dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions In conclusion, the present work demonstrates the appropriateness of using age and populationspecific templates as reference targets for spatial normalization of structural and functional neuroimaging data. This database of age-specific IBTs and IBTAs is made freely available to the wider neuroimaging community of researchers and clinicians worldwide. We hope that these tools will facilitate research into neurological understand in general and into the functional and morphometric changes that occur over life-course in Indian population in particular. Age-specific Indian brain templates.
Acknowledgments This work was partially supported by c -VEDA (Consortium on Vulnerability to Externalizing Disorders and Addictions) ICMR (India) / MRC (UK)  (grant  ICMR/MRC-UK/3/M/2015-NCD-I ) to VB and GS. Wellcome Trust/DBT India Alliance Fellowship Grants  to BH (Award:  IA/RTF/14/1/1002 ), and  UMM  (Award:  IA/E/12/1/500755 ),  DST   Research Grant   SR/CSI/44/2008 (5) to RDB and  DBT Research Grant   BT/PR14315/MED/30/474/2010  to PKP. GV acknowledges the support of the SwarnaJayanti Fellowship by the  Department of Science and Technology, Government of India  ( DST/SJF/LSA-02/2014-15 ). GS was supported by the  Horizon 2020 -funded  ERC Advanced Grant 'STRATIFY'  (brain network-based stratification of reinforcement-related disorders; 695313), ERANID (understanding the interplay between cultural, biological and subjective factors in drug use pathways;  PR-ST-0416-10004 ), BRIDGET (JPND brain imaging, cognition, dementia and next generation GEnomics;  MR/N027558/1 ), the  Human Brain Project  ( SGA 2 ,  785907 , and SGA 3, 945539), the  National Institute of Health (NIH)  ( R01DA049238 , A decentralized macro and micro gene-by-environment interaction analysis of substance use behavior and its brain biomarkers).
To achieve 5 nm spatial resolution, Favia and co-workers  [40]  used in their experiment a small aperture of 10µm which is also limiting the analysis window to achieving larger strain mapping. The best sensitivity obtained on strain measurements using NBED technique is attributed to Beche and co-workers  [41]  who demonstrated a dramatical improvement of the spatial resolution obtained for NBED analysis to reach 2. 7 nm with larger aperture of 50 µm by using a combination of Modern microscope FEI Titan and dedicated software including the algorithm for diffraction fitting and autocorrelation. These authors obtained a strain sensitivity of 6 x 10 -4 and larger field a view compared to the one obtained elsewhere  [40]. In the present work, HRSTEM combined with NBED technique associated with dedicated software Epsilon tools™ initially developed by FEI for semiconductors technology, and a cutting edge FEI Titan Microscope aberration corrected, are employed to quantify and map the elastic strains in two nuances of ferroelectric [BaZrO 3 ] xᴧ /[BaTiO 3 ] (1-x)ᴧ (BZ xᴧ /BT (1-x)ᴧ ) SLs having periods of Λ = 6.
The out-of-plane lattice parameters of BT and BZ in SLs were obtained by modeling ω/2θ X-ray patterns while the in-plane lattice parameters were determined from the RSMs measurements. The post mortem observations and the strains mapping of the sample's cross-section were performed using FEI™ Titan TEM microscope equipped with aberration correction and operating at 300 kV. Thin lamellae were prepared for both SLs using Dual Beam FEI™ Helios focus ion beam (FIB) combined with scanning electron microscope (SEM), following a conventional sample preparation method as described elsewhere  [42]. The strains quantification and mapping were extracted during HRSTEM using NBED and processed off-line using dedicated software (Epsilon FEI™) including the processing capabilities as detailed in the introduction section.
Strain data is acquired using the shifts in diffraction reflections as function of positions on the sample. The post-processing of the acquired diffraction patterns allows calculating the distance between diffraction spots to be compared to a reference diffraction pattern. As mentioned by Beche and co-workers  [41] , the reference diffraction pattern does not need necessarily to be collected from the material under investigation. The strain is then extracted and plotted as function of the (x,y) positions. In the sake of accuracy, the strain measurements error was determined and calibrated using standard calibration sample. The periodicity Λ of SLs can be determined using the angular distance between the satellite peaks based on the following formula:.
