Early Development of Graphical Literacy through Knowledge Building

This study examined growth in graphical literacy for students contributing to an online, multimedia, communal environment as they advanced their understanding of biology, history and optics. Their science and history studies started early in Grade 3 and continued to the end of Grade 4; students did not receive instruction in graphics production, nor were they required to produce graphics. Results show that students spontaneously produced graphics that advanced along seven dimensions, including effective representation of complex ideas, use of source information and captions, and aesthetic quality. On average, the scores for the seven dimensions were higher for Grade 4 students with two years of experience with Knowledge Building pedagogy and technology (Knowledge Forum®) than for Grade 6 students with one year of experience. The overall pattern of results suggests reciprocal enhancement of graphical, textual, digital, and scientific literacy, with students exceeding expectations by available norms, and performance enhanced through extended Knowledge Building experience

CartiMorph: a framework for automated knee articular cartilage morphometrics

We introduce CartiMorph, a framework for automated knee articular cartilage morphometrics. It takes an image as input and generates quantitative metrics for cartilage subregions, including the percentage of full-thickness cartilage loss (FCL), mean thickness, surface area, and volume. CartiMorph leverages the power of deep learning models for hierarchical image feature representation. Deep learning models were trained and validated for tissue segmentation, template construction, and template-to-image registration. We established methods for surface-normal-based cartilage thickness mapping, FCL estimation, and rule-based cartilage parcellation. Our cartilage thickness map showed less error in thin and peripheral regions. We evaluated the effectiveness of the adopted segmentation model by comparing the quantitative metrics obtained from model segmentation and those from manual segmentation. The root-mean-squared deviation of the FCL measurements was less than 8%, and strong correlations were observed for the mean thickness (Pearson's correlation coefficient $\rho \in [0.82,0.97]$), surface area ($\rho \in [0.82,0.98]$) and volume ($\rho \in [0.89,0.98]$) measurements. We compared our FCL measurements with those from a previous study and found that our measurements deviated less from the ground truths. We observed superior performance of the proposed rule-based cartilage parcellation method compared with the atlas-based approach. CartiMorph has the potential to promote imaging biomarkers discovery for knee osteoarthritis.Comment: To be published in Medical Image Analysi

Theoretical Evidence for the Berry-Phase Mechanism of Anomalous Hall Transport: First-principles Studies on CuCr2_2Se4−x_{4-x}Brx_x

To justify the origin of anomalous Hall effect (AHE), it is highly desirable to have the system parameters tuned continuously. By quantitative calculations, we show that the doping dependent sign reversal in CuCr$_{2}$Se$_{4-x}$Br$_{x}$, observed but not understood, is nothing but direct evidence for the Berry-Phase mechanism of AHE. The systematic calculations well explain the experiment data for the whole doping range where the impurity scattering rates is changed by several orders with Br substitution. Further sign change is also predicted, which may be tested by future experiments.Comment: 4 page

Unsupervised Domain Adaptation for Automated Knee Osteoarthritis Phenotype Classification

Purpose: The aim of this study was to demonstrate the utility of unsupervised domain adaptation (UDA) in automated knee osteoarthritis (OA) phenotype classification using a small dataset (n=50). Materials and Methods: For this retrospective study, we collected 3,166 three-dimensional (3D) double-echo steady-state magnetic resonance (MR) images from the Osteoarthritis Initiative dataset and 50 3D turbo/fast spin-echo MR images from our institute (in 2020 and 2021) as the source and target datasets, respectively. For each patient, the degree of knee OA was initially graded according to the MRI Osteoarthritis Knee Score (MOAKS) before being converted to binary OA phenotype labels. The proposed UDA pipeline included (a) pre-processing, which involved automatic segmentation and region-of-interest cropping; (b) source classifier training, which involved pre-training phenotype classifiers on the source dataset; (c) target encoder adaptation, which involved unsupervised adaption of the source encoder to the target encoder and (d) target classifier validation, which involved statistical analysis of the target classification performance evaluated by the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity and accuracy. Additionally, a classifier was trained without UDA for comparison. Results: The target classifier trained with UDA achieved improved AUROC, sensitivity, specificity and accuracy for both knee OA phenotypes compared with the classifier trained without UDA. Conclusion: The proposed UDA approach improves the performance of automated knee OA phenotype classification for small target datasets by utilising a large, high-quality source dataset for training. The results successfully demonstrated the advantages of the UDA approach in classification on small datasets.Comment: Junru Zhong and Yongcheng Yao share the same contribution. 17 pages, 4 figures, 4 table

Abnormal Functional Brain Network Connectivity Associated with Alzheimer's Disease

