检索结果-内蒙古大学图书馆

Latent Regression and Ordination Risk of Infectious Disease and Climate

Procedia Computer science 2021年 179卷 25-32页

作者： Rezzy Eko Caraka Rung Ching Chen Youngjo Lee Prana Ugiana Gio Arif Budiarto Bens Pardamean College of Informatics Chaoyang University of Technology 168 Jifong East Road Wufong Dist. Taichung City 41349 Taiwan (R.O.C.). Department of Statistics College of Natural Science Seoul National University Gwanak-gu Seoul 08826 Korea Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Mathematics Universitas Sumatera Utara Medan 20222 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Indonesia.

Global warming arising from climate change can increase the spread of deadly diseases. Effort is needed to develop a set of policies for the government to stem or reduce health risks from global warming. The purpose of this paper is to examine more detail and comprehensively about the relationship among climate and event disease count in Taiwan using the partial least square latent regression model. The results obtained that of the 17 types of diseases in Taiwan, that has the most significant loading factor is Amoebiasis, Malaria and Chikungunya. At the same time, climate variables that have the biggest most significant factor are Number day with max temp more than 30, Number day Temp more than 25, and Rainfall PH. Cronbach’s Alpha infectious disease 0.9696 and climate 0.2813. At the same time, the value of Dillon Goldstein’s rho infectious disease 0.974 and climate 0.6404, respectively.

关键词： Disease Counts Climate Latent PLS Regression

来源：评论

学校读者我要写书评

暂无评论

Systematic literature review: Accelerate the rice production for global food security

引用

AIP Conference Proceedings 2023年第1期2594卷

作者： N. Dominic T. W. Cenggoro B. Pardamean Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480

By 2050, about 70% of extra food production is needed to feed the world's population. As a majority staple food, the expansion of rice production should be accelerated. This systematic literature review aims to discover the factors that influence rice yields through a methodology that is partitioned into four main stages, i.e., query entry into multiple database sources, article title filtering, abstract filtering, and final article selection. The results show that genetics, irrigation system management, and farmers’ long-evolved local knowledge or experiences are the three big factors that elevate the rice production rates. Nevertheless, global warming is a serious challenge that should be deeply considered due to its great impact on reducing rice yields by up to 14.5%. Thus, the development of genetically modified rice varieties needs to be escalated while still maintaining other exogenous factors. This study contributes to providing a broader perspective for decision-makers and other relevant experts to accelerate rice production to achieve global food security.

关键词：

来源：评论

学校读者我要写书评

暂无评论

AI-Based Ripeness Grading for Oil Palm Fresh Fruit Bunch in Smart Crane Grabber 3

AI-Based Ripeness Grading for Oil Palm Fresh Fruit Bunch in ...

引用

3rd International Conference on Eco Engineering Development, ICEED 2019

作者： Harsawardana Rahutomo, Reza Mahesworo, Bharuno Cenggoro, Tjeng Wawan Budiarto, Arif Suparyanto, Teddy Surya Atmaja, Don Bosco Samoedro, Bayu Pardamean, Bens Institut Pertanian STIPER Yogyakarta55281 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mechatronics and AI Research and Development Center Biotechnic Innovation Yogyakarta55281 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Indonesia is one of the biggest palm oil exporters in the world. For Indonesia to stay competitive in thepalm oil industry, the harvesting and evacuating process in its oil palm plantation need to be optimized. This research introduces machinerycalled as Smart Crane Grabber. This machinery can be used for automatic harvesting and evacuation process of oil palm fresh fruit bunch. To enable automation, Smart Crane Grabber is equipped with an Artificial Intelligencesystem for automatic ripeness sorting. TheArtificial Intelligence system developed for Smart Crane Grabber achieves 71.34% accuracy by using only 400 images as preliminary training data. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Palm oil

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

Analyzing Morphological Changes in Zebrafish Embryos Exposed to Toxic Chemicals

Analyzing Morphological Changes in Zebrafish Embryos Exposed...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Akhil Ambekar Carolin Poschen Chih Lai Stefan Scholz Elisabet Teixidó Graduate Program in Software University of St. Thomas St. Paul MN USA Data Science Fellow on AI Healthcare Duke University NC USA Helmholtz Centre for Environmental Research GmbH - UFZ Leipzig Germany University of Barcelona Barcelona Spain

ISBN: (纸本)9781665429825

Subtle morphological changes caused by toxic chemicals in different regions of organs in small zebrafish embryos can be difficult to detect by human eyes or even by traditional image processing tools. Without brute force testing arbitrary parameters and thresholds in various combinations of imaging algorithms, we present our systematic approach in which we first train convolutional neural networks (CNNs) to recognize which organs of which zebrafish embryos contain significant morphological differences so they can be reliably classified as being exposed to either the fresh water or the toxic water contaminated with chemicals. From the training results, the organ images in each identified image subset were then analyzed by the technique, occlusion sensitivity map, to allocate the exact locations and the scales of morphological changes caused by different chemicals. In the experiment section of this paper, we use multiple chemical exposure scenarios to demonstrate the morphological differences in organs that can be detected by our approach.

