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<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Not only limbs in atypical restless legs syndrome. Restless legs syndrome (RLS) typically affects the limbs, but the involvement of other body parts has also been reported. In this essay, we critically review all literature reports of atypical RLS cases with unusual localizations. Applying the updated diagnostic criteria of the International restless legs syndrome study group (IRLSSG), which also consider symptoms localized outside of the lower limbs, a few of these atypical cases reported in the previous literature resulted in a definitive diagnosis of RLS. We also discuss the relationship between RLS and burning mouth syndrome (BMS) or restless genital syndrome (RGS). We conclude clinical sleep specialists should be aware of unusual RLS localizations because they respond to the usual treatment for RLS. All the IRLSSG diagnostic criteria should be applied in every suspected case, in order to establish a correct diagnosis of this disabling but treatable condition. <|eot_id|><|start_header_id|>assistant<|end_header_id|><|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Real limits in the virtual world: self-limiting behavior of Internet gamblers. The recent expansion of Internet gambling has stimulated debate, policy, and research on this relatively new phenomenon and its potential consequences. The current study focuses on bettors experiencing problems by sampling Internet gamblers who imposed limits on the amount they were allowed to deposit to a betting site. We analyzed the betting transactions over 18 months of all gamblers who subscribed to an online betting site in February, 2005 (N = 47,134), 567 of whom utilized the site's self-limit feature. Self-limiting gamblers played a wider variety of games and placed more bets than others prior to imposing limits. After imposing limits, self-limiters reduced their activity, but did not reduce the amount they wagered per bet. Time spent gambling, not just money spent, appears to be an important indicator of gambling problems. Self-limit programs appear to be promising options for Internet gamblers at-risk for gambling problems. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T4 Non_physical_leisure_time_activities 104 121 Internet gambling T6 Non_physical_leisure_time_activities 833 841 gambling T7 Non_physical_leisure_time_activities 905 913 gambling T8 Non_physical_leisure_time_activities 1009 1017 gambling T9 Non_physical_leisure_time_activities 491 505 online betting T10 Non_physical_leisure_time_activities 388 395 betting T11 Non_physical_leisure_time_activities 418 425 betting T2 Non_physical_leisure_time_activities 644 649 games <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Strategic and contested use of food laws to ban smokeless tobacco products in India: a qualitative analysis of litigation. OBJECTIVE: To understand how food laws are used, contested and interpreted to ban certain forms of chewing tobacco in India. METHODS: A qualitative study analysing all the tobacco-related litigation under the food laws in India. We used an inductive thematic analysis of the litigation contents. RESULTS: The tobacco industry systematically deployed litigation to (1) challenge the categorisation of smokeless tobacco products as food, and hence, questioned the use of food laws for regulating these products; (2) challenge the regulatory power of the state government in banning tobacco products via the food laws; and (3) challenge the applicability of the general food laws that enabled stricter regulations beyond what is prescribed under the tobacco-specific law. CONCLUSION: Despite facing several legal challenges from the tobacco industry, Indian states optimised food laws to enable stricter regulations on smokeless tobacco products than were feasible through use of a tobacco-specific law. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T2 Drugs 48 74 smokeless tobacco products T3 Drugs 524 550 smokeless tobacco products T4 Drugs 1040 1066 smokeless tobacco products T11 Nutrition 554 558 food T12 Drugs 223 238 chewing tobacco T16 Drugs 296 303 tobacco T18 Drugs 704 720 tobacco products T22 Drugs 624 632 products <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Effect of pomegranate (Punica granatum) juice intake on hepatic oxidative stress. Pomegranate juice (PJ) possesses a high antioxidant activity, which has been related to beneficial health properties. However, in vivo confirmation and characterization of these effects on biological systems are lacking and needed. This study was performed in order to investigate the effect of prolonged PJ ingestion on general oxidation status. For this purpose, mice ingested PJ (or water in control group) during four weeks, after which damage to lipids, proteins and DNA were evaluated as oxidative cell biomarkers. Levels of hepatic glutathione and the activities and expression of enzymes involved in its metabolism were determined. Catalase and SOD activities were quantified as these enzymes have a crucial role in antioxidant defence. Protection against protein and DNA oxidation was found in PJ group. There was also a significant decrease in GSH and GSSG, without change in the GSH/GSSG ratio. All studied enzymatic activities (GPx, GST, GR, SOD and catalase) were found to be decreased by PJ treatment. Additionally, RT-PCR results showed that GST and GS transcription were also decreased in this group. These results are compatible with a protective effect of PJ against systemic oxidative stress in mice. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T6 Nutrition 462 464 PJ T9 Nutrition 1085 1087 PJ T10 Nutrition 1257 1259 PJ T1 Nutrition 10 52 pomegranate (Punica granatum) juice intake T2 Nutrition 83 100 Pomegranate juice T3 Nutrition 123 134 antioxidant T4 Nutrition 388 390 PJ T8 Nutrition 469 474 water T11 Nutrition 886 888 PJ T12 Nutrition 102 104 PJ <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Kiwifruit Skin and Flesh Contributions to Fecal Bulking and Bacterial Abundance in Rats. Changes in fecal bulk and bacterial abundance due to separately consumed skin and flesh of four kiwifruit cultivars was determined using a rat model designed to estimate the fecal bulking potential of human foods. Dry matter contribution by skin to 100 g of fresh kiwifruit was less than 5% in all cultivars, whereas flesh dry matter contributed up to 20% of fresh fruit weight. Dietary fiber was 35-49% of skin compared with 8-23% of flesh on a dry weight basis. The skin significantly increased whole fruit fecal bulking, but the total bulk per 100 g kiwifruit was less than 10% of daily fecal bulk recommended for optimal gut health. Kiwifruit (skin or flesh) substantially increased the abundance of Lachnospiraceae and Lactobacillus spp. within the gut. Fermentation and prebiosis therefore probably play a greater role than fermentation-resistant dietary fiber in gut health benefits of kiwifruit. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T3 Nutrition 297 302 foods T6 Nutrition 449 460 fresh fruit T7 Nutrition 469 482 Dietary fiber T8 Nutrition 593 598 fruit T9 Nutrition 643 652 kiwifruit T10 Nutrition 727 736 Kiwifruit T11 Nutrition 983 992 kiwifruit T12 Nutrition 943 956 dietary fiber T1 Nutrition 0 9 Kiwifruit T2 Nutrition 186 195 kiwifruit T4 Nutrition 348 363 fresh kiwifruit <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: The experience of group mindfulness-based interventions for individuals with mental health difficulties: a meta-synthesis. OBJECTIVE: The aim of this paper was to synthesize the findings of qualitative studies examining the experience of individuals with mental health difficulties attending mindfulness-based interventions. METHOD: A meta-synthesis of 15 qualitative studies was carried out, using a thematic synthesis approach. RESULTS: The meta-synthesis identified eight analytical themes: 'prior experiences and expectations', 'normalising and supportive process of the group', 'relating differently to thoughts and feelings', 'acceptance', 'a sense of control and choice', 'relationship with self and others', 'struggles', and 'awareness'. CONCLUSIONS: A conceptual framework is presented which accounts for the participants' experiences and suggests a process through which they renegotiate their relationship with their mental health difficulties. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Mental_health_practices 18 35 group mindfulness T2 Mental_health_practices 293 304 mindfulness <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Virtual setup: application in orthodontic practice. BACKGROUND: A plaster dental model is a patient's traditional three-dimensional (3D) record. If the dental crowns from a plaster model are separated and positioned in wax, this setup of the crowns can be used to simulate orthodontic treatment. The traditional way to make this dental setup requires significant time by the orthodontist and in the orthodontic lab. New developments in dentistry and orthodontics include the possibility of virtual setups. AIM: In this article, the differences between conventional setups with plaster models and virtual setups are discussed. METHODS: A clinical patient is described for whom two different setups were made and compared by model superimposition with Geomagic Qualify software. RESULTS: According to the literature and the results from this study, virtual setups and conventional setups with plaster models are equally accurate. CONCLUSION: Virtual setups present several advantages, e.g., digital storage, digital models cannot be damaged, the same model can undergo several treatment simulations, and communication between dental and surgical professionals and between dental professionals and patients is facilitated. Despite these advantages, considerable time and training are needed for dental professionals to master and adopt the general use of digital models and virtual setups in dentistry. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Beauty_and_Cleaning 30 50 orthodontic practice T2 Beauty_and_Cleaning 274 295 orthodontic treatment T4 Beauty_and_Cleaning 451 463 orthodontics T6 Beauty_and_Cleaning 400 411 orthodontic <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Abnormal visual P300 latency in obstructive sleep apnea does not change acutely upon treatment with CPAP. This study evaluated the effects of 2-4 months of continuous positive airway pressure (CPAP) treatment on previously demonstrated P300 latency prolongations in obstructive sleep apnea (OSA). Subjects with severe OSA (respiratory disturbance index > 40/hour sleep) were administered polysomnograms, auditory and visual P300 testing using 31 scalp electrodes, and multiple sleep latency testing before and after treatment with CPAP for 2-4 months. Despite significant improvements in sleep and respiratory variables and the mean sleep latency, there were no significant P300 changes. Obstructive sleep apnea patients had prolonged visual P300 latency compared to normals, both before and after treatment. Prolongations in P300 latency that are resistant to the acute effects of CPAP may suggest that OSA causes physiological cortical changes that are unrelated to sleepiness and may be resistant to treatment. