dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The authors thank Gae ¨lle  CRE  `S who collected insect material necessary for the RNA-seq analyses,  Clotilde GIBARD , Gaetan CLABOTS and Raphae ¨l BOUSQUET who furnished the insects, and the quarantine insect platform (PIQ), a member of the  Vectopole Sud network  (http://www. vectopole-sud. fr/),for providing the infrastructure needed for insect maintenance. Microscopy observations were made possible thanks to the Montpellier MEA platform (https://mea. edu. umontpellier. fr/) and MRI-DBS platform (https://www. mri. cnrs. fr/). All qPCR analyses were performed thanks to the Montpellier Genomix qPHD platform (http:// www. pbs. univ-montp2. fr/).
| Malformations All sampled fry were conserved on paraformaldehyde at 4% (pH = 6. 9) and after in freezer (4°C). Malformations were identified under X10 objective microscope (OPTIKA microscope, SZP-10 with camera MICROVISION Instruments, Lw1235C-GT1 coupled with the software Archimed MICROVISION Instruments 6. 0. 14). It was identified 20 single malformations (Figure  2 ) and 39 combinations (multiple malformations). The typology of malformations (Figure  2 ) was based on the works of  Afonso et al. (2000) ,  Holm et al. (2005) ,  Jezierska, Ługowska, and Witeska (2009)  and  Lahnsteiner (2012). Skeleton malformations included scoliosis, lordosis and kyphosis. Scoliosis corresponds to a lateral malformed, zig-zag shape  (Afonso et al. , 2000)  or lateral concave curvature of the lumbar region of the skeleton  (Holm et al.
GENOME The linear dsDNA genome of Pyrobaculum filamentous virus 1 is 17 714 bp, including 60 bp terminal inverted repeats, and is predicted to encode 39 proteins (Fig. 2 ), most of which do not show similarities to the sequences in public databases  [2]. Nine gene products of Pyrobaculum filamentous virus 1 share significant sequence similarity with proteins encoded in the partially sequenced genome of Thermoproteus tenax virus 1  [3, 4].
Lateral Root Gene Regulatory Networks: emerging properties Lateral root research has recently moved beyond studying the role of individual or a few genes to characterising many genes that compose regulatory networks controlling lateral root development employing systems and -omics based approaches. LR-related transcriptomic datasets have recently been produced in different conditions, monitoring gene expression dynamics during root branching in an unbiased fashion  [42] [43] [44] [45] [46]. These offer unprecedented resources to explore single gene expression dynamics and identify new candidate regulators of LR formation. More importantly understanding how these components interact to form regulatory networks is crucial if we want to understand the emerging properties of LR development  [47, 48].
LRP can be induced in a highly synchronised manner by gravistimulation  [23, 46, 49]. Researchers have therefore exploited gravistimulation-based LR induction to sequentially sample root bending zones every 3 hours and to generate a high resolution time course transcriptomic dataset spanning LR induction to organ emergence and meristem activation  [46]. This unique transcriptome resource offers unprecedented insights to explore expression dynamics during LR organogenesis, ranging from a single gene up to the network scale. More than 8,000 (out of 22,000) genes were differentially expressed during LRP formation. Clustering identified 77 distinct classes of gene expression profiles, highlighting the complex regulation of LR formation  [46]. Interestingly a number of circadian-clock regulated genes were identified, revealing a surprising clock re-phasing mechanism during LR initiation, which were proposed to 'insulate' the LRP from diurnal fluctuations in water fluxes which may interfere with organ emergence  [46].
This LR transcriptomic dataset has been used to infer the topology of the gene regulatory network coordinating lateral root morphogenesis  [50]. The predicted GRN controlling primordia patterning was organised in two main auxin-regulated modules, an early network dependent on ARF7 and ARF19 (Figure  3 ), and a later network, involving ARF5. In general, genes in the early ARF7-associated sub-network are expressed in all primordia cells during early stages of LRP development and then their expression decreases and is confined to the base of LRP. Concomitant with this transition, expression of genes associated with the ARF5 module rises. This module includes many transcription factors involved in primary root meristem organization whose expression is observed in central and tip LRP zones  [13, 50].
However, very little is currently known about the underlying gene regulatory networks and mechanisms driving the The rich 'omic' resources available for Arabidopsis has helped initiate efforts to elucidate the The authors would like to acknowledge the following funding agencies for financial support: underlying gene regulatory networks that control morphogenesis of the new root organs. J. B, D. v. W. , T. G. and M. J. B. acknowledge the Biological and Biotechnological Science Research GRN approaches have revealed system properties that confer important features in LRP Council (BBSRC) for supporting Doctoral Training, responsive mode and Professorial development: ability to stabilize or, conversely, trigger changes in cell identity, capacity to Fellowship awards (grant numbers BB/G023972/1, BB/R013748/1, BB/L026848/1, organize those changes in time and space (patterning), capacity to reach a similar output from BB/M018431/1, BB/PO16855/1, BB/M001806/1); K.
