VLSP SHARED TASK: SENTIMENT ANALYSIS

Sentiment analysis is a natural language processing (NLP) task of identifying orextracting the sentiment content of a text unit. This task has become an active research topic since the early 2000s. During the two last editions of the VLSP workshop series, the shared task on Sentiment Analysis (SA) for Vietnamese has been organized in order to provide an objective evaluation measurement about the performance (quality) of sentiment analysis tools, and encouragethe development of Vietnamese sentiment analysis systems, as well as to provide benchmark datasets for this task. The rst campaign in 2016 only focused on the sentiment polarity classication, with a dataset containing reviews of electronic products. The second campaign in 2018 addressed the problem of Aspect Based Sentiment Analysis (ABSA) for Vietnamese, by providing two datasets containing reviews in restaurant and hotel domains. These data are accessible for research purpose via the VLSP website vlsp.org.vn/resources. This paper describes the built datasets as well as the evaluation results of the systems participating to these campaigns

VLSP Shared Task: Named Entity Recognition

Named entities (NE) are phrases that contain the names of persons, organizations, locations, times and quantities, monetary values, percentages, etc. Named Entity Recognition (NER) is the task of recognizing named entities in documents. NER is an important subtask of Information Extraction, which has attracted researchers all over the world since 1990s. For Vietnamese language, although there exists some research projects and publications on NER task before 2016, no systematic comparison of the performance of NER systems has been done. In 2016, the organizing committee of the VLSP workshop decided to launch the first NER shared task, in order to get an objective evaluation of Vietnamese NER systems and to promote the development of high quality systems. As a result, the first dataset with morpho-syntactic and NE annotations has been released for benchmarking NER systems. At VLSP 2018, the NER shared task has been organized for the second time, providing a bigger dataset containing texts from various domains, but without morpho-syntactic annotation. These resources are available for research purpose via the VLSP website vlsp.org.vn/resources. In this paper, we describe the datasets as well as the evaluation results obtained from these two campaigns

A Latent Profile Analysis of Aggression and Victimization across Relationship Types Among Veterans Who Use Substances

Objective: This study examined patterns of violence victimization and aggression in both intimate partner and non-partner relationships among veterans, and used latent profile analysis to identify subtypes of violence involvement. Methods: Participants were 841 substance use treatment-seeking veterans (94% male) from a large VA Medical Center who completed screening measures for a randomized controlled trial. Self-report measures were: substance use, legal problems, depression, and violence involvement. Results: Past year violence involvement, including both intimate partner (IPV) and non-partner (NPV) were common in the sample; although NPV occurred at somewhat higher rates. When including either IPV or NPV aggression or victimization, over 48% reported involvement with physical violence, 31% with violence involving injury and 86% with psychological aggression. Latent profile analysis including both aggression and victimization in partner and non-partner relationships indicated a four profile solution: no-low violence (NLV, n = 701), predominantly IPV (n = 35), predominantly NPV (n = 83), and high general violence (HGV, n = 22). Multinomial logistic regression analyses revealed that compared to the no-low violence group, the remaining three groups differed in demographics, depressive symptoms, alcohol and other drug use, and legal involvement. Individuals within each profile had different patterns of substance use and legal involvement with the participants with an HGV profile reporting the most legal problems. Conclusions: IPV and NPV are relatively common among veterans seeking substance use treatment. Characteristics of violence and associated substance use, mental health, and legal difficulties may be useful in considering how to tailor substance use and mental health services

Race/Ethnicity and Economic Differences in Cost-Related Medication Underuse Among Insured Adults With Diabetes The Translating Research Into Action for Diabetes Study

ObjectiveTo examine racial/ethnic and economic variation in cost-related medication underuse among insured adults with diabetes.Research design and methodsWe surveyed 5,086 participants from the multicenter Translating Research Into Action for Diabetes Study. Respondents reported whether they used less medication because of cost in the past 12 months. We examined unadjusted and adjusted rates of cost-related medication underuse, using hierarchical regression, to determine whether race/ethnicity differences still existed after accounting for economic, health, and other demographic variables.ResultsParticipants were 48% white, 14% African American, 14% Latino, 15% Asian/Pacific Islander, and 8% other. Overall, 14% reported cost-related medication underuse. Unadjusted rates were highest for Latinos (23%) and African Americans (17%) compared with whites (13%), Asian/Pacific Islanders (11%), and others (15%). In multivariate analyses, race/ethnicity significantly predicted cost-related medication underuse (P = 0.048). However, adjusted rates were only slightly higher for Latinos (14%) than whites (10%) (P = 0.026) and were not significantly different for African Americans (11%), Asian/Pacific Islanders (7%), and others (11%). Income and out-of-pocket drug costs showed the greatest differences in adjusted rates of cost-related medication underuse (15 vs. 5% for participants with income $50,000 and 24 vs. 7$150 per month vs. ConclusionsOne in seven participants reported cost-related medication underuse. Rates were highest among African Americans and Latinos but were related to lower incomes and higher out-of-pocket drug costs in these groups. Interventions to decrease racial/ethnic disparities in cost-related medication underuse should focus on decreasing financial barriers to medications

Stem cell-based therapy for human diseases

Recent advancements in stem cell technology open a new door for patients suffering from diseases and disorders that have yet to be treated. Stem cell-based therapy, including human pluripotent stem cells (hPSCs) and multipotent mesenchymal stem cells (MSCs), has recently emerged as a key player in regenerative medicine. hPSCs are defined as self-renewable cell types conferring the ability to differentiate into various cellular phenotypes of the human body, including three germ layers. MSCs are multipotent progenitor cells possessing self-renewal ability (limited in vitro) and differentiation potential into mesenchymal lineages, according to the International Society for Cell and Gene Therapy (ISCT). This review provides an update on recent clinical applications using either hPSCs or MSCs derived from bone marrow (BM), adipose tissue (AT), or the umbilical cord (UC) for the treatment of human diseases, including neurological disorders, pulmonary dysfunctions, metabolic/endocrine-related diseases, reproductive disorders, skin burns, and cardiovascular conditions. Moreover, we discuss our own clinical trial experiences on targeted therapies using MSCs in a clinical setting, and we propose and discuss the MSC tissue origin concept and how MSC origin may contribute to the role of MSCs in downstream applications, with the ultimate objective of facilitating translational research in regenerative medicine into clinical applications. The mechanisms discussed here support the proposed hypothesis that BM-MSCs are potentially good candidates for brain and spinal cord injury treatment, AT-MSCs are potentially good candidates for reproductive disorder treatment and skin regeneration, and UC-MSCs are potentially good candidates for pulmonary disease and acute respiratory distress syndrome treatment

Atherosclerosis evaluation and cardiovascular risk estimation using coronary computed tomography angiography

Clinical risk scores based on traditional risk factors of atherosclerosis correlate imprecisely to an individual’s complex pathophysiological predisposition to atherosclerosis and provide limited accuracy for predicting major adverse cardiovascular events (MACE). Over the past two decades, computed tomography scanners and techniques for coronary computed tomography angiography (CCTA) analysis have substantially improved, enabling more precise atherosclerotic plaque quantification and characterization. The accuracy of CCTA for quantifying stenosis and atherosclerosis has been validated in numerous multicentre studies and has shown consistent incremental prognostic value for MACE over the clinical risk spectrum in different populations. Serial CCTA studies have advanced our understanding of vascular biology and atherosclerotic disease progression. The direct disease visualization of CCTA has the potential to be used synergistically with indirect markers of risk to significantly improve prevention of MACE, pending large-scale randomized evaluation

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead