Bioinformatics (Oxford, England)最新文献_第3页

Representation-aware conformal prediction for reliable uncertainty quantification in drug-target affinity. 表征感知的保形预测在药物靶标亲和力中可靠的不确定度定量。

IF 5.4

Bioinformatics (Oxford, England) Pub Date : 2026-10-13 DOI: 10.1093/bioinformatics/btag771

Xinya Li, Yaxin Zhang, Ximin Zeng, Danping Zhou, Yue Cheng, Shaoping Shi

{"title":"Representation-aware conformal prediction for reliable uncertainty quantification in drug-target affinity.","authors":"Xinya Li, Yaxin Zhang, Ximin Zeng, Danping Zhou, Yue Cheng, Shaoping Shi","doi":"10.1093/bioinformatics/btag771","DOIUrl":"https://doi.org/10.1093/bioinformatics/btag771","url":null,"abstract":"Motivation: Quantifying predictive uncertainty is essential for computational drug discovery to reduce experimental costs and false positives. However, most drug-target affinity (DTA) models focus on improving prediction accuracy without reliable uncertainty estimates.Results: We propose RCP-DTA, a representation-aware conformal prediction framework that constructs adaptive prediction intervals based on feature similarity. By constructing local calibration sets, our approach generates sample-specific prediction intervals that achieve empirical coverage close to the nominal confidence level. To enhance representation quality, RCP-DTA employs a cross-modal architecture that integrates pre-trained sequence representations and structural information. Binding site context is incorporated via virtual pocket feedback to enhance residue representations, followed by gated fusion for adaptive integration of sequence and structural modalities. Evaluations across multiple DTA benchmark datasets demonstrate that RCP-DTA achieves competitive affinity prediction performance while providing reliable uncertainty quantification.Availability: Code and data are available at: https://github.com/Lee515628/RCP-DTA.Supplementary information: Supplementary data are available at Bioinformatics online.","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149186945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Letters and Biological Process: Linguistic Structure Encodes Hierarchical Depth in the Gene Ontology. 字母与生物过程：语言结构编码基因本体的层次深度。

IF 5.4

Bioinformatics (Oxford, England) Pub Date : 2026-10-13 DOI: 10.1093/bioinformatics/btag768

Sadiya Ahmad

{"title":"Letters and Biological Process: Linguistic Structure Encodes Hierarchical Depth in the Gene Ontology.","authors":"Sadiya Ahmad","doi":"10.1093/bioinformatics/btag768","DOIUrl":"https://doi.org/10.1093/bioinformatics/btag768","url":null,"abstract":"Motivation: We asked whether the natural language used to name Gene Ontology (GO) Biological Process terms encodes hierarchical position in the GO directed acyclic graph.Results: Semantic representations of term names, combined with UMAP visualization and k-means clustering (k=20) recovered biologically coherent semantic groups whose depth distributions differed strongly (Kruskal-Wallis H=5244, p<10-300; η2=0.217). Term-name length increased with depth (Pearson r=0.34, Spearman ρ=0.37; slope ≈3.2 letters per level), and this positive scaling held under shortest-, longest-, and average-path depth definitions. Vocabulary entropy followed a bell-shaped diversification-specialization curve with an intermediate-depth peak (maximal at depth 4) that lies outside a Monte Carlo null envelope across most depths, and null-model tests supported a genuine linguistic-hierarchical association.Availability and implementation: The go_lingdepth Python package, regenerated result tables, and figure-generation scripts are available at https://github.com/Sadiya-ISU/Letters-and-Biological-Process and archived on Zenodo (DOI: 10.5281/zenodo.21501473).Supplementary information: Supplementary data are available at Bioinformatics online.","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149186872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient and scalable Python implementation of ANCOM-BC and ANCOM-BC2 for omics differential abundance testing. 高效和可扩展的Python实现ANCOM-BC和ANCOM-BC2组学差异丰度测试。

IF 5.4

Bioinformatics (Oxford, England) Pub Date : 2026-10-13 DOI: 10.1093/bioinformatics/btag767

