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Generative model-based fundus photography translation for enhanced cross-device consistency

REPLY TO “The meaningfulness of searching for minimal exposure duration to understand visual perception”

Social marginalization risk and its negative association with socialising preferences in Japanese gamers

For people with a high risk of social withdrawal, such as Japanese “hikikomori” or NEET, socialising with others can be demanding. For some, however, an alternative to direct real-world socialisation comes in the form of videogames. Past research suggests that videogames could help by providing opportunities for virtual social interaction and relational wellbeing management. But would those with a high risk of social marginalization also withdraw from social interaction in games? Past research entertains both possibilities: individuals may avoid socialising in-game as they do offline, but the anonymity of online gaming could afford safer opportunities for social interaction. To better understand the potential of game-based interventions for audiences at high risk of social withdrawal, we investigated gaming preferences of Japanese players (N = 587) with varying levels of NEET-Hikikomori risk. We hypothesized that higher withdrawal risk, measured by the NHR Scale, could either positively or negatively relate to player socialisation and cooperation, as measured by the HEXAD and Game Traits scales. After accounting for age, gender and extraversion, results showed that higher NHR score was associated with lower HEXAD Socialiser, lower HEXAD Philanthropist and lower Game Traits Social orientation scores, suggesting a negative association between NEET-Hikikomori risk and player traits related to socialisation. Furthermore, exploratory analysis showed that a higher NHR score was associated with lower scores in HEXAD Achiever and Game Traits Challenges orientation, suggesting NEET-Hikikomori risk has a negative correlation with challenges and achievement-related gameplay. These findings demonstrate how understanding specific gamer personality types is essential for designing experiences that can act as bridges towards more socialisation, while avoiding the risk of worsening players’ conditions through misaligned design.

Optical spin-orbit interaction induced by magnetic textures

Abstract Contrary to the optical spin angular momentum (SAM), the role played by the orbital angular momentum (OAM) of light in magneto-optics remains largely unexplored. However, the SAM and OAM are known to be coupled when light interacts with non-homogeneous and non-isotropic materials. Here we predict that the OAM carried by each photon in a light beam is modified upon reflection on magnetic textures like skyrmions, and that the sign and magnitude of this variation are governed both by the SAM of the incident field and by the topology of the texture. Our predictions can be readily tested by performing circular dichroism measurements, and may provide new routes to read the topology of magnetization optically, or to shape light’s angular momentum with magnetism.

FcγR- and CD9-dependent synapse-engulfing microglia in the thalamus drive cognitive impairment following cortical brain damage in mice

Abstract Chronic neuroinflammation gives rise to diverse microglial states across the brain, yet how region-specific microglial remodeling contributes to cognitive dysfunction remains unclear. Here we report that synapse-engulfing microglia in the thalamus drive cognitive impairment after cortical brain damage in mice, primarily studied in females. Region-specific manipulations of microglia during the chronic phase show that reactive microglial changes in the thalamus, but not in the hippocampus, impair recognition memory. Single-cell RNA sequencing reveals an enrichment of synapse-engulfing CD9 hi microglia in the thalamus. Antibody-based CD9 blockade in the thalamus, as well as microglia-selective CD9 disruption, rescues thalamic synaptic loss, restores neuronal activity, and improves recognition memory. Further analysis shows that the blood-brain barrier disruption and subsequent γ-immunoglobulin (IgG) extravasation facilitate the generation of CD9 hi microglia in an Fcγ receptor III-dependent manner. These findings demonstrate that the induction of synapse-engulfing CD9 hi microglia in the thalamus by IgG/FcγRIII signaling drives recognition memory deficits following cortical damage.

