dataesr/scientific-paragraphs-categorization huggingface dataset:
For the data used in this section, the acquisition dataset was split into two subsets, one corresponding to stimulation periods (30 s to 60 s within each repeated stimulation pattern) and one corresponding to baseline periods (0 s to 30 s within each repeated stimulation pattern). MB count and velocity maps were computed for these two subsets. They correspond to data shown in Figs. 2d-j and 3a,c, Extended Data Figs. 5  and 7  and Supplementary Table  1. For longitudinal profile metrics (Figs. 2d-h and 3a,c), a skeletonization (MATLAB bwmorph function) was performed on the vasculature binary image (2D filtering). The skeleton corresponding to the vessel under study was selected and eventually removed from short segments to keep only the penetrating vessel centerline. For each pixel of the centerline (corresponding to each cortical depth of the vessel), the flow direction was measured (computed as tan -1 ( Vx Vz ) ) and an 80-µm segment normal to the flow direction and centered on the centerline was chosen to compute the MB count and velocity profiles.
The following metrics were extracted for each depth: max MB count, velocity at max MB count, diameter. The diameter was defined as the width of the vessel at a threshold set by the half maximum of the rest profile. A smoothing filter (window of 200 µm) was applied on max MB count and velocity values along the depth dimension. The same was performed on the diameter value using a median filtering (presence of outliers due to bifurcations). For transversal profiles (Fig. 2i ,j, Extended Data Fig. 7b ,c and Supplementary Table  1 ), a segment crossing the vessel under study at the desired depth was chosen. The flow direction (defined as tan -1 ( Vx Vz ) ) was computed at the pixel corresponding to the maximum MB count (center of the vessel). MB count and velocity profiles were computed by averaging on a 50-µm wide slice of an 80-µm segment normal to the flow direction and centered on the pixel with maximal MB count.
Statistics and reproducibility. Ten animals were used in this study. Supplementary Table  2  summarizes which animal was used for each figure or result. In Extended Data Fig. 3 , we used n = 4 rats for whisker stimulations (20 stimuli repetitions) and n = 3 rats for visual stimulations (20 stimuli repetitions). The ROI were selected using fUS imaging experiments, as described in 'Functional ultrasound activation maps and ROI selection' and were used to extract one temporal response for each ROI and for each animal. In one of the four experiments, no thalamus activation could be seen in the fUS experiment. ULM maps were also registered in the Paxinos Atlas referential  52. The temporal responses shown in Extended Data Fig. 3  are shown as mean ± s. e. m. for those n = 4 (except for thalamus, n = 3) and n = 3 rats.
Microflow rendering for video representation. Animated flow rendering. Computed-generated imagery animation (Supplementary Videos 1 and 2) was created using the software Houdini 18. 5. 563 (SideFX) to visualize the functional information extracted from dynamic bubble positions and tracking information. MB count variation (Supplementary Video 1). The MB flux variation (MBs per pixel per second) and mean velocity vector (Vx, Vz) (corresponding to MBF (x, z, t) and V s (x, z, t), section 'Temporal ULM data construction') acquired from whisker stimulations pattern (70 blocks of 1 s each consisted of 30 s rest, 30 s stimulation and 10 s rest repeated 20 times and summed) were imported into Houdini using a custom Python node (Supplementary Video 1). To produce smooth animation with 420 frames (played at 24 f.
p. s. ) using the initial 70 frames, data were linearly interpolated using a 'retime' node. Next, inside a particle operator (POP) network, particles life was set to 0. 1 s (2. 4 frames) and their position was updated for each frame based on the imported velocity vector field using the node 'POP advect by volumes'. Finally, flux increase, in respect to baseline, was highlighted by replicating particles proportionally to the value of MBs per pixel per second, using the 'POP replicate' node with a maximum of four emitted particles per second having a lifetime of 0. 5 s (12 frames). And to better emphasize the flux increase, particle color was converted from RGB to hue, saturation and value color space and the color value (intensity) was modified proportionally to the value of MBs per pixel per second.