The study's objective is to explore the distinctions in the functional brain network connectivity between Alzheimer's Disease (AD) patients and normal controls using Functional Magnetic Resonance Imaging (fMRI). The study included 590 individuals, with 175 having AD dementia and 415 age-, gender-, and handedness-matched normal controls. The connectivity of functional brain networks was measured using ROI-to-ROI and ROI-to-Voxel connectivity analyses. The findings reveal a general decrease in functional connectivity among the AD group in comparison to the normal control group. These results advance our comprehension of AD pathophysiology and could assist in identifying AD biomarkers.Comment: 23 pages, 19 figures, 1 tabl

General self-efficacy and the effect of hospital workplace violence on doctors’ stress and job satisfaction in China

Objectives: This study aims at exploring associations of general self-efficacy (GSE), workplace violence and doctors' work-related attitudes. Material and Methods: In this study a cross-sectional survey design was applied. Questionnaires were administrated to 758 doctors working in 9 hospitals of Zhengzhou, Henan province, China, between June and October 2010. General information on age, gender, and years of working was collected, and the doctors' experience and witnessing workplace violence, job satisfaction, job initiative, occupational stress as well as GSE were measured. General linear regression analysis was performed in association analyses. Results: Both experiencing and witnessing workplace violence were significantly positively correlated with the level of occupational stress but significantly negatively correlated with job satisfaction, job initiative, and GSE. General self-efficacy significantly modified relationships between both experiencing and witnessing workplace violence with occupational stress (β = 0.49 for experiencing violence; β = 0.43 for witnessing violence; p 0.05). The levels of occupational stress declined significantly with the increase of GSE, while job satisfaction increased significantly along with its increase. The effects of GSE on occupational stress and job satisfaction weakened as the frequency of violence increased. Conclusions: The findings suggest that GSE can modify effects of workplace violence on health care workers' stress and job satisfaction. Enhancing GSE in combination with stress reduction may lead to facilitating health care workers' recovery from workplace violence, and thereby improving their work-related attitudes

Personality modifies the effect of post-traumatic stress disorder (PTSD) and society support on depression-anxiety-stress in the residents undergone catastrophic flooding in Henan, China

Background: To analyze the impact of the flood disasters, social support and personality on the mental health of residents in Henan Province, China, providing fundamental knowledges for making measuring strategies to improve the psychological protection and anti-stress ability of the residents after the disaster. Material and Methods: A cross-section study was conducted via an online survey platform “questionnaire star,” which included 572 residents in Henan Province, which underwent the history of ever flood disaster on July 20. The questionnaires of Impact of Event Scale-Revised Edition (IES-R), Perceived Social Support Scale (PSSS), the Depression Anxiety Stress Scales (DASS-21) and the scales of Eysenck Personality Questionnaire-Revised (EPQ-R) version in Chinese were also administered to each participant. Generalized linear regression model was performed. Results: The residents who live in the flooding areas, are male and married had a significantly higher post-traumatic stress disorder (PTSD) score than their counterparts. The scores of depression-anxiety-stress in the residents with stable emotion were significantly lower than those with unstable emotion (p < 0.001). Machine learning showed that PTSD ranked the top risk factor, followed by neuroticism for Depression-Anxiety-Stress after disaster. The PTSD was negatively correlated with social support (p < 0.01), while it was positively correlated with depression-anxiety-stress and emotional stability (p < 0.01). There was a statistically significant interaction between PTSD, social support and neuroticism on depression-anxiety-stress (p < 0.001), with an independent effect of 1.4% on depression-anxiety-stress. Emotional stability showed the largest association with depression-anxiety-stress. Conclusions: Residents living in the catastrophic flooding areas had significant post-traumatic mental health issues, and the severity of mental problems was differently affected by post-traumatic stress disorder and social support in individuals with different personalities. Introvert and PTSD were the major risk factors for depression-anxiety-stress after the disaster

Thermodynamic investigation of DNA-binding affinity of wild-type and mutant transcription factor RUNX1.

Transcription factor RUNX1 and its binding partner CBFβ play a critical role in gene regulation for hematopoiesis. Mutations of RUNX1 cause ~10% of acute myeloid leukemia (AML) with a particularly poor prognosis. The current paradigm for the leukemogenesis is that insufficient activity of wild-type (WT) RUNX1 impairs hematopoietic differentiation. The majority of mutant RUNX1 proteins lose the DNA-binding affinity and inhibit WT RUNX1 by depletion of CBFβ. Here, isothermal titration calorimetry (ITC) was used to quantitatively study the interactions of WT and three clinical mutant RUNX1, CBFβ and DNA. Our data show that the binding of RUNX1 to DNA is enthalpy-driven, and the affinity decreases in the order of WT > S114L > R139Q >> K83E, which support previous observations and conclusion. To find potentially beneficial RUNX1 mutations that could increase the overall RUNX1 activity, K83R and H179K mutations of RUNX1 were designed, using structure-based computational modeling, and found to possess significantly higher DNA-binding affinities than does WT RUNX1. K83R and H179K mutant RUNX1 could therefore be protein-based RUNX1 activators