Performance evaluation of keyword extraction methods and visualization for student online comments

Topic keyword extraction (as a typical task in information retrieval) refers to extracting the core keywords from document topics. In an online environment, students often post comments in subject forums. The automatic and accurate extraction of keywords from these comments are beneficial to lecturers (particular when it comes to repeatedly delivered subjects). In this paper, we compare the performance of traditional machine learning algorithms and two deep learning methods in extracting topic keywords from student comments posted in subject forums. For this purpose, we collected student comment data from a period of two years, manually tagging part of the raw data for our experiments. Based on this dataset, we comprehensively compared the five typical algorithms of naïve Bayes, logistic regression, support vector machine, convolutional neural networks, and Long Short-Term Memory with Attention (Att-LSTM). The performances were measured by the four evaluation metrics. We further examined the keywords by visualization. From the results of our experiment and visualization, we conclude that the Att-LSTM method is the best approach for topic keyword extraction from student comments. Further, the results from the algorithms and visualization are symmetry, to some degree. In particular, the extracted topics from the comments posted at the same stages of different teaching sessions are, almost, reflection symmetry

A Systematic Review and Meta-Analysis of Symptoms of Anxiety, Depression, and Insomnia in Spain in the COVID-19 Crisis.

BACKGROUND: General population, frontline healthcare workers (HCWs), and adult students in Spain are at risk of anxiety, depression, and insomnia symptoms during the COVID-19 crisis. A meta-analysis of the individual studies on these symptoms would provide systematic evidence to aid policymakers and researchers in focusing on prevalence, risk, and best interventions. OBJECTIVE: This paper aims to be the first meta-analysis and systematic review to calculate the prevalence of anxiety, depression, and insomnia symptoms in Spain's adult population (general population, frontline healthcare workers (HCWs), and adult students) during the Covid-19 epidemic. METHOD: Random-effect meta-analysis was used to estimate the prevalence of anxiety, depression, and insomnia. RESULTS: The meta-analysis includes 28 studies with 38 individual samples in Spain. The pooled prevalence of anxiety symptoms in 22 studies comprising a sample population of 82,024 was 20% (95% CI: 15-25%), that of depression symptoms in 22 articles with a total sample comprising 82,890 individuals was 22% (95% CI: 18-28%), and that of insomnia symptoms in three articles with a sample population of 745 was 57% (95% CI: 48-66%. CONCLUSIONS: The accumulative evidence reveals that adults in Spain suffered higher prevalence rates of mental symptoms during the COVID-19 crisis, with a significantly higher rate relative to other countries such as China. Our synthesis also reveals a relative lack of studies on frontline and general HCWs in Spain

Mental Health during the COVID-19 Crisis in Africa: A Systematic Review and Meta-Analysis.

We aim to provide a systematic review and meta-analysis of the prevalence rates of mental health symptoms among major African populations during the COVID-19 pandemic. We include articles from PubMed, Embase, Web of Science, PsycINFO, and medRxiv between 1 February 2020 and 6 February 2021, and pooled data using random-effects meta-analyses. We identify 28 studies and 32 independent samples from 12 African countries with a total of 15,071 participants. The pooled prevalence of anxiety was 37% in 27 studies, of depression was 45% in 24 studies, and of insomnia was 28% in 9 studies. The pooled prevalence rates of anxiety, depression, and insomnia in North Africa (44%, 55%, and 31%, respectively) are higher than those in Sub-Saharan Africa (31%, 30%, and 24%, respectively). We find (a) a scarcity of studies in several African countries with a high number of COVID-19 cases; (b) high heterogeneity among the studies; (c) the extent and pattern of prevalence of mental health symptoms in Africa is high and differs from elsewhere-more African adults suffer from depression rather than anxiety and insomnia during COVID 19 compared to adult populations in other countries/regions. Hence, our findings carry crucial implications and impact future research to enable evidence-based medicine in Africa

The association of pulmonary function with carotid atherosclerosis in older Chinese: Guangzhou Biobank Cohort Study-CVD Subcohort

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Mental disorder symptoms during the COVID-19 pandemic in Latin America - a systematic review and meta-analysis.

AIMS: There is a lack of evidence related to the prevalence of mental health symptoms as well as their heterogeneities during the coronavirus disease 2019 (COVID-19) pandemic in Latin America, a large area spanning the equator. The current study aims to provide meta-analytical evidence on mental health symptoms during COVID-19 among frontline healthcare workers, general healthcare workers, the general population and university students in Latin America. METHODS: Bibliographical databases, such as PubMed, Embase, Web of Science, PsycINFO and medRxiv, were systematically searched to identify pertinent studies up to August 13, 2021. Two coders performed the screening using predefined eligibility criteria. Studies were assigned quality scores using the Mixed Methods Appraisal Tool. The double data extraction method was used to minimise data entry errors. RESULTS: A total of 62 studies with 196 950 participants in Latin America were identified. The pooled prevalence of anxiety, depression, distress and insomnia was 35%, 35%, 32% and 35%, respectively. There was a higher prevalence of mental health symptoms in South America compared to Central America (36% v. 28%, p < 0.001), in countries speaking Portuguese (40%) v. Spanish (30%). The pooled prevalence of mental health symptoms in the general population, general healthcare workers, frontline healthcare workers and students in Latin America was 37%, 34%, 33% and 45%, respectively. CONCLUSIONS: The high yet heterogenous level of prevalence of mental health symptoms emphasises the need for appropriate identification of psychological interventions in Latin America

Meta-Regression on the Heterogenous Factors Contributing to the Prevalence of Mental Health Symptoms During the COVID-19 Crisis Among Healthcare Workers.

