AI News Archive: July 27, 2026 — Part 13
Sourced from 500+ daily AI sources, scored by relevance.
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- Decentralised Consensus Learning Networks: SME Rotation Without Centralised Reward
Centralised reward signals dominate modern AI learning systems, but they impose a single external definition of correct or valuable knowledge. We present a decentralised, consensus-based multi-agent learning framework in which expertise emerges through peer validation rather than prescribed reward. ...
- Moral Hazard in Multi-Agent Language Models
Cooperation can fail when socially valuable effort is costly, weakly observable, and mainly benefits others. Drawing on Holmström's team moral-hazard model, we introduce the Dialogue Moral Hazard Game, a controlled textual game that operationalizes this hidden-action structure for language agents. I...
- EEGForceFusion: Joint Tokenised-Continuous Representation Learning for Subject-Independent Grasp Force Decoding
Brain-machine interfaces provide a link between neural activity and external devices, enabling restoration of motor function and advancing human-machine interaction using non-invasive electroencephalography (EEG). However, continuous grasp force decoding remains challenging due to complex temporal d...
- Leveling the Playing Field: Temporal Video Segmentation for Individuals with ADHD in Computing Education
Individuals with Attention-Deficit/Hyperactivity Disorder (ADHD) often face significant barriers in computing education. In asynchronous learning environments, instructional videos can impose high extraneous cognitive load, often relying on assumptions about sustained attention and working memory th...
- Mapping the Reddit Bot Ecosystem: Taxonomy and Evolution
Automated agents increasingly participate in online communities, yet their population structure and roles remain poorly understood. Using a dataset of 3,389 identified bots and their full activity histories, we construct a taxonomy of bot "species" on the news aggregation and social media platform R...
- Just Testing, Move Along: Evasion of LLM-based System Log Interpretation by Prompt Injection
Large Language Models (LLMs) are increasingly integrated into Security Operations Center (SOC) workflows, where they support analysts in tasks such as the interpretation of system logs. However, the ability of LLMs to directly process untrusted textual input also introduces new attack surfaces. In p...
- A Cybersecurity MLPS Large Language Model with Multi-Path Retrieval Fusion
The Multi-Level Protection Scheme (MLPS) is a foundational system in China's cybersecurity governance framework. Therefore, accurate analysis and understanding of MLPS requirements are essential. At present, MLPS analysis still relies mainly on manual interpretation of standards and rule-based tools...
- ContainmentBench: Trace-Based Evaluation of Post-Injection Containment in Tool-Using LLM Agents
Tool-using LLM agents process untrusted content, maintain memory, delegate across agents, and invoke side-effecting tools. Existing prompt-injection evaluations typically summarize security with terminal attack or policy outcomes, but equal endpoints can conceal different post-exposure traces and di...
- Development of Vision-Language Model-based GNSS Spoofing Detection for Autonomous Vehicle Navigation
Autonomous vehicles (AVs) depend on Global Navigation Satellite Systems (GNSS) for localization and navigation, making them vulnerable to spoofing attacks that can covertly redirect vehicles or induce unsafe maneuvers. In this paper, we develop the first Vision-Language Model (VLM)-based framework f...
- EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability
Due to the lack of systematic evaluations, we are not yet able to determine which AI-based Windows malware detector to deploy in production, since existing evaluations (i) differ in terms of data used for both training and testing; (ii) do not consider temporal analysis to showcase whether models wi...
- Verification-Conditioned Use: A Qualitative Study on How Generative AI Reshapes Learning, Autonomy, and Market Entry for Junior Software Developers
Objective: to investigate how the use of generative Artificial Intelligence (AI) tools affects the early stages of a career in software development, from the perspective of the newcomers themselves. Method: thirteen interns and junior developers were interviewed individually, by videoconference. Int...
- Industrial Practice of LLM-Based Test Case Carving and Assertion Generation (Experience Paper)
Enterprise regression testing for microservice systems is often constrained by incomplete or outdated documentation. In practice, QA engineers frequently rely on real execution traffic to reconstruct business scenarios; however, turning raw traffic into replayable regression tests with stable valida...
- RESTOR: Automated Test Oracle Generation for RESTful APIs via Reinforcement Learning
Modern REST API testing faces a critical challenge in defining reliable test oracles, particularly in agile industrial environments where formal specifications (e.g., OpenAPI) are frequently missing or outdated, and historical execution logs are unavailable for newly deployed endpoints. In this pape...
- Four Years of GenAI: How Educators and Industry Adapted Their Assessment Strategies
GenAI's ability to solve a wide range of software engineering tasks is reshaping the software industry, raising the question of what an entry-level software engineer looks like today. In this work, we investigate whether the core skills for entry-level engineers have changed with GenAI and how the a...
- Leveraging Gradient Reversal Loss and Multitask Learning for Datasets-Aware Audio Deepfake Detection
Recent advances in speech synthesis and voice conversion, which pose threats to security and privacy, have underscored the need for deepfake detection technology. Although existing detection systems achieve strong performance on individual datasets, they often fail to generalize across diverse datas...
