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Новости за 03.10.2024

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GAN-WGCNA: Calculating gene modules to identify key intermediate regulators in cocaine addiction

Plos.org 
En

by Taehyeong Kim, Kyoungmin Lee, Mookyung Cheon, Wookyung Yu

Understanding time-series interplay of genes is essential for diagnosis and treatment of disease. Spatio-temporally enriched NGS data contain important underlying regulatory mechanisms of biological processes. Generative adversarial networks (GANs) have been used to augment biological data to describe hidden intermediate time-series gene expression profiles during specific biological processes. Developing a pipeline that uses augmented... Читать дальше...

Dynamic dissipative control for fuzzy distributed parameter cyber physical system under input quantization and DoS attack

Plos.org 
En

by Jingzhao Chen, Liming Ding, Tengfei Li

This article explores the dissipative control for a class of nonlinear DP-CPS (distributed parameter cyber physical system) within a finite-time interval. By utilizing a Takagi-Sugeno (T-S) fuzzy model to represent the system’s nonlinear aspects, the studied system is formulated as a class of fuzzy parabolic partial differential equation (PDE). In order to optimize network resources, both the system state and input signal are subjected to quantization using dynamic quantizers. Читать дальше...

Development of a new perceived injustice scale for Bangla speaking population

Plos.org 
En

by Chandrima Mourin, Muhammad Kamruzzaman Mozumder

Background

Perceived injustice is a relatively novel psychosocial construct starting to get some attention among researchers studying health and mental health outcomes. In the context of the widespread perception of being a victim of injustice in Bangladesh, a gap in instruments measuring perceived injustice was evident. The novelty of the construct and lack of similar instruments necessitated the development of a new Perceived Injustice Scale for Bangla speaking population. Читать дальше...

An application of node and edge nonlinear hypergraph centrality to a protein complex hypernetwork

Plos.org 
En

by Sarah Lawson, Diane Donovan, James Lefevre

The use of graph centrality measures applied to biological networks, such as protein interaction networks, underpins much research into identifying key players within biological processes. This approach however is restricted to dyadic interactions and it is well-known that in many instances interactions are polyadic. In this study we illustrate the merit of using hypergraph centrality applied to a hypernetwork as an alternative. Specifically,... Читать дальше...

“It’s not just about you”: International students’ vulnerabilities and capacities during the first phase of the COVID-19 pandemic in Canada

Plos.org 
En

by Ayisha Khalid, Jessica Naidu, Tanvir C. Turin

In Canada, the COVID-19 pandemic was initially characterized by emergency government responses that disrupted daily life, especially for marginalized groups. This study explored the vulnerabilities and capacities of international students studying at a university in Calgary, Canada during the first phase of the pandemic. Guided by the Capacities and Vulnerabilities Analysis framework, we thematically analyzed 11 semi-structured interviews with international students. Читать дальше...

How strong is strong? The challenge of interpreting network edge weights

Plos.org 
En

by Zachary P. Neal

Weighted networks are information-rich and highly-flexible, but they can be difficult to analyze because the interpretation of edges weights is often ambiguous. Specifically, the meaning of a given edge’s weight is locally contingent, so that a given weight may be strong for one dyad, but weak for other dyad, even in the same network. I use backbone models to distinguish strong and weak edges in a corpus of 110 weighted networks, and used the results to examine the magnitude of this ambiguity. Читать дальше...

STAREG: Statistical replicability analysis of high throughput experiments with applications to spatial transcriptomic studies

Plos.org 
En

by Yan Li, Xiang Zhou, Rui Chen, Xianyang Zhang, Hongyuan Cao

Replicable signals from different yet conceptually related studies provide stronger scientific evidence and more powerful inference. We introduce STAREG, a statistical method for replicability analysis of high throughput experiments, and apply it to analyze spatial transcriptomic studies. STAREG uses summary statistics from multiple studies of high throughput experiments and models the the joint distribution of p-values accounting for the heterogeneity of different studies. Читать дальше...