检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Wang, Xinru Deliu, Nina Narita, Yusuke Chakraborty, Bibhas Centre for Quantitative Medicine Duke-NUS Medical School Singapore MRC - Biostatistics Unit University of Cambridge Cambridge United Kingdom Department of Methods and Models for Economics Territory and Finance Sapienza University of Rome Rome Italy Department of Economics and Cowles Foundation Yale University New HavenCT United States Program in Health Services and Systems Research Duke-NUS Medical School Singapore Department of Statistics and Data Science National University of Singapore Singapore Department of Biostatistics and Bioinformatics Duke University DurhamNC United States

Dynamic treatment regimes (DTRs) are sequences of decision rules that recommend treatments based on patients’ time-varying clinical conditions. The sequential multiple assignment randomized trial (SMART) is an experimental design that can provide high-quality evidence for constructing optimal DTRs. In a conventional SMART, participants are randomized to available treatments at multiple stages with balanced randomization probabilities. Despite its relative simplicity of implementation and desirable performance in comparing embedded DTRs, the conventional SMART faces inevitable ethical issues including assigning many participants to the empirically inferior treatment or the treatment they dislike, which might slow down the recruitment procedure and lead to higher attrition rates, ultimately leading to poor internal and external validities of the trial results. In this context, we propose a SMART under the Experiment-as-Market framework (SMART-EXAM), a novel SMART design that holds the potential to improve participants’ welfare by incorporating their preferences and predicted treatment effects into the randomization procedure. We describe the steps of conducting a SMART-EXAM and evaluate its performance compared to the conventional SMART. The results indicate that the SMART-EXAM can improve the welfare of the participants enrolled in the trial, while also achieving a desirable ability to construct an optimal DTR when the experimental parameters are suitably specified. We finally illustrate the practical potential of the SMART-EXAM design using data from a SMART for children with attention-deficit/hyperactivity disorder (ADHD). © 2022, CC BY.

关键词： Design of experiments

来源：评论

学校读者我要写书评

暂无评论

Breast Cancer Immunohistochemical Image Generation: a Benchmark dataset and Challenge Review

arXiv

引用

arXiv 2023年

作者： Zhu, Chuang Liu, Shengjie Yu, Zekuan Xu, Feng Aggarwal, Arpit Corredor, Germán Madabhushi, Anant Qu, Qixun Fan, Hongwei Li, Fangda Li, Yueheng Guan, Xianchao Zhang, Yongbing Singh, Vivek Kumar Akram, Farhan Sarker, Md Mostafa Kamal Shi, Zhongyue Jin, Mulan The School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China Academy for Engineering and Technology Fudan University Shanghai China Beijing Chaoyang Hospital Capital Medical University Beijing China Biomedical Engineering Department Georgia Tech and Emory University United States The Atlanta Veterans Affairs Medical Center United States Biomedical Engineering Department Emory University United States Data Science Institute Imperial College London United Kingdom The School of Computer and Electrical Engineering Purdue University United States The School of Computer Science and Technology Harbin Institute of Technology Shenzhen China The Department of Computer Engineering and Mathematics Rovira I Virgili University Spain The Department of Pathology and Clinical Bioinformatics Erasmus Medical Center Netherlands The Institute of Biomedical Engineering University of Oxford Oxford United Kingdom

For invasive breast cancer, immunohistochemical (IHC) techniques are often used to detect the expression level of human epidermal growth factor receptor-2 (HER2) in breast tissue to formulate a precise treatment plan. From the perspective of saving manpower, material and time costs, directly generating IHC-stained images from Hematoxylin and Eosin (H&E) stained images is a valuable research direction. Therefore, we held the breast cancer immunohistochemical image generation challenge, aiming to explore novel ideas of deep learning technology in pathological image generation and promote research in this field. The challenge provided registered H&E and IHC-stained image pairs, and participants were required to use these images to train a model that can directly generate IHC-stained images from corresponding H&E-stained images. We selected and reviewed the five highest-ranking methods based on their PSNR and SSIM metrics, while also providing overviews of the corresponding pipelines and implementations. In this paper, we further analyze the current limitations in the field of breast cancer immunohistochemical image generation and forecast the future development of this field. We hope that the released dataset and the challenge will inspire more scholars to jointly study higher-quality IHC-stained image generation. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Autoscore-imbalance: An interpretable machine learning tool for development of clinical scores with rare events data

