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Hu R, Xu Y, Han B, Chen Y, Li W, Guan G, Hu P, Zhou Y, Xu Q, Chen L. MiR-202-3p determines embryo viability during mid-blastula transition. Front Cell Dev Biol 2022; 10:897826. [PMID: 36003151 PMCID: PMC9393261 DOI: 10.3389/fcell.2022.897826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 07/13/2022] [Indexed: 11/13/2022] Open
Abstract
Developmental growth is an intricate process involving the coordinated regulation of the expression of various genes, and microRNAs (miRNAs) play crucial roles in diverse processes throughout animal development. The mid-blastula transition (MBT) is a developmental milestone when maternal RNAs are cleared and the zygotic genome programmed asynchronous cell division begins to drive embryogenesis. While mechanisms underlying MBT have been intensively revealed, factors regulating cell proliferation at the transition remain largely unknown. We report here a microRNA, miR-202-3p to be a key factor that determines embryonic fate during MBT in zebrafish. A miR-202-3p antagomir specifically terminated embryo development at the mid-blastula stage. In vivo deletion of the miR-202 locus recapitulated the fatal phenotypes, which were rescued only by miR-202-3p or its precursor. Transcriptome comparison revealed >250 RNAs including both maternal and zygotic origins were dysregulated at MBT in the miR-202−/− embryos, corresponding with arrays of homeostatic disorders leading to massive apoptosis. A trio of genes: nfkbiaa, perp and mgll, known to be intimately involved with cell proliferation and survival, were identified as direct targets of miR-202-3p. Importantly, over- or under-expression of any of the trio led to developmental delay or termination at the blastula or gastrula stages. Furthermore, nfkbiaa and perp were shown to inter-regulate each other. Thus, miR-202-3p mediates a regulatory network whose components interact closely during MBT to determine embryonic viability and development.
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Affiliation(s)
- Ruiqin Hu
- International Joint Research Centre for Marine Biosciences (Ministry of Science and Technology), College of Fisheries and Life Science, Shanghai Ocean University, Shanghai, China
- Key Laboratory of Exploration and Utilization of Aquatic Genetic Resources (Ministry of Education) and International Research Centre for Marine Biosciences, College of Fisheries and Life Science, Shanghai Ocean University, Shanghai, China
| | - Yanna Xu
- International Joint Research Centre for Marine Biosciences (Ministry of Science and Technology), College of Fisheries and Life Science, Shanghai Ocean University, Shanghai, China
- Key Laboratory of Exploration and Utilization of Aquatic Genetic Resources (Ministry of Education) and International Research Centre for Marine Biosciences, College of Fisheries and Life Science, Shanghai Ocean University, Shanghai, China
| | - Bingshe Han
- International Joint Research Centre for Marine Biosciences (Ministry of Science and Technology), College of Fisheries and Life Science, Shanghai Ocean University, Shanghai, China
- Key Laboratory of Exploration and Utilization of Aquatic Genetic Resources (Ministry of Education) and International Research Centre for Marine Biosciences, College of Fisheries and Life Science, Shanghai Ocean University, Shanghai, China
| | - Yi Chen
- International Joint Research Centre for Marine Biosciences (Ministry of Science and Technology), College of Fisheries and Life Science, Shanghai Ocean University, Shanghai, China
- Key Laboratory of Exploration and Utilization of Aquatic Genetic Resources (Ministry of Education) and International Research Centre for Marine Biosciences, College of Fisheries and Life Science, Shanghai Ocean University, Shanghai, China
| | - Wenhao Li
- International Joint Research Centre for Marine Biosciences (Ministry of Science and Technology), College of Fisheries and Life Science, Shanghai Ocean University, Shanghai, China
