Scientific Data Research Articles

Dynamical Sphere Regrouping Particle Swarm Optimization Programming: An Automatic Programming Algorithm Avoiding Premature Convergence

Symbolic regression plays a crucial role in machine learning and data science by allowing the extraction of meaningful mathematical models directly from data without imposing a specific structure. This level of adaptability is especially beneficial in scientific and engineering fields, where comprehending and articulating the underlying data relationships is just as important as making accurate predictions. Genetic Programming (GP) has been extensively utilized for symbolic regression and has demonstrated remarkable success in diverse domains. However, GP’s heavy reliance on evolutionary mechanisms makes it computationally intensive and challenging to handle. On the other hand, Particle Swarm Optimization (PSO) has demonstrated remarkable performance in numerical optimization with parallelism, simplicity, and rapid convergence. These attributes position PSO as a compelling option for Automatic Programming (AP), which focuses on the automatic generation of programs or mathematical models. Particle Swarm Programming (PSP) has emerged as an alternative to Genetic Programming (GP), with a specific emphasis on harnessing the efficiency of PSO for symbolic regression. However, PSP remains unsolved due to the high-dimensional search spaces and local optimal regions in AP, where traditional PSO can encounter issues such as premature convergence and stagnation. To tackle these challenges, we introduce Dynamical Sphere Regrouping PSO Programming (DSRegPSOP), an innovative PSP implementation that integrates DSRegPSO’s dynamical sphere regrouping and momentum conservation mechanisms. DSRegPSOP is specifically developed to deal with large-scale, high-dimensional search spaces featuring numerous local optima, thus proving effective behavior for symbolic regression tasks. We assess DSRegPSOP by generating 10 mathematical expressions for mapping points from functions with varying complexity, including noise in position and cost evaluation. Moreover, we also evaluate its performance using real-world datasets. Our results show that DSRegPSOP effectively addresses the shortcomings of PSO in PSP by producing mathematical models entirely generated by AP that achieve accuracy similar to other machine learning algorithms optimized for regression tasks involving numerical structures. Additionally, DSRegPSOP combines the benefits of symbolic regression with the efficiency of PSO.

Zoonotic outbreak risk prediction with long short-term memory models: a case study with schistosomiasis, echinococcosis, and leptospirosis

BackgroundZoonotic infections, characterized with huge pathogen diversity, wide affecting area and great society harm, have become a major global public health problem. Early and accurate prediction of their outbreaks is crucial for disease control. The aim of this study was to develop zoonotic diseases risk predictive models based on time-series incidence data and three zoonotic diseases in mainland China were employed as cases.MethodsThe incidence data for schistosomiasis, echinococcosis, and leptospirosis were downloaded from the Scientific Data Centre of the National Ministry of Health of China, and were processed by interpolation, dynamic curve reconstruction and time series decomposition. Data were decomposed into three distinct components: the trend component, the seasonal component, and the residual component. The trend component was used as input to construct the Long Short-Term Memory (LSTM) prediction model, while the seasonal component was used in the comparison of the periods and amplitudes. Finaly, the accuracy of the hybrid LSTM prediction model was comprehensive evaluated.ResultsThis study employed trend series of incidence numbers and incidence rates of three zoonotic diseases for modeling. The prediction results of the model showed that the predicted incidence number and incidence rate were very close to the real incidence data. Model evaluation revealed that the prediction error of the hybrid LSTM model was smaller than that of the single LSTM. Thus, these results demonstrate that using trending sequences as input sequences for the model leads to better-fitting predictive models.ConclusionsOur study successfully developed LSTM hybrid models for disease outbreak risk prediction using three zoonotic diseases as case studies. We demonstrate that the LSTM, when combined with time series decomposition, delivers more accurate results compared to conventional LSTM models using the raw data series. Disease outbreak trends can be predicted more accurately using hybrid models.

Context-dependent agricultural intensification pathways to increase rice production in India

Yield gap analysis is used to characterize the untapped production potential of cropping systems. With emerging large-n agronomic datasets and data science methods, pathways for narrowing yield gaps can be identified that provide actionable insights into where and how cropping systems can be sustainably intensified. Here we characterize the contributing factors to rice yield gaps across seven Indian states, with a case study region used to assess the power of intervention targeting. Primary yield constraints in the case study region were nitrogen and irrigation, but scenario analysis suggests modest average yield gains with universal adoption of higher nitrogen rates. When nitrogen limited fields are targeted for practice change (47% of the sample), yield gains are predicted to double. When nitrogen and irrigation co-limitations are targeted (20% of the sample), yield gains more than tripled. Results suggest that analytics-led strategies for crop intensification can generate transformative advances in productivity, profitability, and environmental outcomes.

