Modern Data Analysis Research Articles

MODERN VIEWS ON THE MANAGEMENT OF SAFE ABORTION AND TACTICS FOR MAINTAINING REPRODUCTIVE HEALTH AFTER AN EARLY ABORTION

The article reviews the literature on current views about problem of assistance women, who decided to end an early pregnancy, about the latest achievements in studying the tactics of care such patients, individualization of approaches in order to maintain reproductive health. Purpose of the study. To analyze current literature of authors from different countries to determine the main directions of tactics in management patients, who decided to end an early pregnancy, and evaluate new approaches to improving methods for maintaining reproductive health in these women. Conclusions. The analysis of modern literature data shows the relevance of improving the program of management and individualizing the approach to the rehabilitation of patients, who decided to interrupt unintended early pregnancy. Post-abortion contraception remains an important issue of the program. The main modern method of care such patients is the use of oral contraceptives immediately after artificial abortion, selected individually, taking according to the anamnesis, the medical history before pregnancy, additional necessary examination, plans for the closest intended pregnancy in order to prevent possible side effects. Further research of this problem is relevant for the development and improvement of the patient’s management program after early abortion. The authors have no known conflicts of interests to declaire.

APPROACHES TO RATIONAL NUTRITION OF THE POPULATION OF INDUSTRIALIZED REGIONS EXPOSED TO AEROGENIC IMPACT OF AROMATIC HYDROCARBONS

In the process of sustainable development of regions, negative consequences arise in the form of (1) depletion of natural and energy resources, (2) reduction of biological diversity, (3) violation of the stability of ecosystems, and (4) pollution of environmental objects, including atmospheric air. The constant prolonged chronic aerogenic impact of aromatic hydrocarbons on the human body can make a significant contribution to the formation of additional morbidity and mortality in the population. The ability to neutralize and correct the effect of these compounds is ensured not only by continuous monitoring of the quality of atmospheric air and its impact on human health but also by specifically selected food rations. The analysis of modern theoretical data of international and Russian scientific literature was conducted within the methodological approaches to organizing food for the population in conditions of chemical pollution of atmospheric air. We searched for scientific information in bibliographic and abstract databases. Besides, we presented the average long-term data on (1) the content of benzene and phenol in the atmospheric air and blood in children living under conditions of high chemical aerogenic load with these substances; (2) the state of the antioxidant and conjugation-elimination functions of the benzene biotransformation system; (3) the assessment of the secretion of proteolytic digestive enzymes; and (4) the state of liver cells and excretory-concentration function of the biliary tract. There are examples of food products, the systematic inclusion of which in the diet accelerates the modification and elimination of chemicals; moreover, it has a membrane-stabilizing, hepatoprotective, and antioxidant effect. Acceleration of biotransformation and elimination of toxic substances can be done by including food products with a high content of fiber and pectins in the diet. The acceleration of the metabolism of toxic substances can be carried out by including in the diet foods with a high content of fiber and pectins, polyunsaturated fatty acids, and antioxidant vitamins. The importance of the availability of special diets for all groups of the population living in industrially developed regions under conditions of aerogenic chemical load, including aromatic hydrocarbons, is shown to minimize the negative impact of human-made chemicals and prevent diseases caused by an increased level of atmospheric air pollution.

First-Generation Organic Reaction Intermediates in Zeolite Chemistry and Catalysis.

Zeolite chemistry and catalysis are expected to play a decisive role in the next decade(s) to build a more decentralized renewable feedstock-dependent sustainable society owing to the increased scrutiny over carbon emissions. Therefore, the lack of fundamental and mechanistic understanding of these processes is a critical "technical bottleneck" that must be eliminated to maximize economic value and minimize waste. We have identified, considering this objective, that the chemistry related to the first-generation reaction intermediates (i.e., carbocations, radicals, carbenes, ketenes, and carbanions) in zeolite chemistry and catalysis is highly underdeveloped or undervalued compared to other catalysis streams (e.g., homogeneous catalysis). This limitation can often be attributed to the technological restrictions to detect such "short-lived and highly reactive" intermediates at the interface (gas-solid/solid-liquid); however, the recent rise of sophisticated spectroscopic/analytical techniques (including under in situ/operando conditions) and modern data analysis methods collectively compete to unravel the impact of these organic intermediates. This comprehensive review summarizes the state-of-the-art first-generation organic reaction intermediates in zeolite chemistry and catalysis and evaluates their existing challenges and future prospects, to contribute significantly to the "circular carbon economy" initiatives.