- Qwen-Audio-3.0-TTS: Freely Controllable and Highly Robust Speech Synthesis with Multi-Stage Training Paradigm
In this report, we present Qwen-Audio-3.0-TTS, a production-oriented speech synthesis system that jointly advances content consistency, speaker similarity, prosodic naturalness, audio quality, controllability, multilingual coverage, efficiency, and robustness. It combines a 12.5~Hz low-frame-rate sp...
- Revisiting Vocos: That Phasiness Business in Time-Frequency Neural Vocoding
Recently, time-frequency neural vocoders have been approaching the state-of-the-art quality of time-domain neural vocoders. Vocos is a notable example due to its efficiency, but its audio quality lags behind the time-domain vocoders and the reasons remain debated. Thus, in this study, we revisit Voc...
- LaRec: Unleashing LLM-based Latent Reasoning for Generative Recommendation
Large Language Models (LLMs) have shown great promise in recommendation due to superior reasoning abilities. However, existing methods mainly rely on explicit Chain-of-Thought (CoT), resulting in verbose reasoning texts and inefficient response times. latent reasoning aims to balance efficiency by t...
- One Graph, Multiple Gains: Single High-Quality Item-Item Graph for Multimodal Recommendation
Multimodal recommendation leverages item multimodal features alongside collaborative signals to capture user preferences. While item-item graphs have become a key building block in advanced models, existing methods typically construct them with noisy similarity edges and limit their role to a single...
- Robust Interpretation of Historical Documents in Knowledge Graphs Through Query Inference and Execution
The emergence of Large Language Models (LLMs) has redefined how users interact with information in digital environments. However, their widespread and often indiscriminate integration has raised significant concerns regarding reliability and trustworthiness issues that are particularly critical when...
- Strategy-Aware Parameter-Efficient Adaptation for LLM-based Auto-Bidding
Advertising bidding has evolved from manual strategies to auto-bidding systems better adapted for large-scale, dynamic auction environments. While recent advances in Large Language Models (LLMs) offer strong reasoning for auto-bidding, existing methods suffer from shallow trajectory-text interaction...
- Unifying Generative Recall and Multi-Objective Ranking in a Single Decoder-Only Sequence
Modern industrial recommendation systems typically separate recall and ranking into two independent stages. Although this cascade supports corpus-level retrieval and fine-grained multi-objective scoring, it causes objective inconsistency, information loss at the candidate hand-off, and redundant use...
- CORE: A Unified Cascaded Ordinal Relevance Estimation Framework for E-commerce Search
Ranking relevance is a fundamental task in e-commerce search, directly affecting ranking quality and consumer experience. Although inherently an ordinal classification problem, it is commonly formulated as conventional multi-class classification, which overlooks the natural order among relevance lev...
- CogRec: Structure-Cognitive Fast-and-Slow Reasoning for Generative Recommendation
Semantic-ID-based generative recommendation represents each item as a hierarchical discrete token sequence and reformulates next-item prediction as constrained sequence generation. Existing methods, however, mainly use Semantic IDs as target sequences to be memorized, leaving the hierarchy, intra-la...
- OxygenREC-v2: Internalizing Discrimination into Generative Recommendation
Generative recommendation unifies retrieval and ranking within a single model by autoregressively decoding semantic identifier (SID) sequences. Yet reliably incorporating behavior signals from clicks, cart additions, and orders remains challenging. Existing approaches either jointly optimize generat...
- Integrating Factual and Normative Industrial Knowledge via Constraint-Aware Graph Attention for Process Plan Recommendation
Integrating heterogeneous industrial knowledge, including factual relations and decision constraints, remains a core challenge in industrial information systems. Machining process planning exemplifies this problem because engineers must select operations by combining material properties, feature cha...
- Secrecy Energy Efficiency for IRS-Assisted Low-Altitude Communications: A D3QN-PER Based Approach
To address the security and energy efficiency challenges in low-altitude economy (LAE) wireless communications, we develop a secure synergistic network integrating unmanned aerial vehicle (UAV) and intelligent reflecting surface (IRS), with an emphasis on maximizing secrecy energy efficiency (SEE) f...
- ConAlign: Conditional Alignment Framework for Balancing Biased and Unbiased Recommendation
Industry recommender systems trained on observational data suffer from various biases that create filter bubbles, causing user interests to collapse into narrow categories and severely degrading long-term engagement. While utilizing unbiased uniform data for debiasing has shown promise, existing met...
- SpecFormer: Mitigating Embedding and Attention Collapse via Spectral-Aware Transformer for Recommendation
Transformer architectures have achieved remarkable success across diverse domains; however, directly applying their standard self-attention mechanism to recommendation often yields suboptimal performance, sometimes even trailing behind well-designed simple recommendation models. In this paper, we re...
- Mosaic: A Fleet of User Embedding Specialists for Recommendation at Meta
User representation is one of the highest-leverage modeling problems in industrial recommendation systems: a single advancement in how users are encoded can propagate across retrieval, ranking, and integrity tasks at platform scale. Prior industrial user representation work builds either a single us...
- B-SMART-Former: An Explainable Transformer-Based Deep Learning Model for Predicting Drug-Drug Interactions Between Biotech and Small-Molecule Drugs
Drug-drug interactions between biotech and small-molecule drugs play a critical role in medication safety and therapeutic efficacy. However, most existing computational DDI prediction methods focus primarily on interactions between small-molecule drugs, leaving biotech-small-molecule interactions comparatively underexplored. In this study, we propose B-SMART-Former, an explainable deep learning framework for predicting interaction types between biotech and small-molecule drugs. The proposed framework integrates ChemBERTa embeddings and Morgan molecular fingerprints for small molecules with ProtBERT embeddings for biotech drugs, eliminating the need for similarity-based features while leveraging complementary molecular representations. These multimodal features are processed by a hybrid architecture that combines Transformer-based self-attention, residual convolutional learning, and a multi-layer perceptron classifier to capture both global contextual dependencies and local discriminative patterns. The model is formulated as a multi-class classification task and evaluated using stratified 10-fold cross-validation. To improve model transparency, Integrated Gradients is employed as a post-hoc explainability method to identify the molecular features that contribute most strongly to each prediction. Experimental results demonstrate that B-SMART-Former achieves a micro-averaged AUROC of 0.9978 and an AUPR of 0.9682 while relying solely on intrinsic molecular representations, remaining competitive with similarity-based approaches. The proposed framework offers an effective and explainable solution for biotech-small-molecule DDI prediction and provides a practical foundation for future computational drug interaction studies.
- Multimodal Phenotyping of Myofascial Pain Syndrome Using Rotational Shear Wave Elastography and Clinical Network Analysis
Myofascial pain syndrome (MPS) is characterized by increased muscle stiffness, trigger points, and functional limitations, yet clinical diagnosis remains largely subjective. Shear wave elastography (SWE) provides quantitative assessment of muscle mechanical properties, but its value for identifying biomechanical and clinical phenotypes of MPS is not fully established. This study evaluated whether stiffness parameters derived from multi-angle SWE can reliably characterize upper-trapezius anisotropy, and whether integrating SWE with bioimpedance spectroscopy (BIS), range of motion (ROM), and patient-reported outcomes (PROs) improves differentiation of MPS subgroups. Seventy-one adults completed upper-trapezius SWE, BIS, ROM assessments, and PRO measures. Clinically, 18 were classified as active MPS, 36 as latent, and 17 as normal. Shear wave speed measurements were modeled to estimate longitudinal (uL), transverse (uT), and anisotropy (uE) components. Reliability was examined using intraclass correlation coefficients. Unsupervised clustering and partial-correlation network analysis were applied to biomechanical and clinical variables. uT showed the strongest associations with BIS frequency parameters and ROM measures, indicating sensitivity to fascial composition, and mobility. Multimodal clustering incorporating uT with Fc or ROM identified subgroups with distinct tissue-level and functional characteristics. Network analysis demonstrated a progression in connectivity patterns, shifting from localized mechanical relationships to broader symptom-level coupling involving pain interference, sleep disturbance, emotional distress, and physical function. These findings indicate that SWE-derived stiffness parameters provide reliable, direction-specific quantification of trapezius mechanical properties. Combining SWE with impedance and mobility measures yields physiologically coherent MPS phenotypes that differ in both biomechanical features and clinical network structure, supporting more objective framework for characterizing MPS.
- Diagnosing Rejection Collapse via Uncertainty Decomposition
Standard uncertainty-informed rejection can unexpectedly trigger severe performance collapse, exposing localized vulnerabilities that common machine learning metrics typically do not show. We systematically diagnose this failure dynamic using Levodopa-Induced Dyskinesia prediction in Parkinson's Disease as a proof-of-concept. By training a heterogeneous ML ensemble, decomposing Aleatoric and Epistemic uncertainty and applying unsupervised subgroup discovery, we isolated the precise drivers of these atypical errors. Stratified error analysis revealed two divergent predictive regimes previously hidden by a global evaluation. While the models successfully extracted a predictive signal for one subgroup, the baseline features of a second subgroup lacked discriminative capacity, resulting in a high rate of confident misclassifications. Operating entirely below rejection thresholds, this single subgroup flatlined predictive metrics, driving the collapse of the global rejection curve. Ultimately, we demonstrate that atypical rejection failures stem from subgroup-specific data ambiguity rather than algorithmic deficiencies, making localized uncertainty-aware evaluation a critical methodological requirement prior to real-world deployment.
