Improved REBA: deep learning based rapid entire body risk assessment for prevention of musculoskeletal disorders.

Preventing work-related musculoskeletal disorders (WMSDs) is crucial in reducing their impact on individuals and society. However, the existing mainstream 2D image-based approach is insufficient in capturing the complex 3D movements and postures involved in many occupational tasks. To address this, an improved deep learning-based rapid entire body assessment (REBA) method has been proposed.

Spatial relationship-aware rapid entire body fuzzy assessment method for prevention of work-related musculoskeletal disorders.

In the advent of Industry 5.0, advances in human-centered smart manufacturing (HSM) accentuate the role of humans in human-machine collaboration. This development has catapulted human health in human-machine systems to the forefront of the conversation.

Pedestrian re-identification based on attention mechanism and Multi-scale feature fusion.

Existing pedestrian re-identification models generally have low pedestrian retrieval accuracy when encountering factors such as changes in pedestrian posture and occlusion because the network cannot fully express pedestrian feature information. Therefore, this paper proposes a method to address this problem by combining the attention mechanism with multi-scale feature fusion, and combining the proposed cross-attention module with the ResNet50 backbone network. In this way, the ability of the network to extract strong salient features is significantly improved; at the same time, using the multi-scale feature fusion module to extract multi-scale features from different depths of the network, achieving the complementary advantages between features through feature addition, feature concatenation and feature weight selection.

COVID-19 contact tracking based on person reidentification and geospatial data.

Efficient contact tracing is a crucial step in preventing the spread of COVID-19. However, the current methods rely heavily on manual investigation and truthful reporting by high-risk individuals. Mobile applications and Bluetooth-based contact tracing methods have also been adopted, but privacy concerns and reliance on personal data have limited their effectiveness.

Knowledge platform affordances and knowledge collaboration performance: The mediating effect of user engagement.

Knowledge collaboration is the result of knowledge transfer and social interaction among users on knowledge platforms, and its formation mechanism has attracted much attention. Based on the affordance theory, this paper introduces user engagement as a mediating variable to study the relationship between knowledge platform affordances and knowledge collaboration performance. Data collected from 361 valid questionnaires from the Zhihu platform were analyzed by using SPSS 26.

Artificial intelligence-based intelligent surveillance for reducing nurses' working hours in nurse-patient interaction: A two-wave study.

Aim: To explore the feasibility of applying artificial intelligence in nurse-patient interaction to assist nurses in grasping patient status and reducing their working hours.

Background: Artificial intelligence has been reshaping the health care industry and has immense potential in nursing care, but there is still a lack of suitable artificial intelligence methods to improve the efficiency of the nurse-patient interaction that takes much time of nurses.

Methods: An artificial intelligence-based intelligent surveillance system was developed to reduce nurses' working hours in nurse-patient interaction, and a two-wave follow-up design was adopted in this study.

A model-based approach to assess reproducibility for large-scale high-throughput MRI-based studies.

Magnetic Resonance Imaging (MRI) technology has been increasingly used in neuroscience studies. Reproducibility of statistically significant findings generated by MRI-based studies, especially association studies (phenotype vs. MRI metric) and task-induced brain activation, has been recently heavily debated.

Increased brain volume from higher cereal and lower coffee intake: shared genetic determinants and impacts on cognition and metabolism.

It is unclear how different diets may affect human brain development and if genetic and environmental factors play a part. We investigated diet effects in the UK Biobank data from 18,879 healthy adults and discovered anticorrelated brain-wide gray matter volume (GMV)-association patterns between coffee and cereal intake, coincidence with their anticorrelated genetic constructs. The Mendelian randomization approach further indicated a causal effect of higher coffee intake on reduced total GMV, which is likely through regulating the expression of genes responsible for synaptic development in the brain.

Associations between polygenic risk scores and amplitude of low-frequency fluctuation of inferior frontal gyrus in schizophrenia.

Schizophrenia (SCZ) is a serious and complex mental disorder with high heritability. Polygenic risk score (PRS) is a useful tool calculating the accumulating effects of multiple common genetic variants of schizophrenia. The amplitude of low-frequency fluctuation (ALFF) is an efficient index to reflect spontaneous, intrinsic neuronal activity.

Modeling the Use of Online Knowledge Community: A Perspective of Needs-Affordances-Features.

With the support of network information technology, the Online Knowledge Community (OKC) has emerged. Among different OKC applications, some entered into the new era of popular knowledge production, while others experienced the process to decline. In order to solve the dilemma faced by the OKC platforms, the needs-affordances-features (NAF) perspective on OKC is proposed by taking Zhihu, one of the most popular OKC applications in China as an example.

Polygenic risk for autism spectrum disorder affects left amygdala activity and negative emotion in schizophrenia.

Although the diagnoses based on phenomenology have many practical advantages, accumulating evidence shows that schizophrenia and autism spectrum disorder (ASD) share some overlap in genetics and clinical presentation. It remains largely unknown how ASD-associated polygenetic risk contributes to the pathogenesis of schizophrenia. In the present study, we calculated high-resolution ASD polygenic risk scores (ASD PRSs) and selected optimal ten ASD PRS with minimal P values in the association analysis of PRSs, with schizophrenia to assess the effect of ASD PRS on brain neural activity in schizophrenia cases and controls.

Development and validation of a new score for predicting functional outcome of neurocritically ill patients: The INCNS score.

Aims: To develop and validate a novel score for prediction of 3-month functional outcome in neurocritically ill patients.

Methods: The development of the novel score was based on two widely used scores for general critical illnesses (Acute Physiology and Chronic Health Evaluation II, APACHE II; Simplified Acute Physiology Score II, SAPS II) and consideration of the characteristics of neurocritical illness. Data from consecutive patients admitted to neurological ICU (N-ICU) between January 2013 and June 2016 were used for the validation.

Multi-scale analysis of schizophrenia risk genes, brain structure, and clinical symptoms reveals integrative clues for subtyping schizophrenia patients.

Analysis linking directly genomics, neuroimaging phenotypes and clinical measurements is crucial for understanding psychiatric disorders, but remains rare. Here, we describe a multi-scale analysis using genome-wide SNPs, gene expression, grey matter volume (GMV), and the positive and negative syndrome scale scores (PANSS) to explore the etiology of schizophrenia. With 72 drug-naive schizophrenic first episode patients (FEPs) and 73 matched heathy controls, we identified 108 genes, from schizophrenia risk genes, that correlated significantly with GMV, which are highly co-expressed in the brain during development.

Neural and genetic determinants of creativity.

Creative thinking plays a vital role in almost all aspects of human life. However, little is known about the neural and genetic mechanisms underlying creative thinking. Based on a cross-validation based predictive framework, we searched from the whole-brain connectome (34,716 functional connectivities) and whole genome data (309,996 SNPs) in two datasets (all collected by Southwest University, Chongqing) consisting of altogether 236 subjects, for a better understanding of the brain and genetic underpinning of creativity.