arXiv

引用

arXiv 2021年

作者： Yuan, Han Xie, Feng Ong, Marcus Eng Hock Ning, Yilin Chee, Marcel Lucas Saffari, Seyed Ehsan Abdullah, Hairil Rizal Goldstein, Benjamin Alan Chakraborty, Bibhas Liu, Nan Duke-NUS Medical School National University of Singapore Singapore Singapore Department of Emergency Medicine Singapore General Hospital Singapore Singapore Health Services Research Centre Singapore Health Services Singapore Faculty of Medicine Nursing and Health Sciences Monash University Melbourne Australia Department of Anaesthesiology Singapore General Hospital Singapore Singapore Department of Biostatistics and Bioinformatics Duke University DurhamNC United States Department of Statistics and Data Science National University of Singapore Singapore Singapore Institute of Data Science National University of Singapore Singapore Singapore

来源：评论

学校读者我要写书评

暂无评论

An integrated abundance model for estimating county-level prevalence of opioid misuse in Ohio

arXiv

引用

arXiv 2021年

作者： Hepler, Staci A. Kline, David M. Bonny, Andrea McKnight, Erin Waller, Lance A. Department of Mathematics and Statistics Wake Forest University Winston-Salem United States Department of Biostatistics and Data Science Wake Forest School of Medicine Winston-Salem United States Division of Adolescent Medicine Nationwide Children’s Hospital Department of Pediatrics The Ohio State University Columbus United States Department of Biostatistics and Bioinformatics Emory University Atlanta United States

Opioid misuse is a national epidemic and a significant drug related threat to the United States. While the scale of the problem is undeniable, estimates of the local prevalence of opioid misuse are lacking, despite their importance to policy-making and resource allocation. This is due, in part, to the challenge of directly measuring opioid misuse at a local level. In this paper, we develop a Bayesian hierarchical spatio-temporal abundance model that integrates indirect county-level data on opioid-related outcomes with state-level survey estimates on prevalence of opioid misuse to estimate the latent county-level prevalence and counts of people who misuse opioids. A simulation study shows that our integrated model accurately recovers the latent counts and prevalence. We apply our model to county-level surveillance data on opioid overdose deaths and treatment admissions from the state of Ohio. Our proposed framework can be applied to other applications of small area estimation for hard to reach populations, which is a common occurrence with many health conditions such as those related to illicit *** Codes 62 © 2021, CC BY.

关键词： Monitoring

来源：评论

学校读者我要写书评

暂无评论

SARS-CoV-2-identical protein regions found in mammalian coronaviruses have immunogenic potential and can imply cross-protection

引用

ImmunoInformatics 2024年 14卷

作者： Luciano Rodrigo Lopes Bio-Data Science and Bioinformatics Division Health Informatics Department Universidade Federal de São Paulo–UNIFESP Rua Botucatu 862 Prédio Leal Prado térreo Vila Clementino CEP 04023-062 SP São Paulo Brazil