B. , S. G. and L. L. acknowledge support various transcriptomics scenarios (robustness) and at the same time, capacity to integrate from the French National Research Agency (ANR) through the NewRoot project (ANR-17- external cues that may influence the GRN final state. This latter plasticity property plays a CE13-0004-01); T. G. was supported by the Ministry of Education, Culture, Sports, Science and prominent role for root system adaptation to environmental signals. This will also provide the Technology (MEXT) through a JSPS Research Fellowship for Young Scientists (No. 12J02079) foundation on which to probe the sophisticated adaptive mechanisms that regulate how plants pattern root branching to match spatial availability of resources like water and nutrients in their external environment.
Surprisingly, limited attention has been paid to the role of biomechanics in the regulation of lateral root development, to date. However, increasing evidence points to the central role for mechanical signals during development in both plants and animals[21,85]. Lateral root development is a fascinating system to address these questions given that, unlike leaf primordia formation that takes place on the flanks of the shoot apical meristem, new root organs originate deep within parental root tissues. Live cell imaging, e. g. In parallel with -omics data collection and studies, advances in cell tracking, sampling and transcriptome analysis will generate a more detailed cartography of gene expression in developing LRP at various stages. When combined with time-course transcriptomic datasets, this will allow exploration of GRN at high temporal and spatial resolution.
Statistics Data were anonymized. The questionnaires were available via Google Drive allowing the automatic entry of results into a Microsoft Excel ® database. The randomization list was established by the random function of Excel ®. Statistical analyzes were performed using Excel ® 2013 and SAS ® softwares. For each test, a total score was assigned to each trainee. Comparisons of the means were carried out with a Student's test. The comparison of the percentages of exact lesion diagnosis was carried out by a Chi² test. The comparison of the test score 2 between the experts and the trainees was done with a Wilcoxon test because the number of experts, less than 30, did not correspond to a normal distribution. The comparison of lesion detection rates between experts and trainees was done using a Fisher test.
Thanks The photo database was created from photographs by  Drs. Manuel Aubert ,  Boubekeur Bennadji ,  Jean-François Contou ,  Elise Crochet ,  Nadia Fathallah ,  Philippe Marteau ,  Benoit Mory ,  Vincent de Parades ,  Elise Pommaret ,  Laurent Siproudhis  and  Ghislain Staumont , (in the majority, members of Research Group of Proctology, GREP).
Beyond oxidative phosphorylation (OXPHOS), mitochondria have also immune functions against infection, such as the regulation of cytokine production, the generation of metabolites with antimicrobial proprieties and the regulation of inflammasome-dependent cell death, which seem in turn to be regulated by the metabolic status of the organelle. Although OXPHOS is one of the main metabolic programs altered during infection, the mechanisms by which pathogens impact the mitochondrial electron transport chain (ETC) complexes to alter OXPHOS are not well understood. Similarly, how changes on ETC components affect infection is only starting to be characterized. Herein we summarize and discuss the existing data about the regulation of ETC complexes and super-complexes during infection, in order to shed some light on the mechanisms underlying the regulation of the mitochondrial OXPHOS machinery when intracellular pathogens infect eukaryotic host cells.
Data The data consisted of 193,226 milk TD records from 30,186 first-lactation Saanen goats (234 herds) and 205,841 milk TD records from 32,256 first-lactation Alpine goats (198 herds) from northwestern France, collected between 1995 and 2015. The pedigree consisted of 66,716 and 67,159 Saanen and Alpine goats, respectively. Each lactation included at least four TD between the 7th and 270th day in milk (DIM). Lactation had to last between 180 and 350 days. Goats were milked twice a day and their records were summed to obtain their daily production. More than four animals per herd × test date combination were required. The sires of these goats were artificial-insemination bucks with at least 20 progeny in the dataset, with 379 and 324 sires from the Alpine and Saanen goat breeds, respectively.
These results were consistent with other studies in dairy cows  [14, 23]. Unlike other studies in dairy goats  [4, 5] , we did not use an effect on litter size since this information was not available in our dataset, but Mucha et al. [1]  found no impact of this effect on EBV. We applied a principal component analysis of the variance-covariance matrix of the most complex model, which showed that it was possible to associate biological significance with each PC. As in our study, van der Werf et al. [17] , Olori et al. [24] , Druet et al. [14]  and Togashi and Lin  [25]  highlighted that the first eigenfunction was linked with the average production level throughout lactation, the second eigenfunction was associated with persistency, and the third eigenfunction opposed production around the middle of lactation against production at the beginning and end of lactation.
the present study describes the genetic architecture of the isolated populations of Cilento, through the analysis of exome sequence data of 245 representative individuals of these populations. By annotating the exome variants and cataloguing them according to their frequency and functional effects, we identified 347,684 variants, 67. 4% of which are rare and low frequency variants, and 1% of them (corresponding to 319 variants per person) are classified as high functional impact variants; also, 39,946 (11. 5% of the total) are novel variants, for which we determined a significant enrichment for deleterious effects. By comparing the allele frequencies in Cilento with those from the Tuscan population from the 1000 Genomes Project Phase 3, we highlighted an increase in allele frequency in Cilento especially for variants which map to genes involved in extracellular matrix formation and organization.
Correspondence and requests for materials should be addressed to M. C. (email: marina. ciullo@igb. cnr. it) In this work we used the data coming from the WES study performed on the Cilento isolates to describe their genetic architecture, annotating the identified variants, cataloguing them according to their frequency and functional effects, and comparing their frequencies with those from a general Italian population. