No abstract

The antimicrobial peptide database (APD, http://aps.unmc.edu/AP/) is an original database initially online in 2003. The APD2 (2009 version) has been regularly updated and further expanded into the APD3. This database currently focuses on natural antimicrobial peptides (AMPs) with defined sequence and activity. It includes a total of 2619 AMPs with 261 bacteriocins from bacteria, 4 AMPs from archaea, 7 from protists, 13 from fungi, 321 from plants and 1972 animal host defense peptides. The APD3 contains 2169 antibacterial, 172 antiviral, 105 anti-HIV, 959 antifungal, 80 antiparasitic and 185 anticancer peptides. Newly annotated are AMPs with antibiofilm, antimalarial, anti-protist, insecticidal, spermicidal, chemotactic, wound healing, antioxidant and protease inhibiting properties. We also describe other searchable annotations, including target pathogens, molecule-binding partners, post-translational modifications and animal models. Amino acid profiles or signatures of natural AMPs are important for peptide classification, prediction and design. Finally, we summarize various database applications in research and education.

Abbreviations (no more than 10): CT = Computed Tomography CXR = Chest radiograph RT-PCR = Reverse Transcription Polymerase Chain Reaction SARS = Severe acute respiratory syndrome Key Results: 1. Chest radiograph and CT findings of 21 patients with confirmed 2019 novel coronavirus infection in Shenzhen and Hong Kong are described and compared 2. A literature review and tabulation of the radiographic features in original publications are presented 3. One asymptomatic patient had evidence of consolidation on chest CT Abstract: Background: COVID-19 (formerly known as the 2019 novel coronavirus [2019-nCoV]) has rapidly spread in mainland China and into multiple countries worldwide. The radiographic profile of this infection continues to evolve as more cases present beyond the epicenter of Wuhan, China. Purpose: We present 21 COVID-19 cases from two Chinese centers with CT and chest radiograph (CXR) findings, as well as follow-up imaging in 5 cases. Materials and Methods: Retrospective study in Shenzhen and Hong Kong. Patients with COVID-19 infection were included. A systematic review of the published literature on COVID-19 infection's radiological features. Results: The predominant imaging pattern is of ground-glass opacification with occasional consolidation in the peripheries. Pleural effusions and lymphadenopathy were absent in all cases. Patients demonstrate evolution of the ground-glass opacities into consolidation, and subsequent resolution of the airspaces changes. Ground-glass and consolidative opacities visible on CT are sometimes undetectable on chest radiographs, suggesting that CT is a more sensitive imaging modality for investigation. The systematic review identified 4 other studies confirming the findings of bilateral and peripheral ground glass with or without consolidation as the predominant finding on CT chest examinations. Conclusion: The COVID-19 infection pulmonary manifestation is predominantly characterized by ground-glass opacification with occasional consolidation on CT. Radiographic findings in patients presenting in Shenzhen and Hong Kong are in keeping with 4 previous publications from other sites.

Summary Interleukin (IL)-23 and CD4+ T helper-17 (Th17) cells are thought to be critical in the development of psoriasis. Here, we report that IL-23 predominantly stimulated dermal γδT cells to produce IL-17 that led to disease progression. Dermal γδT cells constitutively expressed the IL-23 receptor (IL-23R), RORγt, and various chemokine receptors. IL-17 production from dermal γδT cells was independent of αβT cells. The epidermal hyperplasia and inflammation induced by IL-23 were significantly decreased in T cell receptor δ deficient (Tcrd−/−) and IL-17 receptor deficient (Il17ra−/−) mice but occurred normally in Tcra−/− mice. Imiquimod-induced skin pathology was also significantly decreased in Tcrd−/− mice. Perhaps further promoting disease progression, IL-23 stimulated dermal γδT cell expansion. In psoriasis patients, γδT cells were also greatly increased in affected skin and produced large amounts of IL-17. Thus, IL-23-responsive dermal γδ T cells are the major IL-17 producers in the skin and may represent a novel target for the treatment of psoriasis.

