Effect of a Web-Based Heartfulness Program on the Mental Well-Being, Biomarkers, and Gene Expression Profile of Health Care Students: Randomized Controlled Trial.

Background: Health care students often experience high levels of stress, anxiety, and mental health issues, making it crucial to address these challenges. Variations in stress levels may be associated with changes in dehydroepiandrosterone sulfate (DHEA-S) and interleukin-6 (IL-6) levels and gene expression. Meditative practices have demonstrated effectiveness in reducing stress and improving mental well-being.

Antimicrobial resistance in plant endophytes associated with poultry-manure application revealed by selective culture and whole genome sequencing.

Poultry manure is widely used as organic fertilizer in agriculture during the cultivation of crops, but the persistent high-level use of antibiotics in poultry production has raised concerns about the selection for reservoirs of antimicrobial resistance genes (ARGs). Previous studies have shown that the addition of poultry manure can increase the abundance of genes associated with resistance to tetracyclines, aminoglycosides, fluoroquinolones, sulfonamides, bacitracin, chloramphenicol, and macrolide-lincosamide-streptogramin in soil and plants. Understanding the microbial populations that harbor these ARGs is important to identify microorganisms that could enter the human food chain.

gRNAde: A Geometric Deep Learning Pipeline for 3D RNA Inverse Design.

Fundamental to the diverse biological functions of RNA are its 3D structure and conformational flexibility, which enable single sequences to adopt a variety of distinct 3D states. Currently, computational RNA design tasks are often posed as inverse problems, where sequences are designed based on adopting a single desired secondary structure without considering 3D geometry and conformational diversity. In this tutorial, we present gRNAde, a geometric RNA design pipeline operating on sets of 3D RNA backbone structures to design sequences that explicitly account for RNA 3D structure and dynamics.

Spatiotemporal control of structure and dynamics in a polar active fluid.

We apply optimal control theory to a model of a polar active fluid (the Toner-Tu model), with the objective of driving the system into particular emergent dynamical behaviors or programming switching between states on demand. We use the effective self-propulsion speed as the control parameter ( the means of external actuation). We identify control protocols that achieve outcomes such as relocating asters to targeted positions, forcing propagating solitary waves to reorient to a particular direction, and switching between stationary asters and propagating fronts.

impact of ethanolic extract of seed on Gir bull spermatozoa: a comprehensive evaluation through transcriptome profiling.

Aim/objectives: This study examines the impact of an ethanolic extract derived from seeds on the Gir bull () spermatozoa. The objective is to thoroughly assess the effects of the seed extract on the physiological parameters of bull spermatozoa, followed by evaluating its effects on X and Y-bearing spermatozoa and its impact on gene expression through transcriptome profiling.

Material Method: For this study, one Gir bull was selected, and 12 ejaculates were collected at one-week time intervals.

Bacterially expressed full length Hemagglutinin of Avian Influenza Virus H5N1 forms oligomers and exhibits hemagglutination.

Avian influenza poses a significant global health threat, with the potential for widespread pandemics and devastating consequences. Hemagglutinin (HA), a critical surface glycoprotein of influenza viruses, plays a pivotal role in viral entry and serves as a primary target for subunit vaccine development. In this study, we successfully cloned, expressed, and purified hemagglutinin from the circulating strain of H5N1 influenza virus using a robust molecular biology approach.

Evaluating Representation Learning on the Protein Structure Universe.

We introduce , a comprehensive benchmark suite for representation learning on protein structures with Geometric Graph Neural Networks. We consider large-scale pre-training and downstream tasks on both experimental and predicted structures to enable the systematic evaluation of the quality of the learned structural representation and their usefulness in capturing functional relationships for downstream tasks. We find that: (1) large-scale pretraining on AlphaFold structures and auxiliary tasks consistently improve the performance of both rotation-invariant and equivariant GNNs, and (2) more expressive equivariant GNNs benefit from pretraining to a greater extent compared to invariant models.

RNA-FrameFlow: Flow Matching for de novo 3D RNA Backbone Design.

We introduce RNA-FrameFlow, the first generative model for 3D RNA backbone design. We build upon SE(3) flow matching for protein backbone generation and establish protocols for data preparation and evaluation to address unique challenges posed by RNA modeling. We formulate RNA structures as a set of rigid-body frames and associated loss functions which account for larger, more conformationally flexible RNA backbones (13 atoms per nucleotide) vs.

Astrocytic stress response is induced by exposure to astrocyte-binding antibodies expressed by plasmablasts from pediatric patients with acute transverse myelitis.

Background: Pediatric acute transverse myelitis (ATM) accounts for 20-30% of children presenting with a first acquired demyelinating syndrome (ADS) and may be the first clinical presentation of a relapsing ADS such as multiple sclerosis (MS). B cells have been strongly implicated in the pathogenesis of adult MS. However, little is known about B cells in pediatric MS, and even less so in pediatric ATM.

gRNAde: Geometric Deep Learning for 3D RNA inverse design.

