Technologies for detecting and monitoring drivers' states: A systematic review.

Driver fatigue or drowsiness detection techniques can significantly enhance road safety measures and reduce traffic accidents. These approaches used different sensor technologies to acquire the human physiological and behavioral characteristics to investigate the driver's vigilance state. Although the driver's vigilance detection technique has attracted significant interest recently, few studies have been conducted to review it systematically.

Cortical signals analysis to recognize intralimb mobility using modified RNN and various EEG quantities.

Electroencephalogram (EEG) signals are critical in interpreting sensorimotor activities for predicting body movements. However, their efficacy in identifying intralimb movements, such as the dorsiflexion and plantar flexion of the foot, remains suboptimal. This study aims to explore whether various EEG signal quantities can effectively recognize intralimb movements to facilitate the development of Brain-Computer Interface (BCI) devices for foot rehabilitation.

New regulatory role of Znf1 in transcriptional control of pentose phosphate pathway and ATP synthesis for enhanced isobutanol and acid tolerance.

To develop a cost-effective microbial cell factory for the production of biofuels and biochemicals, an understanding of tolerant mechanisms is vital for the construction of robust host strains. Here, we characterized a new function of a key metabolic transcription factor named Znf1 and its involvement in stress response in Saccharomyces cerevisiae to enhance tolerance to advanced biofuel, isobutanol. RNA-sequencing analysis of the wild-type versus the znf1Δ deletion strains in glucose revealed a new role for transcription factor Znf1 in the pentose phosphate pathway (PPP) and energy generation.

Prevalence of Depression and Subjective Job Stress Among Men who have Sex with Men and Transgender Community Health Workers in Pakistan.

Objective: To determine the prevalence of depression, subjective work-related stress and associated factors among men who have sex with men and transgender people working in community-based organisations in Pakistan.

Study Design: Cross-sectional descriptive study. Place and Duration of the Study: Community-based organisations in Lahore, October 2022.

Filicide In Depressive Psychosis: Case Report Of An Emotionally Unstable Woman Slaughtering Her Two Children.

Filicide is the murder of a child by the parent. It is associated with various psychiatric and nonpsychiatric conditions. We herein present a case report of a filicide by an emotionally unstable mother suffering from depressive psychosis.

Comparison Of Effectiveness Of Antipsychotics In Schizophrenia: Second-Generation Versus The Irstgeneration.

Background: The choice of the antipsychotic medication is based upon the risks, benefits and the cost. There has been still a debate that which group of antipsychotics is overall better amongst the two so we planned this study with the objective to compare the efficacy of the 1st & 2nd generation antipsychotics for the treatment of schizophrenia.

Methods: This RCT was conducted at in/out patient department of Psychiatry at a tertiary care hospital of Pakistan over the time period of six months.

Projected Increase in Hydropower Production in India under Climate Change.

Hydropower is a valuable renewable energy resource in India, which can help in climate change mitigation and meet the increasing energy demands. However, the crucial role of climate change on hydropower production in India remains unexplored. Here using the observations and model simulations, we show that seven large hydropower projects experienced a significant (p-value < 0.

A wavelet-based technique to predict treatment outcome for Major Depressive Disorder.

Treatment management for Major Depressive Disorder (MDD) has been challenging. However, electroencephalogram (EEG)-based predictions of antidepressant's treatment outcome may help during antidepressant's selection and ultimately improve the quality of life for MDD patients. In this study, a machine learning (ML) method involving pretreatment EEG data was proposed to perform such predictions for Selective Serotonin Reuptake Inhibitor (SSRIs).