This study shows the application of machine learning in health and safety management using operational data sets of mining operations. The findings of this study confirm that fatigue is caused by a wide variety of factors and many are likely very difficult to quantify, but there may be a small but impactful percentage of factors that can be quantified. Fatigue prediction is a matter of predicting the complex interactions between human behavior and the ever-changing work environments at mines. In the social sciences, it is very common to see situations where a low *R* 2 value captures relationships that quantify a relatively high amount of variance in a complex relationship [36]. Individual worker data can be added to the model to increase the accuracy of the prediction model, since only operational data and weather data are utilized in these models.

In all of the models developed, the training scores are substantially better than the validation scores. This is most often attributable to overfitting of the model, but in this case, it is likely largely due to the difficultly in generalizing a model that can predict fatigue due to the complex psychological and physiological factors associated with fatigue. This line of research will become more important as the fitness for duty of equipment operators takes on greater significance in scheduling operator work shifts.

Even when using a fairly simple model with a small data set, the best-performing model in this study is able to achieve excellent results. Many refinements were made to the models during this study, but there are many avenues of exploration that could yield even stronger predictive models. Some key areas to explore in future models could include:

• Looking at individual fatigue events instead of the aggregated fatigue events;

	- Creating common naming conventions between data sets so that they can be linked by location, operator and equipment;
	- Adding more complex features such as the sleep pattern, health condition, fitness or diet of the operator; • Adding some features related to the working schedule of the operator in terms of
	- Adding features that represent information collected during time periods prior to when the fatigue occurred, such as downtime or production on the previous day; fatigue at the time and the day or week before; • Exploring more details of each feature to reduce the number of features that have a
		- Adding some features related to the working schedule of the operator in terms of fatigue at the time and the day or week before; lower impact on fatigue.
	- Exploring more details of each feature to reduce the number of features that have a lower impact on fatigue. **Author Contributions:** Conceptualization, E.T. and W.P.R.; methodology, E.T., W.P.R. and T.M.; software, E.T. and T.M.; validation, W.P.R., E.T. and F.A.D.; formal analysis, E.T.; investigation, E.T.;

resources, W.P.R.; data curation, E.T. and T.M.; writing—original draft preparation, E.T.; writing—

**Author Contributions:** Conceptualization, E.T. and W.P.R.; methodology, E.T., W.P.R. and T.M.; software, E.T. and T.M.; validation, W.P.R., E.T. and F.A.D.; formal analysis, E.T.; investigation, E.T.; resources, W.P.R.; data curation, E.T. and T.M.; writing—original draft preparation, E.T.; writing review and editing, E.T., W.P.R. and F.A.D.; visualization, E.T.; supervision, W.P.R.; project administration, W.P.R.; funding acquisition, W.P.R. All authors have read and agreed to the published version of the manuscript. review and editing, E.T., W.P.R. and F.A.D.; visualization, E.T.; supervision, W.P.R.; project administration, W.P.R.; funding acquisition, W.P.R. All authors have read and agreed to the published version of the manuscript. **Funding:** Funding for the project was provided by the National Institute for Occupational Safety and Health (NIOSH) with grant number 75D30119C05500.

**Funding:** Funding for the project was provided by the National Institute for Occupational Safety and Health (NIOSH) with grant number 75D30119C05500. **Data Availability Statement:** The data presented in this study are not publicly available due to confidentiality.

**Data Availability Statement:** The data presented in this study are not publicly available due to confidentiality. **Acknowledgments:** We would like to express our gratitude to the National Institute for Occupational Safety and Health (NIOSH) for the funding, as well as the mining company, its management

**Acknowledgments:** We would like to express our gratitude to the National Institute for Occupational Safety and Health (NIOSH) for the funding, as well as the mining company, its management and all of the people supporting this research. and all of the people supporting this research. **Conflicts of Interest:** On behalf of all authors, the corresponding author states that there is no conflict of interest.

**Conflicts of Interest:** On behalf of all authors, the corresponding author states that there is no conflict of interest. **Appendix A**

#### **Appendix A**

The ICE plots of the top ten features from the shift-based model are presented in Figure A1. The ICE plots of the top ten features from the shift-based model are presented in Figure A1.

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• Adding some features related to the working schedule of the operator in terms of

• Exploring more details of each feature to reduce the number of features that have a

**Author Contributions:** Conceptualization, E.T. and W.P.R.; methodology, E.T., W.P.R. and T.M.; software, E.T. and T.M.; validation, W.P.R., E.T. and F.A.D.; formal analysis, E.T.; investigation, E.T.; resources, W.P.R.; data curation, E.T. and T.M.; writing—original draft preparation, E.T.; writing review and editing, E.T., W.P.R. and F.A.D.; visualization, E.T.; supervision, W.P.R.; project administration, W.P.R.; funding acquisition, W.P.R. All authors have read and agreed to the published

**Funding:** Funding for the project was provided by the National Institute for Occupational Safety

**Data Availability Statement:** The data presented in this study are not publicly available due to

**Acknowledgments:** We would like to express our gratitude to the National Institute for Occupational Safety and Health (NIOSH) for the funding, as well as the mining company, its management

**Conflicts of Interest:** On behalf of all authors, the corresponding author states that there is no con-

The ICE plots of the top ten features from the shift-based model are presented in

fatigue at the time and the day or week before;

and Health (NIOSH) with grant number 75D30119C05500.

and all of the people supporting this research.

lower impact on fatigue.

version of the manuscript.

confidentiality.

flict of interest.

**Appendix A**

Figure A1.

**Figure A1.** ICE plots of the top ten features (Blue lines identify the dependence of the prediction on a feature for each instance independently, and yellow line is PDP which shows the average of the blue lines). **Figure A1.** ICE plots of the top ten features (Blue lines identify the dependence of the prediction on a feature for each instance independently, and yellow line is PDP which shows the average of the blue lines).

1. Bauerle, T.; Dugdale, Z.; Poplin, G. Mineworker fatigue: A review of what we know and future directions. *Min. Eng.* **2018**, *70*,

2. Drews, F.A.; Rogers, W.P.; Talebi, E.; Lee, S. The Experience and Management of Fatigue: *A Study Mine Haul. Oper. Min. Metall.*

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5. Parker, T.W.; Warringham, C. Fitness for work in mining: Not a 'one size fits all' approach. In Proceedings of the Queensland

6. Hutchinson, B. "Fatigue Management in Mining—Time to Wake up and Act", Optimize Consulting. 2014. Available online:

7. Pelders, J.; Nelson, G. Contributors to Fatigue of Mine Workers in the South African Gold and Platinum Sector. *Saf. Health Work*

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http://www.tmsconsulting.com.au/basic-fatigue-management-in-mining/ (accessed on 25 January 2020).

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## **References**

**Rajive Ganguli 1,\* , Preston Miller 2 and Rambabu Pothina 1**

**Abstract:** To achieve the goal of preventing serious injuries and fatalities, it is important for a mine site to analyze site specific mine safety data. The advances in natural language processing (NLP) create an opportunity to develop machine learning (ML) tools to automate analysis of mine health and safety management systems (HSMS) data without requiring experts at every mine site. As a demonstration, nine random forest (RF) models were developed to classify narratives from the Mine Safety and Health Administration (MSHA) database into nine accident types. MSHA accident categories are quite descriptive and are, thus, a proxy for high level understanding of the incidents. A single model developed to classify narratives into a single category was more effective than a single model that classified narratives into different categories. The developed models were then applied to narratives taken from a mine HSMS (non-MSHA), to classify them into MSHA accident categories. About two thirds of the non-MSHA narratives were automatically classified by the RF models. The automatically classified narratives were then evaluated manually. The evaluation showed an accuracy of 96% for automated classifications. The near perfect classification of non-MSHA narratives by MSHA based machine learning models demonstrates that NLP can be a powerful tool to analyze HSMS data.

