dataesr/scientific-paragraphs-categorization huggingface dataset:
acknowledgements We gratefully acknowledge the funding by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) -Projektnummer  268020496 -TRR 172 , within the  Transregional Collaborative Research Center "ArctiC Amplification: Climate Relevant Atmospheric and SurfaCe Processes , and  Feedback Mechanisms  (AC) 3 ". The aircraft campaign MOSAiC-ACA has been funded by  AWI  in the framework of the international  Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC)  with the tag  MOSAiC20192020 M. Mo. and C. V. have been supported by the  Deutsche Forschungsgemeinschaft  (grant nos. Vo1504/5-1  ( SPP-2115 ,  PROM ) and  Vo1504/7-1  ( SPP-1294 , HALO). O. J. , G. M. , C. G. , and R. D. have been funded by the  Centre National des Etudes Spatiales (CNES)  (grant no  6710 ) and by the  Institute Paul Emile Victor (IPEV)  (grant no  1224 ).
The direct and semi-direct radiative effects of biomass burning aerosols (BBAs) are investigated over southern Africa and the southeastern Atlantic during the Aerosols, Radiation and Clouds in southern Africa (AEROCLO-sA) field campaign in September 2017. A reference convection-permitting simulation has been performed using the Meso-NH model with an online dust emission scheme, a strongly absorbing BBA tracer emitted using the daily Global Fire Emissions Database and online-computed backward Lagrangian trajectories. The simulation captures both the aerosol optical depth and the vertical distribution of aerosols as observed from airborne and spaceborne lidars. The occurrence of stratocumulus over the southeastern Atlantic, deep convective clouds over equatorial Africa and the large-scale circulation are all reproduced by the model.
The dust scheme of  Grini et al. (2006)  includes the Dust Entrainment and Deposition (DEAD) scheme  (Zender et al. , 2003)  and the ORganic and Inorganic Log-normal Aerosols Model (ORILAM)  (Tulet et al. , 2005) , which describes the transport as well as dry and wet deposition of dust. The BBA scheme includes a biomass burning carbon tracer that is emitted from the daily Global Fire Emissions Database (GFED) version 4, available at a spatial resolution of 0. 25 • (van der  Werf et al. , 2017). The BB tracer is emitted in the first vertical layer of the model, allowing its mixing by turbulence within the boundary layer. No more elaborate injection assumptions or BBA removal processes are considered here because they were found in a multimodel evaluation of BBA plume transport to play a minor role  (Das et al.
4. 3. The wind at 12 km shows an anticyclonic circulation with strong westerlies over southern Africa and easterlies above -12 m s -1 at the Equator, along the southern fringe of the tropical easterly jet, for all three datasets. The tropical easterly jet is stronger for NORAD than for the ECMWF analysis and for BBRAD, in coherence with the more active deep convection feeding it. AOD values retrieved from MODIS observations show an arc of high values spanning over the Atlantic Ocean, between 10 and 20 • S, and along the border between Angola and the Democratic Republic of the Congo (Fig. 3d ). BBRAD re-produces this feature well, but with higher values along the border and lower values over the ocean. NORAD mimics BBRAD along the border but misses the area over the ocean. Instead, it spans large values of AOD along the African coast up to the Gulf of Guinea.
Data availability. The LNG lidar data are available via the digital object identifier (DOI) https://doi. org/10. 6096/AEROCLO. 1774 (Flamant, 2018)  and the dropsondes data via DOI https://doi. org/10. 6096/AEROCLO. 1777 (Perrin and Etienne, 2019). The AERONET data were downloaded from the NASA AERONET website (http://aeronet. gsfc. nasa. gov/, last access: 1 July 2022; NASA, 2022a), the MODIS data from the Giovanni web portal (https://giovanni. gsfc. nasa. gov/giovanni/, last access: 1 July 2022; NASA, 2022b), and the SEVIRI and CATS data from ICARE (https://www. icare. univ-lille. fr/, last access: 1 July 2022; AERIS/ICARE Data and Services Center, 2022). The Meso-NH-derived fields and back-trajectory data can be obtained upon request to the corresponding author of the paper.