MB trajectories (Supplementary Video 2). MBs dataset point gathering track ID, position (x, z) and velocity for activated barrel venule or arteriole and control venule or arteriole were imported into Houdini. MB trajectories were reconstructed using an 'add' node using track ID attribute to create a solid line and then animated using a 'carve' node (Supplementary Video 2). Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article. Extended Data Fig. 2  | Temporal profiles of microbubble detection in typical pixels representative of pial vessels, penetrating vessels and 1 st order branching capillaries. (A) time courses of MB count for a pixel in an arteriole (top, γ in D) and smaller blood vessel (bottom,1 st order branching after descending arteriole, ε in D), illustrated in D.
Code availability Data analyses were performed on MATLAB R2020b (MathWorks). Home-made MATLAB codes were used for the ULM algorithms: SVD filtering of tissue signal (according to Demené et al. 54  ) and localization of the MBs. The ULM algorithm includes vesselness filtering available on Mathworks file exchange (2D implementation available on Mathworks file exchange)  60, 61  and a tracking algorithm, simpletracker. m available on Mathworks Tinevez, wrapping the MATLAB munkres algorithm implementation of Cao, 2009. Home-made MATLAB codes were used for the ULM data analysis and the statistical analysis. Super-resolution videos were obtained using a 3D software for visualization (Houdini, 17. 5. 360, SideFX). MATLAB codes for the reading of ULM data  50  are provided on the Zenodo repository website at: https://doi.
al. 2020 ). This increased our ability to generate phylogenetic signal in the simulated networks without affecting their overall structure. Under the BipartiteEvol model, closely related species tend to interact with similar sets of partners (i. e. there is a phylogenetic signal in species interactions) if (and only if): (1) closely related species have similar traits (i. e. there is a phylogenetic signal in species traits) and (2) these traits determine who interacts with whom, i. e. ≠ 0. Similarly, an anti-phylogenetic signal in species interactions (i. e. the tendency for closely related species to associate with dissimilar partners) is expected if there is antiphylogenetic signal in species traits (i. e. closely related species have dissimilar traits) and  ≠ 0. Using the R-package RPANDA  (Morlon et al.
Ecological distances were measured both without accounting for evolutionary relatedness of the interacting partners, using (weighted or unweighted) Jaccard, and accounting for relatedness using (weighted or unweighted) UniFrac distances (Supplementary Methods 3  (Lozupone et al. 2011)). Accounting for evolutionary relatedness of the interacting partners can be particularly relevant for organisms with uncertain species delineations (e. g. microorganisms delineated using only molecular data  (Martos et al. 2012; Sanders et al. 2014) ). We used Pearson, Spearman, and Kendall correlations (R) by extending the mantel function in the Rpackage ecodist  (Goslee and Urban 2007) ; the significance of each correlation was evaluated using 10,000 permutations, except for the computationally intensive Kendall correlation (100 permutations only).
For each network, we considered that there was a significant phylogenetic signal (resp. anti-phylogenetic signal) if the correlation coefficient (R) was higher (resp. lower) than >95% of the randomized correlations; we computed the p-value of each one-tailed Mantel test as the fraction of the randomized correlations above (resp. below) the original value. PBLM: To estimate phylogenetic signal based on PBLM, we modified the function pblm from the R-package picante  (Kembel et al. 2010 ) to more efficiently perform matrix inversions and handle large interaction networks. In short, the parameters dA and dB of the Ornstein-Uhlenbeck processes of PBLM were estimated using generalized least squares  (Ives & Godfray 2006). dA and dB are interpreted as a measure of phylogenetic signal in species interactions: if d=0, there is no effect of the phylogeny (similar as evolution on a star phylogeny, i.
Second, we assessed whether partial Mantel tests successfully correct for phylogenetic signal in the number of partners using networks simulated under a process that generate phylogenetic conservatism in the number, but not the identity, of interacting partners (i. e. partial Mantel tests should not be significant when applied to such networks). To simulate network with only phylogenetic conservatism in the number of partners in guild A, we first simulated phylogenetic trees for guilds A and B using pbtree (R-package phytools; Revell 2012) with a number of species uniformly sampled between 40 and 150 by guild. Next, we simulated the number of partners of the species from guild A using an Ornstein-Uhlenbeck process with an attraction toward 0, a variance of 0. 1 (noise of the Brownian motion), and a selection strength (aA) ranging from 5 (strong stabilizing effect, weak phylogenetic signal) to 0 (Brownian motion, strong phylogenetic signal).
