检索结果-内蒙古大学图书馆

An automatic nuclei segmentation method based on deep convolutional neural networks for histopathology images

BMC biomedical engineering 2019年第1期1卷 24页

作者： Hwejin Jung Bilal Lodhi Jaewoo Kang Department of Computer Science and Engineering Korea University Seoul Republic of Korea. Interdisciplinary Graduate Program in Bioinformatics Korea University Seoul Republic of Korea.

BACKGROUND:Since nuclei segmentation in histopathology images can provide key information for identifying the presence or stage of a disease, the images need to be assessed carefully. However, color variation in histopathology images, and various structures of nuclei are two major obstacles in accurately segmenting and analyzing histopathology images. Several machine learning methods heavily rely on hand-crafted features which have limitations due to manual thresholding. RESULTS:To obtain robust results, deep learning based methods have been proposed. Deep convolutional neural networks (DCNN) used for automatically extracting features from raw image data have been proven to achieve great performance. Inspired by such achievements, we propose a nuclei segmentation method based on DCNNs. To normalize the color of histopathology images, we use a deep convolutional Gaussian mixture color normalization model which is able to cluster pixels while considering the structures of nuclei. To segment nuclei, we use Mask R-CNN which achieves state-of-the-art object segmentation performance in the field of computer vision. In addition, we perform multiple inference as a post-processing step to boost segmentation performance. We evaluate our segmentation method on two different datasets. The first dataset consists of histopathology images of various organ while the other consists histopathology images of the same organ. Performance of our segmentation method is measured in various experimental setups at the object-level and the pixel-level. In addition, we compare the performance of our method with that of existing state-of-the-art methods. The experimental results show that our nuclei segmentation method outperforms the existing methods. CONCLUSIONS:We propose a nuclei segmentation method based on DCNNs for histopathology images. The proposed method which uses Mask R-CNN with color normalization and multiple inference post-processing provides robust nuclei segmentation results. O

关键词： Convolutional neural network Deep learning Histopathology Image analysis Nuclei segmentation

来源：评论

学校读者我要写书评

暂无评论

Titania Nanosheets Generates Peroxynitrite for S-Nitrosylation and Enhanced p53 Function in Lung Cancer Cells

Research Square

引用

Research Square 2021年

作者： Soonnarong, Rapeepun Tungsukruthai, Sucharat Nutho, Bodee Rungrotmongkol, Thanyada Vinayanuwattikun, Chanida Maluangnont, Tosapol Chanvorachote, Pithi Interdisciplinary Program of Pharmacology Graduate School Chulalongkorn University Bangkok Thailand Cell-based Drug and Health Products Development Research Unit Chulalongkorn University Bangkok Thailand Departments of Pharmacology and Physiology Faculty of Pharmaceutical Sciences Bangkok Thailand Department of Pharmacology Faculty of Science Mahidol University Bangkok Thailand Biocatalyst and Environmental Biotechnology Reseach Unit Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok Thailand Program in Bioinformatics and Computational Biology Graduate School Chulalongkorn University Bangkok Thailand Division of Medical Oncology Department of Medicine Chulalongkorn University Bangkok Thailand College of Materials Innovation and Technology King Mongkut’s Institute of Technology Ladkrabang Bangkok Thailand

Background: Metal oxide nanomaterials are increasingly being exploited in cancer therapy thanks to their unique properties, which can enhance the efficacy of current cancer therapies. However, the nanotoxicity and mechanism of Ti0.8O2 nanosheets for specific site-targeting strategies in NSCLC have not yet been investigated. Methods: The effects of Ti0.8O2 nanosheets on cytotoxicity in NSCLC cells and normal cells were examined. The apoptosis characteristics, including condensed and fragmented nuclei, as assessed by positive staining with annexin V. The cellular uptake of the nanosheets and the induction of stress fiber were assessed via transmission electron microscopy (TEM) and scanning electron microscopy (SEM) analyses, respectively. We also evaluated the expression of protein in death mechanism to identify the molecular mechanisms behind the toxicity of these cells. We investigated the relationship between S-nitrosylation and the increase in p53 stability by molecular dynamics. Results: Ti0.8O2 nanosheets caused cytotoxicity in several lung cancer cells, but not in normal cells. The nanosheets could enter lung cancer cells and exert an apoptosis induction. Results for protein analysis further indicated the activation of p53, increased Bax, decreased Bcl-2 and Mcl-1, and activation of caspase-3. The nanosheets also exhibited a substantial apoptosis effect in drug-resistant metastatic primary lung cancer cells, and it was found that the potency of the nanosheets was dramatically higher than that of cisplatin and etoposide. In terms of their mechanism of action, we found that the mode of apoptosis induction was through the generation of cellular ONOO− mediated the S-nitrosylation of p53 at C182. Molecular dynamics analysis further showed that the S-nitrosylation of one C182 stabilized the p53 dimer. Consequently, this nitrosylation of the protein led to an upregulation of p53 through its stabilization. Conclusions: Taking all the evidence together, we provided info

