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  1. Article ; Online: The Complex Interplay between Imbalanced Mitochondrial Dynamics and Metabolic Disorders in Type 2 Diabetes.

    Van Huynh, Tin / Rethi, Lekha / Rethi, Lekshmi / Chen, Chih-Hwa / Chen, Yi-Jen / Kao, Yu-Hsun

    Cells

    2023  Volume 12, Issue 9

    Abstract: Type 2 diabetes mellitus (T2DM) is a global burden, with an increasing number of people affected and increasing treatment costs. The advances in research and guidelines improve the management of blood glucose and related diseases, but T2DM and its ... ...

    Abstract Type 2 diabetes mellitus (T2DM) is a global burden, with an increasing number of people affected and increasing treatment costs. The advances in research and guidelines improve the management of blood glucose and related diseases, but T2DM and its complications are still a big challenge in clinical practice. T2DM is a metabolic disorder in which insulin signaling is impaired from reaching its effectors. Mitochondria are the "powerhouses" that not only generate the energy as adenosine triphosphate (ATP) using pyruvate supplied from glucose, free fatty acid (FFA), and amino acids (AA) but also regulate multiple cellular processes such as calcium homeostasis, redox balance, and apoptosis. Mitochondrial dysfunction leads to various diseases, including cardiovascular diseases, metabolic disorders, and cancer. The mitochondria are highly dynamic in adjusting their functions according to cellular conditions. The shape, morphology, distribution, and number of mitochondria reflect their function through various processes, collectively known as mitochondrial dynamics, including mitochondrial fusion, fission, biogenesis, transport, and mitophagy. These processes determine the overall mitochondrial health and vitality. More evidence supports the idea that dysregulated mitochondrial dynamics play essential roles in the pathophysiology of insulin resistance, obesity, and T2DM, as well as imbalanced mitochondrial dynamics found in T2DM. This review updates and discusses mitochondrial dynamics and the complex interactions between it and metabolic disorders.
    MeSH term(s) Humans ; Diabetes Mellitus, Type 2/metabolism ; Mitochondrial Dynamics ; Mitochondria/metabolism ; Insulin Resistance ; Insulin/metabolism
    Chemical Substances Insulin
    Language English
    Publishing date 2023-04-23
    Publishing country Switzerland
    Document type Journal Article ; Review ; Research Support, Non-U.S. Gov't
    ZDB-ID 2661518-6
    ISSN 2073-4409 ; 2073-4409
    ISSN (online) 2073-4409
    ISSN 2073-4409
    DOI 10.3390/cells12091223
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book ; Online: BANANA at WNUT-2020 Task 2

    Van Huynh, Tin / Nguyen, Luan Thanh / Luu, Son T.

    Identifying COVID-19 Information on Twitter by Combining Deep Learning and Transfer Learning Models

    2020  

    Abstract: The outbreak COVID-19 virus caused a significant impact on the health of people all over the world. Therefore, it is essential to have a piece of constant and accurate information about the disease with everyone. This paper describes our prediction ... ...

    Abstract The outbreak COVID-19 virus caused a significant impact on the health of people all over the world. Therefore, it is essential to have a piece of constant and accurate information about the disease with everyone. This paper describes our prediction system for WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets. The dataset for this task contains size 10,000 tweets in English labeled by humans. The ensemble model from our three transformer and deep learning models is used for the final prediction. The experimental result indicates that we have achieved F1 for the INFORMATIVE label on our systems at 88.81% on the test set.

    Comment: Submitted to 2020 The 6th Workshop on Noisy User-generated Text (W-NUT)
    Keywords Computer Science - Computation and Language ; Computer Science - Social and Information Networks ; covid19
    Publishing date 2020-09-06
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Book ; Online: XLMRQA

    Van Nguyen, Kiet / Do, Phong Nguyen-Thuan / Nguyen, Nhat Duy / Van Huynh, Tin / Nguyen, Anh Gia-Tuan / Nguyen, Ngan Luu-Thuy

    Open-Domain Question Answering on Vietnamese Wikipedia-based Textual Knowledge Source

    2022  

    Abstract: Question answering (QA) is a natural language understanding task within the fields of information retrieval and information extraction that has attracted much attention from the computational linguistics and artificial intelligence research community in ... ...

