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Inferring appropriate information from large datasets has become important. In particular, identifying relationships among variables in these datasets has far-reaching impacts. In this paper, we introduce the uniform information coefficient (UIC), which measures the amount of dependence between two multidimensional variables and is able to detect both linear and non-linear associations. Our proposed UIC is inspired by the maximal information coefficient (MIC) \cite{MIC:2011}; however, the MIC was originally designed to measure dependence between two one-dimensional variables. Unlike the MIC calculation that depends on the type of association between two variables, we show that the UIC calculation is less computationally expensive and more robust to the type of association between two variables. The UIC achieves this by replacing the dynamic programming step in the MIC calculation with a simpler technique based on the uniform partitioning of the data grid. This computational efficiency comes at the cost of not maximizing the information coefficient as done by the MIC algorithm. We present theoretical guarantees for the performance of the UIC and a variety of experiments to demonstrate its quality in detecting associations.

This paper discusses the degree to which some of the most widely-used measures of association in corpus linguistics are not particularly valid in the sense of actually measuring association rather than some amalgam of a lot of frequency and a little association. The paper demonstrates these issues on the basis of hypothetical and actual corpus data and outlines implications of the findings. I then outline how to design an association measure that only measures association and show that its behavior supports the use of the log odds ratio as a true association-only measure but separately from frequency; in addition, this paper sets the stage for an analogous review of dispersion measures in corpus linguistics.

OBJECTIVES To assess depression and its relation with different factors among people with epilepsy in wider range of ages. METHODS A cross-sectional study was conducted in Taif city. The sample size was 150; the inclusion criteria were ≥18-year-old patients diagnosed with epilepsy. The data were collected by interviewing patients following up from rural and urban areas of Taif in neurology clinics at King Faisal Medical Complex (KFMC) and King Abdul-Aziz Specialist Hospital (KAASH). The PHQ-9 score was used to screen for depression. RESULTS Our study showed that approximately 76.7% (n=115) of the patients had some form of depression. A severe form of depression was identified in 8.7% (n=13) of the patients, and a moderately severe form was observed in 13.3% (n=20) of the patients. There was no significant association between depression and gender, duration of epilepsy, type of epilepsy, and medications for epilepsy, while there was a statistical association between depression and age, stress level, and employment status (p

espanolEl objetivo del presente articulo es analizar la asociacion de los errores de refraccion en ninos de 4 a 10 anos con los factores que se relacionan con ellos. Este consistio en un estudio descriptivo transversal con abordaje cuantitativo, realizado en dos sedes de una institucion educativa, con muestreo probabilistico y participacion de 233 ninos en 2018. El error de refraccion (ER) se diagnostico bajo cicloplejia. Para identificar el estado nutricional se hizo la medicion e interpretacion del peso y la talla. Los datos sociodemograficos se obtuvieron por medio de una encuesta, y los estilos de vida saludable se midieron mediante un cuestionario sobre riesgos de la salud en ninos y adolescentes. La prevalencia de hipermetropia, astigmatismo y miopia en ninos fue de 37 %, 21 % y 2,2 %, respectivamente. No se demostro asociacion de los ER con el sexo de la poblacion ni con las condiciones socioeconomicas de los padres. Respecto a la edad, se presento una asociacion directamente proporcional en ninos con astigmatismo y miopia, e inversamente proporcional en ninos hipermetropes. Se destaca la mayor frecuencia de ninos con desnutricion aguda e hipermetropia, y la mayor ingesta de desayunos saludables y lacteos por dia en ninos emetropes en comparacion con los hipermetropes. Como conclusion se obtuvo que la prevalencia de hipermetropia fue la mas alta y la miopia la mas baja en el grupo analizado. Este primer grupo realiza mas actividad fisica que los ninos con otros ER. Ademas, no se encontro asociacion estadistica entre las variables de nutricion ni sueno y descanso con los ER. EnglishThe aim of this study was to analyze the association of refractive errors in children aged 4 to 10 years with the factors related to them. This consisted of a cross-sectional descriptive study with quantitative approach, conducted in two sites of an educational institution, with probabilistic sampling and participation of 233 children in 2018. Refractive error (RE) was diagnosed under cycloplegia. To identify the nutritional status, weight and height were measured and interpreted. Sociodemographic data were obtained by survey, and healthy lifestyles were measured by a questionnaire on health risks in children and adolescents. The prevalence of hyperopia (Farsightedness), astigmatism, and myopia in children was 37 %, 21 %, and 2.2 %, respectively. No association of RE was shown with the sex of the population or with the socioeconomic conditions of the parents. Regarding age, there was a directly proportional association in children with astigmatism and myopia, and inversely proportional in hyperopic children. The higher frequency of children with acute malnutrition and hyperopia, and the higher intake of healthy breakfasts and dairy products per day in emmetropic children compared to hyperopic children are highlighted. As a conclusion, the prevalence of hyperopia was the highest and myopia the lowest in the group analyzed. This first group performs more physical activity than children with other RE. In addition, no statistical association was found between the variables of nutrition, sleep and rest with the RE.

Background The increasing number of genome-wide association studies (GWAS) has revealed several loci that are associated to multiple distinct phenotypes, suggesting the existence of pleiotropic effects. Highlighting these cross-phenotype genetic associations could help to identify and understand common biological mechanisms underlying some diseases. Common approaches test the association between genetic variants and multiple traits at the SNP level. In this paper, we propose a novel gene- and a pathway-level approach in the case where several independent GWAS on independent traits are available. The method is based on a generalization of the sparse group Partial Least Squares (sgPLS) to take into account groups of variables, and a Lasso penalization that links all independent data sets. This method, called joint-sgPLS, is able to convincingly detect signal at the variable level and at the group level. Results Our method has the advantage to propose a global readable model while coping with the architecture of data. It can outperform traditional methods and provides a wider insight in terms of a priori information. We compared the performance of the proposed method to other benchmark methods on simulated data and gave an example of application on real data with the aim to highlight common susceptibility variants to breast and thyroid cancers. Conclusion The joint-sgPLS shows interesting properties for detecting a signal. As an extension of the PLS, the method is suited for data with a large number of variables. The choice of Lasso penalization copes with architectures of groups of variables and observations sets. Furthermore, although the method has been applied to a genetic study, its formulation is adapted to any data with high number of variables and an exposed a priori architecture in other application fields.

Smoothness regularization derives the optimal regression function by minimizing the squared loss combined with a smoothness regularizer that restricts the variation of the function within a neighboring region. Thus, the regression function can effectively accommodate intrinsic data structures, and prediction performance can be improved when the label information is insufficient. In this study, we propose a weighted co-association rate-based Laplacian regularized label description algorithm. In the proposed algorithm, we define a regression function by combining weighted co-association rates and a label descriptive function. We use the weighted co-association rate, computed by summarizing various clustering solutions, to depict the data structure. The label descriptive function identifies a latent label distribution, and hence helps the regression function to accurately involve as much true label information as possible. To derive the optimal label descriptive function, we apply the smoothness regularizer to label descriptive function. Experiments were conducted on various benchmark datasets to examine the properties of the proposed algorithms, and the results were compared with those of the existing methods. The experimental results confirm that the proposed algorithm outperforms the previous methods.

Questionnaire surveys often deal with items by which we would like to identify possible associations. These items/variables can be measured on the basis of nominal, ordinal or interval scale. This paper provides an overview of statistical measures of association between two ordinal variables that are used most commonly. It also offers a display of the outputs from the SPSS program, in addition to the description of correlation coefficient calculations. DOI: https://doi.org/10.24040/sjss.2020.6.2.55-61

Genomics study, as opposed to socio-anthropology, has been demonstrated as an excellent tool to picture biological relatedness and disease risk factors. To analyze the data obtained from the study, Genome-wide Association Study (GWAS) has been more than decades known as the mainstay approach., is the most popular approach in analysing genomics data. The confounding variables selection, being that ancestry estimation or population stratification, is substantial to maintain the quality of GWAS. Researchers have developed various methods in extracting the population stratification information from high dimensional genomics data, especially Single Nucleotide Polymorphisms (SNPs) data. In the present study, we proposed an implementation of Principal Component Analysis (PCA)-complemented Gaussian Mixture Model (GMM) as an unsupervised model to estimate population stratification from samples. The results derived from this approach was further compared to that resulted from K-means and from the commonly used ancestry estimation software, fast STRUCTURE. We figured out that our recent improved approach outperformed the two later mentioned as shown by the average cluster and population scores. Furthermore, it was able to generate the probability distribution of each sample across all population, despite its limited quality. These intriguing results worth further investigations with much more comprehensive population coverage and more advanced algorithm.

Imaging genetics research based on Sparse Canonical Correlation Analysis (SCCA) helps to discover the correlation between pathological features reflected by neuroimaging and genotypic variation. Multi-Task SCCA (MTSCCA) method is to identify bi-multivariate associations between SNPs and multi-modal imaging QTs. However, the MTSCCA method is unsupervised and cannot identify diagnosis-guided genotype-phenotype associations. In order to improve the performance and interpretability of MTSCCA, we propose an improved MTSCCA algorithm, which is a supervised sparse bivariate learning model fused with a linear regression model, in which the regression part plays a guiding role in imaging QT selection. To jointly understand the relationship between genotypes and phenotypes of multiple tasks, in this study, gene expression data and single nucleotide polymorphisms (SNPs) as genetic data are considered in the algorithm. The focus of each task is to determine the genotype-phenotype pattern guided by the diagnostic team to discover the association with SNP/gene and brain region changes. Besides, the Laplacian matrices of three kinds of data are added as prior knowledge to the algorithm penalty item so that the algorithm can analyze the correlation between different features. Compared with other SCCA methods, our algorithm has improved noise resistance and stability, and found some diagnostic-specific SNP/gene-ROI specific to the two diagnostic groups of MCI and AD. Significance: This method provides a way to further study the association of multi-modal biological data and identify the complex association patterns of diseases.

Background and Objectives: Oral squamous cell carcinoma (OSCC) is considered as a major health problem worldwide and has been associated with high recurrence rate and poor progno-sis. Advances in understanding of OSCC have not improved the outcome in their management significantly. Many studies have focused on the roles of biomolecular markers in OSCC. The use of p16 and Ki67 as biomarkers of biological behavior of oral squamous cell carcinoma is contro-versial. This study aimed to determine immunoexpression of P16 and Ki67 in oral squamous cell carcinoma and to evaluate their association with various clinicopathological parameters. Materials and Methods: Fifty cases of squamous cell carcinoma from different locations in the oral cavity were included in this cross sectional study. The cases were collected from Rizgary Teaching Hospital and Private Laboratories in Erbil city during a period of eight months from October 2018 to May 2019. The expression of p16 and Ki 67 were evaluated immunohistochem-ically; the findings were correlated with the age of the patients, gender, site of the tumor and grade of the tumor. Result: A total of 50 patients with oral squamous cell carcinoma were enrolled in this study the age ranged from 33 to 89 years, with a mean age ± SD of (64.24 ±12.01) years and more than half (52.0%) of them were males. Lower lip was the most common site of the tumor followed by upper lip and tongue (42.0%, 26.0% and 18.0%, respectively). Histopathological findings of the tumor showed that (54.0%) of the patients had moderately differentiated squamous cell carci-noma. However, (84.0%) of the patients showed negative expression of P 16, while Ki 67 ex-pression was positive among (76.0%) of them. No significant statistical association were found between immunoexpression of p16 and age, sex of patient, site of the tumor and grade of the tumor (P=0.67, P=0,095, P=0.696, P=0.454 respectively). No significant statistical association were found between immunoexpression of Ki67 and age, sex of patient, site of the tumor and grade of the tumor (P=0.637, P=0,411, P=0.353, P=1.00 respectively). Conclusion: in relation to the results obtained in this study no significant association were found between P16 and Ki 67 immunoexpression in oral squamous cell carcinoma with clinicopatho-logical parameters. Further researches have to be designed to better understand the role of p16 and Ki 67 in OSCC. Keywords: oral squamous cell carcinoma, immunoexpression, P16, Ki67.

Aetiological processes of polypoidal choroidal vasculopathy (PCV) remains poorly understood, but several studies indicate that immunity may play a role and report elevated levels of systemic C-reactive protein (CRP). In this systematic review and meta-analysis, we summarize available evidence in the field. We searched the databases PubMed/MEDLINE, EMBASE, Web of Science and the Cochrane Central on 19 March 2020. Two independent authors reviewed the studies and extracted data. Two independent authors reviewed the studies, extracted data and evaluated risk of bias within individual studies. Studies were reviewed in the text qualitatively and measures of association were included for quantitative analyses. Results from univariate analyses and multivariate-adjusted analyses were included for separate meta-analyses to evaluate whether the association was only due to factors associated with PCV. Four studies (246 patients with PCV and 2861 control individuals) were identified and included for a qualitative and quantitative analysis. Increased CRP was associated with PCV when pooling both univariate measures (OR 3.54, 95% CI: 2.13-5.89, p

First published online: October 2020 This paper is the first tofullycharacterize the relationship among cross-market Bitcoin prices to provide a complete picture ofdirectional predictabilityof Bitcoin traded in various currencies across five developed markets. To exploit full-distributional dynamics, we employ Cross-quantilogram based Correlation and Dependence model to delve deep into the estimates an asymmetric tail dependence across quantiles would reflect on heterogeneous movement pattern of Bitcoin prices. A cross-quantilogram-based analysis reveals new empirical evidence of a heterogeneous tail dependence pattern : whereas Bitcoin-USD and the Northeast Asian market (viz., Japan) depicts a strong co-movement, smaller markets display weak connectedness and strong market-efficiency.

Africa being one of the poorest continents is an ideal platform for the use of bitcoin. Considering the specific nature of the continent, is this digital currency going to be a propelling factor for its growth? In this paper, we investigate the correlation and dependence structure between bitcoin and eight African currencies using two different concepts, including extreme correlation plots and a bivariate extreme value model due to Gumbel (Bull Inst Int de Stat 37:471475, 1960). The currencies are chosen to correspond to eight biggest economies in Africa. We identify those African currencies having a positive correlation with bitcoin and those having a negative correlation with bitcoin. The dependence quantified can have economic implications. We perform a robustness study to see if the results hold also for parts of the data. We provide economic interpretations of the results which could be of interest to researchers, policy makers and bitcoin investors within the region and beyond.

Motivation There is growing interest in the biomedical research community to incorporate retrospective data, available in healthcare systems, to shed light on associations between different biomarkers. Understanding the association between various types of biomedical data, such as genetic, blood biomarkers, imaging, etc. can provide a holistic understanding of human diseases. To formally test a hypothesized association between two types of data in Electronic Health Records (EHRs), one requires a substantial sample size with both data modalities to achieve a reasonable power. Current association test methods only allow using data from individuals who have both data modalities. Hence, researchers cannot take advantage of much larger EHR samples that includes individuals with at least one of the data types, which limits the power of the association test. Results We present a new method called the Semi-paired Association Test (SAT) that makes use of both paired and unpaired data. In contrast to classical approaches, incorporating unpaired data allows SAT to produce better control of false discovery and to improve the power of the association test. We study the properties of the new test theoretically and empirically, through a series of simulations and by applying our method on real studies in the context of Chronic Obstructive Pulmonary Disease. We are able to identify an association between the high-dimensional characterization of Computed Tomography chest images and several blood biomarkers as well as the expression of dozens of genes involved in the immune system. Availability and implementation Code is available on https://github.com/batmanlab/Semi-paired-Association-Test. Supplementary information Supplementary data are available at Bioinformatics online.

Background Ambulance Services are vital in providing pre-hospital treatment and care to help regain life. Timely and safe transport of patients from the emergency scene to the appropriate healthcare facility for further care is a complex process. Administering emergency services in minimal time is a challenge in a country with basic lacunae in infrastructure", making the role of EMTs (emergency medical technicians) and ambulance quality vital in saving lives. Methods A descriptive cross-sectional study using a structured questionnaire for the emergency medical technicians and ten parameters to inspect the quality of ambulances was used. Emergency medical technicians in 150 ambulances were interviewed and ambulance was inspected through convenience sampling. Chi-squared test was applied to investigate the association between quality of ambulance & emergency medical technicians' knowledge. Logistic regression was used to obtain the measure of association adjusted for confounders. SPSS (Statistical Package for Social Sciences) version 25, (Licensed software of IIHMR University, Jaipur) was used for analysis. Results Majority of the ambulances 68% (n = 102) were found to have met the standards. Almost 71% (n = 107) of the EMTs were observed to have below average knowledge. Among ambulances not meeting the standards, 66.7% (n = 101) had EMT with below average knowledge. Ambulance quality was found to be independent of the knowledge of EMTs after adjusting for the effects of years of experience and age of the EMTs. Conclusion Majority of the ambulances were found to have met the standards however public health workers should work towards improving the emergency medical technicians' knowledge on transportation of emergency cases.

Introduction Heated debates have been on-going about tea consumption and the incidence of cancer, especially in head and neck cancer types. This study aimed to review the association between tea consumption habits and nasopharyngeal cancer (NPC). Methods: This review was carried out in accordance with the PRISMA-P protocol. Literature search for journal articles that published studies on the relationship between tea consumption and NPC was performed via databases, such as Elsevier, PubMed, Science Direct, Springer Link, Google, and Google Scholar, for 10 years from 2008 to 2018. Relevant studies were obtained by applying the pre-determined keywords, such as nasopharyngeal cancer, tea consumption and NPC, risk factors of NPC and benefits of tea consumption. Results: A total of 126 articles was retrieved. These articles were subjected to eligibility assessment. Six articles remained after applying the inclusion criteria. Results suggest that habitual tea consumption reduces NPC. Tea consumption significantly reduces NPC with all the studies having a p-value ≤0.05. Meta-analysis showed statistical association between tea consumption and NPC risk with OR=0.865 at 95% CI (0.806-0.929). Conclusion: This study suggests that habitual tea consumption could be associated with prevention of NPC development. Additional studies are needed to further understand the molecular role of bioactive compound and potential health benefit of tea consumption in NPC prevention.

Objective The aim of the study was to determine if the depth of large loop excision of the transformation zone (LLETZ) is a risk factor for presenting affected endocervical margins. Methods A cross-sectional retrospective study was performed on 353 patients that underwent LLETZ after presenting cervical biopsies with CIN grade 2 and grade 3 or persistent CIN grade 1 at Hospital Universitario Santa Lucia, Cartagena, Spain, from November 2011 to December 2016. Automatized measurement of the LLETZ depth was performed in microns and added to cervical canal extensions when these were performed. Other variables studied in positive endocervical margins were age (under or over 35 years), premenopause/postmenopause, number of affected quadrants, CIN grading, the presence of HPV 16 before LLETZ, parity (nulliparous vs multiparous) and the location where the LLETZ was performed (operating room vs consultation room). Results Our multivariant analysis showed that LLETZ depth ≤ 10 mm did not increase the risk in affected endocervical margins (P = 0.366) and no statistically significant difference between the two groups (affected and nonaffected margins) was found. CIN grading and parity did prove a statistically significant association (P = 0.039 and P = 0.011, respectively). Age, menopause, number of affected quadrants, HPV 16 and the location did not show statistical association with positive endocervix margins. Conclusions LLETZ depth equal to or lower than 10 mm was not proven to be a risk factor to have affected endocervical margins after the treatment. Therefore, higher LLETZ depth would not be justified to ensure oncological results.

Because of the nature of social interaction or association data, when testing hypotheses using social network data it is common for network studies to rely on permutations to control for confounding variables, and to not also control for them in the fitted statistical model. This can be a problem because it does not adjust for any bias in effect sizes generated by these confounding effects, and thus the effect sizes are not informative in the presence of counfouding variables.We implemented two network simulation examples and analysed an empirical data set to demonstrate how relying solely on permutations to control for confounding variables can result in highly biased effect size estimates of animal social preferences that are uninformative when quantifying differences in behaviour.Using these simulations, we show that this can sometimes even lead to effect sizes that have the wrong sign and are thus the effect size is not biologically interpretable. We demonstrate how this problem can be addressed by controlling for confounding variables in the statistical dyadic or nodal model.We recommend this approach should be adopted as standard practice in the statistical analysis of animal social network data.

espanolLa Paratuberculosis Bovina (PTB) o Enfermedad de Johne, es una infeccion del tracto gastrointestinal causada por Mycobacterium avium, subespecie paratuberculosis (Map), que se caracteriza por generar enteritis granulomatosa cronica y linfadenitis en rumiantes. La preocupacion mas relevante en relacion con la importancia de la PTB es su posible vinculo con la Enfermedad de Crohn (EC) en humanos, sin embargo, esta asociacion aun esta bajo investigacion. Se determino la seroprevalencia de PTB en el municipio de Sogamoso (Boyaca), donde se recolectaron 604 muestras de sangre, cuyo suero fue procesado mediante la tecnica de ELISA indirecta con el kit comercial PARACHEK® 2 KIT (Prionics, Suiza). La seroprevalencia fue de 10,9% (66/604), donde el grupo etario de 2 a 3 anos y la raza Jersey fueron los de mayor seroprevalencia. Se encontro asociacion estadistica significativa (p≤0,05) entre la edad de los individuos evaluados y el suministro de concentrado. La seroprevalencia encontrada indica que se esta produciendo una transmision activa de la enfermedad y que las medidas de control disponibles no estan siendo llevadas a cabo o no son lo suficientemente efectivas. EnglishBovine Paratuberculosis (BPT), or Johne's Disease, is an infection of the gastrointestinal tract caused by Mycobacterium avium, subspecies paratuberculosis (Map), which is characterized by chronic granulomatous enteritis and lymphadenitis in ruminants. The most relevant concern regarding the importance of BPT is its possible link to Crohn's disease (CD) in humans, however this association is still under investigation. The seroprevalence of BPT was determined in the municipality of Sogamoso (Boyaca), where 604 blood samples were collected, their serum was processed by the indirect ELISA technique with the commercial PARACHEK® 2 KIT (Prionics, Switzerland), following the manufacturer's instructions. The seroprevalence was 10,9% (66/604), with the 2 to 3 years age group and the Jersey breed having the highest seroprevalence. A significant statistical association was found (p≤0,05) with the age of the individuals tested and the supply of concentrate. The seroprevalence found indicates that active transmission of the disease is taking place, and that the available control measures are not being carried out or are not effective enough. portuguesA Paratuberculose Bovina (PTB) ou Doenca de Johne e uma infeccao do trato gastrointestinal causada pela Mycobacterium avium subespecie paratuberculosis (Map), caracterizada por gerar enterite granulomatosa cronica e linfadenite em ruminantes. A preocupacao mais relevante em relacao a importância do PTB e seu possivel vinculo com a Doenca de Crohn (DC) em seres humanos, no entanto, essa associacao ainda esta sob investigacao. A soroprevalencia do PTB foi determinada no municipio de Sogamoso (Boyaca), onde foram coletadas 604 amostras de sangue, cujo soro foi processado pela tecnica ELISA indireta com o kit comercial PARACHEK® 2 KIT (Prionics, Suica). A soroprevalencia foi de 10,9% (66/604), onde a faixa etaria de 2 a 3 anos e a raca Jersey foram as que apresentaram maior soroprevalencia. Foi encontrada associacao estatisticamente significante (p≤0,05) com a idade dos individuos avaliados e o suprimento de concentrado. A soroprevalencia encontrada indica que a transmissao ativa da doenca esta ocorrendo e que as medidas de controle disponiveis nao estao sendo realizadas ou nao sao eficazes o suficiente.

Concerns about the value of features of psychopathy to explanations of offending may be driven by challenges with testing this relationship as opposed to the construct’s limited predictive validity. The current study introduced psychopathology network modeling as an analytic strategy capable of addressing these challenges through a more nuanced description of the structural and statistical association between features of psychopathy and offending. To provide more direct implications for criminological theory and research, this included examining whether within-individual changes in features of psychopathy were associated with within-individual change in offending. Data on male and female young offenders from the Pathways to Desistance Study (n = 1354) were used to examine the association between features of psychopathy and offending versatility, as measured by the Youth Psychopathic Traits Inventory and the Self Report of Offending scale, respectively. Three network structures were modeled that separated the variance in the relationship between features of psychopathy and offending into between-subjects and within-individual networks. Between-subjects and within-individual analyses indicated that interpersonal and affective features of psychopathy were positively associated with offending versatility. The network approach indicated that remorselessness and manipulativeness were central features of psychopathy that were also associated with offending versatility. Remorselessness in particular helped bridge together conceptually distinct features of psychopathy. Psychopathology network modeling illustrated the value of features of psychopathy to mainstream criminological theory and research. These features included interpersonal and affective deficits, which previously were identified as poor predictors of offending outcomes.

Detecting relationships among multivariate data is often of great importance in the analysis of high-dimensional data sets, and has received growing attention for decades from both academic and industrial fields. In this study, we propose a statistical tool named the neighbor correlation coefficient (nCor), which is based on a new idea that measures the local continuity of the reordered data points to quantify the strength of the global association between variables. With sufficient sample size, the new method is able to capture a wide range of functional relationship, whether it is linear or nonlinear, bivariate or multivariate, main effect or interaction. The score of nCor roughly approximates the coefficient of determination (R2) of the data which implies the proportion of variance in one variable that is predictable from one or more other variables. On this basis, three nCor based statistics are also proposed here to further characterize the intra and inter structures of the associations from the aspects of nonlinearity, interaction effect, and variable redundancy. The mechanisms of these measures are proved in theory and demonstrated with numerical analyses.

Problem, research strategy, and findings: The literature widely reports a statistical association between the built environment and obesity. What is less clear is the reason for the association. Is...

Most nontyphoidal Salmonella (NTS) illnesses in the United States are thought to be foodborne. However, transmission routes likely vary among the different serotypes. We developed a relative ranking of NTS serotypes according to the strength of their association with foodborne transmission. We used Laboratory-based Enteric Disease Surveillance data to estimate the proportion of infections for each Salmonella serotype reported from 1998 to 2015 and Foodborne Disease Outbreak Surveillance System data to calculate the proportion of foodborne outbreak-associated Salmonella illnesses caused by each serotype. We calculated the ratios of these proportions to create a foodborne relatedness (FBR) measure for each serotype. Of the top 20 serotypes, Saintpaul (2.14), Heidelberg (1.61), and Berta (1.48) had the highest FBR measures; Mississippi (0.01), Bareilly (0.13), and Paratyphi B variant L(+) tartrate(+) (0.20) had the lowest. The FBRs for the three most prevalent serotypes were 1.22 for Enteritidis, 0.77 for Typhimurium, and 1.16 for Newport. This method provides a quantitative approach to estimating the relative differences in the likelihood that an illness caused by a particular serotype was transmitted by food, which may aid in tailoring strategies to prevent Salmonella illnesses and guide future research into serotype-specific source attribution.

Motivation In gene expression and genome-wide association studies, the identification of interaction effects is an important and challenging issue owing to its ultrahigh-dimensional nature. In particular, contaminated data and right-censored survival outcome make the associated feature screening even challenging. Results In this article, we propose an inverse probability-of-censoring weighted Kendall’s tau statistic to measure association of a survival trait with biomarkers, as well as a Kendall’s partial correlation statistic to measure the relationship of a survival trait with an interaction variable conditional on the main effects. The Kendall’s partial correlation is then used to conduct interaction screening. Simulation studies under various scenarios are performed to compare the performance of our proposal with some commonly available methods. In the real data application, we utilize our proposed method to identify epistasis associated with the clinical survival outcomes of non-small-cell lung cancer, diffuse large B-cell lymphoma and lung adenocarcinoma patients. Both simulation and real data studies demonstrate that our method performs well and outperforms existing methods in identifying main and interaction biomarkers. Availability and implementation R-package ‘IPCWK’ is available to implement this method, together with a reference manual describing how to perform the ‘IPCWK’ package. Supplementary information Supplementary data are available at Bioinformatics online.

The key challenge for multiple vessel cooperative localization is considered as data association, in which state-of-the-art approaches adopt a divide-and-conquer strategy to acquire measurement-to-target association. However, traditional approaches suffer both the computational time and accuracy issues. Here, an improved algorithm under Random Finite Set statistics (RFSs) is proposed, in which the Probability Hypothesis Density (PHD) filter is utilized to address the aforementioned issues, by jointly estimating both the number of vessels and the corresponding states in complex environments. Furthermore, to avoid the prior requirement constrain with respect to the PHD filter, the pattern recognition method is simultaneously utilized to calculate the birth intensities. Simulation results exhibit the proposed approach performs better than normal PHD for multiple vessel cooperative localization, in scenarios of unknown birth intensity.

Chapter 9 describes robust measures of association. Several types are available and their relative merits are discussed. Inferential methods based on these measures of association are described as well. Although Pearson's correlation is not robust, a method for testing the hypothesis that Pearson's correlation is equal to zero is included that allows heteroscedasticity. Methods for comparing measures of association, in the independent case, are described. In terms of detecting an association, there are two methods that test the hypothesis that the regression surface is a horizontal plane. That is, these methods are sensitive to curvature, which might be missed when using Pearson's correlation or even the robust measures of association covered in this chapter. R functions are described and illustrated that provide access to all of the measures of association.

INTRODUCTION: The aim of the study was to investigate the prevalence and severity of adverse reactions (ARs) after immunization of healthcare workers (HCWs) with BNT162b2 vaccine and to associate them with clinical and epidemiological characteristics. METHODS: A form containing demographic and clinical data as well as ARs after both doses of the vaccine was completed, and statistical association analysis was performed. RESULTS: A total of 502 HCWs (females 78.3%) with mean age (±SD) 48.17 years (±12.97) participated. After the first dose, 404 (80.5%) HCWs reported at least one local AR (LAR) and 366 (72.9%) after the second dose (p-value=0.004). After the first dose, 121 (24.1%) HCWs reported at least one systemic AR (SAR) and 275 (54.8%) after the second dose (p-value

Background Innovations in single cell technologies have lead to a flurry of datasets and computational tools to process and interpret them, including analyses of cell composition changes and transition in cell states. The diffcyt workflow for differential discovery in cytometry data consist of several steps, including preprocessing, cell population identification and differential testing for an association with a binary or continuous covariate. However, the commonly measured quantity of survival time in clinical studies often results in a censored covariate where classical differential testing is inapplicable. Results To overcome this limitation, multiple methods to directly include censored covariates in differential abundance analysis were examined with the use of simulation studies and a case study. Results show that multiple imputation based methods offer on-par performance with the Cox proportional hazards model in terms of sensitivity and error control, while offering flexibility to account for covariates. The tested methods are implemented in the package censcyt as an extension of diffcyt and are available at https://bioconductor.org/packages/censcyt. Conclusion Methods for the direct inclusion of a censored variable as a predictor in GLMMs are a valid alternative to classical survival analysis methods, such as the Cox proportional hazard model, while allowing for more flexibility in the differential analysis.

Balancing the distributions of the confounders across the exposure levels in an observational study through matching or weighting is an accepted method to control for confounding due to these variables when estimating the association between an exposure and outcome and to reduce the degree of dependence on certain modeling assumptions. Despite the increasing popularity in practice, these procedures cannot be immediately applied to datasets with missing values. Multiple imputation of the missing data is a popular approach to account for missing values while preserving the number of units in the dataset and accounting for the uncertainty in the missing values. However, to the best of our knowledge, there is no comprehensive matching and weighting software that can be easily implemented with multiply imputed datasets. In this paper, we review this problem and suggest a framework to map out the matching and weighting multiply imputed datasets to 5 actions as well as the best practices to assess balance in these datasets after matching and weighting. We also illustrate these approaches using a companion package for R, MatchThem., Comment: 23 Pages, 3 Figures

El objetivo del presente trabajo es determinar empíricamente la asociación del capital estructural (CE) y el capital relacional (CR) con el desempeño organizacional (DO) en el sector público del estado de Tamaulipas (México). De octubre a diciembre 2019, se aplicaron 269 encuestas de manera presencial y online. Los datos recabados fueron interpretados mediante el análisis factorial exploratorio (AFE) y el coeficiente de correlación de Rho de Spearman. Una estructura de dos factores fue establecida para CE (1. Innovación organizacional y 2. Estructura organizacional), una estructura de tres factores para CR (1. Relaciones a largo plazo con los usuarios, 2. Relaciones a largo plazo con los proveedores y 3. Alianzas estratégicas) y una estructura de tres factores para DO (1. Comportamiento cívico, 2. Compañerismo y 3. Iniciativa). Los factores Relaciones a largo plazo con los usuarios y Alianzas estratégicas poseenel mayor nivel de asociación estadística con el DO.

Nair, Sankaran and John (Metron 76 (2018) 133–153) have defined and studied the properties of reliability functions in terms of copulas. In the present paper, we investigate the utility of such functions in inferring the time-dependent association of bivariate distributions. We consider the Clayton measure of association for the study. A general expression for this measure in terms of the generator of Archimedean copulas is given, and a method of finding nature of association using the generators is provided. We derive the relationship of the association measure with the ageing property of the distribution, associated with the generator. We analyze how the hazard rate of survival copulas can be utilized in studying the association between two random variables. Applications of the results in real life situations are discussed.

