Current Status of Key Competencies among Medical Students: A Latent Profile Analysis

The purpose of this study was to understand the overall level of key competencies of medical students and explore the potential profile of key competencies, promoting quality education, and improving the quality talent cultivation in medical colleges. A stratified random sampling method selected 734 medical students from four medical colleges in Chongqing Province of China. A general information questionnaire and a key competencies survey questionnaire were used to conduct the survey. The overall score and scores of each dimension of key competencies were analyzed. Latent profile analysis was conducted to classify the key competencies of medical students and compare the distribution differences of demographic variables among different categories. The results showed that 26% of medical students have never heard of the concept of key competencies, and 59% of them are not familiar with the content related to key competencies. The score of key competencies is 3.66 ± 0.60, with the highest score in the dimension of responsibility and the lowest score in the dimension of humanistic accomplishment. The latent profile analysis classified them into three categories: “low key competencies group (14.71%)”, “medium key competencies group (36.79%)”, and “high key competencies group (48.50%)”. The R3STEP regression analysis results showed statistically significant differences in educational level and whether they served as student cadres among different key competencies categories of medical students. This paper discusses three different potential key competencies categories among medical students, and the overall level of key competencies is relatively good. However, medical students lack a comprehensive and systematic understanding of key competencies. Humanistic accomplishment, healthy living, and practical innovation are the three dimensions with lower scores and should be given more attention. Medical colleges should integrate the concept of key competencies into teaching and implement it in medical practice to cultivate more high-quality medical talents for society.

User Profile & Attitude Analysis Based on Unstructured Social Media and Online Activity

As social media and online activity continue to pervade all age groups, it serves as a crucial platform for sharing personal experiences and opinions as well as information about attitudes and preferences for certain interests or purchases. This generates a wealth of behavioral data, which, while invaluable to businesses, researchers, policymakers, and the cybersecurity sector, presents significant challenges due to its unstructured nature. Existing tools for analyzing this data often lack the capability to effectively retrieve and process it comprehensively. This paper addresses the need for an advanced analytical tool that ethically and legally collects and analyzes social media data and online activity logs, constructing detailed and structured user profiles. It reviews current solutions, highlights their limitations, and introduces a new approach, the Advanced Social Analyzer (ASAN), that bridges these gaps. The proposed solutions technical aspects, implementation, and evaluation are discussed, with results compared to existing methodologies. The paper concludes by suggesting future research directions to further enhance the utility and effectiveness of social media data analysis.

Prevalence of Latent Tuberculosis Infection [LTBI] in Prison Officers: A Systematic Review and Meta-Analysis

This study aimed to perform a systematic review and meta-analysis to determine the LTBI prevalence in prison officers worldwide. A systematic search was performed in PubMed, WoS, Embase, and BVS, including all articles related to LTBI prevalence and risk factors. After critical evaluation and qualitative synthesis of the identified articles, a meta-analysis was used. Five studies carried out between 2012 and 2022 were included, with a total sample size of 1718 prison officers. The overall LTBI prevalence was 50% [95% confidence interval [CI]: 48% - 52%;n = 816], with high heterogeneity between studies. Smoking [OR = 1.76;CI 95% = 1.26 - 2.46] and males [OR = 2.08;CI 95% = 1.31 - 3.31] were positively related to a higher LTBI prevalence among prison officers. Thus, preventive measures and the rapid and accurate diagnosis of new cases should be emphasized to ensure tuberculosis control, especially among risk groups such as prison officers.

