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Single-cell Long Non-coding RNA Landscape of T Cells in Human Cancer Immunity

Genomics, Proteomics & Bioinformatics 2021年第3期19卷 377-393页

作者： Haitao Luo Dechao Bu Lijuan Shao Yang Li Liang Sun Ce Wang Jing Wang Wei Yang Xiaofei Yang Jun Dong Yi Zhao Furong Li Translational Medicine Collaborative Innovation Center The Second Clinical Medical College(Shenzhen People’s Hospital)Jinan UniversityShenzhen 518020China Shenzhen Key Laboratory of Stem Cell Research and Clinical Transformation Shenzhen 518020China Integrated Chinese and Western Medicine Postdoctoral Research Station Jinan UniversityGuangzhou 510632China Bioinformatics Research Group Key Laboratory of Intelligent Information ProcessingAdvanced Computing Research CenterInstitute of Computing TechnologyChinese Academy of SciencesBeijing 100190China Department of Gastrointestinal Surgery The Second Clinical Medical College(Shenzhen People’s Hospital)Jinan UniversityShenzhen 518020China

The development of new biomarkers or therapeutic targets for cancer immunotherapies requires deep understanding of *** date,the complete landscape and systematic characterization of long noncoding RNAs(lncRNAs)in T cells in cancer immunity are ***,by systematically analyzing full-length single-cell RNA sequencing(scRNA-seq)data of more than 20,000 libraries of T cells across three cancer types,we provided the first comprehensive catalog and the functional repertoires of lncRNAs in human T ***,we developed a custom pipeline for de novo transcriptome assembly and obtained a novel lncRNA catalog containing 9433 *** increased the number of current human lncRNA catalog by 16%and nearly doubled the number of lncRNAs expressed in T *** found that a portion of expressed genes in single T cells were lncRNAs which had been overlooked by the majority of previous *** on metacell maps constructed by the MetaCell algorithm that partitions scRNA-seq datasets into disjointed and homogenous groups of cells(metacells),154 signature lncRNA genes were *** were associated with effector,exhausted,and regulatory T cell ***,84 of them were functionally annotated based on the co-expression networks,indicating that lncRNAs might broadly participate in the regulation of T cell *** findings provide a new point of view and resource for investigating the mechanisms of T cell regulation in cancer immunity as well as for novel cancer-immune biomarker development and cancer immunotherapies.

关键词： Long non-coding RNA Transcriptome assembly Metacell Immune regulation Functional annotation

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On dynamic weighting of data in clustering with K-alpha means

On dynamic weighting of data in clustering with K-alpha mean...

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2010 20th International Conference on Pattern Recognition, ICPR 2010

作者： Chen, Si-Bao Wang, Hai-Xian Luo, Bin Key Lab. of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei Anhui 230039 China Key Lab. of Child Development and Learning Science Research Center for Learning Science Southeast University Nanjing Jiangsu 210096 China

ISBN: (纸本)9780769541099

Although many methods of refining initialization have appeared, the sensitivity of K-Means to initial centers is still an obstacle in applications. In this paper, we investigate a new class of clustering algorithm, K-Alpha Means (KAM), which is insensitive to the initial centers. With K-Harmonic Means as a special case, KAM dynamically weights data points during iteratively updating centers, which deemphasizes data points that are close to centers while emphasizes data points that are not close to any centers. Through replacing minimum operator in K-Means by alpha-mean operator, KAM significantly improves the clustering performances. © 2010 IEEE.

关键词： Clustering algorithms

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Subspace selection using semi-supervised harmonic mean of Kullback-Leibler divergences

Subspace selection using semi-supervised harmonic mean of Ku...

