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A microbiome catalog of Chinese traditional artisanal cheeses provides insights into functional and microbial diversity
Revisiting co-expression-based automated function prediction in yeast with neural networks and updated Gene Ontology annotations
Automated function prediction (AFP) is the process of predicting the function of genes or proteins with machine learning models trained on high-throughput biological data. Deep learning with neural networks has become the dominant machine learning methodology of contemporary AFP models. However, it is unclear what difference exists between neural networks and classical machine learning techniques for AFP. Therefore, we created a model of AFP in yeast using feedforward neural networks that is trained on gene co-expression data to predict Gene Ontology (GO) labels and directly compared it to two previous, experimentally-validated AFP models. When trained on the same input data, our model outperforms these classical machine learning models (a Bayesian network and adaptive query-driven search) when predicting individual genes involved in mitochondrion organization. In particular, we found our neural network model better distinguished mis-annotated negatives in its training data. Finally, we quantified how differences in gene expression data and Gene Ontology annotations affect the performance of our model across each of its predicted GO terms. Our results suggest that feedforward neural networks can be more performant and robust to GO mis-annotations compared to the specific classical approaches examined here for co-expression-based AFP of some biological processes.
Cell-free DNA Screening and Maternal Cancer
The link between work-leisure conflict and cognitive pre-sleep arousal is serially mediated by emotional exhaustion and entrapment
CD4+ T cell protection against murine Salmonella infection is female-specific and estrogen-dependent
A pilot randomized controlled trial of neuromodulation-augmented balance training in people with multiple sclerosis: STIM-Balance Protocol
Background Impairments in balance control and falls are common problems for people with multiple sclerosis (PwMS), resulting in mobility limitations and reduced participation. Non-invasive neuromodulation techniques such as functional electrical stimulation (FES) and transcutaneous spinal stimulation (TSS) have revealed promising results in improving motor functions in other neurological populations; however, their effects during task-specific balance training have not been investigated in PwMS. Objective To evaluate the feasibility, acceptability, safety, and preliminary clinical efficacy of neuromodulation-augmented balance training programs on balance, mobility, and neuroplasticity in PwMS (ClinicalTrials.gov Identifier: NCT07174973). Methods Twenty-four ambulatory PwMS will be randomly assigned into three groups: (1) visual feedback balance training (VFBT) with sham stimulation, (2) VFBT with active (closed-loop) FES for the ankle muscles and sham TSS, and (3) VFBT with active FES and active (open-loop sub-motor-threshold) TSS at the lumbosacral enlargement. Participants in each group will complete 12 training sessions over six weeks. Feasibility, safety, and acceptability will be assessed through recruitment and adherence metrics, adverse-event monitoring, and semi-structured interviews guided by the Technology Acceptance Model questionnaire-2. Performance-based measures of balance, mobility, and walking speed, as well as patient-reported outcomes of balance confidence, walking ability, and fear of falling will be recorded to assess the preliminary efficacy. Modulation in neural pathways excitability will be quantified by recording motor evoked potentials and spinal motor evoked potentials. Conclusion Findings will help to determine whether neuromodulation-augmented balance training is feasible, safe, and acceptable for PwMS and will guide the design of a future fully powered RCT.
Using Data to Inform Decision Making — Borrowing Limits for Graduate Nursing Students
An evaluation of the impact of visual attributes of park landscape spaces on the emotional benefits and behavioral intention of younger older adults
Filamentation-assisted isolated attosecond pulse generation
Edge-assisted adaptive offloading algorithm for 3D object detection tasks
Multimodal 3D object detection is crucial for autonomous systems but suffers from high delay due to significant computational demands. To address this, we propose an edge computing-assisted framework that balances load between terminal devices and edge servers. We introduce dynamic threshold tuning and resolution-adaptive offloading algorithms to optimize performance. Experimental results demonstrate that our approach significantly reduces delay by minimizing offloading frequency while maintaining high accuracy, achieving a superior delay-accuracy trade-off. Furthermore, the framework exhibits robust adaptability across various models and bandwidth conditions, ensuring effectiveness in dynamic environments.
Cardiovascular Outcomes with Tirzepatide in Type 2 Diabetes
Plasma tool as green route for incorporation of flame retardancy and ultraviolet protection of textile fabrics
Abstract Herein, gas plasma was used as an effective route for surface activation for easier incorporation of fire safety, bacterial growth inhibition and UV shielding functions for synthetic polyacrylic fabrics. Oxygen and nitrogen plasma were used with different time exposure for surface activation and their influence for activation was studied. Green nanocomposite-based coating layer was fabricated from ZnONPs prepared using molokhia stems extract of an average nanoparticle diameter of 6.2 nm in conjunction with sodium tripolyphosphate and tetra-n-butylammonium hexafluorophosphate as flame retardant agents. The dispersion of ZnONPs were dispersed in the nanocomposite using ultrasonication process. Afterwards, the plasma treated polyacrylic fabric was coated directly with the fabricated coating layer. The fire safety, antibacterial and UV shielding properties for the treated and untreated polyacrylic fabrics were investigated. The coated polyacrylic fabric achieved good flame retardant properties, recording a decrease in the rate of burning by 83% compared to blank polyacrylic. Additionally, the emission of toxic gases during combustion was suppressed compared to uncoated polyacrylic fabrics. The nanocomposite-based polyacrylic coating inhibits the growth of both gram-positive bacteria of Staphylococcus aureus and gram-negative bacteria of Escherichia coli achieving clear inhibition zone of 7.6 and 7.8 mm, respectively, compared to blank polyacrylic sample. The UV protection ability was enhanced significantly for coated fabric affording more than three-fold superior ultraviolet protection factor value than uncoated one. Moreover, the tensile strength was improved achieving reinforcement by ~ 10%.
Uncovering complex phonon interactions in Mg3Bi2-xSbx: topology and avoided crossings
Distribution, genetic polymorphism and genotype prediction of Rhesus blood group antigens among the Kurdish population of Zakho, Kurdistan Region, Iraq
The Rhesus blood group system exhibits significant polymorphism, with diverse antigen distributions across populations. This study investigates the antigenic profile, haplotype frequencies, and genotype predictions in a cohort from Zakho, Kurdistan Region, Iraq, which is crucial for transfusion medicine and genetic studies. A prospective cross-sectional analysis was performed on 1,000 Kurdish individuals at Zakho Emergency Teaching Hospital. Blood samples were phenotyped for Rh (D, C, c, E, e) antigens. Haplotype assignments were made using Fisher-Race terminology, and probable genotypes were calculated based on allele frequencies and Wiener nomenclature. The most common Rhesus blood antigens are Rh(e) (95%), Rh(D) (91.9%), Rh(C) (76%), and Rh(c) (68%). Rh(E) is found in 25.8% of individuals, while only 8.1% are Rh(D) negative. Among the Rh(D) positive population, the most frequent phenotype/haplotype and presumed genotype was DCe ( DCe/DCe, R1R1 ) at 31.4%, followed by DCce ( DCe/dce, R1r ) at 29.3%, and DCcEe ( DCe/DcE, R1R1 ) at 13.8%. The dce phenotype ( dce/dce, rr , in 7.2%) was the most common among Rh(D) negative individuals. No significant differences were observed between sexes. This study reveals that DCe, DCce, and DCcEe are prevalent phenotypes among Rh(D) positive individuals, whereas the dce haplotype predominates among Rh(D) negative individuals. The Rhesus phenotype/genotype aligns with Kurdish and Arab groups in Iraq and shows partial resemblance to Western European, Indian, and Iranian populations, but significantly differs from African-American populations, except for the dce phenotype ( dce/dce, rr) . These findings are crucial for blood transfusion strategies, donor selection, maternal and fetal health, the prevention of Rh incompatibilities, and genetic research in the region.
Case 11-2026: A 24-Year-Old Man with Depression, Anhedonia, and Fatigue
Taguchi Optimization of Microstructure and Mechanical Properties in Dissimilar TIG Welding of AISI 304 L–409 M Stainless Steel
Light-induced giant random telegraph noise in CuScP2S6/MoS2 heterostructures and their use in noise resilience image inference
Abstract Random telegraph noise (RTN) is usually regarded as a hallmark of nanoscale conduction channels, arising from individual trapping events in semiconductors and oxide dielectrics. Here we show that optical excitation can induce “giant” RTN in macroscopically large-area devices based on CuScP 2 S 6 /MoS 2 heterostructures, revealing a mesoscopic regime in which a sparse set of photo-activated defects in an insulating thiophosphate controls the conductance of an extended channel. Under optical illumination, the device conductance exhibits stochastic two-level fluctuations whose amplitudes are nearly independent of illumination strength, whereas the characteristic trapping-detrapping time constants are strongly governed by the incident light intensity. This behavior implies that photons are absorbed in effectively small packets that modulate a sparse ensemble of active traps, giving rise to bimodal noise statistics and illumination-tunable switching kinetics. We further exploit this controllable stochasticity in a proof-of-concept optical encoder that converts image pixels into RTN-driven spike trains, enhancing the robustness of a spiking neural network (SNN) to noise-corrupted MNIST inputs. Our results identify CuScP 2 S 6 as a model platform in which light-tunable RTN connects microscopic defect dynamics to macroscopic conductance fluctuations, opening opportunities to engineer noise itself as a functional degree of freedom in photonic and neuromorphic hardware.
Expert consensus on designing a metaverse supported blended EFL module in chinese higher education: A Fuzzy Delphi method
Metaverse technologies can provide immersive, interaction-rich experiences for English as a Foreign Language (EFL) learning, yet curriculum-level design principles and implementation guidance remain limited. This study developed, through expert consensus, a ranked blueprint for a metaverse-supported blended learning module for undergraduate EFL learners in Chinese higher education. Using an exploratory design, Phase 1 elicited candidate elements through semi-structured interviews with EFL instructors (n = 5), and Phase 2 applied the Fuzzy Delphi Method (FDM) with a 12-expert panel using a five-point Likert scale. Items were retained only if they met all prespecified criteria (Agreement ≥ 75%, interquartile range [IQR] ≤ 1.0, and fuzzy distance [d] ≤ 0.20). Retained items were prioritized by defuzzified value (DV), with ties resolved by IQR, then d, then Agreement. Actionable consensus was reached across six domains: learning objectives, learning content, instructional strategies, learning activities, assessment methods, and learning resources. Resampling-based stability checks (leave-one-out and bootstrap) supported the robustness of the induced priority ordering. Kendall’s W indicated limited overall concordance across heterogeneous items; accordingly, item inclusion relied on the prespecified thresholds. The study contributes a replicable, ranked blueprint that embeds constructive alignment in immersive EFL contexts and provides an implementation-ready specification to support staged adoption and subsequent validation in higher education.