text:
Assessment of food safety knowledge, attitude, practices of food handlers and microbial contamination in foods at the canteens of a University in Pakistan

Developing nations are striving to assure food safety that rely mainly upon handling procedures. The current study focused upon the understanding level and practices of food handlers working at various canteens of University of the Punjab, Quid-i-Azam campus, Lahore. Different canteens were selected where 300 different food handlers were judged for their approach towards food safety through a closed ended questionnaire. Samples of different food products were also taken randomly for their microbiological safety evaluation. The whole data was analyzed using chi-square to assess the proportion of correct and incorrect responses among various parameters. The non-significant variation was observed among the knowledge, attitude and practices scores of university and its hostels’ canteens food handlers. Overall, 60% of the responses were correct from the university and hostel canteens food handlers. Similarly, 50-60% of the responses regarding attitude and practices were correct from the university and hostel canteen food handlers. With many technical flaws, these food handlers showed moderate level of knowledge. Not only apprentices but the supervisors too, lacked the basic knowledge of temperature as major factor creating potential food safety threats. Lack of knowledge about personal hygiene and workplace sanitation were few contributing factors towards heedless behavior. Incidence of Salmonella and Staphylococcus aureus contamination in food samples was found 29% and 57%, respectively, while 35% food samples were found contaminated with E. coli. It further exposed the negligence, emphasizing proper training of employees as part and parcel of food safety procedures. It was concluded that more training programs along with periodic validation of food manufacturing standards are required to ensure food safety. Furthermore, strict surveillance and implementation of GMPs by the administrative authorities are needed to safeguard the consumers’ health.

Introduction
Developing world is suffering from foodborne diseases for years with the estimated cases of 600 million including 420,000 deaths, annually. The major contributors of foodborne diseases include unhygienic food handling techniques and lack of cleanliness. Food safety knowledge is the learned skills or education, while food safety attitude is the belief about food safety. Food safety practices are the on-ground act or application of food safety. The food safety knowledge, attitude, and practices (KAP) play a crucial role to overcome the incidence of food poisoning among consumers. On the other hand, the lack of food safety knowledge, inappropriate attitude and wrong handling practices can cause severe food safety threats to the consumers. Hence the food safety KAP are critical to make food free from any kind of hazards during preparation, processing and serving as well (Tuglo et al., 2021).
Food handlers plays a significant role in ensuring food safety and preventing food contamination throughout the food supply chain (Lee et al., 2017). The precise attitude of food workers can decline the outbreak of food borne disease and further the sufficient knowledge as well as rigorous administration can also play an important role. The suggested reasons behind food poisoning are substandard cooking and cooling, long duration between preparation and consumption, inappropriate heat treatment, infectious staff, application of contaminated materials, lack of equipment cleaning, cross contamination, low quality food materials and residual food materials. The pathogenic microbial transmission from food handlers to consumer can be reduced by applying the principle of good hygienic practices and correct food handling. Food handlers must follow the proper food safety measures and hygiene practices to reduce the incidence of pathogenic microorganism in foods (Bou-Mitri et al., 2018).
Microbes have evolved into threatening creatures posing real threats to human beings with acquired resistance over the passage of time. These microbes have mastered a skill of existence under toughest conditions and food processing procedures. Foods containing potential pathogenic microbes could be source of food borne illness. Various studies have confirmed that the training of food handlers could be effective in this regard. The success of training programs with consequent of reduction in prevalence of pathogenic microorganism in prepared foods has been established through research (Elobeid et al., 2019). In developing world, food safety is indicator of economic growth that is why both government and private sector recognize the significance of safety in food supply chains (Akhtar et al., 2014). The public sector universities of Pakistan are contributing a lot towards education and well-being of society. In this domain, University of the Punjab is one of the oldest, established in 1882 and the highest site of learning which accommodates more than 40,000 students out of which >10,000 students are residing in its hostels. In addition, almost every department and hostel have its own canteen. Food business in university premises is mainly run by local vendors who are un-professional and lack background knowledge of food safety. The workers involved in food operations mostly belong to rural backgrounds. As, most of the recorded outbreaks have been reported from educational organizations (Soon et al., 2020), this urged us to conduct a food safety study based on knowledge, attitude and practices of food handlers working at different canteens of University of the Punjab. Moreover, the microbiological analysis of different food products being sold at various canteens was also performed to conclude the relationship between KAP of workers and microbial safety of the products being sold.

Materials and methods
The cross-sectional survey was conducted to collect the data from food handlers working at the canteens of hostels and the departments in University of the Punjab, Quaid-i-Azam Campus, Lahore. Total 300 food handlers, 180 from various departments of campus (G-I) and 120 from hostel canteens (G-II) were randomly selected and included in the study. In addition, microbiological quality of different food commodities was also assessed at Food Safety Research Lab in the Department of Food Sciences.

Questionnaire design
A comprehensive questionnaire was designed including four sections i.e., demographic data (4 questions i.e., gender, age, experience and education), second question regarding knowledge, attitude and practices related to personal hygiene (10 questions) whereas workplace cleanliness (22 questions) was part of third section and fourth section contained temperature supervision data (7 questions). Moreover, different questions were asked to assess the knowledge (10 questions), attitude (15 questions) and practice (15 questions) within the aforementioned sections of the questionnaire and the correct responses were analyzed (Luu et al., 2017).

Microbiological assessment
Food samples were collected randomly from selected canteens and placed into sterile glass bottles and the bottles were kept in the ice cooler for transportation. The samples were labeled with date of sample collection, canteen number and name of food sample. All the samples were stored at freezing temperature and transferred to refrigerator at 4°C for thawing before analysis. Types and number of collected food samples are as follows: Samosa 10; Bread 10; Patties 10; Milk Shake 20; Kebab 4; Chaat 12; Drinking Water 12; Yogurt 6; Salad 6; Juice 10.

Microbiological analysis
Enumeration of TPC was done by adopting standard ISO Method 483310. Presumptive test for the Coliforms followed by the detection and confirmation of E. coli was done by the described method (Herrera, 2001). Detection of Staphylococcus aureus and Salmonella from food samples was done via McLandsborough (2004) method.

Statistical analysis
All the data were analyzed using statistical package for social sciences (SPSS) version 26 and were expressed as frequencies and percentages. The chi-square was applied to assess the proportion of correct and incorrect responses among various parameters assessed in the knowledge, attitude, and practices with reference to personal hygiene, workplace cleanliness and temperature supervision.

Results and discussion
The present study was conducted with the aim to assess the knowledge, attitude and practices of food handlers with reference to their place of employment, in which they were asked about the personal hygiene, workplace cleanliness, temperature monitoring and supervision. Overall, 10 questions were asked in the personal hygiene section form which half of the questions were addressed more appropriately by the hostel canteens food handlers and vice versa. Personal hygiene parameters scores were comparable for both hostel and university canteens food handlers. On the contrary, the knowledge attitude and practices scores regarding workplace cleanliness were better in hostel canteen workers. Similarly hostel canteen workers were more accurate in responding to temperature supervision parameters. There was a huge percentage of workers from both placements, who were unable to answer correctly the questions based on simple food safety and food handling practices, depicted from the scoring of the data, as 42-51% of the food handlers from both settings were unable to address simple parameters related to food safety KAP.

Demographic attributes
The demographic information of food handlers included in this study is presented in Figure 1. The food handlers of various age groups (ranging from less than 20 to more than 50 years) were categorized into various subgroups based upon the age range i.e., 20-30 years (41.67%) and 30-40 years (35%) etc. The majority of food handlers were illiterate (48.3%), while only 20% and 16.7% had completed their primary and middle school education, respectively. Only one female employee had bachelor's degree with her name. This clearly defines how lack of education may directly or indirectly influence food safety situation. Majority of the participants were male with only one female out of 300. In Pakistan food business is dominated by males, however, in some countries, females have higher proportion in food business as reported by Samapundo et al., (2015) in which 88.7% women were involved in food supply chain as food handlers. The information for work experience was also gathered and segmented into five groups. The 25% of food handlers had experience of less than one year where 33.3% had working experience of 1-3 years (Has et al., 2018). It is worth mentioning that work experience of all the workers does not include any kind of training for their job.

