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Новости за 03.07.2024

Perturbations in gut microbiota composition in schizophrenia

Plos.org 

by YiMeng Wang, SiGuo Bi, XiaoLong Li, YuTao Zhong, DongDong Qi

Schizophrenia is a severe, complex and long-term psychiatric disorder with unclear etiology. Gut microbes influence the central nervous system via the gut-brain axis. Consequently, investigations of the relationship between gut microbes and schizophrenia are warranted. This study involved 29 patients with schizophrenia and 30 age-matched normal controls. After 16S rRNA gene sequencing and whole-genome shotgun metagenomic sequencing... Читать дальше...



Risks of anxiety disorders, depressive disorders, and sleep disorders in patients with dengue fever: A nationwide, population-based cohort study

Plos.org 

by Hsin-I Shih, Yu-Ping Wang, Chia-Yu Chi, Yu-Wen Chien

Background

Dengue virus (DENV) infection, a common mosquito-borne disease, has been linked to several mental disorders like depression and anxiety. However, the temporal risk of these disorders after DENV infection is not well studied.

Methods

This population-based cohort study encompassed 45,334 recently lab-confirmed dengue patients in Taiwan spanning 2002 to 2015, matched at a 1:5 ratio with non-dengue individuals based on age, gender, and residence (n = 226,670). Читать дальше...

Новости России
Москва

ГУАП отмечен благодарностью Минобрнауки РФ за внедрение программы «Обучение служением»


A variational autoencoder trained with priors from canonical pathways increases the interpretability of transcriptome data

Plos.org 

by Bin Liu, Bodo Rosenhahn, Thomas Illig, David S. DeLuca

Interpreting transcriptome data is an important yet challenging aspect of bioinformatic analysis. While gene set enrichment analysis is a standard tool for interpreting regulatory changes, we utilize deep learning techniques, specifically autoencoder architectures, to learn latent variables that drive transcriptome signals. We investigate whether simple, variational autoencoder (VAE), and beta-weighted VAE are capable of learning reduced... Читать дальше...

Limb-related sensory prediction errors and task-related performance errors facilitate human sensorimotor learning through separate mechanisms

Plos.org 

by Anushka Oza, Adarsh Kumar, Apoorva Sharma, Pratik K. Mutha

The unpredictable nature of our world can introduce a variety of errors in our actions, including sensory prediction errors (SPEs) and task performance errors (TPEs). SPEs arise when our existing internal models of limb-environment properties and interactions become miscalibrated due to changes in the environment, while TPEs occur when environmental perturbations hinder achievement of task goals. The precise mechanisms employed... Читать дальше...


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