dataesr/scientific-paragraphs-categorization huggingface dataset:
8A ), which induced a change in PR2 pocket composition relative to the PR1 pocket. In this section, we studied the link between structural changes observed at residue 45_B between PR1 and PR2 structures and the six substitutions occurring between PR1 and PR2 pockets (T31S, V32I, M46I, I47V, L76M, and V82I). For this purpose, we modelled 63 PR1 mutants containing one to six pocket substitutions using the FoldX software  26. For each mutant, we built five mutant structures that lead to a set of 315 PR1 mutant structures (see Material & Methods). We compared the conformation of residue 45_B in one PR1 (PDB code: 1HPV) and one PR2 (PDB code: 3S45) wild-type structures and in the 315 PR1 mutant structures. To do so, we computed a hierarchical classification of the 315 PR1 mutant structures, the wild-type PR1 and PR2 structures based on the RMSD computed between residue 45_B of all structures (see Method).
In their study, the authors suggested that the larger side chain of residue 82 in PR2 (isoleucine) relative to residue 82 in PR1 (valine) avoid the deformation of the loop 80-83 implied in PI recognition  31. In addition, the putative role of the substitutions V32I, I47V, and V82I in the alteration of PI interactions with residues 32, 47, and 82 have been previously reported in several studies  3, 15, 16. For example, the comparison of the crystallographic structures of PR1 and PR2 complexed with DRV showed that hydrophobic interactions between DRV and PR2 are more elongated than those in PR1-DRV complex and supposed to result from substitutions V32I, I47V, and V82I  15. Tie et al. (2012) compared structural and biochemical features of PR1 (PDB code: 2IEN)  16  , PR2 (PDB code: 3EBZ)  15  , and PR1 mutant with three substitutions (V32I, I47V, and V82I PDB code: 3S43)  16  complexed with DRV.
PR1 mutant and PR2 had similar biochemical properties and DRV had similar interaction network in these two PRs. For example, residue 47_A has two hydrophobic interactions in PR1-DRV complex, whereas it had no contact in PR1 mutant and PR2 complexes that is consistent with our observation that atom 47_A_CD1 is missing in PR2 pocket  16. This difference at position 47 was not observed during the comparison of other PR1-DRV complex (PDB code: 4DQB)  32  with the PR2-DRV complex (PDB code: 3EBZ)  15  , but the lack of an interaction between residue 47 with APV was found in the PR2-APV complex (PDB code: 3S45)  16  but present in the PR1-APV complex (PDB code: 3EKV)  3, 33. This interaction between APV and residue 47 was re-established in the modelled PR2 mutant containing four substitutions I32V, V47I, M76L, and I82V  3.
Material and Methods PR structure. All available PR structures of both HIV-1 and HIV-2 were downloaded from the PDB  20. From these PDB structures, we selected X-ray PR structures with a resolution smaller than 3 Å and structures with wild-type sequences (structures with experimental mutations to decrease spontaneous proteolysis were considered as wild-type sequences). In addition, for PR1, we retained only structures extracted from HIV-1 subtype B. From this PDB structure set, we retained unbound PR structures and PR1 and PR2 structures complexed with the same ligands. This resulted in a set of 26 PDB files: 15 for PR1 and 11 for PR2. Two PDB files (PDB codes: 2MIP and 1HSH) contained two dimers. For these, we separated the two dimers, which were stored in two PDB files named in our work as 2MIP_AB and 2MIP_CD for the 2MIP PDB file and 1HSH_AB and 1HSH_CD for the 1HSH PDB file.
Ultimately, the PR dimer set included 28 PR dimers: 3 unbound PR1 monomers, 1 unbound PR2, 12 bound PR1 and 12 bound PR2 complexed with 8 different ligands (Table  S1 ). For each ligand, we have at least one structure of both PR1 and PR2. The PR chain names of all PR structures were homogenized, and the first chain of each PDB file was labelled A and the second labelled B. In addition, PR residue numbers in each PDB file were also homogenized; each chain began with residue 1 and ended with residue 99. All structures of unbound PR1 contained only one monomer. To compensate for the lack of PR1 unbound dimer structures in the dataset, artificial dimers of PR1 were generated using a symmetric reflection of PR1 unbound monomer structures to reconstruct dimers using symmetry mates function within 5 Å with the PyMOL software  36.