We computed the number of partners per species by calibrating the simulated values between 1 and the number of species in guild B and taking the integer part. For each aA value (5, 1, 0. 5, 0. 05, or 0), we performed 100 simulations using mvSIM (R-package mvMORPH;  Clavel et al. 2015). Finally, for each species in A, we attributed the corresponding number of partners in B at random to obtain binary networks. We checked that our simulations indeed generated a signal in the number of partners by performing simple Mantel tests between phylogenetic and degree difference distances. Finally, we performed on each simulated network a partial Mantel test between phylogenetic and ecological distances, while controlling for the absolute differences in degrees. Given the poor performances of partial Mantel tests (see Results), we tested whether using sequential Mantel tests would provide a good alternative: based on simple Mantel tests, we consider that there is a phylogenetic signal in the identity of the partners if there is a phylogenetic signal in species interactions and no phylogenetic signal in the number of partners.
The phylogenetic tree does not need to be binary, rooted, or ultrametric. For each step, an example of the corresponding function available in the R-package RPANDA is indicated in grey. Step 1: The first step consists in testing for phylogenetic signal in species interactions for guild A (i. e. whether closely related species from guild A tend to interact with similar partners from guild B) using a one-tailed simple Mantel test. This step requires to pick an ecological distance (UniFrac distances are recommended compared to Jaccard distances) and a type of correlation (Pearson correlation by default). Step 2: Next, to assess whether a phylogenetic signal in species interactions really comes from the identity of species interactions, the second step consists in testing whether there is phylogenetic signal in the number of partners of guild A (i.
Acknowledgments: The authors acknowledge  M. Elias ,  E. Thébault , and  D. de Vienne  for helpful discussions. They also thank  I. Overcast ,  S. Lambert ,  I. Quintero ,  C. Fruciano ,  J. Clavel , and  A. Silva  for comments on an early version of the manuscript. This work was supported by a doctoral fellowship from the  École Normale Supérieure de Paris  attributed to BPL and the  École Doctorale FIRE -Programme Bettencourt. Funding of the research of FM was from the  Agence Nationale de la Recherche  ( ANR-19-CE02-0002 ). HM acknowledges support from the  European Research Council  (grant CoG-PANDA).
Data accessibility: The R functions used to measure phylogenetic signals in bipartite interaction networks, including (simple, partial, and clade-specific) Mantel tests and PBLM, are available in the R-package RPANDA  (Morlon et al. 2016 ) (functions phylosignal_network and phylosignal_sub_network). A tutorial and the simulated networks can be found at https://github. com/BPerezLamarque/Phylosignal_network. Amended functions of BipartiteEvol are also included in RPANDA. The scripts for simulating the networks and for measuring the phylogenetic signals in species interactions are available at: https://github. com/BPerezLamarque/Phylosignal_network/tree/master/simulations.
After an epidemiological and microbiological investigation with the help of WGS data, pointed to two types of raw-milk cheeses as vehicles of the S. Dublin outbreak at that time  (30) , a working group was formed to perform a retrospective study focusing on this serotype in the most affected regions. Thanks to the extensive epidemiological and microbiological investigations at the time, a large dataset of strains was collected between 2015 and 2017 in two regions producing the incriminated cheese. The study was designed to understand the circulation of strains and to improve the overall surveillance of S. Dublin through whole genome analysis. We present a retrospective genomic analysis of 480 S. Dublin isolates from collections, field investigations, con-trols and surveillance plans spanning the production process steps from fields to products, all characterised with a set of minimal metadata and contextualised with geographical and temporal links to actual outbreak events.
