dataesr/scientific-paragraphs-categorization huggingface dataset:
Notably, the public health labs in Australia, England and USA from which we sourced genomic surveillance data (Fig. 4 ) all utilise genome-wide SNV-based phylogenetics for S. sonnei analysis  17, 26. Reliance on whole genome comparisons and phylogenetic inference is considerably slower than genotyping and requires expertise and background knowledge both to conduct the analysis and to interpret the results. In contrast, using the genotyping framework, raw sequence data can be turned into simple informative identifiers for each strain without reference to other genomes or databases (and without needing to assemble the genome). The resulting genotyping information can be easily interpreted, compared and stored in (non-sequence) databases for future reference, facilitating epidemiological investigations without need for direct comparisons with any other genome sequences.
Methods Single nucleotide variants (SNVs) and phylogenetic analysisdiscovery dataset. The discovery dataset consisted of 1935 high quality previously published S. sonnei genomes, sequenced using Illumina platforms. Source information for all genomes (year of collection, geographic origin, etc) was extracted from their respective publications  4, 7, [10] [11] [12] [13] 15, 16  (Supplementary Table  1  and Supplementary Data 1). Genom s were mapped to the S. sonnei reference genome 53 G (accession NC_016822) using RedDog (v1beta11; https://github. com/katholt/RedDog). Briefly, RedDog maps reads with Bowtie2 v2. 2. 9  44  with the sensitive local parameter, then uses SAMtools v1. 1  45  to retain high quality SNV calls (phred score ≥20, read depth >5, removes heterozygous calls). SNVs detected in repetitive regions where SNV calls are dubious (e.
monophyletic in the ape package  48  for R); a small number of groups were non-monophyletic and were broken up into smaller groups (2 groups at clade level, n = 388 isolates; 3 groups at subclade level, n = 52 isolates) to result in a final set of monophyletic clusters. The SNV alignment was also analysed using the R package fastbaps 49 to partition the data into groups using Bayesian clustering (using the hc method for prior optimisation, and two levels of clustering using multi_res_baps) (Supplementary Fig. 1a ). Pairwise SNV distances within and between our final genotype groups (Fig. 2b ), and within and between FastBAPS clusters for comparison (Supplementary Fig. 1b ), were calculated from the SNV alignments using the dna. dist function in the ape package  48. Isolates belonging to previously defined epidemiological groups were located in the ML tree, and used to identify subtrees corresponding to each set of group members.
The list of SNV markers is given in Supplementary Data 2. We modified the Mykrobe genotyping software  32  to probe for these marker SNVs and assign S. sonnei genotypes. Probes to detect changes at specific S. sonnei QRDR codons (GyrA-83, GyrA-87, ParC-80) were also included in the S. sonnei panel in Mykrobe 32 (v0. 9. 0) software available at https://github. com/Mykrobetools/mykrobe, using the probe panel stored in Figshare (https://doi. org/10. 6084/ m9. figshare. 13072646). Mykrobe outputs were then parsed using a custom Python script which tabulates results across a set of samples and summarises the support for each genotype call (https://github. com/katholt/sonneityping). We tested the Mykrobe S. sonnei genotyper using the discovery genome set as input (Illumina reads, fastq format), to confirm that the genotype and QRDR mutations reported by this implementation were correct.
Code was also tested using Oxford Nanopore (ONT) long reads (fastq format) using the --ont flag in Mykrobe. Validating the genotyping scheme on independent data. We used an independent validation dataset (i. e. , genomes not included in the discovery set used to define the scheme) a total of 2015 S. sonnei genomes downloaded from the Gen-omeTrakr project 33 on 7 May 7 2019 (listed in Supplementary Data 1). All genomes had been sequenced using Illumina platforms. The validation and discovery datasets were subjected to mapping, SNV calling and phylogenetic analysis as described above, generating a recombination-filtered core-genome alignment of 32,138 SNVs in 3696 isolates (n = 260 SNVs identified as recombinant across 11 genomes), and a ML phylogeny (Supplementary Fig. 3 , interactive tree available in Microreact at https://microreact.
org/project/g8BvA2JCXWaZNDyPyjsWXF). All genomes were assigned a genotype using Mykrobe 32 v0. 9. 0, and these were compared to the ML phylogeny to check that each genotype was monophyletic as expected (using the function is. monophyletic in the ape package 48 for R). In addition, we confirmed that all marker SNVs remained non-homoplasic amongst this larger set of genomes, using output from SNPPar (a total of 2003 homoplastic SNVs were identified amongst this expanded set of genomes). Analysis of AMR determinants amongst genotypes. Genotypes were identified as above, and AMR determinants were identified using SRST2  50  and the CARD database  52  for a further 2644 genomes sourced from public databases, and results combined with those from the discovery and validation sets yielding a total of n = 6595 S.