Computational RNA design tasks are often posed as inverse problems, where sequences are designed based on adopting a single desired secondary structure without considering 3D geometry and conformational diversity. We introduce gRNAde, a geometric RNA design pipeline operating on 3D RNA backbones to design sequences that explicitly account for structure and dynamics. Under the hood, gRNAde is a multi-state Graph Neural Network that generates candidate RNA sequences conditioned on one or more 3D backbone structures where the identities of the bases are unknown.

gRNAde: Geometric Deep Learning for 3D RNA inverse design.

Computational RNA design tasks are often posed as inverse problems, where sequences are designed based on adopting a single desired secondary structure without considering 3D geometry and conformational diversity. We introduce , a eometric sign pipeline operating on 3D RNA backbones to design sequences that explicitly account for structure and dynamics. gRNAde uses a multi-state Graph Neural Network and autoregressive decoding to generates candidate RNA sequences conditioned on one or more 3D backbone structures where the identities of the bases are unknown.

Heterologous expression, purification and single step efficient refolding of recombinant tissue plasminogen activator (Reteplase) from E. coli.

Reteplase (recombinant plasminogen activator, rPA) is a mutant non-glycosylated tissue-type plasminogen activator (tPA) containing 355 amino acids with longer half-life and promising thrombolytic activity than its original counterpart, full length tPA. In this study, we aimed to produce and optimize the purification process of recombinant tissue-type plasminogen activator (tPA) known as Reteplase (rPA). Reteplase cDNA synthesized from total mRNA isolated from human placenta was PCR amplified, cloned into a pET-28a(+) E.

Cloning and characterization of FMN-dependent azoreductases from textile industry effluent identified through metagenomic sequencing.

Azo dyes, when released untreated in the environment, cause detrimental effects on flora and fauna. Azoreductases are enzymes capable of cleaving commercially used azo dyes, sometimes in less toxic by-products which can be further degraded via synergistic microbial cometabolism. In this study, azoreductases encoded by and genes were screened from metagenome shotgun sequences generated from the samples of textile dye industries' effluents, cloned, expressed, and evaluated for their azo dye decolorization efficacy.

lncRNA-miRNA-mRNA network in kidney transcriptome of Labeo rohita under hypersaline environment.

The present study describes the kidney transcriptome of Labeo rohita, a freshwater fish, exposed to gradually increased salinity concentrations (2, 4, 6 and 8ppt). A total of 10.25 Gbps data was generated, and a suite of bioinformatics tools, including FEELnc, CPC2 and BLASTn were employed for identification of long non-coding RNAs (lncRNAs) and micro RNAs (miRNAs).

Unravelling the genomic origins of lumpy skin disease virus in recent outbreaks.

Lumpy skin disease virus (LSDV) belongs to the genus Capripoxvirus and family Poxviridae. LSDV was endemic in most of Africa, the Middle East and Turkey, but since 2015, several outbreaks have been reported in other countries. In this study, we used whole genome sequencing approach to investigate the origin of the outbreak and understand the genomic landscape of the virus.

Untargeted metabolomics of buffalo urine reveals hydracyrlic acid, 3-bromo-1-propanol and benzyl serine as potential estrus biomarkers.

Buffalo is a silent heat animal and doesn't show prominent signs of estrous like cattle so it becomes difficult for farmers to determine the receptivity of the animal based purely on the animal behaviour. India, having a huge population size, needs to produce more milk for the population. Successful artificial insemination greatly depends on the receptivity of the animal.

Herbicide application impacted soil microbial community composition and biochemical properties in a flooded rice field.

Herbicide application is a common practice in intensive agriculture. However, accumulating herbicide residues in the ecosystem affects important soil attributes. The effect of two herbicides, pendimethalin and pretilachlor, on soil biochemical properties and microbial community composition was studied in a transplanted paddy field.

Chronic industrial perturbation and seasonal change induces shift in the bacterial community from gammaproteobacteria to betaproteobacteria having catabolic potential for aromatic compounds at Amlakhadi canal.

Escalating proportions of industrially contaminated sites are one of the major catastrophes faced at the present time due to the industrial revolution. The difficulties associated with culturing the microbes, has been circumvent by the direct use of metagenomic analysis of various complex niches. In this study, a metagenomic approach using next generation sequencing technologies was applied to exemplify the taxonomic abundance and metabolic potential of the microbial community residing in Amlakhadi canal, Ankleshwar at two different seasons.

Exploring genetic landscape of low-density polyethylene degradation for sustainable troubleshooting of plastic pollution at landfills.

Plastic pollution increases globally due to the high volume of its production and inadequate mismanagement, leading to dumps in landfills affecting terrestrial and aquatic ecosystems. Landfills, as sink for plastics, leach various toxic chemicals and microplastics into the environment. We scrutinized the genetic expression for low-density polyethylene (LDPE) degradation via microorganisms to investigate cell viability and metabolic activities for biodegradation and genetic profiling.

Genomic profiling and characteristics of a C1 degrading heterotrophic fresh-water bacterium Paracoccus sp. strain DMF.

Paracoccus species are metabolically versatile gram-negative, aerobic facultative methylotrophic bacteria showing enormous promise for environmental and bioremediation studies. Here we report, the complete genome analysis of Paracoccus sp. strain DMF (P.