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This is data for: "Modelling the filtration efficiency of a woven fabric: The role of multiple lengthscales", on arXiv Files are (this is also in README file): 1) FinalFused.tif : stack of slices taken with confocal at Bristol by Ioatzin Rios de Anda. This is the imaging data of the fabric used 2) processDataTo3D_PAPER.py : Python code to analyse 1) to produce mask of fibre voxels needed for LB simulation, by Jake Wilkins 3) LBregionstack.tiff : image stack for region in LB simulations 4) masknx330ny280nz462_t10.txt : mask in right format to be read in to Palabos LB code to specify which voxels are fibre and so need bounce-back 5) Ioatzin3D.cpp : C++ code for Palabos LB. NB need Palabos LB code: https://palabos.unige.ch/, should go in directory "~/palabos-v2.2.0/examples/Ioatzin/3D ". Needs 4) 6) make_pkl.py : converts output of LB code into Python pickled format for .py codes below. 7) IoatzinDarcy_pkl.py : takes pickled output of LB code and computes Darcy k etc 8) traj2_pkledge.py : computes trajectories of particles and so filtration efficiency, needs pickled output of LBC code and 9) 9) lattice_params.yaml : parameter values for 7) and 8) 10) eff_filter_edges.txt : filtration efficiencies computed by 8) WITH inertia 11) eff_filter0Stokes.txt : filtration efficiencies computed by 8) WITHOUT inertia 12) plot_filtration.py : plots 10) and 11) 13) Final_render.mp4 : rotating animation showing region simulated by LB code, by Jake Wilkins 14) alpha_ofz.txt : alpha - fraction of fibres voxels as function of z 15) plot_justalpha.py : plots 14) 16) vtk01.vti : flow field velocity field in vti format - as used by Paraview 17) vel3D.pkl : flow field velocity field in Python's pkl format 18) slice_heatmap.py : produces heatmap of velocities in xy slice through the flow field 19) plot_sigma_streamlines.py : plots Sigma (curvature lengthscale) from 20), 21), 22), 23) 20) stream4.txt: streamline for flow field 21) stream5.txt: streamline for flow field 22) stream6.txt: streamline for flow field 23) stream7.txt: streamline for flow field 24) plot_Stokes.py : plots Stokes number as function of particle diameter 25) 0traj20.0_47.xyz : trajectory in format that Paraview can read 26) intraj20.0_47.xyz : another trajectory 27) streamlines_pkl.py : calculates streamlines, eg 20), 21), 22) and 23) 28) this README file Abstract of that work: During the COVID-19 pandemic, many millions have worn masks made of woven fabric, to reduce the risk of transmission of COVID-19. Masks are essentially air filters worn on the face, that should filter out as many of the dangerous particles as possible. Here the dangerous particles are the droplets containing virus that are exhaled by an infected person. Woven fabric is unlike the material used in standard air filters. Woven fabric consists of fibres twisted together into yarns that are then woven into fabric. There are therefore two lengthscales: the diameters of: (i) the fibre and (ii) the yarn. Standard air filters have only (i). To understand how woven fabrics filter, we have used confocal microscopy to take three dimensional images of woven fabric. We then used the image to perform Lattice Boltzmann simulations of the air flow through fabric. With this flow field we calculated the filtration efficiency for particles around a micrometre in diameter. We find that for particles in this size range, filtration efficiency is low ($\sim 10\%$) but increases with increasing particle size. These efficiencies are comparable to measurements made for fabrics. The low efficiency is due to most of the air flow being channeled through relatively large (tens of micrometres across) inter-yarn pores. So we conclude that our sampled fabric is expected to filter poorly due to the hierarchical structure of woven fabrics.

Additional file 7: Supplementary table 7.

Additional file 1: Supplementary Figure 1. Schematic diagram of COVID-19 patient (n=37) follow-up, including disease onset, admission, stool sample collection, duration of hospital stay. “CoV” denotes patient with COVID-19. Stool specimens were serially collected for separate shotgun metagenomic sequencing of RNA and DNA virome; “SARS-CoV-2 PCR negative in nasopharyngeal test”: the first negative result for SARS-CoV-2 virus in two consecutive negative nasopharyngeal tests, upon which patient was then discharged.