5 , interactive tree available in Microreact at https://microreact. org/project/kMRoFFXxkB6JAn9bgBAdMz). Application to public health surveillance data. We applied the S. sonnei genotyping framework to analyse 4222 genomes sequenced by public health reference labs between 2016 and 2019 in Australia (n = 644 4 ), England (n = 2867  16, 17  ), and the USA (n = 711, from GenomeTrakr 33 as of 7 May 2019). All genomes were genotyped, and AMR determinants were identified as above. Azithromycin resistance was predicted based on presence of mph(A), ESBL/carbapenemase production based on known beta-lactamase alleles (CTX-M, OXA-66, OXA-181), and ciprofloxacin resistance based on the combination of three QRDR mutations (GyrA-S83L, GyrA-D87Y and ParC-S80I). Data were analysed in R and visualised using the ggplot2 package (Figs.
Acknowledgements K. E. H. is supported by a  Senior Medical Research Fellowship  from the  Viertel Foundation of Australia, and Bill and Melinda Gates Foundation, Seattle  (grant # OPP1175797 ). K. S. B. is supported by a  Wellcome Trust   Clinical Research Career Development Fellowship  ( A/106690/A/14/Z ) and RJB through a  UKRI MRC New Investigator Research Grant  (held by  KSB ,  MR/R020787/1 ). K. S. B. , T. J. D. and C. J. are affiliated to the  National Institute for Health Research Health Protection Research Unit (NIHR HPRU ) in Gastrointestinal Infections at  University of Liverpool  in partnership with  Public Health England (PHE) , in collaboration with  University of Warwick. The views expressed are those of the author(s) and not necessarily those of the  NHS , the  NIHR , the  Department of Health and Social Care or Public Health England.
Code availability Mykrobe v0. 9. 0 is available for download at https://github. com/Mykrobe-tools/mykrobe Instructions for running Mykrobe v0. 9. 0 for S. sonnei and tabulating the results is available at https://github. com/katholt/sonneityping (https://doi. org/10. 5281/ zenodo. 4609813).
The fully electromagnetic particle-in-cell code WarpX is being developed by a team of the U. S. DOE Exascale Computing Project (with additional non-U. S. collaborators on part of the code) to enable the modeling of chains of tens to hundreds of plasma accelerator stages on exascale supercomputers, for future collider designs. The code is combining the latest algorithmic advances (e. g. , Lorentz boosted frame and pseudo-spectral Maxwell solvers) with mesh refinement and runs on the latest computer processing unit and graphical processing unit (GPU) architectures. In this paper, we summarize the strategy that was adopted to port WarpX to GPUs, report on the weak parallel scaling of the pseudo-spectral electromagnetic solver, and then present solutions for decreasing the time spent in data exchanges from guard regions between subdomains.
I. INTRODUCTION Of the possible applications of plasma-based particle accelerators, the development of a plasma-based collider for high-energy physics studies is the most challenging. 1 Fast and accurate simulation tools are required to study the physics toward configurations that enable the production and acceleration of very small beams with low energy spread and emittance preservation over long distances, as required for a collider. The fully electromagnetic particle-in-cell (PIC) code WarpX 2 is being developed by a team of the U. S. DOE Exascale Computing Project 3 (with additional non-U. S. collaborators on part of the code) to enable the modeling of chains of tens to hundreds of plasma accelerator stages on exascale supercomputers, for future collider designs. The code is combining the latest algorithmic advances (e.
While computing on a multi-core CPU, work sharing between individual threads is achieved by tiling the domain in these containers further, which can lead to cache re-use because elements in a tile are grouped spatially close together. An individual thread operates on a tile of data, which aids potential compiler-guided vectorization and avoids cache conflicts between threads. In contrast, the GPU strategy currently assigns individual cells and particles to GPU threads without yet exploiting explicit cache blocking algorithms such as supercell structures. 15 For example, current deposition is performed via hardware-accelerated atomic additions into global device memory. Even without cache blocking particle structures, data locality can be increased. In particular, repeated sorting of particles into spatially close buckets over time was explored and has shown to enable a simulation speedup of up to nearly eightfold over unsorted particles.
16 esides the aforementioned on-node composition of particle and cell data elements, data containers also serve the purpose to domaindecompose a large simulation volume across nodes of a computing cluster. As in on-node tiling, overlapping halo (or guard) regions are managed with container functions that can exchange and populate values from neighboring sub-domains. Abstracting such functionality via AMReX containers is convenient, since complex functionality such as load balancing, changing execution strategies with number of domain-decomposed boxes per device, and organizing data between multiple mesh-refinement levels can be optimized by domain experts who work on abstract modules. For example, one can optimize cost functions and space filling curves for load balancing, numerical details of an algorithm, parallel execution strategy and tiling, and among other aspects independently and in different locations of the software stack.
3. Average clock time (in seconds) per time step as a function of the number of nodes from a weak parallel scaling study with WarpX, using the PSATD solver with 1FFT/GPU, for 8, 16, or 32 guard cells, in double precision (solid lines) and single precision (dashed lines). Each value is the average of 14 time steps. The range between the maximum and the minimum values is plotted as a filled color background behind each curve. [Associated dataset available at https://doi. org/ 10. 5281/zenodo. 4429368] (Ref. 32). on-device memory dominated, and usually only show a speedup of 1:5-1:6Â when switching to single precision. In particular, FDTD kernels do not reach a 2Â speedup when switching to single precision, 39 since many auxiliary operations that are executed per GPU kernel, i. e. , indexing operations (integer math) and atomic reductions in current depositions, cannot be expected to speed up linearly when reducing floating-point memory transfer bandwidth.
V. CONCLUSION The electromagnetic PIC code WarpX has now been fully transitioned from an original mix of Fortran and Cþþ source code to a fully modern Cþþ14 application. In the process, it has been ported to GPUs (with current support for NVidia GPUs), while preserving its ability to run efficiently on CPUs, using a single-source programing approach via abstractions that are provided through the AMReX library in the form of generic interfaces to parallel-for and reduction primitives. 16 This approach will enable support of other GPU architectures (e. g. , from Intel and AMD) with little to no change to numerical algorithms implemented in WarpX. The implementation includes both finite-difference and pseudo-spectral (FFT based) Maxwell solvers. Domain decomposition is used with both types of solvers, and the overall efficiency depends critically on the number of guard cells, that is, used along each dimension, as exchange of data between neighboring subdomains dominate the runtime.
ACKNOWLEDGMENTS This research was supported by the  Exascale Computing Project  (No. 17-SC-20-SC ), a joint project of the  U. S. Department of Energy's Office of Science and National Nuclear Security Administration , responsible for delivering a capable exascale ecosystem, including software, applications, and hardware technology, to support the nation's exascale computing imperative. This work was performed in part under the auspices of the  U. S. Department of Energy by Lawrence Berkeley National Laboratory  under Contract No. DE-AC02-05CH11231 ,  SLAC National Accelerator Laboratory  under Contract No. AC02-76SF00515 , and  Lawrence Livermore National Laboratory  under Contract No. DE-AC52-07NA27344. This research used resources of the  Oak Ridge Leadership Computing Facility , which is a  DOE Office of Science User Facility  supported under Contract No.
A stress resultant shell approach is proposed to simulate the draping of textile composite reinforcements with continuous fibers. This approach naturally makes it possible to take into account the specificity of the deformation of the fibrous reinforcements by considering independent bending and tension stiffness whereas they are linked in standard shell elements. The stress resultants and stress moments are related to membrane strains and curvatures by rate constitutive equations (hypoelastic laws). The experimental tests required to identify the material properties necessary for these constitutive laws in stress resultants are presented. This approach is implemented in the ABAQUS finite element code and can be used by all users of this software. A set of forming tests showed the effectiveness of the approach through comparisons between simulation and experimental results.