- Key Laboratory of Exploration and Utilization of Aquatic Genetic Resources (Ministry of Education) and International Research Centre for Marine Biosciences, College of Fisheries and Life Science, Shanghai Ocean University, Shanghai, China
| | - Guijun Guan
- International Joint Research Centre for Marine Biosciences (Ministry of Science and Technology), College of Fisheries and Life Science, Shanghai Ocean University, Shanghai, China
- Key Laboratory of Exploration and Utilization of Aquatic Genetic Resources (Ministry of Education) and International Research Centre for Marine Biosciences, College of Fisheries and Life Science, Shanghai Ocean University, Shanghai, China
| | - Peng Hu
- International Joint Research Centre for Marine Biosciences (Ministry of Science and Technology), College of Fisheries and Life Science, Shanghai Ocean University, Shanghai, China
- Key Laboratory of Exploration and Utilization of Aquatic Genetic Resources (Ministry of Education) and International Research Centre for Marine Biosciences, College of Fisheries and Life Science, Shanghai Ocean University, Shanghai, China
| | - Yan Zhou
- International Joint Research Centre for Marine Biosciences (Ministry of Science and Technology), College of Fisheries and Life Science, Shanghai Ocean University, Shanghai, China
- Key Laboratory of Exploration and Utilization of Aquatic Genetic Resources (Ministry of Education) and International Research Centre for Marine Biosciences, College of Fisheries and Life Science, Shanghai Ocean University, Shanghai, China
| | - Qianghua Xu
- International Joint Research Centre for Marine Biosciences (Ministry of Science and Technology), College of Fisheries and Life Science, Shanghai Ocean University, Shanghai, China
- Key Laboratory of Exploration and Utilization of Aquatic Genetic Resources (Ministry of Education) and International Research Centre for Marine Biosciences, College of Fisheries and Life Science, Shanghai Ocean University, Shanghai, China
- Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, College of Marine Science, Shanghai Ocean University, Shanghai, China
| | - Liangbiao Chen
- International Joint Research Centre for Marine Biosciences (Ministry of Science and Technology), College of Fisheries and Life Science, Shanghai Ocean University, Shanghai, China
- Key Laboratory of Exploration and Utilization of Aquatic Genetic Resources (Ministry of Education) and International Research Centre for Marine Biosciences, College of Fisheries and Life Science, Shanghai Ocean University, Shanghai, China
- *Correspondence: Liangbiao Chen,
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Emerging Roles of RNA-Binding Proteins in Neurodevelopment. J Dev Biol 2022; 10:jdb10020023. [PMID: 35735914 PMCID: PMC9224834 DOI: 10.3390/jdb10020023] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 06/02/2022] [Accepted: 06/08/2022] [Indexed: 02/06/2023] Open
Abstract
Diverse cell types in the central nervous system (CNS) are generated by a relatively small pool of neural stem cells during early development. Spatial and temporal regulation of stem cell behavior relies on precise coordination of gene expression. Well-studied mechanisms include hormone signaling, transcription factor activity, and chromatin remodeling processes. Much less is known about downstream RNA-dependent mechanisms including posttranscriptional regulation, nuclear export, alternative splicing, and transcript stability. These important functions are carried out by RNA-binding proteins (RBPs). Recent work has begun to explore how RBPs contribute to stem cell function and homeostasis, including their role in metabolism, transport, epigenetic regulation, and turnover of target transcripts. Additional layers of complexity are provided by the different target recognition mechanisms of each RBP as well as the posttranslational modifications of the RBPs themselves that alter function. Altogether, these functions allow RBPs to influence various aspects of RNA metabolism to regulate numerous cellular processes. Here we compile advances in RNA biology that have added to our still limited understanding of the role of RBPs in neurodevelopment.
