Framing 2014 Hong Kong Protest

Numerous newspapers have documented the stories of the 2014 Hong Kong protest, but there was a void of systematic analysis of the protest coverage. To partially fill this gap, this study utilized the most five common news frames- conflict, human interest, responsibility attribution, morality, economic consequence- to probe how they were used in different newspapers within their media systems. The attitudes, or tone, towards the Hong Kong protest was also examined to gain an in-depth understanding in relation to different framing strategies. A total of 105 news article from China Daily (Mainland China), South China Morning Post (Hong Kong) and New York Times (the United States) were selected for content analysis. Through empirical data collected, it was found that conflict, human interest, and responsibility attribution were the most frequent used framing strategies in reporting 2014 Hong Kong protest. The frame of economic consequence was associated with the negative tone of the protest. Neutral tone was the dominant attitude towards the Hong Kong protest

A TOPIC SENSITIVE SIMRANK (TSSR) MODEL FOR EXPERTS FINDING ON ONLINE RESEARCH SOCIAL PLATFORMS

As an efficient online academic information repository and information channel with crowds’ contribution, online research social platforms have become an efficient tool for various kinds of research & management applications. Social network platforms have also become a major source to seek for field experts. They have advantages of crowd contributions, easy to access without geographic restrictions and avoiding conflict of interests over traditional database and search engine based approaches. However, current research attempts to find experts based on features such as published research work, social relationships, and online behaviours (e.g. reads and downloads of publications) on social platforms, they ignore to verify the reliability of identified experts. To bridge this gap, this research proposes an innovative Topic Sensitive SimRank (TSSR) model to identify “real” experts on social network platforms. TSSR model includes three components: LDA for Expertise Extension, Topic Sensitive Network for Reputation Measurement, and Topic Sensitive SimRank for unsuitable experts detection. We also design a parallel computing strategy to improve the efficiency of the proposed methods. Last, to verify the effectiveness of the proposed model, we design an experiment on one of the research social platforms-ScholarMate to seek for experts for companies that need academic-industry collaboration

Double or nothing: old chest X-ray as a clue to lung mass

Mucoepidermoid carcinoma is a young person’s lung cancer with no apparent causal connection to smoking. It exhibits slow growth, which can make it challenging to detect changes in size on serial chest imaging. Another way of describing its growth pattern is that mucoepidermoid carcinoma has an unusually long volume doubling time. We describe a case of an incidental lung nodule diagnosed as mucoepidermoid carcinoma in which a prior chest radiograph provided a clue to the indolent nature of the abnormality and therefore argued against typical lung cancer. In the same context, we underscore the value of volumetric analy-sis in improving the accuracy of nodule growth determinations, which further strengthens the argument that the importance of locating prior imaging has not diminished in contemporary pulmonary practice

Fusing Two Kinds of Virtual Samples for Small Sample Face Recognition

Face recognition has become a very active field of biometrics. Different pictures of the same face might include various changes of expressions, poses, and illumination. However, a face recognition system usually suffers from the problem that nonsufficient training samples cannot convey these possible changes effectively. The main reason is that a system has only limited storage space and limited time to capture training samples. Many previous literatures ignored the problem of nonsufficient training samples. In this paper, we overcome the insufficiency of training sample size problem by fusing two kinds of virtual samples and the original samples to perform small sample face recognition. The two used kinds of virtual samples are mirror faces and symmetrical faces. Firstly, we transform the original face image to obtain mirror faces and symmetrical faces. Secondly, we fuse these two kinds of virtual samples to achieve the matching scores between the test sample and each class. Finally, we integrate the matching scores to get the final classification results. We compare the proposed method with the single virtual sample augment methods and the original representation-based classification. The experiments on various face databases show that the proposed scheme achieves the best accuracy among the representation-based classification methods

The effect of leisure engagement on preschool teachers’ job stress and sustainable well-being

The preschool education profession often faces many challenges and preschool teachers, as important members of the preschool education profession must respond to a variety of emergencies with young children, which also leads to high levels of job stress and can have a negative impact on their ongoing well-being. Past research has pointed out that a healthy lifestyle is one of the key factors in enhancing sustainable well-being in high-stress work situations and many studies have found that good leisure activity engagement as a healthy lifestyle is associated with higher levels of well-being. However, the relationship between preschool teachers’ leisure engagement and sustainable well-being has been less explored. Therefore, this study proposed seven research hypotheses based on the engagement theory proposed by Fredricks et al. (2004) and developed a research model to explore the relationship between three types of leisure engagement, including behavioral, affective and cognitive, and preschool teachers’ job stress and sustainable well-being, using age as a control variable. This study used a cross-sectional web-based questionnaire with a convenience sample of 500 preschool teachers in China. The collected data were analyzed for reliability and validity, model fit testing and structural equation modeling for model validation after removing invalid data and incomplete responses. The results of the study showed that (a) behavioral engagement was not related with either the job stress or sustainable well-being of preschool teachers; (b) Emotional and cognitive engagement were negatively related to job stress but positively related to the sustainable well-being of preschool teachers; and (c) Job stress was negatively related to the sustainable well-being of preschool teachers; (d) Age is an effective control variable. From the above results, it is clear that not all three types of leisure engagement are effective in terms of reducing the work stress of preschool teachers. As well as being related to the sustainable well-being of preschool teachers emotional and cognitive engagement contributed more to sustainable well-being acquisition

Exploring the mechanism of aloe-emodin in the treatment of liver cancer through network pharmacology and cell experiments