**Keywords:** mine safety and health; accidents; narratives; machine learning; natural language processing; random forest classification

#### **1. Introduction**

Workers' health and safety is of utmost priority for the sustainability of any industry. Unfortunately, occupational accidents are still reported in high numbers globally. According to the recent estimates published by the International Labour Organization (ILO), 2.78 million workers die from occupational accidents and diseases worldwide [1]. In addition, 374 million workers suffer from non-fatal accidents, and lost work days represent approximately 4% of the world's gross domestic product [2,3]. It is, therefore, not surprising that researchers are constantly investigating factors that impact safety [4,5], or finding innovations and technology to improve safety [6,7].

As to the U.S. mining industry, for years 2016–2019, the National Institute for Occupational Safety and Health (NIOSH), a division of the US Centers for Disease Control and Prevention (CDC) reports 105 fatal accidents and 15,803 non-fatal lost-time injuries [8]. To bring down the rate of serious injuries and fatalities, the industry analyzes incident reports to conduct root cause analysis and identify leading indicators. Unfortunately, as noted by the International Council on Mining and Metals, a global organization of some of the largest mining companies of the world, the vast trove of incident data is not analyzed as much as it could be due to lack of analytics expertise at mine sites [9]. With the advances in natural language processing (NLP), there is now an opportunity to create NLP-based tools to process and analyze such textual data without requiring human experts at the mine site.

**Citation:** Ganguli, R.; Miller, P.; Pothina, R. Effectiveness of Natural Language Processing Based Machine Learning in Analyzing Incident Narratives at a Mine. *Minerals* **2021**, *11*, 776. https://doi.org/10.3390/ min11070776

Academic Editor: Yosoon Choi

Received: 22 May 2021 Accepted: 14 July 2021 Published: 17 July 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Natural language processing (NLP) has been explored as a tool to analyze safety reports since the 1990s [10,11]. This paper, intended for a mining industry audience, presents in this section, a brief history of NLP and its use in analyzing safety reports. NLP is the automated ability to extract useful information out of written or spoken words of a language. Exploring its application to safety is logical, as safety reports are valuable information. If causation and associated details can be automatically extracted from the safety reports, NLP can be used to quickly gain insight into safety incidents from historical reports that are filed away in the safety management databases. Additionally, with smartphone-based work site observations apps becoming popular, NLP tools can be useful in providing real time insights as incidents and observations are reported in real time. For example, in a confidential project, one of the authors of this paper advised an industrial site about a hazardous practice at the operation using an NLP analysis of data collected using a smartphone-based application. This hazard became apparent after evaluating the data because several employees had noted the practice in their worksite observations.

The efforts to apply NLP to extract causation from safety reports received a major boost when the Pacific Northwest National Laboratory (PNNL) put together a large team in the early 2000s to apply NLP and analyze aviation safety reports from the National Aeronautics and Space Administration's (NASA) aviation safety program [12]. The "meaning" of a sentence depends not just on the words, but also on the context. Therefore, PNNL used a variety of human experts to develop algorithms to extract human performance factors (HPF) from report narratives. HPF definitions were adopted from NASA [13]. The PNNL approach consisted of artificial intelligence (AI) after the text was preprocessed using linguistic rules. The linguistic rules, developed by human experts, considered specific phrases and sentence structures common in aviation reports. When automated, these rules were able to identify causes of safety incidents on par with human experts. The PNNL team, however, noted the reliance of the algorithms on human experts with domainspecific knowledge.

New developments have reduced human involvement in text analysis [14]. These developments include identifying linguistic features such as parts of speech, word dependencies, and lemmas. A million-sentence database (or "corpus" to use NLP terminology) may only contain 50,000 unique words once words such as 'buy' and 'bought' (one is a lemma of the other) are compressed into one; though that is also a choice for the human expert. After vectorization, each sentence in the database is a vector of length 50,000, with most elements being zero (a twelve-word sentence will only have ones in twelve places). When the relative order of words in a sentence is taken into account, common phrases can be identified easily. Thus, after preprocessing with NLP techniques, classical statistics and machine learning techniques can be applied to classify text. Baker et al., 2020 [15] used a variety of NLP and machine learning techniques to classify incident reports and predict safety outcomes in the construction industry. Tixier et al., 2016 developed a rule based NLP algorithm that depends on a library of accident related keywords to extract precursors and outcomes from unstructured injury reports in the construction industry [16]. In another study that was conducted on narratives from Aviation Safety Reporting System (ASRS), NLP-based text preprocessing techniques along with k-means clustering classification were used to identify various safety events of interest [17]. Baillargeon et al., 2021 [18] used NLP and machine learning techniques to extract features of importance to the insurance industry from public domain highway accident data. In an analysis conducted on infraction history of certain mine categories, ML-based classification and regression tree (CART) and random forest (RF) models were used on Mine Safety and Health Administration (MSHA) database narratives in predicting the likely occurrence of serious injuries in near future (the following 12-month period) [19].

The application of NLP-based machine learning to mining industry safety data is relatively new. Yedla et al., 2020 [20] used the public domain (MSHA) database to test the utility of narratives in predicting accident attributes. They found that vectorized forms of narratives could improve the predictability of factors such as days away from work. Other researchers used NLP to analyze fatality reports in the MSHA database [21]. Using co-occurrence matrices for key phrases, they were able to identify some of the common causes of accidents for specific equipment.

#### **2. Importance of this Paper**

In safety-related research, it is typical to demonstrate NLP and machine learning capabilities on public domain databases. Models are first developed on a public domain database, after which its capabilities are demonstrated on an independent subset of the same database. Since modeling and subsequent demonstration of model capabilities happen on the same dataset, there is no certainty that these approaches or models would be effective on databases created by other sources. For example, every entry in an MSHA database is made by a federal employee. Would a federal employee describe an incident the same way as a mining company employee? If yes, then there exists a specific language for mine safety that is shared by safety professionals. This 'language', if it exists, can be leveraged to make NLP-based machine learning of mine safety data very effective.

This paper advances the use and application of NLP to analyze mine safety incident reports by demonstrating that machine learning models developed on public domain mine safety databases can be applied effectively on private sector safety datasets. Therefore, it demonstrates that there is a language of safety that spans organizations. Furthermore, this paper identifies key attributes of specific categories of incidents. This knowledge can be used to improve algorithms and/or understand their performance.

More generally, the paper advances the field of mine safety research. Currently, data-mining-based mine safety researchers focus only on categorical or numerical data. Therefore, gained insights are limited to statistical characterization of data (such as average age, or work experience) or models based on these data [4]. If narratives are available with incident data (as they often are), this paper will encourage researchers to evaluate them to glean more insights into the underlying causes.

#### **3. Research Methodology**

#### *3.1. MSHA Accident Database*

The MSHA accident database [22] has 57 fields used to describe safety incidents including meta-data (mine identification, date of incident, etc.), narrative description of the incident, and various attributes of the incidents. Some of the data is categorical such as body part injured and accident type. More than eighty-one thousand (81,298) records spanning the years 2011 to early 2021 were used in this research. Any operating mine in the United States that had a reportable injury is in the database. Thus, the database reflects many types of mines, jobs, and accidents.

Accidents are classified in the database as belonging to one of 45 accident types. Examples include "Absorption of radiations, caustics, toxic and noxious substances", "Caught in, under or between a moving and a stationary object", and "Over-exertion in wielding or throwing objects". Looking at these definitions, it appears that MSHA defined them to almost answer the question "What happened?" Thus, the category is simply the high level human summary of the narrative, i.e., the category is the "meaning" of the narrative. In this paper, the MSHA accident type is considered a proxy for the meaning of the narrative. Narratives are typically five sentences or less.

