检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Mukherjee, Soham Wethington, Darren Dey, Tamal K. Das, Jayajit Computer Science Department Purdue University West LafayetteIN United States Biomedical Science Graduate Program Wexner College of Medicine The Ohio State University ColumbusOH United States Biophysics Program The Ohio State University ColumbusOH United States Battelle Center for Mathematical Medicine Abigail Wexner Research Institute Nationwide Children’s Hospital ColumbusOH United States Departments of Pediatrics Biomedical Informatics Pelotonia Institute of Immuno-Oncology Wexner College of Medicine The Ohio State University ColumbusOH United States

Cytometry experiments yield high-dimensional point cloud data that is difficult to interpret manually. Boolean gating techniques coupled with comparisons of relative abundances of cellular subsets is the current standard for cytometry data analysis. However, this approach is unable to capture more subtle topological features hidden in data, especially if those features are further masked by data transforms or significant batch effects or donor-to-donor variations in clinical data. We present that persistent homology, a mathematical structure that summarizes the topological features, can distinguish different sources of data, such as from groups of healthy donors or patients, effectively. Analysis of publicly available cytometry data describing non-naïve CD8+ T cells in COVID-19 patients and healthy controls shows that systematic structural differences exist between single cell protein expressions in COVID-19 patients and healthy controls. We identify proteins of interest by a decision-tree based classifier, sample points randomly and compute persistence diagrams from these sampled points. The resulting persistence diagrams identify regions in cytometry datasets of varying density and identify protruded structures such as ‘elbows’. We compute Wasserstein distances between these persistence diagrams for random pairs of healthy controls and COVID-19 patients and find that systematic structural differences exist between COVID-19 patients and healthy controls in the expression data for T-bet, Eomes, and Ki-67. Further analysis shows that expression of T-bet and Eomes are significantly downregulated in COVID-19 patient non-naïve CD8+ T cells compared to healthy controls. This counter-intuitive finding may indicate that canonical effector CD8+ T cells are less prevalent in COVID-19 patients than healthy controls. This method is applicable to any cytometry dataset for discovering novel insights through topological data analysis which may be difficult to ascertain otherwise

关键词： T-cells

来源：评论

学校读者我要写书评

暂无评论

Predicting Microsatellite Instability from Histology Images with Dilated Neighborhood Attention Transformer in Colorectal Cancer

Predicting Microsatellite Instability from Histology Images ...

引用

IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Yun-Chi Chen Tung Chao Wimaya Nitya Phandita Tzu-Yu Sun Huai-Zhi Wang Yi-Hsien Hsieh Le-Yin Hsu Che Lin Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University (NTU) Taipei Taiwan College of Bioresources and Agriculture NTU Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Graduate Institute of Communication Engineering NTU Taipei Taiwan Data Science Degree Program NTU Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan

ISBN: (数字)9798331518622

ISBN: (纸本)9798331518639

Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, whereas histology images, widely available in colorectal cancer diagnosis, offer a valuable alternative for MSI prediction. Although Transformer-based models have demonstrated promising outcomes in predicting MSI from histology images, they are hampered by traditional local attention mechanisms that struggle to capture long-range interdependencies and establish a comprehensive global receptive field. In this study, we introduce DiNAT-MSI, a novel framework for histology-based MSI prediction that incorporates the Dilated Neighborhood Attention Transformer (DiNAT). This model enhances global context recognition and substantially expands receptive fields, all without additional computational burden. Our results demonstrate that DiNAT-MSI achieves a superior patientwise AUROC compared to ResNet18 and Swin Transformer, along with commendable explainability. Our work not only illustrates a more accessible diagnostic tool for leveraging histological data but also underscores the potential of Transformerbased models with sophisticated attention designs in advancing precision medicine for colorectal cancer patients.

