Metagenomic Insights into Pollutants in Biorefinery and Dairy Wastewater: rDNA Dominance and Electricity Generation in Double Chamber Microbial Fuel Cells.

This study evaluates the potential of biorefinery and dairy wastewater as substrates for electricity generation in double chamber Microbial Fuel Cells (DCMFC), focusing on their microbial taxonomy and electrochemical viability. Taxonomic analysis using 16S/18S rDNA-targeted DGGE and high-throughput sequencing identified Proteobacteria as dominant in biorefinery biomass, followed by Firmicutes and Bacteriodota. In dairy biomass, Lactobacillus (77.

BioBERT based text mining for incorporating prior knowledge in the inference of genetic network models.

Reconstruction of Gene Regulatory Networks (GRNs) is essential for understanding gene interactions, their impact on cellular processes, and manifestation of diseases, including drug discovery. Among various mathematical and dynamic models used for GRN reconstruction, S-system model, comprising non-linear differential equations, is widely utilised to capture the behaviour of complex biological systems with non-linear and time-dependent interactions. However, as the network size increases, computational demand for network inference grows due to a greater number of estimation parameters, significantly impacting the performance of optimisation algorithms.

Investigating the Association between Catechol-O-Methyltransferase Gene Activity and Pain Perception in South African Patients with Different Temporomandibular Disorders Diagnoses.

Temporomandibular disorders (TMD) affect a significant portion of the population, with profound psychological, behavioral, and social repercussions. Recent investigations have explored the genetic basis underlying pain perception in individuals with TMD, aiming to elucidate the role of specific genetic factors in modulating the condition. Notably, genetic variations have been implicated in the pathogenesis of TMD, particularly genes involved in pain perception pathways.

Large language model based framework for automated extraction of genetic interactions from unstructured data.

Extracting biological interactions from published literature helps us understand complex biological systems, accelerate research, and support decision-making in drug or treatment development. Despite efforts to automate the extraction of biological relations using text mining tools and machine learning pipelines, manual curation continues to serve as the gold standard. However, the rapidly increasing volume of literature pertaining to biological relations poses challenges in its manual curation and refinement.