Registre telemàtic per administracions públiques

El projecte neix amb la finalitat de reduir aquests costos, creant una infraestructura que permeti realitzar els tràmits amb les Administracions Públiques per via telemàtica. D'aquesta forma se suprimeix la necessitat d'acudir presencialment a una oficina de l'Administració, suposant una gran avantatge per ambdues parts, especialment en quant al cost temporal

A Distance-Based Kernel Association Test Based on the Generalized Linear Mixed Model for Correlated Microbiome Studies

Researchers have increasingly employed family-based or longitudinal study designs to survey the roles of the human microbiota on diverse host traits of interest (e. g., health/disease status, medical intervention, behavioral/environmental factor). Such study designs are useful to properly control for potential confounders or the sensitive changes in microbial composition and host traits. However, downstream data analysis is challenging because the measurements within clusters (e.g., families, subjects including repeated measures) tend to be correlated so that statistical methods based on the independence assumption cannot be used. For the correlated microbiome studies, a distance-based kernel association test based on the linear mixed model, namely, correlated sequence kernel association test (cSKAT), has recently been introduced. cSKAT models the microbial community using an ecological distance (e.g., Jaccard/Bray-Curtis dissimilarity, unique fraction distance), and then tests its association with a host trait. Similar to prior distance-based kernel association tests (e.g., microbiome regression-based kernel association test), the use of ecological distances gives a high power to cSKAT. However, cSKAT is limited to handling Gaussian traits [e.g., body mass index (BMI)] and a single chosen distance measure at a time. The power of cSKAT differs a lot by which distance measure is used. However, choosing an optimal distance measure is challenging because of the unknown nature of the true association. Here, we introduce a distance-based kernel association test based on the generalized linear mixed model (GLMM), namely, GLMM-MiRKAT, to handle diverse types of traits, such as Gaussian (e.g., BMI), Binomial (e.g., disease status, treatment/placebo) or Poisson (e.g., number of tumors/treatments) traits. We further propose a data-driven adaptive test of GLMM-MiRKAT, namely, aGLMM-MiRKAT, so as to avoid the need to choose the optimal distance measure. Our extensive simulations demonstrate that aGLMM-MiRKAT is robustly powerful while correctly controlling type I error rates. We apply aGLMM-MiRKAT to real familial and longitudinal microbiome data, where we discover significant disparity in microbial community composition by BMI status and the frequency of antibiotic use. In summary, aGLMM-MiRKAT is a useful analytical tool with its broad applicability to diverse types of traits, robust power and valid statistical inference

How Criticality of Gene Regulatory Networks Affects the Resulting Morphogenesis under Genetic Perturbations

Whereas the relationship between criticality of gene regulatory networks (GRNs) and dynamics of GRNs at a single cell level has been vigorously studied, the relationship between the criticality of GRNs and system properties at a higher level has remained unexplored. Here we aim at revealing a potential role of criticality of GRNs at a multicellular level which are hard to uncover through the single-cell-level studies, especially from an evolutionary viewpoint. Our model simulated the growth of a cell population from a single seed cell. All the cells were assumed to have identical GRNs. We induced genetic perturbations to the GRN of the seed cell by adding, deleting, or switching a regulatory link between a pair of genes. From numerical simulations, we found that the criticality of GRNs facilitated the formation of nontrivial morphologies when the GRNs were critical in the presence of the evolutionary perturbations. Moreover, the criticality of GRNs produced topologically homogenous cell clusters by adjusting the spatial arrangements of cells, which led to the formation of nontrivial morphogenetic patterns. Our findings corresponded to an epigenetic viewpoint that heterogeneous and complex features emerge from homogeneous and less complex components through the interactions among them. Thus, our results imply that highly structured tissues or organs in morphogenesis of multicellular organisms might stem from the criticality of GRNs.Comment: 34 pages, 17 figures, 1 tabl

The AGE-RAGE axis in an Arab population: The United Arab Emirates Healthy Futures (UAEHFS) pilot study