关键词： Attention mechanisms Histopathology Computational modeling Precision medicine Small satellites Immunotherapy Colorectal cancer Predictive models Transformers Genetics

来源：评论

学校读者我要写书评

暂无评论

The role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net

引用

European Radiology 2023年第9期33卷 6157-6167页

作者： Li, Ya-Hui Lin, Shao-Chieh Chung, Hsiao-Wen Chang, Chia-Ching Peng, Hsu-Hsia Huang, Teng-Yi Shen, Wu-Chung Tsai, Chon-Haw Lo, Yu-Chien Lee, Tung-Yang Juan, Cheng-Hsuan Juan, Cheng-En Chang, Hing-Chiu Liu, Yi-Jui Juan, Chun-Jung Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Department of Medical Imaging China Medical University Hsinchu Hospital No. 199 Sec. 1 Xinglong Rd. Zhubei City Hsinchu County 302 Hsinchu Taiwan Ph.D. Program in Electrical and Communication Engineering Feng Chia University Taichung Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan Department of Management Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Biomedical Engineering and Environmental Sciences National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Radiology School of Medicine College of Medicine China Medical University Taichung Taiwan Department of Medical Imaging Medical University Hospital Taichung Taiwan Department of Neurology China Medical University Hospital Taichung Taiwan Cheng Ching Hospital Taichung Taiwan Master’s Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan Department of Automatic Control Engineering Feng Chia University No. 100 Wenhwa Rd. Seatwen Taichung 40724 Taiwan Department of Biomedical Engineering The Chinese University of Hong Kong ERB1112 11/F William M.W. Mong Engineering Building N.T Shatin Hong Kong Multi-Scale Medical Robotics Center The Chinese University of Hong Kong N.T Shatin Hong Kong Department of Biomedical Engineering National Defense Medical Center Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Background: To evaluate the effect of the weighting of input imaging combo and ADC threshold on the performance of the U-Net and to find an optimized input imaging combo and ADC threshold in segmenting acute ischemic stroke (AIS) lesion. Methods: This study retrospectively enrolled a total of 212 patients having AIS. Four combos, including ADC-ADC-ADC (AAA), DWI-ADC-ADC (DAA), DWI-DWI-ADC (DDA), and DWI-DWI-DWI (DDD), were used as input images, respectively. Three ADC thresholds including 0.6, 0.8 and 1.8 × 10^–3 mm²/s were applied. Dice similarity coefficient (DSC) was used to evaluate the segmentation performance of U-Nets. Nonparametric Kruskal–Wallis test with Tukey–Kramer post-hoc tests were used for comparison. A p <.05 was considered statistically significant. Results: The DSC significantly varied among different combos of images and different ADC thresholds. Hybrid U-Nets outperformed uniform U-Nets at ADC thresholds of 0.6 × 10^–3 mm²/s and 0.8 × 10^–3 mm²/s (p <.001). The U-Net with imaging combo of DDD had segmentation performance similar to hybrid U-Nets at an ADC threshold of 1.8 × 10^–3 mm²/s (p =.062 to 1). The U-Net using the imaging combo of DAA at the ADC threshold of 0.6 × 10^–3 mm²/s achieved the highest DSC in the segmentation of AIS lesion. Conclusions: The segmentation performance of U-Net for AIS varies among the input imaging combos and ADC thresholds. The U-Net is optimized by choosing the imaging combo of DAA at an ADC threshold of 0.6 × 10^–3 mm²/s in segmentating AIS lesion with highest DSC. Key Points: • Segmentation performance of U-Net for AIS differs among input imaging combos. • Segmentation performance of U-Net for AIS differs among ADC thresholds. • U-Net is optimized using DAA with ADC = 0.6 × 10^–3mm²/s. © 2023, The Author(s), under exclusive licence to European Society of Radiology.