Personal hygiene
The participant's response about personal hygiene has been presented in the Table 1. The results indicated that food handlers had an average knowledge of food safety related personal hygiene. The majority of the food workers had a habit of washing their hands before performing any operation as more than 50% workers (from both groups) choose option "Yes" in this case. Handlers who chosen correct option for wearing proper uniform at the workplace were few in numbers and the data showed significant association. However, most of the food handlers were certain about washing their hands before operational work and timely trimming of their nails. Most of the food handlers have very poor level of awareness about other hygienic practices i.e. avoiding touching nose, mouth and hair etc. during operational work. Food handlers from G-I (72.22%) and from G-II (87.5%) had habit of doing such unhygienic activities with significant variation between both groups. The 61.11% of G-I food handlers claimed that they cover their head while working whereas, only 29.16% of G-II food handlers had habit of doing this. However, all the food handlers assured that they don't work when suffering from any disease. In another study conducted by Isoni Auad et al. (2019) in Brazil also reported that the food handlers answer correctly for handwashing. Nevertheless, (95%) food han-dlers were aware about the reduction of food contamination by the use of jewelry, accessories, adornments and (77.5%) food handlers affirmed that they never used adornment and jewelry. The results also revealed that 82% of food handlers wear cap and having no adornment during working. Previous studies reveal that 86.3% food handlers know that it is necessary to take leave from work in case of skin infection and other associated diseases while 88.1% were aware of microbial presence on the nose, skin, and mouth of healthy handlers (Samapundo et al., 2015). Food handlers of G-I (27.78%) and G-II (58.33%) had a positive response about need of having separate washing facility. A contemporary study describes best personal hygiene in which 96.6 % food worker follow safe practices during work. They wear proper uniform and hairnet/caps. More than 90 % food workers washed hands after sneezing, coughing or smoking and more than 70% were well aware of removing their personal belongings i.e., jewelry, ring during their job (Al-Shabib et al., 2016). In addition, Codex Alimentarius Commission (2003)   Article with a conclusion that unscrupulous food handling is the key reason for food borne diseases and proper washed hands is the main risk factor to prevent cross contamination in the food. At every step, food workers must wash their hands during food manufacturing. The food workers suffering from any disease could be compensated at other places and must not permitted to deal with food. Our finding reveals that few workers (less than 30 %) keep working when ill or infected and find it safe for consumers.

Workplace cleanliness
The approach of food handlers towards workplace cleanliness is presented in the Table 2. In G-I, 49.22% participants had habit of smoking whereas all the G-II participants denied having this habit and significant variation was revealed statistically. However, all the smokers were unaware of contamination risks during indoor cooking. Almost half of the participants from both groups did not have any awareness about the importance of neat/clean working environmental conditions and structure of working areas i.e., floors, ceilings etc as concluded from their response in this survey. Condition of food stalls was also pathetic in another study (Okojie & Isah, 2014). Approximately 44.44% participants in G-I and 66.66% participants in G-II, claimed that they wash utensils before use. Less than 40% participants from both groups had an idea of proper storage of food to avoid contamination and damage with significant variations between groups. In contrast, study conducted in South Africa and Ghana explained that 84.4% of food handlers were aware of the importance of storing cooked and raw foods separately in order to prevent food borne diseases (Donkor et al., 2009). Fariba et al. (2018) reported that food handlers 90.5% know how to handle raw and cooked foods separately during storage and among them 80% were used separate utensils for raw and cooked foods. In contrast to a survey conducted by Al-Shabib et al. (2016) where 82.8% food handlers were using separate utensils for raw and cooked food, most of the handlers in current study did not have any such approach. Food handlers were unaware of using separate cutting boards for vegetables and chicken. As assessed in a previous study 30% of the individuals thought that the reheating of foods can contribute to food contamination while 60% answered that preparation of foods in advance can reduce the chances of contamination (Samapundo et al., 2015).
The food workers lack scientific or careful approach. They did not have any idea that dealing with the food and money at the same time may cause food contamina-tion. As discussed in previous studies food safety relies over various factors including personal hygiene so dealing with the money and food at the same time should be avoided (Kaesboher, 2000). Insects and rodents may cause serious food safety issues and considered as a potential source of contamination. Most of the handlers from both groups (i.e. <35% answer correctly) did not have any idea about food safety issues associated with insects and rodents. A survey conducted in India revealed that food handlers did not cover food usually so chances of contamination from insects and rodents were high (Choudhury et al., 2011). However, 94.44% participants from G-I and 58% from G-II agreed with the statement that training of staff may help in creating better food production circumstances. Majority of the participants were aware of checking expiry of the ingredients before usage. Less than 40% of them were reusing the leftover food without informing the costumer, that was surprising and unethical. Consumers who pay for fresh food might get infested or spoiled food in such case. Due to better check and balance by hostel administration, workers at hostel canteens had better performance as compared to departmental canteens. Students at hostels have better ties with the workers, so this might be the other reason for relatively careful approach of the food handlers at hostel canteens.

Food temperature control
Temperature plays a key role in food quality management, so regulation is necessary. Majority of the food handlers were unaware of the basic knowledge and significance of temperature in controlling microbial population within the food. Poor handling results in escalation of microbial growth leading to food hazards subsequently food borne illness. Practices followed by food handlers regarding temperature were assessed and depicted in the Table 3. In G-I, only 19.44%, whereas in G-II, approximately 33.33% considered temperature as important factor to be checked and regulated while others showed no concern. However, larger number of workers cooked the food at right temperature but failed to maintain it properly.
Food handlers of both groups did not have proper knowledge of high risks associated with food and cooking temperature. Merely 27.77% from G-I, and 28.88% from G-II had idea about the risks associated with the cooking temperature of high-risk food to avoid food borne diseases. Most of them were even unaware of temperature and techniques required for proper and safe storage. Despite all these mismanagements, majority of food handlers claimed that their food was safe for consumers. When asked for the possible flaws in provision of unsafe food, most of the handlers failed to justify their opinion. These results match with the study of Baş et al. (2006), who suggested that many of participants lack the information of critical temperature for ready to eat food, refrigeration storage temperature range and cross contamination. However, Bou-Mitri et al. (2018) reported contrary that 97% respondents had the knowledge of temperature in refrigerator/freezer and its role in food spoilage while the 96% respondents check the temperature and thermometer of freezer/refrigerator at regular periods. In addition, knowledge is a key tool for behavioral improvement that will help in improving safety measures. To overcome such kind of behavioral problems, training courses should be incorporated within the food systems for the development of positive attitude in food handlers as a preventive measure for food contamination.

Scoring of food handlers from university and hostel canteens
Several questions were asked to the food handlers of university and hostel canteens and on the basis of knowledge, attitude and practices regarding personal hygiene, workplace cleanliness and temperature control. No differences were observed between the knowledge, attitude and practices scores of hostels and university canteen food handlers as mentioned in the Table 4. Total 60% of the responses were correct from the university and hostel food handlers regarding knowledge of personal hygiene, workplace cleanliness and temperature monitoring. Similarly, 50-60% of the responses regarding attitude and practices were correct from hostel and university canteen food handlers.

Microbiological analysis
Microbial assessment done in the current study is presented in the Table 5. Canteens usually serve food in polyethylene bags and uncovered utensils and workers do not use safety gloves. These factors collectively have major contribution in food contamination. The TPC of 30 plates was ignored because of overgrowth or too few colonies to count. The presumptive test for 35 food samples out of 100 was positive and E. coli. was also found in the same samples. The prevalence of Salmonella and Staphylococcus aureus was found in 29 and 57 samples, respectively. The results unveiled milk shake as the most contaminated food followed by water and juice where contamination of Coliform and E. coli was found. The elevated level of microbes might be due to inappropriate  food handling practices and inadequate washing of utensils. The results of previous study conducted by Akhtar et al. (2013) for exploring the microbiological quality of various juices in Multan, Pakistan indicated the prevalence of total viable count 2.48±0.16 log CFU/ml, coliform 0.70-4.86 ±0.29 log CFU/ml and E.coli. 0.6 -3.83 log CFU/ml. In addition, salmonella spp. was also detected from various samples. Various type of Arcobacter species were detected in South Korea resulting more than 45 % food samples including chicken, meat, and leafy green were contaminated (Kim et al., 2019). Total aerobic bacteria and Coliform that  (17) 20 (17) 30 (25) 20 (17) Chi-Square (α 0.05), data are presented as frequency (percentage) Table 4. Knowledge, attitude and practices scoring of food handlers from university and hostel area.

Food handlers Correct Incorrect P
were found in different types of salads were 3.2x106 and 1.9x104 CFU/g respectively and 23.2% samples were contaminated with S. aureus (Adjrah et al., 2013). A study in Namibia unveiled the presence of Escherichia coli (42%), Listeria monocytogenes (15%), Shigella (6%) and Staphylococcus aureus (52%) in ready to eat food samples. Moreover, Enterobacteriaceae were found in 83 % food samples (Shiningeni et al., 2019). The high microbial load and prevalence of pathogenic microorganisms in food samples under study, endorsed the above results that the participants did not have basic food safety knowledge, attitude and practices mandatory to reduce the contamination.

