Statistical distributions of health risks

Modulation of gene expression is influenced by environmental exposures, genetic variation and stochastic processes. Epigenetic biomarkers that signal the outcome of their combined impact could be important early predictors of susceptibility to chronic noncommunicable diseases. Can epigenetic epidemiology shed light on causality and mechanism? Common complex traits arise over time and are the end result of lifestyle choices and a combination of environmental exposures during periods of developmental vulnerability and epigenetic plasticity in the presence of a combination of susceptible genetic variants. Genome-wide association studies to identify the genetic contributions to complex traits have identified many high probability allelic associations, but almost invariably each with a small effect, explaining only a modest percentage of the heritability of the trait. Notably, many estimates of heritability are inflated and do not adequately take into account-shared environment and other familial factors. It is not new to suggest that tissue-specific epigenetic modulation plays a major role in the development of complex traits, yet few have managed to support this using a robust experimental approach. Whereas the genome remains constant, gene expression is labile, demonstrates cell type and developmental specificity, age and sex differences, and is variably responsive to changes in the environment. What are the relationships between genetic variation and different environments that influence epigenetic responses? How do we begin to understand causal relationships that influence gene expression? From an experimental perspective, it is challenging to design studies that can address these questions, yet present a feasible and affordable approach. Large international and interdisciplinary studies are beginning to provide a good framework to better understand gene expression regulation in humans. Though open to criticism, their value should not be underestimated. They include ENCODE that examined 147 cell lines to search for functional elements in the genome [1]; the Epigenomics Roadmap [2] that examined 127 tissue biopsies from embryos and adults, both diseased and healthy, with a focus on identifying functional regulatory elements; the International Human Epigenome Consortium [3]; and the Genome Tissue Expression project (GTEx) that explores the relationship between genetic variation and tissue-specific gene expression and alternate splicing by sequencing whole genomes and examining gene expression in over 20,000 tissue samples from 900 individuals [4]. These data are beginning to give insight into cell specific, developmental stage, sex, age and ethnicity influenced, and stochastic gene expression differences at an individual and population level. The layered annotations in genome browsers highlight the importance of integrating transcriptome data (mRNA, miRNA and lncRNA) with DNA methylation, histone modification and chromatin interaction data to more fully understand function. Epigenome-wide association studies (EWASs) to identify regulatory mechanisms Epigenetic epidemiology: is there cause for optimism?

The purpose of the article is to study the possibilities of using the RStudio environment to solve problems in mathematics, physics and economics. In particular, the emphasis is on the integration of statistical analysis tools, the use of a set of mathematical functions and operations that ensure efficiency and accuracy in calculations. Research methods. An analysis of scientific papers, methodological recommendations and examples of RStudio application in various fields of knowledge related to mathematics, physics and economics was conducted, the functionality of the RStudio environment was tested on practical tasks, and the results obtained using RStudio were compared with the results obtained using other methods to assess the effectiveness of the environment. The scientific novelty lies in the use of R functionality, which allows developing and implementing new methods and approaches to data analysis and solving scientific problems. Conclusions. The RStudio environment demonstrates its effectiveness for solving problems in mathematics, physics and economics due to a wide range of functions for numerical modeling, statistical analysis and data visualization. Using RStudio allows you to significantly simplify the process of solving complex mathematical problems, in particular, it contributes to the integration of mathematical methods in the study of physical phenomena and economic processes, which allows you to form new approaches to data analysis and modeling. RStudio can be used as an effective tool in education, contributing to the formation of analytical thinking and programming skills in students engaged in mathematical, physical and economic problems. In particular, the use of the Tukey criterion and the functions of the RStudio environment for comparing data in large arrays, such as the results of experiments or measurements. The results of the study confirm the feasibility of implementing RStudio in the practice of teaching and scientific research, and also indicate the need for further research into the capabilities of the environment in solving problems in scientific fields.