The Usefulness of Gradient-Weighted CAM in Assisting Medical Diagnoses

In modern medicine, medical imaging technologies such as computed tomography (CT), X-ray, ultrasound, magnetic resonance imaging (MRI), nuclear medicine, etc., have been proven to provide useful diagnostic information by displaying areas of a lesion or tumor not visible to the human eye, and may also help provide additional recessive information by using modern data analysis methods. These methods, including Artificial Intelligence (AI) technologies, are based on deep learning architectures, and have shown remarkable results in recent studies. However, the lack of explanatory ability of connection-based, instead of algorithm-based, deep learning technologies is one of the main reasons for the delay in the acceptance of these technologies in the mainstream medical field. One of the recent methods that may offer the explanatory ability for the CNN classes of deep learning neural networks is the gradient-weighted class activation mapping (Grad-CAM) method, which produces heat-maps that may offer explanations of the classification results. There are already many studies in the literature that compare the objective metrics of Grad-CAM-generated heat-maps against other methods. However, the subjective evaluation of AI-based classification/prediction results using medical images by qualified personnel could potentially contribute more to the acceptance of AI than objective metrics. The purpose of this paper is to investigate whether and how the Grad-CAM heat-maps can help physicians and radiologists in making diagnoses by presenting the results from AI-based classifications as well as their associated Grad-CAM-generated heat-maps to a qualified radiologist. The results of this study show that the radiologist considers Grad-CAM-generated heat-maps to be generally helpful toward diagnosis.

Neuroscience Cloud Analysis As a Service: An open-source platform for scalable, reproducible data analysis

A key aspect of neuroscience research is the development of powerful, general-purpose data analyses that process large datasets. Unfortunately, modern data analyses have a hidden dependence upon complex computing infrastructure (e.g., software and hardware), which acts as an unaddressed deterrent to analysis users. Although existing analyses are increasingly shared as open-source software, the infrastructure and knowledge needed to deploy these analyses efficiently still pose significant barriers to use. In this work, we develop Neuroscience Cloud Analysis As a Service (NeuroCAAS): a fully automated open-source analysis platform offering automatic infrastructure reproducibility for any data analysis. We show how NeuroCAAS supports the design of simpler, more powerful data analyses and that many popular data analysis tools offered through NeuroCAAS outperform counterparts on typical infrastructure. Pairing rigorous infrastructure management with cloud resources, NeuroCAAS dramatically accelerates the dissemination and use of new data analyses for neuroscientific discovery.

Partial least median of squares regression

AbstractIn modern data analysis, there is an increasing availability of datasets with numerous variables. Linear models that deal with abundant predictor variables often have poor performance because they tend to produce large variances. As well known, partial least squares (PLS) regression standouts because it is serviceable even if the number of variables far exceeds the number of samples. However, PLS, at its core, is a least‐squares method based on latent space, which is spanned by the components extracted from the original predictors. Hence, it is sensitive to outliers. In this study, incorporating the idea of least median of squares, we propose a new robust PLS method, namely, partial least median of squares (PLMS) regression. Unlike most of the robust counterparts, we solve the PLMS problem via modern optimization rather than a heuristic process or a reweighting strategy. A classical PLS method and two of the most efficient robust PLS methods are compared with our method. Results on simulations and two real‐world data sets demonstrate the effectiveness and robustness of our approach.

