The rapid development of proteomics studies has resulted in large volumes of experimental data. The emergence of big data platform provides the opportunity to handle these large amounts of data. The integrated proteome resource, iProX (https://www.iprox.cn), which was initiated in 2017, has been greatly improved with an up-to-date big data platform implemented in 2021. Here, we describe the main iProX developments since its first publication in Nucleic Acids Research in 2019. First, a hyper-converged architecture with high scalability supports the submission process. A hadoop cluster can store large amounts of proteomics datasets, and a distributed, RESTful-styled Elastic Search engine can query millions of records within one second. Also, several new features, including the Universal Spectrum Identifier (USI) mechanism proposed by ProteomeXchange, RESTful Web Service API, and a high-efficiency reanalysis pipeline, have been added to iProX for better open data sharing. By the end of August 2021, 1526 datasets had been submitted to iProX, reaching a total data volume of 92.42TB. With the implementation of the big data platform, iProX can support PB-level data storage, hundreds of billions of spectra records, and second-level latency service capabilities that meet the requirements of the fast growing field of proteomics.

BackgroundThe Pediatric Quality of Life Inventory (PedsQL) is widely used instrument to measure pediatric health-related quality of life (HRQOL) for children aged 2 to 18 years. The purpose of the current study was to investigate the feasibility, reliability and validity of the Chinese mandarin version of the PedsQL 4.0 Generic Core Scales and 3.0 Cancer Module in a group of Chinese children with cancer.MethodsThe PedsQL 4.0 Genetic Core Scales and the PedsQL 3.0 Cancer Module were administered to children with cancer (aged 5-18 years) and parents of such children (aged 2-18 years). For comparison, a survey on a demographically group-matched sample of the general population with children (aged 5-18) and parents of children (aged 2-18 years) was conducted with the PedsQL 4.0 Genetic Core Scales.ResultThe minimal mean percentage of missing item responses (except the School Functioning scale) supported the feasibility of the PedsQL 4.0 Generic Core Scales and 3.0 Cancer Module for Chinese children with cancer. Most of the scales showed satisfactory reliability with Cronbach's α of exceeding 0.70, and all scales demonstrated sufficient test-retest reliability. Assessing the clinical validity of the questionnaires, statistically significant difference was found between healthy children and children with cancer, and between children on-treatment versus off-treatment ≥12 months. Positive significant correlations were observed between the scores of the PedsQL 4.0 Generic Core Scale and the PedsQL 3.0 Cancer Module. Exploratory factor analysis demonstrated sufficient factorial validity. Moderate to good agreement was found between child self- and parent proxy-reports.ConclusionThe findings support the feasibility, reliability and validity of the Chinese Mandarin version of PedsQL 4.0 Generic Core Scales and 3.0 Cancer Module in children with cancer living in mainland China.

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We describe a strategy to ''revive'' putatively pathogenic T cells from frozen specimens of human inflammatory target organs. To distinguish pathogenic from irrelevant bystander T cells, we focused on cells that were (i) clonally expanded and (ii) in direct morphological contact with a target cell. Using CDR3 spectratyping, we identified clonally expanded T cell receptor (TCR) ␤-chains in muscle sections of patients with inflammatory muscle diseases. By immunohistochemistry, we identified those V␤-positive T cells that fulfilled the morphological criteria of myocytotoxicity and isolated them by laser microdissection. Next, we identified coexpressed pairs of TCR ␣-and ␤-chains by a multiplex PCR protocol, which allows the concomitant amplification of both chains from single cells. This concomitant amplification had not been achieved previously in histological sections, mainly because of the paucity of available anti-␣-chain antibodies and the great heterogeneity of the ␣-chain genes. From muscle tissue of a patient with polymyositis, we isolated 64 T cells that expressed an expanded V␤1 chain. In 23 of these cells, we identified the corresponding ␣-chain. Twenty of these 23 ␣-chains were identical, suggesting antigendriven selection. After functional reconstitution of the ␣␤-pairs, their antigen-recognition properties could be studied. Our results open avenues for combined analysis of the full TCR ␣-and ␤-chain repertoire in human inflammatory tissues.autoimmunity ͉ immunopathology ͉ myositis ͉ repertoire ͉ single-cell PCR

Given a scientific collaboration network, how can we find a group of collaborators with high research indicator (e.g., h-index) and diverse research interests? Given a social network, how can we identify the communities that have high influence (e.g., PageRank) and also have similar interests to a specified user? In such settings, the network can be modeled as a multi-valued network where each node has d (d ≥ 1) numerical attributes (i.e., h-index, diversity, PageRank, similarity score, etc.). In the multi-valued network, we want to find communities that are not dominated by the other communities in terms of d numerical attributes. Most existing community search algorithms either completely ignore the numerical attributes or only consider one numerical attribute of the nodes. To capture d numerical attributes, we propose a novel community model, called skyline community, based on the concepts of k-core and skyline. A skyline community is a maximal connected k-core that cannot be dominated by the other connected k-cores in the d-dimensional attribute space. We develop an elegant space-partition algorithm to efficiently compute the skyline communities. Two striking advantages of our algorithm are that (1) its time complexity relies mainly on the size of the answer s (i.e., the number of skyline communities), thus it is very efficient if s is small; and (2) it can progressively output the skyline communities, which is very useful for applications that only require part of the skyline communities. Extensive experiments on both synthetic and real-world networks demonstrate the efficiency, scalability, and effectiveness of the proposed algorithm.