Zijun Wu, Huang Lin, James T Morton, Qiyun Zhu

{"title":"Efficient and scalable Python implementation of ANCOM-BC and ANCOM-BC2 for omics differential abundance testing.","authors":"Zijun Wu, Huang Lin, James T Morton, Qiyun Zhu","doi":"10.1093/bioinformatics/btag767","DOIUrl":"https://doi.org/10.1093/bioinformatics/btag767","url":null,"abstract":"Summary: Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC) and its successor ANCOM-BC2 are critical algorithms for differential abundance analysis. Designed for handling compositional data, they feature the ability to estimate the unknown sampling fractions and correct the bias induced by their differences. While the original R package implementing the two methods is robust for standard dataset sizes, the increasing magnitude of modern multi-omics studies presents computational challenges, often resulting in a runtime bottleneck for high-dimensional data. Here, we present native Python implementations of both ANCOM-BC and ANCOM-BC2 in the scikit-bio package, significantly accelerating the workflow and enabling their application to large-scale datasets. By leveraging vectorization with NumPy and numerical optimization in SciPy, we enhanced the efficiency of both methods without compromising their statistical accuracy. The benchmarking results revealed a dramatic increase in performance in both simulated and real datasets. We demonstrated that our Python implementations generated test results matching the original R code, while achieving 380- and 147-fold faster runtimes and 97.4% and 96.2% lower memory consumption on the largest tested dataset, respectively. The Python implementations allow for the analysis of datasets that are significantly larger than previously possible, and enable applications to alternative omics data types and complex statistical models that vary greatly by the number of samples, features, and covariates.Availability and implementation: https://github.com/scikit-bio/scikit-bio.Supplementary information: N/A.","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149186875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Microenvironment Awareness Pragmatic Analysis for Generative Enzyme Function Reasoning. 生成酶函数推理的微环境意识语用分析。

IF 5.4

Bioinformatics (Oxford, England) Pub Date : 2026-10-13 DOI: 10.1093/bioinformatics/btag769

Jing Hao, Ke Yan, Bin Liu

{"title":"Microenvironment Awareness Pragmatic Analysis for Generative Enzyme Function Reasoning.","authors":"Jing Hao, Ke Yan, Bin Liu","doi":"10.1093/bioinformatics/btag769","DOIUrl":"https://doi.org/10.1093/bioinformatics/btag769","url":null,"abstract":"Motivation: Accurate enzyme function annotation is critical for interpreting rapidly expanding sequence data, yet experimental methods are costly and low-throughput, while computational approaches face two key limitations. Most existing methods are discriminative models confined to known EC labels, making it difficult to generalize to emerging enzymatic activities under cold-start conditions. Moreover, they typically treat enzymes as isolated molecular entities and overlook microenvironment factors such as optimal pH and temperature, which provide important contextual clues for functional annotation.Results: This study proposes MPA-EC, a generative framework that integrates microenvironment as pragmatic context into enzyme function reasoning. By fine-tuning the large language model on a newly constructed Enzyme Microenvironment Database, the model learns to infer microenvironment from sequence and then reason function within that context, generating EC labels with explicit reasoning chains. MEMP imputes missing pH and temperature values to complete the Enzyme Microenvironment Database. On public benchmark dataset, MPA-EC achieves an absolute F1 improvement of 0.1068 over the state-of-the-art GraphEC model. Under cold-start conditions, the model generates reference-worthy function labels, demonstrating its practical utility for emerging enzymatic activities.Availability: The source code and data of MPA-EC can be accessed at https://bliulab.net/MPA-EC, https://zenodo.org/records/22152329 and https://github.com/bliulab/MPA-EC.","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149186938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint enzyme-reaction retrieval and catalytic optima prediction via multimodal fusion. 通过多模态融合的联合酶反应检索和催化最佳预测。

IF 5.4

Bioinformatics (Oxford, England) Pub Date : 2026-10-12 DOI: 10.1093/bioinformatics/btag764

Yueyi Cai, Feng Yang, Juan Liu

{"title":"Joint enzyme-reaction retrieval and catalytic optima prediction via multimodal fusion.","authors":"Yueyi Cai, Feng Yang, Juan Liu","doi":"10.1093/bioinformatics/btag764","DOIUrl":"https://doi.org/10.1093/bioinformatics/btag764","url":null,"abstract":"Motivation: Enzyme-reaction retrieval prioritizes candidate biocatalysts for experimental follow-up, where recommendations should indicate catalytic compatibility and suitable pH and temperature conditions. Existing retrieval models optimize matching scores, whereas catalytic optima predictors are typically enzyme-level regressors because condition annotations are sparse. This separation leaves ranked pairs unevaluated for condition suitability and optima predictions without reaction context.Results: We present GERO, a multimodal fusion framework integrating enzyme sequence semantics, sequence-derived pocket geometry, and molecular reaction representations through feature-gated fusion for condition-aware retrieval and catalytic optima estimation. We define the tolerance-restricted hit rate (Hit@k-TR), which requires both correct top-k retrieval and condition predictions within predefined tolerances. Across enzyme- and reaction-similarity splits, GERO improves Hit@k-TR over two-stage baselines under the primary tolerances. Benchmark examples and an iodinin biosynthesis case study illustrate candidate ranking and assay-condition estimation for experimental prioritization.Availability and implementation: Source code is available at https://github.com/ykxhs/GERO.Supplementary information: Supplementary data are available at XXXX online.","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149171560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CMGL: Confidence-guided Multi-omics Graph Learning for Cancer Subtype Classification. CMGL：用于癌症亚型分类的信心引导多组学图学习。