Conclusions
The study explored that the knowledge, attitude and practices followed by food handlers need to be improved as these do not comply with standard hygienic procedure for food handling. Moreover, microbiological quality of food commodities requires improvement as many samples were contaminated and may contribute a human health hazard. Proper training of employees and strict monitoring of food industries or chains, i.e., hotels, restaurants and canteens may assure better food safety. Raw material suppliers and all the entities directly or indirectly involved in the food business should be considered for monitoring as these may play a critical role in food borne illness. Food handlers lack a scientific approach towards food commodities that needs to be developed via training sessions. Overall, 60% of the responses were answered correctly from the university and hostel food handlers about the knowledge of personal hygiene, workplace cleanliness and temperature control. Likewise, 50-60% of the responses regarding attitude and practices were correct from both groups. Legislative authorities are there, for the implementation of food safety laws but they need to work smartly. Little changes and little efforts give big rewards and that is what required for overall improvement that may in turn reward safety to the consumers and well prestige to the handlers.


extfieldsofstudy:
Biology
Computer Science
Mathematics

provenance: AgriculturalAndFoodSciences,2022-2022-Agricultural And Food Sciences-2022.jsonl.gz.gz

text:
Stimulation of soy seeds using environmentally friendly magnetic and electric fields

The study analyzes the impact of constant and alternating magnetic fields and alternating electric fields on various growth parameters of soy plants: the germination energy and capacity, plants emergence and number, the Yield(II) of the fresh mass of seedlings, protein content, and photosynthetic parameters. Four cultivars were used: MAVKA, MERLIN, VIOLETTA, and ANUSZKA. Moreover, the advanced Machine Learning processing pipeline was proposed to distinguish the impact of physical factors on photosynthetic parameters. It is possible to distinguish exposition on different physical factors for the first three cultivars; therefore, it indicates that the EM factors have some observable effect on soy plants. Moreover, some influence of physical factors on growth parameters was observed. The use of ELM (Electromagnetic) fields had a positive impact on the germination rate in Merlin plants. The highest values were recorded for the constant magnetic field (CMF) - Merlin, and the lowest for the alternating electric field (AEF) - Violetta. An increase in terms of emergence and number of plants after seed stimulation was observed for the Mavka cultivar, except for the AEF treatment (number of plants after 30 days) (...)

Introduction
Soy is one of the most important arable crops cultivated worldwide [1][2][3] . It is a leguminous plant whose symbiosis with Rhizobium bacteria enables it to bind nitrogen. This capacity makes for an inexpensive method of preserving soil fertility and improving plant yields 4 . Soy seeds are a valuable source of protein (30 − 40%) and fat (19 − 20%), as well as minerals and vitamins, owing to which they play an essential part in human and animal nutrition [5][6][7][8][9][10] . Soy is often used in food products and medicinal foodstuffs in the East 5,11 . It is typically consumed in the form of sprouts, oil, paste 12 , or soy meal 13 soy milk 14 .
Soy production is hindered by poor germination rates and low seed vitality. Therefore, it is necessary to employ methods facilitating better seed quality 15 . In order to improve the germination rate, seeds were primed by Ancient Greeks by soaking them in milk or water as early as in 4th century BC 16 . Literature provides reports on studies related to soaking seeds in hydrogen peroxide, potassium nitrate, or potassium chloride 17 . Moreover, stress caused by both abiotic (e.g., low or high temperature, water deficit or excess, salination, excessive sunlight, hail, etc.) and biotic factors (diseases, pests) has a considerable impact on plant growth, Yield(II), and seed storage 18,19 .
Of the same, low temperatures are a major factor inhibiting the growth and development of plants, including soy. Soy germinates the most effectively in temperatures ranging from 10 o C to 30 o C, with the rate increasing as the temperatures go up. Indeed, some authors claim that the optimum temperature is exactly 30 o C [20][21][22][23][24][25] . Pre-soaking seeds further increases the germination rate in higher temperatures but decreases it in cooler conditions 20 . In temperatures between 0 o C and 10 o C, young soy plants tend to be damaged, and the stress has also been confirmed to impact photosynthetic processes negatively 24 .
The seed moisture level is high during the harvest but decreases with long-term storage. This results in decreased seed quality, i.e., loss of vitality with the low vigor negatively influencing the germination rate, seedling growth, and plant regeneration. The conditions of correct cultivation depend on factors such as the vitality and age of seeds, conditions, and duration of storage, as well as the plant genotype. Moreover, reduced seed vitality hinders soy emergence, particularly in agriculture 26 .
A key concern related to achieving higher efficiency in soy cultivation is the improvement of the germination rate, e.g., by employing one of the methods of processing seeds before sowing. The methods discussed in the following article include stimulation with alternating and constant magnetic fields and alternating electric fields [27][28][29][30][31] . Due to their environmental friendliness, the techniques might potentially replace chemical treatment in the future. Other physical treatment methods have also been discussed in the literature, including laser light, gamma radiation, etc. 18,32 . The pre-sowing treatment of seeds affects the physiological and biochemical properties of the material 32 .
Magnetic field stimulation was one of the broadly studied methods of seed processing analyzed in terms of impact on seed germination, plant growth, and biological parameters [33][34][35][36] . The technique remains the subject of considerable research scrutiny, mainly because of its non-invasiveness and environmentally friendly character, as well as its effectiveness in stimulating better seed germination and vigor 37 . Research has also been conducted on the effects of an alternating magnetic field with the ICR cyclotron resonance frequency of Ca2+ ions on wheat seedlings, and the results evidenced a positive influence on the number of sprouts as well as the wet and dry weight of the seedlings 38 .
Stimulating seeds with an alternating electric field increased growth capacity in seven-year-old radish seeds by approx. 80% 39 , and the germination capacity of old tomato seeds (Halicz cultivar) by between 46% and 80% 40 or even 100% 41 .
From the technical perspective, chlorophyll fluorescence is measured using specialist fluorometers. The method can be used in studies on various aspects of photosynthesis in plant production, mainly to detect stress conditions but without identifying their exact nature 42,43 . The method of determining leaf greenness using a portable chlorophyll meter (SPAD-502) can be used for fast and repeatable measurements in various plant species 44,45 .
However, global literature still lacks sufficient reports on the impact of soy seed stimulation using magnetic and electric fields on germination parameters, growth, development, Yield(II), or the efficiency of photosynthesis and the respective parameters of photosynthetic processes in leaves. Despite the ongoing scientific and technological advances, many secrets of the mechanisms related to plant growth are yet to be discovered. For this reason, we were driven to explore this research problem, which our group has been analyzing for over a dozen years.
Moreover, the rising importance of advanced Machine Learning methods in science provides an excellent opportunity to use them in the research to discover non-trivial relationships in the data. The paper contains a well-thought clustering pipeline to check physical factors' influence on soy plants.
The paper is organized as follows: In the next section the methodology is outlined. Next, the results are presented. Finally, a discussion is given.

Parameters of germination and growth of soy plants
The germination energy of soy seeds ( Fig.1) was between 29% (Anuszka AMF) and 82% (Mavka AMF). In the range from 40% (Violetta, AEF) to 82% (Mavka, AMF), the germination capacity of the seeds changed as well (Fig. 2). The significant, and simultaneously the highest, values of the germination parameters were observed for the Merlin and CMF cultivars (23%), whereas the lowest extent of changes in Violetta and AEF plants (25%), relative to the control (C).
Relative to C, an increase in plant emergence was observed (Fig. 3) in all the stimulated samples for the Mavka cultivar, with the highest increase of 38% recorded for AMF. A significant 5% increase was also observed in Anuszka plants, as well as a 105, but insignificant, decrease for Merlin plants. Under CMF stimulation, an insignificant 11% increase was observed for the Mavka cultivar and a decrease by between 2 and 14% in the other three cultivars. The alternating magnetic field (AMF) triggered an increase in plant emergence, by between 2 and 6%, in three of the cultivars and a decrease by 12% in Anuszka plants.
A nearly threefold increase in the number of plants was observed for the Mavka plants and AMF (Fig. 4). A decrease by 56% was recorded for Anuszka and AEF and by approx. 21% for Merlin plants treated with AMF and CMF, otherwise no significant differences.
Similarly, after 30 days (Fig. 5), the increase in the number of plants was the highest for Mavka (AMF). A twofold increase relative to the control was recorded for Mavka (CMF) and Violetta (AMF). A decrease by 31% and 50% was observed for Merlin (AMF and CMF) and Anuszka (CMF), respectively.
The increase in the Yield(II) of fresh mass (Tab. 1) was the highest (relative to C) for AMF, and the lowest for AEF in most of the studied cultivars. The highest recorded increase relative to C was 233% (AMF, Violetta), 93% (AEF, Violetta) and 71% Figure 1. Germination energy of soy seeds subjected to pre-sowing treatment using alternating electric field, constant and alternating magnetic field: C -control, AMF -alternating magnetic field, CMF -constant magnetic field, AEF -alternating electric field. Error bars -standard deviation. a-d -different letters indicate significant changes between the control and stimulated study groups, interaction between groups subjected to stimulation (α < 0.05).

Figure 2.
Germination capacity of soy seeds subjected to pre-sowing treatment using alternating electric field, constant and alternating magnetic field: C -control, AMF -alternating magnetic field, CMF -constant magnetic field, AEF -alternating electric field. Error bars -standard deviation. a-d -different letters indicate significant changes between the control and stimulated study groups, interaction between groups subjected to stimulation (α < 0.05).
In terms of the number of plants after 30 days (Fig. 8), a very high increase (216.7%) was recorded for AMF in the case of Mavka plants, as well as a significant (125%) increase for the Violetta cultivar. Anuszka plants also increased, albeit considerably lower (12.5%). The negative impact of AMF on the number of plants was recorded only for Merlin plants (31.3%). As evidenced by the above, in certain situations the stimulating factors can also have a negative impact. For CMF, positive results were observed for Mavka (100%) and Violetta (25%) plants, but at the same time negative results were returned for Merlin (31.3%) and Anuszka (50%) cultivars.

