Abstract ：

Background: Working memory refers to a process of temporary storage and manipulation of information to support planning, decision-making, and action. Frequently comorbid alcohol misuse and sleep deficiency have both been associated with working memory deficits. However, how alcohol misuse and sleep deficiency interact to impact working memory remains unclear. In this study, we aim to investigate the neural processes inter-relating alcohol misuse, sleep deficiency and working memory. Methods: We curated the Human Connectome Project (HCP) dataset and investigated the neural correlation of working memory in link with alcohol use severity and sleep deficiency in 991 young adults (521 women). The two were indexed by the first principal component (PC1) of principal component analysis of all drinking metrics and Pittsburgh Sleep Quality Index (PSQI) score, respectively. We processed the imaging data with published routines and evaluated the results with a corrected threshold. We used path model to characterize the inter-relationship between the clinical, behavioral, and neural measures, and explored sex differences in the findings. Results: In whole-brain regression, we identified beta estimates of dorsolateral prefrontal cortex response (DLPFC beta) to 2- vs. 0-back in correlation with PC1. The DLPFC showed higher activation in positive correlation with PC1 across men and women (r=0.16, P<0.001). Path analyses showed the model PC1- DLPFC beta- differences in reaction time (2- minus 0-back; RT2-0) of correct trials- differences in critical success index (2- minus 0-back; CSI2-0) with the best fit. In women alone, in addition to the DLPFC, a cluster in the superior colliculus (SC) showed a significant negative correlation with the PSQI score (r=-0.23, P<0.001), and the path model showed the inter-relationship of PC1, PSQI score, DLPFC and SC beta's, and CSI2-0 in women. Conclusions: Alcohol misuse may involve higher DLPFC activation in functional compensation, whereas, in women only, sleep deficiency affects 2-back memory by depressing SC activity. In women only, path model suggests inter-related impact of drinking severity and sleep deficiency on 2-back memory. These findings suggest potential sex differences in the impact of drinking and sleep problems on working memory that need to be further investigated.

Keyword ：

alcohol dependence alcohol use disorder (AUD) insomnia Working memory

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Abstract ：

The rapid advancement of generative artificial intelligence (GAI) and the extensive use of social media have transformed how students engage with educational materials and interact with their peers. Collaborative learning (CL) platforms, empowered by artificial intelligence (AI) algorithms, have gained popularity due to their potential to enhance learning outcomes and provide personalised educational experiences. This research examines the effects of generative AI (ChatGPT-4) and social media use on young students' academic performance and psychological well-being, focusing on CL. The study conceptual framework was examined based on a sample of 441 Chinese university students. The statistical technique PLS-SEM is put into practice to measure the structural framework of academic performance and psychological well-being. The findings of this study show that generative AI (ChatGPT-4) and social media positively influence young students' academic performance and psychological well-being. Additionally, the results of this research study show that CL positively mediates between social media, academic performance and psychological well-being. Conversely, it negatively mediates the association between generative AI (ChatGPT-4), academic performance (AP), and psychological well-being. The findings can facilitate a better understanding of the implications of technologies in the educational context and subsequently aid in formulating evidence-based strategies to optimise their impact on students's academic success and well-being.

Keyword ：

ChatGPT-4 higher education psychological well-being collaborative learning social media academic performance

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Abstract ：

Speeding related-crashes have caused numerous fatalities and become a worldwide health problem. This study aims at investigating the factors affecting injury severity in speeding-related crashes, considering the spatial heterogeneity on rural and urban roads. The data on speeding-related crashes were extracted from the Crash Report Sampling System (CRSS) between 2018 to 2020, including information about the characteristics of drivers, vehicles, crashes, roads, and the environment. Two separate correlated random parameter order probit models with heterogeneity in means (CRPOPHM) were established for speeding-related crashes on rural and urban roads, and the plausibility of separately modelling injury severity was tested by a set of LR tests. The model results showed that some factors were significant in both models, while others were significant in only one particular model. For example, heavy trucks and weekends are significant in the rural model; and young drivers, rear-end crashes, speed limits, and nights with lit roads are significant in the urban model. The results of correlations and heterogeneity in means of random parameters of the two models also showed some similarities and differences for speeding-related crashes on rural and urban roads. Based on these results, some policy recommendations are proposed to mitigate the injury severity of speeding-related crashes.

Keyword ：

Injury severity speeding-related crashes heterogeneity in means rural and urban roads CROPOHM models correlations

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Abstract ：

Alcohol misuse is associated with altered punishment and reward processing. Here, we investigated neural network responses to reward and punishment and the molecular profiles of the connectivity features predicting alcohol use severity in young adults. We curated the Human Connectome Project data and employed connectome-based predictive modeling (CPM) to examine how functional connectivity (FC) features during wins and losses are associated with alcohol use severity, quantified by Semi-Structured Assessment for the Genetics of Alcoholism, in 981 young adults. We combined the CPM findings and the JuSpace toolbox to characterize the molecular profiles of the network connectivity features of alcohol use severity. The connectomics predicting alcohol use severity appeared specific, comprising less than 0.12% of all features, including medial frontal, motor/sensory, and cerebellum/brainstem networks during punishment processing and medial frontal, fronto-parietal, and motor/sensory networks during reward processing. Spatial correlation analyses showed that these networks were associated predominantly with serotonergic and GABAa signaling. To conclude, a distinct pattern of network connectivity predicted alcohol use severity in young adult drinkers. These "neural fingerprints" elucidate how alcohol misuse impacts the brain and provide evidence of new targets for future intervention.

