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arXiv 2018年

作者： Yoon, Wonjin So, Chan Ho Lee, Jinhyuk Kang, Jaewoo Department of Computer Science and Engineering Korea University Seoul02841 Korea Republic of Interdisciplinary Graduate Program in Bioinformatics Korea University Seoul02841 Korea Republic of

Background: Finding biomedical named entities is one of the most essential tasks in biomedical text mining. Recently, deep learning-based approaches have been applied to biomedical named entity recognition (BioNER) and showed promising results. However, as deep learning approaches need an abundant amount of training data, a lack of data can hinder performance. BioNER datasets are scarce resources and each dataset covers only a small subset of entity types. Furthermore, many bio entities are polysemous, which is one of the major obstacles in named entity recognition. Results: To address the lack of data and the entity type misclassification problem, we propose CollaboNet which utilizes a combination of multiple NER models. In CollaboNet, models trained on a different dataset are connected to each other so that a target model obtains information from other collaborator models to reduce false positives. Every model is an expert on their target entity type and takes turns serving as a target and a collaborator model during training time. The experimental results show that CollaboNet can be used to greatly reduce the number of false positives and misclassified entities including polysemous words. CollaboNet achieved state-of-the-art performance in terms of precision, recall and F1 score. Conclusions: We demonstrated the benefits of combining multiple models for BioNER. Our model has successfully reduced the number of misclassified entities and improved the performance by leveraging multiple datasets annotated for different entity types. Given the state-of-the-art performance of our model, we believe that CollaboNet can improve the accuracy of downstream biomedical text mining applications such as bio-entity relation extraction. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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Molecular characterization and expression analysis of Cyclin B and Cell division cycle 2 in gonads of diploid and triploid bighead catfish, Clarias macrocephalus Günther, 1864

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Agriculture and Natural Resources 2017年第2期51卷 117-130页

作者： Wachirachaikarn, Anyalak Rungsin, Wikrom Srisapoome, Prapansak Klinbunga, Sirawut Na-Nakorn, Uthairat Interdisciplinary Program in Genetic Engineering Graduate School Kasetsart University Bangkok 10900 Thailand Department of Zoology Faculty of Science Kasetsart University Bangkok 10900 Thailand Department of Aquaculture Faculty of Fisheries Kasetsart University Bangkok 10900 Thailand Aquatic Molecular Genetics and Biotechnology Laboratory National Center for Genetic Engineering and Biotechnology (BIOTEC) National Science and Technology Development Agency (NSTDA) Khlong Nueng Khlong Luang Pathum Thani 12120 Thailand Center of Excellence for Marine Biotechnology Faculty of Science Chulalongkorn University Bangkok 10330 Thailand

This study investigated the differential expression of genes associated with reproduction in sterile triploid and normal diploid bighead catfish (Clarias macrocephalus Günther, 1864). The triploid fish were produced using cold shock and were reared in the same conditions as the diploid counterpart. The histomicrographs showed completely retarded triploid gonads across the samples aged 2–12 mth, whereas the gonads of the diploids were in developing stages during 2–4 mth, reached the early maturing stage at 6 mth, matured at 8 mth and showed signs of atresia at 10–12 mth. In parallel, the full-length cDNAs of cyclin B1 (CmCcnb1;1539 bp in length with an open reading frame (ORF) of 1194 bp corresponding to 397 amino acids) and cell division cycle 2 (CmCdc2;1355 bp, an ORF of 909 bp, 302 amino acids) of bighead catfish (C. macrocephalus Günther, 1864) were isolated. Phylogenetic analysis revealed that the newly characterized CmCcnb1 should be regarded as a member of cyclin B1 rather than cyclin B2. The expression level of CmCcnb1 mRNA was limited in different stages of the ovaries and testes of triploids. In diploid ovaries, its expression was significantly higher than that in triploid ovaries in fish aged 2 mth (513.43 ± 82.22 fold) and in fish aged 8 mth (2430.87 ± 900.06 fold). The CmCcnb1 level in the testes of diploids was significantly greater than that in triploids in fish aged 2 mth (928.85 ± 208.72 fold). Similarly, expression of CmCdc2 mRNA was also reduced in triploids. Its expression was significantly lower than that in diploid females aged 2 mth (7.66 ± 3.42 fold), 4 mth (59.42 ± 10.50 fold) and 8 mth (42.74 ± 8.36 fold). In males, significantly greater expression of CmCdc2 was observed at age 6 mth (58.61 ± 34.64 fold) and 8 mth (72.70 ± 4.36 fold) diploids compared to triploids. The results illustrated that CmCcnb1 and CmCdc2 are functionally involved in oogenesis and spermatogenesis and reduced expression levels of these transcripts affected the reproduc

