The object of this study was to evaluate the relationship between changes in the alveolar bone density around the teeth and the direction of tooth movement by using cone-beam computed tomography (CBCT). CBCT was used to measure the bone densities around six maxilla anterior teeth before and after 7 months of orthodontic treatment in eight patients. Each root was divided into three levels (cervical, intermediate, and apical) to determine whether the bone density change varied with the tooth level. Moreover, each level was divided into four regions (palatal, distal, mesial, and buccal sides). Three-dimensional computer models of the maxilla before and after orthodontic treatment were created to detect the direction of tooth movement. The percentage for all 144 samples [8 (patients) × 6 (teeth) × 3 (levels)] in which the side (palatal, distal, mesial, or buccal sides) of maximum bone density reduction (before and after orthodontic treatment) coincided with the direction of tooth movement was calculated; this was referred to as the "coincidence percentage". The bone density around the teeth reduced by 24.3 ± 9.5%. The average coincidence percentage for the eight patients was 59.0%. The coincidence percentages for the eight patients were 62.5%, 62.5%, and 52.1% at the cervical, intermediate, and apical levels, respectively. The obtained results demonstrate that the direction of tooth movement is associated with the side of maximum bone density reduction, and that CBCT is a useful approach for evaluating bone density changes around teeth induced by orthodontic treatment.

Quantitative understanding of relationships between students' behavioural patterns and academic performances is a significant step towards personalized education. In contrast to previous studies that were mainly based on questionnaire surveys, recent literature suggests that unobtrusive digital data bring us unprecedented opportunities to study students' lifestyles in the campus. In this paper, we collect behavioural records from undergraduate students' ( = 18 960) smart cards and propose two high-level behavioural characters, orderliness and diligence. The former is a novel entropy-based metric that measures the regularity of campus daily life, which is estimated here based on temporal records of taking showers and having meals. Empirical analyses on such large-scale unobtrusive behavioural data demonstrate that academic performance (GPA) is significantly correlated with orderliness. Furthermore, we show that orderliness is an important feature to predict academic performance, which improves the prediction accuracy even in the presence of students' diligence. Based on these analyses, education administrators could quantitatively understand the major factors leading to excellent or poor performance, detect undesirable abnormal behaviours in time and thus implement effective interventions to better guide students' campus lives at an early stage when necessary.

Detecting abnormal behaviors of students in time and providing personalized intervention and guidance at the early stage is important in educational management. Academic performance prediction is an important building block to enabling this pre-intervention and guidance. Most of the previous studies are based on questionnaire surveys and self-reports, which suffer from small sample size and social desirability bias. In this paper, we collect longitudinal behavioral data from 6, 597 students' smart cards and propose three major types of discriminative behavioral factors, diligence, orderliness, and sleep patterns. Empirical analysis demonstrates these behavioral factors are strongly correlated with academic performance. Furthermore, motivated by social influence theory, we analyze the correlation between each student's academic performance with his/her behaviorally similar students'. Statistical tests indicate this correlation is significant. Based on these factors, we further build a multi-task predictive framework based on a learning-to-rank algorithm for academic performance prediction. This framework captures inter-semester correlation, inter-major correlation and integrates student similarity to predict students' academic performance. The experiments on a large-scale realworld dataset show the effectiveness of our methods for predicting academic performance and the effectiveness of proposed behavioral factors. :2 H. Yao et al. students' academic performance. It has been demonstrated that physical status [23, 29], intelligence quotient [9] and even socioeconomic status [38] are correlated with academic performance. However, these characteristics are relatively stable over the long run and are difficult to change via educational management.Comparatively, more studies are focused on the perspectives of psychology and behavior, partially due to the possibility of intervening on student's mentation and behavior. Extensive experiments about the correlation between the big-five personality traits (Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism) and academic performance have been reported [2,25,33], uncovering conscientiousness as one of the strongest predictors. Behaviors like class attendance [8], lifestyle [34] and sleep habit [10] are also highly associated with academic performance. However, almost all of these results are obtained from questionnaires or self-reports, which usually suffer from small sample size and social desirability bias, resulting in the difficulty to draw a valid and solid conclusion.Thanks to the development of information technology, there is a growing trend to augment physical facilities with sensing, computing and communication capabilities in modern universities. These facilities provide an unprecedented opportunity to collect real-time digital records about students' campus activities in an unobtrusive way and to reveal behavioral predictors for academic performance. In this paper, mainly through campus smart card, we collect 6, 597 students' longitudinal behavio...

BACKGROUNDMedical students have high rates of depression, anxiety, and burnout that have been found to affect their empathy, professional behaviors, and performance as a physician. While studies have examined predictors for burnout and depression in the United States (US), no study, to our knowledge, has compared depression in medical students cross-culturally, or has attempted to examine the effect of factors influencing rates including burnout, exercise, stress, unmet mental health needs, and region.AIMTo examine rates of depression in three international cohorts of medical students, and determine variables that may explain these differences.METHODSConvenience samples of medical students from three countries (US, China, and a Middle Eastern country whose name remains anonymous per request from the school) were surveyed in this observational study. Using the Patient Health Questionnaire-2 (PHQ-2) and a modified Maslach Burnout Inventory, depression and burnout were examined among medical students from the three cohorts (n = 473). Chi-square test and analysis of variance were used to examine differences in demographics, behavioral, and psychological variables across these three schools to identify potentially confounding descriptive characteristics. Analysis of covariance compared depression and the emotional exhaustion component of burnout identified through Principal Component Analysis across countries. Multiple linear regression was used to analyze the impact of demographic, behavioral, and psychological variables on screening positive for depression.RESULTSMedical students from the Middle Eastern country had the highest rates of positive depression screens (41.1%), defined as a PHQ-2 score of ≥ 3, followed by China (14.1 %), and then the US (3.8%). More students in the Middle Eastern school had unmet mental health needs (50.8%) than at the medical school in China (34.8%) or the school in the US (32.8%) (Pearson chi-square significance < 0.05). Thus, PHQ-2 scores were adjusted for unmet mental health needs; however, the Middle Eastern country continued to have the highest depression. Adjusting for PHQ-2 score, medical students from the US scored the highest on emotional exhaustion (a measure of burnout). Demographic variables did not significantly predict medical student depression; however, lack of exercise, unmet mental health needs, stress, and emotional exhaustion predicted nearly half of depression in these cohorts. In comparison to the US, coming from the Middle Eastern country and China predicted higher levels of depression.CONCLUSIONDepression rates differ in three international cohorts of medical students. Measured factors contributed to some observed differences. Identifying site-specific prevention and intervention strategies in medical student mental health is warranted.