Phytochemical Analysis and Antimicrobial Activity of Lawsonia inermis Leaf Extracts from Burkina Faso

Lawsonia inermis is a hairless plant growing in various regions of North Africa, the Indian subcontinent, and the Middle East. It possesses many medicinal attributes, including curative properties against infectious dermatoses. This study was carried out to evaluate the phytochemical profile of the crude ethanolic extract of the plant leaves and its fractions as well as their antimicrobial activities. The phytochemical profile was performed using high-performance thin-layer chromatography (HPTLC), gas chromatography-mass spectrometry (GC-MS), and high-performance liquid chromatography (HPLC). Additionally, the phenolic and flavonoid contents were determined using the Folin-Ciocalteu spectrophotometric and the aluminum trichloride methods. Antimicrobial activity was tested using disc diffusion and microdilution methods. The presence of flavonoids, tannins, sterols, and triterpenes was revealed. GC-MS detected twelve compounds main compounds consisting of saturated and unsaturated fatty acids and phenolic and terpenoid compounds among twenty-seven components. HPLC also detected high contents of phenolic acids and flavonoids. The most abundant triterpene and sterols were ursolic acid (around 43.14 g/100g DW, 13.9 g/100g dry weight (DW), and 0.68 g/100g DW) in the crude ethanolic extract of leaves (FeLi), hexane fraction (FHLi) and dichloromethane fraction (FDLi), respectively and, β-sitosterol in FeLi (56.7 mg/100g DW), FHLi (10.55 g/100g DW), FDLi (106.1 mg/100g DW) and butanol fraction (FBLi) (357.4 mg/100g DW). Among the flavonoids, rutin = 3.24 g/100g and quercetin = 0.63 g/100g in the ethanolic extract, rutin = 15.73 g/100g in the dichloromethane fraction, and rutin = 0.23 g/100g) in the aqueous fraction;and among phenolic compounds, caffeic acid (37.65 g/100g DW) and vanillic acid (22.70 g/100g DW) were the most important in the ethyl acetate fraction (FAeLi). All organic fractions exhibited interesting antibacterial and antifungal activities against the tested strains, with the best activity recorded with the dichloromethane and ethyl acetate fractions. The leaf extracts’ phytochemical profile and antimicrobial activity support the use of Lawsonia inermis against infectious skin diseases.

Kinetic equilibrium reconstruction with internal safety factor profile constraints on EAST tokamak

Reconstruction of plasma equilibrium plays an important role in the analysis and simulation of plasma experiments. The kinetic equilibrium reconstruction with pressure and edge current constraints has been employed on EAST tokamak. However, the internal safety factor(q) profile is not accurate. This paper proposes a new way of incorporating q profile constraints into kinetic equilibrium reconstruction. The q profile is yielded from the Polarimeter Interferometer(POINT)reconstruction. Virtual probes containing information on q profile constraints are added to inputs of the kinetic equilibrium reconstruction program to obtain the final equilibrium. The new equilibrium produces a more accurate internal q profile. This improved method would help analyze EAST experiments.

Analysis of Public Sentiment regarding COVID-19 Vaccines on the Social Media Platform Reddit

This study undertakes a thorough analysis of the sentiment within the r/Corona-virus subreddit community regarding COVID-19 vaccines on Reddit. We meticulously collected and processed 34,768 comments, spanning from November 20, 2020, to January 17, 2021, using sentiment calculation methods such as TextBlob and Twitter-RoBERTa-Base-sentiment to categorize comments into positive, negative, or neutral sentiments. The methodology involved the use of Count Vectorizer as a vectorization technique and the implementation of advanced ensemble algorithms like XGBoost and Random Forest, achieving an accuracy of approximately 80%. Furthermore, through the Dirichlet latent allocation, we identified 23 distinct reasons for vaccine distrust among negative comments. These findings are crucial for understanding the community’s attitudes towards vaccination and can guide targeted public health messaging. Our study not only provides insights into public opinion during a critical health crisis, but also demonstrates the effectiveness of combining natural language processing tools and ensemble algorithms in sentiment analysis.

