dataesr/scientific-paragraphs-categorization huggingface dataset:
7. 9. 0 2012) package. The 'spots' module of the Imaris software (Bitplane, Switzerland) was used for tracking of PGCs and for correction for gastrulation-derived tissue movement. In this procedure, we made use of the labeled nuclei of somatic cells located closest to the tracked germ cells, thereby taking into consideration the effect of whole tissue movement on PGC tracking and speed measurements. Counting of ectopic PGCs. To calculate the number of ectopic germ cells, PGCs expressing LifeAct-EGFP were counted at 22 hpf in the GFP-channel using a ×20 water-immersion objective. Whole-mount immunohistochemistry. Detection of E-cadherin in zebrafish embryos was performed as previously described  53  , using a polyclonal mouse antibody directed against the protein (610181, BD Biosciences) at a 1:200 dilution and detected using a fluorophore-conjugated secondary antibody (Alexa Fluor® 546F(ab′) 2 fragment of goat anti-mouse IgG, Invitrogen).
The nuclei of all cells were labeled by incubating the embryos for 30 min in a 1:10,000 Hoechst dilution in PBT in the dark with gentle agitation. Images were processed using Fiji/ImageJ software. Z-stacks of the embryos were acquired and the first 10 images were subsequently merged into an average intensity projection using an automated Fiji plugin. The mean intensity of an area of a defined size in the centre of the Zprojection was measured. As control for unspecific binding of the fluorophoreconjugated secondary antibody, embryos from the same egg clutches were incubated in blocking solution without the primary antibody. The average fluorescence value of this control was subtracted from the values of all other samples. For each condition, the values of the E-cadherin depleted samples were normalized to the respective controls.
Actin signal intensity measurements. Germ cells expressing LifeAct-EGFP and farnesylated mCherry proteins were used. Polarized (showing an actin enrichment at one aspect of the cell) and motile (showing a net displacement) cells were imaged. Only frames in which the cell front and rear appeared simultaneously in focus (based on the membrane signal, farnesylated mCherry) were further analyzed. To generate actin signal intensity profiles, an ImageJ (V. 1. 51, NIH) plugin was designed. The input for the plugin is a 16-bit grayscale image of the cell of interest, that has been background corrected and thresholded using Fiji software. In this way, irrelevant pixels were designated as possessing intensity values of 0. When required, images of cells were rotated to ensure proper orientation along the y-axis, with the cell front facing up.
The plugin reads and averages the intensities of all relevant pixels in a line along the x-axis. The average values of each line were exported as a table for subsequent analysis and presented as intensity profiles along the y-axis of the original images. To generate the mean curves, the cell length was normalized into 50 segments and the signal intensity level was normalized to the cell area. To calculate the ratios between the mean value of LifeAct-EGFP signal intensity at the cell front (F) and in the middle part of the cell (M), the same 16-bit input images used for the signal intensity profiles were utilized but thresholded using Fiji software in a way that no value was attributed to irrelevant pixels. The cells were then divided into five sectors according to their length. The ratio between the mean value in segment 1 (F) and that in segment 3 (M) was then calculated for each cell.
Rac1-and RhoA-FRET analysis. Analysis was performed using ImageJ and Fiji software as previously described  25  , with the difference that the FRET ratios for the whole-cell measurements were obtained for 15 frames for each cell. Kymographs. Actively migrating cells expressing LifeAct-EGFP and farnesylated mCherry proteins were analyzed. Movies were captured at the focal plane of the cell membrane signal, thereby ensuring that both the front and the rear were in focus. Analysis was performed using Fiji. For each movie, 50 time points at intervals of 500 ms were analyzed. First, all cells were rotated to ensure their orientation along the y-axis (cell front to the left). A line (width 4) was drawn along the axis of cell polarization and kymographs were obtained using the Dynamic Reslice command.
Quantification of actin flow Cell segmentation and intracellular motion fields (BioFlow). Analysed cells were derived from the same sets of cells used in the kymograph analysis. Individual cells were segmented using an active contour model that accounts for region intensity and edge information. The modified optical flow technique described in ref. 35  was then employed to estimate velocity fields v(x, t) within the segmented cells, x 2 C t ð Þ, from successive fluorescence images. The method, automated in the software BioFlow  35  within the Icy 57 platform, is especially robust concerning outof-plane motion and missing information. When the flow-field analysis was performed two channels at a time (cytoplasm and actin or E-cadherin and actin), since the signal pairs were uncoupled, we considered each channel separately and obtained independent velocity measurements for the movement of each protein.
Sliding the time interval back and forth by a moderate amount did not change the measured values significantly, nor did adjusting the thickness. This perturbation analysis shows that our method is robust. To enhance the objectivity of the measurements, the axis of cell polarization p was automatically adjusted using an intensity-weighted version of principal component analysis. In this way, the axis corrected itself with the appearance of new blebs. Setting the axis manually for each individual bleb yielded very similar measured values. The tool presented in this section is available as a custom-made Python script that uses four main libraries: SciPy  58  and NumPy 59 to solve the advection problem and implement a composite trapezoidal time integral, and FEniCS 60 and CGAL  61  to handle the regions as triangulated meshes, and cast the finite elements used for the spatial integral.
Simulations were performed with a home-written Python code. In short, the required input was a fluorescence image consisting of a membrane channel and an actin channel. Both images were blurred with a Gaussian filter with σ = 3 pixels, to remove noise on a pixel level. The membrane image was then thresholded using an Otsu-filter, and the contours of the cell membrane were determined from the binary image. This was then used as a mask for the actin signal, as well as to determine the centre of mass of the cell. From the actin fluorescence image both the entire cell area and the perimeter of the cell were angularly integrated. The front of the cell (defined as 0°) was determined as the angle of the maximum actin signal. This angle was smoothened over time with a σ = 25 s Gaussian filter to follow changes of direction of the cell, but to be insensitive to slight frame-to-frame variations.
The polarized state of the cells was confirmed by the co-expression of LifeAct-EGFP in the PGCs. To calculate the angle between consecutive blebs, Fiji software was used. The multi-line ROI tool was used to manually select points along the fluorescently labeled cell membrane at the moment of initiation of a bleb. The centre of mass of the selected ROI was then determined and connected to the middle point of the bleb neck. The same procedure was repeated for the subsequent bleb and the angle between the two lines was determined. Friction-based transport model describing actin distribution. To describe the spatial distribution of the actin fluorescence signal along the front-rear axis of the cell we use a transport-decay model, where the F-actin network is transported retrogradely with velocity v while polymerizing with a constant rate k + and depolymerizing with a constant rate k -.
Using a Levenberg-Marquardt algorithm in Matlab (Mathworks, USA), λ was determined by fitting the exponential decay to the experimentally measured fluorescence intensity of the LifeAct signal (Supplementary Fig. 4a, d ). The fitting regime started 2 μm from the peak of the actin intensity until the actin signal decreased below 50% of the peak intensity (Fig. 4c  and Supplementary Fig. 9 ). As the velocity v ¼ F contraction γ depends on the myosin mediated contraction and the E-cadherin mediated friction γ / c ecad this model predicts λ ¼ F contraction γ k À and hence an inverse relation between the decay length and the friction λ / γ À1. Morpholino-treated clone experiments. Cell clones were generated by injecting fluorescently labeled morpholinos and RNAs into one out of 32 blastomeres of embryos derived from kop:lifeact-mCherry-nos3′UTR transgenic females.
Embryos containing the labeled transplant were imaged using light-sheet microscopy. Timelapse movies were acquired at a time resolution of 1 min and a z-resolution of 2 μm. x and y-resolution were 0. 286 μm. In the processing step, the x-and y-resolution was lowered to 0. 572 μm. To improve the definition of the moment of contact between PGCs and the morpholino-treated clones, the surfaces of the MOtreated clone and of all germ cells were rendered using the surface plugin of the Imaris software (Bitplane). To determine whether PGCs were in a polar or apolar state, the Lifeact-mCherry signal was used. The behavior of germ cells upon contact with a MO-treated clone was defined as no reaction, change of polarity or loss of polarity based on the reaction of the PGC in the subsequent 10 frames.
The Imaris software was also used to generate Movies 7, 8 and 9 and the snapshots presented in Fig. 5b-d. To this end, the animation option was used and an orthogonal view was employed. Statistical analysis. Figures where statistics was used represent pooled data from independent replicates as indicated in the figure legends. Appropriate controls were included for each biological replicate. Analyses were performed using GraphPad Prism and MATLAB. Normality of data was tested using the D'Agostino-Pearson omnibus normality test. Two-sided Student's t-test was used for normal distributions and two-tailed Mann-Whitney U-test or two-tailed Kolmogorov-Smirnov test was applied in case of non-normal distributions with significance levels of *P < 0. 05, **P < 0. 01, ***P < 0. 001 and ****P < 0. 0001.
Acknowledgements This work was supported by the  European Research Council  ( ERC, CellMig ), the  German Research Foundation  (DFG,  CRC 1348  and  RA 863/11-1 ), The  Medical Faculty of the University of Muenster  and the Cells in Motion cluster. C. G. is a member of the  CiM-IMPRS Graduate School. A. B. -P. is part of the  Pasteur-Paris University (PPU) International PhD Programme , which received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No. 665807 , and from the  Institut Carnot Pasteur Microbes & Santé  ( ANR-16 CARN 0023-01 ). The BIA unit is partially supported by grants from the  Labex IBEID  ( ANR-10-LABX-62-IBEID ),  France-BioImaging infrastructure  ( ANR-10-INBS-04 ), and the programme  PIA INCEPTION  ( ANR-16-CONV-0005 ).
19. The larger systems having the initial size of 20 × 20 × 8 nm were used to study the dynamical characteristics of the lipids and to study the membrane-MGD1 interactions further. protein modelling: loop construction and insertion to the membrane. The primary sequence of MGD1 starts with a chloroplast transit peptide in N-terminal position (amino acids 1-106) followed by a short sequence (amino acids 107-136) predicted to be disordered and not necessary for the activity of the enzyme  16  and by the catalytic domain that comprises the amino acids 137 up to 533. Two crystal structures of the catalytic domain of MGD1 are currently available, in its apo-form (PDB code: 4wyi at resolution 2. 5 Å), and in complex with UDP (PDB code: 4x1t, 2. 25 Å)  15. One of the peculiarities put forward by the resolution of the two structures is the lack of electron density corresponding to a region in the N-domain.
3. MGD1-glycolipid membrane with ElNeDyn restraints on protein, which was repeated without any restraints, over a production period of 10 μs. All MD simulations were performed with the GROMACS 5 package  47  with the standard parameter settings for the Martini force field. All the systems were equilibrated with counter ions Na + Cl -to reach the overall amount of 150 mM. The energy was minimized with the steepest descent algorithm. Then, four equilibration stages followed, where the position restraints on the headgroups were gradually decreased from 200 to 0 kJ mol -1 nm -2 , and the time step for leap-frog integrator was increased from 2 to 10 fs. The production phase was performed with a 10 fs time step according to that used in Ref. 19. The systems were calculated in the isothermal-isobaric (NpT) ensemble.
The lateral diffusion coefficient (D) was calculated as a slope using Einstein relation y = 4Dt + c. The GROMACS gmx mindist tool provides a way to calculate the normalized amount of contacts between lipid species. The lipids are in contact if their glycerol group GL1 beads appear within the first solvation shell of the other lipid beads, i. e. , at the distance 0. 8 nm or less. The lipids of the one type were added to the same cluster if GL1 beads were within 0. 8 nm. The number of lipids in the resulted clusters was calculated using GROMACS gmx clustersize and cluster tool, correspondingly. Flip-flopping of lipids between the leaflets was counted as a number of membrane-crossing-events along the z-axis. The z coordinate as a function of time was calculated using the VMD program  50. K A = kTA 0 N�(A -A 0 ) 2 � , Normal Mode Analysis (NMA) of Cα-based anisotropic network model was applied to the protein molecule for prediction of slow internal motions using ANM Web Server 2.
1  26. Principal Component Analysis (PCA) was performed using the tools implemented in Gromacs package (gmx covar and gmx anaeig). The projections of a trajectory on the eigenvectors of its covariance matrix are called principal components (PC's). The motions of Cα atoms along the first two principal components were calculated. Cross-correlation matrix shows the correlation for each pair of residues, centred on Cα atoms. The translational and rotational motions of the protein as overall were previously removed before the calculation of crosscorrelation matrix. The elements of cross-correlation matrix for i and j atoms were calculated as follows: where � -→ r i (t) = -→ r i (t) --→ r i (t) determining the displacement of atom i from its MD-averaged position. The angular brackets denote an MD-averaged value.
Docking of UDP-Gal, PG and DAG to MGD1. The following docking procedure of UDP-Gal and DAG to MGD1 molecule was performed using HADDOCK2. 2 webserver  51. First, the UDP-Gal was docked to the unliganded MGD1. The correct positioning of UDP moiety was controlled by the knowledge from the crystal structure of MGD1 with UDP bound (PDB code: 4x1t). The knowledge about the position of sugar moiety was taken from the crystal structures of homologous enzymes, i. e. , the red grape enzyme UDP-glucose: flavonoid 3-O-glycosyltransferase (GT1 from Vitus Vinifera, PDB code: 2c1z, and UGT71G1 from MedicagoTruncatula, PDB code: 2acw). Afterwards, the DAG and PG molecules were consequently docked to the MGD1-UDP-Gal complex. The interaction of DAG with the catalytic residue H155 was used as a constraint.
Acknowledgements The calculations were carried out using the equipment of the  High Performance Computing centre of University Grenoble Alpes, Grenoble, France  and the shared research facilities of high-performance computing resources at  Lomonosov Moscow State University, Moscow, Russian Federation. This work was supported by the  Cross-Disciplinary Program Glyco@Alps, within the framework "Investissement d' Avenir" program  [ ANR-15IDEX-02 ]. OM acknowledges funding from the  government assignment for Federal Research Center (FRC) Kazan Scientific Center of Russian Academy of Sciences (RAS)  (in part of AA MD simulations).
Simulation results The model is implemented in the MATLAB® language for technical computing. It runs on a laptop computer and the results are obtained in a few seconds. The presentation focuses on the influence of the rotational frequency on the force repartition applied to the piston and on the resulting torque. Indeed, for identical thermodynamic cycles in the cylinders, the mechanical power produced on the shaft of the engine is proportional to the rotation speed. In order to reduce the size of the system for a given shaft power, it is important to determine the maximum rotational speed which can be accepted. So three frequencies will be studied, ranging from the natural frequency f0 to 5 f0. Table  2  presents the valve timing considered for each rotation speed. In order to obtain appropriate indicated diagrams and mass flowrates through the valves, the valve timing has to be slightly adapted when the rotation speed increases.
7B ). In addition to the mostly hydrophobic interface, four salt bridges further stabilize the interaction (Fig. 7C ). On interaction with ORF9b, the interacting helices on Tom70 move inward to tightly wrap around ORF9b as compared with previously crystallized yeast Tom70 homologs (movie S1). No structure for human Tom70 without a substrate has been reported to date, and therefore we cannot rule out the idea that the conformational differences are because of differences between homologs. However, it is possible that this conformational change upon substrate binding is conserved across homologs, as many of the Tom70 residues interacting with ORF9b are highly conserved, which likely indicates residues essential for endogenous MTS substrate recognition. Although a previously published crystal structure of SARS-CoV-2 ORF9b revealed that it entirely consists of beta sheets [Protein Data Bank (PDB) ID: 6Z4U] (16), we observed that, upon binding Tom70 residues 52 to 68, ORF9b forms a helix (Fig.
We then used generalized summary-based Mendelian randomization (GSMR)  (28, 29)  on the curated GWAS datasets of the COVID-19 Host Genetics Initiative (COVID-HGI)  (30)  and observed that genotypes that predicted higher sIL17RA plasma levels were associated with lower risk of COVID-19 when compared with the population (Fig. 8D  and table  S9 ), which is seemingly consistent with our molecular data. Similar results were obtained when comparing only hospitalized COVID-19 patients to the population. However, there was no evidence of association in hospitalized versus nonhospitalized COVID-19 patients. Though the COVID-HGI dataset is underpowered and this observation needs to be replicated in other cohorts, the clinical observations, functional genetics, and clinical genetics all suggest that SARS-CoV-2 benefits from modulating IL-17 signaling.
To this end, using GWAS datasets of the COVID-HGI (30), we observed that increased predicted sIL17RA plasma levels were associated with lower risk of COVID-19. Notably, we find that IL17RA physically binds to SARS-CoV-2 ORF8, and genetic disruption results in decreased infection. These collective data suggest that future studies should be focused on this pathway as both an indicator and therapeutic target for COVID-19. Furthermore, using medical billing data, we also observed trends in COVID-19 patients on specific drugs indicated by our molecular studies. For example, inpatients prescribed sigma-ligand typical antipsychotics appear to have better COVID-19 outcomes compared with users of atypical antipsychotics, which do not have anti-SARS-CoV-2 activity in vitro. However, we cannot be certain that the sigma receptor interaction is the mechanism underpinning this effect, as typical antipsychotics are known to bind to a multitude of cellular targets, and some atypical antipsychotics, which lack anti-SARS-CoV-2 activity, nonetheless have reported affinity for rodent sigma receptors (table  S10 ).
