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Editorial Note: Machine learning-driven Diabetes Health Tracer (DHT): Optimizing prognosis using RaSK_GraDe and RaSK_GraDeL models

Health-related quality of life in children with HIV/AIDS: Child and parent perspectives at a tertiary ART clinic in Kathmandu, Nepal

Background Children living with human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) face persistent physical, emotional, and social challenges that can reduce their quality of life. In Nepal, evidence describing the health-related quality of life (HRQoL) of this population remains limited. Objective To assess health-related quality of life and the level of agreement between child self-reports and parent proxy reports among children living with HIV/AIDS receiving antiretroviral therapy at a tertiary care hospital in Kathmandu, Nepal. Methods An analytical cross-sectional study was conducted from July to September, 2022 among 105 children aged 6–18 years and their parents attending the antiretroviral therapy clinic at Tribhuvan University Teaching Hospital. HRQoL was assessed using the Pediatric Quality of Life Inventory Version 4.0 Generic Core Scales which comprises physical, emotional, social, and school functioning domains, for both child self-reports and parent proxy reports. Descriptive statistics summarized participant characteristics and HRQoL scores. Chi-square and Fisher’s exact tests examined associations between sociodemographic variables and quality of life. Intraclass correlation coefficients (ICC) were calculated to assess agreement between child self-reports and parent proxy reports. Results More than half of the children (58.1%) had poor HRQoL. The mean total HRQoL score from child self-reports was 68.8 ± 5.7, with higher scores in the social (73.5 ± 8.1) and physical (73.3 ± 6.1) domains compared to the emotional (65.1 ± 8.9) and school functioning (62.8 ± 7.5) domains. Parent proxy reports showed slightly higher mean scores (70.4 ± 3.7). Overall agreement between child self-reports and parent proxy reports was poor (ICC = 0.405), with the highest agreement observed in the physical functioning domain. Conclusion Poor quality of life is common among children living with HIV/AIDS in Nepal, particularly in emotional and school functioning domains. Targeted interventions addressing psychosocial well-being and educational participation are needed to improve overall quality of life in this group.

Preoperative binaural beats reduce remimazolam dosage and enhance safety in anesthesia induction: A randomized controlled trial

Binaural beats, a form of auditory stimulation, are thought to reduce anxiety and anesthetic requirements through brainwave entrainment. Remimazolam offers advantages in terms of rapid onset and offset of action and hemodynamic stability. However, the optimal remimazolam dose for anesthesia induction remains unclear and there are concerns regarding variability in response and potential side effects at higher doses. This study investigated the effects of preoperative binaural beats on the remimazolam dose required for loss of consciousness during general anesthesia induction. In this randomized, prospective, single center study, 72 patients undergoing general anesthesia were allocated to two groups: the binaural sound (B group) or the control group. The B group listened to binaural sounds (1-Hz frequency difference) for 30 min preoperatively, while the control group did not. The B group required a significantly lower remimazolam dose for loss of consciousness (15.0 ± 3.6 vs. 17.7 ± 4.5 mg, p = 0.006) and achieved loss of consciousness faster (140 ± 29 vs. 168 ± 47 s, p = 0.003) than the control group. The incidence of hypotension was lower in the B group than in the control group (6 vs. 28%, p = 0.024). Electroencephalography spectral analysis revealed no significant between-group differences. Binaural beats significantly reduced the remimazolam dose required for loss of consciousness and shortened the time to loss of consciousness, while reducing the incidence of hypotension during anesthesia induction. Binaural beats are an effective, non-invasive method of enhancing efficiency and safety in anesthesia induction when using remimazolam infusion. Trial registration ClinicalTrials.gov NCT06099977

Analytical framework for evaluating NMPC-based robot navigation in fluid environments