Objective: Aloe-emodin (AE) is an anthraquinone compound extracted from the rhizome of the natural plant rhubarb. Initially, it was shown that AE exerts an anti-inflammatory effect. Further studies revealed its antitumor activity against various types of cancer. However, the mechanisms underlying these properties remain unclear. Based on network pharmacology and molecular docking, this study investigated the molecular mechanism of AE in the treatment of hepatocellular carcinoma (HCC), and evaluated its therapeutic effect through in vitro experiments.Methods: CTD, Pharmmapper, SuperPred and TargetNet were the databases to obtain potential drug-related targets. DisGenet, GeneCards, OMIM and TTD were used to identify potential disease-related targets. Intersection genes for drugs and diseases were obtained through the Venn diagram. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of intersecting genes were conducted by the website of Bioinformatics. Intersection genes were introduced into STRING to construct a protein-protein interaction network, while the Cytoscape3.9.1 software was used to visualize and analyze the core targets. AutoDock4.2.6 was utilized to achieve molecular docking between drug and core targets. In vitro experiments investigated the therapeutic effects and related mechanisms of AE.Results: 63 overlapped genes were obtained and GO analysis generated 3,646 entries by these 63 intersecting genes. KEGG analysis mainly involved apoptosis, proteoglycans in cancer, TNF signaling pathway, TP53 signaling pathway, PI3K-AKT signaling pathway, etc. AKT1, EGFR, ESR1, TP53, and SRC have been identified as core targets because the binding energies of them between aloe-emodin were less than -5 kcal/Mol.The mRNA and protein expression, prognosis, mutation status, and immune infiltration related to core targets were further revealed. The involvement of AKT1 and EGFR, as well as the key target of the PI3K-AKT signaling pathway, indicated the importance of this signaling pathway in the treatment of HCC using AE. The results of the Cell Counting Kit-8 assay and flow analysis demonstrated the therapeutic effect of AE. The downregulation of EGFR, PI3KR1, AKT1, and BCL2 in mRNA expression and PI3KR1, AKT,p-AKT in protein expression confirmed our hypothesis.Conclusion: Based on network pharmacology and molecular docking, our study initially showed that AE exerted a therapeutic effect on HCC by modulating multiple signaling pathways. Various analyses confirmed the antiproliferative activity and pro-apoptotic effect of AE on HCC through the PI3K-AKT signaling pathway. This study revealed the therapeutic mechanism of AE in the treatment of HCC through a novel approach, providing a theoretical basis for the clinical application of AE

Patterns of pediatric and adolescent female genital inflammation in China: an eight-year retrospective study of 49,175 patients in China

BackgroundGenital inflammation is one of the most frequent clinical complaints among girls, which was easily overlooked by the general public. This study aimed to investigate the patterns and epidemiological characteristics of pediatric and adolescent female genital inflammation in China.MethodsA retrospective observational study (2011 to 2018) was conducted among all female patients under the age of 0–18 years at the Department of Pediatric and Adolescent Gynecology of The Children’s Hospital, Zhejiang University School of Medicine. Data were collected from the electronic medical records. The abnormal vaginal discharge of patient was collected for microbiological investigation by bacterial and fungal culture. Descriptive analysis was conducted to evaluate the genital inflammation pattern and epidemiological characteristics, including age, season, and type of infected pathogens.ResultsA total of 49,175 patients met the eligibility criteria of genital inflammation and 16,320 patients later came to the hospital for follow-up over the study period. The number of first-visit increased gradually from 3,769 in 2011 to 10,155 in 2018. The peak age of the first visit was 0–6 years old. Non-specific vulvovaginitis, lichen sclerosis, and labial adhesion were the top three genital inflammation. Among the top five potential common pathogens of vaginal infection, the prevalence of Haemophilus influenzae cases was the highest (31.42%, 203/646), followed by Streptococcus pyogenes (27.74%, 176/646), Candida albicans (14.09%, 91/646), Escherichia coli (8.51%, 55/646), and Staphylococcus aureus (6.35%, 41/636). The specific disease categories and pathogens of genital inflammation vary by age groups and season.ConclusionOur study summarizes the pattern of pediatric and adolescent female genital inflammation over an 8-year period in China, emphasizing the need for more public awareness, healthcare services and research in this field

The 2011 Survey on Hypertensive Disorders of Pregnancy (HDP) in China:Prevalence, Risk Factors, Complications, Pregnancy and Perinatal Outcomes

Hypertensive disorders of pregnancy (HDP) are a group of medical complications in pregnancy and also a risk factor for severe pregnancy outcomes, but it lacks a large-scale epidemiological investigation in recent years. This survey represents a multicenter cross-sectional retrospective study to estimate the prevalence and analyze the risk factors for HDP among the pregnant women who had referred for delivery between January 1st 2011 and December 31st 2011 in China Mainland. A total of 112,386 pregnant women were investigated from 38 secondary and tertiary specialized or general hospitals randomly selected across the country, of which 5,869 had HDP, accounting for 5.22% of all pregnancies. There were significant differences in the prevalence of HDP between geographical regions, in which the North China showed the highest (7.44%) and Central China showed the lowest (1.23%). Of six subtypes of HDP, severe preeclampsia accounted for 39.96%, gestational hypertension for 31.40%, mild preeclampsia for 15.13%, chronic hypertension in pregnancy for 6.00%, preeclampsia superimposed on chronic hypertension for 3.68% and eclampsia for 0.89%. A number of risk factors for HDP were identified, including twin pregnancy, age of >35 years, overweight and obesity, primipara, history of hypertension as well as family history of hypertension and diabetes. The prevalence of pre-term birth, placental abruption and postpartum hemorrhage were significantly higher in women with HDP than those without HDP. The possible risk factors confirmed in this study may be useful for the development of early diagnosis and appropriate treatment of HDP