IF 5.4

Bioinformatics (Oxford, England) Pub Date : 2026-10-12 DOI: 10.1093/bioinformatics/btag763

Boyang Fan, Hengchuang Yin, Siyu Yi, Yifan Wang, Zhicheng Li, Leijiyu Zhou, Jiancheng Lv, Wei Ju

引用次数: 0

Polypharmacological Target-Aware Molecule Generation. 多药理学靶标感知分子生成。

IF 5.4

Bioinformatics (Oxford, England) Pub Date : 2026-10-12 DOI: 10.1093/bioinformatics/btag766

Kehan Wu, Wei Zhang, Han Guo, Renhe Liu, Yu Shi, Peiran Jin, Haiguang Liu, Enhong Chen, Tao Qin, Zequn Liu, Yingce Xia

{"title":"Polypharmacological Target-Aware Molecule Generation.","authors":"Kehan Wu, Wei Zhang, Han Guo, Renhe Liu, Yu Shi, Peiran Jin, Haiguang Liu, Enhong Chen, Tao Qin, Zequn Liu, Yingce Xia","doi":"10.1093/bioinformatics/btag766","DOIUrl":"https://doi.org/10.1093/bioinformatics/btag766","url":null,"abstract":"Motivation: Designing compounds capable of modulating multiple target proteins simultaneously (i.e., polypharmacology) is critical for treating complex diseases like cancer and neurodegenerative disorders. However, AI-driven multi-target drug design remains severely hindered by the scarcity of multi-target 3D structural data and the inefficiency of traditional \"generate-then-filter\" pipelines. There is a pressing need for scalable methods that can satisfy diverse binding constraints across multiple proteins without relying on scarce structural priors.Results: To overcome these bottlenecks, we reformulate multi-target drug design as a sequence-based generation task and introduce PolyTamGen, an end-to-end autoregressive framework. We first curate a large-scale ligand-centric bioactivity dataset from ChEMBL and BindingDB, comprising 1.6 million unique ligands and 4.9 million bioactivity entries. PolyTamGen encodes target protein sets using ESM3, compresses these high-dimensional features into compact tokens via an attention module, and feeds them into a sequence-based compound generator to produce the final multi-target ligands. We further apply Direct Preference Optimization (DPO) to improve docking-based affinity proxies and drug-likeness. Extensive experiments on dual- and triple-target benchmarks demonstrate that PolyTamGen improves joint success rates over representative baselines while maintaining high chemical validity and diversity.Availability and implementation: The code is available at https://github.com/bjzgcai/PolyTamGen. The processed data, model checkpoints, and benchmark results are available at Zenodo: https://doi.org/10.5281/zenodo.20594128.Supplementary information: Supplementary data are available at Bioinformatics online.","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149169279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FAMUS: A Few-Shot Learning Framework for Large-Scale Protein Annotation. FAMUS：用于大规模蛋白质注释的几次学习框架。

IF 5.4

Bioinformatics (Oxford, England) Pub Date : 2026-10-12 DOI: 10.1093/bioinformatics/btag761

Guy Shur, David Burstein

{"title":"FAMUS: A Few-Shot Learning Framework for Large-Scale Protein Annotation.","authors":"Guy Shur, David Burstein","doi":"10.1093/bioinformatics/btag761","DOIUrl":"https://doi.org/10.1093/bioinformatics/btag761","url":null,"abstract":"Motivation: Gene function prediction is pivotal in genomic and metagenomic data analysis. Current annotation tools typically rely on the most similar sequence from the query database and struggle to robustly set meaningful hit thresholds, causing a major challenge for reliable annotation.Results: Here, we present a contrastive learning framework for functional annotation. FAMUS (Functional Annotation Method Using Supervised contrastive learning) compares query sequences to a full array of profile Hidden Markov Models, encoding each sequence's complete similarity profile, rather than only its top-scoring hit, into a condensed vector space that minimizes the distance between proteins from the same family. Unannotated sequences are incorporated as negative examples during training, enabling robust detection of proteins that fall outside the reference database without requiring user-defined thresholds. FAMUS outperformed KEGG's native KofamScan for KEGG Orthology annotation and InterPro's InterProScan for PANTHER family annotation, especially on realistic, sparsely annotated data. We used this approach to build four protein annotation models spanning the KEGG Orthology, InterPro, OrthoDB, and EggNOG databases. FAMUS is a comprehensive, modular framework that supports both pre-defined and user-specific databases. It can be readily integrated into genomic and metagenomic analysis pipelines for accurate, large-scale functional annotation.Availability and implementation: All four models are available as a conda package and via our web server at https://app.famus.bursteinlab.org/, and source code is freely available on GitHub at https://github.com/burstein-lab/famus/ as well as a source code snapshot at https://doi.org/10.5281/zenodo.23132542.Supplementary information: Supplementary data are available at Bioinformatics online.","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149171139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LoGicAl: Local ancestry and genotype calling uncertainty-aware ancestry-specific allele frequency estimation from admixed samples. 逻辑：本地祖先和基因型呼叫不确定性意识祖先特异性等位基因频率估计从混合样本。

