DIFFICULTIES IN WRITING AN ESSAY OF ENGLISH-MAJORED SOPHOMORES AT TAY DO UNIVERSITY, IN VIETNAM

Writing is an important skill in English that helps people express thoughts, emotion and viewpoint to readers. However, students face some difficulties when writing. Hence, the survey research presents the process of the study about “Difficulties in writing an essay of English-majored sophomores at Tay Do University, in Viet Nam”. It was conducted to find out some difficulties in learning writing (from 200 to 250 - word essay) of 102 the sophomores from Bachelor of English 10 at Tay Do University. Questionnaire, paper interview and essay samples were the instruments of the study. The results showed that sophomores had many problems in writing such as vocabulary, grammar structures, ideas arrangement, background knowledge, and others. Basing on the results, some solutions would be suggested to help students to get a good writing skill. Viết là một kỹ năng quan trọng trong tiếng Anh giúp người viết thể hiện suy nghĩ, cảm xúc và quan điểm với người đọc. Tuy nhiên, sinh viên thường gặp một số khó khăn khi viết. Do đó, nghiên cứu “Khó khăn khi viết bài luận của sinh viên năm thứ hai chuyên ngành Ngôn Ngữ Anh tại Trường Đại học Tây Đô, Việt Nam” được thực hiện nhằm tìm ra một số khó khăn khi học môn viết (bài luận từ 200 đến 250 từ) của 102 sinh viên Cử nhân Tiếng Anh năm thứ hai, khóa 10 của Trường Đại học Tây Đô. Công cụ nghiên cứu gồm bảng câu hỏi, phỏng vấn trên giấy và phân tích bài luận. Kết quả cho thấy sinh viên năm thứ hai gặp nhiều vấn đề về viết như từ vựng, cấu trúc ngữ pháp, sắp xếp ý tưởng, kiến thức nền tảng và những vấn đề khác. Dựa trên kết quả đạt được, một số giải pháp sẽ được đề xuất để giúp sinh viên có được kỹ năng viết tốt hơn. Article visualizations

Childhood hospitalisation and related deaths in Hanoi, Vietnam: a tertiary hospital database analysis from 2007 to 2014

To describe hospital admission and emergency visit rates and potential risk factors of prolonged hospitalisation and death among children in Hanoi.; A retrospective study reviewed 212 216 hospitalisation records of children (aged 0-17) who attended the Vietnam National Children's Hospital in Hanoi between 2007 and 2014. Four indicators were analysed and reported: (1) rate of emergency hospital visits, (2) rate of hospitalisation, (3) length of hospital stay and (4) number of deaths. The risk of prolonged hospitalisation was investigated using Cox proportion hazard, and the risk of death was investigated through logistic regressions.; During 2007-2014, the average annual rate of emergency visits was 2.2 per 1000 children and the rate of hospital admissions was 13.8 per 1000 children. The annual rates for infants increased significantly by 3.9 per 1000 children during 2012-2014 for emergency visits and 25.1 per 1000 children during 2009-2014 for hospital admissions. Digestive diseases (32.0%) and injuries (30.2%) were common causes of emergency visits, whereas respiratory diseases (37.7%) and bacterial and parasitic infections (19.8%) accounted for most hospital admissions. Patients with mental and behavioural disorders remained in the hospital the longest (median=12 days). Morbidities related to the perinatal period dominated mortality causes (32.5% of deaths among those admitted to the hospital. Among the respiratory diseases, pneumonia was the leading cause of both prolonged hospitalisation and death.; Preventable health problems, such as common bacterial infections and respiratory diseases, were the primary causes of hospital admissions in Vietnam