关键词： Training Embryo Systematics Sensitivity Toxic chemicals Biological systems Water pollution

来源：评论

学校读者我要写书评

暂无评论

AI-based Automatic Segmentation of Prostate on Multi-modality Images: A Review

arXiv

引用

arXiv 2024年

作者： Jin, Rui Li, Derun Xiang, Dehui Zhang, Lei Zhou, Hailing Shi, Fei Zhu, Weifang Cai, Jing Peng, Tao Chen, Xinjian School of Future Science and Engineering Soochow University Suzhou China Department of Urology Peking University First Hospital Xicheng District Beijing100034 China MIPAV Lab The School of Electronic and Information Engineering Soochow University Jiangsu China Graduate Program of Medical Physics and Data Science Research Center Duke Kunshan University Jiangsu Kunshan215316 China Department of Mechanical Engineering and Product Design Engineering Swinburne University of Technology Melbourne Australia Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong Department of Radiation Oncology UT Southwestern Medical Center DallasTX United States The State Key Laboratory of Radiation Medicine and Protection Soochow University Jiangsu China

Prostate cancer represents a major threat to health. Early detection is vital in reducing the mortality rate among prostate cancer patients. One approach involves using multi-modality (CT, MRI, US, etc.) computer-aided diagnosis (CAD) systems for the prostate region. However, prostate segmentation is challenging due to imperfections in the images and the prostate’s complex tissue structure. The advent of precision medicine and a significant increase in clinical capacity have spurred the need for various data-driven tasks in the field of medical imaging. Recently, numerous machine learning and data mining tools have been integrated into various medical areas, including image segmentation. This article proposes a new classification method that differentiates supervision types, either in number or kind, during the training phase. Subsequently, we conducted a survey on artificial intelligence (AI)-based automatic prostate segmentation methods, examining the advantages and limitations of each. Additionally, we introduce variants of evaluation metrics for the verification and performance assessment of the segmentation method and summarize the current challenges. Finally, future research directions and development trends are discussed, reflecting the outcomes of our literature survey, suggesting high-precision detection and treatment of prostate cancer as a promising avenue. Impact Statement — Prostate cancer remains a significant health threat, necessitating early detection to reduce mortality rates. Current diagnostic approaches, particularly in medical imaging, face challenges in accurate prostate segmentation due to the organ's complex structure and imaging imperfections. Our review paper delves into AI-based automatic segmentation methods across various imaging modalities (CT, MRI, US), emphasizing their strengths, weaknesses, and the specific challenges they address. By classifying segmentation techniques based on supervision levels, we provide a comprehensive analys

关键词： Computer aided diagnosis

来源：评论

学校读者我要写书评

暂无评论

Emotion recognition in doctor-patient interactions from real-world clinical video database: Initial development of artificial empathy

引用

Computer Methods and programs in Biomedicine 2023年 233卷 107480-107480页

作者： Huang, Chih-Wei Wu, Bethany C.Y. Nguyen, Phung Anh Wang, Hsiao-Han Kao, Chih-Chung Lee, Pei-Chen Rahmanti, Annisa Ristya Hsu, Jason C. Yang, Hsuan-Chia Li, Yu-Chuan Jack Taipei Medical University Taipei Taiwan Taipei Medical University Ringgold standard institution - Center for Simulation in Medical Education Taipei 116 Taiwan National Taiwan University Children and Family Research Center Sponsored by CTBC Charity Foundation Taipei Taiwan Clinical Data Center Office of Data Science Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University TMU Da'an Campus 15 F No. 172-1 Kee lung Road Section 2 Da-an District Taipei Taiwan Research Center of Big Data and Meta-analysis Wanfang Hospital Taipei Medical University Taipei Taiwan Department of Dermatology Wanfang Hospital Taipei Medical University Taiwan Department of Dermatology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Delta Research Center Taipei Taiwan Department of Health Policy and Management Faculty of Medicine Public Health and Nursing Universitas Gadjah Mada Yogyakarta Indonesia International PhD Program in Biotech and Healthcare Management College of Management Taipei Medical University Taipei Taiwan Research Center of Data Science on Healthcare Industry College of Management Taipei Medical University Taipei Taiwan