This paper presents a proof-of-concept study on a two-dimensional distributed-deflection sensor for tissue palpation and the associated correction mechanism for its performance variation. The core of the sensor is one whole polydimethylsiloxane microstructure embedded with a $3\times 3$ sensing-plate/transducer array. Upon pressing the sensor against a tissue, the sensing-plate array translates its elasticity distribution to the deflection distribution, which registers as resistance changes by the transducer array and is further converted into the stiffness distribution. The connection of the sensing-plate/transducer array into one piece allows the sensor interacting with a tissue in a continuous manner and, thus, unifies misalignment errors from non-ideal normal contact between the sensor and the tissue. A correction mechanism is developed to remove the effect of performance variation among the sensing-plate/transducer array on the measured stiffness distribution of a tissue. The stiffness distribution normalized to the minimum stiffness across a tissue is utilized to identify the existence and location of a tumor. The related measurement errors are both theoretically and experimentally examined.

Many problems in statistics, finance, biology, pharmacology, physics, mathematics, economics, and chemistry involve determination of the global minimum of multidimensional functions. R packages for different stochastic methods such as genetic algorithms and differential evolution have been developed and successfully used in the R community. Based on Tsallis statistics, the R package GenSA was developed for generalized simulated annealing to process complicated non-linear objective functions with a large number of local minima. In this paper we provide a brief introduction to the R package and demonstrate its utility by solving a non-convex portfolio optimization problem in finance and the Thomson problem in physics. GenSA is useful and can serve as a complementary tool to, rather than a replacement for, other widely used R packages for optimization.

Doppler related range measurement error (velocity error) can make a major contribution to the systematic error in radars with linear-frequency modulation of a probing pulse. The velocity error can be eliminated by range prediction. It was difficult to apply this approach to radars with analogue signal processing due to lack of advanced computer aids. In radars with digital signal processing, the velocity error in range measurements is often eliminated by estimating Doppler frequency. This paper covers issues related to the elimination of the velocity error by range prediction without Doppler frequency estimation and presents the experiment results at surveillance radar.

Building fragility curves of sliding failure of concrete gravity dams integrating natural and epistemic uncertainties

s.The least-squares algorithm is usually used to fit data in circle detection. The application of the conventional least-squares algorithm is limited, its’ roundness error is bigger, and precision is lower. For detecting circle with data points of non-uniform distribution, developed an analysis algorithm for assessing the minimum zone roundness error. Center and radius can be accurately solved, without iteration, without truncation error. Visual measurements have been carried out for known diameter D=2.564mm tooth shape chain board’s aperture using the proposed methods, calculated results (Table 1) using four kinds of roundness error evaluation methods. Tooth shape chain board’s aperture diameter errors are 0.0157mm、0.0126mm、0.0117 mm and 0.0218mm, roundness errors are 0.0251 mm、0.0225mm、0.0228mm and 0.0244mm respectively. The minimum zone algorithm are suitable for distributed data of all kinds situation, particularly suitable for the realization of machine vision inspection system, fast speed for high precision, wide range of application.

Inflammatory bowel disease is a chronic condition characterized by immune mediated inflammation and primarily includes Crohn's disease and ulcerative colitis. There is an increase rate of incidence of the disease in Kazakhstan. Therefore, it is important to determine the prevalence of the condition in to improve diagnosis and the treatment for the local population. They current manuscript is a systematic review of the literature that focuses on IBD from Kazakhstan with a focus on prevalence, risk factors including genetic predisposition, disease mechanism, diagnosis and treatment. The prevalence of IBD is higher in urban regions, and more prevalent in individuals under the age of 40 years. Genetic studies reported that familial IBD is quite rare in Kazakhstan, suggesting an environmental cause rather than a genetic reason that is driving the increase of prevalence. There are IBD-associated variants linked to immune function and inflammation pathways that may serve as noninvasive biomarkers for disease diagnosis and severity. However, the mechanisms of disease development and drug response may differ across different populations, therefore, there might be a need implement genetic testing to improve therapy and disease management in Kazakhstan.

Investigating solutions of differential equations has been an important issue for scientists. Researchers around the world have talked about different methods to solve differential equations. The type and order of the differential equation enabled us to decide the method that we could choose to find the solution of the equation. One of these methods is the integral transform. Integral transform is the conversion of a real or complex valued function into another function by some algebraic operations. Integral transforms are used to solve many problems in mathematics and engineering, such as differential equations and integral equations. Therefore, new types of integral transforms have been defined and existing integral transforms have been improved. One of the solution methods of many physical problems also initial and boundary value problems are integral transforms. Integral transforms were introduced in the first half of the 19th century. The first historically known integral transforms are Laplace and Fourier transforms. Over the time, other transforms have emerged that are used in many fields. In this article, mohand transform was described and used to simplify the solution of linear ordinary differential equations with constant coefficients.

