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2024

Новости за 12.02.2024

Unraveling implicit human behavioral effects on dynamic characteristics of Covid-19 daily infection rates in Taiwan

Plos.org 

by Ting-Li Chen, Elizabeth P. Chou, Min-Yi Chen, Fushing Hsieh

We investigate the dynamic characteristics of Covid-19 daily infection rates in Taiwan during its initial surge period, focusing on 79 districts within the seven largest cities. By employing computational techniques, we extract 18 features from each district-specific curve, transforming unstructured data into structured data. Our analysis reveals distinct patterns of asymmetric growth and decline among the curves. Utilizing theoretical... Читать дальше...

Factors affecting the efficiency of equine embryo transfer (EET) in polo mares under subtropical conditions of Pakistan

Plos.org 

by Khalid Mahmood, Aijaz Ali Channa, Aamir Ghafoor, Amjad Riaz

Equine embryo transfer (EET) is a prominent technology in the equine breeding industry, and its efficacy is affected by a number of factors. The current study aimed to determine the effects of the breed of donor/recipient mares, estrus/ovulation induction treatment, cooled transportation of embryos, and synchrony between donor and recipient mares on the efficiency of the EET under subtropical conditions of Pakistan. A total of... Читать дальше...

Comprehensive analysis of single cell and bulk data develops a promising prognostic signature for improving immunotherapy responses in ovarian cancer

Plos.org 

by Huanfei Ding, Bowen Hu, Ruixia Guo

The tumor heterogeneity is an important cause of clinical therapy failure and yields distinct prognosis in ovarian cancer (OV). Using the advantages of integrated single cell RNA sequencing (scRNA-seq) and bulk data to decode tumor heterogeneity remains largely unexplored. Four public datasets were enrolled in this study, including E-MTAB-8107, TCGA-OV, GSE63885, and GSE26193 cohorts. Random forest algorithm was employed to construct a multi-gene prognostic... Читать дальше...



Learning what matters: Synaptic plasticity with invariance to second-order input correlations

Plos.org 

by Carlos Stein Naves de Brito, Wulfram Gerstner

Cortical populations of neurons develop sparse representations adapted to the statistics of the environment. To learn efficient population codes, synaptic plasticity mechanisms must differentiate relevant latent features from spurious input correlations, which are omnipresent in cortical networks. Here, we develop a theory for sparse coding and synaptic plasticity that is invariant to second-order correlations in the input. Going beyond classical Hebbian learning... Читать дальше...





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