dataesr/scientific-paragraphs-categorization huggingface dataset:
Distributions of serovars from potential mono-and multianimal sources The composition of Salmonella serovars from Entero-Base  [58]  were investigated in order to build a genome dataset taking into account the confounding factors of microbial GWAS (Additional file 1), namely genome selection  [43, 44] , recombination  [43, [45] [46] [47] , population structure  [33, 40, 43, 48]  and genome wide significance  [43, 50]. Out of 13,635 records from a curated and synthetic subset of Enterobase, Salmonella isolates were mainly distributed in avian, bovine, fish, plant, shellfish and swine sources, enabling the selection of multiple strains for each studied serovar and source when building our dataset (Additional file 2). Because the detailed records from Enterobase were not enough detailed to determine if the strains from plants and shellfishes were isolated inside or outside tissues, the present study focuses on adaption to the following sources: avian, bovine, swine and fish.
Among strains isolated from these sources (n = 11,450), most (22 out of 35) serovars (Fig. 1 ) had single animal sources (p < 4. 5 × 10 -1 , Chi-square tests of uniformity to find serovars associated with some sources). Respecting high levels of diversity in terms of phylogenomic relationships in agreement with previous studies  [59] , geographical origins, dates of isolation and BioProject accession numbers, a balanced dataset of serovars from putative mono-and multi-animal sources (Fig. 1 ) were selected. This dataset was used to detect mutations and metabolic pathways associated with the adaptation of Salmonella serovars to their animal sources. More precisely, isolates of the Salmonella serovars Newport, Typhimurium and Anatum were selected as multi-animal sources, whereas other serovars were selected as mono-animal sources related to avian (i.
2 ) between strains from mono-and multi-animal sources (Fig. 1 ). NG50 values close to the sizes of the reference circular genomes, low number of long scaffolds (i. e. between 1 and 83 higher than 1000 bp), and almost complete genome fractions (i. e. ≈ 100%) (Additional file 4), were considered as evidences of assembly quality sufficiently high to perform pangenome extraction  [63]. The pangenome extraction revealed logarithmic and hyperbolic forms of curves representing the new and conserved genes according to the sizes of genome dataset, respectively (Additional file 4). According to previous studies that estimated strict coregenome sizes of Salmonella between 1500  [64]  and 2800  [65]  genes, the present open pangenome of Salmonella enterica consists in 2705 core genes and 19,130 accessory genes.
Consideration of confounding factors during microbial GWAS With the objective to take into account the confounding factors during microbial GWAS (Additional file 1), we compared different dataset of genomes to assess the correction of population structure and estimated the impact of the homologous recombination events  [43]. More precisely, 9 microbial GWAS were performed for each animal sources (i. e. 36 analyses) considering different datasets of genomes from multi-(i. e. panmictic expansion) and/or mono-(i. e. clonal expansion) animal sources in the cluster presenting the phenotype of interest, as well as the cluster without this latter one (Additional file 6). Excluding the variants from homologous recombination events, 9 other microbial GWAS (i. e. 36 analyses) were performed with these different datasets of genomes (Additional file 7).
Probably due to the coexistence of purely clonal to nearly panmictic lineages in the dataset of 440 genomes (Additional file 1), the datasets of genomes and variants from homologous recombination events affected the population structure corrections (Additional files 6 and 7). Expected shapes of quantilequantile (QQ) plots referring to suitable population structure corrections (i. e. inflation for only highly significant observed p-values) were systematically checked including genomes from mono-and multi-animal sources in both studied strains and compared strains for the avian, bovine, swine and fish sources (Additional files 6 and 7). Concerning these expected shapes of QQ plots presenting inflations for only highly significant observed p-values, much more stratification of causal mutations were observed including variants from homologous recombination events (Additional file 6), compared to microbial GWAS excluding them (Additional file 7).
Without consensus concerning the genome wide significance of Table  1  Congruency parameters between phylogenomic reconstructions of strains belonging to different serovars of Salmonella enterica subsp. enterica (n = 440) in terms of distance and topology. The phylogenomic reconstructions were performed by maximum likelihood selecting the most appropriate models of evolution and checking ultrafast bootstrap convergences (i. e. IQ-Tree). The compared approaches 'variants' and 'genes' correspond to phylogenomic trees reconstructed using pseudogenomes from variant calling analysis (i. e. iVARCall2) including (A) or excluding (B) variants from recombination events (i. e. ClonalFrameML), and concatenated orthologous genes (C) from pangenome analysis (i. e. Roary), respectively. The cophenetic function of the 'dendextend' R package was used to compute the cophenetic correlations.
The dendrogram function of the 'dendextend' R package was used to compute the Fowlkes-Mallows indexes. The treedist function of the 'phangorn' R package was used to compute the  Robinson  a distance refers to similarity between trees in terms of correlation between the cophenetic distance matrices. Topology refers to differences between two trees in terms of node clustering, respectively microbial GWAS  [43] , and with regards to frequencies of presence and absence of genes and alternative variants (Additional file 8), we estimated and checked visually that associated mutations present p-values of association between p = 8. 78 × 10 -3 and p = 2. 32 × 10 -15 (Fig. 3  and Additional file 8). These mutations associated by microbial GWAS have been retained to apply downstream GOEA.
Discussion Restricted and unrestricted animal sources across Salmonella Salmonella serovars might be considered as having restricted (mono-) or broad (multi-) animal sources. Here we used the Enterobase resource providing both genomic data and metadata to build a dataset to explore the relationships between genotype and adaptation to the animal sources (Fig. 1 ). As exemplified with Escherichia (only host-unrestricted lineages), Campylobacter (both hostrestricted and -unrestricted lineages) and Staphylococcus (only host-restricted lineages), the lineages resulting of phylogenomic reconstructions reflect the genetic structure (i. e. patterns of mutations) established through either host-adapted lineages, physical barriers to colonization, or local clonal spreading induced by selection or genetic drift  [12].
Genetic signatures of Salmonella adaptation to animal sources Especially in highly recombinant bacterial genomes, phylogeographic signatures can be weakened due to dissemination around the world and genomic changes occurring within the reservoir hosts  [70]. Even with a dataset of genomes highly diversified in terms of serovars (i. e. 12 clonal and 3 panmictic serovars including 13 monophyletic and 2 polyphyletic serovars), geographical origin (i. e. 26 countries, 68% from United States) and time of isolation (i. e. 25 th and 75 th percentiles: 2005-2013) origins (Additional file 3), we were able to identify genetic signatures of animal sources (Table  2 , Table  4  and Additional file 8) by microbial GWAS (Fig. [73]  or pre-agriculture wild animal  [73, 74]. Probably because Salmonella enterica subsps.
enterica did not evolve as an obligate intracellular pathogens, we did not observe evidence of accumulation of deleterious mutations and losses of unessential functions (Fig. 2  and Table  2 ), that have been associated  [12] ,. or not  [5] , with host adaptation and restricted host transmission in other organisms. Salmonella genomes from human source were not included in the dataset of genomes, because it would conceal the mutations associated with the avian, bovine, swine and fish sources, but the mutations identified in the present study as associated with animal sources (Table  2  and Additional file 8) could be used as in silico or in vitro markers to identify them from human isolates in a context of source tracking for food safety  [1, 2]. In this framework, we plan to develop in a near future a workflow to attribute animal sources from human samples based on the markers identified in the present study to improve the models of source attribution at the genomic scale  [51] , as recently proposed to prediction sources of S.
Selection of a genome dataset (i) With regard to metadata from Enterobase (i. e. December 2016: 83618 records), we selected 440 isolates in order to depict a high level of genomic diversity of Salmonella enterica subsp. enterica serovars, potentially related to mono-or multi-animal sources  [58]. The corresponding reads were downloaded from the ENA  [115]. With a homemade python script (version 2. 7), the metadata from Enterobase was curated retaining complete records (i. e. BioProject, ENA ID, Host, sample Matrix, serovar, source niche, source origin, source details, country and collection years) and standardizing typos. Based on this curated subset of Enterobase (i. e. 37,747 records), the samples from environment, composite foods of the retail market and humans were not retained because they are considered as vectors of pathogen expositions and exposed susceptible consumers in the present study focusing on adaptation to animal sources (i.
e. 13,635 records of considered sources). Taking into account the unbalanced distributions of serovars and sources in this curated database, indexes representative of the association levels of animal sources were calculated for each serovar and each source. These indexes ( i ¼ log %ð serovar sourceÞ %ð strains sourceÞ ) represent the common logarithm of the number of strains per source for each specific serovar (%ð serovar sourceÞ) divided by the number of strains per source in the full curated database ( %ð strains sourceÞ ). Deviations from 0 correspond to over-or under-representation of the serovar in the source. Chisquare tests of uniformity of these indexes for each serovar allowed sorting of serovars from potential multi-animal sources (p > 0. 02 with i ≈ 0) to potential mono-animal sources (p < 0.