- Hemoglobin-to-Red Cell Distribution Width Ratio Predicts Three-Year Major Adverse Cardiovascular Events After Percutaneous Coronary Intervention: A Vietnamese Multicenter Cohort Study
Introduction Percutaneous coronary intervention (PCI) is a widely adopted strategy for managing coronary artery disease (CAD), leading to improved survival rates, particularly in developing countries. However, the increased survival of these patients imposes a substantial burden on long-term management, especially at the primary healthcare level. Recently, the hemoglobin-to-red cell distribution width ratio (HRR) has emerged as a potentially valuable prognostic biomarker for post-PCI patients. Despite its accessibility and cost-effectiveness, HRR has not been extensively investigated in resource-limited settings. Aim This study aimed to evaluate the prognostic value of the HRR in predicting 3-year major adverse cardiovascular events (MACE) among patients undergoing PCI who were managed at the primary healthcare level. Methods We conducted a multicenter prospective cohort study in Vietnam. A total of 626 post-PCI patients were ultimately included in the final analysis. The study commenced in October 2019 and concluded in October 2025. The association between HRR and 3-year MACE was evaluated using Cox proportional hazards regression models. Results The MACE incidence decreased progressively across the ascending HRR quartiles (p < 0.001). According to the unadjusted Cox model, each unit increase in HRR was associated with a lower risk of MACE (HR = 0.756; 95% CI, 0.703-0.814; p < 0.001). This association persisted after adjustment for age, sex, comorbidities (Model I: HR = 0.810; 95%CI: 0.750-0.878; p < 0.001). HRR outperformed its individual components, hemoglobin and red cell distribution width. Subgroup analyses confirmed the consistency of the association across clinically relevant strata. Calibration and decision-curve analyses further suggested acceptable risk estimation and potential clinical utility of HRR for 3-year MACE risk stratification. In the discriminative analysis for predicting 3-year MACE, the HRR had the highest area under the curve outperforming other inflammation-based indices. Conclusion A lower HRR was independently associated with a greater 3-year MACE risk in post-PCI patients. HRR outperforms commonly used leukocyte- and platelet-derived indices, highlighting its potential utility as a simple, cost-effective prognostic marker in resource-constrained healthcare settings.
- Multimorbidity and Frailty in Older Adults: A Comparative Study of Care Models and Policy Strategies in Spain, Italy, Chile, and Colombia
Background Population aging is rapidly increasing the burden of multimorbidity and frailty, yet there is limited evidence on how healthy aging policies are implemented across different demographic and health system contexts. Methods We conducted a mixed-methods comparative case study in Spain, Italy, Chile, and Colombia, integrating a Targeted review of policy documents and relevant literature, quantitative indicators, and expert interviews. Results Chile and Colombia showed faster aging dynamics (compound annual growth rates of 4.18% and 4.21%) than Italy (1.96%) and Spain (1.39%), with estimated doubling times of 17 versus 36-50 years. All four countries have established policy frameworks; implementation remains uneven. Key barriers included fragmented services, limited community care, workforce constraints, and misaligned financing. Conclusions The central challenge lies not in the absence of policy frameworks but in their effective implementation. Strengthening primary care, embedding frailty assessment, and improving health-social care integration are key priorities for systems facing rapid demographic transition.
- Visual Outcomes After Radiotherapy and Radiosurgery for Optic Pathway Hypothalamic Glioma: A Systematic Review and Meta-analysis
Background Optic pathway hypothalamic glioma (OPHG) can cause irreversible visual impairment. Radiotherapy and radiosurgery are used for progressive, recurrent, or treatment-refractory disease, but their effects on visual outcomes remain incompletely defined. Objective To synthesize visual outcomes following radiotherapy or radiosurgery for OPHG, with visual preservation as the primary outcome. Methods This prospectively registered systematic review and meta-analysis (PROSPERO CRD420261440136) followed the PRISMA 2020 statement. PubMed, Scopus, Web of Science, Embase, the Cochrane Library, Google Scholar, and ClinicalTrials.gov were searched from inception to 1 June 2026. Eligible studies included patients with OPHG or related optic pathway or hypothalamic gliomas treated with radiotherapy or radiosurgery and reporting extractable visual outcomes. Visual preservation was defined as stable or improved vision. Random-effects meta-analyses of logit-transformed proportions were performed. Results Forty-nine studies were included in the systematic review, of which 19 contributed 494 visual outcome observations to the meta-analysis. The pooled visual preservation rate was 75.6% (95% CI 65.1% to 83.7%; I2 = 71.6%). Pooled rates of visual improvement, stability, and worsening were 24.7% (95% CI 19.5% to 30.8%), 46.7% (95% CI 37.0% to 56.6%), and 20.6% (95% CI 12.7% to 31.6%), respectively. Preservation was higher in pure radiotherapy or radiosurgery cohorts than in mixed-treatment cohorts (85% vs 66%; subgroup p = 0.0104), although differences in cohort composition and treatment attribution may have influenced this finding. Conclusion Radiotherapy and radiosurgery were associated with preservation of vision in approximately three-quarters of evaluable outcomes, predominantly through visual stability rather than recovery. Standardized visual outcome definitions are needed to improve future comparisons and treatment evaluation.
