Minicomputer systems have been designed for researchers who have no prior computer experience and, therefore, require no computer training, that is, programming knowledge. Such systems allow immediate access to the data, their retrieval, reduction, and analysis, and the retention of direct control over the data base by the researcher. In addition, CLINFO offers a wide range of calculations and statistical procedures. The three-dimensional, time-oriented data-base format of CLINFO allows for a longitudinal study of a particular variable, and its repeated measure over time in the same patient. This system has been adapted for use in the consultation-liaison setting to provide pedagogic, research, and administrative functions from a field-tested data base.
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Polygenic risk for depression and resting-state functional connectivity of subgenual anterior cingulate cortex in young adults.
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From the Department of Psychiatry, Yale University School of Medicine, New Haven, Conn., USA (Chen, Luo, Ide, C.-S. Li); Yale University, New Haven, Conn., USA (H.-T. Li); the Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing, China (G. Li); the Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing, China (G. Li); the Department of Neuroscience, Yale University School of Medicine, New Haven, Conn., USA (C.-S Li); the Interdepartment Neuroscience Program, Yale University, New Haven, Conn., USA (C.-S. Li); the Wu Tsai Institute, Yale University, New Haven, Conn., USA (C.-S. Li).
Background: Genetic variants may confer risk for depression by modulating brain structure and function; evidence has underscored the key role of the subgenual anterior cingulate cortex (sgACC) in depression. We sought to examine how the resting-state functional connectivity (rsFC) of the sgACC was associated with polygenic risk for depression in a subclinical population.
Methods: Following published protocols, we computed seed-based whole-brain sgACC rsFC and calculated polygenic risk scores (PRS) using data from healthy young adults from the Human Connectome Project.
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Institute of Translational Medicine, School of Medicine, Yangzhou University, Yangzhou, 225009, China. Electronic address:
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View Article and Find Full Text PDFEnhanced detection of measurement anomalies in cartridge cases using 3D gray-level co-occurrence matrix.
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The firing pin impression left on the base of a cartridge case is a critical analytical feature in forensic science. To address the limitations of traditional manual trace analysis and mitigate the risk of secondary damage to physical evidence, we employ a line laser displacement sensor to capture and analyze three-dimensional (3D) traces of fired cartridge cases. However, when using laser displacement sensors to collect traces from metal cartridge cases, the high curvature and reflectivity of the metal surface can cause specular reflections, potentially leading to measurement anomalies in the firing pin impressions.
View Article and Find Full Text PDFA graph neural network-based model with out-of-distribution robustness for enhancing antiretroviral therapy outcome prediction for HIV-1.
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Sapienza University of Rome, Department of Computer Control and Management Engineering Antonio Ruberti, 00185, Rome, Italy. Electronic address:
Predicting the outcome of antiretroviral therapies (ART) for HIV-1 is a pressing clinical challenge, especially when the ART includes drugs with limited effectiveness data. This scarcity of data can arise either due to the introduction of a new drug to the market or due to limited use in clinical settings, resulting in clinical dataset with highly unbalanced therapy representation. To tackle this issue, we introduce a novel joint fusion model, which combines features from a Fully Connected (FC) Neural Network and a Graph Neural Network (GNN) in a multi-modality fashion.
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