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Mutation of Oryza sativa CORONATINE INSENSITIVE 1b(OsCOI1b) delays leaf senescence

Journal of Integrative Plant Biology 2015年第6期57卷 562-576页

作者： Sang-Hwa Lee Yasuhito Sakuraba Taeyoung Lee Kyu-Won Kim Gynheung An Han Yong Lee Nam-Chon Paek Department of Plant Science Plant Genomics and Breeding InstituteResearch Institute of Agriculture and Life SciencesSeoul National University Interdisciplinary Program in Bioinformatics Seoul National University Department of Plant Molecular Systems Biotechnology Crop Biotech InstituteKyung Hee University Department of Agricultural Biotechnology Seoul National University

Jasmonic acid （JA） functions in plant development, including senescence and immunity. Arabidopsis thaliana CORONATINE INSENSITIVE 1 encodes a JA receptor and functions in the JA‐responsive signaling pathway. The Arabidopsis genome harbors a single COI gene, but the rice （Oryza sativa） genome harbors three COI homologs, OsCOI1a, OsCOI1b, and OsCOI2. Thus, it remains unclear whether each OsCOI has distinct, additive, synergistic, or redundant func-tions in development. Here, we use the oscoi1b‐1 knockout mutants to show that OsCOI1b mainly affects leaf senescence under senescence‐promoting conditions. oscoi1b‐1 mutants stayed green during dark‐induced and natural senescence, with substantial retention of chlorophylls and photosyn-thetic capacity. Furthermore, several senescence‐associated genes were downregulated in oscoi1b‐1 mutants, including homologs of Arabidopsis thaliana ETHYLENE INSENSITIVE 3 and ORESARA 1, important regulators of leaf senescence. These results suggest that crosstalk between JA signaling and ethylene signaling affects leaf senescence. The Arabidopsis coi1‐1 plants containing 35S：OsCOI1a or 35S：OsCOI1b rescued the delayed leaf senescence during dark incubation, sug-gesting that both OsCOI1a and OsCOI1b are required for promoting leaf senescence in rice. oscoi1b‐1 mutants showed significant decreases in spikelet fertility and grain weight, leading to severe reduction of grain yield, indicating that OsCOI1‐mediated JA signaling affects spikelet fertility and grain filling.

关键词： Jasmonic acid leaf senescence OsCOI1b rice stay-green

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An analysis of conformational changes upon RNA-Protein binding 14

An analysis of conformational changes upon RNA-Protein bindi...

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5th ACM Conference on bioinformatics, Computational Biology, and Health Informatics, ACM BCB 2014

作者： Sankar, Kannan Jernigan, Robert L. Walia, Rasna R. Honavar, Vasant G. Mann, Carla M. Dobbs, Drena Bioinformatics and Computational Biology Program Dept. of Biochem. Biophysics and Molecular Biology Iowa State University United States Dept. of Biochem. L.H. Baker Center for Bioinformatics and Biological Statistics Iowa State University United States Bioinformatics and Computational Biology Program Dept. of Computer Science Iowa State University United States Bioinformatics and Genomics Program College of Information Sciences and Technology Pennsylvania State University United States Dept. of Genetics Development and Cell Biology L.H. Baker Center for Bioinformatics Iowa State University United States

ISBN: (纸本)9781450328944

RNA-binding proteins (RBPs) have myriad functions in transcription, translation, and post-transcriptional gene regulation, with central roles in normal development as well as in both genetic and infectious diseases. When a protein binds RNA, a conformational change often occurs. For RNA-protein complexes that have been characterized, conformational changes have been observed in the protein, the RNA, or both. These conformational changes have not been sufficiently characterized, however, in part due to the small number of structures of bound and unbound complexes of RNA-binding proteins available until recently. Here we systematically analyze a new dataset of 90 pairs of bound and unbound proteins to evaluate the conformational changes that occur upon RNA binding. Most of the conformational changes were observed in noninterfacial regions of the RNA-binding proteins. Detailed analyses of the modes of RNA binding and any associated conformational changes in proteins are critical for fully understanding the mechanisms of RNA-protein recognition, for developing better RNA-protein docking methods and methods for predicting interfacial residues, and for RNA-based drug design. Copyright © 2014 ACM.