- Death, Culture, and Conflict: A Qualitative Study on Sociocultural Practices and Their Implications for Maternal and Perinatal Death Surveillance in Eastern Democratic Republic of Congo
Background: Death is a social and cultural phenomenon whose meaning shapes how and whether losses are mourned, disclosed, and reported. These dynamics have direct implications for maternal and perinatal death surveillance and response (MPDSR), yet remain understudied, particularly in humanitarian contexts. Methods: This phenomenological qualitative study was conducted in two conflict-affected health zones in Eastern Democratic Republic of Congo. In-depth interviews (n=50) were conducted with bereaved family members of maternal or perinatal deaths, community leaders, and health providers to understand the socio-cultural practices surrounding death and the factors influencing MPDSR. Interviews were transcribed in French and analyzed using inductive thematic content analysis. Results: Four themes characterized the socio-cultural practices surrounding maternal and perinatal deaths: burial practices, mourning and bereavement traditions, rationale for these practices, and the impact of insecurity on customs. Burial and mourning practices differed markedly by type of death, with stillbirths and neonatal deaths accorded significantly less social recognition than maternal deaths. Deaths were commonly attributed to witchcraft or spiritual causality, or blame directed at mothers, husbands, and health providers. Active conflict further disrupted customary practices and eroded community trust. Collectively, these dynamics inhibit disclosure and reporting of deaths, undermining MPDSR case identification. Conclusion: Effective MPDSR in conflict-affected settings requires culturally responsive adaptation, community involvement in case identification, and trust in health sector actors. By documenting specific actors involved in burials, variations in burial and mourning practices, and how conflict changes socio-cultural practices, findings offer actionable entry points for strengthening MPDSR in conflict-affect health zones in Eastern DRC.
- Childhood immunisation coverage in rural and a tribal settings in southern India and assessing effect of the Covid19 pandemic
Purpose: Childhood immunisation witnessed reduced coverage and delays globally during the Covid19 pandemic. This study aimed to identify gaps in routine childhood immunisation in southern India and the effect of the pandemic in censused rural and tribal populations. Methods: This study was conducted between August and September 2021 in a hard-to-reach tribal block (Jawadhu Hills) and rural (Timiri) block in Tamil Nadu. The proportion of full immunisation coverage (FIC), age-appropriate coverage and vaccination delays were calculated in the pre-pandemic (prior to March 2020) and pandemic (lockdown) period. Results: Among 2746 children (aged <30 months, with immunisation cards available) surveyed in these populations, overall FIC among children between 12-23 months of age was 50.8% (95%CI: 46.6-55.0) in Jawadhu and 83.7%; (81.1-86.2) in Timiri. In Jawadhu, coverage of oral polio vaccine3 (OPV3) decreased from 78.3% (73.4-82.6) pre-pandemic to 70.0% (66.6-73.2) during the pandemic; inactivated polio vaccine2 (IPV2) from 76.9% (72.1-81.2) to 69.1% (65.5-72.4); Pentavalent3 from 78.0% (73.1-82.4) to 70.2% (66.8-73.5); and Measles containing vaccine1 (MCV1) from 74.6% (67.6-80.8) to 56.7% (53.3-60.1). In Timiri, only MCV1 coverage decreased from 92.9% (88.7-95.9) to 85.4% (83.3-87.3). Factors associated with not receiving MCV1 included due date of vaccination falling during the pandemic lock downs, higher birth order, younger and less educated parents, and residing in the tribal block. Conclusion: This study highlighted differences in routine childhood immunisation vaccine coverage and impact of the pandemic in a geographically remote tribal block in contrast to a more accessible, rural block. Resilient health systems are essential to ensure sustainability in gains in vaccination coverage, especially in remote areas, and should be part of pandemic preparedness.
- Patterns and Purpose of Social Media Use, and Their Association with Depression Among Students at Three Universities in Tanzania: A Cross-Sectional Study.
Social media has an estimated global user base of 3.5 billion. Adolescents, constituting over 90% of this group, and thereby university students. are extensively involved in social media use, emphasizing its significance in their daily lives. Excessive use of social media poses risks such as depression, affecting academic performance. The objective of this study was to determine the association between social media use and depression among university students in Urban Northern Tanzania. This was a cross-sectional study that involved university students from three universities. Data was collected through an online close-ended questionnaire. Statistical analysis included the use of frequencies, percentages, chi-square and bivariate logistic regression at 95% confidence intervals (CIs) and significance at p value <0.05. A total of 384 participants were enrolled in the study. 36.7% reported that they used social media for 1 to 3 hours daily. 58.1% of all participants reported high frequency of social media use for social purposes. 29.2% of university students who participated in the study were screened to have depression. The prevalence of depression increased with longer daily duration of social media use. Those using social media for more than 5 hours daily, had 3.049 times higher odds of being depressed. Moderate frequency of social media use for social purposes was linked to reduced depression. Daily duration of social media was significantly associated with depression levels among university students. Significant associations were also found between moderate use of social media for socializing, with lower risk of depression. We recommend that universities develop targeted mental health interventions and promote balanced, purposeful social media use among students.
- SSRI treatment of obsessive-compulsive disorder as motion across a bistable fold: a calibrated circuit plasticity model of response, remission, and augmentation
SSRIs are first-line for obsessive-compulsive disorder (OCD), yet benefit is partial, weeks-delayed, and severity-gated, and augmentation is empirical. The stable-states view is established but not drug-specific. We supply the missing coupling: a calibrated object joining serotonin pharmacokinetics, a basal-ganglia-thalamocortical circuit, and corticostriatal plasticity, in which OCD is an elevated fixed point and SSRI treatment moves this bistable state across a saddle-node fold (a tipping point). With ~35 constants fixed, two shared parameters reproduce six SSRIs (RMS 0.83 Y-BOCS points, 84 arm-timepoints) and predict a held-out trial (0.58). A fold-free model fits the means equally well, so the mean cannot separate them; they diverge only on individual-level signatures it cannot produce - a discrete, durable off-drug remission subgroup and critical slowing - with qualitative support in OCD and adjacent disorders, not yet tested as bistability signatures. These, not the mean, decide who remits and relapses; the same geometry reframes memantine and antipsychotic augmentation.