Figure 3.
Emergence of plants grown from soy seeds stimulated using alternating electric field, constant and alternating magnetic field: C -control, AMF -alternating magnetic field, CMF -constant magnetic field, AEF -alternating electric field. Error bars -standard deviation. a-d -different letters indicate significant changes between the control and stimulated study groups, interaction between groups subjected to stimulation (α < 0.05).

Figure 4.
Number of plants grown within 8 days from soy seeds stimulated using alternating electric field, constant and alternating magnetic field: C -control, AMF -alternating magnetic field, CMF -constant magnetic field, AEF -alternating electric field. Error bars -standard deviation. a-d -different letters indicate significant changes between the control and stimulated study groups, interaction between groups subjected to stimulation (α < 0.05).

Leaf protein content
After the field stimulation, the protein content ( Fig. 9) in half of the analyzed soy cultivars was increased compared to the control. The highest values were observed in two cultivars after AMF stimulation, with a significant increase by 13% (Merlin) and 16% (Mavka) relative to C. It is also noteworthy that the stimulation increased the protein content in Anuszka plants by 3 − 5%. The least amounts of protein were observed in the AMF sample (2 − 3%) but the differences were statistically insignificant.

Relative correlations between photosynthetic parameters in soy leaves
In the case of photosynthetic parameters (Figs. 10,11,12 ), in order to determine the impact of the stimulation, we sought correlations between the respective cultivars and the physical stimuli used, to identify similarities and repeatability.
The observed influence of ELM stimulation on the photosynthetic index ( Fig. 10) was both positive and negative compared to the control sample. In this respect, a clearly positive impact of seed treatment was recorded for two of the cultivars (Mavka and Anuszka). The highest value of Yield(II) was obtained for AEF stimulation (Mavka, (increase by 19%), and the lowest for CMF (Violetta, decrease by approx. 2%).
Electron transfer (Fig. 11) was positively influenced by all the fields in Mavka and Anuszka plants, and negatively in the other two cultivars (Merlin and Violetta). The highest value relative to the control was observed for CMF (21% increase, Mavka) and the lowest for AEF (47% decrease, Merlin).
The impact of field treatment on the SPAD greenness index (Fig. 12) was positive in one cultivar (Mavka) and negative in the other three. The highest increase was observed for CMF (58%, Mavka) and the highest decrease for AEF (over 15%, Merlin). A positive impact of alternating magnetic field treatment was identified for the Yield(II) and ETR correlation in the 4/17 Figure 5. Number of plants grown within 30 days from soy seeds stimulated using alternating electric field, constant and alternating magnetic field: C -control, AMF -alternating magnetic field, CMF -constant magnetic field, AEF -alternating electric field. Error bars -standard deviation. a-d -different letters indicate significant changes between the control and stimulated study groups, interaction between groups subjected to stimulation (α < 0.05).  Table 1. C -control, AMF -alternating magnetic field, CMF -constant magnetic field, AEF -alternating electric field. ± standard deviation , mark a-d in rows mean statistical differences between control and research objects, interaction between groups subjected to simulation (α < 0.05).

Variety
Mavka and Anuszka cultivars; the other two cultivars showed negative effects in this respect. Positive effects of ELM fields in the correlation of three parameters: Yield(II), ETR, and SPAD could be observed only in one cultivar (Mavka). A negative impact of alternating electric fields was observed in two of the studied cultivars (Merlin and Violetta) and of constant electric fields in one (Violetta), in a correlation between Yield(II), ETR, and SPAD.
To recapitulate, in the study on using ELM fields to treat soy seeds, the results related to Yield(II), ETR, and SPAD were inconsistent and evidenced both positive and negative effects of the treatment. One should note, in particular, the object where the use of CMF in combination with the Mavka cultivar produced a clearly positive result in terms of protein content and photosynthetic parameters (photosynthesis, electron transfer, greenness index).

Clustering analysis
The previous sections indicate a possible impact on averaged values of soy parameters. Therefore, it is essential to cross-check these relations for individual samples.
Our first attempt to distinguish the impact of physical factors on soy plants was to use supervised learning for the whole range of data (not averaged). In order to maximize the number of records, only four features were selected: Leaf Greenning index after 15 and 30 days and Yield(II) after 15 and 30 days. The data labels were selected as either varieties, electromagnetic factors, or both. We used multiple classes or a one-class-against-others approach with proper weighting. The standard pipeline was applied that consists of the following steps: 1. Standard scaling of data.
2. Dimensional reduction of data in terms of Principal Component Analysis.

Cross validate models.
with models including Naive Bayesian, Decision Tree, Support Vector Machine, and Neural Network. Unfortunately, averaged weighted accuracy and other metrics suggest that the effectiveness of prediction is at the level of about 50%. Figure 6. Graphic representation of the relative germination energy values C -control, AMF -alternating magnetic field, CMF -constant magnetic field, AEF -alternating electric field.

5/17
Due to the ineffectiveness of this approach, we proposed another method that can distinguish various classes based on unsupervised learning (clustering) and nonlinear transformation connected with dimensionality reduction. The redesigned analysis pipeline consists of several steps: 1. Remove outliers.
3. Reduce data dimensionality and nonlinear transform/projection to the three-dimensional space.
4. Compute the optimal number of clusters using the averaged silhouette score.

Designing of clustering algorithm
Since the classical machine learning approach was not conclusive, the new, more robust approach was designed. For the analysis, the algorithms from Scikit Learn 0.22.2.post1 version of Python library 46 was used.
It was checked that the following procedure provides the best uniform clusters for the following parameters: • SPAD greenness index after 15 and 30 days • Yield(II) after 15 and 30 days

• ETR after 15 and 30 days
The first step is to clean data from the outliers. This step is necessary since, by visual inspection, it was spotted that some of the values look unreliable. The following reasoning explained this: some complicated measurements such as ETR or Yield(II) heavily rely on multiple factors such as sample preparation and proper measurement procedure that is error susceptible. Therefore errors should occur when a high number of measurements is produced. The two standard techniques for outlier/anomaly detection were checked: 6/17 Figure 7. Graphic representation of the relative germination capacity values C -control, AMF -alternating magnetic field, CMF -constant magnetic field, AEF -alternating electric field.

• Isolation Forest -sensitive to global outliers • Local Outliers Factory -isolates local outliers
Both algorithms have their advantages and drawbacks 50 . In order to restrict the globality of the Isolation Forest, the run was performed on a specific cultivar, however, it was checked that although Isolation Forest removes more possible outliers, it produces at further steps more uniform clusters. In most cases the outliers detected by the Local Outliers factory are included in the outliers found by the Isolation Forest algorithm.
In the next step the Hopkins statistics was used to check clustering tendency in the data. The value is 0.8784, which indicates a high tendency to cluster.
Then for each object, standardization to the mean 0 and STD 1 was made. Since it was checked that the clusters do not separate well upon linear transformation through PCA, it was decided to perform nonlinear transformation with a dimensional reduction to 3 dimensions using UMAP (Uniform Manifold Approximation and Projection for Dimension Reduction) 51, 52 . This algorithm has similar or better performance for many cases than the other state-of-the-art nonlinear transformation and dimensionality reduction algorithm t-SNE used commonly for clustering 52 .
Finally, the optimal number of clusters was extracted using two independent techniques: the first one is the maximization of averaged silhouette score that also provides the information on the balance of clusters; the second one is the elbow rule on the SSE vs. the number of clusters plot. After this check, the standard k-Means clustering algorithm was used to identify clusters and check their content.
The result of the procedure is presented in the following subsection.

Clustering of cultivars
MAVKA: This class contains 51 records. After scaling and UMAP transformation and projection the averaged silhouette score was optimized for 15 clusters. The k-Means clustering is presented in Fig. 13. and the clusters are presented in Tab. 2. It is visible that most of the clusters are clean and therefore our approach can distinguish seeds exposed by different physical factors.
MERLIN: This class contains 47 samples. The similar procedure as above provided 13 clusters with the content in Tab. 3. The obtained clusters are also distinguished. VIOLETTA: The sample contains 48 records. After processing the cluster content is presented in Tab. 4. In this case some clusters are mixed with no distinguished leading factor, e.g., clusters no. 7 and 9. ANUSZKA: For this cultivar there are 46 records, and the optimal number of clusters is determined to be 10. The content of clusters is presented in Tab. 5. In this case the clusters are the most nonuniform. The proposed method can distinguish some clear clusters, but most are nonuniform.