关键词： Cdc2 Clarias macrocephalus Cyclin B Diploids Expression Triploids

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An Activin A/BMP2 chimera, AB215, blocks estrogen signaling via induction of ID proteins in breast cancer cells

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BMC Cancer 2014年第1期14卷 1-12页

作者： Jung, Jae W. Shim, Sun Y. Lee, Dong K. Kwiatkowski, Witek Choe, Senyon Joint Center for Biosciences Songdo Global University Campus 187 Songdo-dong Incheon Yeonsu-gu 406-840 South Korea Salk Institute for Biological Studies 10010 N. Torrey Pines RD La Jolla CA_apop 92037 United States Seoul National University Interdisciplinary Graduate Program in Genetic Engineering 1 Gwanak-ro Seoul Gwanak-gu South Korea

Background: One in eight women will be affected by breast cancer in her lifetime. Approximately 75% of breast cancers express estrogen receptor alpha (ERα) and/or progesterone receptor and these receptors are markers for tumor dependence on estrogen. Anti-estrogenic drugs such as tamoxifen are commonly used to block estrogen-mediated signaling in breast cancer. However, many patients either do not respond to these therapies (de novo resistance) or develop resistance to them following prolonged treatment (acquired resistance). Therefore, it is imperative to continue efforts aimed at developing new efficient and safe methods of targeting ER activity in breast ***: AB215 is a chimeric ligand assembled from sections of Activin A and BMP2. BMP2's and AB215's inhibition of breast cancer cells growth was investigated. In vitro luciferase and MTT proliferation assays together with western blot, RT_PCR, and mRNA knockdown methods were used to determine the mechanism of inhibition of estrogen positive breast cancer cells growth by BMP2 and AB215. Additionally in vivo xenograft tumor model was used to investigate anticancer properties of ***: Here we report that AB215, a chimeric ligand assembled from sections of Activin A and BMP2 with BMP2-like signaling, possesses stronger anti-proliferative effects on ERα positive breast cancer cells than BMP2. We further show that AB215 inhibits estrogen signaling by inducing expression of inhibitor of DNA binding proteins (IDs). Specifically, we demonstrate that knockdown of ID proteins attenuates the anti-estrogen effects of AB215. Remarkably, we find that AB215 is more effective than tamoxifen in suppressing tumor growth in a xenograft ***: This study shows that IDs have profound role to inhibit estrogen signaling in ERα positive breast cancer cells, and that engineered TGF-beta ligands may have high therapeutic value. © 2014 Jung et al.;licensee BioMed Central Ltd.

关键词： AB215 Bio-better Bone morphogenetic protein Estrogen receptor-positive breast cancer Inhibitor of DNA binding proteins Tamoxifen alternative Transforming growth factor-β

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Enhancing Label Consistency on Document-level Named Entity Recognition

arXiv

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arXiv 2022年

作者： Jeong, Minbyul Kang, Jaewoo Department of Computer Science and Engineering Korea University Seoul02841 Korea Republic of Interdisciplinary Graduate Program in Bioinformatics Korea University Seoul Korea Republic of AIGEN Sciences Seoul04778 Korea Republic of

Summary: Named entity recognition (NER) is a fundamental part of extracting information from documents in biomedical applications. A notable advantage of NER is its consistency in extracting biomedical entities in a document context. Although existing document NER models show consistent predictions, they still do not meet our expectations. We investigated whether the adjectives and prepositions within an entity cause a low label consistency, which results in inconsistent predictions. In this paper, we present our method, ConNER, which enhances the label dependency of modifiers (e.g., adjectives and prepositions) to achieve higher label agreement. ConNER refines the draft labels of the modifiers to improve the output representations of biomedical entities. The effectiveness of our method is demonstrated on four popular biomedical NER datasets;in particular, its efficacy is proved on two datasets with 7.5–8.6% absolute improvements in the F1 score. We interpret that our ConNER method is effective on datasets that have intrinsically low label consistency. In the qualitative analysis, we demonstrate how our approach makes the NER model generate consistent predictions. © 2022, CC BY-SA.