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2010 7th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2010

作者： Chen, Si-Bao Wang, Hai-Xian Luo, Bin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei Anhui 230039 China Key Lab of Child Development and Learning Science of Ministry of Education Research Center for Learning Science Southeast University Nanjing Jiangsu 210096 China

ISBN: (纸本)9781424459346

In many areas of pattern recognition and machine learning, subspace selection is an essential step. Fisher's linear discriminant analysis (LDA) is one of the most well-known linear subspace selection methods. However, LDA suffers from the class separation problem. The projection to a subspace tends to merge close class pairs. A recent result, named maximizing the geometric mean of Kullback-Leibler (KL) divergences of class pairs (MGMD), can significantly reduce the class separation problem. Furthermore, maximizing the harmonic mean of Kullback-Leibler (KL) divergences of class pairs (MHMD) emphasizes smaller divergences more than MGMD, and deals with the class separation problem more effectively. However, in many applications, labeled data are very limited while unlabeled data can be easily obtained. The estimation of divergences of class pairs is unstable using inadequate labeled data. To take advantage of unlabeled data for subspace selection, semi-supervised MHMD (SSMHMD) is proposed using graph Laplacian as normalization. Quasi-Newton method is adopted to solve the optimization problem. Experiments on synthetic data and real image data show the validity of SSMHMD. ©2010 IEEE.

关键词： Discriminant analysis

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Surface repair of wide-bandgap perovskites for high-performance all-perovskite tandem solar cells

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Journal of Energy Chemistry 2024年第6期93卷 64-70,I0003页

作者： Xiaojing Lv Weisheng Li Jin Zhang Yujie Yang Xuefei Jia Yitong Ji Qianqian Lin Wenchao Huang Tongle Bu Zhiwei Ren Canglang Yao Fuzhi Huang Yi-Bing Cheng Jinhui Tong State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of TechnologyWuhan 430070HubeiChina Key Lab of Artificial Micro-and Nano-Structures of Ministry of Education of China School of Physics and TechnologyWuhan UniversityWuhan 430072HubeiChina Department of Electrical and Electronic Engineering Photonics Research Institute(PRI)Research Institute for Intelligent Wearable Systems(iWEAR)The Hong Kong Polytechnic University999077Hong KongChina Laboratory of Advanced Materials Fudan UniversityShanghai 200438China Xianhu Laboratory of the Advanced Energy Science and Technology Guangdong Laboratory Foshan 528200GuangdongChina

Wide-bandgap(WBG)perovskite solar cells(PSCs)play a fundamental role in perovskite-based tandem solar ***,the efficiency of WBG PSCs is limited by significant open-circuit voltage losses,which are primarily caused by surface *** this study,we present a novel method for modifying surfaces using the multifunctional S-ethylisothiourea hydrobromide(SEBr),which can passivate both Pb^(-1)and FA^(-1)terminated surfaces,Moreover,the SEBr upshifted the Fermi level at the perovskite interface,thereby promoting carrier *** proposed method was effective for both 1.67 and 1.77 eV WBG PSCs,achieving power conversion efficiencies(PCEs)of 22.47%and 19.90%,respectively,with V_(OC)values of 1.28 and 1.33 V,along with improved film and device *** this advancement,we were able to fabricate monolithic all-perovskite tandem solar cells with a champion PCE of 27.10%,This research offers valuable insights for passivating the surface trap states of WBG perovskite through rational multifunctional molecular engineering.

关键词： Wide-bandgap perovskite Surface defect Multifunctional molecule All-perovskite tandem solar cells

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GEP-DL4Mol: A Novel Molecular Deep-learning Model Optimization Framework for Boosting Molecular Properties Prediction

GEP-DL4Mol: A Novel Molecular Deep-learning Model Optimizati...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Peng, Yuzhong Zhang, Hao Zhang, Ziqiao Lin, Yanmei Zhou, Shuigeng Qiao, Shaojie Nanning Normal University Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision Nanning China Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen China Fudan University Shanghai Key Lab of Intelligent Information Processing Shanghai China Nanning Normal University Undergraduate College Nanning China Chengdu University of Information Technology School of Software Engineering Chengdu China