Exposure route mediates toxicological effects of sulphur and fluxapyroxad fungicides in a non-target butterfly

Agricultural intensification and the associated increase in pesticide use have raised concerns about impacts on non-target insects. However, fungicides, despite their frequent application, remain poorly studied, particularly for herbivorous species. This is partly caused by the fact that they are considered less harmful than other pesticides. We here investigate the effects of three fungicides commonly used in viticulture, two sulphur- and one fluxapyroxad-based ones, on the cabbage white butterfly ( Pieris rapae ) under controlled laboratory conditions. Larvae were exposed to fungicides orally via treated host plants or by direct contact, and mortality and sublethal effects were assessed during development. In our study, oral exposure did not increase mortality relative to controls. Regarding sublethal effects, larvae exposed orally to sulphur-based fungicides exhibited a longer pupal development compared to controls. Additionally, larvae exposed to fluxapyroxad showed shorter development time and higher fat content than those exposed to sulphur-based fungicides. Contact exposure resulted in increased mortality relative to controls, with no significant differences among fungicides. These findings demonstrate that fungicide effects on non-target Lepidoptera are route-dependent, resulting in sublethal impacts via ingestion and lethal effects via contact exposure. Our results thus emphasize the need to consider (1) the dual risk posed by fungicides through both lethal and sublethal effects, (2) exposure route, and (3) fungicide-specific responses when assessing fungicide risks, particularly in agricultural systems where non-target Lepidoptera may occur. Detrimental effects of direct contact can be minimized through the timing of fungicide applications, thereby reducing the route-dependent risks identified in this study.

Stabilizing distributed OLTP under high contention via measurement-driven adaptive concurrency control

Abstract Distributed online transaction processing (OLTP) systems with high contending workloads can be extremely unstable, with negative impacts such as abort cascades, lock contention, and retry amplifications. Traditional schemes such as two-phase locking (2PL), optimistic concurrency control (OCC) and multi-version concurrency control (MVCC) rely on a fixed policy and are not flexible enough to cater for dynamic contention patterns, resulting in reduced throughput and high latency skew. In the present paper, an Adaptive Concurrency Control (ACC) framework called Measurement-Driven Adaptive Concurrency Control (MD-ACC) is proposed, where the concurrency control is interpreted as a feedback controlled process. The framework provides a thin control layer that is always updated with the metrics at runtime, including the conflict rate, abort rate, queue length and retry intensity. Real-time determination of the contention level can be achieved by using composite contention score, and stable adaptation can be achieved by using a hysteresis-based transition. MD-ACC dynamically applies coordinated policies like wait–abort regulation, adaptive retry backoff, and admission throttling based on observed conditions. The proposed MD-ACC is compared to classical baseline and adaptive/hybrid baseline (such as 2PL, OCC, MVCC, Adaptive OCC, Hybrid OCC/2PL, Dynamic Backoff OCC, CAS, and HACC). Experimental results on a distributed OLTP testbed demonstrate that the proposed approach indeed can provide up to 2.1 × higher throughput, much lower abort rates, with much better execution stability under high contention. These results provide insights into the applicability and deployability of current-state distributed transaction processing systems via measurement-based adaptation.

Unveiling surface and subsurface atomic vacancies in MoS2 with lateral force microscopy

Validating a novel driving simulation-based MWT against the standard MWT in an OSA-cohort challenged by CPAP-withdrawal (DS-MWT2) – Protocol for a monocentric, controlled, randomized, crossover trial

Background Excessive daytime sleepiness (EDS) is a major yet under-recognized contributor to road traffic accidents. Traditional diagnostic tools, such as the Maintenance of Wakefulness Test (MWT), assess wakefulness under passive conditions but may not accurately reflect real-world driving risks. To address this gap, we have piloted a Driving Simulation–based MWT (DS-MWT), designed to evaluate sleepiness in an ecologically valid driving scenario. The present study aims to validate the novel DS-MWT against the classical MWT in a functionally relevant cohort of patients with obstructive sleep apnoea (OSA). Methods The present monocentric, randomized, controlled, within-subject crossover trial will include 54 participants: 36 patients with OSA undergoing ≥ 7-day CPAP withdrawal (W) or continuation (C), and 18 healthy controls. The study employs a well-established CPAP-withdrawal model in patients with prior optimal treatment adherence to transiently induce EDS under controlled conditions. A healthy control group is included to enable between-group comparisons. The primary expected outcome is the difference in mean latencies between MWT and DS-MWT, determined during four standardized test sessions per condition. Secondary exploratory outcomes are defined as the presence, direction, and magnitude of differences or correlations between treatment status (CPAP withdrawal vs. continuation) and driving performance metrics (e.g., lateral position, speed, lane departures, etc.), EEG and eye-tracking features, subjective sleepiness scores, at-home polysomnography (PSG) parameters, and metabolomic biomarkers (saliva, exhaled breath and dried blood spots). Data will be analyzed using linear mixed models, repeated-measures ANOVA, and predictive modeling with cross-validation. Discussion This trial addresses a critical limitation in sleep and traffic medicine by introducing a realistic, supposedly more ecologically valid alternative to standard sleepiness assessment tools. The DS-MWT may enhance clinical decision-making regarding fitness to drive (FTD) and provide a framework for identifying physiological and behavioral markers of sleepiness in realistic conditions. Trial registration ClinicalTrials.gov Identifier: NCT06872593 , released on 12.03.2025, https://clinicaltrials.gov/study/NCT06872593 . Swiss National Clinical Trial Portal SNCTP000006301, released on 19.03.2025, https://www.humanforschung-schweiz.ch/en/trial-search/study-detail/66469 .

Genome-wide identification and expression profiling of the bHLH transcription factor family under salt stress and hormonal treatments in cowpea (Vigna unguiculata)

Publisher Correction: Microbial growth rates captured using Raman-SIP reveal a highly active subsurface biosphere fueled by serpentinization

Childhood trauma, substance use and mental health: Exploring differences across two Tunisian jails

Introduction The increasing rate of incarceration represents a significant public health issue worldwide. However, focusing on mental health issues and exploring gender differences among this vulnerable population is lacking in the literature. We aimed to assess differences in childhood trauma, substance use, and mental health among two Tunisian jails. Materials and methods Two cross-sectional studies were conducted in two Tunisian prisons with different gender compositions, one involving male prisoners in April 2023 and the other involving female prisoners in July 2024. We used Arabic-validated versions of the Adverse Childhood Experiences-International Questionnaire, the Hospital Anxiety and Depression Scale, and the Rosenberg Self-Esteem Scale. Substance use and suicidal thoughts were assessed through a yes or no question. The questionnaire was anonymous and participation was voluntary. Results A total of 568 prisoners answered the questionnaire. The majority were males (71%) with a median age of 31 years [2640]. We found that participants from female prison reported higher rates of childhood sexual abuse (25.6% vs 7.8%, p < 0.001). Physical abuse was more common among the male prison population (74.6% vs 58.8%, p < 0.001). Community and collective childhood violence were significantly more prevalent among participants from the male prison (94.8% vs 74.5%, p < 0.001 and 57.3% vs 43.6%, p = 0.003, respectively). Substance use was more prevalent among participants in the male prison across all substance types assessed. The female prison population reported more severe anxiety symptoms (69.1% vs 23.6%, p < 0.001) and depressive symptoms (96.4% vs 80.1%, p < 0.001). About 46% of participants in the female prison reported suicidal thoughts during incarceration, compared with 35.1% in the male prison (p = 0.014). The male prison population was more likely to have lower self-esteem (87.4% vs 69.1%, p < 0.001). Conclusion Implementing rehabilitation programs for prisoners is essential to reduce incarceration rates and mitigate these alarming negative outcomes.

AR/VR based digital exhibitions as a tool for successful presentation of new products and ideas at trade shows and expos

Directly probing the carrier transfer length in 2D-material transistors

A pathogen lncRNA secreted into rice sequesters a host miRNA for virulence

Author Correction: Vitellogenin receptor mediates heat adaptability of oocyte development in mud crabs and zebrafish

Validity of multiple human pose estimation tools for measuring knee impact angles in video-captured falls of older adults

