Scalable Incomplete Multi-View Clustering with Structure Alignment

The success of existing multi-view clustering (MVC) relies on the assumption that all views are complete. However, samples are usually partially available due to data corruption or sensor malfunction, which raises the research of incomplete multi-view clustering (IMVC). Although several anchor-based IMVC methods have been proposed to process the large-scale incomplete data, they still suffer from the following drawbacks: i) Most existing approaches neglect the inter-view discrepancy and enforce cross-view representation to be consistent, which would corrupt the representation capability of the model; ii) Due to the samples disparity between different views, the learned anchor might be misaligned, which we referred as the Anchor-Unaligned Problem for Incomplete data (AUP-ID). Such the AUP-ID would cause inaccurate graph fusion and degrades clustering performance. To tackle these issues, we propose a novel incomplete anchor graph learning framework termed Scalable Incomplete Multi-View Clustering with Structure Alignment (SIMVC-SA). Specially, we construct the view-specific anchor graph to capture the complementary information from different views. In order to solve the AUP-ID, we propose a novel structure alignment module to refine the cross-view anchor correspondence. Meanwhile, the anchor graph construction and alignment are jointly optimized in our unified framework to enhance clustering quality. Through anchor graph construction instead of full graphs, the time and space complexity of the proposed SIMVC-SA is proven to be linearly correlated with the number of samples. Extensive experiments on seven incomplete benchmark datasets demonstrate the effectiveness and efficiency of our proposed method. Our code is publicly available at https://github.com/wy1019/SIMVC-SA

Efficient Multi-View Graph Clustering with Local and Global Structure Preservation

Anchor-based multi-view graph clustering (AMVGC) has received abundant attention owing to its high efficiency and the capability to capture complementary structural information across multiple views. Intuitively, a high-quality anchor graph plays an essential role in the success of AMVGC. However, the existing AMVGC methods only consider single-structure information, i.e., local or global structure, which provides insufficient information for the learning task. To be specific, the over-scattered global structure leads to learned anchors failing to depict the cluster partition well. In contrast, the local structure with an improper similarity measure results in potentially inaccurate anchor assignment, ultimately leading to sub-optimal clustering performance. To tackle the issue, we propose a novel anchor-based multi-view graph clustering framework termed Efficient Multi-View Graph Clustering with Local and Global Structure Preservation (EMVGC-LG). Specifically, a unified framework with a theoretical guarantee is designed to capture local and global information. Besides, EMVGC-LG jointly optimizes anchor construction and graph learning to enhance the clustering quality. In addition, EMVGC-LG inherits the linear complexity of existing AMVGC methods respecting the sample number, which is time-economical and scales well with the data size. Extensive experiments demonstrate the effectiveness and efficiency of our proposed method.Comment: arXiv admin note: text overlap with arXiv:2308.1654

DealMVC: Dual Contrastive Calibration for Multi-view Clustering

Benefiting from the strong view-consistent information mining capacity, multi-view contrastive clustering has attracted plenty of attention in recent years. However, we observe the following drawback, which limits the clustering performance from further improvement. The existing multi-view models mainly focus on the consistency of the same samples in different views while ignoring the circumstance of similar but different samples in cross-view scenarios. To solve this problem, we propose a novel Dual contrastive calibration network for Multi-View Clustering (DealMVC). Specifically, we first design a fusion mechanism to obtain a global cross-view feature. Then, a global contrastive calibration loss is proposed by aligning the view feature similarity graph and the high-confidence pseudo-label graph. Moreover, to utilize the diversity of multi-view information, we propose a local contrastive calibration loss to constrain the consistency of pair-wise view features. The feature structure is regularized by reliable class information, thus guaranteeing similar samples have similar features in different views. During the training procedure, the interacted cross-view feature is jointly optimized at both local and global levels. In comparison with other state-of-the-art approaches, the comprehensive experimental results obtained from eight benchmark datasets provide substantial validation of the effectiveness and superiority of our algorithm. We release the code of DealMVC at https://github.com/xihongyang1999/DealMVC on GitHub

Testing Two Student Nurse Stress Instruments in Chinese Nursing Students:A Comparative Study Using Exploratory Factor Analysis