#### *3.2. Random Forest Classifier*

The random forest (RF) technique was used to classify the narratives based on accident types. Random forests are simply a group of decision trees. Though described here briefly, those unfamiliar with decision trees are referred to Mitchell, 1997 [23], a good textbook on the topic and the source for the description below. A decision tree is essentially a series of yes or no questions applied to a particular column ("feature") of the input data. The decision from the question (for example, miner experience > 10, where miner experience is a feature in the data set) segments the data. Each question is, thus, a "boundary" splitting

the data into two subsets of different sizes. The segmented data may be further segmented by applying another boundary, though the next boundary may be on another feature. Applying several boundaries one after the other results in numerous small subsets of data, with data between boundaries ideally belonging to a single category. The maximum number of decision trees applied in the longest pathway is called the "tree depth". The method works by applying the sequence of boundaries to a sample, with the final boundary determining its class. Note that while one boundary (also called "node") makes the final decision on the class for one sample, some other boundary may make the decision for another sample. It all depends on the path taken by a particular sample as it travels through the tree. When the final boundary does not result in a unanimous class, the most popular class in the subset is used as the final decision of the class.

Boundaries are set to minimize the error on either side of the boundaries. The combination of a given data set and given boundary criteria will always result in a specific tree. In an RF, a decision tree is formed by randomly selecting (with replacement) the data. Thus, while a traditional decision tree will use the entire modeling subset for forming the tree, a decision tree in an RF will use the same amount of data, but with some samples occurring multiple times, and some not occurring at all. Thus, the same data set can yield multiple trees. In the RF technique, multiple trees formed with a random selection of data are used to classify the data. One can then use any method of choice to combine predictions from the different trees. This method of using a group of trees is superior to using a single decision tree.

In this paper, an RF classifier was applied to model the relationship between a narrative and its accident type. A non-MSHA database would contain narratives, but not any of the other fields populated by MSHA staff. Since the goal of the project is to test it on non-MSHA data, no other field in the database was used to strengthen the model. Half of the records were randomly selected to develop the model. It was tested on the remaining half of the records to evaluate its performance on the MSHA data. In the final step, the model was tested on non-MSHA data. There is no standard for what proportion of data to use for training and testing subsets, though it is expected that the subsets be similar [24]. A 50–50 split is a common practice [25,26]. RF models were developed using the function RandomForestClassifier () in the SCIKIT-LEARN [27] toolkit. As is common practice in machine learning [28], the authors did not code the RF but used a popular tool instead.

Modeling starts by making a list of non-trivial words in the narratives. As is typical in NLP, the narratives were pre-processed before the list of non-trivial words is made. Pre-processing consisted of:

The combined length of all narratives was 1.72 million words, consisting of 31,995 unique words or "features". The list of unique features is called the vocabulary. The input data set is then prepared by selecting the top 300 most frequently occurring words ("max features"). Essentially, the vocabulary is cut from its full length to just the words occurring most frequently. These words are used to vectorize each narrative such that each narrative is represented as a vector of size 300. The value at a given location in the vector would

represent the number of occurrences of that word in that narrative. The top 5 words were: fall, right, left, back, and cause.

The output for the narrative consisted of a 1 or a 0, indicating whether it belonged ("1") to a particular category of accident or not ("0"). "Max features" is a parameter in RF modeling, and was set to 300 after trial and error exercises. Similarly, the number of trees ("n\_estimators") was set to 100. Another parameter is "max\_depth" (maximum depth of tree). This parameter was not set. Whenever a parameter is not specified, the tool uses default values. In the default setting for tree depth, data is continually segmented till the final group is all from the same class. According to the user guide of the tool, the main parameters are the number of trees, and max features. The rest of the parameters were not set, i.e., default values were used. The interested reader can visit the provided links for technical details about the toolkits in the footnotes, including the default values. The tool combines the outputs of the various trees by averaging them to obtain the final classification.

Among the 45 accident types are some whose names start with the same phrase. For example, there are four over-exertion (OE) types, all of which start with the phrase over-exertion. They are (verbatim): Over-exertion in lifting objects, over-exertion in pulling or pushing objects, over-exertion in wielding or throwing objects, and over-exertion NEC. Accident categories whose names begin with the same phrase are considered to belong to the same "type group", with the phrase defining the grouping.

NEC stands for "not elsewhere classified," and is used within some type groups. When it exists, it is often the largest sub-group as it is for everything that is not easily defined. There are 11 types that start with "Fall", including two that start with "Fall to". Five types start with "Caught in". Six start with "Struck by". These accident type groups contain 26 of the 45 accident types, but 86% of all incidents (35,170 out of 81,298). Table 1 shows the four type groups that were modeled in this paper. Separate models were developed for some of the sub-groups to get an understanding of these narrowly defined accidents. These were:

**Table 1.** The four type groups of accidents modeled in the paper.

Thus, a total of nine RF models were developed; four for the four type groups, and five for the specific types. Table 2 shows the characterization of the training and testing subsets that went into developing the models. It is apparent that each category was represented about the same in the two subsets.

**Table 2.** Various accident categories in the training and testing subsets. Each subset has 40,649 samples.

In classification exercises, it is common to develop a single model to classify a data set into multiple categories, rather than develop models for each category individually. The reason for developing nine models instead of one is discussed in the next section.

## **4. Results**

## *4.1. Performance within MSHA Data*

Table 3 shows a summary of the modeling within the MSHA test set. To understand the table, consider the OE type group. Of the 40,649 records in the test set, 8979 records were from this type. The success of an RF model can be determined by identifying the OE type as OE type and/or by classifying a non-OE type (31,670 records) as not belonging to OE. This is shown below through a simple computation.

**Table 3.** Results of RF models in the MSHA test set.

The overall success was 92%, i.e., a very high proportion of narratives were classified correctly as belonging to OE type group, or as not belonging to OE type group. Though it is an indicator of overall success, this type of evaluation is not particularly useful, as classifying a narrative as "not belonging to OE" is not helpful to the user. It is more useful to look at how successful RFs were in correctly identifying narratives from the accident type in question (OE type group in this example). As shown in the table and in the example computation, 81% of these 8918 (7248) were accurately identified. The false positive rate was 4%, i.e., 1331 of the 31,670 non-OE records were identified as OE. The low positive rate implies that if a narrative was classified as belonging to the OE type group, it was highly likely to belong to that type. The success in the other type groups was lower, and ranged from 71% to 75%, with false positives ranging from 1% to 5%. Thus, one could expect RF to accurately identify about 75% of the narratives in the MSHA database from the four type groups, with a good false positive rate.

The success rate takes a dramatic downturn with the individual models. Only 25% to 59% of narratives belonging to the individual types are correctly classified though with a negligible false positive rate. The negligible false positive implies that when the model classifies the narrative as belonging to a specific category, it is almost guaranteed to be in that category. The low number of records in the individual categories is one part of the explanation of the poor performance, as models would be less powerful if they are trained on fewer records. For example, only about 3% of the records were from the OEP category. This means that 97% of the data seen by the OEP model was not relevant to identifying OEP. An additional explanation is obtained from trigram analysis of the narratives that belong to these accident types. Trigrams explore the sets of three words that occur consecutively the most. Trigram analysis was conducted using the NLTK collocations toolkit.

Table 4 shows the tri-word sequences that occur the most frequently in the OE accident types. They are listed in order of frequency. The overlap between the tri-words is immediately apparent. Back, shoulders, knee, abdomen, and groin are injured most in these types of accidents. The overlap between OEP and OEL would cause accidents to be misclassified as belonging to the other category. This issue is also evident in the Fall accident types (Table 5), where losing balance, slipping, and falling seem to be the major attributes. Even the two types "Caught in" and "Struck by" have some overlap (Table 6). Caught in makes it apparent that it is the fingers that are predominantly injured in this type of accident. SFO highlights that eyes and safety glasses are impacted when someone is struck by a flying object.