Coronaviruses are known to infect a wide range of mammals. In humans, coronaviruses have been responsible for causing the common cold. The immune response against common cold coronaviruses appears to elicit a cross-protective response to SARS-CoV-2. This study identified protein regions in the mammalian coronaviruses' proteome that are identical to those of SARS-CoV-2. Using bioinformatics analysis, the study predicted the involvement of SARS-CoV-2-identical protein regions, identified in mammalian coronaviruses, in antigen-presenting processes and their ability to elicit immune responses. The SARS-CoV-2-identical protein regions were predominantly found in the proteomes of betacoronaviruses, with less prevalence in alphacoronaviruses. Alphacoronaviruses, such as FCoV in domestic felines and MCoV in minks, are known to infect species highly susceptible to SARS-CoV-2. In contrast, betacoronaviruses infect mammals with lower susceptibility to SARS-CoV-2, including dogs, mice, and farmed animals. Furthermore, betacoronaviruses exhibited a higher number of peptides with an increased potential for efficient presentation during the antigen-presenting process, indicating their greater immunogenicity. Conversely, the SW1 gammacoronavirus showed a lower count of SARS-CoV-2 protein regions and a reduced potential for efficient antigen presentation. The results suggested that the elevated number of SARS-CoV-2 identical stretches found in betacoronaviruses may provide potential cross-protection between SARS-CoV-2 and mammalian betacoronaviruses. This cross-protection could be similar to that observed between human coronaviruses causing the common cold and SARS-CoV-2. The limited numbers observed in the proteomes of FCoV, MCoV, and SW1-CoV may offer an explanation for the susceptibility of cats and minks to SARS-CoV-2, as well as a potential vulnerability in cetaceans.

关键词： Betacoronaviruses Zoonotic susceptibility Cross-protection Antigen presentation Immunogenicity

来源：评论

学校读者我要写书评

暂无评论

The by-product of oil palm dosage application on Turnera subulata growth supports water supply

引用

AIP Conference Proceedings 2024年第1期3026卷

作者： Pauliz Budi Hastuti Gilang Tri Anggara Wiwin Dyah Ully Parwati Teddy Suparyanto Digdo Sudigyo Bens Pardamean Faculty of Agriculture STIPER Institute of Agriculture Yogyakarta Indonesia 55281 Faculty of Agricultural Technology STIPER Institute of Agriculture Yogyakarta Indonesia 55281 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Plant growth with complex organic matter content can be enhanced by using oil palm by-products. These organic materials can maintain the water supply in the plant body. Therefore, in oil palm plantations with significant groundwater consumption, organic matter’s the ability to sustain water availability. Caterpillars that devour palm leaves are among the numerous pests that threaten oil palm plantations. Predators of leaf-eating caterpillars use the holly rose flower (Turnera subulata) in oil palm farms as a habitat. Therefore, the effect of the type and dose treatment of oil palm by-products on the growth of Turnera subulata was observed to support sustainable water management in oil palm plantations. The research was carried out using a factorial design arranged in a Completely Randomized Design (CRD) consisting of two components: the type of palm oil mill by-product (boiler ash, empty bunches, and solid) and the dose of a by-product (% volume) consisting of four dosages (0, 25, 33, and 50% volume). The research data were analyzed using variance at the 5% test level. A DMRT test at the 5% test level was conducted on the treatment that had a significant effect. The growth of Turnera subulata was most positively impacted by a solid application dose of 50% volume. Turnera subulata plants responded the least favorably to the application of empty bunches in any dose treatment.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Alignments of biomolecular contact maps

Alignments of biomolecular contact maps

引用

作者： Stadler, Peter F. Bioinformatics Group Department of Computer Science and Interdisciplinary Centre for Bioinformatics Universität Leipzig Härtelstraße 16-18 Leipzig04107 Germany Halle-Jena-Leipzig Competence Centre for Scalable Data Services and Solutions Dresden-Leipzig Leipzig Research Centre for Civilization Diseases Centre for Biotechnology and Biomedicine Leipzig University Universität Leipzig Leipzig Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 Leipzig04103 Germany Institute for Theoretical Chemistry University of Vienna Währingerstrasse 17 Wien1090 Austria Facultad de Ciencias Universidad National de Colombia Bogota Colombia Santa Fe Institute 1399 Hyde Park Road Santa FeNM87501 United States

Alignments of discrete objects can be constructed in a very general setting as super-objects from which the constituent objects are recovered by means of projections. Here, we focus on contact maps, i.e. undirected graphs with an ordered set of vertices. These serve as natural discretizations of RNA and protein structures. In the general case, the alignment problem for vertex-ordered graphs is NP-complete. In the special case of RNA secondary structures, i.e. crossing-free matchings, however, the alignments have a recursive structure. The alignment problem then can be solved by a variant of the Sankoff algorithm in polynomial time. Moreover, the tree or forest alignments of RNA secondary structure can be understood as the alignments of ordered edge sets. © 2021 The Author(s).