Objective: This paper used meta-regression to analyze the heterogenous factors contributing to the prevalence rate of mental health symptoms of the general and frontline healthcare workers (HCWs) in China under the COVID-19 crisis. Method: We systematically searched PubMed, Embase, Web of Science, and Medrxiv and pooled data using random-effects meta-analyses to estimate the prevalence rates, and ran meta-regression to tease out the key sources of the heterogeneity. Results: The meta-regression results uncovered several predictors of the heterogeneity in prevalence rates among published studies, including severity (e.g., above severe vs. above moderate, p < 0.01; above moderate vs. above mild, p < 0.01), type of mental symptoms (PTSD vs. anxiety, p = 0.04), population (frontline vs. general HCWs, p < 0.01), sampling location (Wuhan vs. Non-Wuhan, p = 0.04), and study quality (p = 0.04). Conclusion: The meta-regression findings provide evidence on the factors contributing to the prevalence rate of mental health symptoms of the general and frontline healthcare workers (HCWs) to guide future research and evidence-based medicine in several specific directions. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=220592, identifier: CRD42020220592

Light Cigarette Smoking Increases Risk of All-Cause and Cause-Specific Mortality: Findings from the NHIS Cohort Study

Very few studies have examined the association between light cigarette smoking (i.e., = 18 years in the United States were included. Deaths were from all cause, cancer, cardiovascular disease (CVD) and respiratory disease and were confirmed by death certification. During a median follow-up of 8.2 years, 34,862 participants died, of which 8415 were from cancer, 9031 from CVD, and 2040 from respiratory disease. Compared with never-smokers, participants who smoked 1-2 (hazard ratios (HR) = 1.94, 95%CI = 1.73-2.16) and 3-5 cigarettes (HR = 1.99, 1.83-2.17) per day were at higher risk of all-cause mortality after adjustment for demographic variables, lifestyle factors and physician-diagnosis of chronic disease. The associations were stronger for respiratory disease-specific mortality, followed by cancer-specific mortality and CVD-specific mortality. For example, the HRs (95% CIs) of smoking 1-2 cigarettes per day were 9.75 (6.15-15.46), 2.28 (1.84-2.84) and 1.93 (1.58-2.36), respectively, for these three cause-specific mortalities. This study indicates that light cigarette smoking increases risk of all-cause and cause-specific mortality in US adults

Listen to genes : dealing with microarray data in the frequency domain

Background: We present a novel and systematic approach to analyze temporal microarray data. The approach includes normalization, clustering and network analysis of genes. Methodology: Genes are normalized using an error model based uniform normalization method aimed at identifying and estimating the sources of variations. The model minimizes the correlation among error terms across replicates. The normalized gene expressions are then clustered in terms of their power spectrum density. The method of complex Granger causality is introduced to reveal interactions between sets of genes. Complex Granger causality along with partial Granger causality is applied in both time and frequency domains to selected as well as all the genes to reveal the interesting networks of interactions. The approach is successfully applied to Arabidopsis leaf microarray data generated from 31,000 genes observed over 22 time points over 22 days. Three circuits: a circadian gene circuit, an ethylene circuit and a new global circuit showing a hierarchical structure to determine the initiators of leaf senescence are analyzed in detail. Conclusions: We use a totally data-driven approach to form biological hypothesis. Clustering using the power-spectrum analysis helps us identify genes of potential interest. Their dynamics can be captured accurately in the time and frequency domain using the methods of complex and partial Granger causality. With the rise in availability of temporal microarray data, such methods can be useful tools in uncovering the hidden biological interactions. We show our method in a step by step manner with help of toy models as well as a real biological dataset. We also analyse three distinct gene circuits of potential interest to Arabidopsis researchers

An ultrathin rechargeable solid-state zinc ion fiber battery for electronic textiles.

Electronic textiles (e-textiles), having the capability of interacting with the human body and surroundings, are changing our everyday life in fundamental and meaningful ways. Yet, the expansion of the field of e-textiles is still limited by the lack of stable and biocompatible power sources with aesthetic designs. Here, we report a rechargeable solid-state Zn/MnO2 fiber battery with stable cyclic performance exceeding 500 hours while maintaining 98.0% capacity after more than 1000 charging/recharging cycles. The mechanism of the high electrical and mechanical performance due to the graphene oxide–embedded polyvinyl alcohol hydrogel electrolytes was rationalized by Monte Carlo simulation and finite element analysis. With a collection of key features including thin, light weight, economic, and biocompatible as well as high energy density, the Zn/MnO2 fiber battery could seamlessly be integrated into a multifunctional on-body e-textile, which provides a stable power unit for continuous and simultaneous heart rate, temperature, humidity, and altitude monitoring