**Table 4.** Results of trigram analysis on OE accident types.

**Table 5.** Results of trigram analysis on Fall accident types.

**Table 6.** Results of trigram analysis on Caught in and Struck by accident types.

The success rate for classification was dramatically lower when a single RF model was developed to classify the narratives into separate categories. OEP, OEL, FWW, CIMS, SFO had success rates of only 23%, 33%, 19%, 29%, and 17% respectively compared to 37%, 59%, 34%, 55%, 25% respectively. Multiple models for multiple categories would require that multiple models be applied to the same data, resulting in multiple predictions of category. It would be possible then for a particular narrative to be categorized differently by the different models. In such situations, one could determine the similarity between the narrative and the narratives from the multiple categories in the training set to resolve the conflicting classifications. The features (words) of the category within the training set are the foundation behind the model for the category. For example, the words in the "Struck by" category in the training set play a key role in what RF trees are formed in the "Struck by" model. Thus, when a test narrative is classified as "Struck by" by one model, and "Caught in" by another, one could find the similarity between words in the test narrative, and the words in the two categories of the training data, "Struck by" and "Caught in", to resolve the conflict. This is demonstrated in the next section.

#### *4.2. Performance on Non-MSHA Data*

The nine RF models were applied to data from a surface metallic mine in the United States that partnered in this project. The data consisted of narratives that described various safety incidents. Injury severity ranged from very minor incidents to lost time accidents. Narratives were typically longer than MSHA narratives (about twice the length), and formats were sometimes different (such as using a bulleted list). They usually had more details about the incident. The narratives were written by a staff member from the safety department. Narratives from the 119 unique incidents logged in 2019 and 2020 were analyzed. Some narratives were duplicated in the database. Duplicates of narratives were ignored. Each model was applied to the 119 narratives separately.

The RF models classified 76 out of the 119 narratives (Table 7) with a high degree of success. 17 narratives were classified by multiple models, but not misclassified (explained later). Forty-three (43) narratives were ignored by all nine models, i.e., they were not classified as belonging to a particular category. The classifications were manually evaluated by the authors to see if they would match the MSHA Accident Types. In many cases, the MSHA database contained an accident that was not only similar to the narrative being manually evaluated but was also classified into the same accident type as the narrative in question. Therefore, the manual validation was easy. A narrative was deemed as accurately classified if it was also classified as such by the authors. The 43 narratives that were not classified by any of the nine models could possibly belong to one of the 19 MSHA accident types not modeled in this paper. The overall success rate was 96%.

The OE category is quite broad and, therefore, one would expect some narratives to be wrongly classified as OE. Therefore, it is not surprising that 4 out of the 26 classified as OE did not belong in that category. One narrative involved an employee who had a pre-existing soreness in the wrist. The 'incident' was simply the employee reporting to the clinic. Two incidents involved employees backing into or walking into a wall or object while working. The fourth incident involved chafing of the calves from new boots. Some of these incidents would perhaps have been also classified differently had models been developed for the other accident types.

Table 8 shows examples of some of the narratives and the automated classifications. Examples are shown for the narrowest categories as they would normally be the most challenging to identify. Table 9 shows how the overlapping occurred in the 17 narratives. Three narratives were classified as both Fall and FWW, while seven were categorized as both "Caught in" and CIMS. Since nine models were used in parallel, it was possible for each narrative to be categorized into nine different categories. Yet, no narrative was categorized as belonging to three or more different categories. Except for one, these overlaps should be expected. For example, OEL is a subset of OE. Therefore, a narrative classified as OEL by the OEL model is expected to be also classified as OE by the OE model. The overlap between a type group and one of its sub-type is a confirmation that models are working properly. It is good that there was no overlap between OEL and OEP. The overlap between "Caught in" and "Struck by" was surprising as they are different categories. The narrative that was classified as both "Caught in" and "Struck by" is (verbatim): "while installing a new motor/pump assy. using portable a cherry picker, the cherry picker tipped over and the assembly caught the employee leg and ankle between the piping and the motor assembly." Tools and equipment that tip over and cause injury have been reported in the "Struck by" category in the MSHA database. A limb caught in between two objects is reported in the "Caught in" category in the MSHA database. Thus, the RF models were correct in their classification of the narrative. However, the overlap in classification presents a good opportunity to demonstrate how one could use "similarity scores" to resolve the overlap. The steps of the process, to resolve conflicting classifications of "Caught in" and "Struck by" are:

a top word than "leg" did as a top word. Clearly, "catch" is a bigger determiner of "Caught in" than leg is of "Struck by".

6. The decision as to which category the narrative belongs is the one with the highest similarity score. In this case, the narrative is deemed to be of the category "Caught in".

**Table 8.** Examples from the partner mine HSMS, and the automated classifications. Narratives are shown verbatim, but some text has been deleted (identified by . . . ) to not disclose sensitive information.

**Table 9.** Counts of overlapping accident types.

#### **5. Discussion**

Two thirds of the narratives in the partner database could be successfully classified (96% accuracy) without any human intervention. The narratives that are not automatically classified could belong to categories not modeled in this paper. At this time, they were not manually analyzed to determine their nature. The nearly absent overlap in predictions for distinct accident types is encouraging as that allows the multiple-model-for-multiplecategory approach to work. That is further strengthened by the low false positive rates for the distinct categories, i.e., when a particular model for a distinct category (say OEP) claims that a narrative belongs to that category, the classification is most likely valid. The similarity score approach is presented to resolve cases where a narrative is classified into multiple categories due to the use of multiple models.

The classifications done in the paper were not an empty computational exercise thanks to how MSHA classified the accidents. An increase in narratives being classified as SFO would tell management that foreign matter was entering the eyes of their employees. This is the same as humans reading the narratives, understanding them, and reaching that conclusion. Thus, in some sense, the RF models picked up what the narratives "meant". The high classification success rate also meant that there were specific ways safety professionals describe incidents and that NLP tools can extract that language.

These tools have excellent applicability to help the mining industry reach the industry goal of preventing serious injury and fatalities. On noting an increase in SFO classifications, management can deploy eye protection related interventions. An increase in OEL incidents could result in more training about safe lifting. The safety "department" in most mines means a single person with no mandate or expertise to analyze data. These types of tools can assist mines to analyze data without human intervention. As mines deploy smartphonebased apps to collect employee reports on worksites, the volume of information will

explode. However, these tools will help mines process that data and identify hazards before they become incidents.

The detection rate for the narrowest of categories needs to be improved. Improving this would be the most logical next step for this research. A reason why NLP tools were not always effective may be how incidents are described in the narratives. A limitation of the approach is that it is dependent on the terminology and the writing style. For example, "roof bolter" related incidents may not be detected by NLP in narratives when the writer uses the term "pinner" to refer to a bolter (though the diligent NLP developer would notice the frequent occurrence of "pinner" in narratives involving "roof"). "Pinner" is a common term for roof bolters in certain parts of the US. Terminology aside, writing style can vary dramatically depending on the region and the English language abilities of the writer. Considering all of these, the MSHA database may not be a great resource for English based NLP tools in other parts of the world. Regardless, organizations (or nations) developing their own NLP tools could provide training to standardize the writing of safety narratives, so that data is generated to assist automation.

The extremely low false positive rate for the narrowest accident types is a wonderful argument for considering these tools. The overall false positive rate across all accident types is quite low, which is good.

#### **6. Conclusions**

Natural language processing based random forest models were developed to classify narratives in the MSHA database depending on accident types. Nine models were developed. Four of the models, i.e., Over-exertion, Fall, "Caught in" and "Struck by", looked at type groups, i.e., groups of particular accident types. Five models looked at specific accident types within these broad groups. They were: Over-exertion in lifting objects, Over-exertion in pulling or pushing objects, Fall to the walkway or working surface, "Caught in", under or between a moving and a stationary object, and Struck by flying object. All models had high overall success rates (typically 95% or higher) in classification on MSHA data when considering both false positive and false negative rates. The success in detecting an accident type within a narrative was higher for type groups (71–81%) than for individual categories (25–59%). Detection was done with low false positive rates for type groups (1–5%), and extremely low false positive rate (<1%) for individual categories.

When a single model was developed to classify narratives into multiple categories, it did not perform as well as when a separate model was developed for each category. A similarity score based method was developed to resolve situations where a particular narrative may be classified differently according to different models.

When applied to non-MSHA data, the developed models were successful in classifying about two-thirds of the narratives in a non-MSHA database with 96% accuracy. The narratives that are not classified by the models could belong to accident types not modeled in this paper. In classifying the non-MSHA narratives with near perfect accuracy, the paper demonstrates the utility of NLP-based machine learning in mine safety research. It also demonstrates that there exists a language for mine safety, as models developed on narratives written by MSHA personnel apply to narratives written by non-MSHA professionals. They also demonstrate that natural language processing tools can help understand this language automatically.

**Author Contributions:** Conceptualization, R.G.; data curation, P.M. and R.P.; formal analysis, R.G., P.M., and R.P.; funding acquisition, R.G.; investigation, R.G., P.M., and R.P.; methodology, R.G., P.M., and R.P.; validation, R.G., P.M., and R.P.; visualization, R.G. and P.M.; writing—original draft, R.G.; writing—review & editing, P.M. and R.P. All authors have read and agreed to the published version of the manuscript.

**Funding:** This project was partially supported by the National Institute of Occupational Safety and Health.

**Data Availability Statement:** Not Applicable.

**Acknowledgments:** This project was partially supported by the National Institute of Occupational Safety and Health. Their support is gratefully acknowledged.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

#### **References**


**Meet the editors**

Prof. (Dr.) Pranav H. Darji graduated in Mechanical Engineering from the North Gujarat University, Gujarat, India, in 1998. He received his postgraduate degree from the Gujarat University, Gujarat, India, in Mechanical Engineering with a specialization in CAD/CAM in 2001. He received his PhD degree from the S.V. National Institute of Technology, Gujarat, India, in Mechanical Engineering in the specific field of

rolling element bearing in 2015. He has 20 years of academic and administrative experience. He is working as a professor at the Department of Mechanical Engineering, C.U. Shah College of Engineering and Technology, C.U. Shah University, Wadhwan City, Gujarat, India, since 2010 and a principal in the same college and university since 2017. He has worked as an Assistant Professor from 2004 to 2010 in the same organization. He has previously worked as a lecturer at N.M. Gopani Polytechnic, Gujarat, India, from 1998 to 2002 and the Head of the Department of Mechanical Engineering from 2002 to 2004. Since 2015, he is also working as the director of P.G. Studies and Research at the Research, Development, and Innovation Center of C.U. Shah University as an additional responsibility and associated with various research activities. His research interest focuses on the areas of bearing technology, tribology, CAD, FEA, and advanced machine design. On the same area, he has published more than 68 research papers in various reputed national and international journals and conferences. He has edited international books *Advances in Tribology* in 2016 and *Bearing Technology* in 2017. He has organized many conferences and workshops for the benefit of the researchers, academicians, and industrialist. He has guided more than 22 dissertations of postgraduate students and 8 PhD scholars. He has given more than 12 invited presentations.

He is also associated actively with the industries to undertake the projects for the mutual benefits. He serves as an editorial board member of reputed international journals. He is a life member of professional bodies such as Institution of Engineers (India), Tribology Society of India, and Indian Society for Technical Education. He is a registered chartered engineer (Mechanical Engineering).

Prof. (Dr.) Veera P. Darji graduated in Mechanical Engineering from the Bhavnagar University, Gujarat, India, in 2000. She received her postgraduate degree from the Maharaja Sayajirao University, Gujarat, India, in Metallurgy Engineering with a specialization in Materials Technology in 2002. She received her PhD degree from the S.V. National Institute of Technology, Gujarat, India, in Mechanical Engi-

neering in the specific field of multiple criteria decision making for manufacturing and industrial environment in 2016. She has 16 years of academic and administrative experience. She is working as a professor at the Department of Mechanical Engineering since 2010 and the Head of the Department of

Mechanical Engineering since 2018 at the C.U. Shah College of Engineering and Technology, C.U. Shah University, Wadhwan City, Gujarat, India. She has worked as a lecturer from 2004 to 2008 and as an Assistant Professor from 2008 to 2010 in the same organization. She has previously worked as a lecturer at N.M. Gopani Polytechnic, Gujarat, India, from 2002 to 2004 and the Head of the Department of Metallurgy Engineering from 2003 to 2004. Since 2008, she is holding the position of chairperson in various women staff and student committees of C.U. Shah College of Engineering and Technology and C.U. Shah University as an additional responsibility. Her research interest focuses on areas of optimization of various situations for manufacturing and industrial engineering issues. On the same area, she has published more than 35 research papers in various reputed national and international journals and conferences. She has coordinated a good number of conferences and workshops for the benefit of the researchers, academicians and industrialist. She has guided 13 dissertations of postgraduate students and 8 PhD scholars. She is also associated actively with the industries to undertake the projects for the mutual benefits. She is a life member of professional bodies such as Institution of Engineers (India), Society of MCDM, and Indian Society for Technical Education. She is a registered chartered engineer (Mechanical Engineering).

Contents

**Preface VII**

**Section 1 Contact Mechanics 1**

**Sealing Studies 3**

Arnaud Caron

Giuseppe Pintaude

**Contact Models 97** Chiara Gastaldi

**under Impulsive Loading 119**

Lin Jing, Zhihua Wang and Longmao Zhao

Chapter 1 **Contact Mechanics of Rough Surfaces in Hermetic**

**Contacts with Metal Surfaces 27**

Peter Ogar, Sergey Belokobylsky and Denis Gorokhov

Chapter 2 **Experimental Studies of Nanometer-Scaled Single-Asperity**

Chapter 3 **Deformation Regimes for Sphere-Plane Contact: Revisiting Tabor's Criteria for Differential Hardness 45**

Chapter 4 **Adaptive Mesh Refinement Strategy for the Semi-Analytical Solution of Frictionless Elastic Contact Problems 59** Victor Roda-Casanova and Francisco Sanchez-Marin

Chapter 5 **Improving Contact Load-Bearing Resistance of Ultrafine-**

Chapter 6 **Modeling Friction for Turbomachinery Applications: Tuning Techniques and Adequacy Assessment of Heuristic**

Chapter 7 **Single-Curvature Sandwich Panels with Aluminum Foam Cores**

**Grained Materials Through Multilayering and Grading 79** Dengke Yang, Jiangting Wang, Huimin Yang and Peter Hodgson


3.2.1. Preceding Spring Seasons

During the preceding spring seasons (Figure 5A,B), the occurrence of zonal versus meridional NH flows or ECM for extreme and moderate drought years, as well as wet years, was similar to those of spring seasons, overall, from 1970 to 2020. However, regionally, there are some strong differences in the flow field (Figure 6) between springs preceding summer drought or wet summers. In Figure 6A,B, the Pacific Region flow was clearly more zonal with an anomalously strong Aleutian Low present. This explains fewer Pacific Region blocking events (Figure 3A) and the negative PNA Index within NA region for drought springs (Figure 4A). However, for the springs preceding wet summers, the PNA Index is positive (Figure 4A) and there was significantly more blocking (Figure 3A). This is reflected in the strong positive 500 hPa height anomaly over the East Pacific (Figure 6C).

**Figure 3.** The character of blocking events for (**A**) NA spring, (**B**) EE/WR spring, (**C**) NA summer, and (**D**) EE/WR summer. The left-hand ordinate is mean block occurrence (number—leftmost bars) and mean block intensity (BI—middle bars), and the right-hand ordinate is mean blocking days (rightmost bars). Blue, green, and red bars stand for statistical significance at *p* = 0.1, 0.05, and 0.01, respectively. For each set of bars, the display represents extreme drought, moderate drought, and wet years from left to right.

**Figure 4.** The major teleconnection indexes for (**A**) NA spring, (**B**) EE/WR Spring, (**C**) NA Summer, and (**D**) EE/WR summer, where the left, middle, and right group of bars are the AO, NAO, and PNA indexes, respectively. The ordinate is the value of the index.