We crossed the phylogenetic signal with metadata to depict and characterize a region-wide S. Dublin diversity and strain dynamics over several years to unravel the genesis of outbreak events. We investigated the impact of homologous recombination events on phylogenomic reconstructions accuracy (i), the correlation of phylogenomic clustering with isolation date, isolation matrices, geographical origin and epidemiological hypotheses (ii), as well as the potential epidemiological relationships with a WGS-based phylogeographic analysis (iii). We highlighted the benefit of WGS approach on closely related isolates and demonstrated how the results from large datasets can help to understand the contamination dynamics of strains, with the objective of supporting sanitary and health authorities to design appropriate safety policies.
Data availability, privacy and anonymization To comply with statistical, geographical and data privacy, metadata presented in the manuscript were transparently anonymized. Geographical data were anonymized in order to avoid disclosing identity from sparse point distribution, through jittering coordinates with uniform distributions computed separately for longitude and latitude and with amplitudes ranging from -0. 2 to +0. 2. To improve readability in high-density area, an additional uniform distribution jitter was added, proportional to the point density computed on all coordinates with an amplitude of up to +0. 2. All raw sequencing materials used in the manuscript are publicly available (Bioproject: PRJNA737646).
Collection of samples and related metadata In this study, four laboratories from the public and private sectors collected 2249 strains of S. Dublin between 2015 and 2017  (31). Samples were collected all along the production stages from cows to finished products. After harmonization of metadata, 2101 strains of S. Dublin were sub-sampled using the Gower Algorithm  (31)  in order to build a representative collection of samples as described below. The laboratory for food safety in Maisons-Alfort from ANSES provided 77 samples from its collections of strains, through the French food-chain surveillance (SCA) platform and the biennial 'National Salmonella Surveillance plan' 2015 and 2017 (www. plateforme-sca. fr). One sample was collected in December 2014, and was added due to temporality proximity with the dataset.
Strains selection In order to downsample the dataset of isolates representing the S. Dublin diversity, we used a previously described method (31) leveraging available metadata (i. e. year sample, region, production stage and type). Based on Gower coefficient (GC), the distance between two units is the sum of all the variable-specific distances associated to the metadata, whose attributes have a mixed of categorical and numerical values. Each variable can have a weight and consequently change the importance of each metadata class. First, dissimilarity matrix between samples is computed using Gower's distance. Then, hierarchical clustering is applied on the dissimilarity matrix to cluster samples. Finally, the 'silhouette' plot is displayed, measuring how close each point in one cluster is to the points in the neighbouring cluster (Supplementary Figure  S1-A ).
Sequence assembly The assembly was performed with ARTwork, a freely available workflow developed by the team GAMeR at ANSES (https://github. com/afelten-Anses/ARtWORK). In summary, ARTwork estimates the coverage of reads dependently of the LT2 reference genome (bbmap (  33 )), normalizes the reads (bbnorm (34)), controls the quality of the reads (fastqc (https://github. com/s-andrews/FastQC)) and trims them  (35). Then, de novo assembly is performed with SPAdes (36), PubMLST scheme is detected by MSLT (https://github. com/tseemann/mlst) and closest reference is retrieved using Mash  (37). Finally, scaffolding is performed with Medusa  (38) , gap filling is done with GapCloser (39), contigs are trimmed with Biopython  (40)  and an assembly synthesis is carried out with QUAST  (41). Three samples displayed sequencing errors and could not be assembled.
Finally, sample serotyping was performed in silico based on the assembled genomes using SeqSero  (43). Unless conflicting or with reasonable doubt on the error source (metadata, low coverage, etc. ), lab-typed and predicted serotypes other than Salmonella enterica subsp. enterica serotype Dublin have been discarded. To extend the quality check beyond the sample-specific metrics, we enforced a dataset-wide two-factor (breadth of coverage × depth of coverage) criterion. In a large-dataset context, each genome with its own depth of coverage variation along its sequence exhibits segments with low coverage, thus locally preventing a sound SNP calling. When adding up samples in a dataset, the number of regions where the minimal depth of coverage is not met for at least one of the samples steadily increases.