02 with i > 0 for over-represented serovars or i < 0 for under-represented serovars). Based on the curated and synthetic subset, we built a collection of 440 genomes so that its composition was genetically diversified (i. e. 15 serovars) and roughly balanced considering animal sources (i. e. mono-and multi-animal sources from 4 animal sources). More precisely, 20 genomes from each of 3 serovars from potential mono-animal sources were selected for each of the studied sources: avian, bovine, swine and fish (i. e. 240 genomes). Between 60 and 80 genomes from each of 3 serovars from potential multi-animal sources (i. e. 200 genomes) were added in order to get a roughly balanced dataset of potential mono-and multi-animal sources. The balance between mono-and multi-animal sources was deliberate and used to evaluate the impact on several confounding factors during microbial GWAS.
Coregenome variants (ii) The coregenome SNPs and small InDels were detected based on the variant caller HaplotypeCaller implemented in the iVARCall2 workflow  [11] , using Salmonella Typhimurium LT2 (NCBI NC_003197. 1) as a reference genome, and following the best practices proposed by the Genome Analysis ToolKit  [116]. More precisely, secondary alignments around small InDels were performed and duplications were excluded before variant calling analysis via local de novo assembly of haplotypes in active regions. The variants (i. e. SNPs and InDels) were flagged with SnpSift (version 4. 1 g)  [117]  and the functional annotations of these variants were obtained with SNPeff (version 4. 1 g without variants from intron, UTR-5′, UTR-3′, upstream regions, and downstream regions)  [118]. As previously described, variants from homologous recombination events were detected with ClonalFrameML  [13]  and excluded to build set of 38,837 variants, or not to build set of 178,351 variants, with the script 'Clonal_VCFilter'  [11].
Accessory genome (ii) With an in-house called ARTwork, the assembly was performed based on coverage control (i. e. > 100X) with Bbmap  [119] , read normalization (i. e. 100X) with Bbnorm  [120] , control of read quality with FastQC  [121] , read trimming (i. e. > 20 of Quality Control) with Trimmomatic  [122] , de novo assembly with SPAdes  [123] , selection of the closely related reference genomes with MinHash among 74 reference circular genomes  [124] , scaffolding with MeDuSa  [125] , gap filling with GMcloser  [126] , trimming of small scaffolds (i. e. < 200 bases) with Biopython  [127] , as well as control of assembly quality with QUAST  [128] , MultiQC  [129]  and ggplot2  [130]  graphics. Based on these draft genomes, pangenome was constructed with Roary  [131]  setting 95% of identity for blastp and a strict definition of the coregenome (i.
Population structure (iii) The phylogenomic reconstructions were performed based on the coregenome variants including or excluding variants from homologous recombination events (i. e. pseudogenomes from iVARCall2  [11] ), as well as core genes (i. e. concatenated orthologous genes from Roary  [131] ). IQ-Tree  [132]  was applied on our datasets made up of millions of aligned sites to perform fast selections of the models of evolution based on Akaike Information Criteria (AIC)  [133]  and efficient tree reconstructions by maximum likelihood based on the most appropriated model of evolution  [132]. More precisely, the consensus trees were produced considering all possible Nearest-Neighbor-Interchanges (NNIs) instead of only surrounded computed NNIs  [132]. The search in the tree space started from a BIONJ tree  [132]  and an improved version  [134]  of the ultrafast bootstrap  [135]  was applied with 1000 iterations to compute boostrap support values.