Falls are a major cause of injury in older adults. Although bending the knees during a fall has been shown to reduce stress on the hip, knee motion during falls is not well understood because laboratory fall studies are limited by safety concerns and marker occlusion in motion capture systems. AI-based pose estimation may help overcome these challenges, but its accuracy in measuring joint angles during falls has not yet been validated. We evaluated three pose estimation models (OpenPose, VideoPose3D, WHAM) for analyzing knee kinematics in video-captured falls. A total of 121 videos of 13 older adults (64.0 ± 5.9 years) falling sideways, utilizing diverse fall strategies (knee block, stick-like, tuck-and-roll), in a lab setting were analyzed. Each model generated time series of knee angles from the videos, from which knee flexion angles at ground impact were calculated and compared to ground truth data from a motion capture system. Agreement with the ground truth was assessed using mean absolute error (MAE), mean absolute percentage error (MAPE), and bias, analyzed across viewing planes (sagittal vs. frontal) and leg sides (impact vs. opposite). WHAM demonstrated the highest accuracy (MAPE:13.61 ± 10.55%) with minimal bias (<10%), consistently performing well across all views and leg sides. OpenPose performed similar to WHAM in the sagittal view (MAPE:14.38 ± 9.63%) but poorly in the frontal view (MAPE:71.33 ± 17.24%) due to substantial underestimation (bias:-71.33 ± 17.24%). VideoPose3D showed poor accuracy across all conditions (MAPE:39.09 ± 20.54%). WHAM also characterized differences in knee flexion kinematics between fall strategies (e.g., least vs. most knee flexion) but did not fully reproduce side-specific kinematic differences between legs, particularly for tuck-and-roll falls. This is the first study to validate pose estimation algorithms for estimating knee impact angles from video-captured falls in older adults. Future work should fine-tune WHAM using fall-specific data to further improve its performance in tracking body movements during falls.

Uncertainty aware stochastic sampling for efficient object detection

Abstract The effectiveness of deep learning models is strongly influenced by the quality of training data. Traditional training approaches assume that all samples contribute equally to the learning process, leading to uniform data sampling. However, this assumption overlooks the substantial variation in informational content across samples. This paper introduces a novel and computationally lightweight data prioritization methodology for object detection that dynamically adjusts the sampling probability of training data according to its relevance during learning.The proposed Relative Detection Error (RDE) is a new temporal instability metric defined in a joint classification–regression setting, where classification corresponds to object category prediction and regression corresponds to bounding-box parameter estimation. By explicitly quantifying prediction variability over time, RDE identifies samples with higher learning value and guides an exploration-regularized stochastic sampling policy. As a result, the method improves both classification and localization accuracy while imposing minimal computational overhead and integrating seamlessly into standard training pipelines. The approach is validated using YOLO architectures on diverse datasets, demonstrating consistent improvements and strong generalization across models and domains. Experimental results show that prioritizing high-value samples yields higher F1 scores and mean Average Precision, alongside more efficient and stable convergence. The project source code is available at: https://kp-labs-bme.github.io/Object-Detection-Prioritization/

An integrated, scaled approach to resolve TSC2 variants of uncertain significance

Identification of shipping signals with few-shot learning: A distribution-aware approach

Effective identification of shipping signals in underwater environments is essential for maritime operations and ecosystem monitoring. Traditional models require extensive data for each ship type, posing a significant challenge owing to the difficulty of collecting diverse signals, particularly for vessels with security constraints. Few-shot learning offers a promising solution by enabling ships identification from minimal data through accurate template matching. This study proposes a novel few-shot learning approach that leverages stochastic information within and between ship types to improve identification accuracy using limited labeled data. The proposed model is designed based on a Siamese prototype network that integrates intra- and inter-category dissimilarities, employing cosine distance to estimate similarity while accounting for variance within the data. It achieves robust performance even when trained on limited samples with an average accuracy of 87.81% in five-way identification. In addition, its ability to generalize to unseen ship classes highlights its potential for real-time marine applications, further confirming the effectiveness of few-shot learning in constrained data scenarios. This approach provides valuable insights into designing adaptive, efficient systems for underwater signal detection and has potential applications across a wide range of acoustic processing tasks.