关键词： Deep Learning Diffusion Magnetic Resonance Imaging Ischemic Stroke Neural Networks, Computer Retrospective Study

来源：评论

学校读者我要写书评

暂无评论

Revised JNLPBA corpus: A revised version of biomedical NER corpus for relation extraction task

arXiv

引用

arXiv 2019年

作者： Huang, Ming-Siang Lai, Po-Ting Tsai, Richard Tzong-Han Hsu, Wen-Lian Bioinformatics Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Institute of Biomedical Informatics National Yang Ming University Taipei Taiwan Department of Computer Science National Tsing-Hua University Hsinchu Taiwan Intelligent Agent Systems Laboratory Institute of Information Science Academia Sinica Taipei Taiwan Intelligent Information Service Research Laboratory Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan

The advancement of biomedical named entity recognition (BNER) and biomedical relation extraction (BRE) researches promotes the development of text mining in biological domains. As a cornerstone of BRE, robust BNER system is required to identify the mentioned NEs in plain texts for further relation extraction stage. However, the current BNER corpora, which play important roles in these tasks, paid less attention to achieve the criteria for BRE task. In this study, we present Revised JNLPBA corpus, the revision of JNLPBA corpus, to broaden the applicability of a NER corpus from BNER to BRE task. We preserve the original entity types including protein, DNA, RNA, cell line and cell type while all the abstracts in JNLPBA corpus are manually curated by domain experts again basis on the new annotation guideline focusing on the specific NEs instead of general terms. Simultaneously, several imperfection issues in JNLPBA are pointed out and made up in the new corpus. To compare the adaptability of different NER systems in Revised JNLPBA and JNLPBA corpora, the F1-measure was measured in three open sources NER systems including BANNER, Gimli and NERSuite. In the same circumstance, all the systems perform average 10% better in Revised JNLPBA than in JNLPBA. Moreover, the cross-validation test is carried out which we train the NER systems on JNLPBA/Revised JNLPBA corpora and access the performance in both protein-protein interaction extraction (PPIE) and biomedical event extraction (BEE) corpora to confirm that the newly refined Revised JNLPBA is a competent NER corpus in biomedical relation application. The revised JNLPBA corpus is freely available at ***/BNER/Content/Revised_***. Copyright © 2019, The Authors. All rights reserved.

关键词： Proteins

来源：评论

学校读者我要写书评

暂无评论

Air Medical Blood Transfusion as a Trigger of Massive Transfusion Protocol

引用

Air Medical Journal 2023年第5期42卷 353-357页

作者： Cornelius, Brian Thompson, Dennis Kilgore, Phillip Cvek, Urska Trutschl, Marjan Samra, Navdeep Cornelius, Angela Graduate Program in Nurse Anesthesia Texas Wesleyan University Fort Worth TX United States Department of Anesthesia John Peter Smith Hospital Fort Worth TX United States Department of Anesthesia Ochsner LSU Health Shreveport LA United States Department of Emergency Medicine Louisiana State University Health Sciences Center Shreveport LA United States Laboratory for Advanced Biomedical Informatics Department of Computer Science Louisiana State University Shreveport LA Department of Trauma and Surgical Critical Care Louisiana State University Health Sciences Center Shreveport LA United States Department of Emergency Medicine John Peter Smith Hospital Fort Worth TX United States

Air medical services can improve access to blood products at the point of injury. Studies have shown that early activation of mass transfusion protocols (MTPs) can improve the survival of trauma patients by up to 25%. There are several scoring systems to guide early activation, but the use of a single criterion has been elusive. Our study sought to determine if air medical administration of blood products was a risk factor for massive transfusion activation and utilization of prehospital vital signs for calculation of the shock index. In our retrospective study, we evaluated adult trauma patients transfused by helicopter emergency medical services (HEMS) and as a control all patients in our institution receiving the MTP. Our study found HEMS blood transfusion was not a reliable trigger for MTP, although the sample size may have limited our findings. We found that HEMS care resulted in an overall reduction in the volume of transfusion and an improvement in hemodynamic parameters upon trauma center arrival. HEMS transfusion and a higher rate of tranexamic acid administration may have contributed to these findings. Of note, the assessment of blood consumption score and shock index were nonspecific in the study populations. © 2023 Air Medical Journal Associates