- Early life experiences are associated with later life DNA methylation signatures in the Health and Retirement Study
Long term associations of early life experiences with later life DNA methylation are understudied. In the U.S. Health and Retirement Study, participants self-reported early life experiences, including years in school, smoking during childhood, growing up in a rural area, and living with a grandparent. Later life DNA methylation was measured in blood for participants with a mean age of 69.6 years at 731,474 sites. We tested for associations between each early life experience with DNA methylation age acceleration, global and site-specific methylation, and enriched biological pathways. We compared results across early life experiences. Participants (N = 3,562) were 58.8% female and 68.1% non-Hispanic White. They reported 13 mean years in school, 18.3% smoked during childhood, 42.7% grew up in a rural area, and 26.8% lived with a grandparent. Fewer years in school (0.10, 95% CI: 0.06, 0.14) and smoking during childhood (0.65, 95% CI: 0.32, 0.97) were associated with accelerated GrimAge in later life, while living in a rural area and living with a grandparent were not associated. Early life experiences were associated (p<1x10-4) with distinct DNA methylation sites, specifically 574 sites for years in school, 20 for smoking during childhood, 49 for growing up in a rural area, and 23 for living with a grandparent. For example, one fewer year in school was associated with 0.47 (p-value = 4.61x10-15) lower percent methylation at cg07318158 in OTUD7B. Sites associated with our early life exposures were enriched for unique pathways. Years in school was enriched for embryonic development and cell structure pathways, smoking during childhood was enriched for nervous system development, exocytosis, and cell adhesion and structure pathways, growing up in a rural area was enriched for cell and vesicle processing pathways, and living with a grandparent was enriched for hormone regulation and protein breakdown pathways. Findings suggest our early life exposures are associated with unique DNA methylation patterns in later life, which can potentially allow for separate biomarker opportunities aimed at early intervention of adverse later life outcomes associated with these exposures.
- County-Level Risk Mapping of Alpha-Gal Syndrome Using a Bayesian Proxy Approach
Alpha-gal syndrome (AGS) is a tick bite-associated allergic condition induced by the lone star tick (Amblyomma americanum). Illinois had no confirmed AGS case data as of January 2026, when mandatory reporting began under the state's TICK Act, leaving practitioners without data to guide screening or resource allocation. We constructed a county-level proxy risk score using a Bayesian conditional autoregressive spatial model applied to three Illinois surveillance sources from 2019-2022: tick abundance, ehrlichiosis cases, and tick establishment status, all linked by a shared vector. Ehrlichiosis was modeled as a population-adjusted rate, ticks as a relative-intensity index, and establishment status as a fixed ecological component. The combined risk score identified a high-risk cluster in far southern Illinois that remained stable across alternative weighting scenarios. This approach is transferable to jurisdictions lacking direct AGS surveillance and offers a starting point for clinician education pending validation against confirmed case data.
- Addictive plasmids drive hospital transmission of mupirocin-resistant Staphylococcus aureus
Background Mupirocin, a widely used topical agent for decolonization of Staphylococcus aureus, is increasingly compromised by resistance. Although plasmid-mediated mupirocin resistance is a recognized cause of decolonization failure, its role in facilitating hospital-wide transmission is unknown. Methods We conducted genomic surveillance of S. aureus at two interconnected urban hospitals where mupirocin decolonization is routine. Genome sequencing of >10,000 isolates was integrated with patient data to identify transmission and resistance determinants. Bacterial phenotypes and fitness were evaluated in vitro and in murine colonization models. Findings Genome sequencing identified 475 hospital transmission events; none were detected by conventional surveillance. The mupA (ileS2) resistance determinant, carried on conjugative plasmids, was enriched eightfold in methicillin-resistant S. aureus (MRSA) relative to methicillin-susceptible strains. mupA was associated with nearly a threefold greater chance of hospital transmission, especially within endemic healthcare-associated MRSA lineages, and was enriched twofold in hospital-onset infections compared with admission colonizing isolates. Multiple independently evolved inactivating mutations in the essential chromosomal gene ileS1 co-occurred with mupA, creating plasmid addiction in which mupA became indispensable for bacterial survival. Addiction arose most frequently within the dominant community-acquired MRSA lineage, where plasmid carriage reduced colonization fitness in mice. Plasmid-containing strains exhibited stringent-response activation, explaining the fitness costs and collateral tolerance to disinfectants, such as ethanol and peroxide. Although addiction reduced S. aureus fitness, it increased plasmid transfer, and addicted variants spread across hosts, demonstrating adaptation that mitigates these costs. Unexpectedly, we identified a mupirocin-dependent vulnerability to isoleucine limitation, revealing a potential strategy to target mupA-mediated resistance. Interpretation Plasmids promote hospital transmission of mupirocin-resistant S. aureus and create an evolutionary trap in which antibiotic use selects for bacterial dependence on otherwise costly resistance elements. This dependence revealed a collateral bacterial vulnerability that could be exploited to target resistant strains and preserve the effectiveness of mupirocin.
- What Patients Reward and Punish in Physician Communication: A Framework-Based Analysis of 62,319 Indian Reviews
Background: Patient-generated online reviews contain detailed accounts of physician communication that remain systematically underused in health services research. Prior computational analyses have either applied service quality frameworks without grounding in clinical communication theory, or have been geographically and specialty-restricted. Objective: To examine which physician communication dimensions predict patient recommendation, switching intent, and retention across specialties and cities in India. Methods: We analyzed 62,319 patient reviews from the Practo platform (10 cities, 8 specialties) coded against established clinical communication frameworks, the Kalamazoo Consensus Statement, Calgary-Cambridge Guide, and SPIKES protocol, adapted into a 12-dimension codebook. Expert confirmatory review on 200 stratified reviews yielded 98.5% agreement between reviewer and model labels across 11 communication dimensions. Binary recommendation was designated the primary outcome; recommendation intensity is reported as secondary. Logistic regression with 1,000 bootstrap iterations examined associations between communication dimensions and recommendation and switching intent, adjusting for treatment outcome, cost, wait time, and specialty. Results: Reassurance (OR 2.07, 95% CI 1.86-2.42) and empathy (OR 1.89, 95% CI 1.79-2.00) were the strongest positive predictors of recommendation. Excessive directiveness (OR 0.62, 95% CI 0.60-0.64) and rushedness (OR 0.65, 95% CI 0.64-0.67) were the dominant negative predictors; directiveness was present in 33.3% of switching-intent reviews. Cost asymmetry was pronounced: cost concerns reduced recommendation rates by 54.2 percentage points; positive cost comments increased recommendation by 6.2 points. Specialty variance in recommendation intensity exceeded city variance (ratio 2.63). Conclusions: Excessive directiveness and rushedness were the communication behaviors most strongly associated with adverse behavioral outcomes in this corpus; reassurance and empathy are the strongest drivers of recommendation. These effects are modified by specialty context in ways that align with Kalamazoo, Calgary-Cambridge, and SPIKES theoretical predictions. The specialty-stratified findings provide a direct empirical basis for competency-based communication curricula in undergraduate and postgraduate medical education.
- The Impact of Self-Reported Health Factors on Behavioural Difficulties in School-Aged Children: Findings from the HAPPEN Pan-Wales Cohort Using Data Linkage
This study aimed to examine factors associated with behavioural difficulties among school-aged children (aged 7 -11) using self-reported survey data linked with education data from a national cohort study of over 50,000 children (the HAPPEN national cohort) in Wales, UK between 2014 and 2024. Data include self-reported health and wellbeing linked to additional learning needs (ALN) status collected from education data in Wales. Measures included self-reported time spent on screens, frequency of sugary snack, fizzy drink and takeaway food consumption, physical activity levels, sleep duration and behavioural difficulties (as measured by the Me and My Feelings Survey). Associations were analysed using descriptive trends, K-means cluster analysis and multivariate regression modelling. Findings showed a rise in behavioural difficulties during and post Covid, which are now improving in Wales. Cluster analysis identified that children with poor sleep and high takeaway and fizzy drinks dietary habits had the highest levels of behavioural difficulties, a trend that remained significant after adjusting for all potential confounders. Physical activity associated with reduced behavioural difficulty, with 5-6 days of activity associated with the lowest difficulty scores. These findings suggest that behavioural difficulty is driven by health behaviours especially sleep and diet rather than single factors. The results highlight the need for integrated public health approaches that address dietary quality and sleep hygiene rather than focusing on single factors in isolation. Promoting more balanced daily activity patterns, promoting good sleep routines, improving food environments and building movement into the school day may have important benefits for childrens behaviour.
- Patterns of healthcare use in people with narcolepsy: a population-based cohort study in England
People with narcolepsy experience delays in diagnosis and inconsistent post-diagnosis care, but the pattern and scale of their healthcare use is poorly described. In this population-based cohort study, we used primary care and linked hospital activity data to compare healthcare use in people with narcolepsy (n=2,772) and a matched comparison group in England (n=13,860). Narcolepsy was defined by a first coded record in primary care or admitted care data between 02 January 1998 and 31 December 2019; this date was the index date for both groups. People with narcolepsy had approximately double the rate of healthcare use in the period from five years before to five years after the index date. Annually, this corresponded on average to an additional 1.9 (95% confidence interval (CI) 1.8-2.0) outpatient events, 0.36 (95% CI 0.30-0.42) admitted patient care events, 0.25 (95% CI 0.22-0.29) Accident & Emergency events and 4.3 (95% CI 3.9-4.7) primary care events per person. Service use in all settings peaked at index and remained elevated for at least 15 years either side. The elevated rates of possible-sleep related outpatient events (respiratory, neurology paediatric, and ear nose & throat) peaked in the year including and after the index date, at 1.50 (95% CI 1.41-1.59); before declining to <0.5 visits per person-year after five years. Our findings of sustained elevated use of healthcare by people with narcolepsy across NHS settings may reflect diagnostic delay, comorbidities, and ongoing narcolepsy-related healthcare needs being met largely outside specialist sleep care, highlighting opportunities to improve healthcare services.