Background. The development and transformation of nursing within professional tertiary education have exerted a great pressure and challenge upon nursing students. Stress experienced by nursing students is a common precursor of psychological distress and attrition. However, no scale is specifically used to evaluate the sources of stress experienced by nursing students in Mainland China. Aims and Objective. This study is aimed at testing and comparing the reliability and validity including sensitivity and specificity of two nursing students’ stress instruments, the Chinese version of Student Nurse Stress Index Scale (SNSI-CHI), and the Stressors in Student Nursing Scale (SINS-CN) in Chinese nursing students, and describing the stress status of nursing students in China. Methods. A cross-sectional survey was conducted in two nursing schools in Henan Province from August 2017 to January 2018. Data were collected by using a questionnaire comprising the Chinese version of SNSI (SNSI-CHI), the Chinese version of SINS (SINS-CN), and the Chinese Perceived Stress Scale (CPSS). Homogeneity and stability, content, construct and concurrent validity, and sensitivity and specificity were assessed. Results. The Cronbach’s alpha (α) of SNSI-CHI was 0.90, and the item-to-total correlations ranged from 0.35 to 0.66. The Cronbach’s α of SINS-CN was 0.93, and the item-to-total correlations ranged from 0.19 to 0.61. The findings of exploratory factor analysis (EFA) confirmed a good construct validity of SNSI-CHI and SINS-CN. The Pearson’s rank correlation coefficients, between total scores of SNSI-CHI and CPSS and SINS-CN and CPSS, were assessed to 0.38 (P<0.01) and 0.39 (P<0.01), respectively. Regarding the CPSS, as the criterion, the cut-points of SNSI-CHI and SINS-CN for the area under the receiver operator characteristic (ROC) curve were 0.77and 0.66, respectively. Conclusion. Both scales are valid and reliable for evaluating the source of stress of student nurses in China. Each has its own characteristics, but the SNSI-CHI demonstrated marginal advantage over the SINS-CN. The SNSI-CHI is short, is easily understood, and with clear dimension for the nursing students, and the SNSI-CHI is more acceptable for the users in China

Hippo Signaling Suppresses Cell Ploidy and Tumorigenesis through Skp2

大多数真核生物的体细胞是二倍体，即仅含有两组染色体，分别遗传自父本和母本。而一些特定组织如心脏、肝脏等就含有多倍体细胞，特别是肝脏组织含有较高比例的四、八倍体等多倍体细胞。肝脏是人体的重要解毒器官，同时酒精、肝炎病毒等毒性物质或毒性代谢物容易诱发肝细胞的基因突变，多倍体被认为有利于提供代偿性的正常基因来维持肝脏稳态。然而肝脏受损后，多倍体细胞将会受胁迫进行增殖，再生修复受损的肝组织。因此研究机体调控多倍体细胞产生及多倍体细胞进行细胞分裂的调控机理对于理解肝癌的发病机理和肝癌的治疗至关重要。Hippo信号通路在调节组织成体干细胞的分化和增殖，调控器官再生与尺寸大小中具有重要作用。深入研究发现, Hippo信号通路下游效应分子YAP通过AKT-SKP2信号促进二倍体细胞向多倍体转化及多倍体细胞的生长增殖。本项研究阐明了Hippo缺失及YAP激活促进多倍体细胞产生及增殖作为肝癌发生发展中的一个重要机制，为肝癌诊疗提供了新的策略。 周大旺，博士，厦门大学生命科学学院教授、副院长、国家杰出青年基金获得者。【Abstract】Polyploidy can lead to aneuploidy and tumorigenesis. Here, we report that the Hippo pathway effector Yap promotes the diploid-polyploid conversion and polyploid cell growth through the Akt-Skp2 axis. Yap strongly induces the acetyltransferase p300-mediated acetylation of the E3 ligase Skp2 via Akt signaling. Acetylated Skp2 is exclusively localized to the cytosol, which causes hyper-accumulation of the cyclin-dependent kinase inhibitor p27, leading to mitotic arrest and subsequently cell polyploidy. In addition, the pro-apoptotic factors FoxO1/3 are overly degraded by acetylated Skp2, resulting in polyploid cell division, genomic instability, and oncogenesis. importantly, the depletion or inactivation of Akt or Skp2 abrogated Hippo signal deficiency-induced liver tumorigenesis, indicating their epistatic interaction. Thus, we conclude that Hippo-Yap signaling suppresses cell polyploidy and oncogenesis through Skp2.该研究工作获得了国家自然科学基金委、国家重点基础研究发展计划(973)项目、青年千人计划和中央高校基本科研基金的资助。 The Yap (S127A) transgenic mice were kindly provided by Dr. Fernando Camargo from Harvard Medical School, Boston, MA. D.Z. and L.C. were supported by the National Natural Science Foundation of China (31625010,U1505224, and J1310027 to D.Z.; 81422018, U1405225, and 81372617 to L.C.; 81472229 to L.H.), the National Basic Research Program (973) of China (2015CB910502 to L.C.), the Fundamental Research Funds for the Central Universities of China-Xiamen University (20720140551 to L.C. and 2013121034 and 20720140537 to D.Z.)