关键词：

来源：评论

学校读者我要写书评

暂无评论

Computational Methods for Single-Cell Proteomics

引用

biomedical Data Science 1000年第1期6卷 47-71页

作者： Sophia M. Guldberg Trine Line Hauge Okholm Elizabeth E. McCarthy Matthew H. Spitzer 3Gladstone-UCSF Institute for Genomic Immunology San Francisco California USA 2Biomedical Sciences Graduate Program University of California San Francisco California USA 1Department of Otolaryngology–Head and Neck Surgery and Department of Microbiology and Immunology University of California San Francisco California USA email: matthew.spitzer@ucsf.edu 4Helen Diller Family Comprehensive Cancer Center University of California San Francisco California USA 5Institute for Human Genetics Division of Rheumatology Department of Medicine Medical Scientist Training Program and Biological and Medical Informatics Graduate Program University of California San Francisco California USA 6Parker Institute for Cancer Immunotherapy San Francisco California USA 7Chan Zuckerberg Biohub San Francisco California USA

Advances in single-cell proteomics technologies have resulted in high-dimensional datasets comprising millions of cells that are capable of answering key questions about biology and disease. The advent of these technologies has prompted the development of computational tools to process and visualize the complex data. In this review, we outline the steps of single-cell and spatial proteomics analysis pipelines. In addition to describing available methods, we highlight benchmarking studies that have identified advantages and pitfalls of the currently available computational toolkits. As these technologies continue to advance, robust analysis tools should be developed in tandem to take full advantage of the potential biological insights provided by these data.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Global Genome-wide Scan with Optimal Cutoff Mining for Emerging Biomarkers in Head and Neck Squamous Cell Carcinoma

Research Square

引用

Research Square 2020年

作者： Chi, Li-Hsing Wu, Alexander T.H. Hsiao, Michael Li, Yu-Chuan The Ph.D. Program for Translational Medicine College of Medical Science and Technology Taipei Medical University Academia Sinica Taipei Taiwan Division of Oral and Maxillofacial Surgery Department of Dentistry Taipei Medical University Hospital Taipei Taiwan Genomics Research Center Academia Sinica Taipei Taiwan Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan

Background: The survival analysis of the Cancer Genome Atlas (TCGA) dataset is a well-known method to discover the gene expression-based prognostic biomarkers of head and neck squamous cell carcinoma (HNSCC). In order to utilize a continuous gene expression for survival analysis, it is necessary to determine a cutoff point by the dichotomization of the patients. There is some optimization software for cutoff determination. However, those predetermined cutoffs by software usually set at the median, 1/4 quantile, or 3/4 quantile of RNA sequencing (RNA-Seq) value to find a significant P-value of the Kaplan-Meier curve. There are few clinicopathological features available on their pre-processed data sets. Methods: We developed a comprehensive workflow by R script, running on the Rstudio platform. It includes data retrieving and pre-processing, feature selection, cutoff mining engine, Kaplan-Meier survival analysis, Cox proportional hazard modeling, and biomarker discovery. Results: Using this workflow on the TCGA HNSCC cohort, we scanned human protein-coding genes (20,500) programmatically. After adjustment with other confounders, we found that the clinical tumor stage and the surgical margin involvement are independent risk factors in patient survival. According to the resulting tables with Bonferroni adjusted P-value under optimal cutoff as well as hazard ratio (>= 1.5), there were ten candidate biomarkers, named as DKK1, CAMK2N1, STC2, PGK1, SURF4, USP10, NDFIP1, FOXA2, STIP1, and DKC1, which are significantly associated with the poor prognosis of overall survival (OS). At the same time, the other ten genes were over-expressed in the better survival patients (with hazard ratio © 2020, CC BY.

关键词： Tumors

来源：评论

学校读者我要写书评

暂无评论

Physician Compliance With a Computerized Clinical Decision Support System for Anemia Management of Patients With End-stage Kidney Disease on Hemodialysis: Retrospective Electronic Health Record Observational Study

引用

JMIR Formative Research 2023年 7卷 e44373页

作者： Yang, Ju-Yeh Shu, Kai-Hsiang Peng, Yu-Sen Hsu, Shih-Ping Chiu, Yen-Ling Pai, Mei-Fen Wu, Hon-Yen Tsai, Wan-Chuan Tung, Kuei-Ting Kuo, Raymond N. Institute of Health Policy and Management College of Public Health National Taiwan University Taipei Taiwan Division of Nephrology Department of Internal Medicine Far Eastern Memorial Hospital New Taipei City Taiwan Center for General Education Lee-Ming Institute of Technology New Taipei City Taiwan Graduate Institute of Medicine Yuan Ze University Taoyuan Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan

Background: Previous studies on clinical decision support systems (CDSSs) for the management of renal anemia in patients with end-stage kidney disease undergoing hemodialysis have previously focused solely on the effects of the CDSS. However, the role of physician compliance in the efficacy of the CDSS remains ill-defined. Objective: We aimed to investigate whether physician compliance was an intermediate variable between the CDSS and the management outcomes of renal anemia. Methods: We extracted the electronic health records of patients with end-stage kidney disease on hemodialysis at the Far Eastern Memorial Hospital Hemodialysis Center (FEMHHC) from 2016 to 2020. FEMHHC implemented a rule-based CDSS for the management of renal anemia in 2019. We compared the clinical outcomes of renal anemia between the pre- and post-CDSS periods using random intercept models. Hemoglobin levels of 10 to 12 g/dL were defined as the on-target range. Physician compliance was defined as the concordance of adjustments of the erythropoietin-stimulating agent (ESA) between the CDSS recommendations and the actual physician prescriptions. Results: We included 717 eligible patients on hemodialysis (mean age 62.9, SD 11.6 years;male n=430, 59.9%) with a total of 36, 091 hemoglobin measurements (average hemoglobin and on-target rate were 11.1, SD 1.4, g/dL and 59.9%, respectively). The on-target rate decreased from 61.3% (pre-CDSS) to 56.2% (post-CDSS) owing to a high hemoglobin percentage of >12 g/dL (pre: 21.5%;post: 29%). The failure rate (hemoglobin <10 g/dL) decreased from 17.2% (pre-CDSS) to 14.8% (post-CDSS). The average weekly ESA use of 5848 (SD 4211) units per week did not differ between phases. The overall concordance between CDSS recommendations and physician prescriptions was 62.3%. The CDSS concordance increased from 56.2% to 78.6%. In the adjusted random intercept model, the post-CDSS phase showed increased hemoglobin by 0.17 (95% CI 0.14-0.21) g/dL, weekly ESA by 264 (95% CI

关键词： anemia management clinical decision support clinical decision support system decision support electronic health records end-stage kidney disease erythropoietin-stimulating agent hemodialysis kidney disease patient outcome physician compliance

来源：评论

学校读者我要写书评

暂无评论

COWID: an efficient cloud-based genomics workflow for scalable identification of SARS-COV-2

引用

Briefings in Bioinformatics 2023年第5期24卷 bbad280-bbad280页

作者： Lim, Hendrick Gao-Min Fann, Yang C Lee, Yuan-Chii Gladys Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei 11031 Taiwan Department of Medical Research Tzu Chi Hospital Indonesia Pantai Indah Kapuk Greater Jakarta 14470 Indonesia It and Bioinformatics Program Division of Intramural National Institute of Neurological Disorders and Stroke National Institutes of Health Bethesda 20892 MD United States

