Systematic Data Analysis Research Articles

Unbiased complexome profiling and global proteomics analysis reveals mitochondrial impairment and potential changes at the intercalated disk in presymptomatic R14Δ/+ mice hearts.

Phospholamban (PLN) is a sarco-endoplasmic reticulum (SER) membrane protein that regulates cardiac contraction/relaxation by reversibly inhibiting the SERCA2a Ca2+-reuptake pump. The R14Δ-PLN mutation causes severe cardiomyopathy that is resistant to conventional treatment. Protein complexes and higher-order supercomplexes such as intercalated disk components and Ca+2-cycling domains underlie many critical cardiac functions, a subset of which may be disrupted by R14Δ-PLN. Complexome profiling (CP) is a proteomics workflow for systematic analysis of high molecular weight (MW) protein complexes and supercomplexes. We hypothesize that R14Δ-PLN may alter a subset of these assemblies, and apply CP workflows to explore these changes in presymptomatic R14Δ/+ mice hearts. Ventricular tissues from presymptomatic 28wk-old WT and R14Δ/+ mice were homogenized under non-denaturing conditions, fractionated by size-exclusion chromatography (SEC) with a linear MW-range exceeding 5 MDa, and subjected to quantitative data-independent acquisition mass spectrometry (DIA-MS) analysis. Unfortunately, current workflows for the systematic analysis of CP data proved ill-suited for use in cardiac samples. Most rely upon curated protein complex databases to provide ground-truth for analysis; however, these are derived primarily from cancerous or immortalized cell lines and, consequently, cell-type specific complexes (including cardiac-specific machinery potentially affected in R14Δ-PLN hearts) are poorly covered. We thus developed PERCOM: a novel CP data-analysis strategy that does not rely upon these databases and can, furthermore, be implemented on widely available spreadsheet software. Applying PERCOM to our CP dataset resulted in the identification of 296 proteins with disrupted elution profiles. Hits were significantly enriched for mitochondrial and intercalated disk (ICD) supercomplex components. Changes to mitochondrial supercomplexes were associated with reduced expression of mitochondrial proteins and maximal oxygen consumption rate. The observed alterations to mitochondrial and ICD supercomplexes were replicated in a second cohort of "juvenile" 9wk-old mice. These early-stage changes to key cardiac machinery may contribute to R14Δ-PLN pathogenesis.

Multi-Class Imbalanced Data Handling with Concept Drift in Fog Computing: A Taxonomy, Review, and Future Directions

A network of actual physical objects or “IoT components” linked to the internet and equipped with sensors, electronics, software, and network connectivity is known as the Internet of Things (IoT). This ability of the IoT components to gather and share data is made possible by this network connectivity. Many IoT devices are currently operating, which generate a lot of data. When these IoT devices started collecting data, the cloud was the only place to analyze, filter, pre-process, and aggregate it. However, when it comes to IoT, the cloud has restrictions regarding latency and a more centralized method of distributing programs. A new form of computing called Fog computing has been proposed to address the shortcomings of current cloud computing. In an IoT context, sensors regularly communicate signal information, and edge devices process the data obtained from these sensors using Fog computing. The sensors’ internal or external problems, security breaches, or the integration of heterogeneous equipment contribute to the imbalanced data, i.e., comparatively speaking, one class has more instances than the other. As a result of this data, the pattern extraction is imbalanced . Recent attempts have concentrated heavily on binary-class imbalanced concerns with exactly two classes. However, the classification of multi-class imbalanced data is an issue that needs to be fixed in Fog computing, even if it is widespread in other fields, including text categorization, human activity detection, and medical diagnosis. The study intends to deal with this problem. It presents a systematic, thorough, and in-depth comparative analysis of several binary-class and multi-class imbalanced data handling strategies for batch and streaming data in IoT networks and Fog computing. There are five major objectives in this study. First, reviewing the Fog computing concept. Second, outlining the optimization metric used in Fog computing. Third, focusing on binary and multi-class batch data handling for IoT networks and Fog computing. Fourth, reviewing and comparing the current imbalanced data handling methodologies for multi-class data streams. Fifth, explaining how to cope with the concept drift, including novel and recurring classes, targeted optimization measures, and evaluation tools. Finally, the best performance metrics and tools for concept drift, binary-class (batch and stream) data, and multi-class (batch and stream) data are highlighted.