Cells HEK293T/17 (HEK293T) cells were procured from the University of California, San Francisco (UCSF) Cell Culture Facility, and are available through UCSF's Cell and Genome Engineering Core (https://cgec. ucsf. edu/cell-culture-andbanking-services). HEK293T cells were cultured in Dulbecco's modified Eagle's medium (DMEM) (Corning) supplemented with 10% fetal bovine serum (FBS) (Gibco, Life Technologies) and 1% penicillin-streptomycin (Corning) and maintained at 37°C in a humidified atmosphere of 5% CO 2. Short tandem repeat (STR) analysis by the Berkeley Cell Culture Facility on 8 August 2017 authenticates these as HEK293T cells with 94% probability. HeLaM cells (RRID: CVCL_R965) were originally obtained from the laboratory of M. S. Robinson (CIMR, University of Cambridge, UK) and have been routinely tested for mycoplasma contamination.
HeLaM cells were grown in DMEM supplemented with 10% FBS, 100 U/ml penicillin, 100 mg/ml streptomycin, and 2 mM glutamine at 37°C in a 5% CO 2 humidified incubator. A549 cells stably expressing ACE2 (A549-ACE2) were a gift from O. Schwartz. A549-ACE2 cells were cultured in DMEM supplemented with 10% FBS, blasticidin (20 mg/ml) (Sigma) and maintained at 37°C with 5% CO 2. STR analysis by the Berkeley Cell Culture Facility on 17 July 2020 authenticates these as A549 cells with 100% probability. Caco-2 cells (ATTC, HTB-37, RRID:CVCL_ 0025) were cultured in DMEM with GlutaMAX and pyruvate (Gibco, 10569010) and supplemented with 20% FBS (Gibco, 26140079). For Caco-2 cells utilized in Cas9-RNP knockouts, STR analysis by the Berkeley Cell Culture Facility on 23 April 2020 authenticates these as Caco-2 cells with 100% probability.
Coronavirus annotation and plasmid cloning SARS-CoV-1 isolate Tor2 (NC_004718) and MERS-CoV (NC_019843) were downloaded from GenBank and utilized to design 2xStreptagged expression constructs of ORFs and proteolytically mature Nsps derived from ORF1ab (with N-terminal methionines and stop codons added as necessary). Protein termini were analyzed for predicted acylation motifs, signal peptides, and transmembrane regions, and either the N or C terminus was chosen for tagging as appropriate. Finally, reading frames were codon optimized and cloned into pLVX-EF1alpha-IRES-Puro (Takara/Clontech) including a 5′ Kozak motif.
Meta-analysis of immunofluorescence data We first sorted the data concerning viral protein location for all Strep-tagged viral proteins expressed individually in three heatmaps (one per virus) using a custom R script ("pheatmap" package). The information concerning protein localization during SARS-CoV-2 infection was added as a square border color code in the first heatmap, to compare the two different localization patterns. To compare the predicted versus the experimentally determined locations, for each protein we took the top scoring sequence-based localization prediction from DeepLoc (43) if the score was >1. When more than one localization can be assigned to the same protein, we took as many top scoring ones as experimentally assigned localizations we had for the same protein. Finally, for each cell compartment, we count the number of experimentally assigned viral proteins and the subset of them predicted to that same compartment as correct predictions.
MS operation and peptide search Samples were resuspended in 4% formic acid, 2% acetonitrile solution, and separated by a reversed-phase gradient over a nanoflow C18 column (Dr. Maisch). HPLC buffer A was composed of 0. 1% formic acid, and HPLC buffer B was composed of 80% acetonitrile in 0. 1% formic acid. Peptides were eluted by a linear gradient from 7 to 36% B over the course of 52 min, after which the column was washed with 95% B and re-equilibrated at 2% B. Each sample was directly injected by means of an Easy-nLC 1200 (Thermo Fisher Scientific) into a Q-Exactive Plus mass spectrometer (Thermo Fisher Scientific) and analyzed with a 75-min acquisition, with all MS1 and MS2 spectra collected in the orbitrap; data were acquired using the Thermo software Xcalibur (4. 2. 47) and Tune (2. 11 QF1 Build 3006).