In-depth Computational Analysis Reveals The Significant Dysregulation of Key Gap Junction Proteins (GJPs) Driving Thoracic Aortic Aneurysm Development.

Objective: Thoracic Aortic Aneurysm (TAA) represents an aortic pathology that is caused by the deranged integrity of the three layers of the aortic wall, and is related to severe morbidity and mortality. Consequently, it is crucial to identify the biomarkers implicated in the pathogenesis and biology of TAA. The aim of the current computational study was to assess the differential gene expression profile of the gap junction proteins (GJPs) in patients with TAA in order to identify novel potential biomarkers for the diagnosis and treatment of this disease.

Predictive modeling of air quality in the Tehran megacity via deep learning techniques.

Air pollution is a significant challenge in metropolitan areas, where increasing amounts of air pollutants threaten public health and environmental safety. The present study aims to forecast the concentrations of various air pollutants, including CO, O, NO, SO, PM, and PM, from 2013 to 2023 in the Tehran megacity, Iran, via deep learning (DL) models and evaluate their effectiveness over conventional machine learning (ML) methods. Key driving variables, including temperature, relative humidity, dew point, wind speed, and air pressure, were considered.

Application of the Lasso regularisation technique in mitigating overfitting in air quality prediction models.

As a significant global concern, air pollution triggers enormous challenges in public health and ecological sustainability, necessitating the development of precise algorithms to forecast and mitigate its impacts, which has led to the development of many machine learning (ML)-based models for predicting air quality. Meanwhile, overfitting is a prevalent issue with ML algorithms that decreases their efficacy and generalizability. The present investigation, using an extensive collection of data from 16 sensors in Tehran, Iran, from 2013 to 2023, focuses on applying the Least Absolute Shrinkage and Selection Operator (Lasso) regularisation technique to enhance the forecasting precision of ambient air pollutants concentration models, including particulate matter (PM and PM), CO, NO, SO, and O while decreasing overfitting.

From motion to emotion: exploring challenging behaviors in autism spectrum disorder through analysis of wearable physiology and movement.

Objective: This study aims to evaluate the efficacy of wearable physiology and movement sensors in identifying a spectrum of challenging behaviors, including self-injurious behavior (SIB), in children and teenagers with autism spectrum disorder (ASD) in real-world settings.

Approach: We utilized a long-short-term memory (LSTM) network with features derived using the wavelet scatter transform to analyze physiological biosignals, including electrodermal activity and skin temperature, alongside three-dimensional movement data captured via accelerometers. The study was conducted in naturalistic environments, focusing on participants' daily activities.