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. RESULTS: Between January 2020 and March 2020, we identified and hospitalized 279 RT-PCR+ contacts of COVID-19 patients. 63 (23%) remained asymptomatic until discharge; 29 had abnormal and 34 had normal chest CT findings. The mean cohort age was 39.3 years, and 87.3% had no comorbidities. Mean time to diagnosis after close contact with a COVID-19 index patient was 16.0 days, and it was 13.4 days and 18.7 days for those with abnormal and normal CT findings, respectively (P < .05). Nine patients (14.3%) transmitted the virus to others; 4 and 5 were in the abnormal and normal CT strata, respectively. The median length of time for nucleic acid to turn negative was 13 days compared with 10.4 days in those with normal chest CT scans (P < .05). CONCLUSIONS: A portion of asymptomatic individuals were capable of transmitting the virus to others. Given the frequency and potential infectiousness of asymptomatic infections, testing of traced contacts is essential. Studies of the impact of treatment of asymptomatic RT-PCR+ individuals on disease progression and transmission should be undertaken.

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. The purified protein was treated with PNGase F, and the degree of glycosylation was analyzed by SDS-PAGE. Macromolecular crowding was simulated using PEG2000 at the concentrations of 50, 100, and 200 g/L. Deglycosylated-MUC5AC (d-MUC5AC) and glycosylated MUC5AC (g-MUC5AC) were denatured by GdnHCl and renatured by dilution in a refolding buffer. Protein aggregation was monitored continuously by absorbance reading at 488 nm using a UV spectrophotometer at 25 °C. The refolded proteins were centrifuged, the protein concentration of the supernatant was measured, and refolding yield in different refolding buffers was determined. RESULTS: Enzyme activityof ppGalNAc T2 was observed to increase with increasing crowding agent concentration, with highest enzyme activity at 200 g/L. Compared with the group in the absence of crowding reagent, the refolding yield of g-MUC5AC and d-MUC5AC were reduced significantly in the presence of different concentrations of PEG2000 (200, 100, and 50 g/L). Compared with the dilute solution, aggregation increased significantly in the presence of PEG2000, especially at 200 g/L. Moreover, in the crowded reagent with the same concentration, the refolding yield of d-MUC5AC was higher than that of g-MUC5AC, whereas the degree of aggregation of d-MUC5AC was lower than that of g-MUC5AC. CONCLUSION: The crowded intracellular environment reduces the refolding rate of MUC5AC and strongly induces the misfolding and aggregation of glycosylated MUC5AC.

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

Objective 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. Methods 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].The changes in clinical parameters during the course of disease were compared between the two groups. Results On day 14,the oxygenation index was 292.5(252.0,351.0)mmHg in the early intervention group,which was significantly higher than that in the late intervention group [246.0(170.0,292.5)mmHg](P=0.005).The length of hospital stay [(18.56±1.11)d vs.(24.87±1.64)d,P=0.001],duration of ICU stay [(14.12±0.91)d vs.(20.00±1.53)d,P=0.000] and time to negativity [(16.77±1.04)d vs.(22.48±1.66)d,P=0.001] in the early intervention group were significantly shorter than those in the late intervention group.The intubation rate(7.3%)in the early intervention group was significantly lower than that in the late intervention group(30.4%)(P=0.028). Conclusion Early TCM therapy within three days after a diagnosis of severe COVID-19 can shorten the length of hospital stay,duration of ICU stay,and time to negativity and decrease intubation rate.

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. In this paper, we propose a novel Dual consensus Anchor Learning for Fast multi-view clustering (DALF) method, where the cluster structure correspondence between anchor graph and partition is guaranteed on multi-view datasets with large scales. It jointly learns anchors, constructs anchor graph and performs partition under a unified framework with the rank constraint imposed on the built Laplacian graph and the orthogonal constraint on the centroid representation. DALF simultaneously focuses on the cluster structure in the anchor graph and partition. The final cluster structure is simultaneously shown in the anchor graph and partition. We introduce the orthogonal constraint on the centroid representation in anchor graph factorization and the cluster assignment is directly constructed, where the cluster structure is shown in the partition. We present an iterative algorithm for solving the formulated problem. Extensive experiments demonstrate the effectiveness and efficiency of DALF on different multi-view datasets compared with other methods.

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). To be specific, SC aims to learn a similarity matrix for each single view wherein the number of connected components equals to the cluster number of the dataset. SAC aims to minimize the discrepancy for the number of connected components in similarity matrices from different views based on the SAC assumption, that is, different views should produce the same number of connected components in similarity matrices. CiMSC also formulates cluster indicator matrices for different views, and shared similarity matrices simultaneously in an optimization framework. Since each column of similarity matrix can be used as a new representation of the data point, CiMSC can learn an effective subspace representation for the high-dimensional data, which is encoded into the latent representation by reconstruction in a nonlinear manner. We employ an alternating optimization scheme to solve the optimization problem. Experiments validate the advantage of CiMSC over 12 state-of-the-art multiview clustering approaches, for example, the accuracy of CiMSC is 98.06% on the BBCSport dataset.

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. In this paper, we propose a novel method from an energy perspective to eliminate errors in the projected space rather than the input space. Since the block diagonal property can lead to correct clustering, we measure the correctness in terms of a block in the projected space with an energy function. A correct block corresponds to the subset of columns with the maximal energy. The energy of a block is defined based on the unary column, pairwise and high-order similarity of columns for each block. We relax the energy function of a block and approximate it by a constrained homogenous function. Moreover, we propose an efficient iterative algorithm to remove errors in the projected space. Both theoretical analysis and experiments show the superiority of our method over existing solutions to the clustering problem, especially when noise exists.

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. SSSL-M regularizes multiple view-specific affinity matrices into a shared affinity matrix based on reconstruction through a unified framework consisting of backward encoding networks and the self-expressive mapping. The shared affinity matrix is comprehensive and can flexibly encode complementary information from multiple view-specific affinity matrices. An enhanced structural consistency of affinity matrices from different views can be achieved and the intrinsic relationships among affinity matrices from multiple views can be effectively reflected in this manner. Technically, we formulate the proposed model as an optimization problem, which can be solved by an alternating optimization scheme. Experimental results over seven different benchmark datasets demonstrate that better clustering results can be obtained by our method compared with the state-of-the-art approaches.

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. To address these challenges, we integrate these three tasks into a problem and propose a novel framework termed Noise-aware Incomplete Multi-view Learning Networks (NIM-Nets). NIM-Nets fully utilize incomplete data from different views to produce a multi-view shared representation which is consistent, informative and robust to noise. We model the inherent noise in data by defining the distribution Γ and assuming that each observation in the incomplete dataset is sampled from the distribution Γ. To the best of our knowledge, this is the first work to unify learning the consistent and informative representation, alleviating the impacts of noise in data and handling the view-missing patterns in multi-view learning into a framework. We also first give a definition of robustness and completeness for incomplete multi-view representation learning. Based on NIM-Nets, we present joint optimization models for classification and clustering, respectively. Extensive experiments on different datasets demonstrate the effectiveness of our method over the existing work based on classification and clustering tasks in terms of different metrics.