Body Weight Misperception and Weight Disorders among Chinese Children and Adolescents:A Latent Class Analysis

Body misperception plays an important role in the development of weight and dietary disorders among children and adolescents.A school-based health promotion program(2014-2015)was conducted to promote the school health education and improve the teenagers'physical health among Chinese children and adolescents.Based on this program,we intended to examine weight status and weight misperception among Chinese children and adolescents and to explore the relationship between weight misperception and lifestyle behaviors.A total of 10708 Chinese children and adolescents in 3rd and 7th grade from Shandong and Qinghai province participated in the program.The participants,dietary and activity patterns were clustered by latent class analysis(LCA).Logistic regression analysis was undertaken to explore the relationship between weight perception and demographic factors or dietary and activity patterns.Given the gender-specific difference of children and adolescents,analyses were separately conducted among boys and girls.The total prevalence of weight misperception was 44.50%.Boys,especially those in higher grade and living in wealthier district,were more likely to misperceive body weight.Girls were more likely to overestimate their weight(26.10%)while boys tended to underestimate the weight(28.32%).Three latent dietary and activity patterns including obesogenic pattern,malnourished pattern and healthy pattern were derived.The participants who had weight misperception were more likely to choose unhealthy dietary and exercise activities.The high prevalence of weight misperception was closely related to the unhealthy weight pattern and unhealthy dietary or exercise patterns.Our research found that most children and adolescents failed to perceive their weight correctly and boys tended to underestimate their weight while girls were subjected to overestimation.So,comprehensive intervention programs should focus on improving self-weight awareness,and appropriate guidance should be made to lead the adolescents to more healthy weight pattern.

Isolation, Identification and Tissue Expression Profile Analysis of One Novel Differentially Expressed Sequence Tag in the Longissimus dorsi Muscle from Meishan, Meishan × Large White Hybrid and Large White Pigs

In order to detect the molecular mechanism of heterosis in pigs, the mRNA differential display technique was performed to investigate the differences of gene expression in the Longissimus dorsi tissue from Meishan, Meishan × Large White hybrid and Large White pigs with nine 3'-end anchored primers in combination with ten 5'-end arbitrary primers and nearly 3000 reproducible bands were examined. One novel expressed sequence tag (EST4, GenBank accession number: AY553914) that was differentially expressed in Meishan, Meishan× Large White hybrid and Large White pigs was isolated from the Longissimus dorsi muscle tissue and identified through semi-quantitative RT-PCR. BLAST analysis revealed that the 350 bp long EST (EST4) was not homologous to any of the known porcine genes. Tissue expression profile analyses showed that the EST4 was expressed in most of tissues.LIU Yong-gang, Ph D candidate

Semi-supervised learning based probabilistic latent semantic analysis for automatic image annotation

In recent years,multimedia annotation problem has been attracting significant research attention in multimedia and computer vision areas,especially for automatic image annotation,whose purpose is to provide an efficient and effective searching environment for users to query their images more easily. In this paper,a semi-supervised learning based probabilistic latent semantic analysis( PLSA) model for automatic image annotation is presenred. Since it's often hard to obtain or create labeled images in large quantities while unlabeled ones are easier to collect,a transductive support vector machine( TSVM) is exploited to enhance the quality of the training image data. Then,different image features with different magnitudes will result in different performance for automatic image annotation. To this end,a Gaussian normalization method is utilized to normalize different features extracted from effective image regions segmented by the normalized cuts algorithm so as to reserve the intrinsic content of images as complete as possible. Finally,a PLSA model with asymmetric modalities is constructed based on the expectation maximization( EM) algorithm to predict a candidate set of annotations with confidence scores. Extensive experiments on the general-purpose Corel5k dataset demonstrate that the proposed model can significantly improve performance of traditional PLSA for the task of automatic image annotation.

