Applying Corpus-based Genre Analysis into the Teaching of Academic Chinese Writing

With an increasing number of international students coming to China for higher education, the learning needs of academic Chinese increased sharply. However, Chinese for Academic Purposes (CAP) is still in its infancy in both academic research and teaching practice. As a result, students lack the support for their academic Chinese skills and encounter difficulties according to their feedback. Meanwhile, the studies on English for Academic Purposes (EAP) pedagogy are fruitful. Among all the pedagogies, corpus-based instruction with genre analysis were found to be an effective approach in improving students’ EAP writing abilities. This study thus applied corpus-based genre analysis teaching approach to CAP teaching and examined the effectiveness. Through the construction and analysis of a specific corpus, this paper first investigated the move structure and high-frequency words and expressions of Chinese research article (RA) abstracts in the discipline of Economics and Management Science. Afterwards, a mini learning corpus was compiled for students’ exploration. The learning materials and sample tasks were introduced and data were collected from students’ feedback and writing samples before and after the teaching intervention. The results revealed that, firstly, the conventional move structure of Chinese RA abstracts in Economics and Management Science was I-MR-D, which is similar to English RA abstracts. Method and Results are two conventional moves. Second, corpus-based genre analysis can partially improve students’ performances in academic writing. After the teaching intervention, students’ awareness of text structure and the use of academic Chinese expressions improved noticeably. Though based on a small sample size, the research findings contribute towards the understanding of linguistic conventions of Chinese RA abstracts. Moreover, the materials and pedagogy used in this project shed new light on the instruction of Chinese writing as well as CAP curriculum development

Psychological interventions for depression in Chinese university students:A systematic review and meta-analysis

Background: University students in China are vulnerable to depression with a high estimated prevalence. It is currently unknown which types of psychological interventions are being delivered to treat depression in this population and whether they are effective. Therefore, a systematic review was conducted to address this issue. Methods: We searched records in English and Chinese databases up to January 2019. Results: From 2,739 records, we identified 39 randomized controlled trails (RCTs) and 54 non-RCTs. A range of psychological interventions were identified including cognitive behaviour therapy, interpersonal therapy, and local interventions. Hedge's g pooled effect size of 23 comparisons from 21 RCTs (N =858) compared to a control group (N = 802) was 1.08 (95% CI: 0.72 to 1.45). Heterogeneity was moderate with I2 = 47 (95%CI: 14 to 68). Type of control group was significantly associated with the effect size (p =0.039). Comparisons between the intervention condition and the ‘no intervention’ condition yielded a higher effect size (Hedges’ g =1.38, 95% CI: 0.89 to 1.87) than comparisons between the intervention condition and the ‘usual care/control’ condition (Hedges’ g = 0.56, 95% CI 0.08 to 1.05). No other significant differences based on the study characteristics were observed. Limitations: Publication bias and quality of inclusions. Conclusions: Collectively, there is evidence that psychological interventions for depression in Chinese university students are effective as compared to control groups, although the effects merit further examination by research of higher quality. Innovations in treatment delivery could facilitate wider dissemination of evidence-based interventions

Towards Discriminative Representation with Meta-learning for Colonoscopic Polyp Re-Identification

Colonoscopic Polyp Re-Identification aims to match the same polyp from a large gallery with images from different views taken using different cameras and plays an important role in the prevention and treatment of colorectal cancer in computer-aided diagnosis. However, traditional methods for object ReID directly adopting CNN models trained on the ImageNet dataset usually produce unsatisfactory retrieval performance on colonoscopic datasets due to the large domain gap. Additionally, these methods neglect to explore the potential of self-discrepancy among intra-class relations in the colonoscopic polyp dataset, which remains an open research problem in the medical community. To solve this dilemma, we propose a simple but effective training method named Colo-ReID, which can help our model to learn more general and discriminative knowledge based on the meta-learning strategy in scenarios with fewer samples. Based on this, a dynamic Meta-Learning Regulation mechanism called MLR is introduced to further boost the performance of polyp re-identification. To the best of our knowledge, this is the first attempt to leverage the meta-learning paradigm instead of traditional machine learning to effectively train deep models in the task of colonoscopic polyp re-identification. Empirical results show that our method significantly outperforms current state-of-the-art methods by a clear margin.Comment: arXiv admin note: text overlap with arXiv:2307.1062

Relationship between serum irisin level, all-cause mortality, and cardiovascular mortality in peritoneal dialysis patients

Introduction: This study aimed to investigate the prospective role of serum irisin-a novel adipo-myokine-in all-cause mortality and cardiovascular (CV) mortality in patients on peritoneal dialysis (PD). Methods: A prospectively observational study was conducted with 154 PD patients. Baseline clinical data were collected from the medical records. Serum irisin concentrations were determined using enzyme-linked immunosorbent assay. Patients were divided into the high irisin group (serum irisin ≥ 113.5ng/mL) and the low irisin group (serum irisin < 113.5ng/mL) based on the median value of serum irisin. A Body Composition Monitor was used to monitor body composition. Cox regression analysis was utilized to find the independent risk factors of all-cause and CV mortality in PD patients. Results: The median serum irisin concentration was 113.5 ng/mL (interquartile range, 106.2–119.8 ng/mL). Patients in the high irisin group had significantly higher muscle mass and carbon dioxide combining power (CO2CP) than those in the low irisin group (p < 0.05). Serum irisin was positively correlated with pulse pressure, CO2CP, and muscle mass, while negatively correlated with body fat percentage (p < 0.05). During a median of follow-up for 60.0 months, there were 55 all-cause deaths and 26 CV deaths. Patients in high irisin group demonstrated a higher CV survival rate than those in low irisin group (p = 0.016). Multivariate Cox regression analysis showed that high irisin level [hazard ratio (HR), 0.341; 95% confidence interval (CI), 0.135–0.858; p = 0.022], age, and diabetic mellitus were independently associated with CV mortality in PD patients. However, serum irisin level failed to demonstrate a statistically significant relationship with all-cause mortality. Conclusion: Low serum irisin levels at baseline were independently predictive of CV mortality but not all-cause mortality in PD patients. Therefore, serum irisin could be a potential target for monitoring CV outcomes in PD patients