Keyword ：

neurotransmitter receptor alcohol misuse fMRI alcohol use disorder connectome

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Dual phase high entropy ceramics (DPHECs) exhibit excellent high temperature mechanical properties and oxidation resistance. Nevertheless, they cannot be applied in the structural components on large scale due to their intrinsic brittleness. Hence, DPHEC coating is a necessary development direction for industrial applications. In this study, we first fabricated micron-order DPHEC powder with high quality by precursor synthesis, spray drying (SD), and induction plasma spheroidization (IPS). The DPHEC coating was prepared by supersonic atmospheric plasma spraying (SAPS). The microstructure, phase compositions, and mechanical properties of the DPHEC powder and coating are investigated in detail. Results show that the SD &IPS powder and SAPS coating are all composed of HEB and HEC phases, showing slight composition segregation and typical global eutectic microstructure. The nanohardness and Young ' s modulus are 22.17 +/- 2.76 GPa and 309.79 +/- 35.71 GPa, respectively. The fracture toughness was calculated to be 2.23 +/- 0.17 MPa m 1/2 from the energy analysis method. The mechanical properties are enhanced by the solid solution and fine grain strengthening effects and are weakened by the intrinsic defects of plasma sprayed coating. This work provides a new method and a potential material for thermal spraying ultra -high temperature ceramic coatings.

Keyword ：

Plasma spraying High entropy ceramics Spray drying Induction plasma spheroidization

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Abstract ：

Non-invasive fundus images can be used to diagnose various fundus diseases, such as high myopia (HM). Existing deep learning-based research mainly relies on data to drive the model to learn key features. However, the data related to HM is limited (especially for young children), making it difficult for deep networks to accurately focus on key features. Hence, we propose a prior knowledge-guided deep learning network for pediatric HM detection. It comprises four modules: (1) Prior Feature-Based Channel Fusion: This module extracts key features (brightness, edges, texture) from fundus images using image processing methods to obtain corresponding single-channel slices. Through channel-level feature fusion, these slices are used to construct multiple sets of feature-enhanced datasets. (2) Global Fundus Feature Extraction: It uses residual blocks to build the backbone, and builds a U-shaped attention component based on the U-shaped network. This module extracts the global and context information of the original fundus image to obtain a global feature map. (3) Knowledge-Guided Attention Generation: The residual structure is employed to further extract the hidden features of the feature-enhanced data, thereby obtaining local key feature maps. (4) Pediatric HM Classification: By combining local key feature maps (obtained in module 3) with global feature maps (obtained in module 2) through spatial attention mechanism, the deep network is guided to complete the classification task of pediatric HM. Extensive experiments on real-world datasets demonstrate the effectiveness of our method (accuracy is 0.921, F1 score is 0.903).

Keyword ：

deep learning knowledge guidance fundus images pediatric high myopia diagnosis

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Abstract ：

Background: The hypothalamus plays a crucial role in regulating sleep-wake cycle and motivated behavior. Sleep disturbance is associated with impairment in cognitive and affective functions. However, how hypothalamic dysfunction may contribute to inter-related sleep, cognitive, and emotional deficits remain unclear. Methods: We curated the Human Connectome Project dataset and investigated how hypothalamic resting state functional connectivities (rsFC) were associated with sleep dysfunction, as evaluated by the Pittsburgh Sleep Quality Index (PSQI), cognitive performance, and subjective mood states in 687 young adults (342 women). Imaging data were processed with published routines and evaluated with a corrected threshold. We examined the inter-relationship amongst hypothalamic rsFC, PSQI score, and clinical measures with mediation analyses.Results: In whole-brain regressions with age and drinking severity as covariates, men showed higher hypotha-lamic rsFC with the right insula in correlation with PSQI score. No clusters were identified in women at the same threshold. Both hypothalamic-insula rsFC and PSQI score were significantly correlated with anxiety and depression scores in men. Further, mediation analyses showed that PSQI score mediated the relationship between hypothalamic-insula rsFC and anxiety/depression symptom severity bidirectionally in men.Conclusions: Sleep dysfunction is associated with negative emotions and hypothalamic rsFC with the right insula, a core structure of the interoceptive circuits. Notably, anxiety-depression symptom severity and altered hypothalamic-insula rsFC are related bidirectionally by poor sleep quality. These findings are specific to men, suggesting potential sex differences in the neural circuits regulating sleep and emotional states that need to be further investigated.