User Profile System Based on Sentiment Analysis for Mobile Edge Computing

Emotions of users do not converge in a single application but are scattered across diverse applications.Mobile devices are the closest media for handling user data and these devices have the advantage of integrating private user information and emotions spread over different applications.In this paper,we first analyze user profile on a mobile device by describing the problem of the user sentiment profile system in terms of data granularity,media diversity,and server-side solution.Fine-grained data requires additional data and structural analysis in mobile devices.Media diversity requires standard parameters to integrate user data from various applications.A server-side solution presents a potential risk when handling individual privacy information.Therefore,in order to overcome these problems,we propose a general-purposed user profile system based on sentiment analysis that extracts individual emotional preferences by comparing the difference between public and individual data based on particular features.The proposed system is built based on a sentiment hierarchy,which is created by using unstructured data on mobile devices.It can compensate for the concentration of single media,and analyze individual private data without the invasion of privacy on mobile devices.

New approaches to cognitive work analysis through latent variable modeling in mining operations

This paper discusses the utilization of latent variable modeling related to occupational health and safety in the mining industry.Latent variable modeling,which is a statistical model that relates observable and latent variables,could be used to facilitate researchers’understandings of the underlying constructs or hypothetical factors and their magnitude of effect that constitute a complex system.This enhanced understanding,in turn,can help emphasize the important factors to improve mine safety.The most commonly used techniques include the exploratory factor analysis(EFA),the confirmatory factor analysis(CFA)and the structural equation model with latent variables(SEM).A critical comparison of the three techniques regarding mine safety is provided.Possible applications of latent variable modeling in mining engineering are explored.In this scope,relevant research papers were reviewed.They suggest that the application of such methods could prove useful in mine accident and safety research.Application of latent variables analysis in cognitive work analysis was proposed to improve the understanding of human-work relationships in mining operations.

APPLICATION OF THE CENTRAL MOMENT METHODS TO ANALYSIS OF X－RAY DIFFRAC－TION LINE PROFILE

Pure X-ray diffraction profiles have been analysed for polyamide (PA) 1010 and added BMI system by means of multipeak fitting resolution of X-ray diffraction. The methods of variance and fourth moment have been applied to determine the particle size and strain values for paracrystalline materials, and the values thus obtained have been compared. The results indicated that both variance and fourth moment of X-ray diffraction line profile' yielded approximately the 'same values of the particle size and the strain. The particle sizes of (100) reflection have been found tO decrease with increasing BMI content.On the contrary, the strain values were on the increase.

A Comparison of Statistics for Assessing Model Invariance in Latent Class Analysis

Latent class analysis (LCA) is a widely used statistical technique for identifying subgroups in the population based upon multiple indicator variables. It has a number of advantages over other unsupervised grouping procedures such as cluster analysis, including stronger theoretical underpinnings, more clearly defined measures of model fit, and the ability to conduct confirmatory analyses. In addition, it is possible to ascertain whether an LCA solution is equally applicable to multiple known groups, using invariance assessment techniques. This study compared the effectiveness of multiple statistics for detecting group LCA invariance, including a chi-square difference test, a bootstrap likelihood ratio test, and several information indices. Results of the simulation study found that the bootstrap likelihood ratio test was the optimal invariance assessment statistic. In addition to the simulation, LCA group invariance assessment was demonstrated in an application with the Youth Risk Behavior Survey (YRBS). Implications of the simulation results for practice are discussed.

Latent Profiles of Social Media Users, STI Knowledge, and Condom Use among African American Young Adults

The abundance of social networking platforms has increased the frequency and the availability for which individuals communicate with one another. The feasibility and accessibility to go online to find sexual partners pose opportunity for contracting sexually transmitted infections (STI) in the absence of safe sexual practices. Low condom use has been reported among young adults who seek sexual partners online. African American young adults have some of the highest rates of infection for certain STIs. In order to mitigate the incidence and prevalence of STIs in at-risk populations, sexually active young adults must use condoms consistently and correctly during sexual activities. The present study sought to uncover the heterogeneity within African American young adults regarding their online networking utilization, STI knowledge, and sexual risk behavior. African American young adults (N = 236), ages 18 - 23, completed private online survey administration. Using latent class analysis, three classes were identified: Social Network Communicators (43%;N = 101), Social Networking Daters (36%;N = 83), and Media Sharers (21%;N = 52). Social Networking Daters exhibited the highest probability of using online dating sites daily, low STI knowledge, and a zero probability of consistent condom use. All three groups exhibited relatively low STI knowledge. Furthermore, having a history of STI increased the likelihood of being classified into the Social Networking Daters class relative to the other classes. Findings highlight the need to capitalize upon online platforms for African American young adults who utilize online dating sites and other online environments.