关键词： RNA

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Development of Machine Learning Models for Predicting Radiation Dermatitis in Breast Cancer Patients Using Clinical Risk Factors, Patient-Reported Outcomes, and Serum Cytokine Biomarkers

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Clinical Breast Cancer 2025年

作者： Lin, Neil Abbas-Aghababazadeh, Farnoosh Su, Jie Wu, Alison J. Lin, Cherie Shi, Wei Xu, Wei Haibe-Kains, Benjamin Liu, Fei-Fei Kwan, Jennifer Y.Y. Institute of Medical Science Temerty Faculty of Medicine University of Toronto Toronto Canada Temerty Faculty of Medicine University of Toronto Toronto Canada Princess Margaret Bioinformatics and Computational Genomics Laboratory University Health Network Toronto Canada Biostatistics Division Princess Margaret Cancer Centre Toronto Canada Research Institute Princess Margaret Cancer Centre Toronto Canada Department of Computer Science University of Toronto Toronto Canada Ontario Institute for Cancer Research Toronto Canada Vector Institute for Artificial Intelligence Toronto Canada Department of Radiation Oncology Temerty Faculty of Medicine University of Toronto Toronto Canada Department of Medical Biophysics Temerty Faculty of Medicine University of Toronto Toronto Canada Radiation Medicine Program Princess Margaret Cancer Centre Toronto Canada

Background: Radiation dermatitis (RD) is a significant side effect of radiotherapy experienced by breast cancer patients. Severe symptoms include desquamation or ulceration of irradiated skin, which impacts quality of life and increases healthcare costs. Early identification of patients at risk for severe RD can facilitate preventive management and reduce severe symptoms. This study evaluated the utility of subjective and objective factors, such as patient-reported outcomes (PROs) and serum cytokines, for predicting RD in breast cancer patients. The performance of machine learning (ML) and logistic regression-based models were compared. Patients and Methods: Data from 147 breast cancer patients who underwent radiotherapy was analyzed to develop prognostic models. ML algorithms, including neural networks, random forest, XGBoost, and logistic regression, were employed to predict clinically significant Grade 2+ RD. Clinical factors, PROs, and cytokine biomarkers were incorporated into the risk models. Model performance was evaluated using nested cross-validation with separate loops for hyperparameter tuning and calculating performance metrics. Results: Feature selection identified 18 predictors of Grade 2+ RD including smoking, radiotherapy boost, reduced motivation, and the cytokines interleukin-4, interleukin-17, interleukin-1RA, interferon-gamma, and stromal cell-derived factor-1a. Incorporating these predictors, the XGBoost model achieved the highest performance with an area under the curve (AUC) of 0.780 (95% CI: 0.701-0.854). This was not significantly improved over the logistic regression model, which demonstrated an AUC of 0.714 (95% CI: 0.629-0.798). Conclusion: Clinical risk factors, PROs, and serum cytokine levels provide complementary prognostic information for predicting severe RD in breast cancer patients undergoing radiotherapy. ML and logistic regression models demonstrated comparable performance for predicting clinically significant RD with AUC>0.70. ©

关键词： Cancer survivorship Oncology Predictive modeling Radiotherapy Skin toxicity

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Distinguishing genetic correlation from causation across 52 diseases and complex traits (vol 50, pg 1728, 2018)

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NATURE GENETICS 2018年第12期50卷 1753-1753页

作者： O'Connor, Luke J. Price, Alkes L. Department of Epidemiology Harvard T.H. Chan School of Public Health Boston MA USA Program in Bioinformatics and Integrative Genomics Harvard Graduate School of Arts and Sciences Cambridge MA USA Department of Biostatistics Harvard T.H. Chan School of Public Health Boston MA USA Program in Medical and Population Genetics Broad Institute Cambridge MA USA

