A survey on representation learning for multi-view data.

Multi-view clustering has become a rapidly growing field in machine learning and data mining areas by combining useful information from different views for last decades. Although there have been some surveys based on multi-view clustering, most of these works ignore simultaneously taking the self-supervised and non-self supervised multi-view clustering into consideration. We give a novel survey for sorting out the existing algorithms of multi-view clustering in this work, which can be classified into two different categories, i.

Dual Consensus Anchor Learning for Fast Multi-View Clustering.

Multi-view clustering usually attempts to improve the final performance by integrating graph structure information from different views and methods based on anchor are presented to reduce the computation cost for datasets with large scales. Despite significant progress, these methods pay few attentions to ensuring that the cluster structure correspondence between anchor graph and partition is built on multi-view datasets. Besides, they ignore to discover the anchor graph depicting the shared cluster assignment across views under the orthogonal constraint on actual bases in factorization.

Treatment for Covid-19 with SARS-CoV-2 neutralizing antibody BRII-196(Ambavirumab) plus BRII-198(Lomisivir): a retrospective cohort study.

Background: Monoclonal antibody therapy for Covid-19 springs up all over the world and get some efficiency. This research aims to explore the treating effect of BRII-196(Ambavirumab) plus BRII-198(Lomisivir) on Covid-19.

Methods: In this retrospective cohort research, patients received standard care or plus BRII-196 /BRII-198 monoclonal antibodies.

Early high-volume resuscitation with crystalloid solution combined with albumin improves survival of critically ill patients: A retrospective analysis from MIMIC-IV database.

Background: Volume resuscitation is often required in critically ill patients. However, we have no clear consensus on the choice between crystalloid solution and colloidal solution. This study aimed to explore the effect of albumin administration in massive fluid resuscitation.

NIM-Nets: Noise-aware Incomplete Multi-view Learning Networks.

Data in real world are usually characterized in multiple views, including different types of features or different modalities. Multi-view learning has been popular in the past decades and achieved significant improvements. In this paper, we investigate three challenging problems in the field of incomplete multi-view representation learning, namely, i) how to reduce the influences produced by missing views in multi-view dataset, ii) how to learn a consistent and informative representation among different views and iii) how to alleviate the impacts of the inherent noise in multi-view data caused by high-dimensional features or varied quality for different data points.

Impact of establishing a respiratory high dependency unit for SCAP patients on the therapeutic effect, prognosis, and expenditure: a retrospective case-control study.

To explore the effects of establishing a high dependency unit (HDU) on the prognosis, outcome, and expenditure of patients with severe community-acquired pneumonia (SCAP). 108 SCAP patients were recruited from the respiratory intensive care unit (RICU) of the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China. Of these, 87 qualified the study-selection criteria and were divided into HDU group (treated in HDU after discharge from RICU prior to transfer to normal unit) (n = 40) and normal group (not treated in the HDU) (n = 47).

Consistency-Induced Multiview Subspace Clustering.

Multiview clustering has received great attention and numerous subspace clustering algorithms for multiview data have been presented. However, most of these algorithms do not effectively handle high-dimensional data and fail to exploit consistency for the number of the connected components in similarity matrices for different views. In this article, we propose a novel consistency-induced multiview subspace clustering (CiMSC) to tackle these issues, which is mainly composed of structural consistency (SC) and sample assignment consistency (SAC).

Maximum Block Energy Guided Robust Subspace Clustering.

Subspace clustering is useful for clustering data points according to the underlying subspaces. Many methods have been presented in recent years, among which Sparse Subspace Clustering (SSC), Low-Rank Representation (LRR) and Least Squares Regression clustering (LSR) are three representative methods. These approaches achieve good results by assuming the structure of errors as a prior and removing errors in the original input space by modeling them in their objective functions.

Semi-Supervised Structured Subspace Learning for Multi-View Clustering.

Multi-view clustering aims at simultaneously obtaining a consensus underlying subspace across multiple views and conducting clustering on the learned consensus subspace, which has gained a variety of interest in image processing. In this paper, we propose the Semi-supervised Structured Subspace Learning algorithm for clustering data points from Multiple sources (SSSL-M). We explicitly extend the traditional multi-view clustering with a semi-supervised manner and then build an anti-block-diagonal indicator matrix with small amount of supervisory information to pursue the block-diagonal structure of the shared affinity matrix.

[Impact of the Timing of Traditional Chinese Medicine Therapy on the Therapeutic Effect and Prognosis of Severe Coronavirus Disease 2019].

To explore the optimal therapy time for the treatment of severe coronavirus disease 2019(COVID-19)by traditional Chinese medicine(TCM)and its influence on the therapeutic effect and prognosis. The clinical data,laboratory findings,and outcomes of 64 patients with severe COVID-19 treated with TCM and western medicine in Chongqing from January 20,2020, to March 11,2020 were retrospectively analyzed.Patients were divided into early intervention group[TCM was initiated within 3 days (including day 3) after the first diagnosis of severe type/critical type COVID-19]and late intervention group[TCM was initiated after 7 days (including day 7) after the first diagnosis of severe type /critical type COVID-19].

Effects of macromolecular crowding on the folding and aggregation of glycosylated MUC5AC.

Objective: To investigate the effects of macromolecular crowding on the folding and aggregation of MUC5AC with different levels of glycosylation during refolding.

Methods: Part 1:An in vitro catalytic reaction comprising the ppGalNAc T2 enzyme, uridine-5'-diphospho-N-galactosamine (UDP-GalNAc) and an 11-amino acid peptide substrate, was used to assess the enzyme activity of the ppGalNAc T2 enzyme in macromolecular crowding environment respectively with bovine serum albumin (BSA), polyethylene glycol (PEG2000), Dextran70 and Ficoll70 at different concentration and temperature. Part 2: The recombinant MUC5AC was expressed in HEK293 cells and purified by nickel column chromatography.

Characterization of an Asymptomatic Cohort of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infected Individuals Outside of Wuhan, China.

Purpose: We aimed to further clarify the epidemiological and clinical characteristics of asymptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections.

Methods: We identified close contacts of confirmed coronavirus disease 2019 (COVID-19) cases in northeast Chongqing, China, who were confirmed by real-time reverse transcription polymerase chain reaction-positive (RT-PCR+). We stratified this cohort by normal vs abnormal findings on chest computed tomography (CT) and compared the strata regarding comorbidities, demographics, laboratory findings, viral transmission and other factors.