Identifying primary estrogen receptor (ER) agonists in municipal sewage is essential for ensuring the health of aquatic environments. Given the complex and variable chemical composition of sewage, the predominant ER a...
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Identifying primary estrogen receptor (ER) agonists in municipal sewage is essential for ensuring the health of aquatic environments. Given the complex and variable chemical composition of sewage, the predominant ER agonists remain unclear. High-resolution mass spectrometry (HRMS)-based models have been developed to predict compound bioactivity in complex matrices, but further optimization is needed to effectively bridge HRMS features with ER agonists. To address this challenge, an FT-GNN (fragmentation tree-based graphneuralnetwork) model was proposed. Given limited data and class imbalance, data augmentation was performed using model predictions within the applicability domain (AD) and oversampling technique (OTE). model development results demonstrated that integrating the FT-GNN with data augmentation improved the balanced accuracy (bACC) value by 6%-31%. The developed model, with a high bACC to identify more true ER agonists, efficiently classified tens of thousands of unidentified HRMS features in sewage, reducing postprocessing workload in nontargeted screening. Analysis of ER agonist transformation during sewage treatment revealed the anaerobic stage as key to both their removal and formation. Estrogenic effect balance analysis suggests that alpha-E2 and 9,11-didehydroestriol may be two previously overlooked key ER agonists. Collectively, the development and application of the FT-GNN model are crucial advancements toward credible tracking and efficient control of estrogenic risks in water.
Aim Social anxiety disorder (SAD) is a mental disorder that requires early detection and treatment. However, some individuals with SAD avoid face-to-face evaluations, which leads to delayed detection. We aim to predic...
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Aim Social anxiety disorder (SAD) is a mental disorder that requires early detection and treatment. However, some individuals with SAD avoid face-to-face evaluations, which leads to delayed detection. We aim to predict individuals with SAD based on their communication logs and social network data from a massively multiplayer online game (MMOG). Method The study included 819 users of Pigg Party, a popular MMOG in Japan. Participants completed the Japanese version of the Liebowitz Social Anxiety Scale (LSAS-J) and a social withdrawal scale (hikikomori) questionnaire. Participants scoring >= 60 on the LSAS-J were classified as having SAD, while those scoring <60 were classified as not having SAD (non-SAD). A total of 142,147 users' communication logs and 613,618 social edges from Pigg Party were used as input to predict whether participants had SAD or non-SAD. graph sample and aggregated embeddings (graph SAGE) was utilized as a graph neural network model. Results Individuals with SAD were more likely to be socially withdrawn in the physical community (hikikomori), had fewer friends, spent less time in other users' virtual houses, and showed lower entropy in their visitation times in MMOG. Based on their social network data, the graph SAGE model predicted SAD, with an F1 score of 0.717. Conclusion The communication logs and social network data in an MMOG include indicators of interpersonal avoidance behaviors, which is typical of individuals with SAD;this suggests their potential use as digital biomarkers for the early detection of SAD.
The Industrial Internet of Things is an emerging technology that has rapidly penetrated diverse applications. MindSphere from Siemens stands out as a leader among Industrial Internet of Things solutions. Unlike most i...
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The Industrial Internet of Things is an emerging technology that has rapidly penetrated diverse applications. MindSphere from Siemens stands out as a leader among Industrial Internet of Things solutions. Unlike most industrial control systems, MindSphere utilizes robust encryption protocols such as SSL, TLS and even QUIC for data transmission, presenting significant challenges to traditional traffic forensic approaches. Conducting effective user behavior forensics in this context requires the identification of relevant traffic in extensive IP network flows along with the execution of fine-grained classification tasks. The challenges significantly reduce the effectiveness of mainstream encrypted traffic classification methods on MindSphere. This chapter describes a novel flow-correlation framework that is designed to automatically extract user behavior patterns from MindSphere network traffic. The approach engages a hybrid feature set encompassing statistical and sequential data to create feature vectors for flow nodes. By leveraging traffic correlation, the approach incorporates a burst time-domain mechanism to construct a communications diagram. The constituent traffic graphs are processed using a graph neural network model to enable effective classification. Comprehensive experiments demonstrate that the approach exhibits outstanding performance that surpasses state-of-the-art methods.
Performance inefficiencies can lead to performance anomalies in parallel programs. Existing performance analysis tools either have a limited detection scope or require significant domain knowledge to use, which constr...
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