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Minimal clinically important difference for cognitive scales in people with Parkinson’s disease with cognitive impairment
Perceived authenticity drives gaze behavior when watching AI-generated videos of physical scenes
Abstract The growing prevalence of realistic AI-generated videos on media platforms increasingly blurs the line between fact and fiction, eroding public trust. Understanding how people watch AI-generated videos offers a human-centered perspective for improving AI detection and guiding advancements in video generation. However, existing studies have not investigated human gaze behavior in response to AI-generated videos of physical scenes. Here, we collect and analyze the eye movements from 40 participants during video understanding and AI detection tasks involving a mix of real-world and AI-generated videos. We find that given the high realism of AI-generated videos, gaze behavior is driven less by the video’s actual authenticity and more by the viewer’s perception of its authenticity. Our results demonstrate that the mere awareness of potential AI generation may alter media consumption from passive viewing into an active search for anomalies.
Remote cognitive-motor dual-task training and cognition in mild cognitive impairment: a randomized controlled trial
Abstract The rising prevalence of Mild Cognitive Impairment (MCI) demands effective early interventions to delay progression to dementia. This randomized controlled trial evaluated the effects of remote cognitive-motor dual-task training on cognitive function and brain functional connectivity in older adults with MCI. Linear mixed-effects models (subjects as random effects; group, time, and their interaction as fixed effects) revealed significant Group × Time interactions for MoCA scores, Mini-Mental State Examination (MMSE) scores, and brain functional connectivity (FC) (all P < 0.001), indicating that intervention effects differed across groups over time. Post-hoc comparisons showed that the Remote Cognitive-Motor Group (RCMG) achieved significant improvements in both MoCA and MMSE scores (both P < 0.001), and these gains significantly exceeded those of the Control Group (CG) ( P < 0.001). The Remote Cognitive Group (RCG) showed a significant improvement in MoCA ( P < 0.001), whereas no significant improvement was observed in MMSE ( P = 0.188). For brain FC, the RCMG showed significantly greater post-intervention enhancement than the CG ( P < 0.001). Although the RCG showed a nominally significant interaction effect ( P = 0.016), this did not remain significant after correction for multiple comparisons, and no significant difference was observed between RCMG and RCG. Region-of-interest (ROI) analyses revealed that the RCMG exhibited significantly enhanced FC between multiple prefrontal and motor-related regions, including the mPFC, DLPFC, and PMC ( P < 0.05). Trial registration Study on rehabilitation training of cognitive-motor dual tasks for aging-related cognitive decline (ChiCTR2200064684) and the registration date was 10/14/2022.
Comparative nutritional analysis of newly developed interspecific citrus hybrids: multivariate insights for crop improvement
Development of complex technology for pyrite-cobalt concentrate processing by sodium carbonate-carbon reductive smelting
Rational design of a self-cleaning PES/UiO-66-NH2@g-C3N4 mixed-matrix membrane for high-efficiency oil–water separation
Trustworthy and ethical intrusion detection for healthcare internet of medical things using reinforcement learning and governance rules
Protective efficacy of dapagliflozin against schistosomiasis mansoni-induced liver pathology: an in vivo study
Abstract Schistosomiasis is a neglected tropical disease associated with significant morbidity and mortality. Treatment with praziquantel; the sole current medication for human schistosomiasis, is associated with incomplete resolution of Schistosoma mansoni -induced liver pathology. Therefore, there is a crucial need to introduce adjuvant agents that can mitigate the pathological events. This study aims to assess the hepatoprotective effects of the oral anti-diabetic drug dapagliflozin, alone and in combination with praziquantel. In the present research, combined dapagliflozin and praziquantel therapy caused significant decrease in adult worm burden and hepatic egg load, significant increase in the percent of mature and dead eggs and significant reduction in the percent of immature eggs in the small intestine, significant increase in hepatic levels of reduced glutathione and nitric oxide, significant attenuation of histopathological changes, and significant reduction in granuloma count and diameter. Additionally, immunohistochemical study of cleaved-caspase 3 revealed increased expression, compared to the untreated group. Our findings provide insight into dapagliflozin use as an advantageous adjuvant to praziquantel in alleviating schistosomal liver pathology. Future studies are needed to explore other pathways responsible for the hepatoprotective effects of dapagliflozin against Schistosoma-mansoni -induced liver fibrosis.
Reference-guided texture transfer with deformable convolutions for indoor image dehazing
An analysis of the genomic architecture of the worldwide Irish Wolfhound dogs
Spatiotemporal deformation patterns induced by ultradeep excavation in soft soil areas
Machine learning–driven data perturbation techniques for privacy-preserving data mining
Abstract The emergence of digital information has raised many issues over the release of sensitive personal information in data mining activities due to the rapid expansion of the digital information. Privacy-Preserving Data Mining (PPDM) has the goal of allowing a significant analysis of data, as well as safeguarding confidential characteristics. Conventional privacy methods, like anonymization usually decrease the usefulness of data, and cryptographic methods are expensive to compute. This paper suggests a perturbation-based PPDM model, which will combine the K-means + + clustering algorithm with the Flip-and-Rotation Perturbation (FRP) algorithm and decision model based on the Analytic Hierarchy Process (AHP). The suggested solution would take the dimensionality of features before perturbation to ensure increased privacy and maintain classification value. Experimental testing to check on the proposed method proves that it has better performance in accuracy, precision, recall and the F-measure compared to the conventional Naive Bayes and fuzzy-based methods. These findings do confirm that the proposed framework is a useful trade-off in the balance of data utility and protection of privacy in structured datasets.