ISBN: (纸本)9798350337488

High-performance molecular property prediction is one of the essential problems in many research fields. Although Deep Learning models (DLMs) have the strong potential to predict molecular property, the search space of their neural structure and hyperparameters is too huge to enumerate all possibilities, which implies that one will be difficult to get a optimal configuration solution, or even build a DLM with poor performance. To tackle the aforementioned problem, we first abstractly map various types of supervised DLMs for chemical molecular property prediction under a unified molecular DLM conceptualization. Then, we develop a self-learning molecular DLMs construction and optimization framework, called GEP-DL4Mol, based on the unified supervised molecular DLM conceptualization and Gene Expression Programming. Experiments on eight benchmark datasets including 19 property prediction tasks were conducted to evaluate the performance of the proposed method. Experimental results show that the proposed method outperforms other methods. © 2023 IEEE.

关键词： deep learning deep neural network optimization gene expression programming hyper-parameter optimization molecular modeling Molecular property prediction

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Topic discovery of clusters from documents with geographical location

Topic discovery of clusters from documents with geographical...

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作者： Zhang, Li Sun, Xiaoping Zhuge, Hai Knowledge Grid Lab Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Nanjing University of Posts and Telecommunications Nanjing Jiangsu China State Key Laboratory of Software Engineering Wuhan University Wuhan Hubei China University of Chinese Academy of Sciences Beijing China

As smart phones with GPS become popular, more and more textual documents with geographical locations are published on the Web. keyword-based location services like vehicle navigation, tour planning, nearby object querying, and location pattern discovering of spatial objects are becoming popular and important. However, processing both text and geographical locations brings more challenges to information-retrieval techniques. In this paper, we focus on the problem of finding textual topics of clusters containing spatial objects with text descriptions. The key is how to combine clustering techniques with topic-retrieval models to integrate both geo-location information and text information. We investigated methods that combine clustering methods with the Latent Dirichlet Allocation model to discover topics of clusters of documents with geo-locations. Six different methods of combination are investigated, each having outputs with different meanings, which can be further leveraged to answer different types of queries over spatial documents. Experiments are conducted on both synthetic and real data. The results show that the combination of the probabilistic topic model with clustering algorithms is an efficient and effective way to discover meaningful clusters in different facets and levels of documents with textual and geographical information. © 2015 John Wiley & Sons, Ltd.

关键词： Cluster analysis

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Salient object extraction for user-targeted video content association

Salient object extraction for user-targeted video content as...

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作者： Li, Jia Yu, Han-Nan Tian, Yong-Hong Huang, Tie-Jun Gao, Wen Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100049 China School of EE and CS Peking University Beijing 100871 China

The increasing amount of videos on the Internet and digital libraries highlights the necessity and importance of interactive video services such as automatically associating additional materials (e.g., advertising logos and relevant selling information) with the video content so as to enrich the viewing experience. Toward this end, this paper presents a novel approach for user-targeted video content association (VCA). In this approach, the salient objects are extracted automatically from the video stream using complementary saliency maps. According to these salient objects, the VCA system can push the related logo images to the users. Since the salient objects often correspond to important video content, the associated images can be considered as content-related. Our VCA system also allows users to associate images to the preferred video content through simple interactions by the mouse and an infrared pen. Moreover, by learning the preference of each user through collecting feedbacks on the pulled or pushed images, the VCA system can provide user-targeted services. Experimental results show that our approach can effectively and efficiently extract the salient objects. Moreover, subjective evaluations show that our system can provide content-related and user-targeted VCA services in a less intrusive way. © 2010 Zhejiang University and Springer-Verlag Berlin Heidelberg.

关键词： Digital libraries

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New image coding scheme with hierarchical representation and adaptive interpolation

New image coding scheme with hierarchical representation and...

