13 research outputs found
Socio-Demographic and Lifestyle Factors Predict 5-Year Changes in Adiposity among a Group of Black South African Adults
No full textThe rising prevalence of obesity and excessive adiposity are global public health concerns. Understanding determinants of changes in adiposity over time is critical for informing effective evidence-based prevention or treatment. However, limited information is available to achieve this objective. Cultural, demographic, environmental, and behavioral factors including socio-economic status (SES) likely account for obesity development. To this end, we related these variables to anthropometric measures in 1058 black adult Tswana-speaking South Africans who were HIV negative in a prospective study over five years. Body mass index (BMI) and waist circumference increased in both sexes, whereas triceps skinfold thickness remained the same. Over the five years, women moved to higher BMI categories and more were diagnosed with central obesity. Age correlated negatively, whereas SES, physical activity, energy, and fat intake correlated positively with adiposity markers in women. In men, SES, marital status, physical activity, and being urban predicted increases in adiposity. For women, SES and urbanicity increased, whereas menopause and smoking decreased adiposity. Among men, smokers had less change in BMI than those that never smoked over five years. Our findings suggest that interventions, focusing on the urban living, the married and those with the highest SES—the high-risk groups identified herein—are of primary importance to contain morbidity and premature mortality due to obesity in black South Africans
Physically constrained 2D joint inversion of surface and body wave tomography
No full textJoint inversion of different geophysical methods is a powerful tool to overcome the limitations of individual inversions. Body wave tomography is used to obtain P-wave velocity models by inversion of P-wave travel times. Surface wave tomography is used to obtain S-wave velocity models through inversion of the dispersion curves data. Both methods have inherent limitations. We focus on the joint body and surface waves tomography inversion to reduce the limitations of each individual inversion. In our joint inversion scheme, the Poisson ratio was used as the link between P-wave and S-wave velocities, and the same geometry was imposed on the final velocity models. The joint inversion algorithm was applied to a 2D synthetic dataset and then to two 2D field datasets. We Compare the obtained velocity models from individual inversions and the joint inversion. We show that the proposed joint inversion method not only produces superior velocity models, also generates physically more meaningful and accurate Poisson ratio models
Anvers et l'Exposition universelle 1885 /
No full textTitle vignette (arms of Belgium)."Dédié à sa majesté Leopold II, roi des Belges."Mode of access: Internet.Binding c.2: red publisher's cloth with colored illustration on front board.Binding c.1: red publisher's cloth with gilt title on front board
Author Correction: Understanding the role of bitter taste perception in coffee, tea and alcohol consumption through Mendelian randomization
No full textAn amendment to this paper has been published and can be accessed via a link at the top of the paper. The original version of this Article contained an error in the author name Liang-Dar Hwang, which was incorrectly given as Daniel Liang-Dar Hwang
Importance weighted directed graph variational auto-encoder for block modelling of complex networks
No full textThis paper addresses the fundamental challenges of jointly performing node clustering and representation learning in directed and valued graphs, which need both global and local network structures to be captured. While these two tasks are highly interdependent, they are often treated separately in existing works. We propose the deep zero-inflated latent position block model (Deep-ZLPBM) in the context of directed and valued networks characterized by non-symmetric adjacency matrices with positive integer entries. Our approach leverages a variational autoencoder (VAE) framework, combining a directed graph neural network (DirGNN) encoder designed to handle directed edges and a zero-inflated Poisson (ZIP) block modelling decoder to model sparse, integer-weighted interactions. Recognizing the limitations of the standard evidence lower bound (ELBO) in VAEs, we explore the importance weighted ELBO (iw-ELBO), a tighter bound on the marginal log-likelihood optimized via gradient ascent, to enhance inference. Extensive experiments on synthetic datasets demonstrate that iw-ELBO optimization yields significant performance gains. Moreover, our results validate that Deep-ZLPBM effectively models complex network structures, providing interpretable partial memberships and insightful visualizations for directed, valued graphs.→ Use footnote for providing further information about author (webpage, alternative address)-not for acknowledging funding agencies.Preprint. Under review.</div
Importance weighted directed graph variational auto-encoder for block modelling of complex networks