The stress resultant approach is one of the main frameworks in shell and shell finite element formulations  [19] [20] [21]. It is well suited to take into account the specific bending of textile reinforcements. In the present paper, the shell stress resultant approach was implemented in a user subroutine (Vugens) of the commercial finite element software ABAQUS. It can be used by all users of this code and can also be integrated in other finite element codes. The experimental tests required to determine the mechanical behavior in terms of stress resultants versus strain are described in Section 5. It is important to have a good understanding of the necessary mechanical tests and their connection with the simulation method in order to envisage the industrial use of such an approach. Section 6 exhibits the validation of the presented approach in cases of combinations of elementary loadings and Section 7 presents a set of forming processes for which the simulations based on the proposed technique were compared with experimental forming.
In the present work, the behavior laws are rate constitutive equations (hypoelastic models). They relate the stress resultant rates to the membrane strain rates and the stress moment rates to the curvature rates. The simulations described in this paper are performed within the ABAQUS/explicit code and based on the Vugens user subroutine. In this subroutine, the stress resultants are calculated from membrane deformations and curvatures. A semi-discrete shell finite element approach that falls within the framework of stress resultant shells has been proposed in an in-house F. E. code  [47]. This approach considers a finite element as a sum of mesoscopic woven unit cells. It is a rotation free approach; the curvatures are computed from the position of the neighboring elements. A viscoelastic simulation of the deformation of UD tapes has been performed in ABAQUS  [42]  by superimposing built-in membrane finite elements and shell finite elements using a Ugens subroutine considering only the bending behavior.
Experimental setup The experiments presented below were implemented on a Zwi-chRoell 100KN traction machine. The TestXpertII software was used to control the movement's speed (set at 30 mm/min for all the types of shape forming) and distance. As shown in Fig. 11b , the blank holder and the die mold were made of poly (methyl methacrylate) (PMMA), which is transparent, in order to observe the material deformation during the forming process, and they were fixed during the forming process. In Fig. 11a , two cameras were placed at the material top side and bottom side respectively, through the reflection of the mirror, pictures were recorded with imaging software. Marker points were painted on the fabric at equal intervals as shown in Fig. 11c , and the marker-based tracking approach was adopted to obtain the deformation at different locations.
The method was implemented in the ABAQUS software and can therefore be carried out by the users of this code as well as integrated in other finite element codes. Nevertheless, it should be noted that the approach has a limitation. The stress resultant shell approach concerns the stress resultants and stress moments in terms of internal forces as well as the membrane strains and curvatures in terms of deformations. This does not explicitly give the distribution of strains and stresses according to the position in the thickness nor the rotation of material normals. The Kirchhoff hypothesis could be used to establish them from the mean surface determined by the stress resultant approach (curvatures and membrane strains). This was not relevant because it was shown that the material normals did not remain perpendicular to the mean surface during the deformation of the textile reinforcement and that the Kirchoff theory was therefore not valid.
(2016). The low-mass regime (M = 10 8-9 M ) is challenging as galaxies become marginally resolved (when smaller than 3 kpc), and it becomes difficult to recover reliable measurements of both sizes and rotation velocity. This paper aims to show that the j-M relation can be characterized in small and low-mass galaxies at redshifts from z = 0. 4-1. 4, opening up new parameter space thanks to the MUSE instrument and to our 3D modeling approach. Contrary to galaxies in the local universe where the j -M relation can be studied independently for the stellar j or gas components j gas (e. g. ,  Obreschkow & Glazebrook 2014; Obreschkow et al. 2016; Li et al. 2020; Mancera Piña et al. 2021) , this is currently extremely challenging in distant galaxies at z 1. 0 or beyond. One should note that the gas sAM j gas in this context is derived solely from H i data at z = 0, and it is often assumed that the sAM for the starforming gas (molecular or ionized) is similar to j because of the similar extent  (Leroy et al.