This paper presents a novel hybrid simulation framework by combining nonlinear model predictive control (NMPC) and the lattice Boltzmann method (LBM) for autonomous robot navigation in dynamic environments. We evaluated the control algorithm’s resilience by looking at fluid-structure interactions in both laminar ( Re  = 100) and turbulent ( Re  = 2000) flows. To ensure numerical accuracy and physical fidelity, a systematic grid independence study was conducted across various resolutions ( 10 × 10 to 200 × 200 ). The 200 × 200 grid was selected as the benchmark standard, providing 3–5 lattice units within the viscous boundary layer to minimize numerical diffusion and accurately resolve high-frequency vortex shedding patterns. This rigorous validation allowed us to test the NMPC trajectory planning across fundamentally different flow behaviors with high confidence in the underlying hydrodynamics. The aim is to enhance mobile robot navigation by integrating a resilient control algorithm with a comprehensive fluid dynamics study, focusing on enhancing trajectory planning, obstacle avoidance, and overall performance in dynamic fluid environments. Computational fluid dynamics (CFD) analysis is combined with a robust control algorithm. The robot’s interaction with the surrounding fluid is evaluated through different parameters such as Reynolds number, drag forces, the robot’s energy dissipation, and vorticity. Key performance metrics, including a path efficiency of 0.887 and low computational requirements with the LBM-NMPC framework maintaining a linear memory footprint of 2.88 MB at peak resolution, demonstrate NMPC algorithm’s viability as a fast and efficient trajectory planner. The robot maintained safe distances from obstacles, highlighting the effectiveness of the obstacle avoidance strategy and the robustness of the validated simulation environment.

Untargeted metabolomics for the early detection of preeclampsia: A systematic review of human studies

This systematic review synthesizes current evidence on metabolomics-based biomarkers for the early prediction or diagnosis of preeclampsia and highlights promising candidates with potential clinical application. Following PRISMA guidelines, a comprehensive search was performed in PubMed, Cochrane Library, Web of Science, and ClinicalTrials.gov up to September 2024, using predefined terms related to preeclampsia, metabolomics, and pregnancy. Study selection and risk of bias assessment were conducted with CADIMA, applying PICO-based inclusion criteria and predefined quality appraisal standards. Of 112 records identified, 16 were duplicates, 57 were excluded after title and abstract screening, and 31 after full-text review, leaving 12 studies for inclusion. These comprised cohort, case–cohort, case–control, validation, prospective control, and translational designs. Data extraction captured study characteristics, populations, methodologies, biological matrices, and main findings. Considerable heterogeneity was observed across studies, with limited overlap in identified metabolites. Nonetheless, alanine was reported in serum, lactate was observed in both serum and urine, and glutamate and glutamine were detected across serum, plasma, and placental tissue. These metabolites, interconnected through the Cori and glucose–alanine cycles, have been linked to hepatic dysfunction, immune regulation, and excitotoxicity. Overall, metabolomics shows strong potential as a sensitive tool for biomarker discovery in preeclampsia, though further research is required to confirm findings, improve reproducibility, and integrate metabolomic data with clinical parameters to support personalized medicine approaches. Systematic review registration PROSPERO, CRD42024540619.

Joint modelling of left- and interval-censored viral load for couples in Mozambique

Mathematical and statistical models have been essential tools in exploring the dynamics of viral load measures, understanding of the pathogenesis of HIV-1 infection and in assessment of the potency of antiretroviral therapies. However, this can be challenging due to the potential intra-couple correlation as well as the presence of multiple measurements collected from the same study site or cluster. A second complication arises due to censoring of the individual viral load measurements. The aim of this paper is to investigate the association between age and viral load of woman and man within a couple, while accounting for the presence of antiretroviral biomarkers in blood. An additional question of interest is how weakly or strongly are viral load of woman and man correlated, and how does this correlation depend on covariates as well. Joint marginal and random-effects models assuming constant and non-constant correlation were fitted using maximum likelihood while accounting for the left- and interval-censoring nature of the two viral loads. Findings show a weak positive correlation between the viral loads of women and men. Next, interaction between age and antiretroviral biomarker’s presence is only significant in the mean viral load for women, showing that the effect of age on a women’s viral load varies by the antiretroviral biomarker’s status. No age effect on the mean viral load for men is observed. These findings reinforce the need of interventions that stimulate adherence to antiretroviral therapy treatment once one or both partners are HIV infected as well as to monitor their viral load as they get older, especially the female partner.

Assessment of miRNAs as transcriptional regulators in respiratory syncytial virus infection through computational analysis and molecular docking studies

Globally, RSV is a major contributor to severe lower respiratory tract infections among children. Despite the significant medical concern posed by RSV, efforts to develop effective vaccines and antiviral drugs have largely fallen short, with the exception of immune prophylaxis available only for specific high-risk infants. We employed a suite of computational tools to investigate the role of microRNAs in the host’s response to RSV infection. miRanda and RNAHybrid were instrumental in predicting microRNA-mRNA binding sites. For a deeper structural analysis, MC-Fold and MC-Sym were used to predict the 3D structures of both the miRNAs and their target mRNAs. The interactions between these molecules were then studied through RNA-RNA docking, with the resulting poses evaluated based on binding affinities and interaction profiles. This analysis focused on twelve selected miRNAs and their binding to specific sites on RSV mRNA. Finally, molecular dynamics (MD) simulations were conducted to evaluate the stability of the docked complexes. Taken together, these results suggest that two miRNAs, hsa_miR-2278 and hsa_miR-6732-3p, could potentially regulate the transcriptional activity during RSV infection and may warrant consideration as therapeutic agents.

Adaptive traffic signal control using deep reinforcement learning: Toward smarter and safer urban mobility

In today’s rapidly evolving Intelligent Transportation Systems (ITS), traditional systems for controlling traffic signals are often inadequate in optimizing real-time traffic flow due to their dependency on preset schedules and lack of adaptability to dynamically changing traffic signal phases. These systems cannot analyze dynamic signal timing changes, especially at multiple intersections, resulting in inefficient vehicle flow, longer queues, and higher levels of congestion. Thus, the need arises to develop intelligent systems capable of optimizing traffic flow in real time, reducing delays, and addressing the growing challenges of intelligent transportation systems. To address these requirements, a novel deep reinforcement learning framework that combines the Twin Delayed Deep Deterministic Policy Gradient (TD3) with prioritization-based Intelligent Traffic Control (P-ITC) is proposed for real-time traffic signal optimization using stability techniques. The module focuses on TD3’s stability-enhancing techniques, including clipped Q-learning, delayed and targeted policy updates, and smoothing. The system ensures robust signal timing decisions across intersection networks. PER prioritizes critical traffic signal experiences, ensuring the system learns from key events that influence real-time traffic flow. The proposed TD3P-ITC framework achieves maximum reductions in queue length (up to 22 at transport hub intersections and 25 at highways) and a 17.9 percent decrease (compared to baseline approaches) in simulated accident rates.

When the source is a bot: How people adapt their evaluation strategies to assess AI-generated content

Generative artificial intelligence (GenAI) blurs the boundaries between expert and non-expert sources, as it increasingly distributes and creates scientific content. This study examines how individuals adapt evaluation strategies, including content and source evaluation, and corroboration, when using GenAI versus a search engine. Based on performance tasks in which participants evaluated science-related socio-scientific dilemmas and follow-up interviews with 30 adult participants from diverse educational backgrounds, findings reveal that users employed these strategies on both platforms but adapted them in distinct ways. We identified two evaluation strategies that emerged as analytical constructs from the qualitative data. First, to corroborate output, participants frequently used a strategy we titled ‘representation evaluation,’ assessing whether GenAI accurately summarized its sources rather than verifying source agreement independently. Second, participants also applied ‘meta source evaluation,’ relying on their familiarity with sources provided by GenAI instead of directly evaluating the sources themselves. Although all participants engaged in dialogue with the chat, they did not leverage the bot’s dialogue capabilities to assess credibility, and many relied on a “machine heuristic”, assuming GenAI’s inherent correctness, reflecting a well-documented over-trust in automated systems. This research underscores the importance of developing and assessing critical evaluation skills for navigating AI-generated scientific information. Specifically, it extends existing models of online information evaluation to contexts mediated by artificial intelligence.

Support-seeking and rehoming pathways differ by surrender circumstances among pet owners

Animal shelters aim to divert intake by encouraging pet retention in homes (e.g., support services) or through alternative methods of surrender (e.g., self-rehoming); however, it remains unclear what factors contribute to an owner’s decision to seek pet support services, as well as to select different methods to surrender a pet. Using a sample of U.S. and Canadian public members who rehomed a pet within the past five years (n = 452), the present study identified groups of pet owners who share similar patterns of responses to surrender circumstances using latent class analysis (LCA). LCA revealed three heterogeneous classes of owners distinguished largely by the reason for surrender and the length of ownership (Owner Hardships n = 215, New Acquisitions n = 194, Behavioural Incompatibility n = 43). Comparisons revealed differences across classes regarding the proportion of respondents that sought assistance and the type of assistance sought, the pathways used to surrender, and the concerns reported by respondents during surrender. For example, the New Acquisitions class was more likely to relinquish to a shelter, either as the only method or after attempting to self-rehome their animal. The Behavioural Incompatibility class had the highest proportion of participants who attempted to relinquish to a shelter but ended up self-rehoming, and the Owner Hardships class had the highest proportion of participants who self-rehomed as their only method. In addition, the classes varied in the proportion of respondents who reportedly sought assistance to help keep their pet. Qualitative analysis revealed that respondents wanted or sought a variety of different support services, including behavioural support, part-time care, and veterinary care. Future research should consider the heterogeneity in surrender decision-making when addressing issues of intake diversion from animal shelters.

Temporal-spatial trends in childbirth in Ontario, Canada

Introduction The importance of understanding the continuum of care throughout the perinatal/postpartum periods is important for health system monitoring and quality improvement. In this study, we take a broad-ranging and longitudinal perspective to examining long-term changes in obstetric care. Methods This is a retrospective population-based study including all liveborn deliveries from 2010–2023 in Ontario, Canada. We used the hospital Discharge Abstract Database to link delivery and newborn abstracts. We report year-over-year changes in socio-demographics, clinical factors, care patterns, and perinatal and postpartum outcomes. Results The number of in-hospital births decreased from 133,957 in 2010–127,660 in 2023. Over the study period, delivery age increased from a mean 30.6 years (SD 5.5) in 2010 to 32.2 (SD 4.9) in 2023 and there was at least a doubling in the proportion of persons who delivered having preexisting/gestational diabetes (5.6% in 2010, 11.1% in 2023), obesity (1.6% in 2010, 4.6% in 2023), pre-eclampsia/eclampsia (1.1% in 2010, 2.6% in 2023), liver disorders (0.43% in 2010; 1.16% in 2023), and other diseases (4.9% in 2010, 10.7% in 2023), p < 0.0001 for all. The proportion of deliveries performed via C-section increased over time (29.3% in 2010, 34.6% in 2023) but the median length-of-stay decreased 2.68% year-over-year. Use of epidural increased non-linearly over the study period and was less likely at lower-volume hospitals. Although uncommon (<5%), the rate of obstetric trauma and birth trauma increased over the study period, regardless of the mode of delivery (p < 0.0001). Six-month mortality did not change over the study period after delivery, while infant mortality decreased (0.35% in 2010 to 0.26% in 2023). We also observed substantial hospital-level variation in utility of services including midwifery care and access to epidural. Conclusion Over the last 14 years, we found an increasing incidence of people giving birth at an older age and having complicating clinical characteristics at the time of delivery.

Price elasticity of demand for cigarettes in Nepal: Evidence from a lower middle-income country in South Asia using Deaton’s demand model