Особливості розвитку панкреатиту на тлі деяких ревматичних захворювань

The purpose of the study was to evaluate the main pathogenetic mechanisms of pancreatic lesions on the background of systemic connective tissue diseases based on the analytical analysis of modern literature data. Materials and methods. Bibliosemantic and analytical methods were used in the research. Results and discussion. The main pathogenetic link of the pancreatitis development in patients with systemic connective tissue diseases is vasculitis of the pancreas. Increased expression of adhesion molecules with activation of leukocytes and endothelial cells, deposition of circulating immune complexes in the vascular wall, production of antibodies to endothelial cells, capillary basement membranes play an important role in this process. In systemic lupus erythematosus, according to various authors, the frequency of arteritis varies greatly: rates range from 6.2-7.4 to 53%. In rheumatoid arthritis, the frequency of arteritis of the pancreatic vessels reaches 50%, in systemic sclerosis – 17%. Secondary Sjogren's syndrome is associated with autoimmune pancreatitis in a quarter of cases, but is not the cause. In diseases such as rheumatoid arthritis, systemic scleroderma and systemic lupus erythematosus, antibodies that can attack phospholipids of cell membranes are produced. Antiphospholipid syndrome develops often in systemic lupus erythematosus (70% of cases). In rheumatic fever patients’ changes in the pancreas were studied only in single studies. The main mechanism of pathogenesis of both acute and chronic pancreatitis in nodular periarteritis is the involvement of small and medium arteries of the pancreas in the pathological process. In granulomatous polyangiitis in the pancreas reveals vascular-granulomatous changes, resulting in the formation of extravasations, necrotic foci, foci of atrophy, sclerosis. In IgA vasculitis, changes in the structure of the pancreas are minimal or there are isolated small subcapsular hemorrhages. It is established that metabolic disorders occur in many rheumatic diseases. Thus, reduced glucose tolerance is observed in 7–74% of patients, hypercholesterolemia and triglyceridemia – in 50–75%, hypertension – in 25–50% of cases. Conclusion. Thus, the diagnostic approach to the pancreatitis in systemic connective tissue diseases is very difficult. Its manifestations are masked by damage of the other organs. The availability of more sensitive diagnostic methods, their accessibility can provide an opportunity to detect symptoms of pancreatitis earlier, which will contribute to the appointment of optimal treatment, improvement of the prognosis, quality of life and survival of such patients

Concurrent 18. Presentation for: Minimising pipeline leaks and maximising operational life by application of machine learning at Cooper Basin

Presented on Wednesday 18 May: Session 18 The development of technologies in the last few decades has enabled operators to collect significantly more data than previously possible. Despite availability, making data-driven decisions on asset health, and developing efficient asset management strategies, is not common. This is mainly due to challenges with compilation, and alignment of all the data into a comprehensive picture of pipeline integrity, as it consumes significant resources deploying conventional methods. A critical advantage of modern data storage, analysis and visualisation techniques is the relative ease of performing statistical assessments of integrity data. Analysis of correlated data can be equally challenging as algorithms used can be overly simplistic and inaccurate. Machine learning algorithms parse, analyse and learn from data, enabling the operators to make an educated decision. This has been extensively deployed in other industries such as finance, healthcare and supply chain management but has never been fully developed and enhanced in pipeline integrity industry until very recently. This paper provides an overview of the development in machine learning tools in pipeline integrity, allowing enhancement of asset performance, through the application of machine learning and automation, to predict integrity threats, and prevent leaks and failures. It provides a case study where a tool was developed, and this technique was successfully implemented across a significant number of upstream pipelines in the Cooper Basin, enabling the Santos integrity engineering team to make the most effective decisions on asset condition and to develop a data-driven asset management plan. To access the presentation click the link on the right. To read the full paper click here