Background and objective: The promising use of artificial intelligence (AI) to emulate human empathy may help a physician engage with a more empathic doctor-patient relationship. This study demonstrates the application of artificial empathy based on facial emotion recognition to evaluate doctor-patient relationships in clinical practice. Methods: A prospective study used recorded video data of doctor-patient clinical encounters in dermatology outpatient clinics, Taipei Municipal Wanfang Hospital, and Taipei Medical University Hospital collected from March to December 2019. Two cameras recorded the facial expressions of four doctors and 348 adult patients during regular clinical practice. Facial emotion recognition was used to analyze the basic emotions of doctors and patients with a temporal resolution of 1 second. In addition, a physician-patient satisfaction questionnaire was administered after each clinical session, and two standard patients gave impartial feedback to avoid bias. Results: data from 326 clinical session videos showed that (1) Doctors expressed more emotions than patients (t [326] > = 2.998, p © 2023

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

High‑Performance Thick Cathode Based on Polyhydroxyalkanoate Binder for Li Metal Batteries

引用

Advanced Fiber Materials 2024年第1期6卷 214-228页

作者： Dong Hyuk Kang Minhyuck Park Jeonghun Lee Chan Yeol Kim Jimin Park Youn‑Ki Lee Jong Chan Hyun Son Ha Jin Hwan Kwak Juhee Yoon Hyemin Kim Hyun Soo Kim Do Hyun Kim Sangmin Kim Ji Yong Park Robin Jang Seung Jae Yang Hee‑Dae Lim Se Youn Cho Hyoung‑Joon Jin Seungjin Lee Yunil Hwang Young Soo Yun KU‑KIST Graduate School of Converging Science and Technology Korea University145 Anam‑roSeongbuk‑guSeoul 02841South Korea Carbon Composite Materials Research Center Korea Institute of Science and Technology(KIST)92 Chundong‑roBongdong‑eupWanju‑gunJeollabuk‑do 55324South Korea Energy Storage Research Center Korea Institute of Science and Technology(KIST)514‑gil Hwaraong‑roSeoul 02792South Korea Program in Environmental and Polymer Engineering Inha University100 Inha‑ro Michuhol‑guIncheon 22212South Korea Advanced Analysis Data Center Korea Institute of Science and Technology(KIST)514‑gil Hwaraong‑roSeoul 02792South Korea Advanced Nanohybrids Laboratory Department of Chemistry and Chemical EngineeringInha University100 Inha‑roMichuhol‑guIncheon 22212South Korea CJ CheilJedang Corporation 55 Gwanggyo‑ro 42Beon‑gilYeongtong‑guSuwon 16495South Korea Department of Chemical Engineering Hanyang University222 Wangsimni‑roSeongdong‑guSeoul 04763South Korea Department of Integrative Energy Engineering Korea University145 Anam‑roSeongbuk‑guSeoul 02841South Korea

Thick cathodes can overcome the low capacity issues,which mostly hamper the performance of the conventional active cathode materials,used in rechargeable Li ***,the typical slurry-based method induces cracking and flaking during the fabrication of thick *** addition,a significant increase in the charge-transfer resistance and local cur-rent overload results in poor rate capabilities and cycling stabilities,thereby limiting electrode *** this study,a synergistic dual-network combination strategy based on a conductive nanofibrillar network(CNN)and a nano-bridging amor-phous polyhydroxyalkanoate(aPHA)binder is used to demonstrate the feasibility of constructing a high-performance thick *** CNN and aPHA dual network facilitates the fabrication of a thick cathode(≥250μm thickness and≥90 wt%active cathode material)by a mass-producible slurry *** thick cathode exhibited a high rate capability and excel-lent cycling *** addition,the thick cathode and thin Li metal anode pair(Li//t-NCM)exhibited an optimal energy performance,affording high-performance Li metal batteries with a high areal energy of~25.3 mW h cm^(-2),a high volumetric power density of~1720 W L^(-1),and an outstanding specific energy of~470 W h kg^(-1)at only 6 mA h cm^(-2).