关键词： Forecasting

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Application of Acinetobacter indicus to promote cigarette smoke particulate matter phytoremediation: removal efficiency and plant–microbe interactions

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Environmental Science and Pollution Research 2024年第39期31卷 52352-52370页

作者： Nookongbut, Phitthaya Thiravetyan, Paitip Salsabila, Salma Widiana, Ana Krobthong, Sucheewin Yingchutrakul, Yodying Treesubsuntorn, Chairat School of Bioresources and Technology King Mongkut’s University of Technology Thonburi Bangkok10150 Thailand Department of Biology Faculty of Science and Technology State Islamic University Sunan Gunung Djati Bandung West Java Bandung City40614 Indonesia Interdisciplinary Graduate Program in Genetic Engineering Kasetsart University Bangkok10900 Thailand Proteomics Research Team National Omics Center NSTDA Khlong Luang Pathum Thani12120 Thailand Pilot Plant Development and Training Institute King Mongkut’s University of Technology Thonburi Bangkok10150 Thailand

Particulate matter (PM) is one of the most hazardous atmospheric pollutants. Several plant species show high potential to reduce air pollutants and are widely used as green belts to provide clean outdoor spaces for human well-being. However, high PM concentrations cause physiological changes and stress in plants. In this study, 11 species of Thai native perennial plants were exposed to PM generated from tobacco smoke. Wrightia religiosa (Teijsm. & Binn.) Benth. ex Kurz, Bauhinia purpurea DC. ex Walp. and Tectona grandis L.f. reduced PM effectively (which is in the typical range of 43.95 to 52.97%) compared to other plant species. In addition, the responses of perennial plants under PM stress at the proteomic level were also evaluated. Proteomic analysis of these three plant species showed that plants respond negatively to high PM concentrations, such as reducing several photosynthetic-related proteins and increasing plant stress response proteins. To improve PM phytoremediation efficiency and reduce plant stress from PM, perennial plant–microbe interactions were investigated. W. religiosa was inoculated with Acinetobacter indicus PS1, and high biosurfactant-producing strains clearly showed a higher PM removal efficiency than non-inoculated plants (9.48, 9.5 and 12.6% for PM1.0, PM2.5 and PM10, respectively). Inoculating W. religiosa with A. indicus PS1 maintained chlorophyll a and b concentrations. Moreover, the malondialdehyde (MDA) concentration of W. religiosa inoculated with A. indicus PS1 was lower than that of non-inoculated W. religiosa. The leaf wax content (µg/cm2) and biosurfactant (µg/cm2) of W. religiosa inoculated with A. indicus PS1 were also higher than those of non-inoculated W. religiosa. This study clearly showed that inoculating plants with A. indicus PS1 can help plants remediate PM and improve their PM stress response. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Air pollution

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Single-cell RNA-sequencing and subcellular spatial transcriptomics facilitate the translation of liver microphysiological systems for regulatory application

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Journal of Pharmaceutical Analysis 2023年第7期13卷 691-693页

作者： Dan Li Zhou Fang Qiang Shi Nicholas Zhang Binsheng Gong Weida Tong Ahmet F.Coskun Joshua Xu Division of Bioinformatics and Biostatistics National Center for Toxicological ResearchU.S.Food and Drug AdministrationJeffersonAR72079USA Wallace H.Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory UniversityAtlantaGA30332USA Machine Learning Graduate Program Georgia Institute of TechnologyAtlantaGA30332USA Division of Systems Biology National Center for Toxicological ResearchU.S.Food and Drug AdministrationJeffersonAR72079USA Interdisciplinary Bioengineering Graduate Program Georgia Institute of TechnologyAtlantaGA30332USA Parker H.Petit Institute for Bioengineering and Bioscience Georgia Institute of TechnologyAtlantaGA30332USA

Reducing the use of animal models in drug development and safety assessment has long been supported by the *** and Drug Administration(FDA).The report by Royal Society for the Prevention of Cruelty to Animals indicates that in 2020,experiments involved the use of over 100 million animals,with the United States leading the list by utilizing 20 million *** ethical considerations associated with animal testing and the costs in terms of time and money,animal models are not always effective in predicting human reactions to drug *** animal testing has been the traditional method for assessing the safety and efficacy of drugs.

