Scalable one-stage multi-view subspace clustering with dictionary learning

The clustering of large numbers of heterogeneous features is a hot topic in multi-view communities. Most existing multi-view clustering (MvC) methods employ matrix factorization or anchor strategies to handle large-scale datasets. The former operates on the original data and is, therefore, sensitive to noise and feature redundancy, which is reflected in the final clustering performance. The latter requires post -processing steps to generate the clustering results, which may be suboptimal owing to the isolation steps. To address the above problems, we propose one-stage multi-view subspace clustering with dictionary learning (OSMvSC). Specifically, we integrate dictionary learning, representation coefficient matrix learning, and matrix factorization as a unified learning framework, which directly learns the dictionary and representation coefficient matrix to encode the original multi-view data, and obtains the clustering results with linear time complexity without any postprocessing step. By manipulating the class centroid with the nuclear norm, a more compact and discriminative class centroid representation can be obtained to further improve clustering performance. An effective optimization algorithm with guaranteed convergence is designed to solve the proposed method. Substantial experiments on various real-world multi-view datasets demonstrate the effectiveness and superiority of the proposed method. The source code is available at https://github.com/justcallmewilliam/OSMvSC.(c) 2022 Elsevier B.V. All rights reserved.

Evolution of tourism risk perception: A bibliometrics analysis on multi-dimensional model

How can we establish a risk perception model and method to guide safety management has become an important issue that needs to be solved urgently in the field of tourism management. However, the solution to this issue is inseparable from the objective analysis, induction and deduction, and the analysis of the frontier trend towards the multidimensional model of tourism risk perception. In this paper, 211 articles from the Web of Science are selected as the research object, and the bibliometric analysis is applied to find: (1) Research on tourism risk perception based on multidimensional models can be divided into nascent, developmental, and mature stages;(2) The research on the multi-dimensional model of tourism risk perception has formed a group of academic groups with outstanding contributions and representative authors;(3) The research hotspots in multidimensional models of tourism risk perception focus on the comprehensive study of perceived risk, the outbreak of COVID-19, psychological risk, destination image, and behavioral intention. On this basis, this paper proposes some corresponding research suggestions to address the inadequacies of existing studies, and the research findings have significant theoretical implications for the construction of the theoretical system of tourism risk management. © 2022 The Author(s).

Ethical Challenges in Novel Coronavirus Pneumonia Vaccine as Well as New Drug Research and Development

To review systematically the progress of all domestic and foreign clinical trials since the emergence of the COVID-19 epidemic and the overview of human challenge study (HCS) in the past year, grasp relevant information of ethical review, and explores the feasibility of human challenge study and provide references for stake holders. Based on the Chinese Clinical Trial Registration Center and clinical trial. gov, clinical trials that were consistent with COVID-19 was searched, and the deadline was June 11, 2020.Basic information was extracted and the number of trials was selected from the research type, clinical stage, recruitment status, and ethical passing status. Literature search was conducted based on Pubmed between January 1, 2019 and June 11, 2020.According to the included literature, basic information was extracted and opinions were reported on HCS. 638 and 1935 clinical trials were carried out at home and abroad respectively, of which the number of clinical trials carried out in Hubei reached 323. China and the United States conducted more interventional trials, and the United States had the most clinical trials entered in Phase II, there were a total of 142. Most of the clinical trials were in the stage of recruiting research subjects. In addition, a total of 539 clinical trials in China had passed ethical approval. A total of 5 documents were included in the Human Challenge Study, four maintained a positive attitude towards HCS and put forward corresponding thinking about ethical review. Under the COVID-19, clinical trials for new drugs and new coronavirus vaccines have rapidly increased, which puts forward higher requirements for the progress of ethical review. In the face of such public health emergencies and special research, it is urgent to establish a sound ethical review system and an ethical review system, as well as to construct an ethical model for special research. © 2021, Editorial Board of Pharmaceutical Biotechnology. All right reserved.

Molecular characteristics of SARS-CoV-2 Nsp16 protein and analysis of its potential effect on male reproductive function

Objective The possibility of coronavirus disease 2019 (COVID-19) involving injury to reproductive function has attracted attention. This study analyzed the genetic characteristics, molecular structure and biological function of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Nsp16 protein, and explored potential effects of Nsp16 on germ cells following the virus′ invading testicular tissue, aiming to lay basis for studies of pathogenic mechanisms and therapeutic strategies. Methods Bioinformatic techniques and international biological databases were used to analyze nsp16 genetic variability, Nsp16 spatial structure and function, and effects on genes and proteins of germ cells. DrugBank databases were applied in screening for drugs targeted at Nsp16. Results An evolutionary tree was constructed based on the nsp16 sequences of 30 isolates of 3 coronavirus species. The nsp16 conserved property was 99% amongst SARS-CoV-2 isolates. Nsp16 is a hydrophilic protein, with a 1.9 h half-life inside cells in vitro. Nsp16 has methyltransferase activity, showing potential to regulate gene and functional protein methylation of sperm and Leydig cells. Nsp16 has both linear B cell epitopes and CTL cell epitopes, with capacity to induce immune responses and damage to testicular tissue. Two inhibitory drugs targeted at Nsp16 were found by screening the DrugBank database. Conclusions SARS-CoV-2 Nsp16 is a functional protein encoded by a highly conserved gene, may affect germ cell growth and development by promoting methylation of host cellular genes and proteins following the virus′ invasion into testis tissue through angiotensin-converting enzyme 2 receptors. This report presents Nsp16-targeted chemotherapeutic drugs for the first time, showing high reference value for prevention and treatment of COVID-19 and related lesions of the male reproductive system. © 2021, Publication Centre of Anhui Medical University. All rights reserved.

MOLFAS: a Multi-stage Online Learning hffectiveness Assessment Scheme in MOOC

Many universities around the world arc now providing online courses on platforms of MOOC (Massive Open Online Course) related to the COVID-19 outbreak. Therefore, it is important for universities to quantify student-learning effectiveness based on MOOC. However, its operation faces many challenges to educational administrators and teachers. The most important thing is that the completion rate of learners is usually low, and therefore it is not comprehensive and objective to directly use online data to assess and predict the student-learning effectiveness. In order to assess multi-stage learning effectiveness of students in MOOC comprehensively, we proposed MOLEAS, a multi-stage online learning effectiveness assessment scheme. First, MOLEAS uses matrix completion to predict missing learning data of students. We take two open courses offered in the icourse MOOC platform as examples to analyze online data, and then we study the student-learning effectiveness using the matrix completion. The prediction results prove the effectiveness and reliability of our model. Then, combined with the prediction, MOLEAS utilizes the influencing factors of student-learning effectiveness, and a series of measures to improve the entire learning effectiveness of students in MOOC. What is more, we design a simulation practice platform which presents strong support to practice online teaching.