Highlights • Significant inverse bidirectional associations were found between self-reported MVPA and depressive symptoms. • No associations were observed between accelerometer MVPA estimates and depressive symptoms. • Both self-reported and accelerometer-estimated MVPA are valid, but may capture different aspects of the activity profile., Depression affects many aspects of health and may be attenuated through increases in physical activity. While bidirectional associations between physical activity (PA) and depressive symptoms have been examined, few studies have examined these associations using both self-reported and accelerometer-estimated measures. Using data from Years 20 (2005–06, age 38–50) and 30 of the Coronary Artery Risk Development in Young Adults (CARDIA) study (N = 2,871), the bidirectional associations between moderate to vigorous intensity physical activity (MVPA) and depressive symptoms were examined using a cross-lagged panel model. Differences in the observed associations by physical activity assessment method were also examined. An inverse bidirectional association between self-reported MVPA and depressive symptoms was found. In subsequent analyses stratified by intensity category, higher levels of vigorous intensity physical activity at baseline, but not moderate intensity physical activity were associated with lower levels of depressive symptoms at the 10-year follow-up (ϕ = −0.04, p

The advent of high-throughput metagenomic sequencing has prompted the development of efficient taxonomic profiling methods allowing to measure the presence, abundance and phylogeny of organisms in a wide range of environmental samples. Multivariate sequence-derived abundance data further has the potential to enable inference of ecological associations between microbial populations, but several technical issues need to be accounted for, like the compositional nature of the data, its extreme sparsity and overdispersion, as well as the frequent need to operate in under-determined regimes. The ecological network reconstruction problem is frequently cast into the paradigm of Gaussian Graphical Models (GGMs) for which efficient structure inference algorithms are available, like the graphical lasso and neighborhood selection. Unfortunately, GGMs or variants thereof can not properly account for the extremely sparse patterns occurring in real-world metagenomic taxonomic profiles. In particular, structural zeros (as opposed to sampling zeros) corresponding to true absences of biological signals fail to be properly handled by most statistical methods. We present here a zero-inflated log-normal graphical model (available at https://github.com/vincentprost/Zi-LN) specifically aimed at handling such “biological” zeros, and demonstrate significant performance gains over state-of-the-art statistical methods for the inference of microbial association networks, with most notable gains obtained when analyzing taxonomic profiles displaying sparsity levels on par with real-world metagenomic datasets., Author summary The importance of associations in the structuring and dynamics of community members is widely acknowledged, but we are currently unable to co-culture most of the micro-organims sampled from the environment. Computational methods to predict microbial associations can therefore be of practical interest, in particular given the large amounts of multivariate microbial abundance data generated by metagenomics. This data can in theory be leveraged to infer association networks, but with limited success so far, as several of its attributes lead to technical difficulties, including its extreme sparsity, compositionality and overdispersion among others. In particular, structural zeros (as opposed to sampling and technical zeros) corresponding to true absences of biological signals frequently fail to be properly handled, and such non-random absences can lead to high levels of false positives. Given their prevalence, zero values should be properly handled by the modeling process by accounting for the zero generating process in the first place. We describe here a truncated log-normal graphical model that specifically addresses zeros originating from biological absences, and discuss consistent methods for estimating sparse and high-dimensional association networks. We also show that this model generates sparse multivariate counts more close to those derived from real-world microbiomes.

Large datasets of hundreds to thousands of individuals measuring RNA-seq in observational studies are becoming available. Many popular software packages for analysis of RNA-seq data were constructed to study differences in expression signatures in an experimental design with well-defined conditions (exposures). In contrast, observational studies may have varying levels of confounding transcript-exposure associations; further, exposure measures may vary from discrete (exposed, yes/no) to continuous (levels of exposure), with non-normal distributions of exposure. We compare popular software for gene expression—DESeq2, edgeR and limma—as well as linear regression-based analyses for studying the association of continuous exposures with RNA-seq. We developed a computation pipeline that includes transformation, filtering and generation of empirical null distribution of association P-values, and we apply the pipeline to compute empirical P-values with multiple testing correction. We employ a resampling approach that allows for assessment of false positive detection across methods, power comparison and the computation of quantile empirical P-values. The results suggest that linear regression methods are substantially faster with better control of false detections than other methods, even with the resampling method to compute empirical P-values. We provide the proposed pipeline with fast algorithms in an R package Olivia, and implemented it to study the associations of measures of sleep disordered breathing with RNA-seq in peripheral blood mononuclear cells in participants from the Multi-Ethnic Study of Atherosclerosis.

Background The potential association between Parvovirus B19 and heart disease has been controversial. The aim of the present study was to report the prevalence of B19 in myocarditis and dilated cardiomyopathy (DCM) as well as measure the statistical association between them. Methods Our systematic search was carried out to retrieve published articles between January 2000 and March 2021 using three major databases: PubMed, Scopus, and Web of Science, as well as the Google Scholar search engine. The overall prevalence of HAV, pooled odds ratio, and heterogeneity were estimated by comprehensive meta-analysis (V2.2, Biostat) software. Results The overall prevalence results in myocarditis and DCM were 23.7% (95% CI: 18.7%–29.5%) and 34.1% (95% CI: 23.8%–46.1%) respectively; in addition, the overall OR for B19 and myocarditis was 4.317 (95% CI, 1.831–10.180) versus 1.163 (95% CI: 0.706–1.916) for B19 and DCM. Conclusion Our findings have shown a significant association between Parvovirus B19 and myocarditis with a high prevalence. In the case of DCM, no significant association was found while the prevalence of the virus was relatively high.

Introducción. La mastalgia se presenta en el 70 % de las mujeres, siendo la causa de realizar estudios de imagen, en muchos casos sin indicación. La incidencia de cáncer de seno entre las mujeres con mastalgia es del 2,5 % sin demostrar asociación entre ambas. Objetivo. Conocer la prevalencia de BIRADS (Breast imaging reporting and data system) 4-5 entre mujeres con o sin mastalgia, describir las características demográficas, clínicas, y determinar la asociación entre mastalgia y el reporte ecográfico BIRADS 4-5. Métodos. Estudio analítico transversal en 323 usuarias atendidas en la Clínica del Prado en Medellín (Colombia), entre junio y octubre de 2018. Se calculó la proporción de mastalgia y de reportes BIRADS 4-5. Se evaluaron las características demográficas y clínicas. Resultados. La prevalencia de mastalgia fue de 21,4 %, y de BIRAD 4-5 de 6,2 %. Entre las que tuvieron mastalgia, la frecuencia de BIRADS 4-5 fue 2,9 %, sin encontrar asociación estadística OR 2,5 (IC95 % 0,58 a 11,3; p: 0.2). Conclusión. Los hallazgos ecográficos sospechosos no se asociaron a mastalgia, siendo esta una indicación común de ultrasonido mamario; frecuentemente los hallazgos son normales o benignos.

A framework for quantifying dependence between random vectors is introduced. Using the notion of a collapsing function, random vectors are summarized by single random variables, referred to as collapsed random variables. Measures of association computed from the collapsed random variables are then used to measure the dependence between random vectors. To this end, suitable collapsing functions are presented. Furthermore, the notion of a collapsed distribution function and collapsed copula are introduced and investigated for certain collapsing functions. This investigation yields a multivariate extension of the Kendall distribution and its corresponding Kendall copula for which some properties and examples are provided. In addition, non-parametric estimators for the collapsed measures of association are provided along with their corresponding asymptotic properties. Finally, data applications to bioinformatics and finance are presented along with a general graphical assessment of independence between groups of random variables.