关键词： Molecular dynamics

来源：评论

学校读者我要写书评

暂无评论

Caco-2 Cell-Derived Biomimetic Electrochemical Biosensor for Cholera Toxin Detection

SSRN

引用

SSRN 2022年

作者： Kim, Yonghwan Lee, Dongtak Seo, Youngjun Jung, Hyo Gi Jang, Jae Won Park, Dongsung Kim, Insu Lee, Gyudo Hwang, Kyo Seon Kim, Seung-Hyun Lee, Sang Won Lee, Jeong Hoon Yoon, Dae Sung School of Biomedical Engineering Korea University Seoul02841 Korea Republic of Interdisciplinary Program in Precision Public Health Korea University Seoul02841 Korea Republic of Department of Clinical Pharmacology and Therapeutics College of Medicine Kyung Hee University Seoul02447 Korea Republic of Department of Biotechnology and Bioinformatics Korea University Sejong30019 Korea Republic of Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong30019 Korea Republic of School of Engineering Brown University ProvidenceRI02912 United States Department of Electrical Engineering and Computer Science University of California IrvineCA92697 United States Department of Electrical Engineering Kwangwoon University Seoul01897 Korea Republic of Astrion Inc Seoul02841 Korea Republic of

Cholera is a highly contagious and lethal waterborne disease induced by an infection with Vibrio cholerae (V. cholerae) secreting cholera toxin (CTx). Cholera toxin subunit B (CTxB) from the CTx specifically binds with monosialo-tetra-hexosyl-ganglioside (GM1) found on the exterior cell membrane of an enterocyte. Owing to the pathological process of CTx, a biomimetic electrochemical biosensor (CCB) was developed by applying GM1-expressing Caco-2 cell membrane (CCM) on the electrode surface of the biosensor for CTx detection. Briefly, the electrode surface was functionalized with CCM using the vesicle fusion method. The specific binding between GM1 and CTxB on the electrode surface was verified using confocal laser scanning microscopy and atomic force microscopy. The CTxB detection performances of the CCB, including sensitivity, selectivity, and long-term stability, were determined using electrochemical impedance spectroscopy (EIS). Based on the results, the CCB had an excellent limit of detection of ~11.46 nM and a detection range spanning 100 ng/mL - 1 mg/mL. Further, the CCB had high selectivity for the CTxB compared to different interfering molecules, which are abundant constituents of intestinal fluid and fecal matter in the human body. The long-term stability of the CCBs was also verified for 3 weeks using EIS. Overall, the CCB was found to have an excellent potential for application to point-of-care and cost-effective testing for CTxB detection in developing countries. © 2022, The Authors. All rights reserved.

关键词： Electrochemical impedance spectroscopy

来源：评论

学校读者我要写书评

暂无评论

Gene knockout inference with variational graph autoencoder learning single-cell gene regulatory networks

引用

Nucleic acids research 2023年第13期51卷 6578-6592页

作者： Yongjian Yang Guanxun Li Yan Zhong Qian Xu Bo-Jia Chen Yu-Te Lin Robert S Chapkin James J Cai Department of Electrical and Computer Engineering Texas A&M University College Station TX 77843 USA. Department of Statistics Texas A&M University College Station TX 77843 USA. Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University 3663 North Zhongshan Road Shanghai 200062 China. Department of Veterinary Integrative Biosciences Texas A&M University College Station TX 77843 USA. Graduate Institute of Microbiology and Public Health College of Veterinary Medicine National Chung Hsing University Taichung 402 Taiwan. Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan. Program in Integrative & Complex Diseases Department of Nutrition Texas A&M University College Station TX 77843 USA. Interdisciplinary Program of Genetics Texas A&M University College Station TX 77843 USA.