Additional file 10: Table S9. Predicted drugs.

Abstract Peripheral facial paralysis (PFP) has been shown to be a neurological manifestation of COVID-19. The current study presents two cases of PFP after COVID-19, along with a rapid review of known cases in the literature. Both case reports were conducted following CARE guidelines. We also performed a systematic review of PFP cases temporally related to COVID-19 using PubMed, Embase, and Cochrane Library databases on August 30, 2021, using a rapid review methodology. The two patients experienced PFP 102 and 110 days after COVID-19 symptom onset. SARS-CoV-2 RNA was detected in nasal samples through reverse-transcription real-time polymerase chain reaction (RT-qPCR) testing. Anosmia was the only other neurological manifestation. PFP was treated with steroids in both cases, with complete subsequent recovery. In the rapid review, we identified 764 articles and included 43 studies. From those, 128 patients with PFP were analyzed, of whom 42.1% (54/128) were male, 39.06% (50/128) female, and in 23 cases the gender was not reported. The age range was 18 to 59 (54.68%). The median time between COVID-19 and PFP was three days (ranging from the first symptom of COVID-19 to 40 days after the acute phase of infection). Late PFP associated with COVID-19 presents mild symptoms and improves with time, with no identified predictors. Late PFP should be added to the spectrum of neurological manifestations associated with the long-term effects of SARS-CoV-2 infection as a post COVID-19 condition.

Additional file 1: Fig. S1. Inhibition of SARS-CoV-2-reactive CD4+ T cells by helminth antigens in COVID-19 patients from a helminth endemic region. Graph summarizes the frequencies of CD69+ and CD137+ in CD4+ T cells in the different settings. Each symbol represents individual donors. Bars indicate Means ± SEM of the percentage of SARS-CoV-2-reactive T cells. Data were obtained from 6 COVID-19 patients in Benin.

The non-structural protein 15 (NSP15, NendoUSARS-CoV-2) from severe acute respiratory syndrome 2 virus (SARS-CoV-2) is an uridylate-specific endoribonuclease, likely responsible in the viral immune evasion mechanism. This TEP provides a set of reagents for further interrogation of the molecular function of NSP15. We have established a purification protocol for the active protein for biochemical and structural studies. Moreover, we have crystallised the protein and performed a crystallographic fragment screen which yielded several hits. Data generated here will be used for the development of enzyme inhibitors that would illuminate the biological role of the gene product, and eventually point the way to new antiviral therapies. This document represents version 1 of the TEP datasheet and includes all updates on the project as of November 2020. For more information about TEPs and the TEP Programme, please visit https://www.cmd.ox.ac.uk/TEP.

Abstract: Introduction: Preventing and fighting COVID-19 are of the utmost importance. In this context, the importance of using telemedicine tools has grown, including teleconsultations, epidemiological telemonitoring, remote diagnosis, support, and training of health professionals. Objective: This article aims to report the results of a distance-training course on SARS-CoV-2 and COVID-19. We analyze the course adherence, the students’ profile, pre, and post-test proficiency index and satisfaction with the course. Methods: This is a cross-sectional study that evaluated data from the course on SARS-CoV-2 and COVID-19. The data were analyzed in terms of distribution and comparisons of means and frequencies. A paired t-test was used to compare the pre and post-test grades. A p-value <0.05 was considered significant. Data were collected from the Moodle teaching platform, without identifying the participants. Results: From March 23 to May 14, the course was offered to 1,008 medical students and health care providers. Most were from the state of Minas Gerais, some from other Brazilian states, and Mozambique. The majority completed the course, with an 89.8% adherence. The evaluations related to the course, the tutors, the degree of satisfaction, and the security for the professional performance after the course obtained maximum scores. The comparison between the pre and post grades showed proficiency gain (p<0.0001). Conclusion: The course has contributed to the training of medical students and health professionals from Brazil and Mozambique. The organizing committee was able to prepare students and provide knowledge to professionals with difficulty to access good technical and evidence-based information. After the training, the students were selected to work on university projects aiming at supporting city halls, health departments, and the community.