Methods Resting-state functional imaging (rs-fMRI) We used 110 7T resting-state functional MRI datasets from the Human Connectome Project S1200  (Glasser et al. 2013). Images were preprocessed and registered to the MNI152 space as specified in the Human Connectome Project protocol (http://www. humanconnectome. org/storage/app/media/documentation/s1200/HCP_S1 200_Release_Reference_Manual. pdf; Glasser et al. 2013). VAN  and DAN maps in the structural space  VAN  and DAN maps were computed using seed regions of interest defined in the functional cortical parcellation map  (Gordon et al. 2016). This template includes 23 VAN parcels (11 in the left and 12 in the right hemisphere) and 32 DAN parcels (19 in the left and 13 in the right hemisphere). This parcellation was performed according to resting-state functional connectivity patterns.
and DAN maps in the functional space The 110 individual VAN Pearson's correlation maps in the MNI152 space were aligned in a functional space to optimize their inter-individual alignment of functional areas  (Mueller et al. 2013; Robinson et al. 2014; Langs et al. 2015; Glasser et al. 2016). We used the Advanced Normalization Tools (ANTs) script 'buildtemplateparallel. sh' to perform an iterative (n=4) diffeomorphic transformation to a common space  (Avants et al. 2011; Alves et al. 2019 ). Cross-correlation was set as the similarity measure and greedy SyN as the transformation model  (Avants et al. 2008; Klein et al. 2009 ). The resulting transformation warps were applied to the MNI152 aligned VAN maps, using the ANTs' script 'WarpImageMultiTransform' to represent the 110 individual VAN maps in the functional space.
The DISTAL atlas is a high-resolution template of subcortical structures in the MNI space used as a reference to localize targets for deep brain stimulation  (Ewert et al. 2018 ). The DISTAL atlas segmentation was performed manually, based on histology, structural imaging, and diffusion-weighted imaging  (Chakravarty et al. 2006; Ewert et al. 2018). The THOMAS atlas is a template of thalamic nuclei derived from the manual segmentation of 20 White-Matter-Nulled Magnetization Prepared Rapid Gradient Echo (MP-RAGE) 7T datasets warped to the MNI space  (Su et al. 2019).
Tractography analysis We analyzed the structural connectivity of the VAN and DAN, including the new subcortical structures identified in our resting-state functional connectivity analysis. Tractography was computed using 177 diffusion-weighted images from the 7T dataset of the Human Connectome Project  (Vu et al. 2015). The scanning parameters are Normalization Tools pipeline  (Avants et al. 2011). Third, the resulting template was co-registered to the MNI152 2mm template using the FSL's tool 'flirt'  (Jenkinson et al. 2002). Finally, the resulting transformation warps were applied to the individual whole-brain streamline tractography using Tract Querier  (Wassermann et al. 2016). Then, we computed the structural connectome of the VAN and DAN models. The cortical nodes were defined according to  Gordon et al.
Networks lateralization We assessed the lateralization of the VAN and DAN networks. For functional connectivity, the average Pearson's correlation across each hemisphere's VAN and DAN nodes was calculated using the FSL's function 'fslmeants'. The obtained values were compared between the right and left hemispheres. For structural connectivity, we extracted the fiber tracts that crossed two nodes of the same hemisphere. Then, the fiber tracts were converted into volume maps using the MRtrix3's tool 'tckmap', and the individual volumes were compared between the two hemispheres  (Tournier et al. 2019). Data were presented as mean (with standard deviations) or median (with interquartile ranges), and paired analyses were performed with paired t-test or Wilcoxon test, according to their distribution.
While the cortical regions of the DAN and the VAN are quite neatly segregated  (Vossel et al. 2014 ), the subcortical nuclei described in our model probably contributed to both the VAN and the DAN. Future investigations using our model to explore the BOLD signal during task-related fMRI in humans or direct electrical recordings in animals might better dissociate the hierarchical organization and functional role of subcortical regions than resting-state fMRI. In addition, the neurotransmitter systems normative atlas is derived from different samples  (Hansen et al. 2021). As PET and SPECT tracers are radioactive, it is not possible to map several neurotransmitter systems in the same participants. Although the atlas was replicated in an independent autoradiography dataset and all scans were acquired in healthy volunteers  (Hansen et al.
(Hansen et al. 2021;Markello et al. Markello et al. 2022en the spatial distribution of the created structural projection map and the neurotransmitter maps provided by Hansen and colleagues using the neuromaps' tool 'compare_images'(Hansen et al. 2021;Markello et al. Markello et al. 2022; https://netneurolab. github. io/neuromaps/). AAL3 was chosen because it includes cortical and subcortical parcels. The null parcellations were generated from the average VAN and DAN structural projection maps using the.
Acknowledgments This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No. 818521 ; MTS), the  Marie Skłodowska-Curie programme  (grant agreement No. 101028551 ; SJF), a  Donders Mohrmann Fellowship  (SJF, No. 2401512 ), " Prémio João Lobo Antunes 2018" -SCML (PNA) , and " Bolsa de Investigação em Doenças Vasculares Cerebrais 2017 " -SPAVC (PNA). MC was supported by  MIUR -Departments of Excellence Italian Ministry of Research  ( MART_ECCELLENZA18_01 );  Fondazione Cassa di Risparmio di Padova e Rovigo (CARIPARO) (Grant Agreement number  55403 );  Ministry of Health Italy NEUROCONN  ( RF-2008 -12366899 );  Celeghin Foundation Padova  ( CUP C94I20000420007 ); BIAL foundation grant (No.
Data were provided by the McDonnell Center for Systems Neuroscience at Washington University. Data availability statement The presented brain maps are openly available at https://neurovault. org/collections/XONZLGPJ/, the resting-state functional 7T MRI datasets in the Human Connectome Project S1200 dataset, and the processed tractographies at http://opendata. bcblab. com. Code availability statement Analyses were conducted using open software and toolboxes, as specified in the methods. The Funcon-Connectivity code is openly available at https://github. com/chrisfoulon/BCBToolKit; the ANTs scripts 'buildtemplateparallel. sh' and 'WarpImageMultiTransform' at https://github. com/ANTsX/ANTs; the code where these scripts were applied for the functional alignment performed in this work at https://github.
com/Pedro-N-Alves/VAN_DAN_functional_alignment; the 'easythresh_conj. sh' code (used for the conjunction analysis) at https://warwick. ac. uk/fac/sci/statistics/staff/academicresearch/nichols/; the 'tckedit', 'tckmap' and 'tck2connectome' commands' codes at https://github. com/MRtrix3/mrtrix3; the 'ConnectivityMeasure' script at https://github. com/nilearn/nilearn/; the 'betweenness_centrality' and 'degree_centrality' scripts at https://github. com/networkx/networkx; and the 'compare_images', 'Parcellater' and 'nulls. burt202' scripts at https://github. com/netneurolab/neuromaps.
(Hansen et al. 2021;Markello et al. Markello et al. 2022en the spatial distribution of the created structural projection map and the neurotransmitter maps provided by Hansen and colleagues using the neuromaps' tool 'compare_images'(Hansen et al. 2021;Markello et al. Markello et al. 2022; https://netneurolab. github. io/neuromaps/). AAL3 was chosen because it includes cortical and subcortical parcels. The null parcellations were generated from the average VAN and DAN structural projection maps using the neuromaps' function 'nulls. burt2020' (5000 permutations, generating 5000 null parcellations; Burt et al. 2020; Markello et al. 2022; https://netneurolab. github. io/neuromaps/). The graphical representations were created with Matplotlib 3. 4. 2 and Datashader 0. 13. 0 (Hunter 2007; https://datashader.
2022;https://netneurolab. github. io/neuromaps/). To control for spatial autocorrelations and reduce the risk of false positive results, statistical significance was inferred based on null models generation(Alexander-Bloch et al. 2018;Burt et al. 2020;Markello and Misic 2021). Volumetric data was parcellated according to the Automated Anatomical Labeling atlas 3 (AAL3;Rolls et al. 2020), using the neuromaps' utility 'Parcellater' (.
Epidemiological projections point to acquisition of ever-expanding multidrug resistance (MDR) by Escherichia coli, a commensal of the digestive tract and a source of urinary tract pathogens. Bioinformatics analyses of a large collection of E. coli genomes from EnteroBase, enriched in clinical isolates of worldwide origins, suggest the Cytotoxic Necrotizing Factor 1 (CNF1)-toxin encoding gene, cnf1, is preferentially distributed in four common sequence types (ST) encompassing the pandemic E. coli MDR lineage ST131. This lineage is responsible for a majority of extraintestinal infections that escape firstline antibiotic treatment, with known enhanced capacities to colonize the gastrointestinal tract. Statistical projections based on this dataset point to a global expansion of cnf1-positive multidrugresistant ST131 strains from subclade H30Rx/C2, accounting for a rising prevalence of cnf1-positive strains in ST131.
43 o better appreciate cnf1 dynamics, we performed a large-scale screening of the toxin gene distribution in a large dataset of E. coli genomes deposited in EnteroBase. 44 The observed increase of cnf1-positive strains in the ST131-H30Rx/C2 lineage led us to hypothesize that cnf1 may confer a competitive advantage to colonize the GIT. Indeed, the wildtype strain EC131GY from lineage ST131-H30Rx/C2 outcompeted a cnf1-deleted variant when concurrently inoculated into the GIT, arguing for a role of CNF1 in EC131GY selection within the gut that might be linked to CNF1 deamidase activity on Rho GTPases to promote tissue invasion. Surprisingly, we observed no differences in fitness or inflammation in monoinfections of the urinary tract linked to the presence or absence of cnf1. Collectively, these data support although CNF1 does not impact host response to UTI, it acts as an intestinal colonization factor during competition in the GIT.
Analysis of the distribution of cnf genes in a large collection of E. coli genomes At the start of this study, we mined large genomic datasets from EnteroBase to gain more insight into the distribution of the cnf1 gene and its close homologs in the E. coli population. 44 EnteroBase represents an integrated software environment widely used to define the population structure of several bacterial genera, including pathogens. Quantitative information on the collection of 141,234 E. coli genomes deposited in EnteroBase are reported in supplementary Figure  S1. This collection, starting in 1900, aggregates genomes from strains collected worldwide, but mainly from Europe and North America, and from a wide range of sources but primarily human clinical isolates (Sup. Figure  S1a , S1b, S1c). Using a Hidden Markov Model (HMM) approach coupled to amino acid pairwise distance calculation, we retrieved cnflike positive strains and characterized each type of cnf sequence.
In total, we identified 6,411 cnf-positive strains (4. 5% of all E. coli isolates) with a remarkable dominance of cnf1 (87. 8%, n = 5,634), as compared to cnf2 (8. 6%, n = 554) and cnf3 (3. 5%, n = 223). These strains displayed only one type of cnf-like encoding gene. The prevalent cnf1 gene in this genomic dataset was widely distributed among isolates of all origins but most notably in the groups denoted humans (5. 4% of n = 48,518 human isolates) and companion animals (24. 1% of n = 2,652 companion animal isolates) (Sup. Figure  S1c ). We next studied the distribution of cnf1 among E. coli phylogenetic groups and sequence types (STs). The cnf1 gene was preferentially associated with isolates from the phylogroup B2, representing 24. 3% of n = 22,305 retrieved genome sequences (Sup. Figure  S1d ).
The dataset of accessory genes was built from n = 7,678 sequences that were present in at least 50 and no more than 3,931 strains. We conducted a hierarchical clustering of strains and retained 10 distinct accessory gene clusters. Strikingly, this revealed a conservation between phylogenetically defined groups CNF1_LL1 and CNF1_LL2 and groups defined by their accessory gene contents (Figure  2b ). Indeed, the hierarchical clustering was most evident for CNF1_LL2, showing a differential enrichment determined with Scoary of n = 1,434 genes as compared to other strains from clade C (P < 0. 05, Bonferroni-adjusted correction). Together, these data point toward intensive groupspecific diversification of accessory gene content in cnf1-positive clusters in ST131-H30.
Cnf1-positive strains display dominant expansion in ST131-H30Rx/C2 We next analyzed the temporal distribution of cnf1positive strains within clades and subclades. Using a Generalized Linear Models (GLM) approach, we first verified within our dataset the increase of fimH30-positive isolates over time (clade C) in E. coli ST131 that was maximal in H30Rx/C2 (P < 2. 2 10 -16 , Chi-square association test) (Figure  4a ). We also noted a significant increase in the proportion of cnf1-positive strains over time in E. coli ST131 (Figure  4b , top panel). The GLM was then fitted on years, clades, and subclades. We tested the significance of the year effect and P-values were corrected for multiple comparisons using Tukey's method. The year effect was not significant for clade A, B, or subclade H30R/C1 (Figure  4b ).
Instead, we observed a significant increase of the proportion of cnf1-positive strains within H30Rx/ C2 over time (P = 1. 25 10 -11  ). In addition, the GLM fitted curves predicted that the prevalence of cnf1positive strains within H30Rx/C2 subclade would be approximately 50% (confidence interval of 95% [43% to 58%] in 2018; [47% to 64%] in 2019). Predictive values were compared to the prevalence of cnf1 in ST131 strains isolated in 2018 or 2019 in a second independent dataset up-loaded from EnteroBase in September 2020. The prevalence of cnf1-positive strains within the subclade H30Rx/C2 was 45% in 2018 and 48% in 2019, confirming the prediction of a dominant expansion of cnf1positive strains within ST131-H30Rx/C2.
47, 48 Thus, although the EnteroBase E. coli dataset likely includes some bias in the sampling of strains with a high abundance of strains responsible for acute human infections and from specific continents, this database is well-adapted to study toxin gene distribution in human clinical isolates of E. coli. Further in support of a possible advantage conferred by the acquisition of cnf1, we found a high coverage of strains positive for cnf1 in the common clinical STs, ST73, ST12, and ST127, but not in ST95. 24 Collectively, these findings suggest that cnf1 may enhance the capacity of GIT colonization by some ExPEC, in turn contributing to the dissemination of a few E. coli lineages and sublineages, such as ST131-H30Rx/C2. The intestinal tract is a key reservoir for ExPEC strains. 49 A previous study suggested that E.
E. coli genomes dataset The dataset corresponds to 141,234 E. coli genome sequences retrieved from EnteroBase (November 2020) (http://enterobase. warwick. ac. uk)  44  Strains' metadata (collection year, continent, source niche of isolation and sequence type) were also retrieved (Sup. Table  S3 ). Assemblies were downloaded in GenBank format and proteomes generated using annotations provided in GenBank files.
ST131 dataset structure and phylogenomic analysis The database used for phylogenetic and statistical analyses consists of whole-genome sequences of E. coli ST131 isolates collected by mining EnteroBase from 1967 to 2018. 44 Leaning on Find ST(s) tool from EnteroBase, we retained a total of 5,231 genome assemblies and associated metadata, including information of the isolation date, country and source of isolates (Sup. table  S5 ). Phylogeny of ST131 isolates was resolved using core nonrecombinant SNPs defined with Parsnp (in total 37,304 SNPs)  63  and Gubbins v2. 3. 4. 64 A maximumlikelihood tree was then estimated with RAxML v8. 2. 8 applying a general time-reversible substitution-model with a gamma distribution rate across sites and with an ascertainment bias correction  65  and the resulting tree was edited with the interactive Tree of Life (iTol) v4 program.
Pan-genome analysis The pangenome of E. coli ST131 was estimated using Roary, a high-speed pan genome pipeline analysis tool. 67 Roary returns as output, the gene presence/absence matrix. The matrix was curated to retain genes present in at least 50 genomes and less than 3931 genomes (7678 sequences), that constituted our accessory genes pool dataset. Hierarchical clustering analysis was then conducted according to the Ward's minimum variancederived method. The Ward's method is a clustering criterion that aggregates observations into clusters to minimize the within-cluster variance. The method was implemented using the pheatmap package in R (cran. r-project. org/web/ packages/pheatmap/index. html). The gene presence/absence file generated by Roary was further analyzed using Scoary with a significant Bonferroni-adjusted P-value < 0.
In silico antimicrobial resistance and virulence-associated markers GyrA and ParC protein sequences were retrieved from the EnteroBase annotated genomes, and aligned with the mafft L-INS-I approach. 69 After a visual inspection of the alignment, in-house customized perl scripts (https://github. com/rpatinonavarrete/QRDR) were used to identify the amino acids at the quinolone resistance-determining region (QRDR) (positions 83 and 87, and 80 and 84 in GyrA and ParC, respectively). Search for cnf1 and hlyA alleles in ST131 genomes dataset was carried out by Blastn analysis. Sequences were next aligned with Muscle  70  and curated to remove incomplete sequences. SNPs were then extracted using SNP-sites. 71 To determine strain specific VF profiles, annotated VFs from UPEC described in  34  were translated and pBLASTed against ST131 genomes dataset considering only hits with e-value < 10 -5 and identical matches > 95% (sup.
Co-clustering method Statistical analyses were performed using R software version 3. 6. 0. A total of 20 strains from the collection of 5,231 strains of E. coli ST131 were removed from the analysis due to incomplete associated metadata. The clustering of strains with specific virulence or acquired antibiotic-resistance gene profiles was performed with binary latent block model, implemented in the R package blockcluster. 74 The co-clustering of both virulence or resistance genes and strains was performed with a binary latent block model, implemented in the R package blockcluster. 74 This package implements an Expectation Maximization algorithm to compute the maximum likelihood estimator of the parameters of the mixture of Bernoulli distributions used for co-clustering. As proposed by the authors,  74  the number of clusters was estimated by maximizing the integrated complete-data likelihood criterion (ICL) on a bidimensional grid of parameters making this unsupervised classification procedure automatic.
Flow cytometry Mice were sacrificed at 24 hours post-infection (PI) and the bladders removed. Single-cell homogenates were prepared by incubating minced bladders in 0. 34 Units/mL Liberase TM (Roche) diluted in PBS at 37°C for 1 hour, with manual agitation every 15 minutes. 80 Digested tissue was filtered using a 100 μm filter (Miltenyi), washed, blocked with Fc Block (Rat anti-mouse CD16/CD32, BD Biosciences), and immunostained (Supplementary Table  S9 ). Samples were acquired on a BD Fortessa (BD Biosciences) and analyzed using FlowJo Version 10. 7. 1 software.