No full textThis paper addresses the fundamental challenges of jointly performing node clustering and representation learning in directed and valued graphs, which need both global and local network structures to be captured. While these two tasks are highly interdependent, they are often treated separately in existing works. We propose the deep zero-inflated latent position block model (Deep-ZLPBM) in the context of directed and valued networks characterized by non-symmetric adjacency matrices with positive integer entries. Our approach leverages a variational autoencoder (VAE) framework, combining a directed graph neural network (DirGNN) encoder designed to handle directed edges and a zero-inflated Poisson (ZIP) block modelling decoder to model sparse, integer-weighted interactions. Recognizing the limitations of the standard evidence lower bound (ELBO) in VAEs, we explore the importance weighted ELBO (iw-ELBO), a tighter bound on the marginal log-likelihood optimized via gradient ascent, to enhance inference. Extensive experiments on synthetic datasets demonstrate that iw-ELBO optimization yields significant performance gains. Moreover, our results validate that Deep-ZLPBM effectively models complex network structures, providing interpretable partial memberships and insightful visualizations for directed, valued graphs.→ Use footnote for providing further information about author (webpage, alternative address)-not for acknowledging funding agencies.Preprint. Under review.</div
Identification of heart rate-associated loci and their effects on cardiac conduction and rhythm disorders
No full textElevated resting heart rate is associated with greater risk of cardiovascular disease and mortality. In a 2-stage meta-analysis of genome-wide association studies in up to 181,171 individuals, we identified 14 new loci associated with heart rate and confirmed associations with all 7 previously established loci. Experimental downregulation of gene expression in Drosophila melanogaster and Danio rerio identified 20 genes at 11 loci that are relevant for heart rate regulation and highlight a role for genes involved in signal transmission, embryonic cardiac development and the pathophysiology of dilated cardiomyopathy, congenital heart failure and/or sudden cardiac death. In addition, genetic susceptibility to increased heart rate is associated with altered cardiac conduction and reduced risk of sick sinus syndrome, and both heart rate-increasing and heart rate-decreasing variants associate with risk of atrial fibrillation. Our findings provide fresh insights into the mechanisms regulating heart rate and identify new therapeutic targets
Meta-analyses identify 13 loci associated with age at menopause and highlight DNA repair and immune pathways
No full textTo newly identify loci for age at natural menopause, we carried out a meta-analysis of 22 genome-wide association studies (GWAS) in 38,968 women of European descent, with replication in up to 14,435 women. In addition to four known loci, we identified 13 loci newly associated with age at natural menopause (at P < 5 × 10(-8)). Candidate genes located at these newly associated loci include genes implicated in DNA repair (EXO1, HELQ, UIMC1, FAM175A, FANCI, TLK1, POLG and PRIM1) and immune function (IL11, NLRP11 and PRRC2A (also known as BAT2)). Gene-set enrichment pathway analyses using the full GWAS data set identified exoDNase, NF-κB signaling and mitochondrial dysfunction as biological processes related to timing of menopause
Accuracy of Self-Report and Pill-Count Measures of Adherence in the FEM-PrEP Clinical Trial: Implications for Future HIV-Prevention Trials
No full textThe Author(s) 2014. This article is published with open access at Springerlink.com Abstract Oral emtricitabine/tenofovir disoproxil fuma-rate (FTC/TDF) has been evaluated as pre-exposure pro-phylaxis (PrEP). We describe the accuracy of self-reported adherence to FTC/TDF and pill counts when compared to drug concentrations in the FEM-PrEP trial. Using drug concentrations of plasma tenofovir (TFV) and intracellular tenofovir diphosphate (TFVdp) among a random sub-sam-ple of 150 participants assigned to FTC/TDF, we estimated the positive predictive value (PPV) of four adherence measures. We also assessed factors associated with misreporting of adherence using multiple drug-concentra-tion thresholds and explored pill use and misreporting using semi-structured interviews (SSIs). Reporting use of C1 pill in the previous 7 days had the highest PPV, while pill-count data consistent with missing B1 day had the lowest PPV. However, all four measures demonstrated poor PPV. Reported use of oral contraceptives (OR 2.26; p = 0.014) and weeks of time in the study (OR 1.02; p \ 0.001) were significantly associated with misreporting adherence. Although most SSI participants said they did not misreport adherence, participant-dependent adherence measures were clearly unreliable in the FEM-PrEP trial. Pharmacokinetic monitoring remains the measure of choice until more reli-able participant-dependent measures are developed
МОБИЛЬНЫЙ КАМПУС: КОЛЛЕКТИВНО-РЕФЛЕКСИВНОЕ ИЗМЕРЕНИЕ УЧЕБНОЙ ДЕЯТЕЛЬНОСТИ, ОПОСРЕДОВАННОЙ МОБИЛЬНЫМИ ТЕХНОЛОГИЯМИ
No full textThe author considers a definition and general characteristics of the mobile campus allowing to ensure a combination of informal and social types of the educational activities with formal learning in a traditional educational institution (institutes of higher education). A fundamental element of the mobile campus is intelligent algorithms providing learning analyst for personalized learning experience. Also the article examines connections between the mobile campus and a learning community, a personal learning network and electronic student profile.В работе рассмотрено определение и общая характеристика мобильного кампуса, позволяющего обеспечить совмещение неформального и социального видов (каналов) учебной деятельности с формальным обучением в рамках традиционного учебного заведения (ВУЗ, ССУЗ). Принципиальным элементом мобильного кампуса являются интеллектуальные алгоритмы, обеспечивающие учебную аналитику в контексте персонализированного опыта. Также рассмотрена связь мобильного кампуса с учебным сообществом, персональной учебной сетью учащегося и его электронным профайлом