关键词： money utilizing testing

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BioBERT: A pre-trained biomedical language representation model for biomedical text mining

arXiv

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arXiv 2019年

作者： Lee, Jinhyuk Yoon, Wonjin Kim, Sungdong Kim, Donghyeon Kim, Sunkyu So, Chan Ho Kang, Jaewoo Department of Computer Science and Engineering Korea University Seoul02841 Korea Republic of Clova AI Research Naver Corp Seong-Nam13561 Korea Republic of Interdisciplinary Graduate Program in Bioinformatics Korea University Seoul02841 Korea Republic of

Motivation: Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows. With the progress in natural language processing (NLP), extracting valuable information from biomedical literature has gained popularity among researchers, and deep learning has boosted the development of effective biomedical text mining models. However, directly applying the advancements in NLP to biomedical text mining often yields unsatisfactory results due to a word distribution shift from general domain corpora to biomedical corpora. In this article, we investigate how the recently introduced pre-trained language model BERT can be adapted for biomedical corpora. Results: We introduce BioBERT (Bidirectional Encoder Representations from Transformers for Biomedical Text Mining), which is a domain-specific language representation model pre-trained on large-scale biomedical corpora. With almost the same architecture across tasks, BioBERT largely outperforms BERT and previous state-of-the-art models in a variety of biomedical text mining tasks when pre-trained on biomedical corpora. While BERT obtains performance comparable to that of previous state-of-the-art models, BioBERT significantly outperforms them on the following three representative biomedical text mining tasks: biomedical named entity recognition (0.62% F1 score improvement), biomedical relation extraction (2.80% F1 score improvement) and biomedical question answering (12.24% MRR improvement). Our analysis results show that pre-training BERT on biomedical corpora helps it to understand complex biomedical texts. Availability and implementation: We make the pre-trained weights of BioBERT freely available at https://github. com/naver/biobert-pretrained, and the source code for fine-tuning BioBERT available at https://***/dmis-lab/ biobert. Copyright © 2019, The Authors. All rights reserved.

关键词： Natural language processing systems

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HATS: A hierarchical graph attention network for stock movement prediction

arXiv

引用

arXiv 2019年

作者： Kim, Raehyun So, Chan Ho Jeong, Minbyul Lee, Sanghoon Kim, Jinkyu Kang, Jaewoo Department of Computer Science and Engineering Korea University 145 Anam-ro Seongbuk-gu Seoul02841 Korea Republic of Interdisciplinary Graduate Program in Bioinformatics Korea University 145 Anam-ro Seongbuk-gu Seoul02841 Korea Republic of

Many researchers both in academia and industry have long been interested in the stock market. Numerous approaches were developed to accurately predict future trends in stock prices. Recently, there has been a growing interest in utilizing graph-structured data in computer science research communities. Methods that use relational data for stock market prediction have been recently proposed, but they are still in their infancy. First, the quality of collected information from different types of relations can vary considerably. No existing work has focused on the effect of using different types of relations on stock market prediction or finding an effective way to selectively aggregate information on different relation types. Furthermore, existing works have focused on only individual stock prediction which is similar to the node classification task. To address this, we propose a hierarchical attention network for stock prediction (HATS) which uses relational data for stock market prediction. Our HATS method selectively aggregates information on different relation types and adds the information to the representations of each company. Specifically, node representations are initialized with features extracted from a feature extraction module. HATS is used as a relational modeling module with initialized node representations. Then, node representations with the added information are fed into a task-specific layer. Our method is used for predicting not only individual stock prices but also market index movements, which is similar to the graph classification task. The experimental results show that performance can change depending on the relational data used. HATS which can automatically select information outperformed all the existing methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Aggregates

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SARS-CoV-2 surveillance of wastewater in small rural communities identifies lack of vaccine coverage as influence of omicron outbreak

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Water Research 2025年 283卷 123818页