UFBoot convergences were checked after the IQ-Tree computation  [132]. As stated in the literature, Salmonella enterica subsp. indica is the subspecies closest to subsp. enterica and was consequently used as an outgroup to root the tree of the subspecies enterica  [59, 64]. Practically, this subspecies enterica root was identify using three isolates of the subsp. indica (SRR1840570, SRR1060719 and SRR1060512) and three isolates of each studied serovar. The tree distances were compared numerically with the cophenetic function of the 'dendextend' R package based on the Pearson, Kendall and Spearman correlations (i. e. between -1 and + 1, referring to anti-correlated and correlated distances)  [136]. The tree topologies were compared visually with the cophylo function of the 'phytools' R package  [137].
a references completed from Power et al. [43]. ND stands for not determined tree distances were also numerically compared computing the Fowlkes-Mallows index (i. e. between 0 and 1, referring to dissimilar and similar topologies, respectively) with the dendrogram function of the 'dendextend' R package  [138] , and the Robinson-Foulds index (i. e. number of different nodes between both tree) with the treedist function of the 'phangorm' R package  [139]. Genome wide association study (iii) Within a range from 51 Listeria monocytogenes  [29]  to 3701 Streptococcus pneumoniae strains  [35]  and without consensus on the appropriated size of genome dataset, most of the microbial GWAS includes around 500 samples under clonal and/or panmictic status (Table  6 )  [43]. Contrary to human GWAS focusing on the effects of individual SNPs, microbial GWAS has also to access phenotype associations based on presence/absence of genes alongside SNPs  [43].
In addition, microbial GWAS has to take into account confounding factors such as genome selection, homologous recombination events, population structure related to Linkage Disequilibrium (LD), and genome wide significance, because they can induce false positive identifications of seemingly causal mutations  [43, 141]. With regard to the confounding factors (Additional file 1), we applied the developed microbial GWAS (Fig. 5 ) based on GEMMA  [40]. This workflow was applied to 440 genomes, comparing different sizes of genome dataset, taking into account variants from homologous recombination events and checking population structure corrections. The associated mutations (i. e. Wald tests) refer to polygenicity with regard to QQ plots from microbial GWAS (i. e. p < 1 × 10 -2 ), and present high (i.
Gene ontology enrichment analysis (iv) Based on our recently published workflows called 'Get-GOxML' and 'EveryGO' aiming at retrieving GO-terms online from coregenome variants and perform GOEA at any Fig. 5  Developed scripts and published programs (i. e. black letters) with their corresponding effects (i. e. green letters) implemented in the driving script 'microbial-GWAS' performing microbial GWAS integrating Linear Mixed Model (LMM) for population structure correction. Based on the LMM integrated in GEMMA, the sequential workflow called 'microbial-GWAS' is written in R and Python 2. 7. It runs successively scripts called 'binary', 'panGWAS', 'coreGenVarNb', 'overImpacted' and 'AllResults' in order to standardize SNPs, InDels and genes as binary data, compute Kinship matrix, fit a LMM and perform Wald tests, as well as detect coregenome variants presenting high gene densities (i.
e. hotspots of variants) and high functional impacts (i. e. non-synonymous variants) node of a phylogenomic reconstruction  [11] , we developed an improved workflow called 'fastGSEA' (Fig. 6 ). This workflow 'fastGSEA' produces a fast GOEA dependently of a local Uniprot dataset of GO-terms to decrease the execution duration, and provide a complete automatic workflow applicable to many kinds of gene identifiers (i. e. 15 different gene identifiers)  [56]. This workflow can also produce a slower GOEA dependently of the current version of an application programming interface provided by QuickGO (i. e. 'Annotations' https://www. ebi. ac. uk/QuickGO/api/index. ht ml). The driver script 'fastGSEA' is written in Python (version 2. 7) and uses as input a dataset of gene identifiers (i. e. idmapping.
selected. table. gz; current release from Uniprot: ftp://ftp. uniprot. org/). It requires also two lists of gene identifiers from the sample of interest and universe, as well as a file representing the DAG of GO-terms (i. e. go-basic. obo including eukaryotic and prokaryotic GO-terms or gosub-set_prok. obo including only prokaryotic GO-terms). The first step of the workflow 'fastGSEA' aims at selecting from the dataset 'idmapping', a subset of gene identifiers linking the gene identifiers provided by the user, corresponding Uniprot identifiers and associated GO-terms from the sample and universe lists locally (i. e. based on GO-terms from the subset) or online (i. e. based on Uniprot identifiers from the subset). Secondly, the workflow uses the DAG of GOterms to retain prokaryotic GO-terms and avoid obsolete GO-terms.