Large-scale inference with block structure

The detection of weak and rare effects in large amounts of data arises in a number of modern data analysis problems. Known results show that in this situation the potential of statistical inference is severely limited by the large-scale multiple testing that is inherent in these problems. Here, we show that fundamentally more powerful statistical inference is possible when there is some structure in the signal that can be exploited, for example, if the signal is clustered in many small blocks, as is the case in some relevant applications. We derive the detection boundary in such a situation where we allow both the number of blocks and the block length to grow polynomially with sample size. We derive these results both for the univariate and the multivariate settings as well as for the problem of detecting clusters in a network. These results recover as special cases the sparse signal detection problem (Ann. Statist. 32 (2004) 962–994) where there is no structure in the signal, as well as the scan problem (Statist. Sinica 23 (2013) 409–428) where the signal comprises a single interval. We develop methodology that allows optimal adaptive detection in the general setting, thus exploiting the structure if it is present without incurring a relevant penalty in the case where there is no structure. The advantage of this methodology can be considerable, as in the case of no structure the means need to increase at the rate logn to ensure detection, while the presence of structure allows detection even if the means decrease at a polynomial rate.

Imperative or Functional Control Flow Handling

Modern data analysis tasks often involve control flow statements, such as the iterations in PageRank and K-means. To achieve scalability, developers usually implement these tasks in distributed dataflow systems, such as Spark and Flink. Designers of such systems have to choose between providing imperative or functional control flow constructs to users. Imperative constructs are easier to use, but functional constructs are easier to compile to an efficient dataflow job. We propose Mitos, a system where control flow is both easy to use and efficient. Mitos relies on an intermediate representation based on the static single assignment form. This allows us to abstract away from specific control flow constructs and treat any imperative control flow uniformly both when building the dataflow job and when coordinating the distributed execution.

Correlation Analysis Between Teachers' Teaching Psychological Behavior and Classroom Development Based on Data Analysis.

Teachers’ teaching psychological behavior and classroom development are the current research hotspots in the field of educational psychology. How to realize the data analysis of teachers’ teaching psychological behavior and classroom development is a problem that researchers urgently need to solve. Based on the theory of data correlation analysis, this paper uses modern Internet technology and big data analysis teacher teaching system to quantitatively and qualitatively analyze the potential of students, and build a corresponding model. Through rule correlation technology, the article studies various internal correlations between teachers’ teaching psychological behavior, extracts valuable information from various daily data of students through big data analysis technology, and the WEB teacher’s teaching psychological behavior analysis system based on B/S structure solves the problem that the traditional model cannot measure. In the simulation process, the system is implemented by MVC three-tier architecture, the database uses MYSQL 5.0, the prediction questionnaire is formulated on the basis of the literature method and interviews, and the scale is compiled and tested after repeated revisions. Project analysis and factor analysis are performed on the data obtained from the table test to construct and screen indicators. The experimental results show that the teacher’s classroom teaching behavior index system adopted by the system is practical and feasible, including three first-level indicators, 10 s-level indicators, and 21 third-level indicators. The system has 87.1% completeness, which effectively improves teachers’ teaching psychology.

Independence index sufficient variable screening for categorical responses

Variable screening is very popular in modern data analysis and is particularly useful for large p small n data. In this paper, a novel two-stage sufficient variable screening procedure, especially when the response is categorical is proposed. This procedure is very general and model-free, thus is robust against model mis-specification. In addition, the proposed procedure always improves existing screening approach in literature which only uses marginal relation. Asymptotic results and sure screening properties of the proposed methods are proved. Numerical studies and real data analysis are provided to demonstrate the advantages of the proposed method.

Minimising pipeline leaks and maximising operational life by application of machine learning at Cooper Basin

The development of technologies in the last few decades has enabled operators to collect significantly more data than previously possible. Despite availability, making data-driven decisions on asset health, and developing efficient asset management strategies, is not common. This is mainly due to challenges with compilation, and alignment of all the data into a comprehensive picture of pipeline integrity, as it consumes significant resources deploying conventional methods. A critical advantage of modern data storage, analysis and visualisation techniques is the relative ease of performing statistical assessments of integrity data. Analysis of correlated data can be equally challenging as algorithms used can be overly simplistic and inaccurate. Machine learning algorithms parse, analyse and learn from data, enabling the operators to make an educated decision. This has been extensively deployed in other industries such as finance, healthcare and supply chain management but has never been fully developed and enhanced in pipeline integrity industry until very recently. This paper provides an overview of the development in machine learning tools in pipeline integrity, allowing enhancement of asset performance, through the application of machine learning and automation, to predict integrity threats, and prevent leaks and failures. It provides a case study where a tool was developed, and this technique was successfully implemented across a significant number of upstream pipelines in the Cooper Basin, enabling the Santos integrity engineering team to make the most effective decisions on asset condition and to develop a data-driven asset management plan.