Discussion
The method to distinguish plants under various physical factors was proposed and checked on specific examples. The first two cultivars (MAVKA, MERLIN) provide relatively high predictive power, indicating that the physical factors alter Yield(II), ETR, and leaf greenness parameters. This indicates a potential field to explore in more detailed experiments with higher statistics. VIOLETTA shows a smaller distinction under EM factors. The distinction is smaller for ANUSZKA cultivar. This can suggest that ANUSZKA is less susceptible to the used factors. The proposed processing/clustering pipeline can be used in more advanced studies of the influence of external factors on plants growth. Moreover, the failure of standard supervised ML methods indicates that the influence of EM factors can be masked by some nonlinear relations that more advanced transformations like UMAP can unmask.

Conclusions
A positive impact of ELM fields on germination energy and capacity was observed for the Merlin cultivar, for which the significant (and highest) increase reached 23% (CMF). At the same time, in the case of Violetta plants, a significant, approx. 40% decrease was observed in the AMF sample. Emergence and plant numbers increased in all fields sown with Mavka seeds except the electric sample (number of plants after 30 days). The value of the SPAD greenness index in the correlation of 30-day plants to 15-day plants increased relative to the control for most of the analyzed cultivars and respective ELM fields. Howerer, the SPAD value as such for 15-and 30-day plants decreased (or remained unchanged) for most cultivars and stimulation treatments. This may evidence a negative effect of ELM fields on the chlorophyll content, making them a stressogenic factor in this respect.
In the case of protein content in soy plants, in most cultivar and stimulant combinations was observed to increase (a positive effect relative to the control). The most significant results were noted under CMF stimulation in Mavka and Merlin plants.
Using ELM stimulation on soy seeds positively influenced the photosynthetic efficiency, electron transport rate, and SPAD chlorophyll content in Mavka plants. The results were less decisive in the remaining three cultivars, showing both positive and negative impacts. In Merlin plants, a decrease in the greenness index and plant number after 30 days was recorded for the AMF treatment.
To recapitulate, it can be concluded that changes in the parameters of germination and plant growth, as well as the chlorophyll and protein content, depended both on the particular ELM treatment employed and the soy cultivar in question. It is noteworthy that physical factors may indeed be used to stimulate soy seeds in a non-invasive and environmentally friendly manner as they introduce no changes to the natural agricultural ecosystem. Stimulation can improve germination, Yield(II), and photosynthetic efficiency, but further research in this context is definitely needed.
Moreover, we proposed a clustering algorithm that can be used to examine the nonlinear relation between external stimuli

Plants
The research material comprised seeds of four soy cultivars: Mavka, Merlin, Violetta, and Anuszka, harvested in 2017 and stored under room conditions. In 2018, an experiment was conducted at the Chair of Biophysics of the University of Life Sciences in Lublin. It entailed two stages: determination of the germination energy and capacity of seeds placed on Petri dishes, and determination of the rate of emergence, number of plants, fresh mass Yield(II), photosynthetic parameters, and leaf protein content in pot-grown plants.
We confirm that experimental studies on used plants cultivated in the study, are comply with relevant institutional and national guidelines and legislation effectual at Research Centre for Cultivar Testing 47 .

Pre-sowing stimulation of soy seeds
Before sowing, soy seeds were subjected to stimulation using an alternating magnetic field with the induction of B = 30mT and exposure time of t = 60s, a constant magnetic field with the induction of B = 130mT and exposure time t = 17h, or an alternating electric field with the intensity of E = 5kV cm −1 for t = 60s, respectively designated as: AMF, CMF, and AEF as well as C -control sample (non-stimulated seeds).

Laboratory experiment
The germination energy (GE) and capacity (GC) were determined in accordance with the applicable ISTA procedure 48 after 5 and 8 days of the experiment.
where GE -germination energy, GC -germination capacity, N -number of seeds sown on the dish (units), N 5 -number of seeds germinating after 5 days (units), N 8 -number of seeds germinating after 8 days (units).
After stimulation, the soy seeds were sown in 30 parts in each pot (1dm 3 ) filled with universal soil, in 4 replicates each treatment. Seed sowing depth was 2cm and covered with universal soil for 1cm. Soil humidity was maintained by ensuring constant availability of water by placing the pots in horticultural trays filled with water of constant volume throughout the experiment duration. Samples were placed inside a climate chamber, under day/night 16/8 h illumination, day/night temperatures of 23 o C/12 o C(±2 o C), and E = 600lx light intensity.
The experimental measurements of each treatment are presented as mean plant emergence (after 5 days), the number of plants (after 8 days and 30 days), the Yield(II) of the fresh mass of seedlings (after 30 days), SPAD greenness index, quantum photochemical Yield(II), ETR -electron transport rate, and protein content in leaves. The PE (plant emergency) is defined as where N -number of seeds sown in pots (units), N5 -number of seeds germinating after 5 days (units). Figure 12. Representation of relative SPAD greenness index values, C -control, AMF -alternating magnetic field, CMFconstant magnetic field, AEF -alternating electric field.

Photosynthetic parameters
The greenness index, photosynthetic efficiency (Yield(II), and electron transport rate (ETR) were determined after 15 and 30 days for 16 randomly selected leaves, randomly selected from the 4 replications in each treatment. The greenness index was measured with the use of a SPAD-502 Chlorophyll Meter 45,46 . Yield(II) and electron transport rate were measured using a MINI-PAM 2000 WALTZ Photosynthesis Yield Analyzer (Germany).
The following formulas were used: where Yield(II) -current quantum efficiency PSII, M -Maximal fluorescence Yield(II) (M = Fm or Fm') measured during the last saturating light pulse triggered by START, F -Momentary fluorescence Yield(II) displaying small fluctuations. Display of current factor applied for calculation of relative electron transport rate (ETR), which for a standard leaf is defined as follows: ET R = Y IELD(II) × PAR × 0.5 × 0.84, where ETR -relative electron transport rate, PAR -Photosynthetically active radiation. The standard factor 0.84 corresponds to the fraction of incident light absorbed by a leaf. The preset value, which corresponds to an average observed with various leaf species, can be modified via SET and the arrow-keys.

Statistical analysis
The obtained results were processed by way of ANOVA variance analysis, LSD test at the significance level of α < 0.05, using STATISTICA 13.1 software. Significance was determined between the research factors for the respective cultivars and the control (C). Moreover, the research results were processed and presented using an innovative, proprietary method for determining the percentage of relative relation compared to the control. The percentage of relative relation was calculated using: where A -value obtained, C -control value. Positive results indicate the percentage of relative increase, negative valuesrelative decrease, compared to the control.

Machine Learning -clustering
We used Python 3.8.5 interpreter and Machine Learning library: Scikit-Learn 0.22.2 46 . For UMAP analysis we used reference implementation 51 .


text:
The Impact of the Animal Housing System on Immune Cell Composition and Function in the Blood of Dromedary Camels

Simple Summary The present study investigated the impacts of a change in animal housing system on selected parameters of the camel immune system. Samples collected from camels during a free-ranging time were compared with samples collected from the same camels during movement-restricted housing. Movement-restricted camels showed elevated myeloperoxidase activity in their serum, a significant shape-change of their neutrophils, and higher reactive oxygen species content in their monocytes and neutrophils. The leukogram pattern of the camels under restricted housing was characterized by increased numbers of neutrophils, eosinophils, lymphocytes, and monocytes. Within the lymphocyte population, only the helper T cells and B cells were expanded in animals under restricted housing. In addition, restricted housing modulated the expression of several cell surface antigens, including monocyte-polarization markers and cell adhesion molecules. Functional analysis of bacterial phagocytosis indicated impaired antibacterial function of phagocytes in camels under restricted housing. In summary, the present study identified significant changes in blood immune cell composition, phenotype, and function in dromedary camels under restricted-housing conditions, and suggests the development of an excitement leukogram in those animals. Abstract Background: The dromedary camel (Camelus dromedarius) is an important livestock animal of desert and semi-desert ecosystems. In recent years, several elements of the camel immune system have been characterized. Stress and excitement induced by animal housing represent the most important environmental factors with potential modulatory effects on the immune system. The present study evaluated the impacts of a restricted-housing system on some phenotypic and functional properties of blood leukocytes in dromedary camels. Methods: Immunofluorescence and flow cytometry were used to comparatively analyze samples collected from camels during a free-ranging time and samples collected from the same camels during movement-restricted housing. Results: In comparison to blood samples collected from the camels during the free-ranging time, samples from movement-restricted camels showed elevated serum myeloperoxidase activity, a significant shape-change in their neutrophils, and higher reactive oxygen species content in their monocytes and neutrophils, indicating increased cellular oxidative stress under movement-restricted housing. The leukogram pattern of the camels under restricted housing was characterized by leukocytosis with increased numbers of neutrophils, eosinophils, lymphocytes, and monocytes, resembling an excitement leukogram pattern. Within the lymphocyte population, only the helper T cells and B cells were expanded in animals under restricted housing. The upregulation of CD163 together with the downregulation of MHC-II on monocytes from excited camels indicate a modulatory potential of animal excitement to polarize monocytes toward an anti-inflammatory phenotype. Functional analysis of bacterial phagocytosis indicates an impaired antibacterial function of phagocytes in excited camels. The downregulation of several cell adhesion molecules on leukocytes from excited camels suggests a role for impaired cell adhesion and tissue migration and leukocyte retention in blood in the observed leukocytosis in animals under excitement. Conclusions: The present study identified significant changes in blood immune cell composition, phenotype, and function in dromedary camels under restricted-housing conditions. The observed changes in leukocyte composition suggest the development of an excitement leukogram pattern in camels under movement-restricted housing. To evaluate the clinical relevance of the observed changes in immune cell phenotype and function for the immune competence of camels under restricted housing, further studies are required.

