Genome-wide association studies of ischemic stroke based on interpretable machine learning.

Despite the identification of several dozen genetic loci associated with ischemic stroke (IS), the genetic bases of this disease remain largely unexplored. In this research we present the results of genome-wide association studies (GWAS) based on classical statistical testing and machine learning algorithms (logistic regression, gradient boosting on decision trees, and tabular deep learning model TabNet). To build a consensus on the results obtained by different techniques, the Pareto-Optimal solution was proposed and applied.

A prokaryotic Argonaute protein recruits a helicase-nuclease to degrade invading plasmids.

DdmDE is a novel plasmid defense system that was discovered in the seventh pandemic Vibrio cholerae strain of the biotype O1 EI Tor. In this issue of Cell, Yang and coworkers reveal the mechanisms underlying the assembly and activation of the DdmDE defense system.

Categorization of tweets for damages: infrastructure and human damage assessment using fine-tuned BERT model.

Identification of infrastructure and human damage assessment tweets is beneficial to disaster management organizations as well as victims during a disaster. Most of the prior works focused on the detection of informative/situational tweets, and infrastructure damage, only one focused on human damage. This study presents a novel approach for detecting damage assessment tweets involving infrastructure and human damages.

Heparin dosing in patients with Impella-supported cardiogenic shock.

Background: Impella™ is increasingly used in cardiogenic shock. However, thromboembolic and bleeding events are frequent during percutaneous mechanical circulatory support (pMCS).

Objective: Therefore, we aimed to explore the optimal anticoagulation regime for pMCS to prevent thromboembolism and bleedings.

Identifying dyslexia in school pupils from eye movement and demographic data using artificial intelligence.

This paper represents our research results in the pursuit of the following objectives: (i) to introduce a novel multi-sources data set to tackle the shortcomings of the previous data sets, (ii) to propose a robust artificial intelligence-based solution to identify dyslexia in primary school pupils, (iii) to investigate our psycholinguistic knowledge by studying the importance of the features in identifying dyslexia by our best AI model. In order to achieve the first objective, we collected and annotated a new set of eye-movement-during-reading data. Furthermore, we collected demographic data, including the measure of non-verbal intelligence, to form our three data sources.