Understanding information resources for college student mental health: A knowledge graph approach

Many universities and colleges have not provided well-organized and easy to use mental health related information resources to their students although mental illness has become a significant barrier to college student success. This study aims to understand the information resources important to college student mental health (CSMH). We conducted a content analysis of two CSMH websites as the first step to build a knowledge graph for CSMH. Two site maps are developed based on the analysis. Seven types of information are therefore identified and considered important for colleges to provide to their students: Appointment, Mental Disorders, Self-help Resources, Information for Parents, Local Referral Sources, Substance Abuse Prevention, and University Policies on Mental Disorders. The next step of this study is to develop ontology by verifying the seven types of information and establishing their relationships. More CSMH websites will be examined to achieve reliable results

Composite Match Index with Application of Interior Deformation Field Measurement from Magnetic Resonance Volumetric Images of Human Tissues

Whereas a variety of different feature-point matching approaches have been reported in computer vision, few feature-point matching approaches employed in images from nonrigid, nonuniform human tissues have been reported. The present work is concerned with interior deformation field measurement of complex human tissues from three-dimensional magnetic resonance (MR) volumetric images. To improve the reliability of matching results, this paper proposes composite match index (CMI) as the foundation of multimethod fusion methods to increase the reliability of these various methods. Thereinto, we discuss the definition, components, and weight determination of CMI. To test the validity of the proposed approach, it is applied to actual MR volumetric images obtained from a volunteer’s calf. The main result is consistent with the actual condition

A^2-Net: Molecular Structure Estimation from Cryo-EM Density Volumes

Constructing of molecular structural models from Cryo-Electron Microscopy (Cryo-EM) density volumes is the critical last step of structure determination by Cryo-EM technologies. Methods have evolved from manual construction by structural biologists to perform 6D translation-rotation searching, which is extremely compute-intensive. In this paper, we propose a learning-based method and formulate this problem as a vision-inspired 3D detection and pose estimation task. We develop a deep learning framework for amino acid determination in a 3D Cryo-EM density volume. We also design a sequence-guided Monte Carlo Tree Search (MCTS) to thread over the candidate amino acids to form the molecular structure. This framework achieves 91% coverage on our newly proposed dataset and takes only a few minutes for a typical structure with a thousand amino acids. Our method is hundreds of times faster and several times more accurate than existing automated solutions without any human intervention.Comment: 8 pages, 5 figures, 4 table

Dissipationless Spin Current in Anisotropic p-Doped Semiconductors

Recently, dissipationless spin current has been predicted for the p-doped semiconductors with spin-orbit coupling. Here we investigate the effect of spherical symmetry breaking on the dissipationless spin current, and obtain values of the intrinsic spin Hall conductivity for realistic semiconductor band structures with cubic symmetry