The Quality Improvement Review Board: An Innovative Approach to Oversight of Projects That Do Not Meet Criteria of Human Subject Research.

This article describes the development of an institutional quality improvement review board (QIRB) as an effective and efficient method for reviewing and overseeing institutional quality improvement (QI) initiatives. QI projects involve the systematic collection and analysis of data and the implementation of interventions designed to improve the quality of clinical care and/or educational programs for a distinct population in a specific setting. QI projects are fundamentally distinct from human subjects research (HuSR); however, the differences between them are subtle and highly nuanced. Determining whether a project meets the definition of QI or qualifies as HuSR, thus requiring institutional review board (IRB) review, can be confusing and frustrating. Nevertheless, this distinction is highly consequential due to the heavy regulatory requirements involved in HuSR and IRB oversight. Making the correct determination of a project's regulatory status is essential before the project begins. Project leaders may not realize that their work meets the definition of HuSR and, therefore, might conduct the project without appropriate IRB review. Therefore, best practices dictate that project leaders should not decide which type of institutional review is appropriate for their projects. In addition, when QI project teams attempt to disseminate the results of their work, documentation of formal review and approval is generally required by peer-reviewed journals and professional organizations. However, institutional review mechanisms are rarely available. Projects that do not meet the definition of HuSR fall outside the purview of IRBs and most institutions do not have an alternative review body. This creates frustration for both project leaders and IRB administrators. Apart from IRB review, a separate process for reviewing QI projects offers several benefits. These include (1) relieving the burden on busy IRB staff; (2) promoting scholarly activity; (3) protecting the institution, project leaders, and participants from HuSR conducted outside of appropriate IRB review; and (4) promoting rigorous QI methods.

Applying information mining technology in online entrepreneurship training course

The purpose of this study is to deeply explore the application of information mining technology in online entrepreneurship training courses, and to improve students’ learning effects and entrepreneurship success rate through systematic analysis and optimization of key data in the teaching process. With the development of online education, how to effectively use big data technology to meet personalized learning needs has become an important issue. This study takes several online entrepreneurship training courses as the research object, and uses information mining technology to extract and analyze students’ behavioral data during course participation, including data on study time, interaction frequency, assessment results, etc. Through machine learning algorithms and association rule mining, the research revealed the main factors that affect students’ learning effects and entrepreneurial success, and designed targeted teaching strategies, such as dynamically adjusting learning content, providing personalized feedback, optimizing learning paths, etc. Experimental results show that online courses using information mining technology significantly improve students’ knowledge mastery and entrepreneurial success rate, especially in terms of personalized learning experience and teaching efficiency. In addition, this study also explores the application prospects of information mining technology in future online education. It is believed that through the combination with artificial intelligence (AI) technology, the intelligence and adaptability of online courses can be further enhanced to meet more diversified learning needs.

ENSURING SAFETY AND PROTECTION OF PERSONAL DATA ON THE INTERNET: ISSUES OF SELECTION OF OPTIMAL RESEARCH METHODOLOGY

This article explores the methodologies and legal challenges associated with personal data protection within the global information space. We examine the limitations of relying solely on specialized research methods and techniques. We argue that personal data protection issues require a more comprehensive, multidisciplinary methodological approach. The primary goal is to analyze the selected methodological strategies and evaluate their effectiveness. The subject of the study is to analyze the issues of methodological approaches in legal research on personal data protection. We analyze a philosophical framework that seeks to deepen the understanding of personal data protection issues, addressing legal and ethical dimensions. We aim to contribute to the broader discourse on data security and privacy through various lenses, including legal, technological, and philosophical perspectives. The study focuses on four analytical methods for developing legal technologies to protect personal data internationally. A systematic analysis of personal data as a legal phenomenon, functional analysis within the framework of legal regulation, sociological and psychological approach, and axiological analysis to understand the meaning and evolution of personal data protection rights. These approaches aim to create a unified concept of legal technology for personal data protection, which makes it possible to identify new types of data, study the components of legal technology in detail, and increase the efficiency of legal regulation.

The impact of technology on Brazilian social work: potentials and challenges in ethical and socio-educational intervention

The integration of technology into Brazilian social work has presented both potentials and challenges. Social workers, historically engaged in socio-educational interventions from various perspectives, have experienced changes in their working conditions and tools due to technological innovations, especially following the COVID-19 pandemic. The analysis presented reveals that while technology provides greater access to information and connections, it can also be used to amplify neoliberal influences, prioritising low-cost intervention methods at the expense of service quality and distancing professionals from the population they serve. Social work must adopt a critical and proactive approach, guided by its ethical-political project, to identify the challenges and possibilities of this process. Ignoring technological advances can lead to a detachment from social reality or submission to institutional and neoliberal demands. Methodologically, a systematic literature review and qualitative analysis of secondary data were employed, guided by contemporary critical perspectives within social work.

Cell-type-specific mapping of enhancers and target genes from single-cell multimodal data.

Mapping enhancers and target genes in disease-related cell types has provided critical insights into the functional mechanisms of genetic variants identified by genome-wide association studies (GWAS). However, most existing analyses rely on bulk data or cultured cell lines, which may fail to identify cell-type-specific enhancers and target genes. Recently, single-cell multimodal data measuring both gene expression and chromatin accessibility within the same cells have enabled the inference of enhancer-gene pairs in a cell-type-specific and context-specific manner. However, this task is challenged by the data's high sparsity, sequencing depth variation, and the computational burden of analyzing a large number of enhancer-gene pairs. To address these challenges, we propose scMultiMap, a statistical method that infers enhancer-gene association from sparse multimodal counts using a joint latent-variable model. It adjusts for technical confounding, permits fast moment-based estimation and provides analytically derived -values. In systematic analyses of blood and brain data, scMultiMap shows appropriate type I error control, high statistical power with greater reproducibility across independent datasets and stronger consistency with orthogonal data modalities. Meanwhile, its computational cost is less than 1% of existing methods. When applied to single-cell multimodal data from postmortem brain samples from Alzheimer's disease (AD) patients and controls, scMultiMap gave the highest heritability enrichment in microglia and revealed new insights into the regulatory mechanisms of AD GWAS variants in microglia.

From the ground up: stakeholders’ representations of the Irish longitudinal study on ageing (TILDA)

ABSTRACT Healthy ageing, which is the target of life’ s later developmental stage, can be achieved through building a wise perspective towards life and existence. However, it may not be achievable for older people when the societal sources are limited. The TILDA project aimed to evaluate the associated factors with healthy ageing and to transfer that knowledge into practice. Hence, determining the perspectives of older people support and advocacy organisations on the enablers and facilitators of the healthy ageing strategies of TILDA is essential to gain a better understanding of the project and plan future strategies. This study aimed to investigate how the TILDA project has influenced or impacted upon these organisations from education, policy, or practice perspectives. The participants (n = 15) included in the study’s sample were reached through representative organisations acting to support older people in the Republic of Ireland. Semi-structured interviews were conducted online via Zoom. A systematic thematic data analysis procedure was followed, and three themes emerged from the qualitative data, revealing the perceptions of participants about the TILDA project: (1) Limitations of TILDA, (2) Contributions of TILDA, and (3) Future recommendations for TILDA. In conclusion, among the disadvantages of TILDA, the most significant is not being representative and visible enough; it is evident that it is pivotal to develop a more inclusive culture of TILDA with close cooperation and effective marketing strategies. It is also apparent that TILDA has several advantages that include providing insights into ageing and rich data to plan future support for older people.

Assessment and Evaluation Methods for 21st Century Education: Measuring What Matters

In modern education, not only teaching methods, but also assessment methods play an important role. An adequate assessment system is essential to the development of skills that are necessary for successful careers of applicants. Therefore, the purpose of the research is the analysis of modern assessment methods in the educational process and their significance for the formation of 21st century skills. The PRISMA approach was used to search for relevant materials. Google Scholar was chosen as the main database for literature search. Works written in English or with English annotations were taken into analysis. 10 criteria for the inclusion of literature were developed, which related to the relevance, practical importance and scientific novelty of the research. Therefore, 40 positions were selected. The study used the coding method for the systematic analysis of textual data. Hence, the results showed that the main approaches to assessment in modern education are summative and formative assessment. Summative assessment summarizes student achievement after the learning process is complete, while formative assessment improves learning outcomes in real time through feedback. Special attention is paid to the active use of digital technologies, such as computer adaptive testing, e-portfolios and platforms for online testing. The conclusions emphasize that innovative assessment should contribute to the development of key skills of the 21st century. The development of digital literacy is especially important, as it allows all participants of the study to actively use technologies and understand them.

