Technology for Objective Control of Functional Capacities and Stress of Military Servicemen Based on In-Depth ECG Analysis and Machine Learning

Abiotic and biotic stress responses are traditionally thought to be regulated by discrete signaling mechanisms. Recent experimental evidence revealed a more complex picture where these mechanisms are highly entangled and can have synergistic and antagonistic effects on each other. In this study, we identified shared stress-responsive genes between abiotic and biotic stresses in rice (Oryza sativa) by performing meta-analyses of microarray studies. About 70% of the 1,377 common differentially expressed genes showed conserved expression status, and the majority of the rest were down-regulated in abiotic stresses and up-regulated in biotic stresses. Using dimension reduction techniques, principal component analysis, and partial least squares discriminant analysis, we were able to segregate abiotic and biotic stresses into separate entities. The supervised machine learning model, recursive-support vector machine, could classify abiotic and biotic stresses with 100% accuracy using a subset of differentially expressed genes. Furthermore, using a random forests decision tree model, eight out of 10 stress conditions were classified with high accuracy. Comparison of genes contributing most to the accurate classification by partial least squares discriminant analysis, recursive-support vector machine, and random forests revealed 196 common genes with a dynamic range of expression levels in multiple stresses. Functional enrichment and coexpression network analysis revealed the different roles of transcription factors and genes responding to phytohormones or modulating hormone levels in the regulation of stress responses. We envisage the top-ranked genes identified in this study, which highly discriminate abiotic and biotic stresses, as key components to further our understanding of the inherently complex nature of multiple stress responses in plants.

Plants undergo metabolic perturbations under various abiotic stress conditions; due to their sessile nature, the metabolic network of plants requires continuous reconfigurations in response to environmental stimuli to maintain homeostasis and combat stress. The comprehensive analysis of these metabolic features will thus give an overview of plant metabolic responses and strategies applied to mitigate the deleterious effects of stress conditions at a biochemical level. In recent years, the adoption of metabolomics studies has gained significant attention due to the growing technological advances in analytical biochemistry (plant metabolomics). The complexity of the plant biochemical landscape requires sophisticated, advanced analytical methods. As such, technological advancements in the field of metabolomics have been realized, aided much by the development and refinement of separatory techniques, including liquid and gas chromatography (LC and GC), often hyphenated to state-of-the-art detection instruments such as mass spectrometry (MS) or nuclear resonance magnetic (NMR) spectroscopy. Significant advances and developments in these techniques are briefly highlighted in this review. The enormous progress made thus far also comes with the dawn of the Internet of Things (IoT) and technology housed in machine learning (ML)-based computational tools for data acquisition, mining, and analysis in the 4IR era allowing for broader metabolic coverage and biological interpretation of the cellular status of plants under varying environmental conditions. Thus, scientists can paint a holistic and comprehensive roadmap and predictive models for metabolite-guided crop improvement. The current review outlines the application of metabolomics and related technological advances in elucidating plant responses to abiotic stress, mainly focusing on heavy metal toxicity and subsequent osmotic stress tolerance.

The challenges posed by climate change have had a crucial impact on global food security, with crop yields negatively affected by abiotic and biotic stresses. Consequently, the identification of abiotic stress-responsive genes (SRGs) in crops is essential for augmenting their resilience. This study presents a computational model utilizing machine learning techniques to predict genes in Chinese cabbage that respond to four abiotic stresses: cold, heat, drought, and salt. To construct this model, data from relevant studies regarding responses to these abiotic stresses were compiled, and the protein sequences encoded by abiotic SRGs were converted into numerical representations for subsequent analysis. For the selected feature set, six distinct machine learning binary classification algorithms were employed. The results demonstrate that the constructed models can effectively predict SRGs associated with the four types of abiotic stresses, with the area under the receiver operating characteristic curve (auROC) for the models being 81.42%, 87.92%, 80.85%, and 88.87%, respectively. For each type of stress, a distinct number of stress-resistant genes was predicted, and the ten genes with the highest scores were selected for further analysis. To facilitate the implementation of the proposed strategy by users, an online prediction server, has been developed. This study provides new insights into computational approaches to the identification of abiotic SRGs in Chinese cabbage as well as in other plants.