© 2017 The Authors Aims The transformation of the United Arab Emirates (UAE) from a semi-nomadic to a high income society has been accompanied by increasing rates of obesity and Type 2 diabetes mellitus. We examined if the AGE-RAGE (receptor for advanced glycation endproducts) axis is associated with obesity and diabetes mellitus in the pilot phase of the UAE Healthy Futures Study (UAEHFS). Methods 517 Emirati subjects were enrolled and plasma/serum levels of AGE, carboxy methyl lysine (CML)-AGE, soluble (s)RAGE and endogenous secretory (es)RAGE were measured along with weight, height, waist and hip circumference (WC/HC), blood pressure, HbA1c, Vitamin D levels and routine chemistries. The relationship between the AGE-RAGE axis and obesity and diabetes mellitus was tested using proportional odds models and linear regression. Results After covariate adjustment, AGE levels were significantly associated with diabetes status. Levels of sRAGE and esRAGE were associated with BMI and levels of sRAGE were associated with WC/HC. Conclusions The AGE-RAGE axis is associated with diabetes status and obesity in this Arab population. Prospective serial analysis of this axis may identify predictive biomarkers of obesity and cardiometabolic dysfunction in the UAEHFS

A QUANTITATIVE ANALYSIS OF ENTREPRENEURSHIP: THE KOREAN CASE

The main purpose of this dissertation is to construct a synthetic measure of entrepreneurship and to apply it in empirical tests of some hypotheses suggested in the literature. This is done with particular reference to a sample of Korean firms for the period from 1980 through 1982.^ Entrepreneurship is defined with references to functions rather than individuals. These functions are viewed broadly as comprising a number of variations from primary innovations to routine management. They are classified into seven hypothetical categories: product, market, technology, raw materials, organization, management, and government related entrepreneurship.^ A total of twenty-three variables is selected to represent the complex dimensions of entrepreneurship. Data on the variables are gathered through the questionnaire method. To these data, factor analysis is applied in order to demonstrate the pattern of entrepreneurship empirically. The results are fairly supportive of the hypothetical categorization of entrepreneurial functions. Seven factors emerge and they roughly correspond to each of the a priori aspects of entrepreneurship.^ Based on the factor analytic solution, seven separate indices and an overall index of entrepreneurship are computed, and they create many possibilities for further empirical research. With the sample classified by industry, an inter-industry comparison of entrepreneurship is performed. The major finding is that the extent of entrepreneurship differs among various industry groups. The most salient contrast emerges between the electronics and the textile industries. This contrast stems largely from the high performance of the electronics industry in the areas of technology and market related entrepreneurship.^ More rigorous findings are provided as we utilize the entrepreneurial indices in hypothesis testing. First, with regard to the relationship between firm size and entrepreneurship, the empirical results do not support the Schumpeterian hypothesis. Although it is an increasing function of firm size, entrepreneurship exhibits decreasing returns at high levels of firm size. The second test concerns the hypothesis of the contribution of entrepreneurship to the growth of a firm. Due to data limitations, the test is limited to the short-run relationships. No systematic association is found between the magnitude of entrepreneurship and several measures of business success, except for the case of export growth.