Background In South Asia, taxation policies are popular instruments to reduce high cigarette demand which work through directly affecting cigarette prices. The effectiveness of such policies depends on consumers’ behavioural responses to changes in cigarette price, which is commonly expressed in terms of the price elasticity of demand. A limited number of studies in this region have used available cross-sectional and panel survey data to estimate cigarette price elasticity using Deaton’s demand model, and to date, no such study exists for Nepal. Therefore, this study aims to estimate the price elasticity of cigarette demand in Nepal. Methods We applied Deaton’s demand model to estimate cigarette price elasticity utilizing data from the Nepal Household Risk and Vulnerability Survey, a three-year panel microdata (2016–2018). The analysis is based on data from 5,653 households observed over three waves. Deaton’s demand model exploits spatial variation in unit values of cigarettes to measure price elasticity. We adjusted the basic model for possible bias that may arise from panel nature of the data. Results In this study, we found the estimate of price elasticity for cigarettes to be −0.58 [95% CI: −0.79, −0.37]. This indicates that the demand for cigarettes is relatively inelastic. Similarly, we found the estimate of expenditure elasticity to be 1.06 [95% CI: 0.54, 1.57], indicating a more responsive cigarette demand with respect to income. Conclusion The negative price elasticity estimate indicates that the increase in excise taxes can be effective both in reducing cigarette consumption and raising tax revenues. This also provides additional evidence that routine surveys can be valuable for estimating price elasticity using Deaton’s method, which are originally designed to monitor living standards.

Unraveling the toxicological impact of Bisphenol A exposure on dermatomyositis: An integration of network toxicology and machine learning approaches

Background Dermatomyositis is a common immune-mediated skin disorder whose pathogenesis has not been fully elucidated. Environmental factors play a key role in its onset and progression. Bisphenol A (BPA) is a widespread environmental pollutant known to pose risks to human health. Previous studies have indicated that BPA exposure can disrupt immune function and trigger skin inflammation and autoimmune diseases. However, the role and molecular mechanisms of BPA in dermatomyositis remain unclear. This study aims to systematically elucidate whether and how bisphenol A (BPA) may contribute to the development of dermatomyositis by identifying key toxicological targets and underlying molecular mechanisms through an integrated computational framework. Methods The toxicity and pharmacokinetic properties of BPA were predicted using the ProTox 3.0 and ADMElab 2.0 platforms. Network toxicology approaches were employed to explore the pathogenic pathways and mechanisms of BPA in dermatomyositis. Seven machine learning algorithms were applied for cross-validation and identification of core genes. Molecular docking and molecular dynamics (MD) simulations were conducted to evaluate the binding efficiency and stability between BPA and the identified targets. Results Integrated results from both prediction platforms revealed that BPA exhibits significant neurotoxicity, nephrotoxicity, hepatotoxicity, skin sensitization, and immunotoxicity. Network toxicology analysis suggested that BPA may influence the progression of dermatomyositis by regulating key factors such as AKT1, BCL2, MMP9, ESR1, and INS, thereby affecting apoptosis, immune-inflammatory responses, pathways in cancer, and the PI3K-Akt signaling pathway. Using LASSO regression, SVM, random forest (RF), GBM, GLM, KNN, and NNET machine learning algorithms, four core genes were identified: SAA1, NACAD, SLC14A1, and MYBPH, all of which were highly expressed in dermatomyositis lesion tissues. Molecular docking studies demonstrated strong binding affinities between BPA and these targets, with the highest binding energy observed for SAA1 at –8.4 kcal/mol. Molecular dynamics simulations further confirmed the high binding stability of the BPA–SAA1 protein–ligand complex. Collectively, these findings suggest that BPA may increase the risk of dermatomyositis by modulating SAA1 protein. Conclusion This study identifies SAA1 as a potential target in BPA-induced dermatomyositis, highlighting the impact of BPA on immune regulation and providing a foundation for understanding associated health risks and developing mitigation strategies. Given the limited research on dermatomyositis, further experimental validation is essential to elucidate the pathogenic mechanisms of BPA.

Exploring the spatial coupling relationship between green vegetation carbon stock and recreational intensity in urban parks: A case study of Hangzhou

As an important form of green space within densely built urban environments, optimizing the layout of city parks based on the synergistic integration of carbon stock and recreational services level holds significant importance. Taking Hangzhou Jinsha Lake Park as a case, this research employs remote sensing inversion to analyze the spatial distribution of the park’s carbon stock. A set of indexes was used to evaluate the park’s recreational services level. Finally, a coupling coordination model was applied to generate and visualize the coupling coordination degree between the park’s carbon stock and recreational services level. Results indicate that the coupling coordination degree between recreational services level and carbon stock in Jinsha Lake Park exhibits a distribution pattern characterized by higher values in the west and lower values in the east, as well as higher values in the south and lower values in the north. Most of these coupling coordination degrees fall in the “primary coordination” range and exhibit different spatial characteristics, such as “high recreational level – low carbon stock” and “low recreational level – high carbon stock”. Park road accessibility serves as a fundamental prerequisite for activating spaces. The rational allocation of functional zones provides a structural framework for balancing ecological and recreational interests, while optimized vegetation configuration acts as the core engine that directly drives the simultaneous growth of carbon stock and recreational vitality. Appropriate plant configuration not only directly increases regional carbon stock but also boosts recreational vitality by increasing plant diversity and vegetation coverage. This study proposes a park optimization strategy based on the synergy between carbon stock and recreational functions, which is of great significance for advancing the dual objectives of enhancing ecological services and social services in urban parks.

Assessing Local Differential Privacy for Compliance with the Personal Data Protection Law in Integrated Data Systems

Organizations increasingly integrate and share person-level data across internal platforms and external partners to enable analytics, digital services, and evidence-based decision making. However, combining quasi-identifiers across systems and releases can enable re-identification via linkage attacks, creating regulatory compliance and trust risks. This paper proposes an operational methodology for (i) identifying direct identifiers and quasi-identifiers (QIs), (ii) quantifying baseline re-identification risk using uniqueness and prosecutor-style risk proxies, and (iii) applying Local Differential Privacy (LDP) to reduce link-ability prior to data sharing. We implement categorical LDP using a Generalized Randomized Response (GRR) mechanism and evaluate privacy–utility trade-offs through a sensitivity analysis over the privacy budget ε. Utility is quantified using (a) distributional distortion (total variation distance) and (b) downstream task performance (job-title classification). We further address reviewer concerns by discussing repeated releases, privacy accounting as mitigations for longitudinal deployments, and by improving figure readability and updating related work with recent studies.

Filling the gaps between tide gauges: Demonstrating high-resolution seasonal high tide flooding predictions using NOAA’s Coastal Ocean Reanalysis

High Tide Flooding (HTF) is a present and increasing hazard for coastal communities across the United States. NOAA provides HTF outlooks at U.S. tide gauges, however, many coastal communities lie relatively far from a tide gauge and therefore currently lack localized HTF guidance. In this study, we demonstrate an approach to generate spatially-continuous daily predictions of HTF at 400–500 m resolution out to a year into the future, by combining NOAA’s monthly HTF outlook framework with the newly-released Coastal Ocean Reanalysis (CORA). Using CORA to derive daily HTF predictions at tide gauges, as compared to using gauge observations, results in average HTF model skill reduction of ≤5% using three different statistical metrics at one month lead time. Further, stations which obtain statistically skillful HTF predictions using gauge data also do so using CORA for 94% of cases. The results suggest that CORA could enable skillful HTF predictions away from tide gauges, supporting the possibility of providing high resolution HTF outlooks for much of the U.S. coastline. The potential value of these spatially continuous HTF predictions is illustrated by identifying communities near Charleston S.C. with different CORA-derived local HTF risk than that provided by the closest tide gauge. Finally, we describe outstanding questions and needs for the scaling of these results to an operational national-scale monthly HTF outlook.

Evaluation of a training program for rheumatic heart disease screening integrated into the public health system in Uganda

Introduction Echocardiography screening for rheumatic heart disease (RHD) has gained support as a public health approach, but scale up of RHD screening services is complex. We sought to evaluate the effectiveness of a novel training program to build non-expert competency for RHD echocardiography screening within the Uganda public health system and to describe the human and material resources required to support it. Methods Guided by a logic model, we evaluated the Accelerating Delivery of Rheumatic Heart Disease Prevention in Northern Uganda (ADUNU) Program, a novel RHD control program, 15 months after its implementation within the Ugandan public health care system. Results Sixty-one healthcare workers (HCW) across 10 public health facilities started in training under the program, of which 58 (95%) advanced past the initial stage of training and earned conditional certification to screen for RHD with ongoing remote and in-person feedback and oversight. Of these, 17 (29%) completed all stages of training and earned full certification to independently screen for RHD with no ongoing oversight. A total of 17,927 community members were screened through ADUNU during the program’s first 15 months. After receiving final certification, 14 HCWs (93%) continued to perform screening echocardiograms (≥20/month) at median follow-up of 8 months [IQR 8–10]. HCW sensitivity and specificity were 61% and 96%, respectively. Conclusion Development and deployment of a large scale RHD screening echocardiography training program within an existing public health system is feasible. Future program iterations are needed to improve HCW screening sensitivity and decrease the reliance on human resources.