    Abstract Question answering (QA) is a natural language understanding task within the fields of information retrieval and information extraction that has attracted much attention from the computational linguistics and artificial intelligence research community in recent years because of the strong development of machine reading comprehension-based models. A reader-based QA system is a high-level search engine that can find correct answers to queries or questions in open-domain or domain-specific texts using machine reading comprehension (MRC) techniques. The majority of advancements in data resources and machine-learning approaches in the MRC and QA systems especially are developed significantly in two resource-rich languages such as English and Chinese. A low-resource language like Vietnamese has witnessed a scarcity of research on QA systems. This paper presents XLMRQA, the first Vietnamese QA system using a supervised transformer-based reader on the Wikipedia-based textual knowledge source (using the UIT-ViQuAD corpus), outperforming the two robust QA systems using deep neural network models: DrQA and BERTserini with 24.46% and 6.28%, respectively. From the results obtained on the three systems, we analyze the influence of question types on the performance of the QA systems.

    Comment: Accepted by ACIIDS 2022
    Keywords Computer Science - Computation and Language
    Subject code 400
    Publishing date 2022-04-14
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: VLSP 2021 - ViMRC Challenge

    Van Nguyen, Kiet / Tran, Son Quoc / Nguyen, Luan Thanh / Van Huynh, Tin / Luu, Son T. / Nguyen, Ngan Luu-Thuy

    Vietnamese Machine Reading Comprehension

    2022  

    Abstract: One of the emerging research trends in natural language understanding is machine reading comprehension (MRC) which is the task to find answers to human questions based on textual data. Existing Vietnamese datasets for MRC research concentrate solely on ... ...

    Abstract One of the emerging research trends in natural language understanding is machine reading comprehension (MRC) which is the task to find answers to human questions based on textual data. Existing Vietnamese datasets for MRC research concentrate solely on answerable questions. However, in reality, questions can be unanswerable for which the correct answer is not stated in the given textual data. To address the weakness, we provide the research community with a benchmark dataset named UIT-ViQuAD 2.0 for evaluating the MRC task and question answering systems for the Vietnamese language. We use UIT-ViQuAD 2.0 as a benchmark dataset for the challenge on Vietnamese MRC at the Eighth Workshop on Vietnamese Language and Speech Processing (VLSP 2021). This task attracted 77 participant teams from 34 universities and other organizations. In this article, we present details of the organization of the challenge, an overview of the methods employed by shared-task participants, and the results. The highest performances are 77.24% in F1-score and 67.43% in Exact Match on the private test set. The Vietnamese MRC systems proposed by the top 3 teams use XLM-RoBERTa, a powerful pre-trained language model based on the transformer architecture. The UIT-ViQuAD 2.0 dataset motivates researchers to further explore the Vietnamese machine reading comprehension task and related tasks such as question answering, question generation, and natural language inference.

    Comment: The 8th International Workshop on Vietnamese Language and Speech Processing (VLSP 2021)
    Keywords Computer Science - Computation and Language
    Subject code 400
    Publishing date 2022-03-21
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: Sentence Extraction-Based Machine Reading Comprehension for Vietnamese

    Do, Phong Nguyen-Thuan / Nguyen, Nhat Duy / Van Huynh, Tin / Van Nguyen, Kiet / Nguyen, Anh Gia-Tuan / Nguyen, Ngan Luu-Thuy

    2021  

    Abstract: The development of natural language processing (NLP) in general and machine reading comprehension in particular has attracted the great attention of the research community. In recent years, there are a few datasets for machine reading comprehension tasks ...

    Abstract The development of natural language processing (NLP) in general and machine reading comprehension in particular has attracted the great attention of the research community. In recent years, there are a few datasets for machine reading comprehension tasks in Vietnamese with large sizes, such as UIT-ViQuAD and UIT-ViNewsQA. However, the datasets are not diverse in answers to serve the research. In this paper, we introduce UIT-ViWikiQA, the first dataset for evaluating sentence extraction-based machine reading comprehension in the Vietnamese language. The UIT-ViWikiQA dataset is converted from the UIT-ViQuAD dataset, consisting of comprises 23.074 question-answers based on 5.109 passages of 174 Wikipedia Vietnamese articles. We propose a conversion algorithm to create the dataset for sentence extraction-based machine reading comprehension and three types of approaches for sentence extraction-based machine reading comprehension in Vietnamese. Our experiments show that the best machine model is XLM-R_Large, which achieves an exact match (EM) of 85.97% and an F1-score of 88.77% on our dataset. Besides, we analyze experimental results in terms of the question type in Vietnamese and the effect of context on the performance of the MRC models, thereby showing the challenges from the UIT-ViWikiQA dataset that we propose to the language processing community.