All structures were then placed in the same 3D referential by superimposing the 28 structures onto the unbound PR2 (PDB code: 1HSI, arbitrarily chosen) using the pymol software  36. This resulted in a set of 28 PRs, named the PR set. Pocket extraction considering the ligand diversity and flexibility. Estimation of the pockets was based on the identification of PR atoms located in close proximity to all ligands of the dataset. To do so, we first superimposed the 24 bound PR structures onto the unbound PR2 (PDB code: 1HSI). As PR is not fully symmetrical despite its homodimeric composition and to compensate for the arbitrary designation of the two chains, two superimpositions were performed for each structure as follows: (i) chain A of the structure was superimposed onto the 1HSI chain A, and (ii) chain B of the structure was superimposed onto the 1HSI chain A.
Visualization of the pocket space. To visualize the space sampled by the pockets, we performed principal component analysis (PCA) based on the 42 physicochemical and geometric pocket descriptors. The PCA was computed using the FactoMineR package in R  39. PCA projects standardized data-i. e. , the pocket descriptors-into a subspace made of orthogonal linear combinations of pocket descriptors, so-called principal components. Data may then be explored in a smaller dimensional space spanning the most informative view, according to the data variability.
Statistical selection of the most informative descriptors for distinguishing PR1 and PR2 pockets. Random Forest Training. Random forest (RF) model is a supervised, non-parametric method of classification  40. RF consists of a collection of decision trees, wherein each tree is obtained from a subset of the data. We trained and analysed a RF classification model to identify descriptors allowing the discrimination between PR1 and PR2 bound pockets. It was trained on bound PR pockets using their 42 physicochemical and geometric descriptor values. The RF model was computed using the RandomForest package within R  41, 42. The S2 Appendix shows the different steps for the training and analysis of the RF model. Selection of descriptors differentiating PR1 and PR2 pockets. We selected descriptors allowing the differentiation of PR1 and PR2 pockets using the computation of their importance in the RF PR1-PR2 model (S2 Appendix).
For example, Marjan et al. (2014) modelled the structure of 37 COX-1 structures using FoldX to analyse the functional impacts of 37 nsSNPs on aspirin inhibition potency of COX-1  44. Buß et al. (2018)  used Fold-X to predict the ΔΔG value of 8246 single-site mutations of ω-Transaminase to identify the most thermostable variant  45. See Buß et al. (2018) for a review of the FoldX application  46. The core function of FoldX, the empirical force field algorithm, is based on free energy (ΔG) terms aiming to calculate the change of ΔG in kcal mol-1. It is composed of a solvation term, a van der Waals term, H-bond, and electrostatic terms and entropic terms for the backbone and side chains  26. FoldX force field has been validated on a blind-test database of 625 single-point mutants in 27 simple proteins resulting in a correlation coefficient of 0.
73 between predicted and experimental ΔΔG of folding. Several studies had tested the performances of softwares dedicated to the prediction of protein stability  45, 47, 48. For example, Khan and Vihinen (2010) showed that FoldX were one of the most reliable predictors on a set of 1784 single mutations found in 80 proteins with 64% of well prediction  47. See Buß et al. (2018) for a review of the FoldX accuracy  45. To construct PR1 mutant structures, we used the BuildModel command of FoldX suite. This command introduces a mutation on the wild-type structure using a side-chain rotamer library. It then optimizes the configurations of the side chains of amino acids in the vicinity of the mutated residue. In FoldX, the mutated residue vicinity was defined as residues for which their half-spheres were situated at less 5 Å from the mutated residue half-sphere.
The half-sphere of a residue is proportional to the mobility and length of its side chain. In this study, we constructed 3D structures of PR1 mutants based on the wild-type PR1 structure complexed with APV (PDB code: 1HPV) as a template. First, the APV ligand, metal atoms, and water molecules were removed from the 1HPV structures. We built the 3D structure of PR1 mutants using the BuildModel command of FoldX  26. Each model was generated with five runs, and other options were set to default. PR1 mutant structures were superimposed onto the unbound PR2 structure (PDB code: 1HSI). Comparison of the conformation of residue 45_B in wild-type and mutant PR structures. To compare the conformation of residue 45_B in all PR1 mutant structures and the wild-type PR1 and PR2 structures, we first computed the Root Mean Square deviation (RMSD) between residue 45_B (i) of all PR1 mutant structure pairs, (ii) between the PR1 mutant structures and the wild-type PR1 (PDB code: 1HPV) and, (iii) between the PR1 mutant structures and the wild-type PR2 (PDB code: 3S45) structures.
These residues are shown in sticks mode. Green dots represent the van der Waals sphere of the M76_B_CE atom of PR1 L76M mutant number 2. Cyan dots represent the van der Waals sphere of the I32_B_CE atom of PR1 V32I+L76M mutant number 4. PR2 (PDB code: 3S45) is shown in a cartoon mode and coloured red. All ligands of the PR set used to estimate pockets are displayed in sticks mode and coloured grey.