A massive community-science flower color dataset reveals convergent evolution of delayed flowering phenology in North American red-flowering plants.

The radiation of angiosperms is marked by a phenomenal diversity of floral size, shape, color, scent, and reward. Through hundreds of years of documentation and quantification, scientists have sought to make sense of this variation by defining pollination syndromes as the convergent evolution of common suits of floral traits across distantly related species that have evolved by selection to optimize pollination strategies. Now, with the rise in popularity of community science platforms like iNaturalist, anyone - not just scientists - can collect data. Thanks to the availability of high-quality community-science datasets, we have an unprecedented number of observations of natural flowering plant diversity. These datasets provide an opportunity to develop new tools and to examine new traits, such as flowering time, that may help further characterize pollination syndromes. Here we test the hypothesis that flowering phenology can also be an important trait of a pollination syndrome; particularly of the "hummingbird flower" syndrome, which is usually characterized by red color, long corolla tubes, and exserted stamens. We produced a novel flower color dataset by using GPT-4 with Vision (GPT-4V) to assign flower color to 11,729 North American flowering plant species from community science photographs. We then mapped those species-specific colors to 1,674,908 citizen scientist observations of flowering plants. We demonstrate constrained flowering time in the eastern U.S. for species with red or orange flowers relative to species with flowers of other colors. Importantly, the onset of flowering corresponds closely to the arrival of hummingbirds. Our findings reveal an opportunity to expand the suite of traits included in pollination syndrome and suggest that the hummingbird pollination syndrome can include flowering phenology. Our methods demonstrate an effective pipeline for leveraging the enormous amount of community science data by extracting valuable information about patterns of trait variation.

Мелатонин и мужское бесплодие, вызванное окислительным стрессом

Among couples unable to conceive a child, infertility is explained by the male factor in about 50% of cases. The etiology of male infertility remains uncertain in 30-50% of patients. One of the frequent etiological factors of male infertility is oxidative stress, a statusin which an unbalance between the production of reactive oxygen species and the mechanisms of antioxidant protection. Today, the term «male oxidative stressinfertility» has been proposed as a new descriptor for infertile men with a combination of abnormalsperm characteristics and oxidative stress. Along with the use of traditional antioxidants, it is necessary to search for new moleculesto combat oxidative stressin infertile men. A promising strategy is to use melatonin, a hormone secreted by the pineal gland, as an antioxidant. The review examinesthe antioxidant effects of the melatonin metabolic cascade. The results ofstudies on the relationship between the level of melatonin in seminal plasma and male fertility are presented. New scientific data on the protective potential of melatonin against oxidative stress caused by various metabolic disorders, anticancer chemo- and radiation therapy and other pathogenetic conditions and negative effects are presented. The research on the prospects of using melatonin to protectspermatozoa from oxidative damage in the implementation of assisted reproductive technologiesis analyzed. The interim results ofstudies on the possibility of using melatonin as a dietary supplement in infertile men are summarized.

Modeling Enzyme Kinetics: Current Challenges and Future Perspectives for Biocatalysis.

Biocatalysis is becoming a data science. High-throughput experimentation generates a rapidly increasing stream of biocatalytic data, which is the raw material for mechanistic and novel data-driven modeling approaches for the predictive design of improved biocatalysts and novel bioprocesses. The holistic and molecular understanding of enzymatic reaction systems will enable us to identify and overcome kinetic bottlenecks and shift the thermodynamics of a reaction. The full characterization and modeling of reaction systems is a community effort; therefore, published methods and results should be findable, accessible, interoperable, and reusable (FAIR), which is achieved by developing standardized data exchange formats, by a complete and reproducible documentation of experimentation, by collaborative platforms for developing sustainable software and for analyzing data, and by repositories for publishing results together with raw data. The FAIRification of biocatalysis is a prerequisite to developing highly automated laboratory infrastructures that improve the reproducibility of scientific results and reduce the time and costs required to develop novel synthesis routes.