An enhancer-AAV toolbox to target and manipulate distinct interneuron subtypes.

In recent years, we and others have identified a number of enhancers that, when incorporated into rAAV vectors, can restrict the transgene expression to particular neuronal populations. Yet, viral tools to access and manipulate fine neuronal subtypes are still limited. Here, we performed systematic analysis of single cell genomic data to identify enhancer candidates for each of the cortical interneuron subtypes. We established a set of enhancer-AAV tools that are highly specific for distinct cortical interneuron populations and striatal cholinergic neurons. These enhancers, when used in the context of different effectors, can target (fluorescent proteins), observe activity (GCaMP) and manipulate (opto- or chemo-genetics) specific neuronal subtypes. We also validated our enhancer-AAV tools across species. Thus, we provide the field with a powerful set of tools to study neural circuits and functions and to develop precise and targeted therapy.

Analysis of Evaluation of Cognitive, Affective and Psychomotor Levels in RPP Aqidah Moral Class XI MA Muallimat Malang with Revised Bloom's Taxonomy

This research uses Bloom's Taxonomy Theory and analyzes the Learning Implementation Plan (RPP) for Aqidah Akhlak lessons in Class XI Madrasah Aliyah Muallimat Malang City. Systematic data analysis is a descriptive qualitative research method. National Education focuses on cultivating students' talents/potential to become people who are devout/faithful, have good morals, insightful and responsible. In the Revised Method, Bloom's Taxonomy consists of three domains: the cognitive domain focuses on the intellectual aspect, the affective domain contains behavior and activities related to the application of the values of Islamic teachings, for example the application of behavior or customs, and the psychomotor domain focuses on the function of motor skills. or physical skills. and learning media such as direct learning, questions and answers, discussions etc. For research results related to implementation, deviant attitudes and actions, such as lying and lack of discipline. Based on data analysis of the results of the formulation of the RPP MA Muallimat Malang City, the presentation reached 64.5% at level C4.

Exploring the multifaceted vulnerabilities of female street child labor in the capital city of Bangladesh

Numerous children experience vulnerability due to their families' profound economic and socio-economic hardships. Among this demographic, females face heightened susceptibility, particularly those engaged in child labor. Dhaka, the capital city of Bangladesh, hosts a substantial population of female child laborers, compounding their precarious circumstances. Hence this study utilizes a qualitative phenomenological approach to investigate the vulnerabilities affecting these female child laborers thoroughly. A total of 25 in-depth interviews were conducted with female child laborers in Dhaka city, following a semi-structured format. NVivo 14 software was instrumental in the systematic coding and analysis of the extensive text data, enhancing the reliability and validity of the findings. This study, therefore, explores various vulnerabilities faced by female child laborers in Dhaka city, including risky health behavior, abusive behavior, sexual harassment, school dropout, unhygienic dietary habits, hazardous work conditions, and substandard living environments. Their socio-economic conditions make them susceptible to physical and mental setbacks, exploitation, and loss of dignity. The study emphasizes the necessity for comprehensive support and breaking the cycle through educational, health, and social initiatives. It offers a detailed portrayal of the living conditions of female child laborers in Dhaka city, providing valuable insights and evidence-based policy prescriptions for policymakers and Non-Government Organizations to formulate effective policies and measures to safeguard this vulnerable community.

Modeling of specific fuel consumption for compression ignition engines fueled with polymer-based fuel: a response surface methodology approach

Background. Diesel engines play a crucial role in ensuring human comfort and well-being across residential, commercial, transportation, and emergency response sectors because of their reliability and versatility. However, identifying alternative fuels remains a significant challenge. Objective. This study aims to develop a comprehensive mathematical model using Response Surface Methodology (RSM) to optimize the performance of Compression Ignition (CI) engines utilizing different types of plastic pyrolysis oil. Methods. Through systematic data collection and analysis, this study examines the importance of design parameters, specifically injection pressure, compression ratio, engine load, and type of plastic pyrolysis oil, which are important for specific fuel consumption. A prediction model was developed to identify the complex correlations between these factors and the fuel use. Results. The developed model serves as an effective tool for optimizing the CI engine performance under diverse operational conditions. Experimental validation involved testing diesel engines with conventional diesel fuel and various plastic pyrolysis oils, followed by optimization using RSM to achieve optimal engine performance. The engine load was identified as the most significant parameter affecting the specific fuel consumption, followed by the fuel type, injection pressure, and compression ratio. The high R-squared (99.35%) and adjusted R-squared (98.02%) values indicate that the proposed model effectively fits the experimental data. Conclusion. The RSM-based model effectively optimizes CI engine performance under varied operational conditions. It significantly reduces the time and effort required to optimize engine design variables, thus enhancing engine performance and sustainability.

Data science and its future in large neuroscience collaborations

Collaborative neuroscience requires systematic data management and analysis. How this is best done in practice remains unclear. Based on a survey across collaborative neuroscience projects, we document the current state of the art focusing on data integration, sharing, and researcher training. We propose best practices and list actions and policies to attain these goals.

Tribological behavior of TiN, AlTiN, and AlTiCrN coatings in atmospheric and vacuum environments

In this study, the tribological characteristics of TiN, AlTiN, and AlTiCrN coatings sliding against a SUS420J1 stainless steel pin were investigated in atmospheric and vacuum environments. The coatings were deposited on SUS440C substrates using the arc-physical vapor deposition technique. The friction and wear behavior of the coatings were evaluated based on the systematic analyses of the friction coefficient data as well as the physical and chemical state of the wear track. The results revealed that the friction coefficients of the SUS440C specimen and AlTiCrN coatings increased, whereas those of the TiN and AlTiN coatings decreased when the environment was changed from atmospheric to vacuum. It was confirmed that the formation of an oxide layer and adsorption of oxides on the surface were dominant factors that influenced the tribological behavior in the atmospheric environment. On the other hand, the compatibility, oxidation inhibition, and droplets of the surface mainly affected the frictional characteristics in the vacuum environment. The results of this work are expected to aid in the selection of proper coating materials for tribological systems operating in a vacuum.

Data Mining and in Silico Analysis of Ethiopian Traditional Medicine: Unveiling the Therapeutic Potential of Rumex abyssinicus Jacq.

Multicomponent traditional medicine prescriptions are widely used in Ethiopia for disease treatment. However, inconsistencies across practitioners, cultures, and locations have hindered the development of reliable therapeutic medicines. Systematic analysis of traditional medicine data is crucial for identifying consistent and reliable medicinal materials. In this study, we compiled and analyzed a dataset of 505 prescriptions, encompassing 567 medicinal materials used for treating 106 diseases. Using association rule mining, we identified significant associations between diseases and medicinal materials. Notably, wound healing-the most frequently treated condition-was strongly associated with Rumex abyssinicus Jacq., showing a high support value. This association led to further in silico and network analysis of R. abyssinicus Jacq. compounds, revealing 756 therapeutic targets enriched in various KEGG pathways and biological processes. The Random-Walk with Restart (RWR) algorithm applied to the CODA PPI network identified these targets as linked to diseases such as cancer, inflammation, and metabolic, immune, respiratory, and neurological disorders. Many hub target genes from the PPI network were also directly associated with wound healing, supporting the traditional use of R. abyssinicus Jacq. for treating wounds. In conclusion, this study uncovers significant associations between diseases and medicinal materials in Ethiopian traditional medicine, emphasizing the therapeutic potential of R. abyssinicus Jacq. These findings provide a foundation for further research, including in vitro and in vivo studies, to explore and validate the efficacy of traditional and natural product-derived medicines.

Synergies between the circular economy and carbon emission reduction

Climate change and sustainable development drive transformation in economic development models. Carbon emission reduction and the circular economy propel climate change and sustainable development, yet it's unclear if they synergize or counteract each other. This study examines the question from theoretical and practical perspectives. Using a theory-practice framework, bibliometric and big data analyses were conducted on the Web of Science and Chinese case data, totaling 2.29GB, to explore synergies between carbon emission reduction and the circular economy. The study finds predominantly synergistic interactions between the circular economy and carbon emission reduction, with minimal offsetting effects. That is, the circular economy markedly enhances carbon emissions reduction. At the theoretical level, the two fields are gradually evolving towards in-depth research, while at the practical level, collaboration is coalescing around four areas: hot fields, potential fields, auxiliary fields and common goals. A noteworthy contribution of this study is the development of a framework that synergizes theory and practice, providing a structured approach for future research in this domain. By quantifying the synergistic and offsetting relationship between the circular economy and carbon emissions reduction through systematic big data analysis, this research offers insights essential for achieving the UN's Sustainable Development Goals. We also stress the need for diverse case studies and multi-dimensional analyses in ongoing research.

