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.

Myrtenol-loaded niosomes can prevent lung ischemia-reperfusion injury model in rats by balancing the Nrf2/Keap1 and NF-κB signaling pathways.

Lung Ischemia-reperfusion injury (LIRI) is a risk during lung transplantation that can cause acute lung injury and organ failure. In LIRI, the NF-E2-related factor 2(Nrf2)/ Kelch-like ECH-associated protein 1 (Keap1) signaling pathway and the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) signaling pathway are two major pathways involved in regulating oxidative stress and inflammation, respectively. Myrtenol, a natural compound with anti-inflammatory and antioxidant properties, has potential protective effects against IRI.

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.

The activation of the G-protein-coupled estrogen receptor promotes the aggressiveness of MDA-MB231 cells by targeting the IRE1α/TXNIP pathway.

Background And Purpose: This study investigated modulating the G protein-coupled estrogen receptor (GPER) on the IRElα/TXNIP pathway and its role in drug resistance in MDA-MB231 cells.

Experimental Approach: To determine the optimal concentrations of G and 4-hydroxytamoxifen (TAM), GPER expression and ERK1/2 phosphorylation were analyzed using qRT-PCR and western blotting, respectively. Cells were treated with individual concentrations of G (1000 nM), G (1000 nM), and TAM (2000 nM), as well as combinations of these treatments (G + G, TAM + G, and G + TAM) for 24 and 48 h.

Comparing machine learning approaches for estimating soil saturated hydraulic conductivity.

Characterization of near (field) saturated hydraulic conductivity (Kfs) of the soil environment is among the crucial components of hydrological modeling frameworks. Since the associated laboratory/field experiments are time-consuming and labor-intensive, pedotransfer functions (PTFs) that rely on statistical predictors are usually integrated with the existing measurements to predict Kfs in other areas of the field. In this study some of the most appropriate machine learning approaches, including variants of artificial neural networks (ANNs) were used for predicting Kfs by some easily measurable soil attributes.