The third and final section of the paper compares the results obtained by applying the previously mentioned methods to our dataset. Algorithms were tested on data acquired in and around the zoological museum of Strasbourg (France) and an individual house (only indoor). Indoor data were acquired with static laser scanning technique and outdoor data with a mobile mapping system (Stereopolis from IGN). The museum consists of four floors and a vast attic, with an internal courtyard. The ground floor is composed of class rooms and labs. Two floors are dedicated to the visiting tour and are filled with life-sized animal models as well as complete skeletons. The third floor houses the employees' offices. Pieces of furniture obstruct walls of most rooms. In the attic, old exposition models are stored.
For instance, a superpoint can be a group of points belonging to the same plane. Then the graph representing the superpoint is fed to a 3D CNN that recognizes object as a specific combination of superpoints (geometries). In our study, we explored the method that directly takes a 3D point cloud as an input: PointNet  (Qi et al. , 2017). It was already tested on indoor point clouds. As such we wanted to test it to validate the pertinence of the deep learning approach for opening segmentation. We implemented the method described on PointNet's github. We first tested the training algorithm using the dependencies and OS specified on the git. Since most of those are not supported any more (Ubuntu 14. 04), we also tried to use the code on Ubuntu 18. 04 with the newest version of Cuda and Tensorflow.
Segmentation The energy functions let us conclude for each region if it is a window/door or if it is not a real opening. For each window and door, we calculate the coordinates of its centroid and extrema relatively to the centroid of the wall. Then, we edit an. obj file of the walls to extrude doors and windows. For this step, we used the Boolean modifier of the software Blender. We select the. obj of the walls and the. obj file containing the windows. Blender computes the difference i. e. walls with extruded windows.
CONCLUSION AND FUTURE WORKS We developed an algorithm to automatically detect and segment openings in indoor point cloud. While this purpose is pertinent on its own, it is part of a bigger project that aims at automatic registration of indoor and outdoor point cloud. In this regard, the results are very promising. Indeed, in order to register indoor and outdoor point clouds, only a few common points are necessary. For this purpose, detecting 100% of openings is not required. Yet the algorithm is capable of reaching 100% accuracy on point clouds acquired with unobstructed windows. This drawback had the algorithm perform poorly on our main dataset, the museum. However, it proved rather efficient on point clouds corresponding to this requirement. With the individual house, 100% of unobstructed windows were segmented.
Background: Cortical parcellation is an essential neuroimaging tool for identifying and characterizing morphometric and connectivity brain changes occurring with age and disease. A variety of software packages have been developed for parcellating the brain's cortical surface into a variable number of regions but interpackage differences can undermine reproducibility. Using a ground truth dataset (Edinburgh_NIH10), we investigated such differences for grey matter thickness (GM th ), grey matter volume (GM vol ) and white matter surface area (WM sa ) for the superior frontal gyrus (SFG), supramarginal gyrus (SMG), and cingulate gyrus (CG) from 4 parcellation protocols as implemented in the FreeSurfer, BrainSuite, and BrainGyrusMapping (BGM) software packages. Results: Corresponding gyral definitions and morphometry approaches were not identical across the packages.
As expected, there were differences in the bordering landmarks of each gyrus as well as in the manner in which variability was addressed. Rostral and caudal SFG and SMG boundaries differed, and in the event of a double CG occurrence, its upper fold was not always addressed. This led to a knock-on effect that was visible at the neighbouring gyri (e. g. , knock-on effect at the SFG following CG definition) as well as gyral morphometric measurements of the affected gyri. Statistical analysis showed that the most consistent approaches were FreeSurfer's Desikan-Killiany-Tourville (DKT) protocol for GM th and BrainGyrusMapping for GM vol. Package consistency varied for WM sa , depending on the region of interest. Conclusions: Given the significance and implications that a parcellation protocol will have on the classification, and sometimes treatment, of subjects, it is essential to select the protocol which accurately represents their regions of interest and corresponding morphometrics, while embracing cortical variability.