Effect of ontological insecurity on vaccination behavior against COVID-19: a hospital-based cross-sectional study.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) has brought great uncertainty to our society and it may have disrupted people's ontological security. Consequently, this hospital-based study concerns the impact of ontological insecurity on vaccination behavior against COVID-19. STUDY DESIGN: This cross-sectional study was conducted among hospital inpatients. METHODS: A questionnaire survey addressing inpatient ontological insecurity and vaccination behavior against COVID-19 was administered in Taizhou, China. A total of 1223 questionnaires were collected; specifically, 1185 of them were credible, for a validity rate of 96.9%. RESULTS: The score of ontological insecurity was 13.27 ± 7.84, which was higher in participants who did not recommend vaccination for others than those who did (12.95 ± 8.25 vs 14.00 ± 6.78, P = 0.022). There was no difference between the vaccinated and unvaccinated groups (13.22 ± 7.96 vs 13.35 ± 7.67, P = 0.779). Lower ontological insecurity (odds ratio [OR] = 1.40, 95% confidence interval [CI]: 1.08-1.81) and being inoculated with COVID-19 vaccines (OR = 2.17, 95% CI: 1.67-2.82) were significantly associated with recommendation of COVID-19 vaccines to others after adjusting for sex, age, education, and occupation. Associations between low ontological insecurity and recommendations for COVID-19 vaccines were observed in men, adults aged 18-59 years, non-farmers, and vaccine recipients. CONCLUSIONS: This study suggests that the ontological insecurity of participants affects their behavior of recommending the COVID-19 vaccination to others rather than getting vaccinated themselves. This promotion of vaccination can be considered from the perspective of improving ontological security in China.

Analysis of risk factors for early clinical recurrence of inflammatory bowel disease after fecal microbiota transplantation

Objective: To explore the risk factors for early clinical recurrence of inflammatory bowel disease (IBD) after fecal microbiota transplantation (FMT). Methods: A retrospective study was conducted on 192 patients with IBD who received FMT treatment in the Colorectal Disease Specialty/Intestinal Microecology Treatment Center of the Tenth People’s Hospital Affiliated to Tongji University from February 2017 to June 2020. Univariate and multivariate logistic regression models were used to analyze the risk factors for early recurrence of inflammation. Feces from all participants were collected to extract the total bacterial genomic DNA. The V6-8 regions of the bacterial 16S rDNA gene were amplified by polymerase chain reaction (PCR), the PCR products were detected by the denaturing gradient gel electrophoresis (DGGE) method, and the intestinal flora was analyzed by DNA fingerprinting. Stool samples from all patients were tested for 9 bacteria, white blood cells (WBC) and platelet (PLT) counts, as well as the erythrocyte sedimentation rate (ESR) and serum C-reactive protein (CRP) level. Results: Of the 192 patients, 15 cases had inflammation recurrence during FMT and within one week after treatment, including 11 cases of ulcerative colitis (UC) and 4 cases of Crohn’s disease (CD), with a total recurrence rate of 7.8%. High Mayo inflammatory activity score, Mayo endoscopic sub-item score (MES) =3 points, CRP>10 mg/L, anemia, albumin <30 g/L, absolute value of peripheral blood lymphocytes (PBL) <500/mm3, and intolerance to enteral full nutrition were independent risk factors for recurrence during and after FMT in UC patients (P<0.05). Albumin <30 g/L and simultaneous use of immunosuppressive agents were associated with disease recurrence during and after FMT in CD patients. WBC, PLT, and CRP were all negatively correlated with Enterococcus (EC), and ESR was positively correlated with Saccharomyces boulardii (SB) (P<0.01). Conclusion: The low recurrence rate of IBD after FMT indicates the safety of FMT, but this procedure should be cautiously used in patients with severe intestinal barrier dysfunction and/or severe intestinal dysfunction.