Inhibition of hERG K channels by verapamil at physiological temperature: Implications for the CiPA initiative

The Comprehensive in vitro Proarrhythmia Assay (CiPA) initiative reassesses using the inhibition of hERG potassium channels by drugs as the major determinant for the potential to cause drug-induced Torsades de Pointes (TdP) cardiac arrhythmias. Here we report our findings on the next phase of CiPA: Determination of hERG inhibitory properties using the standard CiPA-defined data acquisition protocol, here called the standard protocol, at physiological temperature (37 degrees Celsius). To do this, we measured inhibition of hERG1a potassium channels stably expressed in HEK293 cells by the small molecule verapamil, using manual whole-cell patch-clamp electrophysiology recordings with the standard protocol, which is characterized, in part, by a series of 10 s duration voltage steps to 0 mV, ultimately leading to a cumulative recording time of approximately 30 min. Using the standard protocol, we measured an IC50 for verapamil of 225 nM, a Hill coefficient of 1, and time constant of inhibition at 0 mV of 0.64 s. But, using the standard protocol resulted in a very low (5 %) experimental success rate per cell, which had low practicality for future experiments. To address the 5 % success rate, we generated a revised protocol characterized, in part, by a series of 3 s duration voltage steps to 0 mV, leading to a cumulative recording time of approximately 10 min. Using the revised protocol, we found an IC50 for verapamil of 252 nM, a Hill coefficient of 0.8, and time constant of inhibition at 0 mV of 0.67 s. The values measured with the revised protocol were similar to those measured using the standard protocol and, furthermore, our success rate using the revised protocol rose to 25 %, an increase of 5-fold over the standard protocol, and more in line with the success rate for biophysical studies. In summary, we captured key pharmacological data for subsequent analysis in CiPA using a revised protocol with an increased success rate and an overall enhanced feasibility and practicality. We propose that the revised protocol may be more pragmatic for generation of some hERG channel drug inhibition data for CiPA and other regulatory sciences.

Online homework assignments: A case study for 5<sup>th</sup> grade of primary school in Greece

There is a growing body of scientific literature and research data recognizing the value of using online homework. Online homework may transform the way students learn outside the classroom, offering a dynamic and interactive approach to reinforcing learning objectives. This paper explores the concerns of educators arising from the integration of the online homework assignment and presents an educational intervention for 5<sup>th</sup> grade students. The aim of the intervention was to engage students in online homework as a supplementary material to the educational process. Results from the evaluation of the intervention are presented and the benefits of the intervention are highlighted. Although there are various technological, pedagogical and social challenges that need to be addressed, online homework can contribute to a more positive learning environment. Teachers who assign online homework seem to help enhance students’ intrinsic learning motivation so that students may achieve better learning outcomes.

Bivariate Length-Biased Exponential Distribution under Progressive Type-II Censoring: Incorporating Random Removal and Applications to Industrial and Computer Science Data

Bivariate Length-Biased Exponential Distribution under Progressive Type-II Censoring: Incorporating Random Removal and Applications to Industrial and Computer Science Data

Mapping cognitive activity from electrocorticography field potentials in humans performing NBack task

Objective. Advancements in data science and assistive technologies have made invasive brain-computer interfaces (iBCIs) increasingly viable for enhancing the quality of life in physically disabled individuals. Intracortical microelectrode implants are a common choice for such a communication system due to their fine temporal and spatial resolution. The small size of these implants makes the implantation plan critical for the successful exfiltration of information, particularly when targeting representations of task goals that lack robust anatomical correlates. Approach. Working memory processes including encoding, retrieval, and maintenance are observed in many areas of the brain. Using human electrocorticography (ECoG) recordings during a working memory experiment, we provide proof that it is possible to localize cognitive activity associated with the task and to identify key locations involved with executive memory functions. Results. From the analysis, we could propose an optimal iBCI implant location with the desired features. The general approach is not limited to working memory but could also be used to map other goal-encoding factors such as movement intentions, decision-making, and visual-spatial attention. Significance. Deciphering the intended action of a BCI user is a complex challenge that involves the extraction and integration of cognitive factors such as movement planning, working memory, visual-spatial attention, and the decision state. Examining field potentials from ECoG electrodes while participants engaged in tailored cognitive tasks can pinpoint location with valuable information related to anticipated actions. This manuscript demonstrates the feasibility of identifying electrodes involved in cognitive activity related to working memory during user engagement in the NBack task. Devoting time in meticulous preparation to identify the optimal brain regions for BCI implant locations will increase the likelihood of rich signal outcomes, thereby improving the overall BCI user experience.

Modernizing Public Health Data Systems and Workforce Capacity: The Centers for Disease Control and Prevention's Public Health Informatics Fellowship Program.

The COVID-19 pandemic exposed governmental public health's outdated information technology and insufficient data science and informatics workforce capacity. The Centers for Disease Control and Prevention's Public Health Informatics Fellowship Program (PHIFP) is well positioned to strengthen public health data science and informatics workforce capacity. Established in 1996, PHIFP is a 2-year, full-time, on-the-job training program. PHIFP includes a didactic curriculum, applied learning through informatics projects completed at the assigned host site, short-term technical assistance projects, and a final capstone project. Fellows have learned from and bolstered host site informatics capacity through the development or enhancement of information systems, evaluations, data integration, data visualization, and analysis. Among recent graduates, 54% are employed at Centers for Disease Control and Prevention and 16% are employed at other public health organizations, including local health departments. Fellowships such as PHIFP, which recruit and train promising scientists in public health informatics, are important components of efforts to strengthen public health workforce capacity.

Evaluating Generative AI’s Ability to Identify Cancer Subtypes in Publicly Available Structured Genetic Datasets

Background: Genetic data play a crucial role in diagnosing and treating various diseases, reflecting a growing imperative to integrate these data into clinical care. However, significant barriers such as the structure of electronic health records (EHRs), insurance costs for genetic testing, and the interpretability of genetic results impede this integration. Methods: This paper explores solutions to these challenges by combining recent technological advances with informatics and data science, focusing on the diagnostic potential of artificial intelligence (AI) in cancer research. AI has historically been applied in medical research with limited success, but recent developments have led to the emergence of large language models (LLMs). These transformer-based generative AI models, trained on vast datasets, offer significant potential for genetic and genomic analyses. However, their effectiveness is constrained by their training on predominantly human-written text rather than comprehensive, structured genetic datasets. Results: This study reevaluates the capabilities of LLMs, specifically GPT models, in performing supervised prediction tasks using structured gene expression data. By comparing GPT models with traditional machine learning approaches, we assess their effectiveness in predicting cancer subtypes, demonstrating the potential of AI models to analyze real-world genetic data for generating real-world evidence.

Exploring other clustering methods and the role of Shannon Entropy in an unsupervised setting

In the ever-expanding landscape of digital technologies, the exponential growth of data in information science and health informatics presents both challenges and opportunities, demanding innovative approaches to data curation. This study focuses on evaluating various feasible clustering methods within the Data Washing Machine (DWM), a novel tool designed to streamline unsupervised data curation processes. The DWM integrates Shannon Entropy into its clustering process, allowing for adaptive refinement of clustering strategies based on entropy levels observed within data clusters. Rigorous testing of the DWM prototype on various annotated test samples revealed promising outcomes, particularly in scenarios with high-quality data. However, challenges arose when dealing with poor data quality, emphasizing the importance of data quality assessment and improvement for successful data curation. To enhance the DWM’s clustering capabilities, this study explored alternative unsupervised clustering methods, including spectral clustering, autoencoders, and density-based clustering like DBSCAN. The integration of these alternative methods aimed to augment the DWM’s ability to handle diverse data scenarios effectively. The findings demonstrated the practicability of constructing an unsupervised entity resolution engine with the DWM, highlighting the critical role of Shannon Entropy in enhancing unsupervised clustering methods for effective data curation. This study underscores the necessity of innovative clustering strategies and robust data quality assessments in navigating the complexities of modern data landscapes. This content is structured by the following sections: Introduction, Methodology, Results, Discussion, and Conclusion.

Basic Requirements and Framework Conditions of Real-World Data (RWD) on Herbal Medicinal Products.