Keyword ：

Hypothalamus Anxiety PSQI fMRI rsFC

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Abstract ：

Ventral striatum (VS) processes rewarding and punishing stimuli. Women and men vary in externalizing and internalizing traits, which may influence neural responses to reward and punishment. To investigate sex dif-ferences in how individual traits influence VS responses to reward and punishment, we curated the data of the Human Connectome Project and identified 981 (473 men) subjects evaluated by the Achenbach Adult Self-Report Syndrome Scales. We processed the imaging data with published routines and extracted VS response (beta) to win and to loss vs. baseline in a gambling task for correlation with externalizing and internalizing symptom severity. Men vs. women showed more severe externalizing symptoms and higher VS response to monetary losses (VS-loss beta) but not to wins. Men but not women showed a significant, positive correlation between VS-loss beta and externalizing traits, and the sex difference was confirmed by a slope test. The correlations of VS-loss vs. exter-nalizing and of VS-win vs. externalizing and those of VS-loss vs. externalizing and of VS-loss vs. internalizing traits both differed significantly in slope, confirming its specificity, in men. Further, the sex-specific relationship between VS-loss beta and externalizing trait did not extend to activities during exposure to negative emotion in the face matching task. To conclude, VS responses to loss but not to win and their correlation with externalizing rather than internalizing symptom severity showed sex differences in young adults. The findings highlight the relationship of externalizing traits and VS response to monetary loss and may have implications for psychological models of externalizing behaviors in men.

Keyword ：

Sex differences VS Punishment Externalizing fMRI

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The presence of unobserved factors in the motorcycle involved two-vehicle crashes (MV) data could lead to heterogenous associations between observed factors and injury severity sustained by motorcyclists. Capturing such heterogeneities necessitates distinct methodological approaches, of which random and scale heterogeneity models are paramount. Herein, we undertake an empirical evaluation of random and scale heterogeneity models, exploring their efficacy in delineating the influence of external determinants on the degree of injury severity in crashes. Within the effects of scale heterogeneity, this study delves into two dominant models: the scaled multinomial logit model (S-MNL) and its generalized counterpart, the G-MNL, which encompasses both the SMNL and the random parameters multinomial logit model (RPL). While the random heterogeneity domain is represented by the random parameters multinomial logit and an upgraded variant - the random parameters multinomial logit model with heterogeneity in means and variances (RPLHMV). Motorcycle involved two-vehicle crashes data were extracted from the UK STATS19 dataset from 2016 to 2020. Likelihood ratio tests are computed to assess the temporal variability of the significant factors. The test result demonstrates the temporal variations over a five-year study period. Some very important differences started to show up across the years based on the model estimation results: that the RPLHMV model statistically outperforms the G-MNL model in the 2016, 2018, and 2019 models, while the S-MNL model is statistically superior in the 2017 and 2020 years. These important findings suggest that the origin of heterogeneity in explaining factor weights can be captured by scale effects, not just random heterogeneity. In addition, the model results further show that motorcyclists' injury severities are significantly affected by motorcycle-related characteristics; there is the added factor of external influences, such as non-motorcycle drivers (males, young drivers, and elderly drivers) and vehicles (the moving status, age, and types of vehicles) that collide with motorcycles. The results of this paper are anticipated to help policymakers develop effective strategies to mitigate motorcycle involved two-vehicle crashes by implementing appropriate measures.

Keyword ：

Two-vehicle crashes Random parameters Motorcyclist safety Heterogeneity in the means and variances Temporal stability Scale heterogeneity Generalized mixed logit model

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Abstract ：

Sleep deficits is associated with impaired cognitive and affective processes, including responses to salient stimuli. Monetary win and loss are both salient. We examined how sleep deficits and comorbid negative emotional states are associated with neural markers of win and loss processing. We curated the Human Connectome Project data and employed connectome-based predictive model (CPM) to investigate how functional connectivity (FC) features of win and loss processing associated with sleep deficits, as reflected in the Pittsburgh Sleep Quality Index (PSQI) score, and subjective emotional states in 981 young adults. Imaging data were processed with published routines and evaluated with a corrected threshold. We examined the inter-relationship amongst win/loss FC, PSQI score, and clinical measures with mediation and path analyses. Poorer sleep quality is associated with higher right inferior temporal gyrus connectivity with amygdala (rITG-rAmyg) and lower connectivity with cerebellum (rITG-CBL) during monetary loss but not win. Both rITG FCs and PSQI score were significantly correlated with perceived stress. Further, mediation and path analyses showed that rITG-rAmyg and rITG-CBL antagonistically modulate PSQI to regulate perceived stress. Poorer sleep quality was associated with higher rITG-rAmyg and lower rITG-CBL during monetary loss. Notably, altered rITG-rAmyg and rITG-CBL antagonistically modulated sleep quality via their opposing influences on perceived stress. Thus, rIGT FCs exhibit functional antagonism such that the rITG-rAmyg connectivity worse sleep quality and perceived stress whereas the rITG-CBL serves to counter that effect.

Keyword ：

FC monetary loss PSQI CPM rITG

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