dataesr/scientific-paragraphs-categorization huggingface dataset:
Functional and metabolic pathway analysis In silico reconstruction of metabolic pathways was performed using Pathway tools v23. 5  42  , using a multi-processing wrapper tool mpwt (https://github. com/AuReMe/mpwt)  80. In order to arrange input data into the appropriate format, and subsequently parse the output, a collection of Python and R scripts were written (https://github. com/ djw533/pathwaytools_gff2gbk). Further specific information about how to run this can be found in the readme hosted at the github repository. In brief: Representative protein sequences for each of the 47,743 protein family groups identified in the pan-genome analysis were extracted from the pan-genome graph-associated data using Cytoscape v3. 7. 1, and functionally annotated using EggNOG-mapper v1. 0. 3 81 , using the following flags '-m diamond -d none -tax_scope auto -go_evidence non-electronic -target_orthologs all -seed_ortholog_evalue 0.
001 -seed_ortholog_score 60 -query-cover 20 -subject-cover 0 -override'. Using the EggNOG annotations from representative protein sequences, annotated genomes (as. gff files) were updated with the Enzyme Commision (EC) numbers, Gene Ontology (GO) terms and predicted function for each protein family group from the pan-genome analysis, using the script gffs2gbk. py in pathwaytools_gff2gbk. This script also appropriately organises the input data required for mpwt given a file listing the taxon IDs for each genome. Pathway tools was then run by running mpwt with the '-patho' and '-taxon_id' flags, whilst providing a file containing taxon ids for each genome. The in silico-reconstructed metabolic pathways for all genomes were then collated using com-pare_pgdbs. R in pathwaytools_gff2gbk, and downstream analysis conducted in R, as shown in https://github.
Plasmid replicon identification Plasmid sequences were identified in the collection of Serratia genome assemblies with the MOB-recon tool using the MOB-suite v3. 0. 3 databases and default settings  82. Characterisation of the identified plasmids, including predicted transferability of the plasmid, was performed with MOB-typer from the MOB-suite package. Charts illustrating plasmid counts and features were generated in R using ggplot2  83. K-mer-based sketches of the plasmid sequences (s = 1000, k = 21) were generated with the mash v2. 3 sketch algorithm  46. Pairwise mutation distances between sketches were estimated using mash dist with a distance threshold of 0. 05 and otherwise default settings. The resulting all-pairs distance matrix was used for graphbased clustering of the plasmid sequences in Cytoscape v3.
GC content analysis Whole-genome GC content was calculated using Quast v4. 6. 0. GC content for each gene, and the average GC value for codon positions 1, 2 and 3 for each gene was then calculated using the script GC_from_-panaroo_gene_alignments. py, which uses the gene_data. csv file created from Panaroo (detailed above). Intragenic nucleotide sequence was extracted for all protein-encoding sequences using gal-s_parser_with_fasta. py with the '-t nuc' flag. Intergenic GC values were then calculated by using Bedtools 85 complement from Bedtools v2. 29. 0 to identify the inverse of all coding regions (i. e all intergenic regions). Bedtools getfasta from Bedtools v2. 29. 0 was then used to extract the intergenic regions as nucleotide sequence. Average GC values for the total intergenic and intragenic regions were then calculated using get_gc_content.
Retrieval of specific gene clusters Gene clusters containing co-localised pig genes (pigA-M) were identified using Hamburger (github. com/djw533/hamburger), which uses protein HMM profiles for each target gene in the gene cluster. User-set parameters define the minimum number of hmmsearch (from HMMER suite v3. 2. 1  86  ) hits required to report the presence of each system in a genome, in addition to the maximum number of non-hit genes that are permitted between two hit genes in a contiguous set of genes. Gene clusters were reported as prodigiosin clusters for loci encoding at least nine genes containing Pfam domains characteristic of 11 of the 14 pig genes with no more than five non-model genes between any hits. Extracted genomic sequences were then compared using blast+ v2. 2. 31  87  and genoplotR v0.
8. 11  88. Blastn was used with the flags '-task Blastn -perc_identity 20 -evalue 10000'. Functions created to use these can be found in micro. gen. extra on https://github. com/djw533/ micro. gen. extra. Gene clusters around other genes of interest, such as the plasticity zone in S. marcescens located between tRNA-Pro ggg and tRNA-Ser tga , were extracted using the script pull_out_around_point. py, and, if in the unwanted orientation, flipped using gff_reverse. py.