The article presents the results of an empirical study of the relationship between basic beliefs and the resistance to stress of teenagers under the conditions of war. The article presents the results of an empirical study of the relationship between basic beliefs and stress resistance of teenagers in wartime conditions. In general, the level of both stress resistance and the formation of basic beliefs were found to be insufficient for a significant number of teenagers living in the conditions of war in Ukraine. Differences in the manifestations of stress resistance of teenagers depending on gender (boys are characterized by lower indicators than girls), age (with age, indicators of stress resistance of teenagers become higher), number of children in the family (in families with 2 or more children, the stress resistance of teenagers is higher than in families with one child), and distance from the front line (teenagers who live near the front line have lower stress resistance than those who are far from it ) are characterized. Features of the basic beliefs of teenagers and their connection with stress resistance are presented. It is shown that the beliefs about the value of one's own "Self", the degree of luck and the randomness of events are insufficiently formed, and teenagers who live close to the front line believe less in the kindness of people, the possibility of self-control, luck, favour of the world and, instead, more in randomness of events. It was established that teenagers with high stress resistance are characterized by formed basic beliefs, they believe more than teenagers with low stress resistance in affection, justice and controllability of the world, the kindness of people, the possibility of self-control and less in the randomness of events. Prospects for further research are outlined; in particular, theoretical substantiation and practical development of a program for the development of stress resistance of teenagers, taking into account their basic beliefs.

Human resources are the core resources of an enterprise, and the demand forecasting plays a vital role in the allocation and optimization of human resources. Starting from the basic concepts of human resource forecasting, this paper employs the backpropagation neural network (BPNN) and radial basis function neural network (RBFNN) to analyze human resource needs and determine the key elements of the company's human resource allocation through predictive models. With historical data as reference, the forecast value of current human resource demand is obtained through the two types of neural networks. Based on the prediction results, the company managers can carry out targeted human resource planning and allocation to improve the efficiency of enterprise operations. In the experiment, the actual human resource data of a certain company are used as the experimental basic samples to train and test the two types of machine learning tools. The experimental results show that the method proposed in this paper can effectively predict the number of personnel required and can support the planning and allocation of human resources.

Relevance. Last decades have seen an increase in local wars and armed conflicts, that more often than not are associated with manifestations of combat stress and other types of stress-associated psychic disorders in the military and civilians. Prompt prevention of acute (short-term) events of combat psychic trauma (combat stress) can be associated with subsequent adaptive stress response and general increase physical adaptability to extreme pathogenic impacts (including combat-specific factors); in absent, such events transform into chronic (persistent) conditions within clinically defined stress-associated psychic disorders, including post-traumatic stress disorders (PTSD) in the long-term and comorbid psychosomatic pathology.The objective is to use VOSviewer software to study research prospects in publications by Russian investigators on combat stress (2005–2021).Methods. The search engine yielded 894 references to publications on combat stress issues, indexed with the Russian Science Citation Index from 2005 through 2021. Publications on the special military operation in Ukraine were not considered. In terms of content, the papers were aligned with rubrics of the classifier. Investigators who had published the largest number of articles underwent scientometric assessment. VOSviewer software was used to identify the largest scientific clusters and networks. The paper reports median values, the upper and lower quartiles (Me [q25; q75]) of mean annual number of published papers.Results and discussion. Annually, fifty-seven 57 [44; 64] papers on combat stress published in Russia were indexed. The distribution by research field included general combat stress problems issues – 7 %, biological aspects – 11.1 %, medical aspects – 23 %, social and psychological aspects – 58.9 %. Content structure dynamics revealed an upward trend in the number of papers devoted to general, biological, social and psychological problems, with a decrease in the number of papers on medical issues. With 9 repetitive key words or 4 repetitive authors, VOSviewer software identified 5 clusters of papers and 11 academic co-authorships. Cluster 1 included a set of papers on combat stress disorder with Total Link Strength of 40.1 %, cluster 2 – social and psychological problems of combat stress (22.2 %), cluster 3rd – psychosomatic disorders in combat veterans (13.1 %), cluster 4 – human behavior in extreme environments (12.4 %), cluster 5 – stress manifestations in civilians during combat operations (12.2 %).Conclusion. The conducted research demonstrates a focus shift of content in Russian academic publications on combat stress from medical issues to social and psychological repercussions, as well as increased number of papers on the diagnostics of human behavior amid vital stress conditions, development of stress-related mental resistance, psychoprophylaxis, psychological correction and psychotherapy of stress and post-stress disorders. An academic e-library provides researchers with for excellent information resources and tools, with about 80% of papers on combat stress available in full version free of charge.

