Prediction of Deoxynivalenol contamination in wheat kernels and flour based on visible near-infrared spectroscopy, feature selection and machine learning modelling.

Contamination of wheat by the mycotoxin Deoxynivalenol (DON), produced by Fusarium fungi, poses significant challenges to the quality of crop yield and food safety. Visible and near-infrared (vis-NIR) spectroscopy has emerged as a promising, non-destructive, and efficient tool for detecting mycotoxins in cereal crops and foods. This study aims to utilize vis-NIR spectroscopy, coupled with a feature selection technique and machine learning modelling, to predict and classify DON contamination in wheat kernels and flour.

Prediction of exam scores using a multi-sensor approach for wearable exam stress dataset with uniform preprocessing.

Background: Physiological signals, such as skin conductance, heart rate, and temperature, provide valuable insight into the physiological responses of students to stress during examination sessions.

Objective: The primary objective of this research is to explore the effectiveness of physiological signals in predicting grades and to assess the impact of different models and feature selection techniques on predictive performance.

Methods: We extracted a comprehensive feature vector comprising 301 distinct features from seven signals and implemented a uniform preprocessing technique for all signals.

Effect of graphene oxide on the uptake, translocation and toxicity of metal mixture to Lepidium sativum L. plants: Mitigation of metal phytotoxicity due to nanosorption.

Due to its unique structure and exceptional properties, graphene oxide (GO) is increasingly used in various fields of industry and therefore is inevitably released into the environment, where it interacts with different contaminants. However, the information relating to the ability of GO to affect the toxicity of contaminants is still limited. Therefore, the aim of our study was to synthesize GO, to examine the phytotoxicity of different concentrations of GO and its co-exposure with the metal mixture using garden cress (Lepidium sativum L.

Toxicological effects of different-sized Co-Fe (CoFeO) nanoparticles on Lepidium sativum L.: towards better understanding of nanophytotoxicity.

Due to their widespread therapeutic and agricultural applicability and usefulness in removing metals and metalloids from water, cobalt ferrite nanoparticles (NPs) are currently receiving increasing attention from researchers. However, their potential phytotoxicity is still poorly understood. Thus, the aim of the current study was to assess the effects of synthesized cobalt ferrite (CoFeO) NPs on biological (morphological, physiological, and biochemical) parameters of edible plant garden-cress (Lepidium sativum L.

The Ets transcription factor Elf5 specifies mammary alveolar cell fate.

Hormonal cues regulate mammary development, but the consequent transcriptional changes and cell fate decisions are largely undefined. We show that knockout of the prolactin-regulated Ets transcription factor Elf5 prevented formation of the secretory epithelium during pregnancy. Conversely, overexpression of Elf5 in an inducible transgenic model caused alveolar differentiation and milk secretion in virgin mice, disrupting ductal morphogenesis.