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2011 IEEE Visual Communications and Image processing, VCIP 2011

作者： Zhang, Xinfeng Xiong, Ruiqin Ma, Siwei Gao, Wen Institute of Digital Media Peking University Beijing 100871 China Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100049 China

ISBN: (纸本)9781457713200

In this paper, we propose a new image coding scheme which combines the advantage of hierarchical (i.e. multi-resolution) representation, adaptive interpolation and rate-distortion optimization capability of block-based coding. We divide the coding process into two layers by partitioning the image pixels into four groups with chessboard structure. In the first layer, pixels in one of these partitions are encoded and reconstructed using the state-of-the-art coding scheme. In the second layer, directional adaptive interpolation is employed to predict and code the pixels in the remaining three partitions. Joint mode decision is performed for the co-located macroblocks of these three partitions. Skip mode is introduced to save coding bits for blocks where the interpolation process works very well. Experimental results demonstrate that the proposed coding scheme achieves very promising performance. © 2011 IEEE.

关键词： Image coding

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DeTrack: In-model Latent Denoising Learning for Visual Object Tracking 38

DeTrack: In-model Latent Denoising Learning for Visual Objec...

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38th Conference on Neural information processing Systems, NeurIPS 2024

作者： Zhou, Xinyu Li, Jinglun Hong, Lingyi Jiang, Kaixun Guo, Pinxue Ge, Weifeng Zhang, Wenqiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Shanghai Engineering Research Center of AI & Robotics Academy for Engineering and Technology Fudan University Shanghai China

Previous visual object tracking methods employ image-feature regression models or coordinate autoregression models for bounding box prediction. Image-feature regression methods heavily depend on matching results and do not utilize positional prior, while the autoregressive approach can only be trained using bounding boxes available in the training set, potentially resulting in suboptimal performance during testing with unseen data. Inspired by the diffusion model, denoising learning enhances the model's robustness to unseen data. Therefore, We introduce noise to bounding boxes, generating noisy boxes for training, thus enhancing model robustness on testing data. We propose a new paradigm to formulate the visual object tracking problem as a denoising learning process. However, tracking algorithms are usually asked to run in real-time, directly applying the diffusion model to object tracking would severely impair tracking speed. Therefore, we decompose the denoising learning process into every denoising block within a model, not by running the model multiple times, and thus we summarize the proposed paradigm as an in-model latent denoising learning process. Specifically, we propose a denoising Vision Transformer (ViT), which is composed of multiple denoising blocks. In the denoising block, template and search embeddings are projected into every denoising block as conditions. A denoising block is responsible for removing the noise in a predicted bounding box, and multiple stacked denoising blocks cooperate to accomplish the whole denoising process. Subsequently, we utilize image features and trajectory information to refine the denoised bounding box. Besides, we also utilize trajectory memory and visual memory to improve tracking stability. Experimental results validate the effectiveness of our approach, achieving competitive performance on several challenging datasets. The proposed in-model latent denoising tracker achieve real-time speed, rendering denoising learning

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Learning label-Specific Features and Class-Dependent labels for Multi-label Classification

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IEEE Transactions on Knowledge and Data Engineering 2016年第99期PP卷 3309-3323页

作者： Huang, Jun Li, Guorong Huang, Qingming Wu, Xindong School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing101480 China Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Department of Computer Science University of Vermont 33 Colchester Avenue BurlingtonVT05405 United States

Binary Relevance is a well-known framework for multi-label classification, which considers each class label as a binary classification problem. Many existing multi-label algorithms are constructed within this framework and utilize identical data representation in the discrimination of all the class labels. In multi-label classification, however, each class label might be determined by some specific characteristics of its own. In this paper, we seek to learn label-specific data representation for each class label, which is composed of label-specific features. Our proposed method LLSF can not only be utilized for multi-label classification directly, but also be applied as a feature selection method for multi-label learning and a general strategy to improve multi-label classification algorithms comprising a number of binary classifiers. Inspired by the research works on modeling high-order label correlations, we further extend LLSF to learn class-Dependent labels in a sparse stacking way, denoted as LLSFDL. It incorporates both second-order and high-order label correlations. A comparative study with the state-of-the-art approaches manifests the effectiveness and efficiency of our proposed methods. © 2016 IEEE.

关键词： Classification (of information)

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