In contrast, for sample BTBZ09 both layers BZ and BT have larger size showing fairly strain variation, namely, BZ layers have a strain ranging from 6. 3% to 7% and BT layers have a strain ranging from 1. 7% to 4. 8%. The layers thicknesses and period obviously influence the deformation of the different layers. To further investigate these effects, lattice parameters have been extracted/computed from Figure  7  and plotted in Figure  8. The data are integrated in the vertical direction to obtain more representative lattice parameter values. The lattice parameter of STO obtained using XRD (a STO = 3. 905 Å) was used to retrieve BT and BZ lattice parameter variations. For comparison purposes, lattice parameters of BT and BZ obtained from the literature and XRD measurements are also plotted. In-plane lattice parameters are in close accordance with XRD measurements for both BTBZ03 and BTBZ09.
Conclusion A local survey at atomic scale of the elastic strains in ferroelectric SLs was demonstrated using HRSTEM and NBED in conjunction with integrated software showing that strain is strongly correlated to the SL periods. Recall that a precise distribution of the strain in ferroic oxide materials is one of the key parameters that could contribute to tuning the functional properties of SLs. This work allowed capturing the elastic strains both in-plane and outof-plane to be correlated to the inter-diffusion interfaces. The choice of thin film SL was motivated by their complex structure composed of alternating layers of two compounds in which the elastic strain is presumably different. In addition, compared to single films, SLs systems offer the possibility to study the strain at both the substrate-layer and layer-layer interfaces in which elastic deformations are quite different from their respective bulk materials.
Bacterial biosynthetic gene clusters analysis The bacterial biosynthetic gene clusters (BGCs) of each genome and each metagenomic bin were analyzed with antiSMASH 5. 0 33 (Figure  S1 ) with the following analysis options: strict detection, and activation of search for KnownClusterBlast, ClusterBlast, SubClusterBlast and Active site finder. BGCs smaller than 5kb were then filtered out of the data and were not included. The taxonomic classification of each cluster was verified to the genus level using the software Blast+ 34. NaPDos 35 was used to classify the ketosynthase (KS) domain and condensation (C) domain sequences required in the biosynthesis of PK and NRP respectively and to infer the KS and C phylogenies.
Distance matrix calculations The published Cephalotes phylogeny was retrieved  36  and the packages ape  37  and phytools  38  of the R software version 3. 6. 1  39  were used to exclude from this phylogeny the Cephalotes species from which no genome or metagenome was available. The cophenetic distance option in the R package stats  39  was used on this pruned phylogeny to create the Cephalotes phylogenetic distance matrix. The cophenetic distance replaces original pairwise distances between the Cephalotes species by the computed distances between their clusters. To calculate the metagenomic BGCs distance matrix, a BGC matrix containing the numbers and types of each BGC found in each metagenome was created. Then the distance function (Euclidean method) of the R package stats was used on the BGC matrix to calculate the metagenomic BGCs distance matrix.
BGCs genomic core gene analysis Although BGCs are made of many domains, some have been deemed the "genomic core". The genomic core is a group of one or a few domains within a single BGC, which contain all the required modules needed for the BGC to be functional and are highly conserved across bacteria. The genomic core of four types of BGCs (arylpolyene, NRP, siderophore and T1PK) were analyzed using the CORASON software version 1. 0 40 (Figure  S1 ). First, a database for each type of BGCs was created from all the BGCs of the same type recovered in the genomes and metagenomes, which were obtained from our previous AntiSMASH analysis. Then a BGC from each of our database was chosen as the reference BGC for its database, and a gene from the biosynthetic core of this reference BGC was chosen as the query protein.
The choice of the reference BGC and query protein for a database were made taking different variables into account: (1) the reference BGC must be one of the longest BGCs in the database, (2) the query (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. protein must come from a biosynthetic core gene or an additional biosynthetic gene close to the core, and (3) the query protein must be present in at least half of the BGCs in the database. Once a reference BGC and a protein query were selected for a database, the CORASON software was used to determine the genomic core of this type of BGCs, and to infer the phylogenies. If less than half of the BGCs present in a database appear in the phylogeny, or if many BGCs appear more than once in the phylogeny (which may happen if the selected query protein belongs to a biosynthetic gene that can be found multiple times in the same BGC), the reference BGC and query protein previously selected were deemed to be inadequate for the analysis.