Motivation The flexibility of a Bayesian framework is promising for GWAS, but current approaches can benefit from more informative prior models. We introduce a novel Bayesian approach to GWAS, called Structured and Non-Local Priors (SNLPs) GWAS, that improves over existing methods in two important ways. First, we describe a model that allows for a marker’s gene-parent membership and other characteristics to influence its probability of association with an outcome. Second, we describe a non-local alternative model for differential minor allele rates at each marker, in which the null and alternative hypotheses have no common support. Results We employ a non-parametric model that allows for clustering of the genes in tandem with a regression model for marker-level covariates, and demonstrate how incorporating these additional characteristics can improve power. We further demonstrate that our non-local alternative model gives symmetric rates of convergence for the null and alternative hypotheses, whereas commonly used local alternative models have asymptotic rates that favor the alternative hypothesis over the null. We demonstrate the robustness and flexibility of our structured and non-local model for different data generating scenarios and signal-to-noise ratios. We apply our Bayesian GWAS method to single nucleotide polymorphisms data collected from a pool of Alzheimer’s disease and cognitively normal patients from the Alzheimer’s Database Neuroimaging Initiative. Availability and implementation R code to perform the SNLPs method is available at https://github.com/lockEF/BayesianScreening.

Variable selection is a vital problem in expert systems and in knowledge discovery. However, traditional variable selection methods achieve low accuracies on large and complex datasets because they do not consider the importance of variable combinations. A variable combination is a variable set in which the combined variables fit better than do other, redundant variables, but each of which, considered individually, may fit worse. Therefore, an association-based evolutionary ensemble variable selection method for multiple linear regression is proposed. Both greedy algorithms and stochastic algorithms are applied under an ensemble framework. Instead of ranking variables according to selection frequency, the associations among variables in local solutions are considered to identify important variable combinations. Experiments show that the proposed approach has a competitive performance for variable selection and outperforms some classic methods on large-scale datasets. The study is important for automated feature discovery for complex systems with multiple combined input variables.

Measuring dependence is a very important tool to analyze pairs of functional data. The coefficients currently available to quantify association between two sets of curves show a non robust behavior under the presence of outliers. We propose a new robust numerical measure of association for bivariate functional data. We extend in this paper Kendall coefficient for finite dimensional observations to the functional setting. We also study its statistical properties. An extensive simulation study shows the good behavior of this new measure for different types of functional data. Moreover, we apply it to establish association for real data, including microarrays time series in genetics.

El objetivo de esta investigación es analizar la asociación entre el uso de plaguicidas y malformaciones congénitas en municipios brasileños con exposición por mayor producción de commodities agrícolas entre 1994 y 2014, así como evaluar la correlación entre la tendencia observada y el volumen de plaguicidas dados como disruptores endocrinos. Es un estudio transversal, de carácter exploratorio, descriptivo y cuantitativo. Se analizaron las informaciones de los nacidos vivos (SINASC/Ministerio de Salud), elaborándose tasas de malformaciones ocurridas de 1994-2003 y 2004-2014. La asociación entre los tipos de malformaciones y las variables fue probada por odds ratio. Se constató que las tasas referentes a las malformaciones congénitas fueron mayores para los años de mayor exposición (2004-2014) y tuvieron asociación estadísticamente significativa. Los resultados indicaron que las malformaciones congénitas identificadas en el sitio de estudio están fuertemente correlacionadas a exposición de plaguicidas.

The well-established association of psychological stress and pathogenesis emphasizes the need for early detection of psychological stress to prevent the progression of diseases and hence saving human lives. The purpose of this research paper is to present a new framework for using phonocardiography (PCG) signal to detect psychological stress based on non-linear entropy based features extracted using empirical mode decomposition (EMD). These PCG signals are used to extract time duration of cardiac cycles consisting of consecutive S1 peaks to form Inter-beat Interval (IBI) signal. The IBI signal is decomposed to sub-band signals using EMD to form Intrinsic Mode Functions (IMFs). Then non-linear features namely — Permutation Entropy (PEn), Fuzzy Entropy (FzEn) and K-Nearest Neighbour (K-NN) entropy estimator are evaluated. Ranking methods namely — Entropy method, Bhattacharya space algorithm, Receiver Operating Characteristic (ROC) method and Wilcoxon method are then used in order to optimize the system. The extracted entropy features are fed to Least-Square Support Vector Machine (LS-SVM) for classification and highest accuracy, sensitivity and specificity obtained using the proposed system is 96.67%, 100% and 93.33% respectively. The proposed system opens a new research area of using PCG signal for psychological stress detection which can be easily used for home-care and is relatively inexpensive in comparison to other biophysical measures like Electroencephalography (EEG) and Electrocardiography (ECG).

Antenatal depression affects ~9–19% of pregnant women and can exert persistent adverse effects on both mother and child. There is a need for a deeper understanding of antenatal depression mechanisms and the development of tools for reliable diagnosis and early identification of women at high risk. As the use of untargeted blood metabolomics in the investigation of psychiatric and neurological diseases has increased substantially, the main objective of this study was to investigate whether untargeted gas chromatography–mass spectrometry (GC–MS) plasma metabolomics in 45 women in late pregnancy, residing in Uppsala, Sweden, could indicate metabolic differences between women with and without depressive symptoms. Furthermore, seasonal differences in the metabolic profiles were explored. When comparing the profiles of cases with controls, independently of season, no differences were observed. However, seasonal differences were observed in the metabolic profiles of control samples, suggesting a favorable cardiometabolic profile in the summer vs. winter, as indicated by lower glucose and sugar acid concentrations and lactate to pyruvate ratio, and higher abundance of arginine and phosphate. Similar differences were identified between cases and controls among summer pregnancies, indicating an association between a stressed metabolism and depressive symptoms. No depression-specific differences were apparent among depressed and non-depressed women, in the winter pregnancies; this could be attributed to an already stressed metabolism due to the winter living conditions. Our results provide new insights into the pathophysiology of antenatal depression, and warrant further investigation of the use of metabolomics in antenatal depression in larger cohorts.

We describe an approach to multivariate analysis, termed structured kernel principal component regression (sKPCR), to identify associations in voxel-level connectomes using resting-state functional magnetic resonance imaging (rsfMRI) data. This powerful and computationally efficient multivariate method can identify voxel-phenotype associations based on the whole-brain connectivity pattern of voxels, and it can detect linear and non-linear signals in both volume-based and surface-based rsfMRI data. For each voxel, sKPCR first extracts low-dimensional signals from the spatially smoothed connectivities by structured kernel principal component analysis, and then tests the voxel-phenotype associations by an adaptive regression model. The method's power is derived from appropriately modelling the spatial structure of the data when performing dimension reduction, and then adaptively choosing an optimal dimension for association testing using the adaptive regression strategy. Simulations based on real connectome data have shown that sKPCR can accurately control the false-positive rate and that it is more powerful than many state-of-the-art approaches, such as the connectivity-wise generalized linear model (GLM) approach, multivariate distance matrix regression (MDMR), adaptive sum of powered score (aSPU) test, and least-square kernel machine (LSKM). Moreover, since sKPCR can reduce the computational cost of non-parametric permutation tests, its computation speed is much faster. To demonstrate the utility of sKPCR for real data analysis, we have also compared sKPCR with the above methods based on the identification of voxel-wise differences between schizophrenic patients and healthy controls in four independent rsfMRI datasets. The results showed that sKPCR had better between-sites reproducibility and a larger proportion of overlap with existing schizophrenia meta-analysis findings. Code for our approach can be downloaded from https://github.com/weikanggong/sKPCR. [Abstract copyright: Copyright © 2018 Elsevier Inc. All rights reserved.]