2010; Daddi et al. 2010; Bacchini et al. 2019). In fact, the Hα size R e,Hα to continuum size ratio R e, has been found to be ≈ 1 3  from a very large sample of 3200 galaxies at 0. 7 < z < 1. 5 with WFC3 imaging in  Nelson et al. (2016) , confirmed in our data as discussed in § 4. 2. In this paper, we measure the star formation rate (SFR)weighted sAM j sfr determined from the star-forming gas properties, namely v [O ii] (r) and Σ [O ii] (r), as a proxy for j following other studies (e. g. ,  Burkert et al. 2016; Contini et al. 2016; Swinbank et al. 2017). This has the advantage that the measurement is self-contained (from Hα or [O ii]) and that a comparison to similar quantities from simulations can be performed. We use the notation j to designate j sfr and in Section 3. 4, we quantify the potential bias between j and j in the TNG50 galaxies.
Since its initial release, the following improvements and new features have been implemented in GalPaK 3D. First, in order to quantify the chain convergence for each parameter, GalPaK 3D performs the Geweke (1992)'s convergence diagnostic test for each of the parameters, which is a Z-test of equality of means (of two parts of the chain) where auto-correlation in the samples is taken into account. Second, GalPaK 3D can now adapt the widths of the Cauchy proposal distribution automatically. Third, and most importantly, GalPaK 3D is now (since v1. 20) compatible with the emcee code  (Foreman-Mackey et al. 2013). emcee has the advantage that its multi-walkers remove the need to tune the proposal distribution, but it has the disadvantage to retain the Normal proposal distribution which is very slow and inefficient in reaching convergence because the Gaussian function does not have broad wings.
The j -M -(V/σ) global fit We use a Bayesian algorithm to fit the 5 parameters (α, β, γ, a, b) using a Student-t likelihood 11 with the NoU-Turn Sampler (NUTS) from the PyMC3 python package  (Salvatier et al. 2016 ). In Fig. 11 , we show the resulting fit to the j -M -(V/σ t ) relation using Eq. 9 whose fitted parameters are listed in Table  1. From this fit, one sees that the j -M relation is a nonlinear function of M , as the j -M relation flattens toward low masses given that the quadratic factor α is significantly non zero (α = 0. 04 ± 0. 01). Specifically, the slope of the sAM relation s ≡ d log j /d logM goes from s ≈ 0. 5 at logM /M = 10. 5, consistent with numerous results at z = 0 (e. g. ,  Obreschkow & Glazebrook 2014; Cortese et al. 2016; Posti et al. 2018a; Fall & Romanowsky 2018)  or high-redshifts (e.
Acknowledgments This work made use of the following open source software: matplotlib (Hunter 2007),  NumPy  ( Van Der Walt  et al. 2011), SciPy (Jones et al. 2001),  Astropy  (The Astropy Collaboration et al. 2018). We thank the referee for his/her constructive comments that improved the quality of the paper. We thank  L. Posti  for insightful discussions that led to an improved manuscript. This work has been carried out thanks to the support of the  ANR 3DGasFlows  ( ANR-17-CE31-0017 ), the  OCEVU Labex  ( ANR-11-LABX-0060 ). B. E. acknowledges financial support from the  program National Cosmology et Galaxies (PNCG)  of  CNRS/INSU  with  INP  and  IN2P3 , cofunded by  CEA  and  CNES. R. B. acknowledges support from the  ERC  advanced grant 339659-MUSICOS. S. G. , through the  Flatiron Institute , is supported by the  Simons Foundation.
Software Architecture for Numerical Resolution of The Model To compute the solution of the nonlinear, coupled multi-phase model, we use our in-house high-performance software platform MMICADO: Mesh based Multi-phase Interaction Capabilities for severe Accident Dedicated cOdes. MMICADO is a CEA's proprietary software designed by following the Object-Oriented Programming (OOP) principles in the modern C++. MMICADO provides a flexible framework for meshes, parallelism (using DUNE library  [37] ), numerical methods, input data parsers (TinyXML library  [38] ), solvers (mainly with PETSc library  [39] ), primarily for developing any new multi-phase application. An overview of such a platform is shown in Figure  2. For example, to solve system  (39)  we have used PETSc's Linear Search Newton (SNESNEWTONLS)  [40]  method (a non-linear solver) within the framework of MMICADO, where the Jacobian matrix is computed numerically using a finite difference approximation.