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Nussinov R, Tsai CJ, Jang H. Allostery, and how to define and measure signal transduction. Biophys Chem 2022; 283:106766. [PMID: 35121384 PMCID: PMC8898294 DOI: 10.1016/j.bpc.2022.106766] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 01/21/2022] [Accepted: 01/24/2022] [Indexed: 12/15/2022]
Abstract
Here we ask: What is productive signaling? How to define it, how to measure it, and most of all, what are the parameters that determine it? Further, what determines the strength of signaling from an upstream to a downstream node in a specific cell? These questions have either not been considered or not entirely resolved. The requirements for the signal to propagate downstream to activate (repress) transcription have not been considered either. Yet, the questions are pivotal to clarify, especially in diseases such as cancer where determination of signal propagation can point to cell proliferation and to emerging drug resistance, and to neurodevelopmental disorders, such as RASopathy, autism, attention-deficit/hyperactivity disorder (ADHD), and cerebral palsy. Here we propose a framework for signal transduction from an upstream to a downstream node addressing these questions. Defining cellular processes, experimentally measuring them, and devising powerful computational AI-powered algorithms that exploit the measurements, are essential for quantitative science.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD 21702, USA; Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
| | - Chung-Jung Tsai
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD 21702, USA
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD 21702, USA
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Yang P, Huang H, Liu C. Feature selection revisited in the single-cell era. Genome Biol 2021; 22:321. [PMID: 34847932 PMCID: PMC8638336 DOI: 10.1186/s13059-021-02544-3] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 11/15/2021] [Indexed: 12/13/2022] Open
Abstract
Recent advances in single-cell biotechnologies have resulted in high-dimensional datasets with increased complexity, making feature selection an essential technique for single-cell data analysis. Here, we revisit feature selection techniques and summarise recent developments. We review their application to a range of single-cell data types generated from traditional cytometry and imaging technologies and the latest array of single-cell omics technologies. We highlight some of the challenges and future directions and finally consider their scalability and make general recommendations on each type of feature selection method. We hope this review stimulates future research and application of feature selection in the single-cell era.
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Affiliation(s)
- Pengyi Yang
- School of Mathematics and Statistics, University of Sydney, Sydney, NSW, 2006, Australia.
- Computational Systems Biology Group, Children's Medical Research Institute, University of Sydney, Westmead, NSW, 2145, Australia.
- Charles Perkins Centre, University of Sydney, Sydney, NSW, 2006, Australia.
| | - Hao Huang
- School of Mathematics and Statistics, University of Sydney, Sydney, NSW, 2006, Australia
- Computational Systems Biology Group, Children's Medical Research Institute, University of Sydney, Westmead, NSW, 2145, Australia
| | - Chunlei Liu
- Computational Systems Biology Group, Children's Medical Research Institute, University of Sydney, Westmead, NSW, 2145, Australia
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5
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Yuan F, Li Z, Chen L, Zeng T, Zhang YH, Ding S, Huang T, Cai YD. Identifying the Signatures and Rules of Circulating Extracellular MicroRNA for Distinguishing Cancer Subtypes. Front Genet 2021; 12:651610. [PMID: 33767734 PMCID: PMC7985347 DOI: 10.3389/fgene.2021.651610] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Accepted: 02/10/2021] [Indexed: 12/24/2022] Open
Abstract
Cancer is one of the most threatening diseases to humans. It can invade multiple significant organs, including lung, liver, stomach, pancreas, and even brain. The identification of cancer biomarkers is one of the most significant components of cancer studies as the foundation of clinical cancer diagnosis and related drug development. During the large-scale screening for cancer prevention and early diagnosis, obtaining cancer-related tissues is impossible. Thus, the identification of cancer-associated circulating biomarkers from liquid biopsy targeting has been proposed and has become the most important direction for research on clinical cancer diagnosis. Here, we analyzed pan-cancer extracellular microRNA profiles by using multiple machine-learning models. The extracellular microRNA profiles on 11 cancer types and non-cancer were first analyzed by Boruta to extract important microRNAs. Selected microRNAs were then evaluated by the Max-Relevance and Min-Redundancy feature selection method, resulting in a feature list, which were fed into the incremental feature selection method to identify candidate circulating extracellular microRNA for cancer recognition and classification. A series of quantitative classification rules was also established for such cancer classification, thereby providing a solid research foundation for further biomarker exploration and functional analyses of tumorigenesis at the level of circulating extracellular microRNA.