Abstract: Background: The dromedary camel (Camelus dromedarius) is an important livestock animal of desert and semi-desert ecosystems. In recent years, several elements of the camel immune system have been characterized. Stress and excitement induced by animal housing represent the most important environmental factors with potential modulatory effects on the immune system. The present study evaluated the impacts of a restricted-housing system on some phenotypic and functional properties of blood leukocytes in dromedary camels. Methods: Immunofluorescence and flow cytometry were used to comparatively analyze samples collected from camels during a free-ranging time and samples collected from the same camels during movement-restricted housing. Results: In comparison to blood samples collected from the camels during the free-ranging time, samples from movement-restricted camels showed elevated serum myeloperoxidase activity, a significant shape-change in their neutrophils, and higher reactive oxygen species content in their monocytes and neutrophils, indicating increased cellular oxidative stress under movement-restricted housing. The leukogram pattern of the camels under restricted housing was characterized by leukocytosis with increased numbers of neutrophils, eosinophils, lymphocytes, and monocytes, resembling an excitement leukogram pattern. Within the lymphocyte population, only the helper T cells and B cells were expanded in animals under restricted housing. The upregulation of CD163 together with the downregulation of MHC-II on monocytes from excited camels indicate a modulatory potential of animal excitement to polarize monocytes toward an anti-inflammatory phenotype. Functional analysis of bacterial phagocytosis indicates an impaired antibacterial function of phagocytes in excited camels. The downregulation of several cell adhesion molecules on leukocytes from excited camels suggests a role for impaired cell adhesion and tissue migration and leukocyte retention in blood in the observed leukocytosis in animals under excitement. Conclusions: The present study identified significant changes in blood immune cell composition, phenotype, and function in dromedary camels under restricted-housing conditions. The observed changes in leukocyte composition suggest the development of an excitement leukogram pattern in camels under movement-restricted housing. To

Introduction
The dromedary camel (Camelus dromedarius) is one of the most important livestock animals in arid and semi-arid regions [1][2][3][4]. In comparison to other domestic species, camels can reproduce and produce high-quality food under extremely harsh conditions, including heat stress and very limited food and water resources [5][6][7]. In addition, camels show relatively higher resistance to several pathogens than many other farm animal species [8].
For the dromedary camel, several components of the immune system have been recently characterized [8]. For most immunological studies, peripheral blood leukocytes, which are a valuable source of innate and adaptive immune cells, are collected from the animals for ex vivo analysis of their phenotypes and functions. The leukocyte count in peripheral blood is regulated by the balance between their production in the bone marrow and their migratory activity from blood to tissues [9]. Leukocyte adhesion to blood vessel endothelial cells and their transmigration and extravasation from blood are regulated by several cell surface adhesion molecules [10][11][12][13].
The phenotypes and functions of leukocytes are influenced by several physiological [14] and pathological factors [15]. Stress and excitement during animal housing represent major environmental factors with significant modulatory effects on the immune system [16]. Studies in several animal species have shown that measurements of many immunological parameters in samples taken from stressed animals can be highly variable, ranging from almost normal baseline to extremely high [16][17][18][19][20]. Cattle and sheep under short-term stress have shown impaired immune responses [17,20]. Bovine heifers with higher handling-stress responsiveness showed reduced antibody and cellular immune responses [16].
Animal stress may result from several factors, which may differently affect animal welfare. Animal stressors may be either physiological, including excitement, restraint, novelty, or handling; or physical stressors, such as hunger, fatigue, injury, or heat-stress [21,22]. According to recent reports, housing management, including space allowance and the freedom of movement, are critical factors for camel welfare [23][24][25]. Restricted housing agitates and excites animals, resulting in changes in several body systems, including the immune system [17,22,26]. In cattle, a limited space allowance has been linked to higher aggressiveness and reduced immune responses [27,28].
The methods of camel housing are now changing due to the increased cultivation, resulting in a significant decrease in free grazing areas. Camel keepers of free-grazing management systems are currently looking for alternative intensive and semi-intensive housing systems [29]. In semi-intensive keeping systems, camels are set free during the daytime and collected at the evening to be herded in closed pen during night. The limited space in intensive animal housing systems is usually associated with several environmental stressors that could affect their welfare [29]. As the impact of housing system on the camel immune system has not been investigated so far, the present study aimed at the evaluation of leukocyte composition, phenotype, and function in camels after movementrestricted housing.

Animals and Blood Sampling
Nine apparently healthy female dromedary camels were selected from a herd of 25 camels reared at a private farm in the Al-Ahsa region in Saudi Arabia. The experimental camels were from the black Majahim breed ranging in age between 9 and 13 years. The she-camels were non-pregnant and non-lactating animals with an average body weight of 345 ± 12.0 kg. As the studied camels were from an associated homogenous herd, the selection criteria were based on the animal age and weight. The study was performed in the arid area of the Al-Ahsa region (N 25 • 17 8.0844", E 49 • 29 11.3316") in the eastern province of Saudi Arabia. The annual temperature of the area varies from 15 to 45 • C with a mean annual rainfall of 85 mm. The study was conducted on two consecutive days in November 2020 with an average ambient temperature and relative humidity of 23 • C (range: 19-28 • C) and 31.3% (range: 20-32.5%), respectively. The animals were kept under a traditional management system and were not within the breeding season. During the daytime, the camels were grazing, browsing from 06:00 a.m. to 08:00 p.m. In the evening, the animals were guided by the caretaker on foot into the farm to be corralled in a group-field fence during the nighttime (08:00 p.m. to 06:00 a.m.). The camels were fed on hay and barley in addition to bread and discarded dates with a mineral supplement (copper sulphate, zinc sulphate, manganese sulphate, calcium iodine, cobalt sulphate, and sodium selenite). Drinking water was available for the camels in the stall and the grazing range. For the first sampling, blood samples were collected from the free-ranging animals during the daytime (at 09:00 a.m. to 12:00 p.m.), where the animals were distanced from each other (1-3 km distance). The second blood sample was taken from the same camels on the second day at 05:00 a.m., where the animals were still on the farm. Blood samples were obtained by venipuncture of the jugular vein (vena jugularis externa) into vacutainer tubes containing ethylenediaminetetraacetic acid (EDTA). After collection, blood samples were transported to the testing laboratory in a cool box. Cell separation from the collected blood samples was performed within one hour from the sampling. For blood serum collection, blood was taken into sterile tubes without anticoagulants (Becton Dickinson, Heidelberg, Germany). After clotting, cell-free serum was prepared by centrifugation of the blood samples at 3000 × g for 15 min.

Serum Myeloperoxidase Activity
Serum myeloperoxidase activity was estimated based on the procedure described previously [30,31] with some modifications. Briefly, the test was performed in a flatbottomed 96-well microtiter plate. Serum was diluted 1 to 5 in phosphate buffered saline (PBS) and 50 µL of diluted serum was incubated with 50 µL of peroxidase substrate and chromogen buffer (33.3 mmol/L citric acid, 66.7 mmol/L NaH 2 PO 4 , pH 5.0, supplemented with 130 µg/mL 3,3 ,5,5 -tetramethylbenzidine and 0.01% (v/v) H 2 O 2 ; all chemicals from Sigma, Darmstadt, Germany). After a short incubation of 8-10 min at RT in the dark, the reaction was stopped by adding 50 µL of 0.5 M H 2 SO 4 to each well. The density of color developed was measured spectrophotometrically (MR 5000, Dynatech, Denkendorf, Germany) at 450 nm.

Cell Separation
Camel white blood cells were isolated from EDTA blood samples after removing the red blood cells using osmotic hypotonic lysis. For this, 1 mL blood sample was incubated in 5 mL distilled water for 20 s, and 5 mL double concentrated PBS were added to restore tonicity. This procedure was repeated until complete hemolysis. After that, the cells were washed two times in PBS (500× g, 250× g, 10 min, 10 • C). Separated leukocytes were finally suspended in staining buffer (PBS containing 5 g/L BSA, 100 mg/L NaN 3 ) at 5 × 10 6 cells/mL for flow cytometry.