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T2 Sleep 364 369 sleep T4 Sleep 478 491 sleep latency T5 Sleep 634 647 sleep latency T6 Sleep 589 594 sleep <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Treatment of adolescents with Hansaplate/headgear. Influence on face in profile and on dentition. The treatment effects of Hansaplate/headgear in the course of 1 year were analyzed. Twenty-one girls and 19 boys, 10 and 11 years old, respectively, with postnormal occlusion and an average overjet of 8 mm, made up the treatment group. Forty untreated patients with the same type of malocclusion and dentofacial morphology as the treated children, and paired and matched for sex and age, made up the controls. Measurements were obtained from cephalometric headfilms. Matched-intrapairs analysis of control versus treatment changes after 1 year of treatment was done. This showed that the treatment resulted in inhibited sagittal growth of the maxilla, increased anterior face height and a flattened soft tissue profile with a less protrusive upper lip and a less pronounced sulcus mentolabialis. The overjet decreased by 5 mm on average. Only slight retroclination of the upper incisors without extrusion and retroclination of lower incisors to the mandibular plane occurred. No significant anterior positioning of the mandible as compared with the controls was recorded in this study. <|eot_id|><|start_header_id|>assistant<|end_header_id|><|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Toothpaste use by children, oral hygiene, and nutritional education: an assessment of parental performance. PURPOSE: The aim of this study was to determine oral health habits and educational needs of children as reported by their parents or guardians attending a health fair in West Virginia. METHODS: Parents and/or guardians completed a questionnaire about the oral hygiene care, food and beverage consumption of their children. They also demonstrated the amount of toothpaste applied to their children's toothbrush. Toothpaste samples were then weighed. RESULTS: Eighty-seven parents or guardians participated. An average of 0.53 mg of toothpaste was dispensed per brushing, almost double the recommended amount. Most of the parents or guardians (75%) indicated their children had brushed twice the day prior to completing the questionnaire. Only 21% reported that their children's teeth had been flossed. Most children had a limited soda, sweet drinks, and fruit juice intake. CONCLUSIONS: Participants were apparently knowledgeable about prevention, the need to limit sugary beverages, and the importance of brushing twice a day. They were not as knowledgeable about the need for flossing, providing fruits and vegetables to their children, the significance of not skipping a meal, or the appropriate amount of toothpaste use. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Beauty_and_Cleaning 0 26 Toothpaste use by children T5 Beauty_and_Cleaning 1317 1331 toothpaste use T6 Beauty_and_Cleaning 28 40 oral hygiene T7 Beauty_and_Cleaning 364 376 oral hygiene T10 Nutrition 383 387;401 412 food consumption T11 Nutrition 392 412 beverage consumption T2 Nutrition 938 942 soda T3 Nutrition 944 956 sweet drinks T4 Nutrition 962 973 fruit juice T12 Nutrition 1074 1090 sugary beverages T13 Beauty_and_Cleaning 1114 1122 brushing T14 Nutrition 1206 1212 fruits T15 Nutrition 1271 1286 skipping a meal T16 Beauty_and_Cleaning 469 479 toothpaste T17 Beauty_and_Cleaning 640 650 toothpaste T18 Beauty_and_Cleaning 520 530 Toothpaste T19 Beauty_and_Cleaning 157 175 oral health habits T8 Nutrition 46 67 nutritional education T20 Nutrition 1217 1227 vegetables <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Evaluating negative process: a comparison of working alliance, interpersonal behavior, and narrative coherency among three psychotherapy outcome conditions. The aim of this study was to investigate the interrelationships of three measures of the therapeutic relationship and their validity in predicting treatment outcome, including the early identification of two treatment-failure conditions. Forty-eight patient-therapist dyads, in 30-session therapies for personality-disordered patients, were classified as premature dropout (DO), poor outcome (PO), or good outcome (GO) cases. Poor and Good Outcomes were determined by a reliable change score. Dropout cases were terminated during the first third of treatment, and patients cited dissatisfaction with the therapy or therapist. Assessment of working alliance, interpersonal behavior and a new measure of narrative coherency in the first third of treatment revealed that DO dyads had significantly poorer alliances and less coherent narratives in early sessions, while PO dyads, who ultimately completed the 30-session treatment protocol, unexpectedly demonstrated the highest degree of hostile complementarity. Clinical implications of the results are discussed. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Mental_health_practices 123 136 psychotherapy T2 Socioeconomic_factors 63 85 interpersonal behavior T3 Socioeconomic_factors 816 838 interpersonal behavior T6 Mental_health_practices 447 456 therapies T7 Mental_health_practices 762 769 therapy <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Denicotinized versus average nicotine tobacco cigarette smoking differentially releases striatal dopamine. INTRODUCTION: Nicotine has long been recognized as a necessary but insufficient component of tobacco cigarettes to maintain a psychophysiological need to smoke. This study examined venous plasma concentrations effects of nicotine in cigarette smoking after overnight abstinence to release striatal dopamine (DA). METHODS: Twenty-two male smokers smoked either denicotinized (denic) or average nicotine (nic) cigarettes under single blind conditions. Each was given [(11)C]raclopride and scanned in a positron emission tomography (PET) facility. RESULTS: Smoking either denic or nic cigarettes released striatal DA. Denic cigarette smoking released DA primarily in the right striatum, whereas nic cigarette smoking released DA in both striata, but especially in the left. Increases in venous plasma nicotine concentrations correlated positively with increased DA release in the left caudate nucleus. Smoking denic cigarettes reduced craving as much as smoking nic cigarettes. Craving reduction after nic tobacco smoking correlated with increases in plasma nicotine. CONCLUSIONS: Nonnicotine factors in tobacco smoking produce important right brain effects. Nicotine is a pharmacological factor during tobacco smoking that releases bilateral striatal DA, but more in the left brain. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T2 Drugs 122 130 Nicotine T3 Drugs 329 337 nicotine T5 Drugs 1264 1272 Nicotine T8 Drugs 201 219 tobacco cigarettes T9 Drugs 1209 1224 tobacco smoking T10 Drugs 1107 1126 nic tobacco smoking T11 Drugs 1308 1323 tobacco smoking T12 Drugs 262 267 smoke T13 Drugs 341 358 cigarette smoking T4 Drugs 468 489;516 526 denicotinized (denic) cigarettes T16 Drugs 1007 1031 Smoking denic cigarettes T17 Drugs 1059 1081 smoking nic cigarettes T18 Drugs 723 746 Denic cigarette smoking T19 Drugs 800 821 nic cigarette smoking T20 Drugs 662 669 Smoking T21 Drugs 686 700 nic cigarettes T23 Drugs 29 63 nicotine tobacco cigarette smoking T24 Drugs 493 526 average nicotine (nic) cigarettes T15 Drugs 677 682;690 700 denic cigarettes <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Effectiveness of a continuing education course in local anesthesia for dental hygienists. The knowledge, skill level, and clinical practice habits of 97 dental hygienists were assessed after the hygienists completed a 24-hour continuing education course in local anesthesia. Three survey methods were used to assess changes: a written pretest/posttest, self-reports, and employer-reported behavior change. The self-report was completed prior to the course and again one month and six months after completion of the course. The dentists/employers completed the survey after six months. Data were analyzed by paired t-tests. There was a significant improvement in knowledge between the pretest and posttest, an increase in the number of dental hygienists administering local anesthesia, and an increased frequency of its use in the practice setting at both one and six months after the course. The majority of the dentists/employers stated that completion of the course by the dental hygienists had a positive impact on their practices as demonstrated by the practice running more smoothly, improved quality of dental hygiene services, more satisfied patients, and increased productivity of the dental hygienists. <|eot_id|><|start_header_id|>assistant<|end_header_id|><|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Segmentation in low-penetration and low-involvement categories: an application to lottery games. Market segmentation is accepted as a fundamental concept in marketing and several authors have recently proposed a segmentation model where personal and environmental variables intersect with each other to form motivating conditions that drive behavior and preferences. This model of segmentation has been applied to packaged goods. This paper extends this literature by proposing a segmentation model for low-penetration and low involvement (LP-LI) products. An application to the lottery games in Chile supports the proposed model. The results of the study show that in this type of products (LP-LI), the attitude towards the product category is the most important factor that distinguishes consumers from non consumers, and heavy users from light users, and consequently, a critical segmentation variable. In addition, a cluster analysis shows the existence of three segments: (1) the impulsive dreamers, who believe in chance, and in that lottery games can change their life, (2) the skeptical, that do not believe in chance, nor in that lottery games can change their life and (3) the willing, who value the benefits of playing. <|eot_id|><|start_header_id|>assistant<|end_header_id|><|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Clinical symptomatology and treatment of restless legs syndrome and periodic limb movement disorder. Patients with restless legs syndrome (RLS) suffer from sensory and motor symptoms evoked in the limbs at rest. Symptoms increase in the evening and during the night. The circadian rhythm and the presence of involuntary periodic limb movement in sleep (PLMS) which are frequently associated with arousals probably cause the leading symptom of sleep disturbances in RLS. Patients who do not have typical RLS symptoms but whose polysomnography shows PLMS that either impact on sleep continuity or daytime functioning are diagnosed as having the periodic limb movement disorder (PLMD). Dopaminergic agents such as levodopa/dopa decarboxylase inhibitor (DDI) and dopamine agonists are considered the treatment of choice for RLS and PLMS. This article gives an overview of the epidemiology, pathophysiology, clinical symptomatology and diagnosis of RLS and PLMD and focuses on treatment strategies in both disorders. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Sleep 272 288 circadian rhythm T2 Sleep 347 352 sleep T3 Sleep 444 462 sleep disturbances T4 Sleep 576 592 sleep continuity <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Why do psychotherapists participate in psychotherapy research and why not? Results of the Attitudes to Psychotherapy Research Questionnaire with a sample of experienced German psychotherapists. OBJECTIVE: Psychotherapy research needs to convince psychotherapists to contribute their time and effort to participate. The present paper describes the development and first results of the Attitudes to Psychotherapy Research Questionnaire (APRQ). METHOD: The APRQ and additional qualitative questions about exclusion criteria for participation were filled out by a sample of 365 therapists (psychoanalytic, psychodynamic, and cognitive-behavioral) during an Internet-based research survey. RESULTS: A principle component analysis yielded six factors: Benefits, Damage, Legitimation, Self-doubt, Effectiveness Doubt and External Reasons. Age, external reasons and benefits predicted willingness to participate independent of therapeutic school. DISCUSSION: Results show a changing trend to more positive attitudes to psychotherapy research. To be willing to participate in future research, therapists expected high quality designs, financial compensation, and personal gains. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T4 Mental_health_practices 39 52 psychotherapy T6 Mental_health_practices 206 219 Psychotherapy T8 Mental_health_practices 1012 1025 psychotherapy <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Multiheaded mandibular condyles. OBJECTIVES: This study aimed to evaluate the prevalence of multiheaded condyles (MHC) in a group of Turkish subjects using cone-beam computed tomography (CBCT). METHODS: In this retrospective study, CBCT scans of 975 patients (546 female, 429 male; age 15-89 years, mean age 42.46 years) who were referred to Istanbul University Faculty of Dentistry, Department of Dentomaxillofacial Radiology between 2015 and 2021 were evaluated. Bifid and trifid mandibular condyle cases were analyzed according to the patients' age, gender, and laterality, and their frequencies were determined statistically. RESULTS: A multiheaded mandibular condyle (MHC) was detected in 57 of the 975 patients (546 female, 429 male). MHC was diagnosed unilaterally in 49 patients; in 8 patients, it was observed bilaterally. A bifid mandibular condyle (BMC) was detected in 54 patients. A trifid mandibular condyle (TMC) was found in 2 women, and one tetrafid mandibular condyle was seen in 1 woman. CONCLUSIONS: The prevalence of MHC was 5.84% in our proband, which is higher than in previous studies. There was no statistically significant difference between the prevalence of MHC in males or females. <|eot_id|><|start_header_id|>assistant<|end_header_id|><|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Brain activity during anticipation of smoking-related and emotionally positive pictures in smokers and nonsmokers: a new measure of cue reactivity. Previous studies have shown that a brain wave pattern known as stimulus preceding negativity (SPN) can index the anticipation of motivationally relevant events. The present study was the first to use SPN as an index of the motivational significance of smoking-related pictures. Emotionally positive and neutral pictures served as controls. The paradigm involved the following sequence: (a) presentation of a picture (S1) for 500 ms, (b) a fixation cross for 3,500 ms, (c) a second presentation (S2) of the same picture for 2,000 ms, and (d) another fixation cross signifying the beginning of a new S1-S2 trial using a different picture. The participants (N = 24, half smokers and half nonsmokers) viewed pictures from three categories (smoking-related, emotionally positive, or emotionally neutral). Consistent with predictions, smokers exhibited significantly greater mean SPN amplitudes in anticipation of smoking-related pictures relative to neutral pictures. Among nonsmokers the SPN was significantly smaller in anticipation of smoking pictures compared with neutral pictures. These findings are consistent with the incentive sensitization theory of addiction and other conditioning and cue-reactivity models. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Drugs 38 45 smoking T2 Drugs 401 408 smoking T3 Drugs 1057 1064 smoking T4 Drugs 1182 1189 smoking T5 Drugs 885 892 smoking T6 Drugs 1304 1313 addiction <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Sugar replacers and the FDA noncariogenicity claim. In 1996, the U.S. Food and Drug Administration (FDA) approved the use of a "health claim" based on the association between sugar replacers and the nonpromotion of dental caries. Small packages of products containing such sweeteners may state "Does not promote tooth decay" or "May reduce the risk of tooth decay." Larger packages must include additional information, such as "Frequent eating of foods high in sugars and starches as between-meal snacks can promote tooth decay." FDA criteria state that these products must not only be "sugar-free," but they must also contain a noncariogenic sugar replacer and not lower plaque pH below 5.7 either during consumption or up to 30 minutes after consumption when tested by in vivo pH telemetry. Many people might reduce their caries risk by using sugar-free products, and the FDA claim can help consumers identity such products. Dental hygienists are ideal sources of consumer information about sugar replacers, and can help clients distinguish between the new FDA claim and advertising claims that are not regulated by FDA. The purpose of this article is to provide dental hygienists with information on sugar replacers that will help clients make appropriate nutrition choices. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Nutrition 0 15 Sugar replacers T2 Nutrition 176 191 sugar replacers T3 Nutrition 274 284 sweeteners T6 Nutrition 473 481 starches T7 Nutrition 498 504 snacks T9 Nutrition 588 598 sugar-free T10 Nutrition 630 658 noncariogenic sugar replacer T13 Nutrition 994 1009 sugar replacers T15 Nutrition 1204 1219 sugar replacers T19 Nutrition 448 468 foods high in sugars T4 Nutrition 846 865 sugar-free products T5 Nutrition 1260 1277 nutrition choices T16 Nutrition 561 569 products T18 Nutrition 918 926 products <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Research on electroencephalogram to measure thermal pleasure in thermal alliesthesia in temperature step-change environment. Thermal pleasure is currently measured along psychological and physiological variables. However, in transient environments where temperatures change, it is hard to correlate psychological and physiological measures, because there is a delay in physiological changes. This study tests a method for correlating both measures using electroencephalogram (EEG), which can capture physiological feedback with a rapid response rate. In this experimental study, thermal pleasure was induced in a temperature step-change environment, one of non-uniform and transient environments. During the experiment, EEG was monitored and psychological responses of thermal sensation and thermal comfort votes were collected via survey questionnaire. A total of 50 males in their twenties participated in a climate chamber experiment. An experimental group of 25 men were exposed to temperature step-change between two different room conditions (32degreesC, 65% and 25degreesC, 50%), experiencing thermal pleasure. The control group of the remaining 25 men were exposed to an unchanging condition, experiencing thermal comfort close to thermal neutrality. The EEG spectral analysis demonstrated that EEG frequency band associated with pleasant emotional (theta) increased while frequency band related to pleasantness, satisfaction or relaxation (beta) decreased with thermal pleasure. <|eot_id|><|start_header_id|>assistant<|end_header_id|><|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Atmospheric nitrogen compounds-issues related to agricultural systems. Workable pollution abatement policies and effective legislation must be based on sound science. However, despite many years of research, there are still uncertainties about the effects of atmospheric nitrogen compounds on crops and other vegetation. This paper reviews the current state of knowledge of the main compounds, focussing on the concentrations and combinations of pollutants that occur in rural areas. The sources, concentrations and effects of oxidised, reduced and organic nitrogen compounds are considered in turn, then the effects of deposited nitrogen on ecosystems are discussed. Research priorities on the effects of deposited nitrogen in Europe and the USA are compared. Finally, the review leads to a list of issues for discussion and recommendations for research. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Environmental_exposures 0 30 Atmospheric nitrogen compounds T2 Environmental_exposures 260 290 atmospheric nitrogen compounds T5 Socioeconomic_factors 472 483 rural areas T3 Environmental_exposures 81 109 pollution abatement policies T4 Environmental_exposures 707 725 deposited nitrogen T6 Environmental_exposures 550 576 organic nitrogen compounds T7 Environmental_exposures 621 639 deposited nitrogen T8 Nutrition 294 299 crops T11 Environmental_exposures 447 457 pollutants <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Prodynorphin gene deficiency potentiates nalbuphine-induced behavioral sensitization and withdrawal syndrome in mice. Dynorphin is the presumed endogenous ligand for the kappa-opioid receptor. The dynorphin gene may play a role in psychotropic agent-mediated behavioral changes via dopaminergic modulation. Therefore, in this study, possible involvement of the dynorphin gene in nalbuphine-mediated behavioral responses was examined using prodynorphin (Pdyn) gene knock-out (-/-) mice. Pdyn gene deficiency potentiates nalbuphine-induced behavioral sensitization of locomotor activity and accumbal c-Fos expression. Administration of nalbuphine induced a significant increase in the dialysate dopamine level in the nucleus accumbens. This increase was more pronounced in the Pdyn (-/-) mice than in the wild-type (WT) mice. In addition, Pdyn (-/-) mice were more vulnerable to the naloxone-precipitated withdrawal syndrome (i.e., teeth chattering, wet dog shakes, forepaw tremors, jumping, weight loss, and global withdrawal score) after repeated treatment with nalbuphine than the WT mice. Consistently, nor-binaltorphimine, a kappa-opioid receptor antagonist, significantly potentiated nalbuphine-induced behavioral effects in WT mice, whereas U-50488H, a kappa-opioid receptor agonist, significantly attenuated these changes in Pdyn (-/-) mice in a dose-dependent manner. Our data suggest that the kappa-opioid receptor/dynorphin system is specifically modulated in response to behavioral sensitization and withdrawal signs induced by nalbuphine. <|eot_id|><|start_header_id|>assistant<|end_header_id|><|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: "Experience-sharing" as an antidote to dependence-making behavior of general practitioners. This article explores how group-dynamic interventions can be used to teach health professionals to counter impulses to make clients dependent on them. The basic assumption, derived from Kohut's work on narcissism, is that dependence-making behavior is connected with deep-rooted feelings of insufficiency resulting from a defective autonomy. A part of the standard Dutch General Practice (GP) training program, experience-sharing in groups of 12 GP trainees, offers opportunities to explore these feelings. The format of this experience-sharing (well-defined boundaries, minimal structure, facilitating leaders) often evokes behavior in which the peculiarities of the dependence-making behavior of the GP's vis-a-vis patients are mirrored: that is, dependent behavior. In order to analyze and influence this behavior, use can be made of Levine's model of group psychotherapy, which focuses on enhancing autonomy. In this paper we show, by means of four group cases, how Levine's framework can be used to enhance autonomy, lessen dependence, and thus diminish the tendency of GP's toward dependence-making behavior. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T4 Mental_health_practices 948 967 group psychotherapy <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Sexual function changes attributed to hormonal contraception use - a qualitative study of women experiencing negative effects. Objective: To increase the understanding of women who experience negative effects on sexual function when using hormonal contraception.Methods: We performed 24 in-depth interviews with women who had previously experienced negative sexual function effects while using hormonal contraceptives. The thematic analysis method was used.Results: 'After experience comes insight', 'Lubrication and desire go hand in hand', 'Mental wellbeing comes before desire' and 'The contraceptive counsellor potentially facilitates insight and decision-making' were the main themes found in the study.Conclusions: This selected group of women described lubrication difficulties and decreased sexual desire associated with both contraceptive use and the menstrual cycle. Contraceptive use became easier with age and with better understanding. The contraceptive counsellor could facilitate the process. Further choice between hormonal or non-hormonal contraceptive methods depended primarily on experienced adverse effects on mood, and secondarily on sexual function, weighed against the advantages or disadvantages experienced during the person's own menstrual cycle. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Socioeconomic_factors 38 64 hormonal contraception use T7 Socioeconomic_factors 240 262 hormonal contraception T8 Socioeconomic_factors 395 418 hormonal contraceptives T11 Socioeconomic_factors 835 852 contraceptive use T13 Socioeconomic_factors 878 895 Contraceptive use T15 Socioeconomic_factors 1044 1070 non-hormonal contraceptive T4 Socioeconomic_factors 1032 1040;1057 1070 hormonal contraceptive <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Destabilization in self-ratings of the psychotherapeutic process is associated with better treatment outcome in patients with mood disorders. Objective: While destabilization periods characterized by high variability and turbulence in a patient's psychological state might seem obstructive for psychotherapy, a complex systems approach to psychopathology predicts that these periods are actually beneficial as they indicate possibilities for reorganization within the patient. The present study tested the hypothesis that destabilization is related to better treatment outcome.Method: 328 patients who received psychotherapy for mood disorders completed daily self-ratings about their psychotherapeutic process. A continuous measure of destabilization was defined as the relative strength of the highest peak in dynamic complexity, a measure for variability and turbulence, in the self-ratings of individual patients.Results: Destabilization was found to be related to better treatment outcome. When improvers and non-improvers were analyzed separately, destabilization was found to be related to better treatment outcome in improvers but not in non-improvers.Conclusions: Destabilization in daily self-ratings of the psychotherapeutic process is associated with better treatment outcome. The identification of destabilization periods in process-monitoring data is clinically relevant. During destabilization, patients are believed to be increasingly sensitive to the effects of therapy. Clinicians could tailor their interventions to these sensitive periods. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T4 Mental_health_practices 295 308 psychotherapy T5 Mental_health_practices 612 625 psychotherapy T1 Mental_health_practices 1480 1487 therapy <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: A coupled airflow and source/sink model for simulating indoor VOC exposures. In this paper, a numerical model is presented to study the indoor air quality (IAQ) in a room with different emission sources, sinks, and ventilation methods. A computer program, ACCESS-IAQ, is developed to simulate the airflow pattern, the time history of the contaminant concentrations in the occupied zone, and the inhalation exposures. The program developed may be useful for IAQ professional to design healthy and comfortable indoor environments. A numerical study has been carried out to predict the effectiveness of a displacement ventilation and a mixing ventilation on volatile organic compound (VOC) removal in a model office. Results from the numerical predictions show that when a "wet" emission source (a freshly painted wood stain) is distributed uniformly across the floor area with sinks (gypsum board) from the four vertical walls, displacement ventilation has consistently lower exposure at the breathing level of the occupant in the room. Such an effect is mainly due to the higher ventilation efficiency of displacement ventilation compared to the mixing ventilation. The simulation results also show that the walls adsorb significant amounts of VOCs during the first hour and act as secondary sources thereafter. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Environmental_exposures 55 75 indoor VOC exposures T2 Environmental_exposures 138 155 ndoor air quality T10 Environmental_exposures 458 461 IAQ T11 Environmental_exposures 396 416 inhalation exposures T12 Environmental_exposures 771 792 """wet"" emission source" T3 Environmental_exposures 157 160 IAQ T4 Environmental_exposures 187 195 emission T5 Environmental_exposures 216 227 ventilation T7 Environmental_exposures 339 350 contaminant T9 Environmental_exposures 603 627 displacement ventilation T13 Environmental_exposures 634 652 mixing ventilation T14 Environmental_exposures 656 695 volatile organic compound (VOC) removal T15 Environmental_exposures 796 822 freshly painted wood stain T16 Environmental_exposures 883 895 gypsum board T17 Environmental_exposures 927 951 displacement ventilation T18 Environmental_exposures 1079 1090 ventilation T19 Environmental_exposures 1105 1129 displacement ventilation T20 Environmental_exposures 1146 1164 mixing ventilation T21 Environmental_exposures 1244 1248 VOCs <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Religion and attitudes towards alcohol use in the Western Isles. Research on religion and alcohol has produced conclusive results with regard to the relationship between religion and alcohol consumption but ambiguous findings with regard to more general attitudes towards alcohol use. This paper considers these issues in depth by a secondary analysis of data collected in the Western Isles of Scotland. It finds that differences between Protestants and Catholics do exist: Protestants are more likely to endorse an abstinent position, while Catholics are more permissive in their attitudes towards drinking. In terms of attitudes towards drunkenness, however, differences between the two groups are slight. The paper concludes by giving data on differences in attitudes towards alcoholism and service use and calls for future studies in the area to take the cultural context of these issues into account. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Socioeconomic_factors 0 8 Religion T2 Socioeconomic_factors 78 86 religion T3 Socioeconomic_factors 171 179 religion T4 Nutrition 31 42 alcohol use T5 Nutrition 273 284 alcohol use T6 Nutrition 91 98 alcohol T7 Nutrition 184 203 alcohol consumption T8 Nutrition 600 608 drinking T9 Nutrition 640 651 drunkenness T10 Nutrition 780 790 alcoholism <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Suitability of using sieved or unsieved maize mash for production of "OGI"--a fermented cereal food. Proximate analysis of sieved and unsieved maize mash revealed that there was a decrease in the protein and lipid content of the sieved maize mash as compared to that of the unsieved maize mash. Crude fibre and ash was completely absent in the sieved maize mash, while they were present in the unsieved mash. Chemical analysis of the fermented unsieved maize mash revealed an increase in the protein content from 9.9% (unfermented) to 13.4% after 3 days of fermentation, whereas the protein content of the sieved maize mash increased from 7.1% (unfermented) to 8.4% after the same period of fermentation. Furthermore, the results revealed that the protein content of the fermented unsieved maize mash was 32.1% higher than that of the fermented sieved maize mash indicating that the unsieved maize mash was of a better nutrient quality and should be preferred to sieved maize mash for use in "Ogi" production. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Nutrition 78 99 fermented cereal food T2 Nutrition 197 204 protein T5 Nutrition 230 247 sieved maize mash T6 Nutrition 275 294 unsieved maize mash T7 Nutrition 836 863 fermented sieved maize mash T8 Nutrition 884 903 unsieved maize mash T9 Nutrition 607 624 sieved maize mash T10 Nutrition 964 981 sieved maize mash T11 Nutrition 435 464 fermented unsieved maize mash T12 Nutrition 772 801 fermented unsieved maize mash T4 Nutrition 31 50 unsieved maize mash T13 Nutrition 135 154 unsieved maize mash T14 Nutrition 345 362 sieved maize mash T15 Nutrition 493 508 protein content T16 Nutrition 584 599 protein content T17 Nutrition 749 764 protein content T18 Nutrition 920 936 nutrient quality T19 Nutrition 395 408 unsieved mash T3 Nutrition 21 27;40 50 sieved maize mash T20 Nutrition 70 73 OGI T21 Nutrition 994 997 Ogi T22 Nutrition 124 130;144 154 sieved maize mash T23 Nutrition 209 214 lipid T24 Nutrition 296 307 Crude fibre T25 Nutrition 312 315 ash T26 Nutrition 558 570 fermentation T27 Nutrition 692 704 fermentation <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: The relationship of child protection worker attitudes to attrition from the field. Child protection workers in 33 states and the District of Columbia participated in a survey conducted by the C. Henry Kempe National Center for the Prevention and Treatment of Child Abuse and Neglect to determine the attitudes and attributes of workers who terminate their careers in child protection. A year after the survey, telephone follow-up was done to identify respondents to the survey who had left the child protection field. Attributes and attitudes were then linked directly to whether workers had terminated or continued child protective service. Just 8.0% (15 of 187) of the workers had left the field. There were few differences between workers that departed and those whose services were retained. Both were generally dissatisfied with their profession at the time of the survey. The shrinking social service labor market and worker investment in the field are discussed as factors impeding turnover among child protection workers. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T4 Socioeconomic_factors 260 271 Child Abuse T6 Socioeconomic_factors 260 265;276 283 Child Neglect <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Metaphor and psychotherapy. The authors describe the role of metaphor in the theory and practice of psychotherapy. Metaphors are considered fundamental elements of our worlds of language and concepts and not just figures of speech, poetic devices, parables, or creative ways to make interpretations. Metaphors shape the process of therapy by structuring the therapist's perceptions, stance, and attitude. They also organize the way problems are discussed as well as the solution that are seen as effective. The authors review the literature on metaphor in psychotherapy and explore common psychotherapeutic metaphors such as "psychotherapy is war," "the mind is a brittle object," "the conduit metaphor," and a variety of somatic metaphors. They conclude with a discussion of how metaphors function in the process of psychotherapy. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Mental_health_practices 13 26 psychotherapy T3 Mental_health_practices 557 570 psychotherapy T4 Mental_health_practices 818 831 psychotherapy T5 Mental_health_practices 627 640 psychotherapy T2 Mental_health_practices 101 114 psychotherapy T7 Mental_health_practices 332 339 therapy <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Cognitive function during low-intensity walking: a test of the treadmill workstation. BACKGROUND: The purpose of this study was to examine the effects of walking at self-selected speed on an active workstation on cognitive performance. METHODS: Sixty-six participants (n = 27 males, 39 females; mean age = 21.06 +/- 1.6 years) completed a treadmill-desk walking and a seated control condition, separated by 48 hours. During each condition, participants completed computerized versions of the Stroop test, a modified flanker task, and a test of reading comprehension. RESULTS: No significant differences in response speed or accuracy were found between walking and sitting conditions for any the cognitive tests. CONCLUSIONS: These findings reveal that performance on cognitive tasks, including executive control processes, are not impaired by walking on an active workstation. Implementing active workstations into offices and classrooms may help to decrease sedentariness without impairing task performance. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Physical_activity 63 72 treadmill T2 Physical_activity 340 354 treadmill-desk T3 Physical_activity 155 185 walking at self-selected speed T4 Physical_activity 960 973 sedentariness T5 Physical_activity 653 660 walking T6 Physical_activity 355 362 walking T7 Physical_activity 844 851 walking T8 Physical_activity 26 47 low-intensity walking T10 Physical_activity 665 672 sitting <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: The relationship between daytime exposure to light and night-time sleep in 6-12-week-old infants. This project investigated the relationship between exposure to light and 24-h patterns of sleep and crying in young, healthy, full-term babies living at home and following a normal domestic routine. Measures included an ankle worn activity monitor, an external light monitor and the Barr Baby Day Diary in which parents recorded periods of sleep, crying, feeding and other behaviours at 5-min intervals throughout the 24-h period. Fifty-six babies (26 males and 30 females) were monitored across three consecutive days at 6, 9 and 12 weeks of age. There was an early evening peak in crying which was associated with reduced sleep at 6 weeks. Across the trials there was a gradual shift towards a greater proportion of sleep occurring at night. Sleeping well at 6 weeks was a good indication of more night-time sleep at 9 and 12 weeks. Babies who slept well at night were exposed to significantly more light in the early afternoon period. These data suggest that light in the normal domestic setting influences the development of the circadian system. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Environmental_exposures 25 50 daytime exposure to light T2 Sleep 55 71 night-time sleep T3 Sleep 898 914 night-time sleep T4 Environmental_exposures 150 167 exposure to light T5 Sleep 177 194 patterns of sleep T6 Sleep 428 444 periods of sleep T7 Sleep 715 728 reduced sleep T8 Sleep 817 822 sleep T9 Sleep 843 851 Sleeping T10 Environmental_exposures 970 1005 exposed to significantly more light T11 Sleep 1132 1148 circadian system T12 Sleep 945 964 slept well at night T14 Environmental_exposures 1061 1066 light <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: The capture of heavy metals from incineration using a spray dryer integrated with a fabric filter using various additives. This study investigated the effects of feedstock additives [polyvinyl chloride (PVC) and NaCl] and spray dryer additives (SiO2, CaCl2, NaHCO3) on heavy metal and fly ash removal efficiencies, and on particle size distribution of heavy metals. A spray dryer with an integrated fabric filter was used as an air pollution control device (APCD). Removal efficiencies for fly ash and heavy metals were greater than 95 and 90%, respectively. When additives of PVC or NaCl were used, the concentration of heavy metals distributed in fly ash apparently varied when the particle diameter was <1 microm. Although the effects of the additives SiO2, CaCl2, and NaHCO3 on the elemental size distribution of Cr were insignificant, these additives did slightly increase concentrations of Cd, Zn, and Pb partitioning in coarser particles (>1 microm). <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Environmental_exposures 429 457 air pollution control device T2 Environmental_exposures 184 202 polyvinyl chloride T3 Environmental_exposures 578 581 PVC T4 Environmental_exposures 15 27 heavy metals T5 Environmental_exposures 84 97 fabric filter T7 Environmental_exposures 204 207 PVC T10 Environmental_exposures 270 281 heavy metal T11 Environmental_exposures 286 293 fly ash T13 Environmental_exposures 353 365 heavy metals T14 Environmental_exposures 400 413 fabric filter T15 Environmental_exposures 491 498 fly ash T16 Environmental_exposures 503 515 heavy metals T17 Environmental_exposures 622 634 heavy metals T18 Environmental_exposures 650 657 fly ash T23 Environmental_exposures 459 463 APCD <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Selection of Resistance Exercises for Older Individuals: The Forgotten Variable. Resistance training has been widely recommended as a strategy to enhance the functional autonomy and quality of life in older individuals. Among the variables that comprise a training session, the selection of exercises stands out as an important consideration for the elderly. Although a wide range of resistance exercise options exists, current guidelines generally do not indicate which exercises should be included and which muscles should be prioritized when prescribing training for older individuals. Therefore, given the lack of evidence-based information on the topic, this paper endeavors to establish recommendations to help guide the prescription of resistance exercises for older adults. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Physical_activity 13 33 Resistance Exercises T2 Physical_activity 744 764 resistance exercises T3 Physical_activity 82 101 Resistance training T4 Physical_activity 385 404 resistance exercise T5 Physical_activity 257 265 training T6 Physical_activity 292 301 exercises T7 Physical_activity 472 481 exercises T8 Physical_activity 558 566 training <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Ventilation rates in recently constructed U.S. school classrooms. Low ventilation rates (VRs) in schools have been associated with absenteeism, poorer academic performance, and teacher dissatisfaction. We measured VRs in 37 recently constructed or renovated and mechanically ventilated U.S. schools, including LEED and EnergyStar-certified buildings, using CO2 and the steady-state, build-up, decay, and transient mass balance methods. The transient mass balance method better matched conditions (specifically, changes in occupancy) and minimized biases seen in the other methods. During the school day, air change rates (ACRs) averaged 2.0+/-1.3 hour(-1) , and only 22% of classrooms met recommended minimum ventilation rates. HVAC systems were shut off at the school day close, and ACRs dropped to 0.21+/-0.19 hour(-1) . VRs did not differ by building type, although cost-cutting and comfort measures resulted in low VRs and potentially impaired IAQ. VRs were lower in schools that used unit ventilators or radiant heating, in smaller schools and in larger classrooms. The steady-state, build-up, and decay methods had significant limitations and biases, showing the need to confirm that these methods are appropriate. Findings highlight the need to increase VRs and to ensure that energy saving and comfort measures do not compromise ventilation and IAQ. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T7 Socioeconomic_factors 132 143 absenteeism T8 Socioeconomic_factors 145 172 poorer academic performance T4 Environmental_exposures 949 952 IAQ T5 Environmental_exposures 1354 1357 IAQ T6 Environmental_exposures 0 11 Ventilation T9 Environmental_exposures 71 82 ventilation T10 Environmental_exposures 605 615 air change T11 Environmental_exposures 710 721 ventilation T12 Environmental_exposures 995 1006 ventilators T14 Environmental_exposures 1338 1349 ventilation T13 Socioeconomic_factors 98 105 schools T15 Socioeconomic_factors 47 53 school T16 Socioeconomic_factors 292 299 schools T17 Socioeconomic_factors 593 599 school T18 Socioeconomic_factors 763 769 school T19 Socioeconomic_factors 972 979 schools T20 Socioeconomic_factors 1038 1045 schools <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: The efficacy of evidence-based psychotherapies versus usual care for youths: controlling confounds in a meta-reanalysis. In the present meta-analysis, the authors examined how several confounding moderator variables may relate to the advantage of evidence-based psychological treatments (EBTs) over usual care (UC) in the treatment of youths, including the extent to which each therapy was bona fide, supervision provided to therapists, therapist caseload, whether EBT was provided by research therapists, and whether specialized training was provided for EBT therapists. When comparing EBT with bona fide UC while controlling for each confound individually, the advantage for EBT was small and nearly always statistically nonsignificant. Additionally, an increasing total number of confounds favoring EBT was significantly predictive of a greater effect size advantage for EBT. The superior efficacy of EBTs for youths may be an artifact of confounded research designs. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T4 Mental_health_practices 466 469 EBT T5 Mental_health_practices 875 878 EBT T6 Mental_health_practices 678 681 EBT T8 Mental_health_practices 803 806 EBT T9 Mental_health_practices 588 591 EBT T1 Mental_health_practices 16 46 evidence-based psychotherapies T10 Mental_health_practices 905 909 EBTs T2 Mental_health_practices 248 287 evidence-based psychological treatments T11 Mental_health_practices 557 560 EBT T14 Mental_health_practices 289 293 EBTs T15 Mental_health_practices 379 386 therapy <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Carotenoids and their metabolites are naturally occurring activators of gene expression via the pregnane X receptor. Carotenoids are important micronutrients in the human diet and are present in human serum at micromolar concentrations. In addition to their antioxidant potential, carotenoids obtain physiologically relevant properties such as influencing cellular signal pathways, gene expression or induction of detoxifying enzymes. In this study, we determined the transactivation of PXR by cotransfection with the full-length receptor and a PXR-responsive reporter gene. Carotenoids and retinol revealed a 5-6 fold reporter gene activity in HepG2 cells in comparison to a 7-fold induction by the well-known PXR agonist rifampicin, whereas apo-carotenals and lycopene exerted less or no activation potential. The inductive efficacy was hereby concentration-dependent. In addition, carotenoid- or retinol-mediated gene expression of PXR-responsive genes like CYP3A4/CYP3A7, CYP3A5, MDR-1 and MRP-2 has been determined in HepG2 cells by RT-PCR with up-regulative properties of beta-carotene or retinol being comparable to or even higher than that of rifampicin. In conclusion, PXR-mediated up-regulation of CYP3A4/CYP3A7 and CYP3A5 as well as MDR1 and MRP2 by carotenoids points to a potential interference on the metabolism of xenobiotic and endogenous relevant compounds. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Nutrition 0 11 Carotenoids T2 Nutrition 118 129 Carotenoids T3 Nutrition 282 293 carotenoids T4 Nutrition 576 587 Carotenoids T5 Nutrition 1262 1273 carotenoids T6 Nutrition 144 158 micronutrients T7 Nutrition 172 176 diet T8 Nutrition 259 270 antioxidant T9 Nutrition 885 895 carotenoid T10 Nutrition 1079 1092 beta-carotene T11 Nutrition 1096 1103 retinol T12 Nutrition 900 907 retinol T13 Nutrition 592 599 retinol T14 Nutrition 744 758 apo-carotenals T15 Nutrition 763 771 lycopene T16 Environmental_exposures 1330 1340;1365 1374 xenobiotic compounds <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Can High-Intensity Interval Training Promote Skeletal Muscle Anabolism? Exercise training in combination with optimal nutritional support is an effective strategy to maintain or increase skeletal muscle mass. A single bout of resistance exercise undertaken with adequate protein availability increases rates of muscle protein synthesis and, when repeated over weeks and months, leads to increased muscle fiber size. While resistance-based training is considered the 'gold standard' for promoting muscle hypertrophy, other modes of exercise may be able to promote gains in muscle mass. High-intensity interval training (HIIT) comprises short bouts of exercise at or above the power output/speed that elicits individual maximal aerobic capacity, placing high tensile stress on skeletal muscle, and somewhat resembling the demands of resistance exercise. While HIIT induces rapid increases in skeletal muscle oxidative capacity, the anabolic potential of HIIT for promoting concurrent gains in muscle mass and cardiorespiratory fitness has received less scientific inquiry. In this review, we discuss studies that have determined muscle growth responses after HIIT, with a focus on molecular responses, that provide a rationale for HIIT to be implemented among populations who are susceptible to muscle loss (e.g. middle-aged or older adults) and/or in clinical settings (e.g. pre- or post-surgery). <|eot_id|><|start_header_id|>assistant<|end_header_id|>T2 Physical_activity 73 90 Exercise training T3 Nutrition 119 138 nutritional support T6 Physical_activity 423 448 resistance-based training T7 Physical_activity 532 540 exercise T1 Physical_activity 4 36 High-Intensity Interval Training T10 Physical_activity 832 851 resistance exercise T11 Physical_activity 859 863 HIIT T12 Physical_activity 953 957 HIIT T14 Physical_activity 1158 1162 HIIT T15 Physical_activity 1230 1234 HIIT T5 Nutrition 263 279 adequate protein T4 Physical_activity 212 246 single bout of resistance exercise T9 Physical_activity 636 659 short bouts of exercise T8 Physical_activity 586 618 High-intensity interval training T17 Physical_activity 620 624 HIIT <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Drug attitudes and discrimination between drugs among a group of English schoolchildren. This study investigates the attitudes of a group of 185 London schoolchildren towards a number of legal and illegal drugs, and examines the ways in which they discriminate between these drugs. Heroin and LSD were seen as the most dangerous drugs, and alcohol and cigarettes as the safest. Subjects who had a negative attitude towards drugs in general regarded cannabis, amphetamines, alcohol and tobacco as more dangerous than subjects with a neutral or pro-drug attitude. There was also a sex difference: boys claimed to know more about drugs, were more likely to know a cannabis user and to have been "really drunk" than the girls. The implications of these results for drug and alcohol education programmes are discussed. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T2 Drugs 42 47 drugs T4 Drugs 762 766 drug T8 Drugs 276 281 drugs T9 Drugs 283 289 Heroin T10 Drugs 294 297 LSD T11 Drugs 330 335 drugs T12 Drugs 341 348 alcohol T13 Drugs 353 363 cigarettes T14 Drugs 424 429 drugs T15 Drugs 450 458 cannabis T16 Drugs 460 472 amphetamines T17 Drugs 474 481 alcohol T18 Drugs 486 493 tobacco T19 Drugs 662 670 cannabis T20 Drugs 628 633 drugs T3 Drugs 548 552 drug T1 Drugs 0 4 Drug T7 Drugs 198 211 illegal drugs <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Health care providers' experiences with problem gamblers. The purpose of this study was to document health care providers' awareness of problem gambling and frequency of recognition and treatment. An exploratory survey was designed to collect data on awareness and knowledge of problem gambling. A structured questionnaire was completed anonymously by 180 health care providers (nurses, physicians, social workers, and other allied health professionals). Almost all respondents (96%) reported knowledge of problem gambling, and 30% reported asking clients about gambling problems when they presented with stress related symptoms. A third of the respondents reported identifying and treating clients for a gambling problem. The most frequently reported intervention was referral to counseling and other gambling resources. Respondents were generally interested in learning more about gambling problems, particularly those providers who have had a client discuss gambling related concerns. There were no significant differences in findings between provider groups. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Non_physical_leisure_time_activities 48 56 gamblers T2 Non_physical_leisure_time_activities 145 153 gambling T3 Non_physical_leisure_time_activities 287 295 gambling T4 Non_physical_leisure_time_activities 515 523 gambling T5 Non_physical_leisure_time_activities 706 714 gambling T6 Non_physical_leisure_time_activities 563 571 gambling T7 Non_physical_leisure_time_activities 962 970 gambling T8 Non_physical_leisure_time_activities 803 811 gambling T9 Non_physical_leisure_time_activities 884 892 gambling T17 Mental_health_practices 782 792 counseling <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Father's and mother's psychological violence and adolescent behavioral adjustment. Maternal and paternal psychological violence were examined as potential risk factors for internalized and externalized behavior problems displayed by adolescents. Childhood family violence (physical and psychological parental violence), current extrafamily violence (bullying and dating violence), and family structure were taken into account. A sample of 278 adolescents (mean age: 14.2) were recruited in three public schools located in low to high socioeconomic areas. Participants were in the 7th, 8th, and 9th grades, and each completed a self-administered questionnaire. Frequency of current psychological violence is about the same for each parental figure. The three most frequent and least frequent psychologically violent parental practices were also the same for both parental figures. Psychological violence of both parents was related to internalized and externalized behavior problems over and above family structure, childhood family violence, and current extrafamily violence. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Socioeconomic_factors 0 44 Father's and mother's psychological violence T2 Socioeconomic_factors 84 128 Maternal and paternal psychological violence T3 Socioeconomic_factors 247 272 Childhood family violence T4 Socioeconomic_factors 287 318 psychological parental violence T5 Socioeconomic_factors 329 349 extrafamily violence T6 Socioeconomic_factors 351 359 bullying T7 Socioeconomic_factors 364 379 dating violence T8 Socioeconomic_factors 497 511 public schools T9 Socioeconomic_factors 530 554 high socioeconomic areas T10 Socioeconomic_factors 881 903 Psychological violence T11 Socioeconomic_factors 682 704 psychological violence T12 Socioeconomic_factors 1055 1075 extrafamily violence T13 Socioeconomic_factors 1016 1041 childhood family violence T14 Socioeconomic_factors 792 834 psychologically violent parental practices T15 Socioeconomic_factors 274 282;301 318 physical parental violence T16 Socioeconomic_factors 523 526;535 554 low socioeconomic areas <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: A review on pharmacological activities and utilization technologies of pumpkin. Dietary plants and herbal preparations have been traditionally used as medicine in developing countries and obtained a resurgence of use in the United States and Europe. Research carried out in last few decades has validated several such claims of use of traditional medicine plants. Popularity of pumpkin in various systems of traditional medicine for several ailments (antidiabetic, antihypertensive, antitumor, immunomodulation, antibacterial, antihypercholesterolemia, intestinal antiparasitia, antiinflammation, antalgic) focused the investigators' attention on this plant. Considerable evidence from several epidemiological studies concerning bioactivities leads have stimulated a number of animal model, cell culture studies and clinical trials designed to test this pharmacological actions. In addition, it was found that technologies such as germination and fermentation could reduce antinutritional materials and affect the pharmacological activities of pumpkin. This review will focus on the main medicinal properties and technologies of pumpkin, and point out areas for future research to further elucidate mechanisms whereby this compound may reduce disease risk. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Nutrition 81 95 Dietary plants T2 Socioeconomic_factors 164 184 developing countries T5 Nutrition 336 363 traditional medicine plants T6 Nutrition 71 78 pumpkin T7 Nutrition 379 386 pumpkin T8 Nutrition 1130 1137 pumpkin T9 Nutrition 1045 1052 pumpkin T10 Nutrition 100 119 herbal preparations T11 Nutrition 653 658 plant <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: A randomized controlled trial to investigate brackets bonded with a hydrophilic primer. OBJECTIVE: To compare the clinical failure rates of brackets bonded using a prototype hydrophilic primer, designed to be insensitive to moisture, with brackets bonded with a conventional primer. DESIGN: Single centre randomised controlled clinical study. Thirty-three patients were bonded using a split mouth technique: randomly allocating the hydrophilic primer to one side of the mouth and a conventional primer to the other. SETTING: Hospital orthodontic department, Bradford, UK. SUBJECTS: Orthodontic patients requiring fixed appliances. MAIN OUTCOME MEASURES: The site and time to bond failure was recorded for each bracket that failed over 6 months. RESULTS: Using survival analysis, there was an increased risk of bracket failure when bonded with the hydrophilic primer compared with the conventional primer (hazard ratio = 2.2, 95% confidence interval: 1.1 to 4.5, P = 0.01). CONCLUSIONS: This hydrophilic primer cannot be recommended for routine clinical use. <|eot_id|><|start_header_id|>assistant<|end_header_id|><|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Airborne transmission between room occupants during short-term events: Measurement and evaluation. This study experimentally examines and compares the dynamics and short-term events of airborne cross-infection in a full-scale room ventilated by stratum, mixing and displacement air distributions. Two breathing thermal manikins were employed to simulate a standing infected person and a standing exposed person. Four influential factors were examined, including separation distance between manikins, air change per hour, positioning of the two manikinsand air distribution. Tracer gas technique was used to simulate the exhaled droplet nuclei from the infected person and fast tracer gas concentration meters (FCM41) were used to monitor the concentrations. Real-time and average exposure indices were proposed to evaluate the dynamics of airborne exposure. The time-averaged exposure index depends on the duration of exposure time and can be considerably different during short-term events and under steady-state conditions. The exposure risk during short-term events may not always decrease with increasing separation distance. It changes over time and may not always increase with time. These findings imply that the control measures formulated on the basis of steady-state conditions are not necessarily appropriate for short-term events. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T3 Environmental_exposures 840 857 airborne exposure T1 Environmental_exposures 501 511 air change <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: The Effect of Pre-Post Differences in Walking, Health, and Weight on Retirees' Long-Term Quality of Life. This research examined the effect of pre-post differences in walking duration, health, and weight on retirees' long-term quality of life (QoL). It used data from a 2018 randomized mail survey of 483 suburban New Jersey retirees. Ordinary least squares and three-stage least squares models were used. The analysis showed that changes in walking duration during the first 2 years of retirement are directly associated with health change, health change has an effect on long-term QoL, and weight variation of 10 lb or more has an effect on health change and long-term QoL. Although QoL peaks for the sample of retirees at around age 75, people whose average walking duration increased, health improved, and weight did not increase substantially after retirement continued to experience high QoL for a longer time. The results show that people can achieve high long-term QoL by choosing an active lifestyle when transitioning to retirement. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Physical_activity 38 45 Walking T2 Physical_activity 168 184 walking duration T3 Physical_activity 762 778 walking duration T4 Physical_activity 443 459 walking duration T6 Physical_activity 993 1009 active lifestyle T7 Socioeconomic_factors 488 498 retirement T8 Socioeconomic_factors 855 865 retirement T9 Socioeconomic_factors 1032 1042 retirement <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Influence of Badminton Practice on Age-Related Changes in Patellar and Achilles Tendons. Regular sport practice could prevent age-related changes in tendinous tissues. The purpose of the study was to investigate the effect of regular badminton practice on patellar and Achilles tendon mechanical properties in senior competitive badminton players (>35 years old) and to compare the results with physically active people matched by age. One hundred ninety-two badminton players and 193 physically active people were divided by age into four groups, between 35 and 44 (U45), between 45 and 54 (U55), between 55 and 64 (U65), and over 65 (O65) years old. A LogiqS8 transducer in elastography mode and a MyotonPRO myotonometer were used to assess patellar and Achilles mechanical properties. Achilles tendon stiffness was higher in the control group than the badminton players for the U45, U55, and O65 age groups (p < .01). Also, the elastography index was higher in the control group than the badminton players for the U45, U55, U65, and O65 age groups (p < .05). In conclusion, regular badminton practice could prevent the decline in mechanical properties of the patellar and Achilles tendons. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Physical_activity 13 31 Badminton Practice T3 Physical_activity 1086 1104 badminton practice T2 Physical_activity 235 253 badminton practice T4 Physical_activity 98 112 sport practice T5 Physical_activity 992 1001 badminton T6 Physical_activity 330 339 badminton T7 Physical_activity 856 865 badminton T8 Physical_activity 460 469 badminton <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Analysis of trans-resveratrol and trans-piceid in vegetable foods using high-performance liquid chromatography. Trans-resveratrol and resveratrol glucoside are natural phenolic compounds existed in a wide variety of plant species, which are extensively consumed in many countries. The existing studies excessively focused on grapes and their products, and little about daily vegetable foods. Actually, in much more countries, vegetable foods are residents' principal food and nutrient origins. This study was to investigate the levels of trans-resveratrol and trans-piceid in daily vegetable foods of China using high-performance liquid chromatography (HPLC) method with fluorescence detection (FLD). Trans-piceid was the major form existing in most vegetable foods, and most of the samples contained higher trans-piceid than trans-resveratrol. The contents of trans-resveratrol and trans-piceid in different varieties and regions were different. Moreover, peculiar vegetable foods to some region were also one of the most important sources of trans-resveratrol and trans-piceid. Therefore, vegetable foods were other significant sources of trans-resveratrol and trans-piceid except the foods published. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Nutrition 12 29 trans-resveratrol T2 Nutrition 34 46 trans-piceid T3 Nutrition 50 65 vegetable foods T4 Nutrition 113 130 Trans-resveratrol T5 Nutrition 539 556 trans-resveratrol T6 Nutrition 1142 1159 trans-resveratrol T7 Nutrition 1045 1062 trans-resveratrol T8 Nutrition 827 844 trans-resveratrol T9 Nutrition 862 879 trans-resveratrol T10 Nutrition 169 187 phenolic compounds T11 Nutrition 135 156 resveratrol glucoside T12 Nutrition 326 332 grapes T13 Nutrition 376 391 vegetable foods T14 Nutrition 427 442 vegetable foods T15 Nutrition 583 598 vegetable foods T16 Nutrition 751 766 vegetable foods T17 Nutrition 967 982 vegetable foods T18 Nutrition 1092 1107 vegetable foods T20 Nutrition 458 472 principal food T21 Nutrition 477 493 nutrient origins T22 Nutrition 561 573 trans-piceid T23 Nutrition 702 714 Trans-piceid T24 Nutrition 884 896 trans-piceid T25 Nutrition 1164 1176 trans-piceid T26 Nutrition 1067 1079 trans-piceid T27 Nutrition 809 821 trans-piceid T28 Nutrition 1188 1193 foods T29 Nutrition 217 222 plant T30 Nutrition 343 351 products <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Tarasoff decision: a decade later dilemma still faces psychotherapists. The landmark cases on the "duty to warn" concept are reviewed. Two trends are noted in the court rulings. The first trend narrowly interprets the duty to warn as applying only to situations involving a serious threat to a specific individual. The second trend has broadened the doctrine to include warnings about patients who do not make threats and whose potential victims are unspecified. The authors argue that the original Tarasoff Doctrine is sound both from the perspective of public policy and psychotherapeutic practice, but that its broader interpretation is problematic for psychotherapists. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T3 Mental_health_practices 574 600 psychotherapeutic practice <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Tobacco price elasticity in Serbia: evidence from a middle-income country with high prevalence and low tobacco prices. In this paper, we use Deaton's demand model and Household Budget Survey data from 2006 to 2017 to provide a first robust and reliable estimate of cigarettes price elasticity for Serbia. The case of Serbia is particularly interesting and important as it provides evidence for a country in which tobacco market is characterised by the high tobacco consumption, low prices and large perceived impact of multinational tobacco companies on public revenues, export and employment, given their considerable cigarette production in Serbia. The price elasticity of cigarettes is estimated at -0.639, in line with the previous estimates for the low-income and middle-income countries. Estimated negative cigarettes price elasticity for Serbia suggests that tobacco tax policy could be used effectively to reduce cigarette consumption in Serbia, which could lower the harmful health effects of cigarettes. Furthermore, a calculation based on the estimated elasticity suggests that increasing tobacco taxes could also have positive fiscal effects, as the expected revenue from the taxes would increase. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Drugs 0 7 Tobacco T3 Socioeconomic_factors 52 73 middle-income country T4 Drugs 103 110 tobacco T5 Drugs 266 276 cigarettes T8 Drugs 414 421 tobacco T9 Drugs 458 477 tobacco consumption T11 Drugs 620 629 cigarette T13 Drugs 676 686 cigarettes T14 Socioeconomic_factors 770 792 middle-income countrie T15 Drugs 814 824 cigarettes T17 Drugs 867 874 tobacco T18 Drugs 922 943 cigarette consumption T20 Drugs 1003 1013 cigarettes T21 Drugs 1101 1108 tobacco T22 Socioeconomic_factors 583 593 employment T24 Socioeconomic_factors 755 765;784 793 low-income countries <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: A psychotherapy researcher's perspective on therapist self-awareness and self-focused attention after a decade of research. In this Early Career Award paper, the interplay between quantitative and qualitative approaches to self-awareness in the psychotherapy literature is described. In addition, the linkages between therapist self-awareness and self-focused attention discussed in the cognitive and social psychology literatures are also addressed. Contradictory sets of findings are examined; for example, momentary states of therapist self-awareness have at times been associated with anxiety or poor performance and at other times with positive interpersonal processes and client perceptions. Some of the studies on the management of distracting self-awareness are also reviewed, and suggestions for future research in the areas of mindfulness, attentional flexibility, and the person of the therapist are provided. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Mental_health_practices 2 15 psychotherapy T2 Mental_health_practices 246 259 psychotherapy T3 Mental_health_practices 838 849 mindfulness <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Optimization of a fermented soy product formulation with a kefir culture and fiber using a simplex-centroid mixture design. The objective of this work was to optimize a fermented soy product formulation with kefir and soy, oat and wheat fibers and to evaluate the fiber and product characteristics. A simplex-centroid mixture design was used for the optimization. Soymilk, soy, oat and wheat fiber mixtures, sucrose and anti-foaming agent were used for the formulation, followed by thermal treatment, cooling and the addition of flavoring. Fermentation was performed at 25 degreesC with a kefir culture until a pH of 4.5 was obtained. The products were cooled, homogenized and stored for analysis. From the mathematical models and variables response surface and desirability an optimal fermented product was formulated containing 3% (w/w) soy fiber. Compared with the other formulations, soy fermented product with 3% soy fiber had the best acidity, viscosity, syneresis, firmness and Lactococcus lactis count. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Nutrition 18 39 fermented soy product T2 Nutrition 59 64 kefir T3 Nutrition 170 191 fermented soy product T4 Nutrition 209 214 kefir T5 Nutrition 219 222 soy T6 Nutrition 224 227 oat T7 Nutrition 232 244 wheat fibers T8 Nutrition 365 372 Soymilk T9 Nutrition 374 377 soy T10 Nutrition 379 382 oat T11 Nutrition 387 398 wheat fiber T12 Nutrition 409 416 sucrose T13 Nutrition 530 539 flavoring T15 Nutrition 889 910 soy fermented product T16 Nutrition 919 928 soy fiber T14 Nutrition 840 849 soy fiber T17 Nutrition 590 595 kefir T18 Nutrition 421 439 anti-foaming agent T19 Nutrition 77 82 fiber T20 Nutrition 265 270 fiber T21 Nutrition 483 500 thermal treatment T22 Nutrition 502 509 cooling T23 Nutrition 541 553 Fermentation T24 Nutrition 275 282 product T25 Nutrition 640 648 products T26 Nutrition 787 804 fermented product <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Depressive symptoms among female college students experiencing gender-based violence in Awassa, Ethiopia. Little epidemiologic research has focused on the mental health effects of gender-based violence among sub-Saharan African women. The objective of this study was to assess risk of depression and depressive symptoms among 1,102 female undergraduate students who were victims of gender-based violence. Students who reported experience of any gender-based violence were nearly twice as likely to be classified as having moderate depression during the academic year (OR = 1.98, 95% CI = 1.39-2.82) as compared with nonabused students. Compared with nonabused students, those who had experienced both physical and sexual abuse were 4 times more likely to report either moderately severe (OR = 4.32, 95% CI = 2.00-9.31) or severe depressive symptoms (OR = 4.19, 95% CI = 1.01-17.43). Our findings, consistent with previous studies, support the thesis that women's mental health status is adversely affected by exposure to gender-based violence. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Socioeconomic_factors 63 84 gender-based violence T2 Socioeconomic_factors 181 202 gender-based violence T3 Socioeconomic_factors 383 404 gender-based violence T4 Socioeconomic_factors 1022 1043 gender-based violence T5 Socioeconomic_factors 446 467 gender-based violence T12 Socioeconomic_factors 26 49 female college students T7 Socioeconomic_factors 702 727 physical and sexual abuse T9 Socioeconomic_factors 333 362 female undergraduate students <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: The influence of air travel on athletic performance. Rapid transmeridian flight is a common reality for modern athletes and it has often been assumed that air travel has detrimental effects on athletic performance. The plausibility of this assumption is supported by established deteriorations in sleep and mood following transmeridian flight. However, the scientific evidence supporting the assumption is neither consistent nor compelling. Studies that have assessed athletic performance following transmeridian flight have produced mixed results and are characterised by major methodological flaws. Recent retrospective assessments of athletic team performance based on distance travelled have generally failed to indicate performance impairments following transmeridian flight. The plausibility of transmeridian air travel impairing athletic performance would be indicated by demonstration of an internally-driven circadian rhythm of athletic performance, or of deleterious performance consequences following sleep deprivation or desynchronisation between the circadian system and the environment. More rigorous research is needed to establish whether athletic performance is influenced by air travel. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T7 Non_physical_leisure_time_activities 156 166 air travel T8 Non_physical_leisure_time_activities 1194 1204 air travel T10 Sleep 298 303 sleep T11 Sleep 1013 1030 sleep deprivation T9 Non_physical_leisure_time_activities 816 826 air travel T12 Sleep 918 934 circadian rhythm T13 Sleep 1064 1080 circadian system T1 Non_physical_leisure_time_activities 17 27 air travel <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: In vivo effectiveness of enamel sealants around orthodontic brackets. OBJECTIVE: In this randomized study, the caries-protective effect on vestibular enamel of two fluoride-containing sealants (Protecto(R) and Light Bond(R)) during multibracket treatment was investigated. MATERIALS AND METHODS: In all, 40 orthodontic patients about to receive a multibracket appliance with the brackets bonded to the vestibular tooth surfaces were randomly included in this study. Each one was randomly assigned to one of four groups. A crossover design was selected in which a sealed quadrant was contralateral to an unsealed quadrant, then choosing the reverse configuration in the opposite jaw. Two sealants were, thus, tested on vestibular enamel on left and right anterior teeth and premolars in both jaws of each patient over 6 months of multibracket treatment. A DIAGNOdent(R) pen measuring laser fluorescence was used to analyze the relevant enamel surfaces both at baseline and after 6 months. RESULTS: Neither the incidence nor the characteristics of the demineralization we observed during the study differed between the 4 groups. CONCLUSION: Single application of smooth-surface sealants did not protect enamel around brackets from incipient carious lesions during the first 6 months of multibracket treatment. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Beauty_and_Cleaning 48 68 orthodontic brackets T3 Beauty_and_Cleaning 348 370 multibracket appliance <|eot_id|>

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a highly accurate Named Entity Recognition (NER) system, specialized in extracting lifestyle-related entities from textual data. Your task is to identify and extract lifestyle entities based on the provided definitions and guidelines. Guidelines for LIFESTYLE Entities: 1. Valid Categories: Lifestyle factors include but are not limited to: - Nutrition (e.g., dietary habits, food groups, macronutrients). - Socioeconomic factors (e.g., income, education, wealth). - Environmental exposures (e.g., air pollution, water quality). - Substance use (e.g., smoking, drug abuse). - Physical activities (e.g., exercise, leisure activities). - Non-physical leisure activities (e.g., reading, hobbies). - Personal care (e.g., hygiene, cosmetic procedures). - Sleep (e.g., habits, quality). - Mental health practices (e.g., meditation, psychotherapy). 2. Exclusions - Exclude general or vague terms unless explicitly tied to health outcomes in the context. 3. Output Format: - Use BRAT format: Each entity must include its label, start offset, end offset, and text. - Provide only the extracted entities, ordered by occurrence in the Abstract. 4. Context Matching: - Extract entities with a clear health-related context. - Avoid ambiguous or irrelevant terms unless contextually justified. Example Input Abstract: Sun exposure is the main cause of skin cancer. Example Annotated Output: T1 LIFESTYLE 0 12 Sun exposure <|eot_id|><|start_header_id|>user<|end_header_id|>Using the provided guidelines specified in the system instructions, extract lifestyle factor entities (label: LIFESTYLE) from the Abstract below. Strictly adhere to the definitions, exclusions, and formatting rules. Return the extracted entities in BRAT format, ordered by their occurrence. Abstract: Does physical activity affect the predictive value of health-related fitness tests on walking difficulty? BACKGROUND: Low levels of physical activity (PA) and poor fitness tend to predict a decline in mobility. The current study investigated whether PA modifies the predictive value of health-related fitness (HRF) tests on difficulty in walking 2 km (WD). METHODS: PA was assessed by self-reported questionnaires in 1990 and 1996. Subjects age 55 to 69 years and free of self-reported WD participated in assessment of HRF in 1996. Occurrence of WD was assessed by questionnaire in 2002 (n=537). RESULTS: There were no statistically significant interactions between PA and HRF tests; thus, PA and HRF were both independent predictors of WD. Regardless of the PA level, the subjects in the poorest performing third in each HRF test had higher risk of WD than the subjects in the best performing third. CONCLUSIONS: PA and HRF seemed to be independent predictors of WD, although the association of PA with WD was weaker than the association of HRF. Thus, PA did not modify the predictive value of HRF on WD. <|eot_id|><|start_header_id|>assistant<|end_header_id|>T1 Physical_activity 5 22 physical activity T2 Physical_activity 133 150 physical activity T3 Physical_activity 367 369 PA T4 Physical_activity 251 253 PA T5 Physical_activity 667 669 PA T6 Physical_activity 691 693 PA T7 Physical_activity 997 999 PA T9 Physical_activity 915 917 PA T10 Physical_activity 1054 1056 PA T11 Physical_activity 86 93 walking T12 Physical_activity 339 346 walking T8 Physical_activity 760 768 PA level T13 Physical_activity 152 154 PA <|eot_id|>