Capturing the Long-Sought Small-Bandgap Endohedral Fullerene Sc3N@C-82 with Low Kinetic Stability

通讯作者地址: Yang, SFThe long-sought small-bandgap endohedral fullerene Sc3N@C-82 with low kinetic stability has been successfully synthesized and isolated for the first time, for which the molecular structure has been unambiguously determined as Sc3N@C-82-C-2v(39718) by single crystal X-ray diffraction. The C-82-C-2v(39718) (or labeled as C-82-C-2v(9) according to the conventional numbering of the isolated pentagon rule (IPR) isomers based on the Fowler-Monolopoulos spiral algorithm) isomeric cage of Sc3N@C-82 agrees well with its most stable isomer previously predicted by DFT computations and is dramatically different to those of the reported counterparts M3N@C-82-Cs(39663) (M = Gd, Y) based on a non-IPR C-82 isomer, revealing the strong dependence of the cage isomeric structure on the size of the encaged metal for C-82-based metal nitride clusterfullerenes (NCFs).National Natural Science Foundation of China 21132007 2137116 U1205111 National Basic Research Program of China 2011CB921400 973 project 2014CB84560

Hippo信号通路通过调控Skp2活性从而抑制细胞多倍体产生及肝癌发生

文章简介在这项研究中,课题组揭示了Hippo信号通路在限制肝脏细胞的染色体由两倍体向多倍/非整倍体转变过程中起关键作用,该机制异常将导致基因组不稳定继而诱发肝癌的发生发展。课题组通过对Hippo信号通路重要成员(WW45,Mst1/2,Lats1/2)肝脏特异性敲除和过表达国家自然科学基金委;;国家重点基础研究发展计划(973)项目;;青年千人计划;;中央高校基本科研基金的资

A comprehensive update on CIDO: the community-based coronavirus infectious disease ontology

The current COVID-19 pandemic and the previous SARS/MERS outbreaks of 2003 and 2012 have resulted in a series of major global public health crises. We argue that in the interest of developing effective and safe vaccines and drugs and to better understand coronaviruses and associated disease mechenisms it is necessary to integrate the large and exponentially growing body of heterogeneous coronavirus data. Ontologies play an important role in standard-based knowledge and data representation, integration, sharing, and analysis. Accordingly, we initiated the development of the community-based Coronavirus Infectious Disease Ontology in early 2020. As an Open Biomedical Ontology (OBO) library ontology, CIDO is open source and interoperable with other existing OBO ontologies. CIDO is aligned with the Basic Formal Ontology and Viral Infectious Disease Ontology. CIDO has imported terms from over 30 OBO ontologies. For example, CIDO imports all SARS-CoV-2 protein terms from the Protein Ontology, COVID-19-related phenotype terms from the Human Phenotype Ontology, and over 100 COVID-19 terms for vaccines (both authorized and in clinical trial) from the Vaccine Ontology. CIDO systematically represents variants of SARS-CoV-2 viruses and over 300 amino acid substitutions therein, along with over 300 diagnostic kits and methods. CIDO also describes hundreds of host-coronavirus protein-protein interactions (PPIs) and the drugs that target proteins in these PPIs. CIDO has been used to model COVID-19 related phenomena in areas such as epidemiology. The scope of CIDO was evaluated by visual analysis supported by a summarization network method. CIDO has been used in various applications such as term standardization, inference, natural language processing (NLP) and clinical data integration. We have applied the amino acid variant knowledge present in CIDO to analyze differences between SARS-CoV-2 Delta and Omicron variants. CIDO's integrative host-coronavirus PPIs and drug-target knowledge has also been used to support drug repurposing for COVID-19 treatment. CIDO represents entities and relations in the domain of coronavirus diseases with a special focus on COVID-19. It supports shared knowledge representation, data and metadata standardization and integration, and has been used in a range of applications