Adaptive Statistical Methods for Microbiome Association Studies

The human microbiome studies have been accelerated by the advances in next-generation sequencing technologies. There has also been increasing interest in discovering microbial taxa that are associated with diverse host phenotypes, environmental factors or clinical interventions. Here, I first describe unique features of microbiome data and the resulting demand for adaptive association analysis which robustly suits different association patterns, while providing valid statistical inferences. Then, I introduce two adaptive microbiome association tests as follows. My first method, namely, optimal microbiome-based association test (OMiAT), relates microbial composition with continuous (e.g., body mass index) or binary (e.g., disease status) traits. OMiAT is a data-driven adaptive testing method which approximates to the most powerful performance among different candidate tests from the sum of powered score tests (SPU) and microbiome regression-based kernel association test (MiRKAT). I illustrate that OMiAT robustly discovers underlying association signals arising from highly imbalanced microbial abundances and phylogenetic tree structure, while correctly controlling type I error rates. I also propose a way to apply it to fine association mapping of diverse higher-level taxa at different taxonomic levels within a newly introduced microbial taxa discovery framework, microbiome comprehensive association mapping (MiCAM). My second method, namely, optimal microbiome-based survival analysis (OMiSA), relates microbial composition with survival (i.e., time to event) traits. OMiSA approximates to the most powerful association test within two test domains, 1) microbiome-based survival analysis using linear and non-linear bases of OTUs (MiSALN) and 2) microbiome-based kernel association test for survival traits (MiRKAT-S). I illustrate that OMiSA powerfully discovers underlying associated lineages whether they are rare or abundant and phylogenetically related or not, while correctly controlling type I error rates. OMiAT and OMiSA are attractive in practice due to the high complexity of microbiome data and the unknown true nature of the state. MiCAM also provides a hierarchical microbiome association map through a breadth of taxonomic levels, which can be used as a guideline for further investigation on the roles of discovered taxa in human health or disease

MiTree: A Unified Web Cloud Analytic Platform for User-Friendly and Interpretable Microbiome Data Mining Using Tree-Based Methods

The advent of next-generation sequencing has greatly accelerated the field of human microbiome studies. Currently, investigators are seeking, struggling and competing to find new ways to diagnose, treat and prevent human diseases through the human microbiome. Machine learning is a promising approach to help such an effort, especially due to the high complexity of microbiome data. However, many of the current machine learning algorithms are in a “black box”, i.e., they are difficult to understand and interpret. In addition, clinicians, public health practitioners and biologists are not usually skilled at computer programming, and they do not always have high-end computing devices. Thus, in this study, we introduce a unified web cloud analytic platform, named MiTree, for user-friendly and interpretable microbiome data mining. MiTree employs tree-based learning methods, including decision tree, random forest and gradient boosting, that are well understood and suited to human microbiome studies. We also stress that MiTree can address both classification and regression problems through covariate-adjusted or unadjusted analysis. MiTree should serve as an easy-to-use and interpretable data mining tool for microbiome-based disease prediction modeling, and should provide new insights into microbiome-based diagnostics, treatment and prevention. MiTree is an open-source software that is available on our web server

A powerful microbiome-based association test and a microbial taxa discovery framework for comprehensive association mapping

Abstract Background The role of the microbiota in human health and disease has been increasingly studied, gathering momentum through the use of high-throughput technologies. Further identification of the roles of specific microbes is necessary to better understand the mechanisms involved in diseases related to microbiome perturbations. Methods Here, we introduce a new microbiome-based group association testing method, optimal microbiome-based association test (OMiAT). OMiAT is a data-driven testing method which takes an optimal test throughout different tests from the sum of powered score tests (SPU) and microbiome regression-based kernel association test (MiRKAT). We illustrate that OMiAT efficiently discovers significant association signals arising from varying microbial abundances and different relative contributions from microbial abundance and phylogenetic information. We also propose a way to apply it to fine-mapping of diverse upper-level taxa at different taxonomic ranks (e.g., phylum, class, order, family, and genus), as well as the entire microbial community, within a newly introduced microbial taxa discovery framework, microbiome comprehensive association mapping (MiCAM). Results Our extensive simulations demonstrate that OMiAT is highly robust and powerful compared with other existing methods, while correctly controlling type I error rates. Our real data analyses also confirm that MiCAM is especially efficient for the assessment of upper-level taxa by integrating OMiAT as a group analytic method. Conclusions OMiAT is attractive in practice due to the high complexity of microbiome data and the unknown true nature of the state. MiCAM also provides a hierarchical association map for numerous microbial taxa and can also be used as a guideline for further investigation on the roles of discovered taxa in human health and disease