Data visualisation Phylogenetic trees were visualised using the R package ggtree v2. 4. 2  90. Synteny of regions of bacterial genomes extracted by Hamburger were visualised using the R package genoplotR v0. 8. 11  88. Genetic organisation of genes were plotted using the R package gggenes v0. 4. 1 (https:// wilkox. org/gggenes/). Other plots were created using the R package ggplot2 v3. 3. 5  83. As mentioned above, networks were viewed using Open Access This article is licensed under a Creative Commons Attribution 4. 0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Acknowledgements This work was supported by  Wellcome  (grant numbers:  104556 , Senior Research Fellowship S. J. C. ; 220321, Senior Research Fellowship Renewal S. J. C. ; 109118, Ph. D. studentship; 206194, N. R. T. ), the  NIHR  ( NIHR200639 ,  AMR Capital Award to University of Dundee ), and  Institut Pasteur  and  INSERM  (P. A. D. G. and F. X. W. ). Firstly, we would like to acknowledge the contribution of, and thank, all those colleagues who contributed over many years to the collection of the Serratia isolates forming the  Institut Pasteur collection of Patrick Grimont. We also thank  Alistair Leanord ,  Teresa Inkster ,  James Chalmers ,  Gillian Orange  and  Nigel Smith  for providing recent isolates of Serratia marcescens from UK hospitals, and  Hazel Auken  and  George Salmond  for sharing isolates reported previously.
Data availability The sequencing data generated in this study have been deposited in the European Nucleotide archive (ENA; https://www. ebi. ac. uk/ena/), under accession numbers ERP106480 for the Pasteur collection (https://www. ebi. ac. uk/ena/browser/view/PRJEB24638) and ERP135711 for the UK hospitals collection (https://www. ebi. ac. uk/ena/browser/ view/PRJEB51113). The annotated genome assemblies for both collections have been deposited in the ENA under the project accession numbers above: ERP106480 for the Pasteur collection (https://www. ebi. ac. uk/ena/browser/view/PRJEB24638) and ERP135711 for the UK hospitals collection (https://www. ebi. ac. uk/ena/browser/view/ PRJEB51113). The other whole genome sequences used in this study are available in the NCBI GenBank (https://ftp. ncbi.
nlm. nih. gov/ genomes/genbank/), with the accession numbers for the individual sequences provided in Supplementary Data 1. The gene annotations generated for all 664 genomes analysed in this study, together with all the genome assemblies generated during this study, are available through Figshare (https://doi. org/10. 6084/m9. figshare. 18051824)  92. All other data used in figure generation, including the output of pangenome analysis, are available in the same repository (https://doi. org/ 10. 6084/m9. figshare. 18051824)  92. The MiniKraken reference database used for species identification is available at https://ccb. jhu. edu/ software/kraken/dl/minikraken_20171019_8GB. tgz and the MOB-suite reference database used for plasmid replicon identification is available from Zenodo (https://doi.
Code availability All custom scripts for which github repositories are not specified above can be found at https://github. com/djw533/Serratia_genus_ paper/analysis_scripts. Other packages used can be found at https:// github. com/djw533/hamburger  93 , https://github. com/djw533/micro. gen. extra  94  , and https://github. com/djw533/pathwaytools_gff2gbk  95. Rscripts used to plot figures can also be found in this repository at https://github. com/djw533/Serratia_genus_paper/figure_scripts. Rscripts make use of the tidyverse 96 collection of packages. R version 4. 0. 3 was used for all analysis and generation of plots.
MRI Data Acquisition In all participants, 11 min of resting-state fMRI (rs-fMRI) data were acquired on the same 3 T MR scanner (Intera Achieva; Philips, Best, the Netherlands). Monitoring of vital measures was performed by a senior intensivist throughout the experiment (BS, HV, FF, WF, SS). Highresolution anatomical image, using 3D T1-weighted sequence and apparent diffusion coefficient were also acquired. Concerning sMRI data, gray matter morphometry was applied on 3D-T1-weighted images by using voxel-based morphometry  [5] , obtaining an estimation of gray matter volume (GM). To study white matter integrity, DTI models were created to fit at each voxel, generating FA and mean diffusivity (MD) maps  [4]. Cortical frontal (mPFC) and posterior parietal (PreCun and PCC) regions of interest (ROI) were defined to be used in ROIs vs.