Background: Maize (Zea mays L.) is a principal cereal crop cultivated worldwide for human food, animal feed, and more recently as a source of biofuel. However, as a direct consequence of water insufficiency and climate change, frequent occurrences of both biotic and abiotic stresses have been reported in different regions around the world, and recently, this has become a major threat in increasing global maize yields. Plants respond to abiotic stresses by utilizing the activity of transcription factors, which are families of genes coding for specific transcription factor proteins whose target genes form a regulon which is involved in the repression/ activation of genes associated with abiotic stress responses. Therefore, it is of uttermost importance to have a systematic study on each family of the transcription factors, the downstream target genes they regulate, and the specific transcription factor genes which are involved in multiple abiotic stress responses in maize and other main crops. Method: In this review, the main transcription factor families, the specific transcription factor genes and their regulons which are involved in abiotic stress regulation will be momentarily discussed. Great emphasis will be given on maize abiotic stress improvement throughout this review, although other examples from other plants like rice, Arabidopsis, wheat, and barley will be used. Results: We have described in detail the main transcription factor families in maize which take part in abiotic stress responses together with their regulons. Furthermore, we have also briefly described the utilization of high-efficiency technologies in the study and characterization of TFs involved in the abiotic stress regulatory networks in plants with an emphasis on increasing maize production. Examples of these technologies include next-generation sequencing, microarray analysis, machine learning and RNA-Seq technology. Conclusion: In conclusion, it is hoped that all the information provided in this review may in time contribute to the use of TF genes in the research, breeding, and development of new abiotic stress tolerant maize cultivars.

BackgroundMaize (Zea mays L.) is a principal cereal crop cultivated worldwide for human food, animal feed, and more recently as a source of biofuel. However, as a direct consequence of water insufficiency and climate change, frequent occurrences of both biotic and abiotic stresses have been reported in various regions around the world, and recently, this has become a constant threat in increasing global maize yields. Plants respond to abiotic stresses by utilizing the activities of transcription factors (TFs), which are families of genes coding for specific TF proteins. TF target genes form a regulon that is involved in the repression/activation of genes associated with abiotic stress responses. Therefore, it is of utmost importance to have a systematic study on each TF family, the downstream target genes they regulate, and the specific TF genes involved in multiple abiotic stress responses in maize and other staple crops.MethodIn this review, the main TF families, the specific TF genes and their regulons that are involved in abiotic stress regulation will be briefly discussed. Great emphasis will be given on maize abiotic stress improvement throughout this review, although other examples from different plants like rice, Arabidopsis, wheat, and barley will be used.ResultsWe have described in detail the main TF families in maize that take part in abiotic stress responses together with their regulons. Furthermore, we have also briefly described the utilization of high-efficiency technologies in the study and characterization of TFs involved in the abiotic stress regulatory networks in plants with an emphasis on increasing maize production. Examples of these technologies include next-generation sequencing, microarray analysis, machine learning, and RNA-Seq.ConclusionIn conclusion, it is expected that all the information provided in this review will in time contribute to the use of TF genes in the research, breeding, and development of new abiotic stress tolerant maize cultivars.