In this paper, we introduce Gene Knockout Inference (GenKI), a virtual knockout (KO) tool for gene function prediction using single-cell RNA sequencing (scRNA-seq) data in the absence of KO samples when only wild-type (WT) samples are available. Without using any information from real KO samples, GenKI is designed to capture shifting patterns in gene regulation caused by the KO perturbation in an unsupervised manner and provide a robust and scalable framework for gene function studies. To achieve this goal, GenKI adapts a variational graph autoencoder (VGAE) model to learn latent representations of genes and interactions between genes from the input WT scRNA-seq data and a derived single-cell gene regulatory network (scGRN). The virtual KO data is then generated by computationally removing all edges of the KO gene-the gene to be knocked out for functional study-from the scGRN. The differences between WT and virtual KO data are discerned by using their corresponding latent parameters derived from the trained VGAE model. Our simulations show that GenKI accurately approximates the perturbation profiles upon gene KO and outperforms the state-of-the-art under a series of evaluation conditions. Using publicly available scRNA-seq data sets, we demonstrate that GenKI recapitulates discoveries of real-animal KO experiments and accurately predicts cell type-specific functions of KO genes. Thus, GenKI provides an in-silico alternative to KO experiments that may partially replace the need for genetically modified animals or other genetically perturbed systems.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Author Correction: Type-2 Diabetics Reduces Spatial Variation of Microbiome Based on Extracellular Vesicles from Gut Microbes across Human Body

引用

Scientific reports 2020年第1期10卷 5368页

作者： Geumkyung Nah Sang-Cheol Park Kangjin Kim Sungmin Kim Jaehyun Park Sanghun Lee Sungho Won Interdisciplinary Program in Bioinformatics Seoul National University Seoul South Korea. Department of public health sciences Seoul National university Seoul South Korea. Inje University College of Medicine and Haeundae Paik Hospital Busan South Korea. Department of medical consilience Graduate school Dankook university Yongin-si South Korea. integrative@***. Interdisciplinary Program in Bioinformatics Seoul National University Seoul South Korea. won1@snu.ac.kr. Department of public health sciences Seoul National university Seoul South Korea. won1@snu.ac.kr. Institute of Health and Environment Seoul National University Seoul South Korea. won1@snu.ac.kr.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Pandemics are catalysts of scientific novelty: Evidence from COVID-19

arXiv

引用

arXiv 2020年

作者： Liu, Meijun Bu, Yi Chen, Chongyan Xu, Jian Li, Daifeng Leng, Yan Freeman, Richard Barry Meyer, Eric Yoon, Wonjin Sung, Mujeen Jeong, Minbyul Lee, Jinhyuk Kang, Jaewoo Min, Chao Song, Min Zhai, Yujia Ding, Ying Institute for Global Public Policy Fudan University Shanghai China Department of Information Management Peking University Beijing China School of Information University of Texas at Austin AustinTX United States School of Information Management Sun Yat-Sen University Guangzhou China McCombs School of Business University of Texas at Austin AustinTX United States Department of Economics Harvard University CambridgeMA United States CambridgeMA United States Department of Computer Science and Engineering Korea University Seoul Korea Republic of Interdisciplinary Graduate Program in Bioinformatics Korea University Seoul Korea Republic of School of Information Management Nanjing University Nanjing China Department of Library and Information Science Yonsei University Seoul Korea Republic of Department of Information Resource Management School of Management Tianjin Normal University Tianjin China School of Information Management Wuhan University Wuhan China Dell Medical School University of Texas at Austin AustinTX United States