IF 5.4

Bioinformatics (Oxford, England) Pub Date : 2026-10-10 DOI: 10.1093/bioinformatics/btag752

Jiongming Wang, Sebastian Zöllner

{"title":"LoGicAl: Local ancestry and genotype calling uncertainty-aware ancestry-specific allele frequency estimation from admixed samples.","authors":"Jiongming Wang, Sebastian Zöllner","doi":"10.1093/bioinformatics/btag752","DOIUrl":"https://doi.org/10.1093/bioinformatics/btag752","url":null,"abstract":"Motivation: In admixed groups, it is of interest to estimate allele frequencies of their ancestral contributing populations. These ancestry-specific allele frequencies inform genetic drivers of disease etiologies, facilitate genome-wide association study interpretations, enhance polygenic risk prediction and portability, and provide insights into demographic history. Their estimation leverages inferred locus-specific ancestry background, i.e., local ancestry, generated by tools like RFMix. However, existing estimation methods lose accuracy and incur biases by failing to model uncertainty from upstream local ancestry inference and genotype calling.Results: Here, we introduce LoGicAl, a novel likelihood-based method for estimating ancestry-specific allele frequencies from admixed samples, simultaneously accommodating uncertainty from ancestry calling, genotyping, and statistical phasing. We demonstrate that modeling these uncertainties substantially reduces estimation errors, resulting in superior accuracy for both sequence-based and array-based genotyping with different levels of local ancestry inference quality. By integrating an accelerated fixed-point algorithm, LoGicAl achieves high scalability and enhanced computational efficiency compared with existing approaches. Applying LoGicAl to admixed cohorts in the 1000 Genomes Project, we illustrate the benefits of local-ancestry-based allele frequency estimates. Together, LoGicAl contributes to genomic analyses of admixed samples by providing precise and rapid ancestry-specific allele frequency estimates, and to constructing the spatial landscape and dynamics of genetic variations in admixed populations at a finer scale.Availability: The LoGicAl software is available at https://github.com/student-jw/LoGicAl.","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149167164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TL-iGeneCombo: Transfer Learning for Individual Sample Specific Gene Combination Effect Prediction and Selection in Cancer Cells. TL-iGeneCombo：肿瘤细胞中个体样本特异性基因组合效应预测和选择的迁移学习。

IF 5.4

Bioinformatics (Oxford, England) Pub Date : 2026-10-10 DOI: 10.1093/bioinformatics/btag760

Birkan Gökbağ, Lingling Wang, Chen Yang, Yun Shen, Ping Zhang, Lijun Chengi, Lang Li

{"title":"TL-iGeneCombo: Transfer Learning for Individual Sample Specific Gene Combination Effect Prediction and Selection in Cancer Cells.","authors":"Birkan Gökbağ, Lingling Wang, Chen Yang, Yun Shen, Ping Zhang, Lijun Chengi, Lang Li","doi":"10.1093/bioinformatics/btag760","DOIUrl":"https://doi.org/10.1093/bioinformatics/btag760","url":null,"abstract":"Motivation: Identifying effective gene combinations is critical for developing combinatorial cancer therapies. Synthetic lethality (SL) has been widely used to characterize genetic interactions based on cell viability, but predictive models are often limited by the availability of combinatorial double-knockout (CDKO) data for individual cancer cells. How to borrow information from CDKO experiments in other cancer cells to improve prediction in a targeted cancer cell remains an important challenge.Results: We investigated the CDKO effects of paired genes using both SL as an indirect measure and gene combination effect as a direct measure. We developed TL-iGeneCombo, a transfer learning approach, to borrow data from CDKO experiments in other cancer cells for the prediction of SL and gene combination effect in a targeted cancer cell. Applying TL-iGeneCombo improved the accuracy of SL classification by 19% and increased the Spearman correlation of gene combination effect scores by 29%. Using data and models built from the first CDKO experiment in a prostate cancer cell, 22RV1, we further designed gene pairs for a second CDKO experiment in the same cell. It validated the superior prediction of gene combination effect, but not SL prediction.Availability: The software is freely available at https://github.com/BirkanGokbag/TL-iGeneCombo under the GNU General Public License v3.0.Supplementary information: Supplementary data are available at Bioinformatics online.","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149168079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0