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Affiliation(s)
- Fei Yuan
- School of Life Sciences, Shanghai University, Shanghai, China
- Department of Science and Technology, Binzhou Medical University Hospital, Binzhou, China
| | - Zhandong Li
- College of Food Engineering, Jilin Engineering Normal University, Changchun, China
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Tao Zeng
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yu-Hang Zhang
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Shijian Ding
- School of Life Sciences, Shanghai University, Shanghai, China
| | - Tao Huang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai, China
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Li JF, Ma XJ, Ying LL, Tong YH, Xiang XP. Multi-Omics Analysis of Acute Lymphoblastic Leukemia Identified the Methylation and Expression Differences Between BCP-ALL and T-ALL. Front Cell Dev Biol 2021; 8:622393. [PMID: 33553159 PMCID: PMC7859262 DOI: 10.3389/fcell.2020.622393] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Accepted: 12/15/2020] [Indexed: 02/06/2023] Open
Abstract
Acute lymphoblastic leukemia (ALL) as a common cancer is a heterogeneous disease which is mainly divided into BCP-ALL and T-ALL, accounting for 80–85% and 15–20%, respectively. There are many differences between BCP-ALL and T-ALL, including prognosis, treatment, drug screening, gene research and so on. In this study, starting with methylation and gene expression data, we analyzed the molecular differences between BCP-ALL and T-ALL and identified the multi-omics signatures using Boruta and Monte Carlo feature selection methods. There were 7 expression signature genes (CD3D, VPREB3, HLA-DRA, PAX5, BLNK, GALNT6, SLC4A8) and 168 methylation sites corresponding to 175 methylation signature genes. The overall accuracy, accuracy of BCP-ALL, accuracy of T-ALL of the RIPPER (Repeated Incremental Pruning to Produce Error Reduction) classifier using these signatures evaluated with 10-fold cross validation repeated 3 times were 0.973, 0.990, and 0.933, respectively. Two overlapped genes between 175 methylation signature genes and 7 expression signature genes were CD3D and VPREB3. The network analysis of the methylation and expression signature genes suggested that their common gene, CD3D, was not only different on both methylation and expression levels, but also played a key regulatory role as hub on the network. Our results provided insights of understanding the underlying molecular mechanisms of ALL and facilitated more precision diagnosis and treatment of ALL.
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Affiliation(s)
- Jin-Fan Li
- Department of Pathology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Xiao-Jing Ma
- Department of Pathology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Lin-Lin Ying
- Department of Pathology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Ying-Hui Tong
- Department of Pharmacy, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Xue-Ping Xiang
- Department of Pathology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
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7
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Zhang YH, Li H, Zeng T, Chen L, Li Z, Huang T, Cai YD. Identifying Transcriptomic Signatures and Rules for SARS-CoV-2 Infection. Front Cell Dev Biol 2021; 8:627302. [PMID: 33505977 PMCID: PMC7829664 DOI: 10.3389/fcell.2020.627302] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 12/14/2020] [Indexed: 12/26/2022] Open
Abstract
The world-wide Coronavirus Disease 2019 (COVID-19) pandemic was triggered by the widespread of a new strain of coronavirus named as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Multiple studies on the pathogenesis of SARS-CoV-2 have been conducted immediately after the spread of the disease. However, the molecular pathogenesis of the virus and related diseases has still not been fully revealed. In this study, we attempted to identify new transcriptomic signatures as candidate diagnostic models for clinical testing or as therapeutic targets for vaccine design. Using the recently reported transcriptomics data of upper airway tissue with acute respiratory illnesses, we integrated multiple machine learning methods to identify effective qualitative biomarkers and quantitative rules for the distinction of SARS-CoV-2 infection from other infectious diseases. The transcriptomics data was first analyzed by Boruta so that important features were selected, which were further evaluated by the minimum redundancy maximum relevance method. A feature list was produced. This list was fed into the incremental feature selection, incorporating some classification algorithms, to extract qualitative biomarker genes and construct quantitative rules. Also, an efficient classifier was built to identify patients infected with SARS-COV-2. The findings reported in this study may help in revealing the potential pathogenic mechanisms of COVID-19 and finding new targets for vaccine design.