Scientific novelty drives the efforts to invent new vaccines and solutions during the pandemic. First-time collaboration and international collaboration are two pivotal channels to expand teams' search activities for a broader scope of resources required to address the global challenge, which might facilitate the generation of novel ideas. Our analysis of 98,981 coronavirus papers suggests that scientific novelty measured by the BioBERT model that is pre-trained on 29 million PubMed articles, and first-time collaboration increased after the outbreak of COVID-19, and international collaboration witnessed a sudden decrease. During COVID-19, papers with more first-time collaboration were found to be more novel and international collaboration did not hamper novelty as it had done in the normal periods. The findings suggest the necessity of reaching out for distant resources and the importance of maintaining a collaborative scientific community beyond nationalism during a pandemic. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

来源：评论

学校读者我要写书评

暂无评论

Multi-stream deep learning framework to predict mild cognitive impairment with Rey Complex Figure Test

arXiv

引用

arXiv 2024年

作者： Park, Junyoung Seo, Eun Hyun Kim, Sunjun Yi, SangHak Lee, Kun Ho Won, Sungho Department of Neurology and Neurological Sciences Stanford University School of Medicine StanfordCA94305 United States RexSoft Inc Seoul08826 Korea Republic of Gwangju Alzheimer's & Related Dementia Cohort Research Center Chosun University Gwangju61452 Korea Republic of Premedical Science College of Medicine Chosun University Gwangju61452 Korea Republic of Neurozen Inc. Seoul06160 Korea Republic of Department of Neurology Wonkwang University Hospital Wonkwang University School of Medicine Iksan15865 Korea Republic of Department of Biomedical Science Chosun University Gwangju61452 Korea Republic of Korea Brain Research Institute Daegu41068 Korea Republic of Department of Public Health Sciences Graduate School of Public Health Seoul National University Seoul08826 Korea Republic of Interdisciplinary Program in Bioinformatics Seoul National University Seoul08826 Korea Republic of Institute of Health and Environment Seoul National University Seoul08826 Korea Republic of

Drawing tests like the Rey Complex Figure Test (RCFT) are widely used to assess cognitive functions such as visuospatial skills and memory, making them valuable tools for detecting mild cognitive impairment (MCI). Despite their utility, existing predictive models based on these tests often suffer from limitations like small sample sizes and lack of external validation, which undermine their reliability. We developed a multi-stream deep learning framework that integrates two distinct processing streams: a multi-head self-attention based spatial stream using raw RCFT images and a scoring stream employing a previously developed automated scoring system. Our model was trained on data from 1,740 subjects in the Korean cohort and validated on an external hospital dataset of 222 subjects from Korea. The proposed multi-stream model demonstrated superior performance over baseline models (AUC = 0.872, Accuracy = 0.781) in external validation. The integration of both spatial and scoring streams enables the model to capture intricate visual details from the raw images while also incorporating structured scoring data, which together enhance its ability to detect subtle cognitive impairments. This dual approach not only improves predictive accuracy but also increases the robustness of the model, making it more reliable in diverse clinical settings. Our model has practical implications for clinical settings, where it could serve as a cost-effective tool for early MCI screening. © 2024, CC BY.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Author Correction: SARS-CoV-2 infection engenders heterogeneous ribonucleoprotein interactions to impede translation elongation in the lungs

