Influence of differential arsenic exposure on cellular redox homeostasis of exposed rural women of West Bengal.

The metalloid arsenic (As) induces oxidative stress is a well-known fact. However, the extent of variation of oxidative stress according to different exposure levels of As in groundwater and the mechanism responsible for As mediated oxidative stress is yet to be elucidated in a human population of West Bengal. In the present study, we have investigated the impact of low level (> 10 ≤ 50 µg/L) and high-level groundwater As (> 50 µg/L) on cellular redox status, DNA damage, and repair mechanisms in chronically exposed rural women of West Bengal.

Predicting sustainable arsenic mitigation using machine learning techniques.

This study evaluates state-of-the-art machine learning models in predicting the most sustainable arsenic mitigation preference. A Gaussian distribution-based Naïve Bayes (NB) classifier scored the highest Area Under the Curve (AUC) of the Receiver Operating Characteristic curve (0.82), followed by Nu Support Vector Classification (0.

COVID-19: A master stroke of Nature.

This article presents the status of countries affected by COVID-19 (as of mid-May 2020) and their preparedness to combat the after-effects of the pandemic. The report also provides an analysis of how human behavior may have triggered such a global pandemic and why humans need to consider living sustainably to make our future world livable for all. COVID-19 originated in the city of Wuhan, China in December 2019.

Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms.

Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark algorithms-Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine-in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran.

Developing robust arsenic awareness prediction models using machine learning algorithms.

Arsenic awareness plays a vital role in ensuring the sustainability of arsenic mitigation technologies. Thus far, however, few studies have dealt with the sustainability of such technologies and its associated socioeconomic dimensions. As a result, arsenic awareness prediction has not yet been fully conceptualized.

Groundwater Arsenic Contamination in the Ganga River Basin: A Future Health Danger.

This study highlights the severity of arsenic contamination in the Ganga River basin (GRB), which encompasses significant geographic portions of India, Bangladesh, Nepal, and Tibet. The entire GRB experiences elevated levels of arsenic in the groundwater (up to 4730 µg/L), irrigation water (~1000 µg/L), and in food materials (up to 3947 µg/kg), all exceeding the World Health Organization's standards for drinking water, the United Nations Food and Agricultural Organization's standard for irrigation water (100 µg/L), and the Chinese Ministry of Health's standard for food in South Asia (0.15 mg/kg), respectively.