作者： Solana Narum Thibault Stalder Benjamin Ridenhour Erik R. Coats Bioinformatics and Computational Biology Graduate Program (BCB) University of Idaho 875 Perimeter Drive MS 1103 Moscow ID 83844 United States Institute for Modeling Collaboration and Innovation (IMCI) University of Idaho 875 Perimeter Drive MS 1122 Moscow ID 83844 United States Department of Biological Sciences University of Idaho 875 Perimeter Drive MS 3051 Moscow ID 83844 United States Institute for Interdisciplinary Data Sciences (IIDS) University of Idaho 875 Perimeter Drive MS 3051 Moscow ID 83844 United States Department of Mathematics and Statistical Science University of Idaho 875 Perimeter Drive MS 1103 Moscow ID 83844 United States Department of Civil and Environmental Engineering University of Idaho 875 Perimeter Drive MS 1022 Moscow ID 83844 United States

Wastewater-based epidemiology (WBE) can provide critical early warnings to aid public health, which can be particularly beneficial in rural communities with limited access to health care. Spikes of SARS-CoV-2 RNA concentration in wastewater have been used to represent infections in a community, but wastewater holds a wealth of information that has not been explored yet. The objectives of this research were to expand the use of WBE to 1) determine the dynamic of SARS-CoV-2 variants in rural communities, and 2) evaluate the relationship between community vaccination status and the outbreak of a variant. We quantified the concentration of SARS-CoV-2 RNA, as well as specific mutations that are consistent with Delta and Omicron in influent raw wastewater samples collected from wastewater treatment facilities (WWTFs) for five populations with <1000 residents and one larger population in Latah County, ID. A binomial generalized linear model using the percent of the population with protection against Omicron from the initial vaccines and the booster shot was able to predict the probability of an uptick in Omicron concentration in wastewater with an accuracy of 0.96. Evaluation of vaccination data indicate that the spike in Omicron infections in December 2021 in the studied towns was linked to low levels of population protection from the initial shots of the COVID-19 vaccine against Omicron infection and limited uptake of booster shots in these communities. Despite difficulties with applying WBE in rural regions, this study shows that beyond evaluating spikes of viral infections, WBE can be used to evaluate the effect of a population’s vaccine coverage on SARS-CoV-2 variant dynamics.

关键词： COVID-19 Delta Modeling Omicron Population coverage Prediction

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Elimination of senescent cells by β-galactosidase-targeted prodrug attenuates inflammation and restores physical function in aged mice

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细胞研究（英文版） 2020年第7期30卷 574-589页

作者： Yusheng Cai Huanhuan Zhou Yinhua Zhu Qi Sun Yin Ji Anqi Xue Yuting Wang Wenhan Chen Xiaojie Yu Longteng Wang Han Chen Cheng Li Tuoping Luo Hongkui Deng The MOE Key Laboratory of Cell Proliferation and Differentiation College of Life Sciences Peking-Tsinghua Center for Life Sciences and School of Basic Medical Sciences State Key Laboratory of Natural and Biomimetic Drugs Peking University Health Science Center Peking University Beijing 100191 China State Key Laboratory of Chemical Oncogenomics School of Chemical Biology & Biotechnology Peking University Shenzhen Graduate School Shenzhen Guangdong 518055 China Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary Studies Peking University Beijing 100871 China Key Laboratory of Bioorganic Chemistry and Molecular Engineering Ministry of Education and Beijing National Laboratory for Molecular Science College of Chemistry and Molecular Engineering Peking University Beijing 100871 China School of Life Sciences Joint Graduate Program of Peking-Tsinghua-NIBS Peking University Beijing 100871 China School of Life Sciences Center for Bioinformatics Center for Statistical Science Peking University Beijing 100871 China

Cellular senescence,a persistent state of cell cycle arrest,accumulates in aged organisms,contributes to tissue dysfunction,and drives age-related *** clearance of senescent cells is expected to decrease chronic,low-grade inflammation and improve tissue repair capacity,thus attenuating the decline of physical function in aged ***,selective and effective clearance of senescent cells of different cell types has proven ***,we developed a prodrug strategy to design a new compound based on the increased activity of lysosomalβ-galactosidase (β-gal),a primary characteristic of senescent *** prodrug SSK1 is specifically activated by β-gal and eliminates mouse and human senescent cells independently of senescence inducers and cell *** aged mice,our compound effectively cleared senescent cells in different tissues,decreased the senescence-and age-associated gene signatures,attenuated low-grade local and systemic inflammation,and restored physical *** results demonstrate that lysosomalβ-gal can be effectively leveraged to selectively eliminate senescent cells,providing a novel strategy to develop anti-aging interventions.

关键词： Apoptosis Senescence

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