dataesr/scientific-paragraphs-categorization huggingface dataset:
Phenotypic, enzymatic and biochemical characteristics. After 4 days of culture on blood-enriched Columbia agar, colonies from strain EG were small (0. 4 mm of diameter), transparent, round with a convex shape and smooth. Bacterial cells were Gram-negative (Fig. 2 ), rod-shaped, motile, and non-spore-forming bacilli. Using the Image J software, their mean length and width were 1. 98 µm and 0. 69 µm, respectively (Fig. 3 ), without any flagellum. For the two strains EG and P. nyackensis NWG-II14 T , no growth was obtained in anaerobic or microaerophilic conditions. Both strains grew at temperatures ranging from 6 to 30 °C in aerobic atmosphere at pH values ranging from 5. 5 to 9; the strains also grew at salinity concentrations lower than 25 g/L. Catalase and oxidase activities were positive for both strains.
Genomic characteristics. The genome sequence from strain EG was assembled into one contig of 6,198,518 bp with a G + C content of 41. 13%. We identified a total of 5,012 predicted protein-coding genes, in addition to 3 complete rRNA operons, 52 tRNAs and 1 tmRNA. Comparison of these genomic data with those from of closely related species is presented in Table  5. The distribution of genes in functional categories (COGs) is shown within Table  6. In addition, using the Prokka software, we identified a two-component sensor histidine kinase system. This system exhibited an identity of 92% (E-value of 10 -3 ) and a score of 52 with the GacS   [GacS/GacA two-component system] 41,42 sensor histidine kinase/response regulator in Pseudomonas putida strain KT2440 according to the VFDB database. It has been reported that the two-component system signaling pathways are the major signaling mechanisms in bacteria and as well in Archaea  43  to monitor external and internal stimuli (including concentration of ions and gas, redox states, levels of nutrients and cell density)  44.
conclusion Using the taxono-genomic approach, we concluded that strain EG is the representative strain of the new species P. schmidteae sp. nov. Interestingly, P. schmidteae sp. nov. is present in 84. 6% of S. mediterranea worms tested. To best of our knowledge, P. schmidteae sp. nov has never been identified anywhere else than in S. mediterranea. Indeed, no nucleotide sequence linked to this new strain has been found in the nr database (BLASTn https:// blast. ncbi. nlm. nih. gov). To date, we assume that P. schmidteae sp. nov is unique to S. mediterranea, but we cannot exclude formally that it might be present in other living organisms of the environment. Table  3. Differential biochemical characteristics of Pedobacter schmidteae and phylogenetically-related species of the genus Pedobacter.
Strains: 1, P. schmidteae strain EG T ; 2, P. nyackensis strain NWG-II14 T ; 3, P. heparinus strain DSM 2366 T ; 4, P. africanus DSM 12126 T. The data were completed using previously described characteristics  38, 40  of taxa 3 and 4, and those obtained in the present study. Data presented for the taxa 3 and 4 were collected in previously published work, only results obtained with the same methodologies than used in the present study for the taxa 1 and 2 (see material and methods) were considered. +, positive; -, negative; V, variable; NA, data not available. protologue. The protologue is to standardize the format of descriptions of new taxa, supported by the Judicial Commission of the International Committee on Systematic Bacteriology  47. Pedobacter schmidteae (schmid. te'ae. N. L. gen. n.
Figure1. : 16S rRNA-based maximum likelihood phylogenetic tree highlighting the position of Pedobacter schmidteae strain EG T relative to other closely related species. The respective GenBank accession numbers for 16S rRNA genes are indicated in parentheses. Sequences were aligned using CLUSTAL W with default parameters and phylogenies were inferred by the software MEGA 7 18. Numbers at the nodes are percentages of bootstrap values obtained by repeating the analysis 500 times to generate a majority consensus tree. Only bootstrap values ≥50% were retained. The scale bare indicates a 0. 5% sequence divergence.
D-melibiose, D-raffinose, D-ribose, D-saccharose, D-sorbitol, D-tagalose, D-trehalose, D-turanose, dulcitol, D-xylose, erythritol, gentiobiose, glycogen, inositol, inulin, L-arabinose, L-arabitol, L-fucose, L-rhamnose, L-sorbose, L-xylose, methyl-αD-Glucopyranoside, methyl-αD-Mannopyranoside, methyl-βD-xylopyranoside, potassium 2-CetoGluconate, potassium 5-Cetogluconate, potassium gluconate, salicin, xylitol. In addition, strain EG T use potassium nitrate, L-tryptophane, L-arginine, urea, gelatin, D-mannitol, malic acid, phenylacetic acid, L-lysin, trinatriumcitrate, L-ormithin, Esculin ferric citrate, Glucose, D-mannose, N-Acetyl-glucosamine and D-maltose. The genome of strain EG T is 6,198,518 bp-long with a G + C content of 41. 13%. The 16S rRNA gene and genome sequence are deposited in GenBank under accession numbers LS453293 and LS999839, respectively.
MALDI-TOF MS spectrum. The MALDI-TOF MS spectrum of Pedobacter schmidteae strain EG T is available at http://www. mediterranee-infection. com/article. php?laref=936 nucleotide sequence accession number. The 16S rRNA gene sequence and genome sequence were deposited in GenBank under accession numbers LS453293 and LS999839, respectively. The Digital Protologue database Taxonumber for strain EG T is TA00631 in the http://imedea. uib-csic. es/dprotologue/ website. Deposit in culture collections. Strain EG T (Pedobacter schmidteae sp; nov. ) was deposited in the CSUR (Collection de Souches de l'Unité des Rickettsies WDCM 875) and CSCT (Colección Española de Cultivos Tipo) strain collections under numbers CSUR P6417 and CECT9771, respectively.
Comparison between the fatty acid cell composition of Pedobacter schmidteae and related species of the genus Pedobacter. Taxa: 1, P. schmidteae strain EGT; 2, P. nyackensis strain NWG-II14T; 3, P. heparinus strain DSM 2366 T; 4, P. africanus DSM 12126 T. The data were completed using previously described characteristics 38,40 and those obtained in the present study. Data presented for the taxa 3 and 4 were collected in previously published work, only results obtained with the same methodologies than used in the present study for the taxa 1 and 2 (see material and methods) were considered. Tr, Trace (<1%); -, not detected; NA, not applicable.
Acknowledgements The study was funded by the  Méditerranée-Infection foundation , the  National Research Agency  under the program " Investissements d'avenir ", reference  ANR-10-IAHU-03  and by  Région Provence Alpes Côte d' Azur and European funding FEDER IHUBIOTK. We also thank  Aurelia Caputo  for submitting the genomic sequences to GenBank. LJK is fellow of the Méditerranée-Infection foundation.
1 )Figure 1. 11 Figure1. : 16S rRNA-based maximum likelihood phylogenetic tree highlighting the position of Pedobacter schmidteae strain EG T relative to other closely related species. The respective GenBank accession numbers for 16S rRNA genes are indicated in parentheses. Sequences were aligned using CLUSTAL W with default parameters and phylogenies were inferred by the software MEGA 7 18. Numbers at the nodes are percentages of bootstrap values obtained by repeating the analysis 500 times to generate a majority consensus tree. Only bootstrap values ≥50% were retained. The scale bare indicates a 0. 5% sequence divergence.
m Matlab code developed in  [65]. The core of the research work consists in the definition of a convenient cost function based on sensitivity fields which may be recovered in a straightforward manner from Hessian matrices in DIC-FEMU. Additional ingredients are then introduced in the cost function in order to enhance stability and convergence properties of the optimization procedure. Several issues, such as the setting of initial conditions or multi-parameter optimization, are also addressed. The performance of the approach is illustrated on several numerical examples, and an experimental validation of the optimized specimen is also shown. The remainder of the paper is organized as follows : in Section 2, basic aspects of DIC and FEMU methods for parameter identification from full-field measurements are recalled ; the coupling between DIC-FEMU and topology optimization, which is the main contribution of the paper, is addressed in Section 3 ; several numerical experiments, as well as an experimental validation, are reported in Section 4 ; eventually, conclusions and prospects are drawn in Section 5.
Numerical results In this section, we design and analyze performance of the proposed approach on several numerical experiments. They are all based on two-dimensional test-cases with linear elastic materials and plane stress assumption. During the iterative process described in Fig. 4 , gradients of c(ρ) were obtained using the finite difference method. For each elementary density, we thus considered ∂c/∂ρ e ≈ (c(ρ + δρ e I e ) -c(ρ))/δρ e with I e the vector of size N e with component 1 in line e and 0 elsewhere, and we chose δρ e = 0. 001 in practice. All numerical results dealing with topology optimization were obtained from a home-made Matlab software, while the Correli software (under patent  [44] ) was used to perform DIC.
15 ). In practice, boundaries of the specimen are clamped on the jaws during the experimental test. Therefore, in order to reproduce similar boundary conditions as in the traction numerical test (with free vertical displacement on the right side, see Fig. 1 ), it is chosen to double the specimen and to use symmetry properties to recover such boundary conditions experimentally. A Nikon 24. 5 objective is used on the camera with 700×300 pixels and a pixel size of 100 µm. The analysis of a first set of 10 unloaded images enables one to recover the initial state and information on the measurement noise. DIC is then performed from the Correli software  [35]. The obtained displacement field (transverse component) for the optimized and doubled specimen is given in Fig. 16  ; it illustrates the originality of the designed specimen, which creates dissymmetric and heterogenous fields.
Acknowledgements The authors thank  Stéphane Roux  ( LMT, ENS Paris-Saclay ) for the fruitful discussions on the topic and helpful comments and advice, which enhanced the quality of the work. We observe that there is a clear convergence to a local minimum in a rapid and effective manner. Oscillations which can be observed in the value of the cost function are purely due to geometrical reasons. Indeed, the hole is defined from pixels which induces that the shape of the hole surface numerically evolves during the iterations.
Next-generation sequencing (NGS) is now widely used in microbiology to explore genome evolution and the structure of pathogen outbreaks. Bioinformatics pipelines readily detect single-nucleotide polymorphisms or short indels. However, bacterial genomes also evolve through the action of small transposable elements called insertion sequences (ISs), which are difficult to detect due to their short length and multiple repetitions throughout the genome. We designed panISa software for the ab initio detection of IS insertions in the genomes of prokaryotes. PanISa has been released as open source software (GPL3) available from https:// github. com/ bvalot/ panISa. In this study, we assessed the utility of this software for evolutionary studies, by reanalysing five published datasets for outbreaks of human major pathogens in which ISs had not been specifically investigated.
We reanalysed the raw data from each study, by aligning the reads against reference genomes and running panISa on the alignments. Each hit was automatically curated and IS-related events were validated on the basis of nucleotide sequence similarity, by comparison with the ISFinder database. In Acinetobacter baumannii, the panISa pipeline identified ISAba1 or ISAba125 upstream from the ampC gene, which encodes a cephalosporinase in all third-generation cephalosporin-resistant isolates. In the genomes of Vibrio cholerae isolates, we found that early Haitian isolates had the same ISs as Nepalese isolates, confirming the inferred history of the contamination of this island. In Enterococcus faecalis, panISa identified regions of high plasticity, including a pathogenicity island enriched in IS-related events.
Isolates with available data Eppinger et al. [9]  Vibrio cholerae O1 biovar El tor str. N16961 (AE003852. 1; AE003583. 1) 116 110 Martinez-Urtaza et al. [10]  Vibrio parahaemolyticus RIMD 2210633 (BA000031. 2; BA000032. 2) 48  16  Raven et al. [11]  Enterococcus faecalis V583 (AE016830. 1) 168 168 Wilson et al. [12]  Salmonella Tennessee str. TXSC_TXSC08-19 (CP007505. 1) 69 68 Holt et al. [13]  Acinetobacter baumannii strain A1 (CP010781. 1) 44 35 Genes are inactivated by the insertion of an IS into their coding sequences. ISs can also modulate the expression of a gene if they disrupt its promoter or create an alternative promoter  [4]. Most known examples of IS transposition are linked to antibiotic resistance, because the resulting phenotypes are easy to detect. For example, the insertion of IS1 or IS10 upstream from the efflux pump gene acrEF increases the resistance of Salmonella enterica to fluoroquinolones  [5].
Similarly, the insertion of ISAba1 or ISAba125 upstream from ampC increases resistance to third-generation cephalosporin in Acinetobacter baumannii  [6, 7]. This ability of IS insertion to affect bacterial resistance to antibiotics and virulence can help bacterial pathogens to adapt to new niches  [4]. IS dynamics are rarely investigated, but an understanding of these dynamics during outbreaks of pathogenic prokaryotes could be highly informative in evolutionary studies. The detection of ISs is challenging, because read lengths are usually shorter (<300 bp) than ISs and the same IS may be repeated in the genome. We have developed the panISa program to detect new and unknown insertions ab initio (i. e. with a database-free approach) in bacterial genomes, based on the detection of structural variants in short-read data.
PanISa requires only short reads and a reference genome as input. The presence of ISs and their repeated nature renders the IS localization from the assembly very challenging, which is why we used a software that localizes ISs from the raw reads. The program has been validated on simulated data and compared with existing tools, but the benefits of IS detection for epidemiological studies remain to be evaluated  [8]. We therefore assessed the dynamics of IS insertion during the spread of bacterial pathogens, by reanalysing the WGS data from five published studies describing genome evolution for major epidemic bacterial pathogens.
Selection of the datasets We reanalysed five published datasets from studies aiming to decipher the evolution of a specific clone of a bacterial pathogen during its spread (Table  1 ). All the species concerned were major human pathogens for which ISs have already been described. The first dataset was from a study describing the global epidemiology of the seventh cholera pandemic that aimed to identify the geographic origin of the contamination of Haiti  [9]. We also selected a dataset for isolates of the transcontinental epidemic strain of Vibrio parahaemolyticus ST36  [10]. The authors reconstructed the evolution of this clone over the last 25 years, by genome-wide analysis. We selected a dataset for genomes from a global collection of isolates of Enterococcus faecalis retrieved between 1958 and 2012 from bloodstream infections in Ireland, the UK and the USA  [11].
The proportion of genomes displaying IS insertions varied considerably between bacterial species, but was at least 86 % for Acinetobacter baumannii, Vibrio cholerae, and Enterococcus faecalis. PanISa is a useful addition to the bioinformatics toolbox for analyses of prokaryote evolution. It will help us to determine the role of ISs in pathogen evolution, which has probably been underestimated. The third column shows the number of insertion events identified by panISa from WGS datasets of bacterial pathogens, the fourth column gives the number of hits matching sequences in the ISFinder database, considered to correspond to IS-related events, the fifth column gives the number of different ISs found among the IS-related events, and the last column gives the proportion of isolates concerned. that the contamination was of environmental origin.
Downloading and pretreatment of short-read data For the five datasets, we first determined the number of isolates for which short-read data were available in the sequence read archive (SRA) database (1. Eppinger et al. [9] , 2. Martinez-Urtaza et al. [10] , 3. Raven et al. [11] , 4. Wilson et al. [12] , 5. Holt et al. [13] ) ((Table  1 ; Data S1, available in the online version of this article). We used prefetch and fastqdump command-line tools from NCBI toolkits to extract and convert the data into an optimized input format for panISa  [14]. Reads were subsampled to a final coverage of 60× and mapped against the same reference genome as in the original studies with the Burrows-Wheeler aligner  [15].
PanISa search PanISa identifies IS insertions through comparison with the pysam library. Briefly, each read that maps partially on the reference genome was detected by panISa as a clipped read. When clipped reads were in opposite directions on two close positions of the genome, the program identified a potential IS insertion (also called a hit). The flanking parts of the clipped reads defined the boundaries of the insertion, with start clipped reads defining the IRR and end clipped reads defining the IRL (see Treepong et al. [8]  for a detailed description of the functioning of the software). PanISa has been released as open-source software (GPL3) available from https:// github. com/ bvalot/ panISa. Each alignment file (. bam) was used as input for panISa with the minimum clipped reads option set to 10 (default settings for all other options).
As panISa detects all insertion events, manual curation of the list of potential ISs is required. We checked for sequence similarity (identity >90 % over>80 % of the length of the sequence) between the boundaries of the IS (IRR and IRL), with the ISFinder database, to confirm IS insertions  [16]. To clarify the terms used thereafter, panISa produces a 'hit' for each detection of whichever insertion, while an 'IS-related event' describes the insertion of an IS. In other words, if one specific IS inserted at two different positions, we described it as one IS and two IS-related events.
RESuLTS AnD DISCuSSIon In this study, we used panISa to reanalyse published WGS datasets. Unsurprisingly, none of these published studies could identify new ISs or new IS insertion sites. However, Holt et al. [13]  reported the detection of ISAba1 and ISAba125 upstream from the cephalosporinase-encoding ampC gene by a PCR approach for the identification of genetic events leading to antibiotic resistance  [13]. ISs can contribute to antibiotic resistance and virulence, depending on the nature of the genes that are disrupted or imported. They can also, more widely, contribute to bacterial genome rearrangements. The identification of IS-related events with an accurate tool would therefore improve our understanding of genome evolution in epidemic pathogens.
IS detection in the genomes of epidemic bacterial pathogens PanISa retrieved 692 to 15 878 hits from the five datasets, the smallest number of hits being obtained for the study of V. parahaemolyticus genomes and the largest number for the study of E. faecalis (Table  2 ). Only hits matching sequences in the ISFinder database were considered to be IS-related events. Thus, 207 of the 2913 hits were identified as IS-related events in the V. cholerae dataset  [9]. PanISa identified 1348 IS-related events among the 15 878 hits for the E. faecalis dataset and 345 IS-related events among the 1371 hits for the A. baumannii dataset  [11, 13]. None of the 692 hits for the Vibrio parahaemolyticus dataset was associated with an IS [10]. We identified four IS-related events among the 727 hits for S. Tennessee, all involving the same IS in a single isolate  [12].
The small numbers of IS-related events detected in these last two datasets may be due to either the short length of the reads (<100 bp) creating artefactual repeat sequences or the low coverage (<40×) for several genomes  [8]. Furthermore, as Wilson et al. [12]  studied genomes of S. Tennessee isolates collected between 2006 and 2008, the study period may have been too short for IS insertion to happen  [12]  (Data S2). In addition, the short length of the reads or the presence of repeated regions may account for the high level of background noise.
Phylogeny of the Vibrio cholerae outbreak Haiti had been free from V. cholerae for 100 years before being hit, in 2010, by an outbreak that lasted 2 years. Phylogenetic analysis revealed that Haiti had been contaminated by a Nepalese strain brought to the island by United Nations peacekeepers  [17]. We reanalysed datasets of genome sequences for V. cholerae isolates from Haiti, Nepal and Bangladesh  [9]. Phylogenetic analysis of these sequences performed by Eppinger et al. [9]  revealed these isolates to be highly homogeneous. PanISa identified 207 insertions of five different ISs. The IS256-like element, inserted at position 2 440 662, was common to all isolates of the dataset. The IS256-like element in position 327 875 was specific to the genomes of isolates from cluster 'a' , which grouped together isolates obtained in Haiti in 2010 and isolates from Nepal (Fig.
IS insertions in the antibiotic-resistant Acinetobacter baumannii global clone 1 We reanalysed the A. baumannii genome dataset with panISa and retrieved 345 IS-related events in 35 genomes, with 86 % of the genomes displaying at least one IS-related event  [13]  (Table  2 ; Data S4). The wide temporal coverage of the collection (~50 years) probably accounts for the large number of IS events retrieved (Fig. 2a ). Likewise, the isolates were selected to represent the maximum diversity among the global clone 1, the high variability of sources and locations presumably accounted for the higher number of IS-related events detected. We performed hierarchical clustering based on the presence/absence of the 345 IS-related events, which revealed similarities between the isolates (Fig. 2b ). Similar IS-related event profiles were observed in some clusters of two or three isolates, such as Canada-BC1 and Canada-BC5, and isolates D78 and D81.
Conclusion PanISa is a software pipeline for detecting IS insertions in prokaryotic genomes from short-read data. It expands the toolkit available for exploring the evolution of prokaryotic lineages. ISs are difficult to detect in short-read sequencing data, and this has probably resulted in an underestimation of the impact of these mobile elements on the evolution of their bacterial hosts. Studies of IS dynamics in bacterial genomes have also been hindered by difficulties in genome assembly. PanISa can increase our understanding of the evolution of bacterial pathogens during their spread. For example, we show here that panISa can consolidate phylogenetic analyses of large datasets. It can also accelerate the identification of IS events with a known biological impact, such as those triggering resistance to third-generation cephalosporins in A.
This paper will demonstrate 1. a clustering method to group similar time series together, 2. a computer vision method for extracting vortex convection speeds from pressure data, and 3. how to detect outliers and inspect the convergence of the dataset. Furthermore, we will provide a future perspective on the way that machine learning may help us in modeling airfoil stall in simulations. While the specific methods used in this paper should prove to be useful and while we will point out some specific aerodynamic effects in the examples section, these are only examples. This paper is trying to communicate that machine learning more broadly is approachable and useful for unsteady aerodynamics, wind energy and other adjacent fields. Before jumping into the new methods we should establish what kind of techniques have been used previously.