PLoS ONE最新文献

[This corrects the article DOI: 10.1371/journal.pone.0259597.].

Callosal abnormalities disrupt interhemispheric communications that impair the recovery of a series of neurological and psychiatric disorders. Understanding interhemispheric communication could help identify treatments for disorders with dysfunctional callosal transmission. This study aimed to validate the sensitivity of a TMS-EEG-based measure of interhemispheric signal propagation (ISP) to GABAB receptor modulation, while independently replicating the pharmacological mechanisms of interhemispheric inhibition. A group of 29 participants received an oral dose of either baclofen, a GABAB receptor agonist, or a placebo. Before and after drug intake, single-pulse TMS-EEG was applied to assess ISP in both the motor and prefrontal regions. Our findings indicated that baclofen increased interhemispheric inhibition over the motor region, indexed by significantly decreased ISP (p = 0.037). No significant effect was observed in the dorsolateral prefrontal region (p = 0.186), suggesting regional specificity in GABAB receptor modulation of ISP. These findings uncovered the pharmacological mechanisms of interhemispheric inhibition as indexed by ISP. They also highlight the potential of ISP for characterising interhemispheric communication, which could inform the treatment for a series of disorders with dysfunctional callosal transmission.

Artificial intelligence-generated content (AIGC) is increasingly embedded in short-video platforms, where algorithmic recommendation systems expose users to high volumes of rapidly produced synthetic media. Although prior research has examined generative AI from the perspectives of creativity, productivity, and technology adoption, less is known about how ordinary users evaluate repeated exposure to AI-generated short videos. This study develops and tests an appraisal-based model of AI content fatigue, distinguishing perceived AIGC saturation from users' appraisals of low-quality prevalence, narrative homogenization, and content overload. A cross-sectional online survey was conducted among Chinese short-video users in May 2026. After excluding invalid responses, 513 valid questionnaires were analyzed using confirmatory factor analysis and structural equation modeling. The adjusted structural model showed acceptable fit to the data, χ²(1321) = 1991.541, χ²/df = 1.508, CFI = .946, TLI = .929, IFI = .947, and RMSEA = .031. Perceived AIGC saturation was positively associated with perceived low-quality prevalence, perceived narrative homogenization, and perceived content overload. Perceived low-quality prevalence had the largest estimated coefficient, followed by perceived content overload, whereas perceived narrative homogenization was not supported under bootstrap inference. AI content fatigue was negatively associated with satisfaction and continued viewing intention. Bootstrap analyses indicated selected path-specific indirect associations from perceived AIGC saturation to AI content fatigue, satisfaction, and continued viewing intention. Indirect pathways through perceived low-quality prevalence and perceived content overload were supported, whereas those through perceived narrative homogenization were not. The findings indicate that fatigue in AIGC-rich short-video environments is not simply a response to AI-generated content itself. It is more closely related to users' appraisals of content quality, repetitiveness, and cognitive burden. The study contributes to social media fatigue research by conceptualizing AI content fatigue as a content-specific evaluative state and offers implications for platform governance, AIGC labeling, recommendation diversity, and quality-sensitive moderation.

Semantic segmentation of high-resolution remote sensing imagery in complex urban scenes often suffers from insufficient modeling of boundary neighborhoods, causing ambiguity near class interfaces and weakening the continuity of elongated structures. To address this issue, we propose a Boundary-Topology-Guided Structural Refinement Network (BTSR-Net). Built upon HRNet-OCR, BTSR-Net introduces a Geometric Boundary-Distance Branch (GBD) with joint boundary-distance supervision to capture spatial transitions around class boundaries, a Multi-scale Topology-Guided Structural Fusion (MTSF) module to enhance connectivity representation of elongated structures, and a dual-stream structural refinement mechanism to perform local residual correction within a boundary-distance-defined refinement narrow-band. Experiments on the ISPRS Potsdam and Vaihingen datasets demonstrate improvements in overall segmentation accuracy, boundary quality, and structural connectivity. On Potsdam, BTSR-Net achieves 84.70% mIoU, 43.17% mBIoU, and 81.63% clDice, exceeding the baseline by 2.91, 5.66, and 3.74 percentage points, respectively. On Vaihingen, it achieves 93.94% mIoU, 73.90% mBIoU, and 95.46% clDice, with corresponding improvements of 2.27, 9.39, and 2.12 percentage points. These results demonstrate the effectiveness of BTSR-Net for structure-sensitive semantic segmentation in high-resolution remote sensing imagery.

Joint failures can disrupt gait coordination, velocity tracking, and body stability in quadruped robots by reducing the available control authority. This study investigates faults in which joints remain locked at fixed angles and presents Adjudicate Reconfiguration and Evolving Regularization Meta Reinforcement Learning (ARER-MetaRL). The framework combines task inference from context, modular routing tailored to each task through an Adjudicate Reconfiguration (AR) network, and Evolving Gradient Regularization (ER) to support rapid adaptation. Tasks differ in the identities and locking angles of the faulty joints, the number of simultaneous faults, the commanded motion direction, and the terrain. Experiments in NVIDIA Isaac Sim cover all 12 actuated joints, with 0-3 stuck joints during meta-training and severity tests involving up to 6 simultaneously stuck joints. ARER-MetaRL achieves higher Mean Episode Linear Velocity Tracking Reward (MELV) and Mean Episode Roll-Pitch Tracking Reward (MERP) than single-task reinforcement learning, ablated variants, and the evaluated learning and adaptation baselines. Cumulative rewards further demonstrate the benefit of combining the proposed components. In the study of fault severity, ARER-MetaRL achieves MELV/MERP values of 0.86/0.84 with one stuck joint and 0.55/0.54 with six stuck joints, compared with 0.21/0.18 and 0.05/0.03, respectively, for the single-task baseline. These results show improved adaptation across different configurations of locked joints and terrain conditions.