Harnessing marketing analytics for enhanced decision-making and performance in SMEs

In the contemporary business landscape, small and medium-sized enterprises (SMEs) are increasingly leveraging marketing analytics to enhance decision-making and improve performance. This paper explores the transformative impact of marketing analytics on SMEs, emphasizing its role in driving data-driven decisions that optimize marketing strategies, enhance customer engagement, and boost overall business performance. Through the systematic collection and analysis of data, SMEs can gain deeper insights into customer behaviors, preferences, and trends, enabling more targeted and effective marketing campaigns. The implementation of marketing analytics facilitates the measurement of key performance indicators (KPIs) such as click-through rates, conversion rates, and return on investment (ROI), providing a robust framework for evaluating the effectiveness of marketing initiatives. By utilizing various types of analytics, including descriptive, predictive, and prescriptive analytics, SMEs can make informed decisions that not only respond to current market conditions but also anticipate future trends and opportunities. Moreover, the adoption of marketing analytics supports the personalization of customer experiences, allowing SMEs to deliver tailored content and offers that resonate with specific audience segments. This level of personalization fosters deeper customer relationships and increases the likelihood of conversions and customer loyalty. The continuous monitoring and analysis of marketing data enable SMEs to remain agile and responsive to changing market dynamics, ensuring that their marketing strategies remain relevant and effective. By embracing a data-driven approach, SMEs can minimize the risk of ineffective marketing efforts and allocate resources more efficiently, ultimately driving sustainable growth and competitive advantage. In conclusion, harnessing marketing analytics is crucial for SMEs aiming to enhance decision-making and performance. The ability to derive actionable insights from data empowers SMEs to optimize their marketing strategies, improve customer engagement, and achieve better business outcomes. As SMEs continue to navigate an increasingly data-driven world, the strategic use of marketing analytics will be pivotal in securing long-term success and growth.

온라인 리뷰를 활용한 전통시장 인식 및 이미지 분석

Recently, in efforts to revitalize traditional markets, the Ministry of Culture, Sports and Tourism of South Korea has put forth the 'K-Tourism Market' project that seeks to develop entertainment, food, and other attractions and turn them into tour routes. Traditional markets are where local residents and tourists interact with one another and enjoy various social and economic benefits. Directing tourists to traditional markets can help address the population decline issue pervasive in South Korea. This study tried to understand the behaviors and preferences of tourists to traditional markets by examining their perceptions and images. To this end, Google online review data were collected and analyzed via big data analysis, frequency analysis, N-gram analysis, centrality analysis, and CONCOR cluster analysis. From this, four major clusters were identified. This study helped expand and enrich research methodology by conducting systematic big data analysis of online reviews from traditional market tourists. Furthermore, by identifying the needs and expectations of tourists, this study can help practitioners improve traditional market service and experience quality.

Risk factors for diminished ovarian reserve in women: Current state of the problem

Ovarian reserve is the basis of female fertility. The main markers of ovarian reserve are the level of anti-Mullerian hormone and the number of antral follicles. In addition to the natural age-related loss of follicles, many women experience a premature diminished ovarian reserve associated with a number of factors. This can be caused by both various diseases and environmental factors, lifestyle, and social aspects.The aim of this review was to examine the influence of external factors on the ovarian reserve and women fertility. A systematic analysis of data from modern scientific literature, domestic and foreign sources was carried out. The search involved such resources as PubMed, MEDLINE, Science Direct, eLibrary, Scopus, Cyberleninka. A detailed analysis of the influence of environmental pollution, lifestyle (sleep, nutrition, physical activity), previous surgeries, bad habits, obesity, psychological and social factors on the ovarian reserve and reproductive function of women was carried out. Significantly diminished ovarian reserve was noted with low sleep quality, excessive physical activity, and an unbalanced diet poor with animal proteins. Regular consumption of alcohol, smoking and exposure to certain chemical environmental pollutants cause premature follicle apoptosis and the onset of menopause. Circadian dysrhythmia, chronic stress and obesity can lead to the ovarian menstrual cycle disorders and the development of infertility in women. Previous parovarium surgeries are a significant risk factor for diminished ovarian reserve. Further population-based studies are needed to determine the precise mechanisms of influence of various factors on female fertility.