Real-world data (RWD) is becoming increasingly relevant for evaluating safety and effectiveness of medicinal products, including non-prescription medicines and herbal medicinal products in particular. In order to use real-world data for regulatory decision-making in the field of herbal medicinal products, it is crucial to have an accurate description the herbal substances and preparations, as herbal medicinal products, in contrast to chemically defined medicinal products, contain a complex mixture of natural compounds. However, it remains challenging to get standardised information on herbal products from current literature. This publication gives a brief overview of necessary information of herbal medicinal products in scientific literature and data sources to ensure high-quality real-world data.

Iron Deficiency and its Relationship with Chronic Heart Failure- A Review.

This review article discusses multifactorial pathophysiology, the relationship between clinical characteristics, functional and absolute ID, and the advantages of medicinal intervention in chronic heart failure (CHF). It also covers how iron shortage affects other body parts. The most recent publications that included substantial scientific data on the connection between CHF and ID, with or without anaemia, were selected. Complex physiopathological interactions, including higher hepcidin levels, systemic inflammation, and activation of the renin-angiotensin-aldosterone system, have been identified in these patients. These mechanisms exacerbate the outcomes for patients by amplifying the severity of anemia, chronic heart failure (CHF), and Chronic kidney disease (CKD). Research in this area has been limited and has shown inconsistent findings. Still, it has also examined evidence-based treatment approaches and diagnostic guidelines, especially in relation to iron supplements and erythropoietin-stimulating medications. Anemia is a frequent chronic heart failure consequence and a poor prognostic factor. We still don't completely understand the many complex causes of anemia. Iron deficiency screening is highly recommended for people with cardiac ailments because of its significance for their prognoses. Due to the paucity of research proving its effectiveness, the high incidence of unfavourable gastrointestinal side effects, and the prolonged length of time required for treatment to boost haemoglobin levels, an oral iron supplement is not advised for people with chronic heart failure. An insufficient amount of iron not only impacts the heart but also various other body components.

On the Harm of Science Popularization

The article discusses some aspects of the relationship between science and society through the prism of such a social phenomenon as the popularization of science. The usual narrative around science popularization today assumes the availability of scientific knowledge and its results for the whole society. However, the academic community quite clearly expresses its concerns about the need and, most importantly, the possibility of conveying scientific knowledge to laypeople in a simplified form. It is this opposition that is playing an increasingly prominent role today, including in the development of state science and technology policy. At the same time, as the author shows, the problem of science popularization turns out to be much more multilayered than it seems at first glance. The popularization of science is only a good indicator of the attitude of the state and society towards science and is a derivative of a more fundamental problem – the relationship between science and the democratic structure of society. Usually, they note several positive trends associated with the popularization of science: helping people to learn about new scientific discoveries and research, which can lead to an increase in interest in science in general; scientific knowledge becomes more accessible to a wide range of people (this contributes to an increase in the general level of education and development of society); science popularization can help to reduce anti-science prejudices and stereotypes as well as to increase the level of scientific literacy and critical thinking in society. However, as the author demonstrates using numerous characteristic examples, this is only one side of the coin. Popularization has another, negative side, which has a no less powerful influence on both society in general and the scientific community in particular. Thus, it is the popularization of science that can lead to the “wildness”of society, to ignoring expert scientific opinion. Due to the need to transfer scientific information in an accessible form, there is a simplification or even distortion of scientific data. This can lead to misunderstandings of science and the spread of myths and false beliefs. Obviously, it is important to find a balance between the availability of scientific information and the preservation of its quality and accuracy. But is it possible to find a balance in such a situation? Or is scientific popularization destined to become part of the entertainment business?

Time series forecasting using transformer neural network

Time series forecasting is a vital component of data science, giving essential insights that help decision-makers to predict future trends across a number of sectors. This paper focuses on projecting complicated stock market price dynamics, weather data variations, and hourly traffic occupancy rates. The Informer-Based Time Series Forecasting (IBTSF) model, which employs a transformer-based architecture to capture long-term relationships and give probabilistic predictions, is proposed.This model is assessed against Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) model to benchmark its performance. Simulation findings demonstrate that the IBTSF model considerably outperforms other models across all datasets. For traffic data, the IBTSF model achieves a Mean Squared Error (MSE) of 10.1 and a Mean Absolute Scaled Error (MASE) of 1.69, compared to other model's larger mistakes. In stock data forecasting, the IBTSF model reports an MSE of 8.04 and a MASE of 1.53, again outperforming other models. Similarly, using meteorological data, the IBTSF model obtains an MSE of 86.12 and a MASE of 0.7, whereas other models fall short. These results underline the IBTSF model's resilience and accuracy in capturing subtle patterns and relationships within time series data, giving precise insights that benefit decision-making processes.