Statistically Empirical Integrated Approach for Knowledge Refined Text Classification

Automated text mining is an especially important task in modern data analysis, both from theoretical and experimental points of view. This particular problem has a major interest in the digital age that is related to “Artificial Intelligence, Machine learning and Information Retrieval”. Feature selection and classification of high dimensionality of text data are challenging tasks. In this paper, we adopted an optimal method for dealing with high dimensionality of data. Later, we chose an appropriate strategy (learning algorithm) for an effcient model training. Our empirical evaluation and experimental analysis show that the proposed method performs better compared with other variable selection-based dimension reduction and further text categorisation methods. We exploited several systematic and careful experimentation scenarios in this work to discover what architecture works best for this BBC news dataset. We used 3 hidden layers, each layer with 128 neurons. We observed this architecture optimal as per our specific problem experimentation. Moreover, our proposed method can be useful for improving efficiency and speed-up the calculations on certain datasets.

Diagnostic and therapeutic approaches to the treatment of iron deficiency conditions in atletes

Based on the analysis of modern literature data, an algorithm was formed for the diagnosis and correction of iron deficiency states in sports in general and in the dynamics of the real training process of qualified representatives of Olympic sports, in particular, for further evaluation of the effectiveness of the innovative liposomal form of bivalent iron. To achieve the goal, we used the analysis data and synthesis of research results available in the modern scientific literature on the issue under study, as well as conducting research on assessing the safety and effectiveness of the liposomal form of bivalent iron in the dynamics of the training process of athletes. The article presents modern algorithms for the diagnosis of anemias, in particular iron deficiency anemia and latent iron deficiency, presents their laboratory and clinical manifestations, and postulates a negative impact on the effectiveness of the competitive process. The frequency of occurrence of iron deficiency states in the population of Ukrainian athletes has been analyzed and it has been shown that these data correspond to world trends. In a randomized, double-blind, placebo-controlled study in 67 athletes, a detailed analysis of the effect of the course use (30 days) of a modern special food supplement “Santeferra” based on ferrous pyrophosphate in liposomal form on indicators of hematological homeostasis and indicators of content, metabolism and transport of iron in representatives of cyclic sports in the dynamics of the training process. Positive shifts in the indicators of the erythrocyte link were found in athletes with a predominantly aerobic mechanism of energy supply, which has a beneficial effect on the oxygen transport function of the blood and, accordingly, on the effectiveness of training and competitive activities of representatives of cyclic sports. Thus, the relatively high frequency of occurrence of iron deficiency states in cyclic sports, especially among female athletes, necessitates serial screening studies and not only therapeutic, but also preventive measures aimed at preventing a decrease in the iron content in the body.

Bayesian Kernel Two-Sample Testing

In modern data analysis, nonparametric measures of discrepancies between random variables are particularly important. The subject is well-studied in the frequentist literature, while the development in the Bayesian setting is limited where applications are often restricted to univariate cases. Here, we propose a Bayesian kernel two-sample testing procedure based on modeling the difference between kernel mean embeddings in the reproducing kernel Hilbert space using the framework established by Flaxman et al. The use of kernel methods enables its application to random variables in generic domains beyond the multivariate Euclidean spaces. The proposed procedure results in a posterior inference scheme that allows an automatic selection of the kernel parameters relevant to the problem at hand. In a series of synthetic experiments and two real data experiments (i.e., testing network heterogeneity from high-dimensional data and six-membered monocyclic ring conformation comparison), we illustrate the advantages of our approach. Supplementary materials for this article are available online.