Statistical analysis To test for correlations between the phylogeny and geography of the Cephalotes ants and the BGCs found in the bacterial metagenomes the Chi-squared function of the R package stats  48  was implemented. The Cephalotes phylogenetic distance matrix and the bacterial BGCs distance matrix from the metagenome analysis were compared using the Mantel test of the R package ape  37. The PCoA between the number and types of bacterial BGCs from the metagenome analysis and Cephalotes geography were performed using the prcomp function of the R package stats and the R package ggplot  49. The correlations between the number of each type of bacterial BGCs from the metagenome analysis and Cephalotes geography were calculated using the one-way ANOVA function with the Tukey's pairwise of the Past software version 3.
Indeed, bacterial genomes and metagenomes of the same bacterial order and same host ant species fall together in the genetic network (Figure  3 ), the genomic core gene analysis (Figure  4 ), and the C and KS domain phylogenies (Figure  5 ). Together, these results show that the BGCs analysis of metagenomes are a good representation of the BGCs diversity in samples indicating that the retrieved BGCs from metagenomic data are not chimeras created by misassembling metagenomic bins. As the 17 species of Cephalotes were collected in Brazil, Peru and the USA we questioned whether the host phylogeny and/or geography have an impact on symbiont BGC diversity. Our results show that the number and type of bacterial BGCs are not correlated with Cephalotes evolutionary history (p=0. 205; Mantel test) but are more similar in Cephalotes samples collected in the same geographic area (p=0.
This pattern suggests a possible convergence of the core genes in the gut of C. rohweri possibly via horizontal gene transfer. In insect-bacteria symbiosis horizontal gene transfer has been shown to occur even in bacteria going through genome reduction  26. However the three sequences did not match any known sequence in the database limiting the identification of the bacterial origin of these genes. On the other hand, the T1PK and siderophore core gene phylogenies (Figure  4B  and 4C) show that BGCs from Burkholderiales across different host species share a strong likely genetic conservation. Several identical genes of T1PK and siderophore BGCs are present across the core gene phylogenies supporting a conserved architecture which suggests a common origin or convergent evolution of Burkholderiales BGCs across Cephalotes species.
out of 89) from genomes and metagenomes belong to the cyclization domain class catalyzing both peptide bond formation and subsequent cyclization. The retrieved KS domain sequences (N=13) from genomes and metagenomes fall into a single clade corresponding to Cis-AT modular class or the iterative domain class. Unfortunately, no close similarity with the identified C and KS sequences of known non-ribosomal peptide and polyketide were found when comparing with NaPDos and the NCBI database, thus no prediction could be made about the chemical structure of bacterial metabolites.
Funding This work received financial support from an " Investissement d'Avenir " grant managed by  Agence Nationale de la Recherche  (CEBA, ref. ANR-10-LABX-25-01 ) to CD, a  National Science Foundation  ( NSF DEB 1900357 ) to CSM. We are grateful to the  University of French Guiana for Anaïs Chanson 's Ph. D. fellowship and traveling grants.
sequences ( 66 66 out of 89) from genomes and metagenomes belong to the cyclization domain class catalyzing both peptide bond formation and subsequent cyclization. The retrieved KS domain sequences (N=13) from genomes and metagenomes fall into a single clade corresponding to Cis-AT modular class or the iterative domain class. Unfortunately, no close similarity with the identified C and KS sequences of known non-ribosomal peptide and polyketide were found when comparing with NaPDos and the NCBI database, thus no prediction could be made about the chemical structure of bacterial metabolites.
Since 2018, this compression device is under clinical evaluation (Scar Wars study, NCT03312166) at 'Centre d'Investigation Clinique' CIC INSERM 1431  (Lihoreau et al. 2020). The device is applied on earlobe after earlobe keloid surgery in order to prevent from keloid recurrency. A wide mechanical characterization of earlobe keloid would provide us information to improve the efficiency of such pressotherapy, i. e. , compression level and load duration. As the ear lobe surface is small, few mechanical tests can be performed within a restricted area, such as indentation and suction tests. Developed by Courage þ Khazaka Electronic, the CutiscanV R is a promising tool to quantify the elastic and viscoelastic properties, as well as anisotropy and the tension lines of the human skin in vivo  (Rosado et al.