Early Cretaceous Thrust and Nappe Tectonics in North Qilian Shan,Northern Tibetan Plateau:Evidence from Field Mapping,Geochronology,and Deep Structural Analysis

The North Qilian Shan fold and thrust belt,located at the northern Tibetan Plateau and southern margin of the Hexi Corridor,is a key tectonic unit to decode the formation and expansion of the plateau.Previous studies emphasize the Cenozoic deformation due to the far-field response to the Indo-Asian collision,but the Mesozoic deformations are poorly constrained in this area.We conducted detailed field mapping,structural analysis,geochronology,and structural interpretation of deep seismic reflectional profiling and magnetotelluric(MT)sounding,to address the superposed results of the Mesozoic and Cenozoic deformation.The results recognized the North Qilian thrust and nappe system(NQTS),the root and the frontal belt are the North Qilian thrust(NQT),and the Yumu Shan klippe(YK),respectively.The middle belt is located between the NQT and the YK.Monzonitic granite zircon U-Pb dating from the middle belt yields an age of ca.415 Ma,which is similar to south NQT.The thrusting displacement is estimated at ca.48 km by structural interpretation of deep profiles.The timing is constrained in the early stage of the Early Cretaceous by the formation of simultaneous growth strata.We suggest that the NQTS has resulted from the far-field effect of the Lhasa-Qiangtang collision,and the Yumu Shan is uplifted by the superposed Cenozoic deformation.

Serological diagnostics of myocardium diseases based on multivariate analysis of cardiotrophic autoantibodies’ profiles

We analyzed profiles of IgG autoantibodies to 16 cardiac specific proteins and their main immunogenic region B-epitopes, in the groups of already verified cardiac pathology: acute and chronic lymphocytic myocarditis, ST elevation myocardial infarction, postinfarction remodeling of myocardium, dilated cardiomyopathy and in healthy controls along with patients, suffered from gastritis (to evaluate immune response against cross-reactive B-epitopes). AAB specific patterns allowed us to distinguish cases among themselves by means of multiparametrical canonical discriminant analysis in approximately 95% of cases. Positive predictive value in the group of MYO reached 95%, in the STEMI—89%, in the PIR—99%, in the DCM—99%, in the group of gastritis—88%. Principal component analysis of mentioned cardiac pathologies extended current clinical knowledge of their immunopathogenesis. Obtained data markedly proved a usability of serum AAB profiling for non invasive screening, differential diagnostics and working hypothesis composition.

A search for evidence of small-scale inhomogeneities in dense cores from line profile analysis

In order to search for intensity fluctuations on the HCN（1-0） and HCO＋（1-0） line pro- files, which could arise due to possible small-scale inhomogeneous structure, long-term observations of high-mass star-forming cores S140 and S199 were carried out. The data were processed by the Fourier filtering method. Line temperature fluctuations that exceed the noise level were detected. Assuming the cores consist of a large number of randomly moving small thermal fragments, the total number of frag- ments is - 4 × 106 for the region with linear size - 0.1 pc in S140 and - 106 for the region with linear size - 0.3 pc in S 199. Physical parameters of fragments in S 140 were obtained from detailed modeling of the HCN emission in the framework of the clumpy cloud model.

