Combining Deep Learning and Structural Modeling to Identify Potential Acetylcholinesterase Inhibitors from .

Alzheimer's disease (AD) is the most common type of dementia, affecting over 50 million people worldwide. Currently, most approved medications for AD inhibit the activity of acetylcholinesterase (AChE), but these treatments often come with harmful side effects. There is growing interest in the use of natural compounds for disease prevention, alleviation, and treatment.

Soil Microbiomes and their Arsenic Functional Genes in Chronically High-Arsenic Contaminated Soils.

Microbial arsenic transformations play essential roles in controlling pollution and ameliorating risk. This study combined high-throughput sequencing and PCR-based approaches targeting both the 16 S rRNA and arsenic functional genes to investigate the temporal and spatial dynamics of the soil microbiomes impacted by high arsenic contamination (9.13 to 911.

Combined Deep Learning and Molecular Modeling Techniques on the Virtual Screening of New mTOR Inhibitors from the Thai Mushroom Database.

The mammalian target of rapamycin (mTOR) is a protein kinase of the PI3K/Akt signaling pathway that regulates cell growth and division and is an attractive target for cancer therapy. Many reports on finding alternative mTOR inhibitors available in a database contain a mixture of active compound data with different mechanisms, which results in an increased complexity for training the machine learning models based on the chemical features of active compounds. In this study, a deep learning model supported by principal component analysis (PCA) and structural methods was used to search for an alternative mTOR inhibitor from mushrooms.

Effects of Arsenic and Iron on the Community and Abundance of Arsenite-Oxidizing Bacteria in an Arsenic-Affected Groundwater Aquifer.

Arsenic (As) contamination of groundwater aquifers is a global environmental problem, especially in South and Southeast Asian regions, and poses a risk to human health. Arsenite-oxidizing bacteria that transform As(III) to less toxic As(V) can be potentially used as a groundwater As remediation strategy. This study aimed to examine the community and abundance of arsenite-oxidizing bacteria in groundwater with various As concentrations from Rayong Province, Thailand using PCR-cloning-sequencing and quantitative PCR (qPCR) of catalytic subunit of arsenite oxidase gene (aioA).

A model of infection in honeybee colonies with social immunity.

Honeybees (Apis mellifera) play a significant role in the pollination of various food crops and plants. In the past decades, honeybee management has been challenged with increased pathogen and environmental pressure associating with increased beekeeping costs, having a marked economic impact on the beekeeping industry. Pathogens have been identified as a contributing cause of colony losses.

Microbial community structure in aquifers associated with arsenic: analysis of 16S rRNA and arsenite oxidase genes.

The microbiomes of deep and shallow aquifers located in an agricultural area, impacted by an old tin mine, were explored to understand spatial variation in microbial community structures and identify environmental factors influencing microbial distribution patterns through the analysis of 16S rRNA and genes. Although , , , , , and were widespread across the analyzed aquifers, the dominant taxa found in each aquifer were unique. The co-dominance of and potentially controlled arsenic immobilization in the aquifers.

Decision-making in honeybee swarms based on quality and distance information of candidate nest sites.

In the nest-site selection process of honeybee swarms, an individual bee performs a waggle dance to communicate information about direction, quality, and distance of a discovered site to other bees at the swarm. Initially, different groups of bees dance to represent different potential sites, but eventually the swarm usually reaches an agreement for only one site. Here, we model the nest-site selection process in honeybee swarms of Apis mellifera and show how the swarms make adaptive decisions based on a trade-off between the quality and distance to candidate nest sites.