Predictive modeling of diazinon residual concentration in soils contaminated with potentially toxic elements: a comparative study of machine learning approaches.

The widespread use of pesticides, including diazinon, poses an increased risk of environmental pollution and detrimental effects on biodiversity, food security, and water resources. In this study, we investigated the impact of Potentially Toxic Elements (PTE) including Zn, Cd, V, and Mn on the degradation of diazinon in three different soils. We investigated the capability and performance of four machine learning models to predict residual pesticide concentration, including adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), radial basis function (RBF), and multi-layer perceptron (MLP).

Green synthesis of copper oxide nanoparticles using walnut shell and their size dependent anticancer effects on breast and colorectal cancer cell lines.

Metal oxide nanoparticles(NPs) contain unique properties which have made them attractive agents in cancer treatment. The CuO nanoparticles were green synthesized using walnut shell powder in different calcination temperatures (400°, 500°, 700°, and 900 °C). The CuO nanoparticles are characterized by FTIR, XRD, BET, SEM and DLS analyses.

prediction of amino acids involved in cCPE interaction with claudin 4.

Among the 26 human claudin proteins, the food-poisoning bacterium produces an enterotoxin (~ 35.00 kDa) that specifically targets human claudin 4, causing diarrhea by fluid accumulation in the intestinal cavity. The enterotoxin (CPE) C-terminal domain (cCPE ~ 15.