Visual Interpretation To shed light on CNN classification, we applied a previously reported visualization technique  [34]. According to this method, the output from each convolutional layer was retrieved. Only positive activation values were considered for the classification. Because data dimension is reduced going deeper in the network, these visualization maps were interpolated to match the input size. To merge results, first the maps were averaged over filter number for each convolution and then over the total number of convolutions to obtain the final visualization map. The 3D CNN model is schematized with fundamental building blocks. Feeding as input each MR index, we examined their discriminant power by using standard evaluation metrics and visualization maps to discover the most relevant voxels taken into account for CNN prediction.
Statistical Analysis Continuous data are expressed as mean ± standard deviation and/or median (range), according to their distribution (Kolmogorov-Smirnov test). Categorical variables were expressed as numbers and percentages. Sensitivity, specificity, and diagnostic accuracy were calculated using standard formulas as well as positive predictive value and negative predictive value. Receiver operating characteristic curves were calculated for each predictive model, and the highest sum of sensitivity and specificity was considered as being the optimal threshold. All p values were two-tailed, and statistical significance was defined as a p value of less than 0. 05. Misclassified patients were examined in relation with the known outcome at 3 months after the primary brain injury event. Hence, we computed the false negative (FN) good outcome rate, i.
Mastcam-Z is a novel pair of variable focal length multispectral charge-coupled device (CCD) cameras used by the Perseverance rover team to document the color, size, shape, texture, and spectral properties of rocks and soils, and the properties and behavior of dust and ice aerosols in the boundary layer and the sky above the crater (see Materials and Methods for more details on the cameras and calibration and processing of their imaging data). These cameras provide a unique combination of wavelength range, high spatial resolution, and variable field of view for extensive landscape coverage, compared to other rover instruments. Their matched focal length ranges, plus the unprecedented downlink data volume from the mission so far that has been enabled by substantial NASA and ESA investments in Mars orbiter communications relay infrastructure, have also allowed almost all Mastcam-Z images to be collected in stereo, enabling detailed three-dimensional (3D) interpretations of outcrop topology at the fine (millimeter to centimeter to decimeter) scales required for precise stratigraphic mapping in the near field and at greater observation distances than other imaging systems on the rover.
1 ), show details of Jezero's western delta, its erosional remnants, and other hills, buttes, fan deposits, impact craters, and the looming crater rim itself, providing context for the materials and units that the rover is exploring directly, including the rock and regolith samples being collected for eventual return to Earth. Third, surface-atmosphere interactions, current meteorological conditions, and dynamic processes and events are assessed with a rich dataset acquired at an unprecedented cadence of intra-and intersol solar and atmospheric imaging observations, including images and videos of the horizon, Sun, sky, and Martian satellites. The Mastcam-Z atmospheric observations and results reported here provide the highest temporal resolution image-based monitoring of the Martian environment to date.
2) 3D triangulation to generate a 3D mesh for each corresponding point pair, making use of the known stereo geometry. 3) Projection of image texture onto the mesh. 4) Data fusion between adjacent meshes. 5) For the 2D mosaic case: Projection of camera textures onto an infinite virtual cylindrical or spherical gridded model and mosaicking between adjacent patches with optimization criteria to minimize visual effects on the borders between patches. 6) Optimization of 3D data structures to enable the following real-time rendering of huge datasets: Production of an OPC (ordered point cloud). Using the specialized software PRo3D (short for Planetary Rover 3D Viewer), these 3D products can be viewed, analyzed, and annotated for scientific analyses [e. g. ,  (52) ]. Possible analyses include but are not necessarily limited to the measurement of scales (figs.
S1, S4, and S5), dip and strike (figs. S4 and S5), altitude, areal coverage, slopes, and other geometric relationships that can be directly obtained from a high-resolution OPC. The spatial information uses the Perseverance pose (position and rotation angles), which is provided by the Jet Propulsion Laboratory engineering team for each position change. This allows the application of a 3D Helmert transform of all obtained datasets OPCs, meshes, and mosaics into a Mars-global reference frame for 3D data fusion in a geometrically consistent manner.