An Integrated Analysis Method for miRNA, lncRNA and mRNA Profiles Based on Their Functional and Positional Relationships

ncRNAs have been identified as potential regulatory molecules and have multiple biological roles. Aberrant expression of specific ncRNAs contributes to multiple biological processes and many human diseases. Herein, we simultaneously profiled miRNA, lncRNA and mRNA in human HepG2 and L02 cells applying high-throughput sequencing and micro-array technologies. Abnormal miRNA, lncRNA and mRNA profiles were assessed through fold change filtering. A cross-platform integrated analysis method was developed to analyze differentially expressed miRNA, lncRNA and mRNA profiles. miRNA-mRNA interaction was analyzed according to their functional relationships. Target mRNAs of aberrantly expressed miRNAs were obtained from experimentally validated datasets or predicted using some programs. Generally, multiple target mRNAs were involved, and they have versatile roles by functional enrichment analysis. Ac-cording to actual expression datasets in the study, compared to deregulated miRNAs, these theoretical target mRNAs showed various expression patterns. The consistent or inconsistent expression was mainly derived from complex, mul-tiple, flexible and alternative regulatory relationships between miRNA and mRNA. Further, miRNA/mRNA and lncRNA were completely surveyed based on their location distributions on human chromosomes. Many miRNA-lncRNA and mRNA-lncRNA pairs always were located on the same strand or different strands in the specific genomic region. Due to the location distributions, they might have partly or completely overlapped regions or they could be reverse complementarily binding. These miRNA/mRNA-lncRNA pairs showed consistent or inconsistent expression pat-terns, although they might have functional relationships through reverse complementarily binding events. Moreover, we also detected and analyzed various isomiRs from a given miRNA locus, including those isomiRs with 3’ additional non-template nucleotides. These isomiRs, especially for those 5’ isomiRs with the new “seed sequences” through “seed shifting” events, maybe have potential biological roles as well as isomiR repertoire and their expression patterns. The integrative analysis provides potential functional relationships between miRNA, lncRNA and mRNA across different datasets. The complex and various expression patterns suggest a robust regulatory network across different regulatory molecules and their targets.

Metaphor Analysis Method Based on Latent Semantic Analysis

Current research on metaphor analysis is generally knowledge-based and corpus-based,which calls for methods of automatic feature extraction and weight calculation.Combining natural language processing(NLP),latent semantic analysis(LSA),and Pearson correlation coefficient,this paper proposes a metaphor analysis method for extracting the content words from both literal and metaphorical corpus,calculating correlation degree,and analyzing their relationships.The value of the proposed method was demonstrated through a case study by using a corpus with keyword“飞翔(fly)”.When compared with the method of Pearson correlation coefficient,the experiment shows that the LSA can produce better results with greater significance in correlation degree.It is also found that the number of common words that appeared in both literal and metaphorical word bags decreased with the correlation degree.The case study also revealed that there are more nouns appear in literal corpus,and more adjectives and adverbs appear in metaphorical corpus.The method proposed will benefit NLP researchers to develop the required step-by-step calculation tools for accurate quantitative analysis.

Profiles of depressive disorders and influencing factors among the elderly in China: protocol for a secondary data analysis

Background:Depressive disorders have become a major risk factor that influences people’s health worldwide,but few studies have focused on the prevalence of depressive disorders among the Chinese elderly and their characteristics of depressive disorders.The current study is a secondary data analysis designed to explore the profiles of depressive disorders in the Chinese elderly by latent profile analysis.Methods:The China Health and Retirement Longitudinal Study(CHARLS 2018)database will be used for analysis.Latent profile analysis will be employed to identify the profiles of depressive disorders using data from the subsection“CESD Depression”in the section“Cognition and Depression”.Stepwise multinomial logistic regression will be used to explore the influencing factors of different profiles of depressive disorders among the Chinese elderly.Discussion:The prevalence of depressive disorders among the Chinese elderly and their profiles of depressive disorders will be reported.Possible influencing factors may include some demographic characteristics and associated psychological elements,which will provide a reference for further research and precise intervention.