Диагностика и лечение бактериальных, хламидийных и аденовирусных конъюнктивитов

In the review article, based on the analysis of modern literature data and own long-term clinical experience, some practical issues of conjunctivitis are presented, which continue to be an urgent problem of modern ophthalmology. In particular, this paper presents modern methods of diagnosis and treatment of the most common conjunctivitis, namely, we are talking about bacterial, chlamydial and adenoviral lesions of the mucous membrane of the eye. At the same time, much attention is paid to antibiotic therapy of bacterial and chlamydial conjunctivitis, in which a special place is occupied by antibiotics- fluoroquinolones, which have a wide spectrum of action and are the first choice drugs for bacterial and chlamydial conjunctivitis, especially resistant to other antibiotics. Keywords: bacterial, chlamydial, adenoviral conjunctivitis, diagnosis, treatment.

Differentiation of natural scrub communities of the Cotoneastro-Amelanchieretum group in Central Europe.

Most of Central European rocky scrub communities formed by Cotoneaster integerrimus, Juniperus communis and Amelanchier ovalis are included in the association Cotoneastro-Amelanchieretum (= Junipero-Cotoneasteretum). However, this leads to the creation of syntaxon whose internal diversity is so great that it seems necessary to examine validity of its existence in the current form. This diversity entails species composition, habitat requirements and geographical distribution. Therefore, we posed the following objectives: i) to investigate the variability of species composition of the rocky scrub; (ii) to determine if there are ecological differences between the communities distinguished by species variability; (iii) to determine the geographical ranges of individual syntaxa. Altogether we analyzed 387 phytosociological relevés from Central Europe. Vegetation types of rocky scrubs were identified using the unsupervised K-means algorithm and detrended correspondence analysis. Mean Ellenberg’s indicator values were applied to identify the environmental gradients shaping the plant communities. Obtained results confirmed the validity of dividing this broadly defined syntaxon into six distinct vegetation units. In order to present the studied communities in a broader context, we included into our analyses other rocky scrub with the occurrence of Cotoneaster sp. and A. ovalis, which formed the remaining three clusters. The observed differences in species composition were further supported by significant differences in soil reaction, temperature and continentality between the syntaxa. Moreover, the distinguished communities differed among one another in geographical range. Our study provides a new typology of the selected type of natural rocky scrub vegetation in Central Europe which involves environmental aspects, hence better reflects community-habitat relationships. This study also indicates a need for further revision of the classification of other types of natural scrub communities. Such classification should be based on modern data analysis methods and should primarily focus on lower vegetation units at pan-European scale.

Pollution of surface waters with phosphates and heavy metals

It is substantiated that the current ecological state of the surface waters of Ukraine is characterized by very dirty. The research results indicate that insufficiently treated wastewater enters surface sources. The main pollutants include phosphates as a constituent of detergents, washout of phosphate fertilizers and pesticides from agricultural communities, and industrial effluents containing heavy metals. The analysis of modern data on the sources and intensity of entry, content, transformation and influence of phosphates and heavy metals on aquatic organisms and surface water bodies was carried out. It is emphasized that an excess of phosphorus compounds leads to the development of eutrophication in water bodies. It is shown that heavy metals belong to the group of the most dangerous toxic substances entering aquatic ecosystems, mainly as a result of technogenic action. In terms of toxicity, they are second only to pesticides. Unlike organic pollution, heavy metals are elements that do not undergo destruction in the aquatic ecosystem, but are only distributed among its components. A scheme of the intensity of pollution of individual components of the biosphere is given. Modern methods of water treatment both from phosphates and from heavy metal ions, which have their own advantages and disadvantages, are considered. A scheme for reducing the level of pollution of surface waters with phosphates and heavy metals is given.

CARDIOVASCULAR EFFECTS OF CHEMIO- AND RADIATION THERAPY I N CANC E R PAT IEN TS : WHAT A CARDIO-ONCOLOGIST SHOULD KNOW PART II)

In the second part of the review, the analysis of modern literature data demonstrating the pathophysiological mechanisms of the development of cardiovascular complications of chemo- and radiation therapy in oncological patients should be continued.