This figure rises sharply when poor quality samples are included in the data set, drastically reducing the breadth of sequence actually used for the phylogeny reconstruction and isolate discrimination. To ensure data consistency and maximize callable SNP positions in a core genome SNPs (cgSNP) analysis context, we rejected high core-losing samples as identified by iteratively comparing depth of coverage drawn from N-samples to that drawn from N-1 samples. Depth of coverage was calculated for each isolate using samtools depth  (44). We implemented this method using an in-house filter that keeps each sample containing more than 4. 0M positions covered at more than 30×, altogether resulting in a dataset-wide >30×, with a core genome size estimated at 3. 8 Mb.
Robustness was tested with IQTree parameters -alrt 1000 and -bb 1000. In order to comply with ClonalFrameML, the primary tree was rooted using a midpoint method as implemented in FigTree (https://github. com/rambaut/figtree/). Following evidences sustaining that phylogenetic inferences relying on a Markov chain model of nucleotide substitutions should only take into account points mutations  (22). Although filtering of SNPs from homologous recombination events might induce partial loss of information  (45, 50) , we decided to characterize the impact of recombination filtering (Supplementary Figure  S2 ) and subsequently to discuss the results excluding recombinant variants (Figure  1 ).
Construction of the genomic dataset After asserting presence of compulsory metadata and harmonizing values, 2 101 samples of S. Dublin were sub-sampled using the Gower Algorithm down to 398 (Supplementary Table  S1 , Random, 'RND' throughout the manuscript) (Supplementary Table  S1 , Gower). Additional samples (n = 104) were included to resolve intricate strain detections in unexpected contexts, understand transmission routes and contribute to food production quality standards, 34 of which did not pass filters (Supplementary Table S1, Targeted, containing selections SELEC A + SE-LEC C + SELEC D + SELEC E). In order to contextualize the regional study with the epidemiological investigation of 2015-2017, paired-reads from Ung et al. (30)  encompassing samples from food sources and human cases were included in the analysis as well as 77 strains from the ANSES strain collection.
Analysis of the diversity at the core genome level All samples from the study are predicted as Salmonella enterica subsp. enterica serotype Dublin by SeqSero  (43) , and sequence type (ST) 10 by MLST (https://github. com/ tseemann/mlst). According to prior knowledge about the clonal expansion of S. Dublin  (53, 54)  and within a limited time and geographical span, mutation rate and recombination events were quite low through the genomes of interest. Altogether, 1041 SNPs were detected along the 480 genomes, of which 17 lay within 8 homologous recombination events spanning a total of 299 bp (6 on leaves, 2 on internal nodes). After exclusion of the variants located in homologous recombination segments, 1024 SNPs remained. The core genome SNP-based maximum likelihood (ML) phylogeny topology was slightly impacted by the removal of homologous recombination variants, with only few minor differences observed between nodes (Supplementary Figure  S2 ).
This result shows that strains isolated as part of surveillance plans can provide an early warning of potential future human contamination. The targeted selections (SELEC A, C, D and E) were built on the intuitions of local actors that some isolates might be related. Phylogenomic clustering showed that each of these 4 datasets is polyphyletic. For SELEC D, corresponding to milk samples in a restricted area, the strains were scattered throughout the phylogenetic tree. As a general trend, targeted epidemiological investigations were in every case not in agreement with the genomic evidence. On one hand, some of the genomes were not clustered together, and on the other hand, samples originally considered as not related to any outbreak events were clustered with outbreak strains (i. e. identified as genetically very close to defined genetic clusters) (Figure  1 ).
S. Dublin outbreaks had multiple origins During the present study, we first analysed a sample set without isolates from human cases. We observed that adding samples from human cases did not change the tree topology. We also noticed that human samples are located in different clusters, showing different contamination origins as shown in Ung et al. (30). 4 samples from human cases are singleton, meaning that their origin remains elusive at the time. This observation suggests that the surveillance plan, despite its size and meshing, does not fully cover the diversity that exists in these two regions or that our subsampling failed to represent rare clusters from which those cases arose. The granularity of the surveillance should however not be held as a sole source of data scarcity as a large part of Salmonella cases are undetected or unreported  (2).