Mendelian randomization, a method to infer causal relationships, is confounded by genetic correlations reflecting shared etiology. We developed a model in which a latent causal variable mediates the genetic correlation; trait 1 is partially genetically causal for trait 2 if it is strongly genetically correlated with the latent causal variable, quantified using the genetic causality proportion. We fit this model using mixed fourth moments \({\it{E}}({\it{\alpha }}_1^2{\it{\alpha }}_1{\it{\alpha }}_2)\) and \({\it{E}}\left( {{\it{\alpha }}_2^2{\it{\alpha }}_1{\it{\alpha }}_2} \right)\) of marginal effect sizes for each trait; if trait 1 is causal for trait 2, then SNPs affecting trait 1 (large \({\it{\alpha }}_1^2\)) will have correlated effects on trait 2 (large α₁α₂), but not vice versa. In simulations, our method avoided false positives due to genetic correlations, unlike Mendelian randomization. Across 52 traits (average n = 331,000), we identified 30 causal relationships with high genetic causality proportion estimates. Novel findings included a causal effect of low-density lipoprotein on bone mineral density, consistent with clinical trials of statins in osteoporosis.

关键词： Genetics Genome-wide association studies

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Evolution Of SARS-CoV-2 In The Presence Of Vaccines.

Evolution Of SARS-CoV-2 In The Presence Of Vaccines.

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2022 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2022

作者： Marathe, Vishwajeet Yan, Changhui North Dakota State University Department of Computer Scienceline FargoND United States North Dakota State University Department of Computer Science Genomics Phenomics and Bioinformatics Graduate Program FargoND United States

ISBN: (纸本)9781665468190

The COVID pandemic has caused tremendous loss worldwide. Now vaccines are the primary weapon to combat the pandemic. Understanding how SARS-CoV-2, the virus that causes the COVID, may mutate in the presence of the vaccines is critical for designing drugs and vaccines for future variants of the virus. In this study, we investigated the numbers of mutations that SARS-CoV-2 accumulated on each protein over time. We found that different proteins of the virus accumulated different levels of mutations and their mutation rates changed over time following different patterns. We also presented evidence that the mutation of the Spike protein might have been suppressed by the vaccines. This is the first time that such a relation was reported based on real world data. Although the discovery was not meant to be conclusive, this study sheds light onto how the virus may response to the vaccines. If confirmed by further studies, the discovery will have significant impacts on many fields, including drug and vaccine designs. © 2022 IEEE.

关键词： Coronavirus

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Noninvasive assessment of hydroquinidine effect in Brugada syndrome (QUIET BrS)

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Heart Rhythm 2025年

作者： Isbister, Julia C. Strocchi, Marina Riedy, Matthew Yeates, Laura Gray, Belinda Singer, Emma S. Bagnall, Richard D. Ingles, Jodie Raju, Hariharan Semsarian, Christopher Niederer, Steven A. Sy, Raymond W. Faculty of Medicine and Health University of Sydney Sydney NSW Australia Department of Cardiology Royal Prince Alfred Hospital Sydney NSW Australia National Heart and Lung Institute Imperial College London London United Kingdom School of Biomedical Engineering and Imaging Sciences King's College London London United Kingdom Medtronic Australasia Sydney NSW Australia Agnes Ginges Centre for Molecular Cardiology at Centenary Institute The University of Sydney Sydney NSW Australia Genomics and Inherited Disease Program Garvan Institute of Medical Research University of New South Wales Sydney NSW Australia Bioinformatics and Molecular Genetics Group at Centenary Institute The University of Sydney Sydney NSW Australia Faculty of Medicine Health and Human Sciences Macquarie University Sydney NSW Australia The Alan Turing Institute London United Kingdom

Background: Hydroquinidine reduces arrhythmic events in patients with Brugada syndrome (BrS). The mechanism by which it exerts antiarrhythmic benefit and its electrophysiological effects on BrS substrate remain incompletely understood. Objective: This study aimed to determine the effect of hydroquinidine on ventricular depolarization and repolarization in patients with BrS in vivo. Methods: Twelve patients with BrS underwent electrocardiography (standard, high-lead, and signal averaged) and electrocardiographic imaging at baseline and “on-treatment” with hydroquinidine 300 mg twice daily. ST-segment elevation, activation time, repolarization time, and activation-recovery interval (ARI) were computed for the ventricles and right ventricular outflow tract (RVOT). Serum hydroquinidine levels were determined, and adverse drug events were captured through a medication survey. Results: Hydroquinidine increased repolarization time (301.1 ± 24.1 ms vs 348.8 ± 28.3 ms;P<.001), repolarization gradients (1.1 ± 0.4 ms/mm vs 1.6 ± 0.4 ms/mm;P<0.001), and ARI (241.3 ± 18.1 ms vs 284.8 ± 21.5 ms;P<.001) in the RVOT, with a greater change in the RVOT than in the rest of the ventricles. In contrast, activation parameters did not change significantly on-treatment with hydroquinidine, although there was a subtle increase in ST-segment elevation over the RVOT (1.5 ± 0.7 mV vs 1.8 ± 0.8 mV;P<.001). Hydroquinidine levels did not correlate with electrophysiological changes or occurrence of adverse drug reactions. One patient developed frequent nonsustained ventricular tachycardia on-treatment with hydroquinidine. Conclusion: Hydroquinidine primarily affects ventricular repolarization and action potential duration (indicated by ARI) in patients with BrS and demonstrates regional variation with more significant changes in the RVOT. © 2024 Heart Rhythm Society

关键词： Activation-recovery interval Brugada syndrome Electrocardiographic imaging Hydroquinidine Noninvasive Ventricular activation Ventricular repolarization

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Designing metabolic engineering strategies with genome-scale metabolic flux modeling

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Advances in genomics and Genetics 2015年 5卷 93页

作者： Yen, Jiun Y Tanniche, Imen Fisher, Amanda K Gillaspy, Glenda E Bevan, David R Senger, Ryan S Department of Biological Systems Engineering Virginia Tech Department of Biochemistry Virginia Tech Genomics Bioinformatics and Computational Biology Interdisciplinary Program Virginia Tech

New in silico tools that make use of genome-scale metabolic flux modeling are improving the design of metabolic engineering strategies. This review highlights the latest developments in this area, explains the interface between these in silico tools and the experimental implementation tools of metabolic engineers, and provides a way forward so that in silico predictions can better mimic reality and more experimental methods can be considered in simulation studies. The several methodologies for solving genome-scale models (eg, flux balance analysis [FBA], parsimonious FBA, flux variability analysis, and minimization of metabolic adjustment) all have unique advantages and applications. There are two basic approaches to designing metabolic engineering strategies in silico, and both have demonstrated success in the literature. The first involves: 1) making a genetic manipulation in a model; 2) testing for improved performance through simulation; and 3) iterating the process. The second approach has been used in more recently designed in silico tools and involves: 1) comparing metabolic flux profiles of a wild-type and ideally engineered state and 2) designing engineering strategies based on the differences in these flux profiles. Improvements in genome-scale modeling are anticipated in areas such as the inclusion of all relevant cellular machinery, the ability to understand and anticipate the results of combinatorial enrichment experiments, and constructing dynamic and flexible biomass equations that can respond to environmental and genetic manipulations.

关键词： genome-scale modeling flux balance analysis flux variability analysis minimization of metabolic adjustment metabolic bottleneck pathway optimization

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DNA-binding specificity and in vivo targets of Caenorhabditis elegans nuclear factor I (vol 106, 12049, 2009)

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PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2009年第36期106卷 15514-15514页

作者： Whittle, Christina M. Lazakovitch, Elena Gronostajski, Richard M. Lieb, Jason D. [a]Department of Biology Carolina Center for the Genome Sciences and Lineberger Comprehensive Cancer Center University of North Carolina Chapel Hill NC 27599-3280 and [b]Department of Biochemistry Program in Neurosciences Developmental Genomics Group and Center of Excellence in Bioinformatics and Life Sciences State University of New York at Buffalo 701 Ellicott Street Buffalo NY 14203

The conserved nuclear factor I (NFI) family of transcription factors is unique to animals and essential for mammalian development. The Caenorhabditis elegans genome encodes a single NFI family member, whereas vertebrate genomes encode 4 distinct NFI protein subtypes (A, B, C, and X). NFI-1-deficient worms exhibit abnormalities, including reduced lifespan, defects in movement and pharyngeal pumping, and delayed egg-laying. To explore the functional basis of these phenotypes, we sought to comprehensively identify NFI-1-bound loci in C. elegans. We first established NFI-1 DNA-binding specificity using an in vitro DNA-selection strategy. Analysis yielded a consensus motif of TTGGCA(N)₃TGCCAA, which occurs 586 times in the genome, a 100-fold higher frequency than expected. We next asked which sites were occupied by NFI-1 in vivo by performing chromatin immunoprecipitation of NFI-1 followed by microarray hybridization. Only 55 genomic locations were identified, an unexpectedly small target set. In vivo NFI-1 binding sites tend to be upstream of genes involved in core cellular processes, such as chromatin remodeling, mRNA splicing, and translation. Remarkably, 59 out of 70 (84%) of the C. briggsae orthologs of the identified targets contain conserved NFI binding sites in their promoters. These experiments provide a foundation for understanding how NFI-1 is recruited to unexpectedly few in vivo sites to perform its developmental functions, despite a vast over-representation of its binding motif.

关键词： ChIP-chip protein-DNA interactions transcription factor binding site selection cis regulatory elements

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Reliable Radiologic Skeletal Muscle Area Assessment – A Biomarker for Cancer Cachexia Diagnosis

arXiv

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arXiv 2025年

作者： Ahmed, Sabeen Parker, Nathan Park, Margaret Jeong, Daniel Peres, Lauren Davis, Evan W. Permuth, Jennifer B. Siegel, Erin Schabath, Matthew B. Yilmaz, Yasin Rasool, Ghulam Department of Machine Learning H. Lee Moffitt Cancer Center and Research Institute TampaFL United States Department of Health Outcomes and Behavior H. Lee Moffitt Cancer Center and Research Institute TampaFL United States Department of GI Oncology H. Lee Moffitt Cancer Center and Research Institute TampaFL United States Diagnostic Imaging and Interventional Radiology H. Lee Moffitt Cancer Center and Research Institute TampaFL United States Department of Cancer Epidemiology H. Lee Moffitt Cancer Center and Research Institute TampaFL United States Department of Biostatistics and Bioinformatics H. Lee Moffitt Cancer Center and Research Institute TampaFL United States Department of Electrical Engineering University of South Florida TampaFL United States Epidemiology and Genomics Research Program National Cancer Institute NIH United States

Cancer cachexia is a common metabolic disorder characterized by severe muscle atrophy which is associated with poor prognosis and quality of life. Monitoring skeletal muscle area (SMA) longitudinally through computed tomography (CT) scans, an imaging modality routinely acquired in cancer care, is an effective way to identify and track this condition. However, existing tools often lack full automation and exhibit inconsistent accuracy, limiting their potential for integration into clinical workflows. To address these challenges, we developed SMAART-AI (Skeletal Muscle Assessment-Automated and Reliable Tool-based on AI), an end-to-end automated pipeline powered by deep learning models (nnU-Net 2D) trained on mid-third lumbar level CT images with 5-fold cross-validation, ensuring generalizability and robustness. SMAART-AI incorporates an uncertainty-based mechanism to flag high-error SMA predictions for expert review, enhancing reliability. We combined the SMA, skeletal muscle index, BMI, and clinical data to train a multi-layer perceptron (MLP) model designed to predict cachexia at the time of cancer diagnosis. Tested on the gastroesophageal cancer dataset, SMAART-AI achieved a Dice score of 97.80% ± 0.93%, with SMA estimated across all four datasets in this study at a median absolute error of 2.48% compared to manual annotations with SliceOmatic. Uncertainty metrics—variance, entropy, and coefficient of variation—strongly correlated with SMA prediction errors (0.83, 0.76, and 0.73 respectively). The MLP model predicts cachexia with 79% precision, providing clinicians with a reliable tool for early diagnosis and intervention. By combining automation, accuracy, and uncertainty awareness, SMAART-AI bridges the gap between research and clinical application, offering a transformative approach to managing cancer cachexia. © 2025, CC BY-NC-ND.

关键词： Diseases

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Peak analysis of cell-free RNA finds recurrently protected narrow regions with clinical potential

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Genome Biology 2025年第1期26.0卷 1-25页

作者： Bao, Pengfei Wang, Taiwei Liu, Xiaofan Xing, Shaozhen Ruan, Hanjin Ma, Hongli Tao, Yuhuan Zhan, Qing Belmonte-Reche, Efres Qin, Lizheng Han, Zhengxue Mao, Minghui Li, Mengtao Lu, Zhi John MOE Key Laboratory of Bioinformatics State Key Lab of Green Biomanufacturing Center for Synthetic and Systems Biology School of Life Sciences Tsinghua University Beijing 100084 China Institute for Precision Medicine Tsinghua University Beijing 100084 China Peking University–Tsinghua University–National Institute of Biological Sciences Joint Graduate Program School of Life Sciences Tsinghua University Beijing China Department of Rheumatology and Clinical Immunology Chinese Academy of Medical Sciences & Peking Union Medical College Beijing 100730 China National Clinical Research Center for Dermatologic and Immunologic Diseases (Ministry of Science & Technology) MOE Key Laboratory of Rheumatology and Clinical Immunology State Key Laboratory of Complex Severe and Rare Diseases Peking Union Medical College Hospital Beijing 100730 China Academy for Advanced Interdisciplinary Studies (AAIS)and Sciences Joint Graduate Program (PTN) Peking University Beijing China Department of Oral and Maxillofacial & Head and Neck Oncology Beijing Stomatological Hospital Capital Medical University Beijing China Centre for Genomics and Oncological Research (GENYO) Avenida de La Ilustración 114 Granada 18016 Spain Department of Biochemistry and Molecular Biology II Faculty of Pharmacy University of Granada Granada Spain Instituto de Investigación Biosanitaria Ibs.GRANADA Hospital Virgen de Las Nieves Granada Spain The Center for Regeneration Aging and Chronic Diseases School of Basic Medical Sciences Tsinghua University Beijing China

Background: Cell-free RNAs (cfRNAs) can be detected in biofluids and have emerged as valuable disease biomarkers. Accurate identification of the fragmented cfRNA signals, especially those originating from pathological cells, is crucial for understanding their biological functions and clinical value. However, many challenges still need to be addressed for their application, including developing specific analysis methods and translating cfRNA fragments with biological support into clinical applications. Results: We present cfPeak, a novel method combining statistics and machine learning models to detect the fragmented cfRNA signals effectively. When test in real and artificial cfRNA sequencing (cfRNA-seq) data, cfPeak shows an improved performance compared with other applicable methods. We reveal that narrow cfRNA peaks preferentially overlap with protein binding sites, vesicle-sorting sites, structural sites, and novel small non-coding RNAs (sncRNAs). When applied in clinical cohorts, cfPeak identified cfRNA peaks in patients’ plasma that enable cancer detection and are informative of cancer types and metastasis. Conclusions: Our study fills the gap in the current small cfRNA-seq analysis at fragment-scale and builds a bridge to the scientific discovery in cfRNA fragmentomics. We demonstrate the significance of finding low abundant tissue-derived signals in small cfRNA and prove the feasibility for application in liquid biopsy. © The Author(s) 2025.

关键词： Cell-free RNA CfRNA fragment Liquid biopsy biomarker Noninvasive cancer detection Peak calling SncRNA Tissue-of-origin

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