A metric learning-based method for biomedical entity linking

Biomedical entity linking task is the task of mapping mention(s) that occur in a particular textual context to a unique concept or entity in a knowledge base, e.g., the Unified Medical Language System (UMLS). One of the most challenging aspects of the entity linking task is the ambiguity of mentions, i.e., (1) mentions whose surface forms are very similar, but which map to different entities in different contexts, and (2) entities that can be expressed using diverse types of mentions. Recent studies have used BERT-based encoders to encode mentions and entities into distinguishable representations such that their similarity can be measured using distance metrics. However, most real-world biomedical datasets suffer from severe imbalance, i.e., some classes have many instances while others appear only once or are completely absent from the training data. A common way to address this issue is to down-sample the dataset, i.e., to reduce the number instances of the majority classes to make the dataset more balanced. In the context of entity linking, down-sampling reduces the ability of the model to comprehensively learn the representations of mentions in different contexts, which is very important. To tackle this issue, we propose a metric-based learning method that treats a given entity and its mentions as a whole, regardless of the number of mentions in the training set. Specifically, our method uses a triplet loss-based function in conjunction with a clustering technique to learn the representation of mentions and entities. Through evaluations on two challenging biomedical datasets, i.e., MedMentions and BC5CDR, we show that our proposed method is able to address the issue of imbalanced data and to perform competitively with other state-of-the-art models. Moreover, our method significantly reduces computational cost in both training and inference steps. Our source code is publicly available here

Load Shedding in Microgrids with Dual Neural Networks and AHP Algorithm

This paper proposes a new load shedding method based on the application of a Dual Neural Network (NN). The combination of a Back-Propagation Neural Network (BPNN) and of Particle Swarm Optimization (PSO) aims to quickly predict and propose a load shedding strategy when a fault occurs in the microgrid (MG) system. The PSO algorithm has the ability to search and compare multiple points, so the proposed NN training method helps determine the link weights faster and stronger. As a result, the proposed method saves training time and achieves higher accuracy. The Analytic Hierarchy Process (AHP) algorithm is applied to rank the loads based on their importance factor. The results of the ratings of the loads serve as a basis for constructing the load shedding strategies of a NN combined with the PSO algorithm (ANN-PSO). The proposed load shedding method is tested on an IEEE 25-bus 8-generator MG power system. The simulation results show that the frequency recovery of the power system is positive. The proposed neural network adapts well to the simulated data of the system and achieves high performance in fault prediction

Virulence of Mycobacterium tuberculosis Clinical Isolates Is Associated With Sputum Pre-treatment Bacterial Load, Lineage, Survival in Macrophages, and Cytokine Response

It is uncertain whether differences in Mycobacterium tuberculosis (Mtb) virulence defined in vitro influence clinical tuberculosis pathogenesis, transmission, and mortality. We primarily used a macrophage lysis model to characterize the virulence of Mtb isolates collected from 153 Vietnamese adults with pulmonary tuberculosis. The virulence phenotypes were then investigated for their relationship with sputum bacterial load, bacterial lineages, bacterial growth, and cytokine responses in macrophages. Over 6 days of infection, 34 isolates (22.2%) showed low virulence (< 5% macrophages lysed), 46 isolates (30.1%) showed high virulence (≥90% lysis of macrophages), and 73 isolates (47.7%) were of intermediate virulence (5–90% macrophages lysed). Highly virulent isolates were associated with an increased bacterial load in patients' sputum before anti-tuberculosis therapy (P = 0.02). Isolate-dependent virulence phenotype was consistent in both THP-1 and human monocyte-derived macrophages. High virulence isolates survived better and replicated in macrophages one hundred fold faster than those with low virulence. Macrophages infected with high virulence isolates produced lower concentrations of TNF-α and IL-6 (P = 0.002 and 0.0005, respectively), but higher concentration of IL-1β (P = 5.1 × 10−5) compared to those infected with low virulence isolates. High virulence was strongly associated with East Asian/Beijing lineage [P = 0.002, Odd ratio (OR) = 4.32, 95% confident intervals (CI) 1.68–11.13]. The association between virulence phenotypes, bacterial growth, and proinflammatory cytokines in macrophages suggest the suppression of certain proinflammatory cytokines (TNF-α and IL-6) but not IL-1β allows better intracellular survival of highly virulent Mtb. This could result in rapid macrophage lysis and higher bacterial load in sputum of patients infected with high virulence isolates, which may contribute to the pathogenesis and success of the Beijing lineage