Background The full range of long-term health consequences of COVID-19 in patients who are discharged from hospital is largely unclear. The aim of our study was to comprehensively compare consequences between 6 months and 12 months after symptom onset among hospital survivors with COVID-19. MethodsWe undertook an ambidirectional cohort study of COVID-19 survivors who had been discharged from Jin Yin-tan Hospital (Wuhan, China) between Jan 7 and May 29, 2020. At 6-month and 12-month follow-up visit, survivors were interviewed with questionnaires on symptoms and health-related quality of life (HRQoL), and received a physical examination, a 6-min walking test, and laboratory tests. They were required to report their health-care use after discharge and work status at the 12-month visit. Survivors who had completed pulmonary function tests or had lung radiographic abnormality at 6 months were given the corresponding tests at 12 months. Non-COVID-19 participants (controls) matched for age, sex, and comorbidities were interviewed and completed questionnaires to assess prevalent symptoms and HRQoL. The primary outcomes were symptoms, modified British Medical Research Council (mMRC) score, HRQoL, and distance walked in 6 min (6MWD). Multivariable adjusted logistic regression models were used to evaluate the risk factors of 12-month outcomes. Findings 1276 COVID-19 survivors completed both visits. The median age of patients was 59•0 years (IQR 49•0-67•0) and 681 (53%) were men. The median follow-up time was 185•0 days (IQR 175•0-198•0) for the 6-month visit and 349•0 days (337•0-361•0) for the 12-month visit after symptom onset. The proportion of patients with at least one sequelae symptom decreased from 68% (831/1227) at 6 months to 49% (620/1272) at 12 months (p<0•0001). The proportion of patients with dyspnoea, characterised by mMRC score of 1 or more, slightly increased from 26% (313/1185) at 6-month visit to 30% (380/1271) at 12-month visit (p=0•014). Additionally, more patients had anxiety or depression at 12-month visit (26% [331/1271] at 12-month visit vs 23% [274/1187] at 6-month visit; p=0•015). No significant difference on 6MWD was observed between 6 months and 12 months. 88% (422/479) of patients who were employed before COVID-19 had returned to their original work at 12 months. Compared with men, women had an odds ratio of 1•43 (95% CI 1•04-1•96) for fatigue or muscle weakness, 2•00 (1•48-2•69) for anxiety or depression, and 2•97 (1•50-5•88) for diffusion impairment. Matched COVID-19 survivors at 12 months had more problems with mobility, pain or discomfort, and anxiety or depression, and had more prevalent symptoms than did controls.Interpretation Most COVID-19 survivors had a good physical and functional recovery during 1-year follow-up, and had returned to their original work and life. The health status in our cohort of COVID-19 survivors at 12 months was still lower than that in the control population.

The antimicrobial peptide database (APD, http://aps.unmc.edu/AP/main.php) has been updated and expanded. It now hosts 1228 entries with 65 anticancer, 76 antiviral (53 anti-HIV), 327 antifungal and 944 antibacterial peptides. The second version of our database (APD2) allows users to search peptide families (e.g. bacteriocins, cyclotides, or defensins), peptide sources (e.g. fish, frogs or chicken), post-translationally modified peptides (e.g. amidation, oxidation, lipidation, glycosylation or d-amino acids), and peptide binding targets (e.g. membranes, proteins, DNA/RNA, LPS or sugars). Statistical analyses reveal that the frequently used amino acid residues (>10%) are Ala and Gly in bacterial peptides, Cys and Gly in plant peptides, Ala, Gly and Lys in insect peptides, and Leu, Ala, Gly and Lys in amphibian peptides. Using frequently occurring residues, we demonstrate database-aided peptide design in different ways. Among the three peptides designed, GLK-19 showed a higher activity against Escherichia coli than human LL-37.

One of the most fundamental questions in biology is what types of cells form different tissues and organs in a functionally coordinated fashion. Larger-scale single-cell sequencing and biology experiment studies are now rapidly opening up new ways to track this question by revealing substantial cell markers for distinguishing different cell types in tissues. Here, we developed the CellMarker database (http://biocc.hrbmu.edu.cn/CellMarker/ or http://bio-bigdata.hrbmu.edu.cn/CellMarker/), aiming to provide a comprehensive and accurate resource of cell markers for various cell types in tissues of human and mouse. By manually curating over 100 000 published papers, 4124 entries including the cell marker information, tissue type, cell type, cancer information and source, were recorded. At last, 13 605 cell markers of 467 cell types in 158 human tissues/sub-tissues and 9148 cell makers of 389 cell types in 81 mouse tissues/sub-tissues were collected and deposited in CellMarker. CellMarker provides a user-friendly interface for browsing, searching and downloading markers of diverse cell types of different tissues. Furthermore, a summarized marker prevalence in each cell type is graphically and intuitively presented through a vivid statistical graph. We believe that CellMarker is a comprehensive and valuable resource for cell researches in precisely identifying and characterizing cells, especially at the single-cell level.

Rain streaks can severely degrade the visibility, which causes many current computer vision algorithms fail to work. So it is necessary to remove the rain from images. We propose a novel deep network architecture based on deep convolutional and recurrent neural networks for single image deraining. As contextual information is very important for rain removal, we first adopt the dilated convolutional neural network to acquire large receptive field. To better fit the rain removal task, we also modify the network. In heavy rain, rain streaks have various directions and shapes, which can be regarded as the accumulation of multiple rain streak layers. We assign different alpha-values to various rain streak layers according to the intensity and transparency by incorporating the squeeze-and-excitation block. Since rain streak layers overlap with each other, it is not easy to remove the rain in one stage. So we further decompose the rain removal into multiple stages. Recurrent neural network is incorporated to preserve the useful information in previous stages and benefit the rain removal in later stages. We conduct extensive experiments on both synthetic and real-world datasets. Our proposed method outperforms the state-of-the-art approaches under all evaluation metrics. Codes and supplementary material are available at our project webpage: https://xialipku.github.io/RESCAN.