**Figure 5.** The fraction of the observed ECM (ordinate) that are zonal and meridional (left bars) and the information entropy (*H*(*x*)—ordinate) (right bars) for (**A**) NA spring, (**B**) EE/WR Spring, (**C**) NA Summer, and (**D**) EE/WR summer. For the left-hand bars, the black and orange colors represent the fraction of observed zonal versus meridional ECM.

Only during the spring preceding the extreme summer drought for the EE/WR study region was the NH flow more meridional relative to other spring seasons during the study period at a statistically significant level (*p* = 0.05) (Figure 5B). This is reflected in a strongly positive NAO (and AO) in Figure 4B, but also in Figure 7A. Lebedeva et al. [55] demonstrated the long-term correspondence between the NAO and AO, as these teleconnections were highly correlated. The correlations between the AO and NAO are similarly high here for spring and summer seasons and for each subsample in Figure 4 (*p* = 0.01). There is also a negative correlation between the AO and PNA at *p* = 0.05, but only in extreme and moderate dry springs and summers. For wet seasons, the PNA and AO do not correlate.

Within the EE/WR, the NAO is weakly negative for springs preceding moderate drought summers (Figures 4B and 7B). However, a negative (EU1—see Reference [56]) Eurasian (EU) teleconnection pattern emerges (e.g., References [79,80]) which is somewhat clear in Figure 7A also. A negative EU is characterized by a positive 500 hPa height anomaly near 20◦ E and 145◦ E and a trough near 75◦ E. Thus, the reversal of the NAO in spring may be an indicator of an extreme dry summer versus a moderately dry summer in the presence of a weakly negative EU. However, for spring seasons preceding wet summers, there is little signal in the NAO but a very distinctly positive EU (EU2—see Reference [56]) pattern (Figure 7C). Finally, there was no preference toward more or fewer blocking events in any of the spring seasons (Figure 3B); however, weaker blocking in the study region during the spring seasons preceding moderate drought summers and negative NAO phase is consistent with Reference [62].

**Figure 6.** The 500 hPa height anomalies (m) in the NA region during the spring season (March–May) preceding (**A**) extreme drought (Table 3), (**B**) moderate drought (Table 3), and (**C**) wet summer seasons (Table 5). The warm (cold) colors are positive (negative) height anomalies. The contour interval is (**A**,**C**) 2.5 m and (**B**) 5 m.

**Figure 7.** As in Figure 6, except for the EE/WR spring seasons in Tables 4 and 5. The contour interval in (**A**,**B**) is 2.5 m, and in (**C**), it is 5 m.


*4.1. Literature Search*

This review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines [76]. The objective of the study, search strategy, inclusion and exclusion criteria, and study evaluation method were planned in advance, refined, and approved by all authors. MEDLINE was searched from 1 January 2015 to 31 May 2020. Key terms like "kidney", "renal", "transplant/transplantation", "urine/urinary", "marker/biomarker", and "rejection" were combined in the search strategy. Additional relevant articles were searched from scanning reference lists of included studies and added if not detected by the original literature search.


**Author details**

responses and become long-term survivors, being some of them cured, the goal of curing MM in a significant proportion of patients is still far away. In the current scenario, getting and maintaining a minimal residual disease has become the primary objective of therapy before cure [29]. In the meantime, the importance of improving quality of life should be pointed out [138].

Based on data from the SEER Program [42], MM is the fourteenth leading cause of cancer death in the United States. The number of deaths was 3.3 per 100,000 men and women per year

Comorbidity has also an impact on mortality, in particular, early mortality [57]. Infection remains as the first cause of death in most studies, frequently associated with aging, renal failure, and relapse. Therefore, every effort to avoid serious infection should be taken into

Little is known about effective measures to avoid the development of MM. Early treatment of asymptomatic patients with high-risk SMM or even high-risk MGUS, now only in the context of clinical trials, may prevent the appearance of MM, increasing the probability of cure [140]. Currently, the potentially preventable risk factors for MM are obesity and the exposure to MM-related carcinogens, particularly in the context of farming. Efforts should be made to

On the other hand, the prevention of treatment-related adverse events is a matter of concern

• MM is a very complex and heterogeneous disease. Heterogeneity is largely responsible for the great variability in the outcome of patients and can be stratified in several levels. Epidemiology should be considered the first level of heterogeneity. Therefore, the knowledge of the epidemiological background should be taken into account in both real-life and clinical trials settings to accurately assess the outcome, allowing a precise comparison between studies. • MM epidemiology is an exciting research topic. In the era of precision and personalized medicine, both clinical and molecular epidemiology should be integrated as a mandatory

• MM is a multistep malignancy. Virtually all patients with NDMM had a previous precursor disease. However, the proportion of NDMM patients with a previously known precursor disease is remarkably small. Both MGUS and SMM have also a heterogeneous pattern of risk progression. Early treatment in high-risk SMM is expected to increase the

account. In this regard, prophylactic antibiotics [139] and vaccines are key measures.

**2.4. Mortality**

20 Update on Multiple Myeloma

**2.5. Prevention**

[141].

**3. Conclusions**

rate of cure.

(age-adjusted rates), based on 2010–2014 deaths.

fight globally and effectively against these risk factors.

step in the optimized workup of every patient.

Rafael Ríos-Tamayo1,2,3,4,5\*, Dolores Sánchez Rodríguez1,7, Yoe-Ling Chang-Chan4,6 and María-José Sánchez Pérez4,5,6

\*Address all correspondence to<EMAIL_ADDRESS>
1 Monoclonal Gammopathies Unit, University Hospital Virgen de las Nieves, Granada, Spain

2 Department of Hematology, University Hospital Virgen de las Nieves, Granada, Spain

3 Genomic Oncology Area, GENYO, Centre for Genomics and Oncological Research, Pfizer/ University of Granada/Andalusian Regional Government, PTS, Granada, Spain

4 Biosanitary Research Institute of Granada (Ibs.GRANADA), University Hospitals of Granada/University of Granada, Granada, Spain

5 CIBER of Epidemiology of Public Health (CIBERESP), Madrid, Spain

6 Granada Cancer Registry, Andalusian School of Public Health, Granada, Spain

7 FIBAO, Granada, Spain


*Edited by Narongsak Chaiyabutr*

*Milk Protein - New Research Approaches* discusses the biology and synthesis of milk protein at both the cellular and molecular levels. It also presents related information on animal nutrition and management, including animal breeding. It is a useful resource for students, researchers, and professionals in veterinary, dairy, food, and animal science, among others.

ISBN 978-1-80355-201-9

Milk Protein - New Research Approaches

Published in London, UK © 2022 IntechOpen © wacomka / iStock

### Milk Protein New Research Approaches

*Edited by Narongsak Chaiyabutr*


**1. Introduction**

Many countries have been trying to eradicate knotweed due to its invasiveness, although it has many practical uses, including improving the health of animals through feed supplementation. This report suggests that it could be used as a feed supplement for horses, thereby improving their fitness. This study was part of a larger project in which knotweed had been administered to several animal species, the health of which improved.

Japanese knotweed (*Reynoutria japonica*, syn. *Polygonum cuspidatum*), including its hybrid (*R.* × *bohemica*), has been thoroughly studied from a range of perspectives, both as an "enemy" of native flora due to its invasiveness and as a plant with many beneficial uses, including positive health effects [1–4]. As a source of resveratrol and other substances with anti-ageing and other positive effects on both animals and humans, the entire knotweed plant can also be expected to have positive effects on the health of horses. Resveratrol has already been used in horses with positive results. Resveratrol is, however, extracted mainly from roots and rhizomes; the aboveground portions of knotweed, which contain equally powerful antioxidants, deserve more thorough investigation [5]. Neochlorogenic acid was identified as an efficient antioxidant in the aboveground knotweed biomass [6]. High amounts of carotenoids in knotweed leaves are comparable to those in spinach [7]. In addition, knotweed plants produce a significant number of bioactive constituents, namely, phenolic substances derived from resveratrol, such as piceid, piceatannol, astringin and emodin, which are also found in aboveground biomass albeit in smaller quantities than in rhizomes and roots.