Differentiation of breast cancer stem cells by knockdown of CD44: promising differentiation therapy

<p>Abstract</p> <p>Background</p> <p>Breast cancer stem cells (BCSCs) are the source of breast tumors. Compared with other cancer cells, cancer stem cells show high resistance to both chemotherapy and radiotherapy. Targeting of BCSCs is thus a potentially promising and effective strategy for breast cancer treatment. Differentiation therapy represents one type of cancer stem-cell-targeting therapy, aimed at attacking the stemness of cancer stem cells, thus reducing their chemo- and radioresistance. In a previous study, we showed that down-regulation of CD44 sensitized BCSCs to the anti-tumor agent doxorubicin. This study aimed to determine if CD44 knockdown caused BCSCs to differentiate into breast cancer non-stem cells (non-BCSCs).</p> <p>Methods</p> <p>We isolated a breast cancer cell population (CD44⁺CD24^-cells) from primary cultures of malignant breast tumors. These cells were sorted into four sub-populations based on their expression of CD44 and CD24 surface markers. CD44 knockdown in the BCSC population was achieved using small hairpin RNA lentivirus particles. The differentiated status of CD44 knock-down BCSCs was evaluated on the basis of changes in CD44⁺CD24^-phenotype, tumorigenesis in NOD/SCID mice, and gene expression in relation to renewal status, metastasis, and cell cycle in comparison with BCSCs and non-BCSCs.</p> <p>Results</p> <p>Knockdown of CD44 caused BCSCs to differentiate into non-BCSCs with lower tumorigenic potential, and altered the cell cycle and expression profiles of some stem cell-related genes, making them more similar to those seen in non-BCSCs.</p> <p>Conclusions</p> <p>Knockdown of CD44 is an effective strategy for attacking the stemness of BCSCs, resulting in a loss of stemness and an increase in susceptibility to chemotherapy or radiation. The results of this study highlight a potential new strategy for breast cancer treatment through the targeting of BCSCs.</p

Constructing a biodiversity terminological inventory.

The increasing growth of literature in biodiversity presents challenges to users who need to discover pertinent information in an efficient and timely manner. In response, text mining techniques offer solutions by facilitating the automated discovery of knowledge from large textual data. An important step in text mining is the recognition of concepts via their linguistic realisation, i.e., terms. However, a given concept may be referred to in text using various synonyms or term variants, making search systems likely to overlook documents mentioning less known variants, which are albeit relevant to a query term. Domain-specific terminological resources, which include term variants, synonyms and related terms, are thus important in supporting semantic search over large textual archives. This article describes the use of text mining methods for the automatic construction of a large-scale biodiversity term inventory. The inventory consists of names of species, amongst which naming variations are prevalent. We apply a number of distributional semantic techniques on all of the titles in the Biodiversity Heritage Library, to compute semantic similarity between species names and support the automated construction of the resource. With the construction of our biodiversity term inventory, we demonstrate that distributional semantic models are able to identify semantically similar names that are not yet recorded in existing taxonomies. Such methods can thus be used to update existing taxonomies semi-automatically by deriving semantically related taxonomic names from a text corpus and allowing expert curators to validate them. We also evaluate our inventory as a means to improve search by facilitating automatic query expansion. Specifically, we developed a visual search interface that suggests semantically related species names, which are available in our inventory but not always in other repositories, to incorporate into the search query. An assessment of the interface by domain experts reveals that our query expansion based on related names is useful for increasing the number of relevant documents retrieved. Its exploitation can benefit both users and developers of search engines and text mining applications