To provide for successful training of qualified staff that can ensure the state economic development, the government needs to improve health care, improving the performance of students. Therefore, the role of the university as a social institution is both to train specialists and also to help them to become socially active, responsible people with good physical and moral health and high stress resistance. Stress resistance is a common object of study of different scientific fields. Most studies deal with the psychological problems of adaptation, motivation and the ability of the individual to cope with different issues; the body's resistance to negative influences is maintained by systems organized in a certain way and subordinate to each other, their constant multilateral interaction leads to coordination of the levels of their functional activity, which is defined as a functional state (FS). The aim of the study is to analyze the psychophysiological and neuropsychological correlates of stress resistance and to develop approaches to the diagnosis and correction of stress resistance in students. The methods used in the study are: psychological tests, individual lateral profile determination, polyparametric method of body functional state determination. Statistical analysis proved the reliability of the ratio of stress resistance indicators only by the parameter of hemispheric asymmetry. Indicators of the anxiety scale are higher in all cases of insufficient stress resistance, but are not specific for determining the type of disorders and correction targets. Detection of dysfunction of the functional state of the body requires physical, psychological and neuropsychological correction.

Abiotic stress in cucumber (Cucumis sativus L.) may trigger distinct transcriptome responses, resulting in significant yield loss. More insight into the molecular underpinnings of the stress response can be gained by combining RNA-Seq meta-analysis with systems biology and machine learning. This can help pinpoint possible targets for engineering abiotic tolerance by revealing functional modules and key genes essential for the stress response. Therefore, to investigate the regulatory mechanism and key genes, a combination of these approaches was utilized in cucumber subjected to various abiotic stresses. Three significant abiotic stress-related modules were identified by gene co-expression network analysis (WGCNA). Three hub genes (RPL18, δ-COP, and EXLA2), ten transcription factors (TFs), one transcription regulator, and 12 protein kinases (PKs) were introduced as key genes. The results suggest that the identified PKs probably govern the coordination of cellular responses to abiotic stress in cucumber. Moreover, the C2H2 TF family may play a significant role in cucumber response to abiotic stress. Several C2H2 TF target stress-related genes were identified through co-expression and promoter analyses. Evaluation of the key identified genes using Random Forest, with an area under the curve of ROC (AUC) of 0.974 and an accuracy rate of 88.5%, demonstrates their prominent contributions in the cucumber response to abiotic stresses. These findings provide novel insights into the regulatory mechanism underlying abiotic stress response in cucumber and pave the way for cucumber genetic engineering toward improving tolerance ability under abiotic stress.

BackgroundClinical medicine offers a promising arena for applying Machine Learning (ML) models. However, despite numerous studies employing ML in medical data analysis, only a fraction have impacted clinical care. This article underscores the importance of utilising ML in medical data analysis, recognising that ML alone may not adequately capture the full complexity of clinical data, thereby advocating for the integration of medical domain knowledge in ML.MethodsThe study conducts a comprehensive review of prior efforts in integrating medical knowledge into ML and maps these integration strategies onto the phases of the ML pipeline, encompassing data pre-processing, feature engineering, model training, and output evaluation. The study further explores the significance and impact of such integration through a case study on diabetes prediction. Here, clinical knowledge, encompassing rules, causal networks, intervals, and formulas, is integrated at each stage of the ML pipeline, resulting in a spectrum of integrated models.ResultsThe findings highlight the benefits of integration in terms of accuracy, interpretability, data efficiency, and adherence to clinical guidelines. In several cases, integrated models outperformed purely data-driven approaches, underscoring the potential for domain knowledge to enhance ML models through improved generalisation. In other cases, the integration was instrumental in enhancing model interpretability and ensuring conformity with established clinical guidelines. Notably, knowledge integration also proved effective in maintaining performance under limited data scenarios.ConclusionsBy illustrating various integration strategies through a clinical case study, this work provides guidance to inspire and facilitate future integration efforts. Furthermore, the study identifies the need to refine domain knowledge representation and fine-tune its contribution to the ML model as the two main challenges to integration and aims to stimulate further research in this direction.