Finally, we also investigated the effect of obliterating blood flow on F-actin and ZO1 organization. We managed to obtain reasonably good images after 2D-maping with the Tg(kdrl:Ras-  mCherry; kdrl:Lifeact-eGFP) fish line, although with difficulties because of the strong distortion of the aortic shape in the sih embryos. Images clearly revealed a perturbation of F-actin organization in EHT cells, with little reinforcement of that staining at the antero-posterior poles of the cells in comparison to the control (Figure  6B , bottom panels). Unfortunately, in the case of eGFP-ZO1, fluorescence signals in the sub-cortical regions of hemogenic cells were too low and aorta too distorted to perform 2D-maping. However, 3D-projections and 3D-analysis with the Imaris software also revealed changes in eGFP-ZO1 localization in cells of the hemogenic endothelium, with altered recruitment at the sub-plasmalemmal level and apparent increase of the eGFP-ZO1 cytoplasmic pool (Figure  6C  and see Figure  6 -video 3 for 3D-Imaris rendering).
EHT sequential steps and key features Time-lapse imaging of Lifeact-eGFP in embryos in which the EHT cells were best aligned with the A-P axis also suggested an active role of the adjoining endothelial cells in the process. The A and P neighbors moved toward each other over the actin-rich poles and appeared to extend actin-rich filopodia to establish contacts. Subsequently, filopodia anastomosis should contribute to the sealing of the aorta floor (Figure  7 -video 1, with the main sequential steps composing Figure  7 , left panels). The live-imaging data presented so far led us to define the main, sequential steps of the EHT (see the cartoons illustrating steps 1-5, Figure  7 , middle and right panels, and details in the legend). We propose that the key features of the process are: (i) the initiation of the bending of an hemogenic cell toward the sub-aortic space (with its antero-posterior axis more or less aligned in the direction of blood flow and the inward bending of its luminal/apical membrane depending on hemodynamic forces); (ii) the anisotropic organization of circumferential F-actin and of junctional contacts influenced by the blood flow (with enrichment at trijunctions and membrane interfaces perpendicular to blood flow/aorta A-P axis); (iii) the anisotropic, stepwise and oscillatory constriction of the cell apex, accompanied by concomitant contraction of a circumferential F-actin belt (essential molecular components of this contractile activity are myosin and associated light chains, see below); (iv) the apparent deployment of pushing forces by adjoining endothelial cells possibly contributing to cellular bending and apical constriction; (v) the extension of filopodia by adjoining endothelial cells; (vi) the anastomosis of filopodia to seal the aortic floor; (vii) the release of the EHT cell after remodelling of the luminal membrane (see Discussion) and down-regulation of remaining junctional complexes.
GFP-positive cells were then scored at 48 hpf, in the sub-aortic space (AGM) (to be most proximal to the site of HSPCs emergence), and in the caudal hematopoietic tissue  (CHT), the first niche where aorta-derived HSPCs home. In both knock-downs, we observed a significant decrease in HSPC number in the AGM and the CHT (Figure  8B-E ). This phenotype was phenocopied by two additional morpholinos targeting myl9b and myl9a mRNAs respectively and was rescued for the spe3i3 myl9b morpholino (Figure  8 figure supplement 2). These data support the involvement of both Myl9a and Myl9b in definitive hematopoiesis, and possibly in the EHT process. Finally, in order to address the intracellular localization and dynamics of the two Myl9 isoforms during the EHT, we generated Tg(kdrl: Myl9a-eGFP) and Tg(kdrl:Myl9b-eGFP) fish lines.
In vivo spinning-disk confocal imaging Embryos were imaged on an inverted microscope (UltraVIEW VOX, Perkin Elmer) equipped with a Yokogawa CSU-X1 spinning-disk, an ImagEM-X2 camera (Hamamatsu), 488 nm and 561 nm laser lines for excitation, a Zeiss LD C-Apochromat 40x corrM27 water objective (NA 1. 1, WD 0. 62 mm at cover glass 0. 17 mm) and the Volocity (Perkin Elmer, http://www. perkinelmer. com/) acquisition software. Optical Z planes were spaced by 0. 6 mm, the power of the lasers (around 150 mW) and exposure time (between 50 ms and 200 ms) were adjusted depending on the fluorophore and the transgenic line used. In all cases, acquisitions of a Z stacks containing the whole dorsal aorta was reduced to 1 min or less and spaced every 2 min. For TL sequences longer than 1 hr, samples were maintained at 28.