Aims This study aims to determine the association between depression with glycemic control (HbA1c) and its complications. Materials and methods This was a cross-sectional study that included 514 diabetic patients. The patients were randomly selected from among all type 2 diabetes patients referred to health and medical centers in Khorramabad town (Iran). Two questionnaires used for data collection. The first questionnaire included demographic information, diabetes and its complications and the second questionnaire was Beck Depression Inventory (BDI-II) which was used to assess depression. The stata software version 14 was used for data analysis. Then, for evaluate the association between depression with glycemic contol and its complications, Univariate and multiple logistic regression analysis were employed. Results The prevalence of depression in diabetic patients under study was 46.3% and for female and male was 49.6 and 36.8%; respectively. The results showed that 48.6% of diabetic patients did not have appropriate glycemic control status (HbA1c > 8). There was no significant association between glycemic control expressed as HbA1c levels and depression (OR: 1.11, 95% CI: 0.87–1.57). By contrast, sex (OR: 2.03, CI 95%: 1.03–3.99), residence (OR: 1.92, 95% CI: 1.28–2.91) and sexual complications (OR: 5.54, 95% CI: 1.07–27.87) have a significant statistical association with depression. Conclusion The study highlights the high prevalence of depression in diabetic patients. However, there was no significant association between depression and glycemic control. The implementation of mental health screening for rapid diagnosis and timely treatment of depression seems essential in diabetic patients.

Multiple ordinal output classification (MOOC) which specifically refers to learning an association between individual inputs (e.g. face images) and a set of discrete ordinal response/output variables (e.g. facial action units), is a special case of multi-output classification and also a relatively-understudied topic in machine learning. It is very challenging in how to jointly model the relationship among multiple output variables and their discrete ordinal values. In this paper, we propose an effective formulation to address the above challenging problems. Under this formulation, the objective function is convex and thus leads to a convex multiple ordinal output classification (ConMOOC). Specifically, we use a regularization formulation to model the relationships among multiple output variables and an effective threshold-based loss function to fit their ordinal values. To enhance ability of the model, we also apply the kernel trick to provide a nonlinear extension. For efficiency, we use an alternating iteration method to learn the optimal model parameters for each variable as well as the relationships between different variables. Experiments conducted on synthetic and real datasets demonstrate that ConMOOC not only achieves effective classification performance but also reveals the structures among output variables. To the best of our knowledge, MOOC as a general machine learning task is the first time to be studied.

Bayesian network has powerful expression and reasoning ability of uncertainty knowledge. At present, it is widely used in many fields, such as fault diagnosis, medical diagnosis and prediction, financial analysis and prediction, decision support, etc. How to construct the Bayesian network effectively is an important research problem in the area of data mining. The constraint-based learning algorithm is commonly used for the construction of Bayesian network (BNs), such as Fast Incremental Association (Fast-IAMB) and Max-Min Parents & Children (MMPC), etc. At present, the Chi square test and correlation coefficient are often used to calculate the correlation between variables in these algorithms. But the calculation accuracy of these methods is not high, thus to influence the effectiveness of the constraint-based BNs learning algorithms. On the basis of the entropy estimation approach of Gaussian kernel probability density estimator, this paper uses the (conditional) mutual information to calculate the correlation between different variables and thus to improve the learning accuracy of the constraint-based BNs learning algorithms. The concrete calculation process about the data type of discrete, continuous and mixed has been described separately. Experiment simulations on the Alarm network prove that our algorithm can improve the learning accuracy of the existing three kinds of constraint-based BNs learning algorithms without affecting the learning efficiency. It also has better learning effect when to process the concrete and continuous data with sparsity. The method in this work can be used to learn more accurate Bayesian network, and then served the research of artificial intelligence, expert system decision making, etc.

Since 2005, genome-wide association (GWA) datasets have been largely biased toward sampling European ancestry individuals, and recent studies have shown that GWA results estimated from self-identified European individuals are not transferable to non-European individuals due to various confounding challenges. Here, we demonstrate that enrichment analyses which aggregate SNP-level association statistics at multiple genomic scales—from genes to genomic regions and pathways—have been underutilized in the GWA era and can generate biologically interpretable hypotheses regarding the genetic basis of complex trait architecture. We illustrate examples of the robust associations generated by enrichment analyses while studying 25 continuous traits assayed in 566,786 individuals from seven diverse self-identified human ancestries in the UK Biobank and the Biobank Japan, as well as 44,348 admixed individuals from the PAGE consortium including cohorts of African-American, Hispanic and Latin American, Native Hawaiian, and American Indian/Alaska Native individuals. We identify 1,000 gene-level associations that are genome-wide significant in at least two ancestry cohorts across these 25 traits, as well as highly conserved pathway associations with triglyceride levels in European, East Asian, and Native Hawaiian cohorts.

Introduction To achieve the Sustainable Development Goals (SDGs) target 2030, the United Nations (UN) endorsed tobacco use reduction, which is essential in decreasing unnecessary deaths caused by tobacco-induced disease. This study investigates the association between tobacco use and Human Papillomavirus (HPV) infection in clinically normal uterine cervix populations from the SDGs perspective. Methods This study is a 7-year cross-sectional study of a clinically normal uterine cervix population, based on negative Visual Inspection of Acetic Acid (VIA). Subjects were recruited consecutively from the medical records of several public and private health providers in Jakarta. The Statistical Product and Service Solutions (SPSS) for Windows version 20.0 were used to analyze the data. Results A total of 1397 negative VIA subjects were collected, consisting of 4.9% (69/1397) tobacco users, and 95.1% (1328/1397) non-users. HPV-DNA positive were 4.3% (3/69) in the tobacco user group and 3.7% (49/1328) in the non-user group. Statistical analysis showed unadjusted OR was 1.19 (95% CI: 0.36-3.91, p=0.778) and adjusted OR was 1.18 (95% CI: 0.36-3.89, p=0.786). High-risk HPV (hrHPV) infections of tobacco and non-tobacco users' groups were found in 2/3 and 27/49 (55.1%), respectively. Conclusions This study showed an insignificant statistical association between tobacco use and HPV infection in normal uterine cervix. However, the proportion of hrHPV infection was higher in tobacco users than non-users. From the SDGs perspective, cervical cancer is closely related to tobacco use and poverty. Further study is needed to support this result and evaluate other co-factor role-related cervical cancer history to achieve SDGs in 2030.