Implementing a specific cloud resource to analyze extensive genomic data on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a challenge when resources are limited. To overcome this, we repurposed a cloud platform initially designed for use in research on cancer genomics (https://***) to enable its use in research on SARS-CoV-2 to build Cloud Workflow for Viral and Variant Identification (COWID). COWID is a workflow based on the Common Workflow Language that realizes the full potential of sequencing technology for use in reliable SARS-CoV-2 identification and leverages cloud computing to achieve efficient parallelization. COWID outperformed other contemporary methods for identification by offering scalable identification and reliable variant findings with no false-positive results. COWID typically processed each sample of raw sequencing data within 5 min at a cost of only US$0.01. The COWID source code is publicly available (https://***/hendrick0403/COWID) and can be accessed on any computer with Internet access. COWID is designed to be user-friendly;it can be implemented without prior programming knowledge. Therefore, COWID is a time-efficient tool that can be used during a pandemic. © 2023 Published by Oxford University Press.

关键词： cloud repurposing cloud workflow COVID-19 Illumina sequencing parallel computation SARS-CoV-2

来源：评论

学校读者我要写书评

暂无评论

The recency and geographical origins of the bat viruses ancestral to SARS-CoV and SARS-CoV-2

引用

Cell 2025年

作者： Pekar, Jonathan E. Lytras, Spyros Ghafari, Mahan Magee, Andrew F. Parker, Edyth Wang, Yu Ji, Xiang Havens, Jennifer L. Katzourakis, Aris Vasylyeva, Tetyana I. Suchard, Marc A. Hughes, Alice C. Hughes, Joseph Rambaut, Andrew Robertson, David L. Dellicour, Simon Worobey, Michael Wertheim, Joel O. Lemey, Philippe Institute of Ecology and Evolution University of Edinburgh Edinburgh United Kingdom Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla 92093 CA United States Department of Biomedical Informatics University of California San Diego La Jolla 92093 CA United States Department of Medicine University of California San Diego La Jolla 92093 CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Medical Research Council University of Glasgow Centre for Virus Research Glasgow United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles 90095 CA United States Department of Immunology and Microbiology The Scripps Research Institute La Jolla 92037 CA United States Institute of Genomics and Global Health Redeemer's University Osun State Ede Nigeria Department of Computer Science and Engineering University of California San Diego La Jolla 92093 CA United States Department of Mathematics School of Science and Engineering Tulane University New Orleans LA United States Department of Population Health and Disease Prevention University of California Irvine Irvine 92617 CA United States Department of Biostatistics Fielding School of Public Health University of California Los Angeles Los Angeles 90095 CA United States Department of Computational Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles 90095 CA United States School of Biological Sciences University of Hong Kong Hong Kong Hong Kong China Biodiversity Green Development Foundation Beijing China Spatial Epidemiology Lab (SpELL) Université Libre de Bruxelles CP160/12 50 av. FD Roosevelt Bruxelles 1050 Belgium Department of Microbiology Immunology and Transplantation Rega Institute

The emergence of SARS-CoV in 2002 and SARS-CoV-2 in 2019 led to increased sampling of sarbecoviruses circulating in horseshoe bats. Employing phylogenetic inference while accounting for recombination of bat sarbecoviruses, we find that the closest-inferred bat virus ancestors of SARS-CoV and SARS-CoV-2 existed less than a decade prior to their emergence in humans. Phylogeographic analyses show bat sarbecoviruses traveled at rates approximating their horseshoe bat hosts and circulated in Asia for millennia. We find that the direct ancestors of SARS-CoV and SARS-CoV-2 are unlikely to have reached their respective sites of emergence via dispersal in the bat reservoir alone, supporting interactions with intermediate hosts through wildlife trade playing a role in zoonotic spillover. These results can guide future sampling efforts and demonstrate that viral genomic regions extremely closely related to SARS-CoV and SARS-CoV-2 were circulating in horseshoe bats, confirming their importance as the reservoir species for SARS viruses. © 2025 The Authors

关键词： molecular epidemiology phylogeography sarbecoviruses virus evolution

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：