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Affiliation(s)
- Yu-Hang Zhang
- School of Life Sciences, Shanghai University, Shanghai, China
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Hao Li
- College of Food Engineering, Jilin Engineering Normal University, Changchun, China
| | - Tao Zeng
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Zhandong Li
- College of Food Engineering, Jilin Engineering Normal University, Changchun, China
| | - Tao Huang
- Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai, China
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Li D, Lin H, Li L. Multiple Feature Selection Strategies Identified Novel Cardiac Gene Expression Signature for Heart Failure. Front Physiol 2020; 11:604241. [PMID: 33304275 PMCID: PMC7693561 DOI: 10.3389/fphys.2020.604241] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 10/15/2020] [Indexed: 02/02/2023] Open
Abstract
Heart failure (HF) is a serious condition in which the support of blood pumped by the heart is insufficient to meet the demands of body at a normal cardiac filling pressure. Approximately 26 million patients worldwide are suffering from heart failure and about 17–45% of patients with heart failure die within 1-year, and the majority die within 5-years admitted to a hospital. The molecular mechanisms underlying the progression of heart failure have been poorly studied. We compared the gene expression profiles between patients with heart failure (n = 177) and without heart failure (n = 136) using multiple feature selection strategies and identified 38 HF signature genes. The support vector machine (SVM) classifier based on these 38 genes evaluated with leave-one-out cross validation (LOOCV) achieved great performance with sensitivity of 0.983 and specificity of 0.963. The network analysis suggested that the hub gene SMOC2 may play important roles in HF. Other genes, such as FCN3, HMGN2, and SERPINA3, also showed great promises. Our results can facilitate the early detection of heart failure and can reveal its molecular mechanisms.
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Affiliation(s)
- Dan Li
- Department of Cardiovascular Medicine, First Hospital Affiliated to Harbin Medical University, Harbin, China
| | - Hong Lin
- Internal Medicine-Cardiovascular Department, Harbin Chest Hospital, Harbin, China
| | - Luyifei Li
- Department of Cardiovascular Medicine, First Hospital Affiliated to Harbin Medical University, Harbin, China
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Zhang YH, Li Z, Zeng T, Pan X, Chen L, Liu D, Li H, Huang T, Cai YD. Distinguishing Glioblastoma Subtypes by Methylation Signatures. Front Genet 2020; 11:604336. [PMID: 33329750 PMCID: PMC7732602 DOI: 10.3389/fgene.2020.604336] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 11/02/2020] [Indexed: 11/13/2022] Open
Abstract
Glioblastoma, also called glioblastoma multiform (GBM), is the most aggressive cancer that initiates within the brain. GBM is produced in the central nervous system. Cancer cells in GBM are similar to stem cells. Several different schemes for GBM stratification exist. These schemes are based on intertumoral molecular heterogeneity, preoperative images, and integrated tumor characteristics. Although the formation of glioblastoma is remarkably related to gene methylation, GBM has been poorly classified by epigenetics. To classify glioblastoma subtypes on the basis of different degrees of genes' methylation, we adopted several powerful machine learning algorithms to identify numerous methylation features (sites) associated with the classification of GBM. The features were first analyzed by an excellent feature selection method, Monte Carlo feature selection (MCFS), resulting in a feature list. Then, such list was fed into the incremental feature selection (IFS), incorporating one classification algorithm, to extract essential sites. These sites can be annotated onto coding genes, such as CXCR4, TBX18, SP5, and TMEM22, and enriched in relevant biological functions related to GBM classification (e.g., subtype-specific functions). Representative functions, such as nervous system development, intrinsic plasma membrane component, calcium ion binding, systemic lupus erythematosus, and alcoholism, are potential pathogenic functions that participate in the initiation and progression of glioblastoma and its subtypes. With these sites, an efficient model can be built to classify the subtypes of glioblastoma.
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Affiliation(s)
- Yu-Hang Zhang
- School of Life Sciences, Shanghai University, Shanghai, China
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Zhandong Li
- College of Food Engineering, Jilin Engineering Normal University, Changchun, China
| | - Tao Zeng
- Shanghai Research Center for Brain Science and Brain-Inspired Intelligence, Shanghai, China
| | - Xiaoyong Pan
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Dejing Liu
- Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Hao Li
- College of Food Engineering, Jilin Engineering Normal University, Changchun, China
| | - Tao Huang
- Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai, China
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