Analysis of Neutrophils' Shape-Change
For the analysis of changes in neutrophil side scatter characteristic (SSC), which is indicative of neutrophil degranulation and secretion of their granular contents [32,33], separated leukocytes (5 × 10 6 cells/mL) were analyzed for their mean SSC values using the Accurie C6 flow cytometer (equipped with a blue (488 nm) and a red (640 nm) laser, two light scatter detectors (FSC and SSC), and four fluorescence detectors; BD Biosciences, Heidelberg, Germany). Neutrophil SSC height (SSC-H) was measured using the CFlow ® software (version 1.0.264.21, BD Biosciences, Heidelberg, Germany) and compared between cells from normal range animals and animals during restricted housing.

Measurement of Reactive Oxygen Species Production in Neutrophils and Monocytes
For monocyte identification, separated leukocytes were firstly labeled with anti-CD14 antibody. For this, 100 µL per well of 1 × 10 6 leukocyte suspension were incubated with an APC-conjugated mouse IgG2a against human CD14. After 15 min at 4 • C, the cells were washed twice with MIF buffer. Labeled leukocytes were then incubated (20 min; 37 • C, 5% CO 2 ) with 500 ng/mL dihydrorhodamine-123 (DHR-123, Mobitec, Goettingen, Germany). After that, the cells were washed once with PBS (300× g for 3 min) and the median fluorescence intensity of FL1 (indicative for ROS amount) was determined by flow cytometry (Accurie C6 flow cytometer, BD Biosciences, Heidelberg, Germany). The fluorochromes allophycocyanin (APC) and DHR-123 were excited by the red and the blue lasers and detected in FL-1 and FL-4, respectively.

Monoclonal Antibodies
The antibodies used for cell staining are presented in Supplementary Table S1. All monoclonal antibodies were directed against leukocyte antigens of other animals, including lama (CD44 and CD45R), human (CD14 and CD18), bovine (CD14, CD163, CD4, WC1, CD11a), and swine (MH II). All antibodies were tested for reactivity against camel leukocytes in previous studies. The cross-reactivity was based on the expression pattern in flow cytometry [15,[34][35][36][37]. For the CD4 and WC1 antibodies, a cross-reactivity with camel leukocytes antigens has been indicated by the manufacturer (Supplementary Table S1).

Membrane Immunofluorescence
Cell labeling was performed in a round-bottomed 96-well microtiter plate using 5 × 10 5 leukocytes per well as previously described [38]. All incubation and centrifugation steps were performed at 4 • C. Separated leukocytes were incubated with unlabeled primary monoclonal antibodies (mAbs) (Supplementary Table S1) specific for the cell surface molecules CD4, WC-1, CD14, CD163, CD172a, MHC-II, CD11a, CD18, CD44, and CD45R [15,[34][35][36][37] for 15 min in the dark. After two washings in staining buffer, the cells were incubated with fluorochrome-labeled anti-mouse IgM, IgG1, and IgG2a secondary antibodies (Invitrogen, Schwerte, Germany) for 15 min in the dark. Parallel setups were incubated only with antibody isotype controls. After two washings, labeled cells were analyzed on an Accurie C6 flow cytometer (BD Biosciences, Heidelberg, Germany) by the acquisition of at least 100,000 total leukocytes. Collected flow cytometric data were analyzed using the CFlow Software (V 1.0.264.21; BD Biosciences, Heidelberg, Germany). Leukocyte count was estimated under a microscope using the Neubauer counting chamber after staining of the blood sample with Türk Solution.

Flow Cytometric Analysis of Bacterial Phagocytosis
For the phagocytosis assay, heat killed Staphylococcus aureus (S. aureus) bacteria (Pansorbin, Calbiochem, Merck, Nottingham, UK) were labeled with fluoresceinisothiocyanate (FITC, Sigma-Aldrich, St. Louis, MO, USA) according to manufacturer instructions. For monocyte identification, separated leukocytes were firstly labeled with APC-conjugated mouse IgG2a against CD14 as described above. Labeled leukocytes were then incubated in 96 well plates (1 × 105/well) with FITC-labeled S. aureus (50 bacteria/cell) for 45 min at 37 • C and 5% CO 2 . After incubation (15 min; 4 • C), the percentage of monocytes and neutrophils with elevated green fluorescence among total cells was calculated after flow cytometric analysis.

Statistical Analyses
For statistical analysis, the values for the two groups were compared using the Prism (GraphPad software version 5, GraphPad Software, San Diego, CA, USA). The Kolmogorov-Smirnov test (with the Dallal-Wilkinson-Lilliefor p value) was performed to check the normal distribution of data. For normal-distributed data, the paired student's t-test was used to compare the mean of the two groups. For the data that failed to pass the normality test, the Wilcoxon matched-pairs signed ranks test was used to compare the means. The results for each analyzed parameter are presented graphically as mean ± standard error of the mean (SEM). The p-values indicating the significance of the differences between means are presented for each parameter.

Results
In the present study, the leukogram and some phenotypic and functional properties of leukocytes were analyzed in dromedary camels under restricted housing. Samples from free-ranging camels (range camels) were compared with samples from camels during restricted-housing. During the first sampling, where the camels were free-ranging and distanced from each other, the animals were calm and easy to handle. In contrast to this, the movement-restricted camels on the second sampling day were excited and agitated.

Camels under Movement-Restricted Housing Showed Elevated Serum Peroxidase Activity
The analysis of the enzymatic activity of peroxidase in serum samples collected from range and housed camels revealed significantly (p = 0.004) higher activity in housed camels (mean ± SEM; 2822 ± 287 optical density units (OD)) with two times higher values than range camels (1397 ± 175 OD) ( Figure 1A). In addition, the SSC values of neutrophils (indicative of granularity) were significantly (p = 0.03) reduced in samples collected from housed camels (409,097 ± 9520), when compared to samples collected during the free-range time (452,361 ± 11,230) ( Figure 1B). Analysis of serum myeloperoxidase activity and neutrophil shape-change. (A) Serum samples were collected from the camels before and after restricted housing and analyzed for myeloperoxidase activity using a colorimetric assay. Optical density (OD) values are presented for free-range and restricted-housing animals as mean ± standard error of the mean (SEM). (B) Neutrophil shape-change was analyzed by the assessment of neutrophil side scatter values using flow cytometry. The student's t-test was used to compare the means, and the * p-value is indicated on the graph.

Camels Leukocytes Showed Higher Spontaneous Production of Reactive Oxygen Species (ROS) during Restricted Housing
The analysis of spontaneously produced ROS by neutrophils and monocytes revealed significantly higher levels in cells from housed camels compared to free-range camels (Figure 2A,B). The MFI values of dehydrorohdamin-123 were three to five-time higher in neutrophils (252,724 ± 12,262; p = 0.0001) and monocytes (481,461 ± 44,440; p = 0.0006) from housed animals than neutrophils (55,201 ± 4272) and monocytes (66,280 ± 2330) from free-range animals.

Changes in the Leukogram Pattern after Restricted Housing
Flow cytometric analysis of the relative white blood cell (WBC) composition did not identify any significant differences between housed and free-range camels regarding the percentages of neutrophils (p = 0.21), eosinophils (p = 0.06), lymphocytes (p = 0.14), and monocytes (p = 0.45) (Figure 3A,B). The estimations of absolute cell numbers in blood samples from housed and free-range camels revealed significant leukogram changes ( Figure 3C). The total leukocyte number in the blood from housed camels (19,880 ± 1214 cell/µL blood) was two-times higher (p = 0.002) than in the blood from the free-range camel group (10,080 ± 991 cell/µL blood). The elevated leukocyte number in the blood from housed camels included significantly increased numbers of neutrophils (p = 0.004), eosinophils (p = 0.003), lymphocytes (p = 0.02), and monocytes (p = 0.04) ( Figure 3C).

Relative and Absolute Quantification of Blood Lymphocyte Subsets
While the fractions of B cells did not differ between housed and range animals (p = 0.42), and the fraction of CD4-positive T helper cells was only slightly (p = 0.09) reduced in the blood from housed camels, the percentage of γδ T cells was significantly (p = 0.03) lower in the blood from housed camels ( Figure 4A,B). In contrast to their relative fractions, the absolute numbers of B cells (p = 0.04) and helper T cells (p = 0.03) were significantly increased in the blood from housed animals ( Figure 4C).

Restricted Housing Modulated the Phenotype of Camel Monocytes
Monocytes from housed camels showed a significantly different phenotype in comparison to free-range animals ( Figure 5). The median fluorescence intensity (MFI) values of CD172a (p = 0.03), CD14 (p = 0.03) and major histocompatibility complex (MHC) class II molecules (p = 0.001) were lower for monocytes collected from housed camels when compared with those from free-range animals. In contrast to this, the scavenger receptor CD163 was upregulated (p = 0.001) on monocytes from housed camels in comparison to those from free-range camels ( Figure 5).