Chapter 9 - Transcriptomics in agricultural sciences: capturing changes in gene regulation during abiotic or biotic stress

Deep learning methods for biotic and abiotic stresses detection and classification in fruits and vegetables: State of the art and perspectives

Both biotic and abiotic stresses pose serious threats to the growth and productivity of crop plants, including maize worldwide. Identifying genes and associated networks underlying stress resistance responses in maize is paramount. A meta-transcriptome approach was undertaken to interrogate 39,756 genes differentially expressed in response to biotic and abiotic stresses in maize were interrogated for prioritization through seven machine learning (ML) models, such as support vector machine (SVM), partial least squares discriminant analysis (PLSDA), k-nearest neighbors (KNN), gradient boosting machine (GBM), random forest (RF), naïve bayes (NB), and decision tree (DT) to predict top-most significant genes for stress conditions. Improved performances of the algorithms via feature selection from the raw gene features identified 235 unique genes as top candidate genes across all models for all stresses. Three genes such as Zm00001eb176680, Zm00001eb176940, and Zm00001eb179190 expressed as bZIP transcription factor 68, glycine-rich cell wall structural protein 2, and aldehyde dehydrogenase 11 (ALDH11), respectively were commonly predicted as top-most candidates between abiotic stress and combined stresses and were identified from a weighted gene co-expression network as the hub genes in the brown module. However, only one gene Zm00001eb038720 encoding RNA-binding protein AU-1/Ribonuclease E/G, predicted by the PLSDA algorithm, was found commonly expressed under both biotic and abiotic stress. Genes involved in hormone signaling and nucleotide binding were significantly differentially regulated under stress conditions. These genes had an abundance of antioxidant responsive elements and abscisic acid responsive elements in their promoter region, suggesting their role in stress response. The top-ranked genes predicted to be key players in multiple stress resistance in maize need to be functional validated to ascertain their roles and further utilization in developing stress-resistant maize varieties.

There has been a lot of research on biotic stresses in lentils because they are visible and lead to decline in production and quality losses. Abiotic stresses, on the other hand, are rapidly being identified as key reasons for the low and unpredictable yield of lentils in many regions. Changes in climate, soils, and climate-soil interactions affect lentil productivity and quality directly or indirectly through their influence on foliar and soil-borne diseases, pests, and rhizobia in each growing zone. Furthermore, the relative tolerance of a cultivar and/or the effect of specific cultural control approaches can vary the effects of a specific stress. Salinity, waterlogging, cold, drought, and heat are the key abiotic factors that affect lentil output, and it is critical to produce climate-robust lentil cultivars to address these issues. The implications of several abiotic stresses on lentil production, genetics, and genomics, including mapping of quantitative traits and incorporating the identified genes with the help of marker-assisted selection breeding, and transcriptomics for the advancement of abiotic stress tolerance in lentil are all covered in this chapter. By identifying candidate genes, gene mapping, and marker-assisted selection, advanced genomic tools can supplement traditional breeding procedures to accelerate breeding projects by enhancing accuracy and saving time. There are few reports on lentil resilience to abiotic stress factors, and more work is needed to investigate the inherited biological process. Evaluating germplasm and breeding material for cultivars resistant to abiotic stressors necessitates the use of rigorous and reproducible phenotypic testing approaches. Systemic application of pan-omics with novel omics technologies will fast-track lentil breeding programmes. Additionally, artificial intelligence (AI) algorithms can help in simulating yield under climate change, leading to predicting the genetic gain. Use of machine learning (ML) in quantitative trait locus (QTL) mining will further enhance the understanding of genetic determinants of abiotic stress in lentils.KeywordsLentilAbiotic stressStressToleranceResistanceBreedingConventionalMolecular