Ambiguous landscapes: A framework for assessing robustness and uncertainties in archaeological point pattern analysis.

Landscape research in archaeology has greatly benefited from the increasing application of computational methods over the last decades. Spatial statistical methods such as point pattern analysis have been particularly revolutionary. Archaeologists have used point pattern analysis to explore spatial arrangements and relations between 'points' (e.g., locations of artefacts or archaeological sites). However, the results obtained from these techniques can be greatly affected by the uncertainty coming from the fragmentary nature of archaeological data, their irregular distribution in the landscape, and the working methods used to study them. Furthermore, the quantification of uncertainty in spatial data coming from non-systematic surveys has never been fully addressed. To overcome this challenge, archaeologists have increasingly relied on applying advanced methods from statistics, data science, and geography. While the application of advanced methods from formal sciences will provide robustness to models based on uncertain datasets, as with uncertainty, robustness must be assessed in relation to the case study, the regional context, and the methods used. These issues are of great importance when the models from advanced methods are directly used to create narratives about past landscapes. In this paper, we gather previous research on uncertainty quantification in archaeology and formalize its best practices into a framework to assess robustness and uncertainty in spatial statistical models, particularly focusing on one commonly used in the discipline, i.e., the Pair Correlation Function. This framework allows us to understand better how incomplete data affect a model, quantify the model uncertainties, and assess the robustness of the results achieved with spatial point processes.

Safeguarding Privacy in Genome Research: A Comprehensive Framework for Authors.

As genomic research continues to advance, sharing of genomic data and research outcomes has become increasingly important for fostering collaboration and accelerating scientific discovery. However, such data sharing must be balanced with the need to protect the privacy of individuals whose genetic information is being utilized. This paper presents a bidirectional framework for evaluating privacy risks associated with data shared (both in terms of summary statistics and research datasets) in genomic research papers, particularly focusing on re-identification risks such as membership inference attacks (MIA). The framework consists of a structured workflow that begins with a questionnaire designed to capture researchers' (authors') self-reported data sharing practices and privacy protection measures. Responses are used to calculate the risk of re-identification for their study (paper) when compared with the National Institutes of Health (NIH) genomic data sharing policy. Any gaps in compliance help us to identify potential vulnerabilities and encourage the researchers to enhance their privacy measures before submitting their research for publication. The paper also demonstrates the application of this framework, using published genomic research as case study scenarios to emphasize the importance of implementing bidirectional frameworks to support trustworthy open science and genomic data sharing practices.

Modern Approaches to Integrated Assessment of Public Health in Russian Federation and the Republic of Kazakhstan

Public health management plays an important role in Russian healthcare system. However, the task is impossible without an integrated assessment. In order to understand modern approaches to integral assessment of public health as well as to develop effective methods for its determination, it is necessary to systematize the existing research data. Countries of vast geographical extent, such as Kazakhstan, are of particular interest, as their regional peculiarities do not allow to elaborate a unified approach aimed at improving public health. The purpose was to analyze the scientific research findings on integrated assessment of public health in the Russian Federation and the Republic of Kazakhstan. Material and methods. A keyword search of scientific data available in eLibrary.RU, PubMed, Google Scholar scientometric databases, subsequent study and analysis were carried out. As a result, 35 articles were included in the review for further analysis. Results. In the scientific literature, there is ambiguity in the choice of criteria for an integrated assessment of public health. The use of different approaches and public health indicators reveals the complexity and multilevel nature of the phenomenon. The revealed weight contributions of various public health indicators, as well as the change in their role in different regions, age groups and other cohort groups indicate the need to adapt unified and standardized approaches using correction factors taking into account the above factors. The existing scientific groundwork on integrated assessment of public health in Russian Federation and the Republic of Kazakhstan allows us to predict an upcoming appearance of scientific research that will contain the results of the most accurate and adequate integrated assessment of public health. Conclusion. A comprehensive assessment of public health is a complex multi-level system that should be measured by integral indicators. In this regard, in order to improve these indicators, it is necessary to clarify the criteria characterizing the high indicative value and coverage of various aspects that determine the level of public health.