引用

EXPERIMENTAL AND MOLECULAR MEDICINE 2023年第12期55卷 2609-2609页

作者： Kim, Junsoo Youn, Daehwa Choi, Seunghoon Lee, Youn Woo Sumberzul, Dulguun Yoon, Jeongeun Lee, Hanju Bae, Jong Woo Noh, Hyuna On, Dain Hong, Seung-Min An, Se-Hee Jang, Hui Jeong Kim, Seo Yeon Kim, Young Been Hwang, Ji-Yeon Lee, Hyo-Jung Kim, Hong Bin Park, Jun Won Yun, Jun-Won Shin, Jeon-Soo Seo, Jun-Young Nam, Ki Taek Choi, Kang-Seuk Lee, Ho-Young Chang, Hyeshik Seong, Je Kyung Cho, Jun Center for RNA Research Institute for Basic Science (IBS) Seoul National University Seoul Republic of Korea Interdisciplinary Program in Bioinformatics Seoul National University Seoul Republic of Korea School of Biological Sciences Seoul National University Seoul Republic of Korea Korea Mouse Phenotyping Center (KMPC) Seoul National University Seoul Republic of Korea Laboratory of Developmental Biology and Genomics Research Institute for Veterinary Science and BK 21 PLUS Program for Creative Veterinary Science Research College of Veterinary Medicine Seoul National University Seoul Republic of Korea Interdisciplinary Program and BIO MAX Institute Seoul National University Seoul Republic of Korea Department of Biomedical Science and Engineering Gwangju Institute of Science and Technology (GIST) Gwangju Republic of Korea Department of Nuclear Medicine Seoul National University Bundang Hospital Seongnam Republic of Korea Department of Nuclear Medicine Seoul National University College of Medicine Seoul Republic of Korea Laboratory of Avian Diseases Research Institute for Veterinary Science and BK 21 PLUS Program for Creative Veterinary Science Research College of Veterinary Medicine Seoul National University Seoul Republic of Korea Preclinical Research Center Seoul National University Bundang Hospital Seongnam Republic of Korea Department of Periodontology Section of Dentistry Seoul National University Bundang Hospital Seongnam Republic of Korea Department of Internal Medicine Seoul National University Bundang Hospital Seoul National University College of Medicine Seongnam Republic of Korea Division of Biomedical Convergence College of Biomedical Science Kangwon National University ChunCheon Republic of Korea Laboratory of Veterinary Toxicology College of Veterinary Medicine Seoul National University Seoul Republic of Korea Severance Biomedical Science Institute Graduate School of Medical Science Brain Korea 21 Project Yonsei University College of Medicine

来源：评论

学校读者我要写书评

暂无评论

Front Cover: Synthesis and Immunomodulatory Activity of Fluorine-Containing Bisphosphonates (ChemMedChem 4/2019)

引用

ChemMedChem 2019年第4期14卷 360-360页

作者： Dr. Satoshi Mizuta Mohammed S. O. Tagod Dr. Masashi Iwasaki Prof. Yoichi Nakamura Dr. Hiroaki Senju Prof. Hiroshi Mukae Prof. Craig T. Morita Prof. Yoshimasa Tanaka Center for Bioinformatics and Molecular Medicine Graduate School of Biomedical Sciences Nagasaki University 1-12-4 Sakamoto Nagasaki 852-8523 Japan Program for Nurturing Global Leaders in Tropical and Emerging Infectious Diseases Graduate School of Biomedical Sciences Nagasaki University 1-12-4 Sakamoto Nagasaki 852-8523 Japan Center for Innovation in Immunoregulative Technology and Therapeutics Graduate School of Medicine Kyoto University Yoshidakonoe-cho Kyoto 606-8501 Japan Department of Respiratory Medicine Nagasaki University School of Medicine 1-7-1 Sakamoto Nagasaki 852-8501 Japan Department of Internal Medicine and the Interdisciplinary Graduate Program in Immunology University of Iowa Carver College of Medicine Iowa City Veterans Affairs Health Care System Iowa City IA 52246 USA Hyogo College of Medicine 1-1 Mukogawa Nishinomiya Hyogo 663-8501 Japan

来源：评论

学校读者我要写书评

暂无评论

Correction: Resistance QTLs controlling leaf and neck blast disease identified in a doubled haploid rice population

引用

Euphytica 2023年第7期219卷 1-1页

作者： Korinsak, Siripar Sriprakhon, Saengchai Sirithunya, Kanyanat Sriwongchai, Tanee Wongsaprom, Chanakarn Plabpla, Anucha Vanavichit, Apichart Toojinda, Theerayut National Center for Genetic Engineering and Biotechnology (BIOTEC) National Science and Technology Development Agency (NSTDA) Khlong Luang Thailand Faculty of Science and Agricultural Technology Rajamangala University of Technology Lanna Chiangmai Thailand Department of Agronomy Faculty of Agriculture Kasetsart University Bangkok Thailand Interdisciplinary Graduate Program in Genetic Engineering and Bioinformatics Kasetsart University Bangkok Thailand Department of Agronomy Faculty of Agriculture at Kamphaeng Saen Kasetsart University Nakhon Pathom Thailand Rice Science Center Kasetsart University Nakhon Pathom Thailand

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：