**Citation:** Kováˇrová, M.; Madˇera, P.; Frantík, T.; Novák, J.; Vencl, Š. Effects of Knotweed-Enriched Feed on the Blood Characteristics and Fitness of Horses. *Agriculture* **2022**, *12*, 109. https://doi.org/10.3390/ agriculture12010109

Academic Editors: Lubomira Gresakova and Emilio Sabia

Received: 28 November 2021 Accepted: 10 January 2022 Published: 13 January 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Among the bioactive components of knotweed, resveratrol and, more recently, piceid (also called polydatin) have been thoroughly studied. Resveratrol was found to modulate many different pathways, as it binds to numerous cell signalling molecules, modulates cell regulatory genes, activates transcription factors, suppresses pro-inflammatory genes' expression and inflammatory biomarkers, induces antioxidant enzymes and inhibits protein kinases and the expression of angiogenic and metastatic gene products. It thus has strong potential as a treatment for inflammatory, cardiovascular, pulmonary and age-related diseases such as cancer, diabetes and even Alzheimer's disease. These chronic illnesses and neurological and autoimmune diseases are accompanied by the dysregulation of multiple cell regulating pathways and are connected with inflammation. Resveratrol targets sirtuin, adenosine monophosphate kinase, nuclear factor-κB, inflammatory cytokines, anti-oxidant enzymes along with cellular processes such as gluconeogenesis, lipid metabolism, mitochondrial biogenesis, angiogenesis and apoptosis. Immunity is regulated by its interfering with immune cell regulation, pro-inflammatory cytokines' synthesis and gene expression [8]. Similarly, piceid regulates lipid metabolism, helps treat cardiovascular diseases [9] and diabetic cardiomyopathy via its anti-inflammatory and anti-oxidative effects [10], preserves mitochondrial function in the central nervous system, offers a therapeutic option for Spinal cord ischemia/reperfusion injury [11], decreases the levels of reactive oxygen species in neurons from the ischemic cortex, ameliorates oxidative stress and mitochondria-dependent apoptosis [12], fights neurodegenerative diseases including Alzheimer's, Parkinson's, cognition/memory dysfunction, brain/spinal cord injuries, ischemic stroke and miscellaneous neuronal dysfunctionalities [13], promotes a radiosensitising effect on osteosarcoma cancer cells, reduces clonogenic survival of tumor cells and induces osteogenic differentiation, alone and in the presence of ionising therapy [14]. Emodin has recently garnered increased interest, with refs. [15,16] reporting its antiviral and anti-inflammatory activities against SARS-CoV-2, for the reason that it is one of the components of the Chinese medicine LQF, the Lianhua-Qingwen formula based on 11 herbs with 61 compounds that is used for the prevention and treatment of viral diseases, including coronavirus disease 2019 (COVID-19).

Three resveratrol analogue glucosides, namely, piceid, piceatannol glucoside and resveratroloside, were found to have antibacterial effects [11], and their bioactivity was comparable to that of resveratrol, which is released in the gut due to glucoside hydrolysis. Piceid exhibited more powerful effects than resveratrol against hepatitis B virus [17,18]. Piceatannol had even more efficient anti-inflammatory, immunomodulatory, anti-proliferative, anti-leishmanial, and anti-leukaemic activities than resveratrol. Resveratroloside competitively inhibited α-glucosidase, thus alleviating postprandial hyperglycemia in diabetic mice [19]. Crude extract from knotweed roots and rhizomes inhibited such troublesome bacteria as *Staphylococus aureus*, *Pseudomonas aeruginosa*, *Acinetobacter baumannii*, *Bacillus cereus*, *Escherichia coli*, *Listeria monocytogenes* and *Salmonella anatum* [20,21].

It might therefore be expected that there are even greater health effects of knotweed plants than there are of their individual components.

In horses, resveratrol has already been used with positive results. Inflammation is accompanied by increased enzymatic activity of the granulocytic enzyme myeloperoxidase (MPO), resulting in the production of the highly oxidative acid HOCl [22,23]. Resveratrol was shown [24] to substantially decrease HOCl production and mitigate inflammation in horses. Inhibitory effects of resveratrol on equine neutrophil myeloperoxidase were described [25,26]. It was found that four weeks of supplementation with 1 g/day resveratrol in old horses decreased the inflammation-induced production of cytokinin both in vitro and in vivo, indicating that resveratrol has substantial potential for the treatment of acute and chronic inflammatory horse diseases [27]. Resveratrol was also reported to reduce the gene expression of inflammatory mediators, thus allowing even old horses to move freely in training and competitions [28,29]. Resveratrol has been administered to performance horses in a number of food supplements, such as Equithrive Joint® and Resverasyn®, with the aims of reducing the effects of laminitis and slowing ageing.

Metabolic efficiency in animals decreases with age, leading to increased levels of creatine kinase and glucose. A decrease in both creatine kinase and glucose levels in horses receiving resveratrol and hyaluronic acid (EquithriveJoint®) was found [30]. EquithriveJoint® was administered to old, lame horses and a decreased serum malondialdehyde (MDA) concentration and modulated serum levels of glutathionperoxidase (GPx), catalase and superoxiddismutase (SOD) were found, indicating protective effects of EquithriveJoint® against oxidative stress and ageing [31]. A beneficial effect of EquithriveJoint® on the performance of horses with hindlimb lameness treated with triamcinolone was also demonstrated [32]. Supplementation with these plant-derived phenolics in old horses was suggested [33] to lower the doses of nonsteroidal anti-inflammatory drugs and thus reduce their side effects. Since laminitis and other diseases impair older horses, the effects of resveratrol on the health of young horses are unclear.

Based on the above evidence, there is good reason to assume that knotweed would help treat horse health issues in a similar way to resveratrol. Although the diseases occur mainly in older horses, the aim of this project was to find out whether knotweed can also improve the fitness of young horses. Reports on the health effects of supplementation with knotweed herbs in animals are scarce; a drug containing knotweed (Praziver®) helped cure equine helminthiasis [34].

Knotweed is not only one of the best sources of resveratrol, its derivatives, carotenoids [7] and neochlorogenic acid [6], but it also contains other substances, the effects of which are still not well known. A conservative approach to the use of knotweed as a dietary supplement in animals thus persists, although there have been many reports of various animal species grazing on it without problems. It is also on the list of safe plants for fodder, as it was introduced to Europe for use as feed for domesticated animals. Aboveground parts of knotweed are consumed by humans in some areas, e.g., in Japan and North America [6].

This study thus aimed to fill the knowledge gap regarding the effects of knotweed on the health status of young horses. We performed two consecutive experiments, during which the dry aboveground knotweed biomass was administered as a dietary supplement to a large number of young stallions.

#### **2. Materials and Methods**

#### *2.1. Horses and Husbandry*

Most of the horses involved in the two experiments belonged to the warmblood breed known as the Czech Warmblood, while the minority of coldblood horses belonged to two breeds, the Czech-Moravian Belgian Horse, a breed constituted from the Czech Coldblood and Moravian Coldblood, and the Silesian Norik. These horses were kept at the Regional Stud Farm Tlumaˇcov, Czech Republic, which provides breeding services, rears foals and young stallions and performs early testing and training of stallions. Most of the stallions are sold at the age of 2–3 years, and only the selected ones are further kept for breeding and training. In October 2021, i.e., 2 years after the experiments, those warmblood horses that had been involved in the experiments and later sold to the horse keepers in the Czech Republic who got them involved in sport activities, were tracked in the records of the Czech Equestrian Federation: https://www.jezdectvi.org/kone (accessed on 25 October 2021).

In winter, the horses were kept in individual boxes on a deep straw bed and fed individually with hay, with a feed supplement and with an experimental mixture, which were always consumed without leftovers. From May to October/November they stayed on pastures at grass only. In boxes, only the 3-year-old stallions were under training, receiving 30 min of daily exercise. All of the horses were healthy and under veterinary control.


*3.2. Fungal Material*

The strain DHS-8 of *Phomopsis asparagi* was isolated from a healthy tree root of the mangrove plant *Rhizophora mangle*, which was collected in the Dong Zhai Gang-Mangrove Garden (110◦320-–110◦37- E, 19◦51-–20◦01- N) in Hainan Province in October 2015. The strain was isolated under sterile conditions from the inner tissue of the root, following an isolation protocol described previously [36] and identified using a molecular biological protocol by DNA amplification and sequencing of the ITS region (GenBank Accession no. MT126606). A voucher strain was preserved on potato dextrose agar slants stored at 4 ◦C at one of the authors' laboratory (J. X.).

#### *3.3. Fermentation and Extraction*

The fungus was fermented onto autoclaved rice solid-substrate medium (thirty 1000 mL Erlenmeyer flasks, each containing 100 g of rice and 100 mL of 0.3% saline water) and incubated for 28 days at 28 ◦C. In total, 140 flasks of culture were extracted three times with EtOAc, and the filtrate was evaporated under reduced pressure to yield crude extract (65 g).

## *3.4. Purification and Identification*

The crude extract was partitioned with petroleum ether (PE), dichloromethane, ethyl acetate (EA), and *n*-butyl alcohol (BA). The dichloromethane fraction and ethyl acetate fraction were combined (30 g) and then subjected to silica gel column chromatography using gradient elution with CH2Cl2–MeOH mixtures of increasing polarity (100:0–0:100, *v*/*v*) to afford 8 fractions (Fr. 1–Fr. 8). Fr. 2 was subjected to open silica gel CC using gradient elution with CH2Cl2-EtOAc (4:1–1:1, *v*/*v*) to yield fractions Fr. 2.1–2.6. Then Fr. 2.3 and Fr. 2.4 were purified by semipreparative reversed-phase HPLC using MeOH–H2O (50:50, *v*/*v*) to afford **3** (5.0 mg) (Figures S19–S27). Purification of Fr. 2.5 was conducted by CC over RP-18 with a MeOH–H2O gradient (50:50–90:10, *v*/*v*) to yield **2** (8.0 mg) (Figures S10–S18) and **6** (119.2 mg) (Figures S34–S36). Fr. 2.6 was separated using Sephadex LH-20 CC with CHCl3–MeOH (1:1, *v*/*v*) to afford **1** (105 mg) (Figures S1–S9). Fr. 4 was subjected to CH2Cl2-EtOAc (1:1, *v*/*v*) and further fractionated by RP C-18 CC eluted with CH3OH-H2O (70:30, *v*/*v*) to produce the major component **7** (818.3 mg) (Figures S37–S39). Fr. 5 was subjected to silica gel CC using CH2Cl2-CH3OH as the eluent (100:4–100:6, *v*/*v*). The promising subfraction Fr. 5.1 was separated by semipreparative reversed-phase HPLC using MeOH–H2O (60:40, *v*/*v*) to obtain **8** (2.7 mg) (Figures S40–S42). Separations of Fr. 5.4 following a procedure similar to that used for Fr. 5.1 gave **4** (semiprep. HPLC, MeOH–H2O = 60:40, *v*/*v*, 120 mg) (Figures S28–S30). Fr. 6.2, collected from Fr. 6 was subjected to silica gel CC with CH2Cl2-EtOAc (100:10, *v*/*v*), followed by Sephadex LH-20 CC using CHCl3–MeOH (1:1, *v*/*v*) as the eluent to yield **5** (6.1 mg) (Figures S31–S33).

**Phomoparagin A** (**1**): colorless amorphous residue (MeOH); [α]20D -13 (*c* 0.001, MeOH); UV (MeOH) λmax 215 nm; 1H and 13C NMR data, see Table 1; HRESIMS *m/z* 434.2644 [M + H]+ (calcd for C28H36NO3, 434.2695).

**Phomoparagin B** (**2**): colorless amorphous residue (MeOH); [α]20D +54 (*c* 0.001, MeOH); UV (MeOH) λmax 212 nm; 1H NMR data, see Table 1; HRESIMS *m/z* 516.2733 [M + Na]+ (calcd for C30H39NO5Na, 516.2726).

**Phomoparagin C** (**3**): colorless amorphous residue (MeOH); [α]20D -14 (*c* 0.001, MeOH); UV (MeOH) λmax 212 nm; 1H and 13C NMR data, see Table 1; HRESIMS *m/z* 434.2678 [M + H]+ (calcd for C28H36NO3, 434.2695).

#### *3.5. Electron Circular Dichroism Calculation*

Monte Carlo conformational searches were run by employing Spartan's 14 software using the Merck Molecular Force Field (MMFF). Conformers with a Boltzmann population of over 0.4% were chosen for ECD calculations (Tables S1 and S2). Then, the conformers were initially optimized at the B3LYP/6-31 g level in gas using the PCM polarizable conductor calculation model. The theoretical calculation of ECD was conducted in MeOH using time-dependent density functional theory (TD-DFT) at the B3LYP/6-31+g (d, p) level for all conformers of Compounds **1**–**3**. Rotatory strengths for a total of 30 excited states were calculated. ECD spectra were generated using the program SpecDis 1.6 (University of Würzburg, Würzburg, Germany) and GraphPad Prism 5 (University of California San Diego, USA) from dipole-length rotational strengths by applying Gaussian band shapes with sigma = 0.3 eV.

## *3.6. Splenocyte Proliferation Assay*

Spleen cells were collected from BALB/c mice under aseptic conditions, plated in a 96-well plate at a density of 1.5 × 106 cells/well and activated by Con A (5 μg/mL) or LPS (10 μg/mL) in the presence of various concentrations of compounds or cyclosporine A (CsA) at 37 ◦C and 5% CO2 for 48 h. Then, 20 μL CCK-8 was added to each well, 4 h before the end of the incubation. Absorbance at OD450 was measured on an ELISA reader, and the IC50 value was calculated from the correlation curve between the compound concentration and the OD450.

## *3.7. Cell Viability Assessment*

Cell viability was evaluated using the CCK-8 method. Spleen cells were plated in a 96- well plate at a density of 1.5 × 106 cells/well. Then, the cells were incubated with various concentrations of compounds or 0.1% DMSO at 37 ◦C and 5% CO2 for 72 h. Proliferation was measured using the CCK-8 assay, as described above. Cyclosporin A (CsA) and cytochalasin D were used as positive controls.

## *3.8. CN Phosphatase Assay*

A spectrophotometric assay was used to determine the activities of CN with 20 mM *p*-nitrophenyl phosphate (*p*-NPP) as the substrate in 1 mM CaCl2, 0.5 mM MnCl2, 2 mM CaM, 2 mM CNB, 1 mM DTT, 0.1 mg/mL bovine serum albumin (BSA), and 50 mM Tris-HCl (pH 7.4) at 4 ◦C. The reaction was initiated by the addition of CNA, various concentrations of compounds (6.25, 12.5, 25, 50 or 100 μM) were added, and the solution was preincubated for 10 min at 4 ◦C. The OD410 value was measured, and the inhibitory concentration (IC50) was calculated. DMSO (2%) was used as a vehicle control. CsA was used as a positive control.

## *3.9. Western Blot Analysis*

Splenocytes were washed with PBS and lysed in PMSF lysate. After centrifugation at 12,000 rpm for 5 min, the protein concentration was determined using a BSA Protein Assay Kit. Protein lysates were separated using 10% sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS–PAGE) and transferred to polyvinylidene difluoride (PVDF) membranes. After blocking, the membranes were incubated overnight with the respective primary antibodies against NFAT1, NFAT-P, and β-actin in 5% BSA, and the secondary antibody was horseradish peroxidase (HRP)-conjugated goat anti-rabbit IgG. Immunoreactive bands were visualized by incubation with luminescent liquid and exposed to lightsensitive film.