Leukocytes from Movement-Restricted Animals Changed Their Adhesion Molecule Expression Profiles
Restricted animal housing resulted in marked decreases in the abundance of the cell adhesion molecules CD44 and CD45 on all leukocyte populations, including granulocytes, monocytes, and lymphocytes ( Figure 6). The average CD44 downregulation was 47.9% (compared to MFI values in free-range animals) for monocytes (p = 0.001), 57.9% for granulocytes (p = 0.006), and 58.6% for lymphocytes (p = 0.009). (Figure 6). Similarly, CD45 was downregulated on granulocytes (38.3% of MFI values in free-range animals; p = 0.004), monocytes (28.2%; p = 0.001), and lymphocytes (56%; p = 0.03) from housed animals in comparison to free-range animals. In addition, housed animals showed increased abundance of CD11a on granulocytes but decreased CD18 expression on monocytes and granulocytes when compared to free-range animals ( Figure 6).  . Flow cytometric assessment of the abundances of the adhesion molecules CD11a, CD18, CD44, and CD45 on the surfaces of leukocytes. Leukocytes were labeled with monoclonal antibodies against CD11a, CD18, CD44, and CD45, and labeled cells were analyzed by flow cytometry. After gating on camel neutrophils, lymphocytes, or monocytes, the expression levels of CD11a, CD18, CD44, and CD45 were measured as MFI of analyzed markers, and the results are presented as mean ± SEM (t-test).

The Phagocytic Activity of Monocytes and Neutrophils Was Reduced after Restricted Housing
Flow cytometric evaluation of the capacity of monocytes and neutrophils to uptake staphylococcus aureus bacteria revealed a negative effect of restricted housing on the phagocytosis of myeloid cells in camels ( Figure 7A,B). In housed animals, the percentages of neutrophils (52.51 ± 1.2% of total cells) and monocytes (55.83 ± 1.1) with ingested bacteria were significantly reduced in comparison to their percentages in free-range animals (73.35 ± 2.1% for neutrophils and 80.48 ± 1.7% for monocytes) (Figure 7A,B).

Discussion
A functional immune system is essential to maintaining health in animals. The competence of the immune system may, however, be influenced by several physiological and pathological factors. Animal excitement results in changes in several body systems, including the immune system [17,22,26]. According to recent reports, housing management, including space allowance and the freedom of movement, are critical factors for camel welfare [23]. The present study investigated the impacts of movement-restricted housing on selected parameters of the camel immune system.

Increased Cellular Stress in Animals under Restricted Housing
Myeloperoxidase (MPO) represents the most abundant inflammatory enzyme stored in the primary granules of neutrophils, and it can be released upon neutrophil degranulation [39]. Increased serum MPO activity has been identified as an indicator of inflammation and sepsis [40]. MPO, which has strong oxidative activity, catalyzes the formation of reactive oxygen species (ROS). Although ROS produced during oxidative phosphorylation serve as essential regulators of several cellular processes [41], high ROS concentrations in neutrophils may have negative effects on fundamental cellular processes. When released, ROS may also cause damage to various biological structures, such as proteins, carbohydrates, lipids, and nucleic acids, and may enhance inflammatory responses, increasing the risk of tissue damage [42,43]. In the present study, serum samples collected from the camels during restricted housing showed elevated serum MPO enzymatic activity in comparison to samples collected from free-range animals. The induced shape-change of neutrophilsseen through a significant decrease in their mean side scatter values (proportional to cell granularity [32,33,39])-suggests that a restricted-housing system is associated with neutrophil activation and degranulation. These results together with the significantly higher amounts of ROS metabolites in neutrophils and monocytes from housed camels indicate increased cellular stress in animals under restricted housing, which may be associated with enhanced cellular and tissue damage.

Restricted Housing Modulated the Composition, Phenotype, and Function of Camel Leukocytes
Flow cytometric analysis of changes in camel leukogram identified marked leukocytosis in the camels during restricted housing. In other veterinary species, physiologic leukocytosis is usually associated with animal stress, excitation, exercise, or parturition [44]. The typical pattern of physiologic leukocytosis is characterized by neutrophilia and lymphocytosis, which are results of a shift from the marginal leukocyte pool to the circulating pool [44,45]. On the other hand, the corticosteroid-associated leukogram is characterized by lymphopenia and neutrophilia [46,47]. In the present study, the unchanged leukocyte composition together with increased total WBC numbers in housed camels resulted in general leukocytosis with elevated cell numbers of all leukocyte populations, including polymorphonuclear granulocytes (neutrophils and eosinophils) and mononuclear cells (lymphocytes and monocytes). These results argue against a selective effect of restricted housing on the hematopoiesis of distinct immune cell types and suggest the development of an excitement leukogram in camels under restricted housing. However, the analysis of lymphocyte subsets suggests selective increases in only B cells and helper T cells, but not in γδ T cells.
Monocytes are key effector cells of the early immune response to pathogens [48]. Their phenotype is characterized by plasticity, with the ability to differentiate to different functional subtypes of macrophages [49]. The cell surface molecules CD172a, CD14, MHC II, and CD163 are important markers of monocyte phenotype [37,50]. In the present study, monocytes from housed camels displayed a lower abundance of the antigen-presenting molecule MHC II and higher expression of the scavenger receptor CD163, compared to their values before restricted housing, resembling an anti-inflammatory phenotype [48,[51][52][53][54][55][56]. This is also supported by the reduced expression of the LPS receptor CD14 on monocytes in addition to their decreased phagocytic activity when compared to cells collected before restricted housing.
We further investigated whether these changes in cell composition and phenotype were associated with functional alterations in the camel immune system. Phagocytosis is an effector innate mechanism with a major role during the first phase of bacterial infection [57,58]. The marked decrease in the ability of neutrophils and monocytes from housed animals to ingest S. aureus indicates a negative effect of animal excitement on the innate immune function of camels. The clinical relevance of the observed changes in phagocytic activity, however, requires further clinical studies comparing the in vivo antibacterial capacity of free-range and housed camels.
Leukocyte recruitment to the site of infection and inflammation is an essential part of an effective immune response, which requires their effective interaction with blood vessel endothelial cells and extracellular matrix in a very complex process regulated by the expression of several cell surface adhesion molecules [10][11][12][13]. The hyaluronan receptor CD44 is widely expressed on all leukocyte populations, and it has a crucial role in leukocyte trafficking and inflammatory processes [59][60][61][62]. The lack of CD44 expression was found to be associated with reduced leukocyte recruitment to the site of bacterial infection, which negatively affected the course of infection [63]. The common leukocyte antigen CD45 is a leukocyte cell surface glycoprotein expressed on all cells of the hematopoietic system [64]. CD45 functions as a protein tyrosine phosphatase with a pivotal role in T-and B-cell antigen receptor signaling [64]. It has also an essential role in leukocyte adhesion and migration. The absence of CD45 has been linked to impaired adhesion and migration of macrophages [65]. In the present study, the reduced expression of CD44 and CD45 on all leukocyte populations from the housed camels indicates significant effects of animal excitement due to restricted housing on cell migration and signaling in camel blood leukocytes. The surface molecule CD11a dimerizes with the integrin beta chain-2 CD18 to form the lymphocyte function antigen-1 (LFA-1), a cell adhesion molecule with a major role in leukocyte adhesion and migration [66,67]. The opposite effect of restricted housing on the expression of CD11a and CD18 on granulocytes may argue against a role for LFA-1 in the observed excitement-induced leukocytosis.
The identification of B cells based on their positive staining with MHCII and negative staining with CD14 represents a limitation of the present study, as MHCII is not a cellspecific marker for B cells. However, we tested several monoclonal antibodies against CD20, CD19, IgM, and CD79 of other species for camel leukocytes but did not identify any cross-reactivity with camel B cells (Data not shown). B cells express MHCII, as do other antigen presenting cells, including monocytes and dendritic cells [68]-so using a combination of MHCII and CD14 to exclude monocytes was the only way for us to identify camel B cells. However, as dendritic cells are a minor population in blood, we believe that their contribution to the CD14-MHCII+ lymphocyte compartment would not significantly affect our results. In addition, we found a similar expression pattern using a combination of MHCII and the myeloid marker CD172a, which is also expressed on DC.

Conclusions
In summary, the present study identified significant changes in blood immune cell composition, phenotype, and function in dromedary camels under restricted housing conditions. Blood samples collected from the camels during movement-restricted housing showed elevated MPO enzymatic activity and higher ROS content in monocytes and neutrophils when compared to samples collected from the same animals during free-ranging time. The leukogram pattern of the camels under restricted housing was characterized by leukocytosis with increased numbers of neutrophils, eosinophils, lymphocytes, and monocytes, resembling an excitement leukogram pattern. Regarding lymphocytes, only the numbers of helper T cells and B cells were affected by restricted housing. In addition, monocytes from excited camels displayed an anti-inflammatory phenotype with elevated CD163 expression and reduced MHC-II expression. Functional analysis of bacterial phagocytosis indicates an impaired antibacterial function of phagocytes in excited camels. The downregulation of several cell adhesion molecules on leukocytes from excited camels suggests roles for impaired cell adhesion, tissue migration, and leukocyte retention in blood in the observed leukocytosis in animals under housing-induced excitement. The evaluation of the clinical relevance of the observed changes in immune cell phenotype and function for the immune competence of camels under restricted housing requires further studies.