The relationship between quadratic variation for compound renewal processes and M-Wright functions is discussed. The convergence of quadratic variation is investigated both as a random variable (for given t) and as a stochastic process.

A result quantity in a numerical algorithm is considered as a function of the input data, roundoff and truncation errors. In order to investigate this functional relationship using the methods of mathematical analysis a structural model of the numerical algorithm calledR-automaton is introduced. It is shown that the functional dependence defined by anR-automaton is a continuous rational function in a neighborhood of any data point except in a point set, the Lebesgue measure of which is zero. An effective general-purpose algorithm is presented to compute the derivative of any result quantity with respect to the individual roundoff and truncation errors. Some ways of generalizing theR-automation model without losing the results achieved are finally suggested.

Remaining useful life (RUL) prediction of machinery plays a significant role for predictive maintenance, thus attracting more and more attentions in recent years. Stochastic process model-based methods are widely used in the RUL prediction of machinery. One of the major issues in the stochastic process model-based methods is that how to deal with the unit-to-unit variability during the RUL prediction process. Traditional methods generally handle this issue by introducing a unit-to-unit variability parameter into the model expression and estimate the parameter using the maximum likelihood estimation (MLE) algorithm. There exist two major limitations in the traditional methods. 1) The degradation processes are assumed to be dependent on only the age, which restricts their implementation in the cases of the state-dependent degradation processes. 2) They do not discuss the influence of the unit-to-unit variability in the RUL prediction processes systematically. To deal with these two limitations, a new RUL prediction method based on age- and state-dependent stochastic process models is proposed in this paper. In the proposed method, a generalized expression of the age- and stage-dependent stochastic process models is generated. An enhanced MLE algorithm is developed to estimate the model parameters according to the measurements of the available training units. And the unit-to-unit variability parameter is updated according to the real-time measurements of the testing unit. The effectiveness of the proposed method is demonstrated using a numerical simulation dataset of fatigue crack-growth.

Trapping of oblique surface gravity waves by dual porous barriers near a wall is studied in the presence of step type varying bottom bed that is connected on both sides by water of uniform depths. The porous barriers are assumed to be fixed at a certain distance in front of a vertical rigid wall. Using linear water wave theory and Darcy's law for flow past porous structure, the physical problem is converted into a boundary value problem. Using eigenfunction expansion in the uniform bottom bed region and modified mild-slope equation in the varying bottom bed region, the mathematical problem is handled for solution. Moreover, certain jump conditions are used to account for mass conservation at slope discontinuities in the bottom bed profile. To understand the effect of dual porous barriers in creating tranquility zone and minimum load on the sea wall, reflection coefficient, wave forces acting on the barrier and the wall, and surface wave elevation are computed and analyzed for different values of depth ratio, porous-effect parameter, incident wave angle, gap between the barriers and wall and slope length of undulated bottom. The study reveals that with moderate porosity and suitable gap between barriers and sea wall, using dual barriers an effective wave trapping system can be developed which will exert less wave force on the barriers and the rigid wall. The proposed wave trapping system is likely to be of immense help for protecting various facilities/ infrastructures in coastal environment.

SUMMARY We investigate the robustness to rounding and truncation measurement error of several scale-free goodness-of-fit tests for exponentiality. Moran's statistic is so sensitive to rounding and truncation error as to be potentially misleading in practice. Tests based on the sample Lorenz curve, Gini's statistic and the Shapiro-Wilk test are remarkably insensitive to rounding errors and relatively insensitive to truncation errors. Other tests exhibit intermediate sensitivity to such errors. Trimming Moran's statistic and adding appropriate constants to observations in rounded or truncated samples improves robustness to measurement error.

Presented is a subsection of a larger project to understand and facilitate students' use of mathematics when solving physics problems. Specifically, this study is an examination of how students group/pair problems in math and physics. The results show that whether students examine surface or structure is in part dependent on context.