Dust particle size is determined by a nonlinear least-squares minimization between the sky images and a forward model calculated using a multiple-scattering radiative transfer algorithm. The data are constrained to have scattering angles between 7° and 20°, selecting only the portion of the images most sensitive to particle size (e. g. , the diffraction lobe) and avoiding the potential issue of scattered light associated with a bright source just outside of the FOV. The forward model is based on the well-known, robust DISORT package (http://www. rtatmocn. com/disort/) [e. g. ,  (58, 59) ], accessed through the Python-based front-end library pyRT_DISORT (https://github. com/kconnour/pyRT_DISORT) (60). An advantage of pyRT_DISORT is the ease-of-use with the selected optimization library, LMFIT (https://lmfit.
github. io/lmfit-py/intro. html) (61). By fixing the optical depth using the contemporaneous direct solar observation, the best-fit effective radius is found using a grid of radiative properties (and linear interpolation) constructed using oblate cylinders and refractive indices  (62) (63) (64). These observations enable separate fits for dust and water ice based on the distinctly different shape of the scattering phase function outside of the diffraction lobe. This, in turn, enables higher fidelity calculations of the effects of aerosol particle size on radiative budgets in dynamical calculations of both surface and atmospheric heating rates [e. g. ,  (65) ], especially when correlated with other relevant meteorological and remote sensing observations from the Perseverance rover [e. g. ,  (21, 32) ].
Acknowledgments: We are grateful to and inspired by the hundreds of engineers, scientists, managers, administrators, and others around the world who contributed to the design, building, testing, launch, and successful landing of the  Mars 2020 mission's Perseverance rover and its science instruments , as well as the hundreds of engineering and science colleagues who contributed to Mastcam-Z design, fabrication, and testing and who are actively contributing to mission operations on Mars to this day. We also thank  K. Edgett  for detailed reviews and suggestions that significantly improved this manuscript,  R. Barnes  for assistance with 3D modeling visualizations and analyses, and  Y. Liu  for advice on the search for potential volcanic constructs within Jezero crater. Funding: Funding for the U.
Statistical Analysis Statistical analyses were performed using JASP (version 0. 15). All data were reported as mean ± standard deviation (SD). We checked distribution of normality and the homogeneity of variances with the Shapiro-Wilk and Levene tests, respectively. The effect of the training program on cardiorespiratory parameters according to CR approach was evaluated using two-way repeated measures ANOVA, i. e. , CR groups (telerehabilitation vs. conventional center-based) x time (pre-post). Where a significant interaction difference occurred, Tukey's post hoc analyses were performed. For all statistical comparisons, the level of significance was set at p < 0. 05.
The lack of systematic serological follow-up of pregnant women limits the informative data on actual seroprevalence and the incidence of seroconversion, especially as the infection is asymptomatic, potentially hiding a considerable number of CT cases. Although many malaria or generated by this study on toxoplasmosis. The newly acquired data on toxoplasmosis, linked to information on inclusion dates in the STOPPAM study, are available from the Open Science Framework database (https://osf. io/ut5ey/), DOI 10. 17605/OSF. IO/UT5EY. Regarding the malaria data from the STOPPAM project, they are not currently publicly available. However, collaborations are encouraged and any researcher interested in exploring data may contact directly the researcher responsible for the local STOPPAM committee: Nicaise Tuikue Ndam (STOPPAM data, email: nicaise.
A total of 1,037 women were enrolled, and 982 were followed-up through pregnancy, including 891 until delivery  [29]. At each visit (e. g. , monthly antenatal care (ANC) visit, in case of an emergency and at delivery), clinical and obstetrical data as well as biological samples (including blood samples which were used for this study) were collected. The STOPPAM project followed WHO recommendations in effect at that time, so women received IPTp, iron and folic acid as per national guidelines. When diagnosed malariainfected, women were treated with quinine or SP following the recommended WHO process  [29].
falciparum and also a conserved apicomplexan protein, were measured by Enzyme-Linked ImmunoSorbent Assay (ELISA) as previously described  [33]. Associations were then assessed using a linear univariate analysis followed by a multivariate linear regression model to account for potential confounding factors such as maternal socio-demographic and clinical characteristics. The differences of anti-PfAMA1 IgG levels between inclusion and delivery were established using a multivariate mixed model where antibody levels at inclusion and at delivery were paired for each woman. Data were analyzed with Stata 1 Software, Version 13 (StatCorp LP, College Station, TX, USA) and the graphics were done using Graph Pad Prism (Version 8. 1. 2). 5 samples due to technical failure, 512 and 462 women were found serologically positive and negative to toxoplasmosis, respectively.