In vivo laser scanning confocal imaging Embryos were imaged using an inverted microscope (Leica TCS SP8, CTR 6500), equipped with 488 nm and 561 nm diodes for excitation, a Hybrid Detector, a Leica HC PL APO CS2 40x water objective (NA 1. 1, WD 0. 65 mm at cover glass 0. 17 mm) and the LAS X acquisition software (Leica, http:// www. Leica-microsystems. com). Optical planes were spaced by 0. 43 mm and Z stacks containing the whole dorsal aorta were acquired every 5-10 min. For long time-lapse imaging, samples were maintained at 28 ˚C using a INU stage Top Incubator (Tokai Hit) and the objective was immersed in 1:1 AquaSound Clear (Free-med, Cat#: FU00071) ultrasound gel with water. For cell counting in live embryos, an HC PL APO CS2 20x IMM objective (NA 0. 75, WD 0. 67 mm) and a resonant scanner were used for high-speed imaging of the whole AGM or CHT.
Subsequent maximum projection and manual counting was performed. Some embryos were imaged with an upright microscope (Leica SPE, CTR 6000), equipped with 488 nm and 532 nm diodes for excitation, an immersion HCX APO LUV-I 40x water objective (NA 0. 8, WD 3. 3 mm) and the LAS AF version 2. 6. 0. 7266 acquisition software (Leica). Optical planes were spaced by 0. 6 mm and Z stacks containing the whole dorsal aorta were acquired every 5 min.
Manual A-P distance measurements We considered the Antero-Posterior luminal distance (A-P distance), as the length between the most anterior and posterior luminal poles of the EHT undergoing cell, aligned with the A-P axis. In the case of EHT cells emerging parallel to the A-P axis, we used the 'Straight Line' tool on Fiji software (NIH, https://imagej. net/Fiji) and measured the A-P distance (for each time points in Figure  1 -figure supplement 1) on 2 or three consecutive Z-planes localized in the central part of the EHT cell (where the apparent A-P distance was the longest) and averaged. For cells emerging at a small angle relative to the A-P axis, we measured the apparent A-P distance on the maximum projection along the Z-axis; when the angle was larger, we first performed a 3D-rotation on Imaris software so as to align the cell in the X-Y plane before doing the measurement.
Semi-automatic A-P distance and closing speed measurements In order to measure A-P luminal distances and dynamics (Figure  2 ), dense eGFP-ZO1 extremities were independently tracked using the Fiji plugin TrackMate  (Tinevez et al. , 2017 , https://imagej. net/TrackMate) after maximum projection along the Z-axis and X,Y translation using the Fiji plugin 'Descriptor based series registration' to correct the X,Y drift  (Preibisch et al. , 2010). Tracks display and subsequent distance analysis were performed using the MATLAB software (MATLAB 2017a, , The MathWorks, https://fr. mathworks. com). To focus on long scale activity rather than short displacements, the distance between the two extremities was filtered twice using an unweighted sliding-average over 20 min. Following this process, we characterized the speed at which the A-P luminal distance decreases by automatically extracting the closing speed local minima and filtering them based on their prominence (>0.
01 mm/min). For additional details see https://fr. mathworks. com/help/matlab/ref/islocalmin. html. Distance between successive minima was used to measure the acceleration-deceleration cycle duration distribution (for the algorithm and codes in MATLAB (version 2017b), see the link: https://github. com/sebherbert/EHT-analysis (Herbert, 2018 ; copy archived at https://github. com/elifesciences-publications/EHT-analysis).
Acknowledgements We are grateful to  Romain Levayer  for stimulating discussions,  Denise Montell  and  Jocelyn McDonald  for advice on myosin light chain mutagenesis,  Emi Murayama  for critical reading of the manuscript. We thank  Leonard Zon  for the p5E-R1+23 -1-beta-globin plasmid,  Markus Affolter  and  Heinz Georg Belting  for the pG1-flk1 and pUAS:eGFP-hs-ZO1 plasmids. This work was supported by the  Institut Pasteur , the  CNRS , the  Agence Nationale de la Recherche Laboratoire d'Excellence Revive  ( Investissement d'Avenir ;  ANR-10-LABX-73 ), and by grants from the  Fondation pour la Recherche Medicale (Equipe FRM   DEQ20120323714  and  DEQ20160334881  to PH) and the  Fondation ARC pour la Recherche sur le Cancer  (to PH). ML was supported by fellowships from the  Ligue Nationale contre le Cancer  and the  Fondation pour la Recherche Medicale.
S. H was supported by the  Agence Nationale de la Recherche Laboratoire d'Excellence Revive  ( Investissement d'Avenir ;  ANR-10-LABX-73 ). This work was partially funded through  ANR-10-INBS-04-06 FranceBioImaging grant  to JCOM. The authors acknowledge the  Imagopole France-BioImaging infrastructure , supported by the  French National Research Agency  ( Investissement d'Avenir ;  ANR-10-INSB-04-01 ) for advice and access to the UltraVIEW VOX system, as well as the  Image Analysis Hub of the Imagopole.
Cooper et al  35  found that there was substantial between-country variation in objectively measured MVPA in the International Children's Accelerometry Database, even between apparently similar countries, and concluded that further research is needed to explore environmental and sociocultural explanations for these differences. To develop a better understanding of childhood physical activity and inactivity across countries, the first Global Matrix (Global Matrix 1. 0) of Report Card grades on physical activity was launched in 2014. 36 Report Cards, based on the Canadian Report Card model,  37  were developed by research teams from 15 countries (including 8 very high HDI countries) using a harmonized process for data gathering, assessing, and assigning grades. 36 For each participating country, grades were assigned to 9 common physical activity indicators: Overall Physical Activity, Organized Sport Participation, Active Play, Active Transportation, Sedentary Behaviors, Family and Peers, School, Community and Environment, and Government Strategies and Investments.
Finally, the description of the data sources used to inform the Overall Physical Activity grade for each country (except for Japan that had an INC) are presented in Supplemental Material [available online]. In total, 87 surveys or studies informed the grades of the very high HDI countries, representing a total sample size of 1,005,989 children and youth. A total of 21 surveys/studies used an objective measurement of physical activity, while 66 used a subjective method, mostly via self-administered questionnaires. Out of 21 surveys/studies that measured physical activity objectively, 20 used accelerometry and one study used pedometry. A minimum wear time for accelerometer ranged between 3 and 7 consecutive days, and 9 different accelerometer cut points were applied for analysis across 20 surveys/studies with the most commonly used cut points (n = 8) from Evenson et al.
The grades ranged from "F" to "A-" for this indicator; however, Slovenia was the only country with a "good" grade ("A-"), and all the other countries' grades fell between "F" and "C. " Five countries received an "F" (Flanders, Scotland, South Korea, Taiwan, and the United Arab Emirates), 4 countries assigned "C" grades (England, Hong Kong, Lithuania, and The Netherlands), and all the remaining countries had a "D-," "D," or "D+. " The comparison and interpretation of the Overall Physical Activity grades should be conducted carefully given the important variation in the methods used to measure Overall Physical Activity between the countries, as seen in Supplemental Material [available online]. In fact, objective data were used in 13 of 29 countries with an Overall Physical Activity letter grade, and subjective data in 27 countries (11 countries combined subjective and objective data to inform their Overall Physical Activity indicator).
98 he benchmark for this indicator was the average percentile achieved on certain health-related physical fitness component based on the normative values published by Tomkinson et al. 41  On average, Physical Fitness was graded "C-. " The highest grade was obtained by Japan ("A"), closely followed by Slovenia ("A-"); and 4 other countries (Canada, Chile, Hong Kong, and Jersey) shared the lowest grade ("D"). An "INC" grade was assigned to Physical Fitness in 13 countries. Various health-related physical fitness components and different normative values were used to calculate the percentile achieved by their sample of children and youth and inform this indicator from one country to another. For example, in Hong Kong this indicator was graded based on peak oxygen consumption, estimated with the performance on the 20-m shuttle run performance among 9-to 17-year olds  99  ; Jersey had data on cardiorespiratory fitness, muscular strength, muscular endurance, flexibility, and motor fundamental movement skills development for school-aged children.
Gwyddion (open-source software) was used for all subsequent processing of the flattened AFM images  52. For periodicity analysis from high-resolution images, a two-dimensional (2D) fast Fourier transform (FFT) filter was applied to get rid of high frequency periodic noise along the slow scan axis followed by a median filter of 3 pixel-width (Supplementary Fig. 22 ) and Gaussian filter with σ = 2. All images in the paper have the same pixel size of 0. 15 nm, except for the Type II G-wire in Fig. 5 , which was acquired using biolever mini at much slower imaging speed. The filtering described above, facilitated the splining process which otherwise led to irreducible branching of the spline. No FFT filtering was done for the images used for height analysis. No Gaussian filtering was used on the images from which the 0.