A trial of patient-oriented problem-solving system for immunology teaching in China: a comparison with dialectic lectures

BACKGROUND: The most common teaching method used in China is lecturing, but recently, efforts have been widely undertaken to promote the transition from teacher-centered to student-centered education. The patient-oriented problem-solving (POPS) system is an innovative teaching-learning method that permits students to work in small groups to solve clinical problems, promotes self-learning, encourages clinical reasoning and develops long-lasting memory. To our best knowledge, however, POPS has never been applied in teaching immunology in China. The aim of this study was to develop POPS in teaching immunology and assess students’ and teachers’ perception to POPS. METHODS: 321 second-year medical students were divided into two groups: I and II. Group I, comprising 110 students, was taught by POPS, and 16 immunology teachers witnessed the whole teaching process. Group II including the remaining 211 students was taught through traditional lectures. The results of the pre- and post-test of both groups were compared. Group I students and teachers then completed a self-structured feedback questionnaire for analysis before a discussion meeting attended only by the teachers was held. RESULTS: Significant improvement in the mean difference between the pre- and post-test scores of those in Groups I and II was seen, demonstrating the effectiveness of POPS teaching. Most students responded that POPS facilitates self-learning, helps them to understand topics and creates interest, and 88.12% of students favored POPS over simple lectures. Moreover, while they responded that POPS facilitated student learning better than lectures, teachers pointed out that limited teaching resources would make it difficult for wide POPS application in China. CONCLUSIONS: While POPS can break up the monotony of dialectic lectures and serve as a better teaching method, it may not be feasible for the current educational environment in China. The main reason for this is the relative shortage of teaching resources such as space, library facilities and well-trained teachers

Transcriptional profile of human thymus reveals IGFBP5 is correlated with age-related thymic involution

Thymus is the main immune organ which is responsible for the production of self-tolerant and functional T cells, but it shrinks rapidly with age after birth. Although studies have researched thymus development and involution in mouse, the critical regulators that arise with age in human thymus remain unclear. We collected public human single-cell transcriptomic sequencing (scRNA-seq) datasets containing 350,678 cells from 36 samples, integrated them as a cell atlas of human thymus. Clinical samples were collected and experiments were performed for validation. We found early thymocyte-specific signaling and regulons which played roles in thymocyte migration, proliferation, apoptosis and differentiation. Nevertheless, signaling patterns including number, strength and path completely changed during aging, Transcription factors (FOXC1, MXI1, KLF9, NFIL3) and their target gene, IGFBP5, were resolved and up-regulated in aging thymus and involved in promoting epithelial-mesenchymal transition (EMT), responding to steroid and adipogenesis process of thymic epithelial cell (TECs). Furthermore, we validated that IGFBP5 protein increased at TECs and Hassall’s corpuscle in both human and mouse aging thymus and knockdown of IGFBP5 significantly increased the expression of proliferation-related genes in thymocytes. Collectively, we systematically explored cell-cell communications and regulons of early thymocytes as well as age-related differences in human thymus by using both bioinformatic and experimental verification, indicating IGFBP5 as a functional marker of thymic involution and providing new insights into the mechanisms of thymus involution

Inside the NIGM Grid Service: Implementation, Evaluation and Extension

Chinese and Western medicine s have a different understanding and approach to life, health, and illness -joining their complementary work and support them by an advanced information technology could result in an improved health system. The Non-Invasive Blood Glucose Measurement (NIGM) Service is a grid based implementation of a novel non-invasive method for measuring human blood glucose values exploiting Chinese meridian theory. In this paper, we describe the implementation of the NIGM service in detail, present an initial performance evaluation and discuss an extension towards other non-invasive long term diabetic relevant measurement. Additionally, the adaption of the ontology-based Medical records Annotation Tool (MedAT) framework towards usage in NIGM trails is elaborated. ? 2008 IEEE.EI