Single-Nucleotide Polymorphism Calling The UNEAK pipeline  (Lu et al. , 2013) , which is part of the TASSEL 3. 0 bioinformatics analysis package  (Bradbury et al. , 2007) , was used to process the raw reads, since no complete reference genome is yet available for H. glycines. This pipeline is designed to call SNPs de novo, without a reference genome with high stringency. Only sequences containing a single putative SNP (1-bp mismatch) per sequencing read were kept. Before analysis, the SNPs were further filtered with a minimum coverage (minCov) of 20 reads, a maximum coverage (maxCov) of 10,000 reads, and a minimum allele frequency (MAF) of 0. 01.
Population Genetics Clustering of SCN populations using PCA was carried out using the prcomp function from the stats package in R software  (R Core Team, 2017). The poppr package (v2. 4. 1) in R  (Kamvar et al. , 2014)  was used to investigate the genetic relationship between populations and to build a phylogenetic tree (Provesti's distance, 10,000 bootstraps, neighbor-joining algorithm counting missing data as equivalent in the distance computation). Visualization of sample coordinates and phylogenetic relationship analyses were carried out using the phytools 0. 6-20 package in R  (Revell, 2017). Fixation index (F ST ) values  (Wright, 1943)  were calculated using the classical approach  (Hartl and Clark, 2007)  with the PoPoolation2 software  (Kofler et al. , 2011)  to evaluate the genetic differentiation between each pair of populations.
In our dataset, the closest sample to the oldest population was located in Clarkton, Missouri (MO1)  (Hegge, 1957; Tylka and Marett, 2017). To access the genetic isolation from the MO1 location, a Spearman rank correlation (r s ) test was performed at a 0. 95 confidence level. Populations from three states (North Dakota, Minnesota, Illinois) were selected as case studies to evaluate the local (shortscale) genetic differentiation. These states were chosen because there were sufficient samples for comparison and because (i) North Dakota shows a recent introduction of SCN and a continuous northward dispersal of the nematode  (Nelson and Bradley, 2003; Mathew et al. , 2015) ; (ii) Minnesota has a longer history of SCN, with the nematode being first detected in 1978 and the infested area still expanding each year  (Zheng et al.
Outlier Detection and Their Association With Environmental Variables Two geographic and four climatic covariables were investigated as possible factors explaining loci under selection: latitude (LAT), longitude (LONG), annual mean air temperature (BIO1), maximum air temperature of the warmest month (BIO5), annual precipitation (BIO12), and total precipitation of the warmest quarter (BIO18). All climatic variables were retrieved from the WorldClim global climate database, version 1. 4 (Fick and Hijmans, 2017) , corresponding to historic conditions. The spatial resolution used for the bioclimatic analysis was set at 30 s or 0. 86 km 2. To detect correlations between variations in population allele frequencies of SNPs and environmental factors, we used three different Bayesian methods (software programs): BayPass, version 2.
1  (Gautier, 2015) , BayeScan, version 2. 1  (Foll and Gaggiotti, 2008), and BayeScEnv, version 1. 1 (de Villemereuil and Gaggiotti, 2015). For each program, triplicate runs with different random seeds were performed with a pilot run of 10,000 iterations to estimate starting parameters, a burn-in length of 50,000 iterations, and a minimum of 50,000 iterations, accounting for the small number of SNPs being investigated. Post-run diagnostics were carried out using the coda package in R software in order to ensure sufficient iterations and normality of the Markov chain  (Plummer et al. , 2006). The first software, BayPass, identifies genetic markers subject to selection by covariates such as phenotypic or environmental variables associated with the population of interest  (Gautier, 2015). This application is based on the BAYENV model proposed by  Coop et al.
05 (eBPis < 0. 05). The second software, BayeScan, uses a Bayesian likelihood method that assumes a Dirichlet distribution of allele frequencies between populations  (Foll, 2012). This program estimates the probability that each locus is subject to selection by using a logistic regression on the two locus-population F ST coefficients. This Bayesian method uses a reversible-jump MCMC algorithm to calculate a posterior probability that each locus is under selection. The decision criterion to determine whether a locus is likely to be under a strong selection is the q-value (Foll, 2012), analog to a false discovery rate (FDR) p-value, that must be under 0. 05. A second decision criterion was applied to further endorse the selected outliers: the ratio of posterior probabilities (PO). The PO threshold to affirm that a locus was under selection, in comparison with a neutral model, was set to 0.
