Bayesian Network Modeling and Inference of GWAS Catalog

Genome-wide association studies (GWASs) have received an increasing attention to understand genotype-phenotype relationships. The Bayesian network has been proposed as a powerful tool for modeling single-nucleotide polymorphism (SNP)-trait associations due to its advantage in addressing the high computational complex and high dimensional problems. Most current works learn the interactions among genotypes and phenotypes from the raw genotype data. However, due to the privacy issue, genotype information is sensitive and should be handled by complying with specific restrictions. In this work, we aim to build Bayesian networks from publicly released GWAS statistics to explicitly reveal the conditional dependency between SNPs and traits. First, we focus on building a Bayesian network for modeling the SNP-categorical trait relationships. We construct a three-layered Bayesian network explicitly revealing the conditional dependency between SNPs and categorical traits from GWAS statistics. We then formulate inference problems based on the dependency relationship captured in the Bayesian network. Empirical evaluations show the effectiveness of our methods. Second, we focus on modeling the SNP-quantitative trait relationships. Existing methods in the literature can only deal with categorical traits. We address this limitation by leveraging the Conditional Linear Gaussian (CLG) Bayesian network, which can handle a mixture of discrete and continuous variables. A two-layered CLG Bayesian network is built where the SNPs are represented as discrete variables in one layer and quantitative traits are represented as continuous variables in another layer. Efficient inference methods are then derived based on the constructed network. The experimental results demonstrate the effectiveness of our methods. Finally, we present STIP, a web-based SNP-trait inference platform capable of a variety of inference tasks, such as trait inference given SNP genotypes and genotype inference given traits. The current version of STIP provides three services which are SNP-trait inference, Top-k trait prediction and GWAS catalog exploration

Mindfulness-Based Intervention For Nurses In AIDS Care In China: A Pilot Study.

Background/purpose:Workplace stress among nurses providing care for people living with human immunodeficiency virus is a serious problem in China that may increase rates of job burnout and affect quality of care. Mindfulness-based intervention has been shown to be effective in relieving stress and burnout in nurses. Therefore, we designed a mixed-method pilot study to evaluate a mindfulness-based intervention for nurses providing care for people living with human immunodeficiency virus. Methods:Twenty nurses caring for people living with human immunodeficiency virus in the First Hospital of Changsha, China participated in a mindfulness-based intervention for 2 hr sessions weekly for 6 weeks. The Perceived Stress Scale, Maslach Burnout Inventory, Five Facets Mindfulness Questionnaire, State-Trait Anxiety Inventory, and the Beck Depression Inventory were used to collect data before and after the mindfulness-based intervention. Participants were invited to attend an in-depth interview 1 week after the end of the mindfulness-based intervention to give feedback. Results:The quantitative analyses revealed a significant change in Five Facets Mindfulness Questionnaire scores. There were no significant differences between pre- and post-intervention measures of any other variables. Qualitative results showed nurses experienced a decrease in work and daily life pressures; improvements in communications with patients, colleagues and families, with better regulation of negative emotions, and acceptance of other people and attention. Conclusion:This study supports the acceptability and potential benefits of the mindfulness-based intervention in helping nurses caring for people living with human immunodeficiency virus to manage stress and emotions, and improve their acceptance of others and attention. A larger study with a randomized controlled trial design is warranted to confirm the effectiveness of this mindfulness-based intervention

The proper class generated by weak supplements

We show that, for hereditary rings, the smallest proper classes containing respectively the classes of short exact sequences determined by small submodules, submodules that have supplements and weak supplement submodules coincide. Moreover, we show that this class can be obtained as a natural extension of the class determined by small submodules. We also study injective, projective, coinjective and coprojective objects of this class. We prove that it is coinjectively generated and its global dimension is at most 1. Finally, we describe this class for Dedekind domains in terms of supplement submodules.TUBITAK (107T709

Land use change and climate variation in the Three Gorges Reservoir Catchment from 2000 to 2015 based on the Google Earth Engine

Possible environmental change and ecosystem degradation have received increasing attention since the construction of Three Gorges Reservoir Catchment (TGRC) in China. The advanced Google Earth Engine (GEE) cloud-based platform and the large number of Geosciences and Remote Sensing datasets archived in GEE were used to analyze the land use and land cover change (LULCC) and climate variation in TGRC. GlobeLand30 data were used to evaluate the spatial land dynamics from 2000 to 2010 and Landsat 8 Operational Land Imager (OLI) images were applied for land use in 2015. The interannual variations in the Land Surface Temperature (LST) and seasonally integrated normalized difference vegetation index (SINDVI) were estimated using Moderate Resolution Imaging Spectroradiometer (MODIS) products. The climate factors including air temperature, precipitation and evapotranspiration were investigated based on the data from the Global Land Data Assimilation System (GLDAS). The results indicated that from 2000 to 2015, the cultivated land and grassland decreased by 2.05% and 6.02%, while the forest, wetland, artificial surface, shrub land and waterbody increased by 3.64%, 0.94%, 0.87%, 1.17% and 1.45%, respectively. The SINDVI increased by 3.209 in the period of 2000-2015, while the LST decreased by 0.253 °C from 2001 to 2015. The LST showed an increasing trend primarily in urbanized area, with a decreasing trend mainly in forest area. In particular, Chongqing City had the highest LST during the research period. A marked decrease in SINDVI occurred primarily in urbanized areas. Good vegetation areas were primarily located in the eastern part of the TGRC, such as Wuxi County, Wushan County, and Xingshan County. During the 2000–2015 period, the air temperature, precipitation and evapotranspiration rose by 0.0678 °C/a, 1.0844 mm/a, and 0.4105 mm/a, respectively. The climate change in the TGRC was influenced by LULCC, but the effect was limited. What is more, the climate change was affected by regional climate change in Southwest China. Marked changes in land use have occurred in the TGRC, and they have resulted in changes in the LST and SINDVI. There was a significantly negative relationship between LST and SINDVI in most parts of the TGRC, especially in expanding urban areas and growing forest areas. Our study highlighted the importance of environmental protection, particularly proper management of land use, for sustainable development in the catchment

Overseas Chinese Communities in Transition: Capable Agency, Translocal Positioning, and Community Re-organisation

© 2019 Qiuping PanThe rapid growth of China-born immigrants around the world has attracted intense attention from researchers, the public, and policymakers. However, much understanding of this population is clouded by speculation and misinformation, thus resulting in heated debates and even social anxieties. This research seeks to inform these debates concerning overseas Chinese communities by presenting an empirical case study conducted in Victoria, Australia. It argues that the significant influx of China-born immigrants since the late 1980s and early 1990s has seen an ongoing community reorganising process. Although this process has been influenced by convoluted forces at macro-, meso-, and micro-levels, the thesis demonstrates that it is more endogenous than exogenous. In other words, the process is fundamentally driven by Chinese immigrants who have strong inclinations and capabilities to self-organise for personal advancement and collective betterment. This research is grounded in offline and online ethnographic fieldwork spanning over three years. It is also informed by Chinese and Australian government reports, Australian national censuses, archival resources generated by the local Chinese community, as well as historical and cross-sectional comparisons. The five discussion chapters of this thesis identify and elaborate on the following key manifestations of the reorganising process respectively: a parallel rise of (1) homeland-engaging (transnational) and (2) hostland-embedding (local) activism, (3) heightened community engagement activism, (4) a feminisation turn, and (5) a redrawn organisational landscape. Addressed in this thesis is an under-researched and under-theorised topic central to the study of Chinese Overseas. This research also problematises state-centric analysis and demonstrates how a community-focused perspective can effectively illustrate and account for the dynamism and mechanism of immigrant community development. The co-evolutionary model developed in this research has proved constructive to unpack these convoluted and dynamic processes. In addition, this perspective also sheds light on the lived experiences of transnational mobility, the promises and pitfalls of accelerated transnational migration, and changes unleashed by accelerating globalisation. In so doing, it offers new avenues to studies on international migration and globalisation

Modeling SNP and quantitative trait association from GWAS catalog using CLG Bayesian network

Genome-wide association studies (GWAS) are a type of genetic methods that have recently received intensive attention. In this paper, we study the construction of the Bayesian network from the GWAS catalog for modeling SNP and quantitative trait associations. Existing methods in the literature can only deal with categorical traits. We address this limitation by leveraging the Conditional Linear Gaussian (CLG) Bayesian network, which can handle a mixture of discrete and continuous variables. A two-layered CLG Bayesian network is built where the SNPs are represented as discrete variables in one layer and quantitative traits are represented as continuous variables in another layer. We propose the method for specifying the CLG Bayesian network, focusing on the specification of the CLG distribution for quantitative traits. We empirically evaluate the construction method, and results demonstrate the effectiveness of our method

STIP: An SNP-trait inference platform

Genome-wide association studies (GWASs) have received increasing attention to understand how a genetic variation affects different human traits. Recent works show that the Bayesian network is powerful in modeling the conditional dependency between single-nucleotide polymorphisms (SNPs) and traits using only the GWAS statistics. In this paper, we present STIP, a web-based SNP-trait inference platform capable of a variety of inference tasks, such as trait inference given SNP genotypes and genotype inference given traits. The core of STIP is two Bayesian networks which model the SNP-categorical trait associations and SNP-quantitative trait associations, respectively. Both Bayesian networks are derived from the public GWAS catalog. The inference tasks are based on the dependency relationship captured in the Bayesian networks. The current version of STIP provides three services which are SNP-trait inference, Top-k trait prediction and GWAS catalog exploration

Building Bayesian networks from GWAS statistics based on Independence of Causal Influence

Genome-wide association studies (GWASs) have received an increasing attention to understand genotype-phenotype relationships. In this paper, we study how to build Bayesian networks from publicly released GWAS statistics to explicitly reveal the conditional dependency between single-nucleotide polymorphisms (SNPs) and traits. The key challenge in building a Bayesian network is the specification of the conditional probability table (CPT) of an variable with multiple parent variables. We employ the Independence of Causal Influences (ICI) which assumes that the causal mechanism of each parent variable is mutually independent. Specifically, we derive a formulation from the Noisy-or model, one of the ICI models, to specify the CPT using the released GWAS statistics. We prove that the specified CPT is accurate as long as the underlying individual-level genotype and phenotype profile data follows the Noisy-or model. We empirically evaluate the Noisy-or model and its derived formulation using data from openSNP. Experimental results demonstrate the effectiveness of our approach

DPWeka: Achieving Differential Privacy in WEKA

In this paper, we present DPWeka, a differentially private prototype based on a widely used data mining software WEKA, for practical data analysis. DPWeka includes a suite of differential privacy preserving computation blocks which support a variety of data analysis tasks including test statistics calculation, regression analysis, and interactive exploratory data analysis. We illustrate the use of DPWeka on genome wide association studies that include privately selecting significant SNPs and running logistic regression based on various differential privacy mechanisms