Acknowledgments We thank the teams of the microbiology and virology units from the  Saint Louis Hospital as well  as the virology team from the  Bichat-Claude Bernard Hospital in Paris  for welcoming us. We are particularly grateful to the team of the parasitology unit from the  Bichat-Claude Bernard Hospital  for their warm welcome and assistance. This study was part of the STOPPAM " Strategy To Prevent Pregnancy-Associated Malaria" collaborative project. We are grateful to all the women who participated in the study. Finally, we thank  Sharon Calandra  for her proofreading.
Preliminaries In this work, we use a notation borrowed from Matlab ®. Fixing i 1 ∈ {1,. , N 1 } and i 2 ∈ {1,. , N 2 }, if A ∈ C N1×N2×M ×M , then A i1,i2,:,: stands for the matrix A i1,i2,:,: := [A i1,i2,j1,j2 ] M j1,j2=1. This notation similarly applies to 3-mode tensors, matrices, and vectors. Table  1  summarizes the notation used in the paper.
e. , the tensor R * (T n ) := I * + ∞ k=1 (T n ) k * , and θ := h        1 0 0. 0 1 1 0. 0. 1 1. 1 0 1 1. 1 1        ∈ C M ×M. Overall, the accuracy of the approximation in  (11)  can not be better than O(h). This is due to the fact that, as explained in  [23] , the discretization (  10 ) is based on a rectangular quadrature rule. Finally, using the Path-sum method  [21]  we get the following explicit expression for the * -resolvent in terms of a continued fraction R * (T n ) 1,1,:,: = α 1 -β 2 α 2 -β 3 • • • β n-1 α -1 n γ n-1 • • • -1 γ 3 -1 γ 2 -1 , (12) with α i = I M -α i. Equation (  12 ) is computed from the most inner term moving outward, where the inversion operation is performed using the backslash operator in Matlab ®. Note that the * -resolvent and all inverses appearing in (  12 ) are expected to exist for h small enough, since their continuous counterparts exist under certain regularity conditions on A(t); see  [22, 24].
, 2n -1. Finally, to quantify the quality of the solution, we consider as error measure for (11) the quantity err sol := ŝ -s (M ) n 2 ŝ 2 , where, if no analytic expression is available, an approximation of s is obtained by using ode45 in Matlab ®. In the formula above, • 2 stands for the usual Euclidean norm. The rate at which err sol decreases as M increases is expected to be O(h) = O(1/M ), i. e. , the accuracy of the discretization used here.
Acknowledgements The authors want to thank  Enikö Baligács  and  Christian Bonhomme  ( Laboratoire de chimie de la matière condensée de Paris, Sorbonne University ) for providing the data from real world applications used in Section 5. This work was supported by  Charles University Research  programs No. PRIMUS/21/SCI/009  and  UNCE/SCI/023 , and by the  Magica  project  ANR-20-CE29-0007  funded by the  French National Research Agency. The authors S. P. and M. R. -Z. are members of the  GNCS-INdAM group.
Whether corticosteroids improve outcome in patients with acute complications of sickle cell disease (SCD) is still debated. We performed a systematic review of the literature with the aim of estimating effects of corticosteroids on the clinical course of vaso-occlusive crisis (VOC) or acute chest syndrome (ACS) in patients with SCD. The primary outcome was transfusion requirement during hospitalization. Studies were identified by search of MEDLINE and CENTRAL database. Three randomized clinical trials (RCT) and three retrospective cohort studies (RCS) were included, involving 3,304 participants and 5,562 VOC or ACS episodes. There was no difference between corticosteroids and standard treatment regarding transfusion overall [OR=0. 98 (95% CI 0. 38 to 2. 53)], but with a significant interaction of study type (P<0.