91, which corresponds to a strong relationship on the Jeffreys scale  (Foll, 2012). We used BayeScan with the default parameters, but we set the minimum number of iterations to 50,000, the length of 20 pilot runs to 10,000 iterations, and the burn-in length to 50,000 iterations. The third software, BayeScEnv, is similar to BayeScan and uses the F ST index to detect loci with a high level of differentiation in comparison with the entire genome. This program allows a normalization vector to be applied to the environmental data instead of only a binary combination, thus generating a lower number of false positives, according to the creators of the software  (de Villemereuil and Gaggiotti, 2015). We used BayeScEnv with the default parameters, with the number of iterations set to 50,000 and 20 pilot runs with a length of 10,000 iterations.
Localization of Outlier Loci, Gene Function, and Genic Environment The SNPs identified in short reads were first retrieved from a draft SCN genome available from SCNBase (https://www. scnbase. org/) by means of BLASTN with the default parameters, except for a smaller word size of 4, with the Blast2GO application  (Conesa et al. , 2005). Many of the identified SNP-containing fragments matched multiple genes or genome locations (see Supplementary Tables  3 4 5 ). To assign a putative gene function to each SNP, we compared the aligned sequences to the National Center for Biotechnology Information (NCBI) protein database by means of BLASTX and BLASTP  (Altschul et al. , 1990 ) on a subset of sequences  (nematodes, taxid: 6231)  or to all of the NCBI non-redundant (nr) sequence database with an E-value significance cutoff of 1e -5.
As our GBS sequencing covers approximately 0. 8% of the genome (see Results section), we explored the genomic regions around outlier loci for genetic hitchhiking in whole-genome sequences from four populations of different origin and distinct virulence profile (ON1, ON34, IL4, and KS2). Sequencing libraries were prepared using the Nextera XT DNA Library Preparation Kit (Illumina, San Diego, CA) and sequenced on a MiSeq sequencer (Illumina) using the MiSeq Reagent Kit v3 (600-cycle). Reads were demultiplexed using the Sabre software program (https://github. com/najoshi/sabre) and processed with Trimmomatic (v0. 32)  (Bolger et al. , 2014)  to remove adapters and barcodes. Alignment on the draft reference genome (see above) was done using  SAMtools (v0. 1. 19) (Li et al. , 2009)  and BWA (v0.
7. 12)  (Li and Durbin, 2009). Only a window of ± 50 kb around the 15 loci under selection was retained for the genic environment analysis. Variants were called with freebayes (v1. 0. 2)  (Garrison and Marth, 2012)  and snpeff (v4. 2)  (Cingolani et al. , 2012). Distribution of allele frequencies in these four populations for each gene variant was compared with the allele frequency of the corresponding loci under selection in the same population in the genotyping-by-sequencing dataset. Only SNPs exhibiting a similar allele frequency and having a high impact on the predicted protein product were retained in our analysis.
Genotyping-by-Sequencing A total of 192,576,709 short reads were obtained from the sequencing of the DNA from the 64 SCN populations, following digestion by the PstI/MspI restriction enzymes. The H. glycines effective genome length was estimated to be 96,752,286 bp, with an average of one PstI restriction site every 12,688 bases (E. Lord, private communication, 2017). On the basis of the sequence size used by the UNEAK (Universal Network-Enabled Analysis Kit) pipeline (first 64 bp of each read), the expected horizontal coverage was approximately 0. 8% of the total SCN genome, and the vertical coverage at each locus, considering the number of reads obtained, was 400×. Before filtering, the UNEAK pipeline identified 3,172 variants. After filtering for low coverage and selecting only SNP variants, two datasets were generated.
Principal Component Analysis A principal component analysis (PCA) was performed using the dataset without missing data, which contained 245 SNPs. The PCA plot of the 64 North American SCN populations revealed a geographically ordered pattern (Figure  1A ). Overall, the Ontario populations showed the greatest dispersion, indicating they are more genetically differentiated, while the central regions of the US showed more clustered populations. Also, the group containing populations from Minnesota and North Dakota (in green in Figure  1A ) was clearly different from the Ontario populations by the first axis, which explained 21% of the total variation. All populations from the central states, although they originated from many states and covered a much wider area, were less diverse and more clustered together.