关键词： Thick cathode Polyhydroxyalkanoate binder Nano-bridging Conductive nano-fibrillar network Lithium metal battery

来源：评论

学校读者我要写书评

暂无评论

Experimental and real-world evidence supporting the computational repurposing of bumetanide for APOE4-related Alzheimer's disease ( Oct, 10.1038/s43587-021-00122-7, 2021)

NATURE AGING

引用

NATURE AGING 2021年第12期1卷 1202-1202页

作者： Taubes, Alice Nova, Phil Zalocusky, Kelly A. Kosti, Idit Bicak, Mesude Zilberter, Misha Y. Hao, Yanxia Yoon, Seo Yeon Oskotsky, Tomiko Pineda, Silvia Chen, Bin Jones, Emily Aery A. Choudhary, Krishna Grone, Brian Balestra, Maureen E. Chaudhry, Fayzan Paranjpe, Ishan De Freitas, Jessica Koutsodendris, Nicole Chen, Nuo Wang, Celine Chang, William An, Alice Glicksberg, Benjamin S. Sirota, Marina Huang, Yadong Gladstone Institute of Neurological Disease Gladstone Institutes San Francisco CA USA Biomedical Sciences Graduate Program University of California San Francisco CA USA Gladstone Center for Translational Advancement Gladstone Institutes San Francisco CA USA Department of Neurology University of California San Francisco CA USA Development and Stem Cell Biology Graduate Program University of California San Francisco CA USA Department of Pathology University of California San Francisco CA USA Bakar Computational Health Sciences Institute University of California San Francisco CA USA Department of Pediatrics University of California San Francisco CA USA Department of Surgery University of California San Francisco CA USA Hasso Plattner Institute for Digital Health at Mount Sinai Icahn School of Medicine at Mount Sinai New York NY USA Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New York NY USA Gladstone Institute of Data Science and Biotechnology Gladstone Institutes San Francisco CA USA

The evident genetic, pathological and clinical heterogeneity of Alzheimer’s disease (AD) poses challenges for traditional drug development. We conducted a computational drug-repurposing screen for drugs to treat apolipoprotein E4 (APOE4)-related AD. We first establishedAPOEgenotype-dependent transcriptomic signatures of AD by analyzing publicly available human brain databases. We then queried these signatures against the Connectivity Map database, which contains transcriptomic perturbations of more than 1,300 drugs, to identify those that best reverseAPOEgenotype-specific AD signatures. Bumetanide was identified as a top drug forAPOE4-related AD. Treatment ofAPOE4-knock-in mice without or with amyloid β (Aβ) accumulation using bumetanide rescued electrophysiological, pathological or cognitive deficits. Single-nucleus RNA sequencing revealed transcriptomic reversal of AD signatures in specific cell types in these mice, a finding confirmed inAPOE4induced pluripotent stem cell (iPSC)-derived neurons. In humans, bumetanide exposure was associated with a significantly lower AD prevalence in individuals over the age of 65 years in two electronic health record databases, suggesting the effectiveness of bumetanide in preventing AD.

关键词： OBSTRUCTIVE SLEEP-APNEA APOLIPOPROTEIN-E APOE EPSILON-4 EXPRESSION RISK E4 AGE ASSOCIATION METABOLISM MECHANISMS

来源：评论

学校读者我要写书评

暂无评论

CellBiAge: Improved single-cell age classification using data binarization

引用

Cell Reports 2023年第12期42卷 113500页

作者： Yu, Doudou Li, Manlin Linghu, Guanjie Hu, Yihuan Hajdarovic, Kaitlyn H. Wang, An Singh, Ritambhara Webb, Ashley E. Molecular Biology Cell Biology and Biochemistry Graduate Program Brown University Providence 02912 RI United States Data Science Institute Brown University Providence 02912 RI United States Neuroscience Graduate Program Brown University Providence 02912 RI United States Department of Applied Mathematics & Statistics Johns Hopkins University Baltimore 21218 MD United States Department of Computer Science Brown University Providence 02912 RI United States Center for Computational Molecular Biology Brown University Providence 02912 RI United States Department of Molecular Biology Cell Biology and Biochemistry Brown University Providence 02912 RI United States Center on the Biology of Aging Brown University Providence 02912 RI United States Carney Institute for Brain Science Brown University Providence 02912 RI United States Center for Translational Neuroscience Brown University Providence 02912 RI United States

Aging is a major risk factor for many diseases. Accurate methods for predicting age in specific cell types are essential to understand the heterogeneity of aging and to assess rejuvenation strategies. However, classifying organismal age at single-cell resolution using transcriptomics is challenging due to sparsity and noise. Here, we developed CellBiAge, a robust and easy-to-implement machine learning pipeline, to classify the age of single cells in the mouse brain using single-cell transcriptomics. We show that binarization of gene expression values for the top highly variable genes significantly improved test performance across different models, techniques, sexes, and brain regions, with potential age-related genes identified for model prediction. Additionally, we demonstrate CellBiAge's ability to capture exercise-induced rejuvenation in neural stem cells. This study provides a broadly applicable approach for robust classification of organismal age of single cells in the mouse brain, which may aid in understanding the aging process and evaluating rejuvenation methods. © 2023 The Authors

关键词： aging brain CP: Cell biology hypothalamus machine learning single-cell RNA-seq

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：