The Ninth Visual Object Tracking VOT2021 Challenge Results

acceptedVersionPeer reviewe

Unraveling Implicit Knowledge in Information Technology Jobs

Job seekers are used to looking at job postings published on the main websites like Glassdoor and Google Jobs. Typically, an online job posting provides a piece of text that describes the job in a more qualitative way. Most job seekers, who would have to view hundreds of postings every day, tend to pay attention to the explicit information exposed by the textual description, such as required skills, salary, and benefits, which are information that the author wishes to convey to the job seekers directly. However, this would lead to overlook of a large part of implicit information which is hidden deeper in the linguistic characteristics of the textual description, such as readability of the text, status of the employer, and domain-unrelated concerns of the text. These implicit aspects of the job description can give job seekers knowledge into the job culture and personal characteristics of future colleagues, helping them to prepare for job interviews more efficiently, and integrate into future job environment more smoothly. Using text mining methods, this study extracts various types of implicit information/knowledge of a collection of more than 24 thousand job postings and depicts the implicit characteristics of IT-related jobs compared to non-IT jobs. Analysis results show that IT-related and non-IT job descriptions have distinct profiles in terms of implicit characteristics

Stream management within the CloudMiner

Nowadays cloud computing has become a major trend that enterprises and research organizations are pursuing with increasing zest. A potentially important application area for clouds is data analytics. In our previous publication, we introduced a novel cloud infrastructure, the CloudMiner, which facilitates data mining on massive scientific data. By providing a cloud platform which hosts data mining cloud services following the Software as a Service (SaaS) paradigm, CloudMiner offers the capability for realizing cloud-based data mining tasks upon traditional distributed databases and other dataset types. However, little attention has been paid to the issue of data stream management on the cloud so far. We have noticed the fact that some features of the cloud meet very well the requirements of data stream management. Consequently, we developed an innovative software framework, called the StreamMiner, which is introduced in this paper. It serves as an extension to the CloudMiner for facilitating, in particular, real-world data stream management and analysis using cloud services. In addition, we also introduce our tentative implementation of the framework. Finally, we present and discuss the first experimental performance results achieved with the first StreamMiner prototype

The Spatial, Temporal, and Individual Dimensions of Child Maltreatment Recurrence in the United States: A Survival Analysis

The current research aims at improving the efficacy of applying data analytics methods such as survival analysis (Coeurderoy, Guilmot, & Vas, 2014) on big human data to create insights into complex social problems recurrent child maltreatment as a representative case. Social welfare agencies collect and produce vast volumes of data from various sources that can be utilized to illuminate social issues and facilitate effective solutions (Coulton, Goerge, Putnam-Hornstein, & de Haan, 2015). However, social welfare agencies face several challenges in converting data into analytical power. First, effective analysis of a massive amount of data that requires recording a large number of features associated with diverse individuals can become challenging. Furthermore, even though the models produced by data analytics are inherently predictive, taking primitive action at the individual level in most social problems is very difficult, if not impossible. Additionally, the interaction between humans and society can be highly intricate, making it difficult to determine if certain features of an individual are essential regarding the occurrence of an event. Responding to these challenges, the current study proposes three inter-connected categories of features available in most big human data sets: spatial, temporal, and individual/event-related features. These three categories of features are recognized based on two theoretical frameworks, the Routine Activities Theory (Felson & Cohen, 1980; Miró, 2014) and Fogg Behavior Model (Fogg, 2009). Supported by the results of an empirical study on an extensive, national data set of child maltreatment cases in the United States (US Department of Health and Human Services, 2017), we argue that features in each of these categories can have a strong indication of social welfare-related occurrence events. In contrast, analysis of these features—individually or jointly—can reveal spatial, temporal, and individual patterns. Therefore, the current study aims to answer the following research questions: (1) What are the spatial, temporal, and individual-related features to be considered to predict recurrent child maltreatment patterns? (2) How is the relative significance of these features when victims of different geographical locations, time frames, demographic groups, and maltreatment types are considered? Survival analysis was conducted in each of or across the victim groups above. Victims’ survival rate of recurrent child maltreatment was examined as the prediction target. Based on the results of survival analysis, the discussion was made on how to improve the predictive capability of individual features in the use of big human data. The results of our study can be helpful for both researchers and policymakers. Researchers can apply the results to improve the efficacy of data analytics methods on big human data. Researchers can also correlate the observed trends with other social and economic factors to explain and predict the target problem\u27s prevalence. Policymakers may use the resulting temporal trends to fine-tune the child welfare policies for the current and subsequent years to optimize child welfare resource allocation