关键词： Alignment

来源：评论

学校读者我要写书评

暂无评论

Categorization and analysis of 14 computational methods for estimating cell potency from single-cell RNA-seq data

arXiv

引用

arXiv 2023年

作者： Wang, Qingyang Zhai, Zhiqian Lian, Qiuyu Song, Dongyuan Li, Jingyi Jessica Department of Statistics and Data Science University of California Los AngelesCA90095-1554 United States Wellcome Trust Cancer Research UK Gurdon Institute University of Cambridge Cambridgeshire CambridgeCB2 1QN United Kingdom Bioinformatics Interdepartmental Ph.D. Program University of California Los AngelesCA90095-7246 United States Department of Human Genetics University of California Los AngelesCA90095-7088 United States Department of Computational Medicine University of California Los AngelesCA90095-1766 United States Department of Biostatistics University of California Los AngelesCA90095-1772 United States Radcliffe Institute for Advanced Study Harvard University CambridgeMA02138 United States

In single-cell RNA sequencing (scRNA-seq) data analysis, a critical challenge is to infer hidden cellular dynamic processes from measured static cell snapshots. To tackle this challenge, many computational methods have been developed from distinct perspectives. Besides the common perspectives of inferring pseudotime trajectories and RNA velocities, another important perspective is to estimate the differentiation potential of cells, which is commonly referred to as the "cell potency." In this review, we provide a comprehensive summary of 14 computational methods that define cell potency from scRNA-seq data under different assumptions, some of which are even conceptually contradictory. We divide these methods into three categories: average-based, entropy-based, and correlation-based methods, depending on how a method summarizes gene expression levels of a cell or cell type into a potency measure. Our review focuses on the key similarities and differences of the methods within each category and between the categories, providing a high-level intuition for each method. Moreover, we use a unified set of mathematical notations to detail the 14 methods’ methodologies and summarize their usage complexities, including the number of parameters, the number of required inputs, and the changes from the method description in publications to the method implementation in software packages. In conclusion, we regard cell potency estimation as an open question that lacks a consensus on the optimal approach, calling for benchmark datasets and studies. Through categorization and detailed description of existing methods, we aim to provide a solid foundation for future benchmark studies of cell potency estimation methods. A broader open challenge is to understand the comparative advantages of methods that infer cellular dynamics from scRNA-seq data using diverse perspectives, including pseudotime trajectories, RNA velocities, and cell potency. © 2023, CC BY-NC-ND.

关键词： Gene expression

来源：评论

学校读者我要写书评

暂无评论

Developing a practical neurodevelopmental prediction model for targeting high-risk very preterm infants during visit after NICU: a retrospective national longitudinal cohort study

引用

BMC Medicine 2024年第1期22卷 68-68页

作者： Chung, Hao Wei Chen, Ju-Chieh Chen, Hsiu-Lin Ko, Fang-Yu Ho, Shinn-Ying Chang, Jui-Hsing Tsou, Kuo-Inn Tsao, Po-Nien Mu, Shu-Chi Hsu, Chyong-Hsin Lien, Reyin Lin, Hung-Chih Hsiao, Chien-Chou Huang, Chao-Ching Chen, Chih-Cheng Division of Neonatology Department of Pediatrics Kaohsiung Medical University Chung-Ho Memorial Hospital Kaohsiung Medical University Kaohsiung Taiwan Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Pediatrics Kaohsiung Municipal Siaogang Hospital Kaohsiung Medical University Kaohsiung Taiwan Center for Big Data Research Kaohsiung Medical University Kaohsiung Taiwan Department of Respiratory Therapy College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu Taiwan Center for Intelligent Drug Systems and Smart Bio-Devices National Yang Ming Chiao Tung University Hsinchu Taiwan College of Health Sciences Kaohsiung Medical University Kaohsiung Taiwan