    Comment: Accepted by KSEM 2021 (International Conference on Knowledge Science, Engineering and Management)
    Keywords Computer Science - Computation and Language
    Subject code 400
    Publishing date 2021-05-19
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Book ; Online: Job Prediction

    Van Huynh, Tin / Van Nguyen, Kiet / Nguyen, Ngan Luu-Thuy / Nguyen, Anh Gia-Tuan

    From Deep Neural Network Models to Applications

    2019  

    Abstract: Determining the job is suitable for a student or a person looking for work based on their job's descriptions such as knowledge and skills that are difficult, as well as how employers must find ways to choose the candidates that match the job they require. ...

    Abstract Determining the job is suitable for a student or a person looking for work based on their job's descriptions such as knowledge and skills that are difficult, as well as how employers must find ways to choose the candidates that match the job they require. In this paper, we focus on studying the job prediction using different deep neural network models including TextCNN, Bi-GRU-LSTM-CNN, and Bi-GRU-CNN with various pre-trained word embeddings on the IT Job dataset. In addition, we also proposed a simple and effective ensemble model combining different deep neural network models. The experimental results illustrated that our proposed ensemble model achieved the highest result with an F1 score of 72.71%. Moreover, we analyze these experimental results to have insights about this problem to find better solutions in the future.

    Comment: Submitted RIVF 2020 Conference
    Keywords Computer Science - Computation and Language
    Publishing date 2019-12-27
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Online: New Vietnamese Corpus for Machine Reading Comprehension of Health News Articles

    Van Nguyen, Kiet / Van Huynh, Tin / Nguyen, Duc-Vu / Nguyen, Anh Gia-Tuan / Nguyen, Ngan Luu-Thuy

    2020  

    Abstract: Large-scale and high-quality corpora are necessary for evaluating machine reading comprehension models on a low-resource language like Vietnamese. Besides, machine reading comprehension (MRC) for the health domain offers great potential for practical ... ...

    Abstract Large-scale and high-quality corpora are necessary for evaluating machine reading comprehension models on a low-resource language like Vietnamese. Besides, machine reading comprehension (MRC) for the health domain offers great potential for practical applications; however, there is still very little MRC research in this domain. This paper presents ViNewsQA as a new corpus for the Vietnamese language to evaluate healthcare reading comprehension models. The corpus comprises 22,057 human-generated question-answer pairs. Crowd-workers create the questions and their answers based on a collection of over 4,416 online Vietnamese healthcare news articles, where the answers comprise spans extracted from the corresponding articles. In particular, we develop a process of creating a corpus for the Vietnamese machine reading comprehension. Comprehensive evaluations demonstrate that our corpus requires abilities beyond simple reasoning, such as word matching and demanding difficult reasoning based on single-or-multiple-sentence information. We conduct experiments using different types of machine reading comprehension methods to achieve the first baseline performances, compared with further models' performances. We also measure human performance on the corpus and compared it with several powerful neural network-based and transfer learning-based models. Our experiments show that the best machine model is ALBERT, which achieves an exact match score of 65.26% and an F1-score of 84.89% on our corpus. The significant differences between humans and the best-performance model (14.53% of EM and 10.90% of F1-score) on the test set of our corpus indicate that improvements in ViNewsQA could be explored in the future study. Our corpus is publicly available on our website for the research purpose to encourage the research community to make these improvements.
    Keywords Computer Science - Computation and Language
    Subject code 420
    Publishing date 2020-06-19
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Book ; Online: Hate Speech Detection on Vietnamese Social Media Text using the Bi-GRU-LSTM-CNN Model

    Van Huynh, Tin / Nguyen, Vu Duc / Van Nguyen, Kiet / Nguyen, Ngan Luu-Thuy / Nguyen, Anh Gia-Tuan

    2019  

    Abstract: In recent years, Hate Speech Detection has become one of the interesting fields in natural language processing or computational linguistics. In this paper, we present the description of our system to solve this problem at the VLSP shared task 2019: Hate ... ...

    Abstract In recent years, Hate Speech Detection has become one of the interesting fields in natural language processing or computational linguistics. In this paper, we present the description of our system to solve this problem at the VLSP shared task 2019: Hate Speech Detection on Social Networks with the corpus which contains 20,345 human-labeled comments/posts for training and 5,086 for public-testing. We implement a deep learning method based on the Bi-GRU-LSTM-CNN classifier into this task. Our result in this task is 70.576\% of F1-score, ranking the 5th of performance on public-test set.

    Comment: We have some errors in experiments
    Keywords Computer Science - Computation and Language
    Publishing date 2019-11-09
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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