Proteome-based investigation of O-GlcNAcylation in a C. elegans model of ageing and Alzheimer’s disease: Functional support for earlier hypothesis-generating findings

O-linked N-acetylglucosamine (O-GlcNAc) is a post-translational modification of serine and threonine residues on nuclear and cytoplasmic proteins. The identity of O-GlcNAcylated proteins during ageing and neurodegenerative disease remains incompletely defined. Thus, animal models play a crucial role for the systematic characterization and cataloguing of O-GlcNAc-modified proteins. In this study, proteomic analysis was performed to identify O-GlcNAc-modified proteins in both L1 larval and adult stages of wild-type N2 and of aex-3 p::Tau(V337M) transgenic Caenorhabditis elegans , a nematode model of ageing and Alzheimer’s disease (AD) using high-resolution nano-LC–ESI mass spectrometry. O-GlcNAcylated proteins identified in the N2 strain were mapped to nuclear- and RNA-related processes in both stages. In the tau-expressing strain, functional enrichment analysis of the identified proteins indicated a predominance of stress-response–related pathways. Together, these data present an analysis of O-GlcNAc-modified proteins across early development, adulthood, and a tau-related C. elegans model, providing a resource for future functional and comparative studies of O-GlcNAcylation in ageing and AD.

Dual-channel feature fusion network for sheep diseases question classification

To address the challenges of feature sparsity, semantic ambiguity, and insufficient feature extraction in sheep disease question classification, this paper proposes a novel model named Dual-Channel Feature Fusion Network for Sheep Diseases Question Classification (DFF-SDQC). The model leverages the CINO pre-trained model to generate dynamic word embeddings, thereby enriching semantic representations. Subsequently, global textual features are captured through BiLSTM, while deeper local contextual features are extracted using an attention mechanism. To further enhance the robustness and generalization of the model, a question-word attention mechanism is introduced, enabling the attention matrix to better capture the intentions expressed by interrogative words, thus strengthening the overall feature representation of the question. Finally, dual-channel feature information is fused to obtain the final textual representation. Experimental results on the D-SDQC and D-TQC datasets show that DFF-SDQC achieves an F1-score of 93.18% on D-SDQC, improving 2.22 percentage points over the strongest baseline, demonstrating the effectiveness of the dual-channel fusion and attention design.

Vibration-based bearing fault diagnosis in noisy conditions using matrix pencil mean frequency and multilayer perceptron neural networks

Bearings are critical elements in rotating machinery, where failures often accelerated by noise interference can cause severe economic and safety consequences. Reliable fault detection in noisy environments remains a major challenge. This paper proposes a novel approach combining advanced signal processing with machine learning to enhance diagnostic robustness. The matrix pencil (MP) method is applied to vibratory signals to extract matrix pencil mean frequency (MPMF) features, offering a noise-resilient spectral representation that highlights fault signatures. To improve generalization, additive white Gaussian noise (AWGN) is introduced both into the extracted features and directly into the vibratory signals, generating diverse datasets with varying signal-to-noise ratios (SNRs). This dual augmentation strategy effectively simulates real-world conditions and strengthens model resilience. A multilayer perceptron (MLP) classifier trained on the enriched feature set achieves outstanding performance, as validated on the University of Ottawa dataset (UORED-VAFCLS). The results demonstrate that the proposed method significantly enhances fault detection accuracy under noisy conditions, offering a promising solution for real-time, reliable condition monitoring in industrial applications.