MFCosface: a masked-face recognition algorithm based on large margin cosine loss

The world today is being hit by COVID-19. As opposed to fingerprints and ID cards, facial recognition technology can effectively prevent the spread of viruses in public places because it does not require contact with specific sensors. However, people also need to wear masks when entering public places, and masks will greatly affect the accuracy of facial recognition. Accurately performing facial recognition while people wear masks is a great challenge. In order to solve the problem of low facial recognition accuracy with mask wearers during the COVID-19 epidemic, we propose a masked-face recognition algorithm based on large margin cosine loss (MFCosface). Due to insufficient masked-face data for training, we designed a masked-face image generation algorithm based on the detection of the detection of key facial features. The face is detected and aligned through a multi-task cascaded convolutional network;and then we detect the key features of the face and select the mask template for coverage according to the positional information of the key features. Finally, we generate the corresponding masked-face image. Through analysis of the masked-face images, we found that triplet loss is not applicable to our datasets, because the results of online triplet selection contain fewer mask changes, making it difficult for the model to learn the relationship between mask occlusion and feature mapping. We use a large margin cosine loss as the loss function for training, which can map all the feature samples in a feature space with a smaller intra-class distance and a larger inter-class distance. In order to make the model pay more attention to the area that is not covered by the mask, we designed an Att-inception module that combines the Inception-Resnet module and the convolutional block attention module, which increases the weight of any unoccluded area in the feature map, thereby enlarging the unoccluded area’s contribution to the identification process. Experiments on several masked-face datasets have proved that our algorithm greatly improves the accuracy of masked-face recognition, and can accurately perform facial recognition with masked subjects. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

MetaP: Meta Pattern Learning for One-Shot Knowledge Graph Completion

Knowledge Graphs (KGs) are widely used in various applications of information retrieval. Despite the large scale of KGs, they are still facing incomplete problems. Conventional approaches on Knowledge Graph Completion (KGC) require a large number of training instances for each relation. However, long-tail relations which only have a few related triples are ubiquitous in KGs. Therefore, it is very difficult to complete the long-tail relations. In this paper, we propose a meta pattern learning framework (MetaP) to predict new facts of relations under a challenging setting where there is only one reference for each relation. Patterns in data are representative regularities to classify data. Triples in KGs also conform to relation-specific patterns which can be used to measure the validity of triples. Our model extracts the patterns effectively through a convolutional pattern learner and measures the validity of triples accurately by matching query patterns with reference patterns. Extensive experiments demonstrate the effectiveness of our method. Besides, we build a few-shot KGC dataset of COVID-19 to assist the research process of the new coronavirus. © 2021 ACM.

Prediction of potential therapeutic drugs against SARS-CoV-2 by using Connectivity Map based on transcriptome data.

OBJECTIVE: Transcriptome data related to severe acute respiratory syndrome-related coronavirus 2 (a novel coronavirus discovered in 2019, SARS-CoV-2) in GEO database were downloaded. Based on the data, influence of SARS-CoV-2 on human cells was analyzed and potential therapeutic compounds against the SARS-CoV-2 were screened. MATERIALS AND METHODS: R package "DESeq2" was used for differential gene analysis on the data of cells infected or non-infected with SARS-CoV-2. The "ClusterProfiler" package was used for GO functional annotation and KEGG pathway enrichment analysis of the differentially expressed genes (DEGs). A protein-protein interaction (PPI) network of the DEGs was constructed through STRING website, and the key subset in the PPI network was identified after visualization by Cytoscape software. Connectivity Map (CMap) database was used to screen known compounds that caused genomic change reverse to that caused by SARS-CoV-2. RESULTS: By intersecting DEGs in two datasets, a total of 145 DEGs were screened out, among which 136 genes were upregulated and 9 genes were downregulated in SARS-CoV-2-infected cells. Functional enrichment analyses revealed that these genes were mainly associated with the pathways involved in viral infection, inflammatory response, and immunity. The CMap research found that there were three compounds with a median_tau_score less than -90, namely triptolide, tivozanib and daunorubicin. CONCLUSIONS: SARS-CoV-2 can cause abnormal changes in a large number of molecules and related signaling pathways in human cells, among which IL-17 and TNF signaling pathways may play a key role in pathogenic process of SARS-CoV-2. Here, three compounds that may be effective for the treatment of SARS-CoV-2 were screened, which would provide new options for improving treatment of patients infected with SARS-CoV-2.

Research on emotional analysis of netizens and topic distribution under public health emergencies : - - A Case Study of COVID-19

[Purpose/meaning] The COVID-19 epidemic that has swept the world has caused people to fall into fear. It conducts sentiment analysis on netizens under public health emergencies and provides a reference for the government to sort out netizens' emotions during the epidemic. [Method/Procedure] LSTM sentiment classification model is built based on deep learning technology, sentiment analysis is carried out on the comments of netizens in Weibo, and the topic distribution of different sentiments of netizens is studied based on the LDA topic model. [Results/Conclusion] The results show that the negative emotions of netizens are about the same as positive emotions. Most positive emotions pay tribute to medical staff, and most of the negative emotions focus on the problem of not being able to buy a mask. © 2020 IEEE.