- Dual foundation models for accelerometry predict future health
Wrist accelerometers are ubiquitous and capture activity, sleep, and cardiorespiratory motion, but how this relates to future disease across the phenome is unclear. We encoded one week of UK Biobank accelerometry from 97,696 participants using two frozen self-supervised models and trained a multilabel survival model on these embeddings with participant age and sex for 390 outcomes. In 5,253 held-out participants, mean concordance was 0.688. A single component explained 76% of predicted risk variance and was associated with future disease burden and mortality. Nevertheless, disease-specific scores added discrimination beyond this shared axis for 85 of 101 well-powered outcomes. A higher-powered full-cohort out-of-fold analysis identified prodromal neurodegenerative signatures, strongest for Parkinson's disease (five-year time-dependent AUROC 0.90; 428 cases), with limited attenuation after lead-time washout. Daytime movement contributed most, whereas sleep-related and genetic information contributed selectively. These findings establish one week of wrist movement as a scalable, low-cost representation of future health for wearable-based risk assessment.
- Statistical Analysis Plan for the SImPLE Trial: A Study to test if illustrations and plain language ImProve cLinical trial Education
Background Traditional participant information and consent forms (PICFs) have changed little over time and are often lengthy and complex. A novel consent process incorporating plain language, an infographic brochure and a short explanatory video was developed to improve communication of clinical trial information. The SImPLE study was designed to evaluate whether this novel consent process improves comprehension of clinical trial information, participant engagement and the consent experience compared with a traditional PICF. Design and Setting SImPLE is a randomised two-period crossover study involving adults receiving cancer treatment who are potentially eligible for clinical trial participation. Study materials were developed for the ANZadapt (ANZUP2101) prostate cancer clinical trial; however, participants are not being recruited to ANZadapt itself. Participants are randomised to receive either a standard 17-page NHMRC-style PICF followed by a novel consent process, or the reverse sequence, with a washout period of at least 7 days between study periods. The novel consent process comprises a simplified 4-page PICF, infographic brochure and short explanatory video. Twenty-four hours following each PICF review, participants complete a comprehension assessment and structured interview. Outcomes and Endpoints The primary endpoint is comprehension of clinical trial information, measured using a study-specific 22-item questionnaire comprising 10 true/false and 12 multiple-choice questions. Secondary endpoints include acceptability, assessed through participant preference for communication format, and engagement, assessed through confidence in explaining the trial and likelihood of agreeing to participate. Planned Analyses The primary analysis will compare comprehension scores between the novel and standard consent approaches using a paired t-test among participants completing both study periods. Secondary outcomes relating to acceptability and engagement will be summarised descriptively and compared between consent approaches as appropriate. Sensitivity analyses will be performed to assess the robustness of the primary findings and the potential impact of crossover design effects.
- A Longitudinal Neuromelanin MRI Processing Framework Reduces Measurement Variability and Improves Precision in Parkinson's Disease
Background: Neuromelanin-MRI enables in vivo assessment of the substantia nigra (SN) and locus coeruleus (LC) in individuals with Parkinson's disease (PD), yet longitudinal studies rely on cross-sectional processing that may introduce measurement variability and confound estimates of change over time. Objectives: In this paper, a longitudinal neuromelanin-MRI processing framework is presented that is designed and validated to improve measurement stability and reduce processing-related variability across repeated scans. Methods: Imaging and clinical data from the Quebec Parkinson Network were analyzed in 268 participants (199 PD, 69 controls), including a longitudinal subset of 74 participants (49 PD, 25 controls) scanned approximately one year apart. Validation experiments evaluated slice-by-slice intensity normalization for slice dependent intensity variation, bias field correction for LC signal asymmetry, and the effects of longitudinal registration on measurement stability and PD-control discrimination. Results: Slice-by-slice intensity normalization significantly reduced brainstem intensity variability by 3.6%. A systematic leftward signal asymmetry was observed in the LC and persisted following N4 bias field correction, suggesting a scanner-related effect not captured by conventional bias field modeling. Longitudinal registration reduced annualized change variability by 25-36% for SN_CR and 27-34% for LC_CR metrics in controls, indicating improved within-subject measurement stability. Residual variability was also reduced for contrast-based metrics by up to 28%. Longitudinal registration generally produced larger PD-control effect sizes at baseline and follow-up, particularly for SN volume metrics. However, no significant method x group x time interactions were observed, indicating that estimated longitudinal trajectories did not differ significantly between longitudinal and conventional cross-sectional processing. Conclusions: Longitudinal registration reduced technical variability and improved the precision of NM-MRI measurements. Although it did not significantly enhance detection of longitudinal PD-control differences over the follow-up interval examined here, it provides a more robust framework for longitudinal NM-MRI studies and may improve sensitivity to subtler biological effects in future investigations.