BACKGROUND: Follow-up visits for very preterm infants (VPI) after hospital discharge is crucial for their neurodevelopmental trajectories, but ensuring their attendance before 12 months corrected age (CA) remains a challenge. Current prediction models focus on future outcomes at discharge, but post-discharge data may enhance predictions of neurodevelopmental trajectories due to brain plasticity. Few studies in this field have utilized machine learning models to achieve this potential benefit with transparency, explainability, and transportability. METHODS: We developed four prediction models for cognitive or motor function at 24 months CA separately at each follow-up visits, two for the 6-month and two for the 12-month CA visits, using hospitalized and follow-up data of VPI from the Taiwan Premature Infant Follow-up Network from 2010 to 2017. Regression models were employed at 6 months CA, defined as a decline in The Bayley Scales of Infant Development 3rd edition (BSIDIII) composite score > 1 SD between 6- and 24-month CA. The delay models were developed at 12 months CA, defined as a BSIDIII composite score < 85 at 24 months CA. We used an evolutionary-derived machine learning method (EL-NDI) to develop models and compared them to those built by lasso regression, random forest, and support vector machine. RESULTS: One thousand two hundred forty-four VPI were in the developmental set and the two validation cohorts had 763 and 1347 VPI, respectively. EL-NDI used only 4-10 variables, while the others required 29 or more variables to achieve similar performance. For models at 6 months CA, the area under the receiver operating curve (AUC) of EL-NDI were 0.76-0.81(95% CI, 0.73-0.83) for cognitive regress with 4 variables and 0.79-0.83 (95% CI, 0.76-0.86) for motor regress with 4 variables. For models at 12 months CA, the AUC of EL-NDI were 0.75-0.78 (95% CI, 0.72-0.82) for cognitive delay with 10 variables and 0.73-0.82 (95% CI, 0.72-0.85) for motor delay with 4 variable

关键词： Machine learning Neurodevelopmental outcome Prediction model Very preterm

来源：评论

学校读者我要写书评

暂无评论

Prediction of Diblock Copolymer Morphology via Machine Learning

arXiv

引用

arXiv 2023年

作者： Park, Hyun Yu, Boyuan Park, Juhae Sun, Ge Tajkhorshid, Emad de Pablo, Juan J. Schneider, Ludwig Theoretical and Computational Biophysics Group NIH Resource Center for Macromolecular Modeling and Bioinformatics Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Center for Biophysics and Quantitative Biology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Pritzker School of Molecular Engineering University of Chicago 5640 Ellis Ave ChicagoIL60637 United States Theoretical and Computational Biophysics Group Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Biochemistry University of Illinois at Urbana-Champaign UrbanaIL61801 United States

A machine learning approach is presented to accelerate the computation of block polymer morphology evolution for large domains over long timescales. The strategy exploits the separation of characteristic times between coarse-grained particle evolution on the monomer scale and slow morphological evolution over mesoscopic scales. In contrast to empirical continuum models, the proposed approach learns stochastically driven defect annihilation processes directly from particle-based simulations. A UNet architecture that respects different boundary conditions is adopted, thereby allowing periodic and fixed substrate boundary conditions of arbitrary shape. Physical concepts are also introduced via the loss function and symmetries are incorporated via data augmentation. The model is validated using three different use cases. Explainable artificial intelligence methods are applied to visualize the morphology evolution over time. This approach enables the generation of large system sizes and long trajectories to investigate defect densities and their evolution under different types of confinement. As an application, we demonstrate the importance of accessing late-stage morphologies for understanding particle diffusion inside a single block. This work has implications for directed self-assembly and materials design in micro-electronics, battery materials, and membranes. Copyright © 2023, The Authors. All rights reserved.

关键词： Block copolymers

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：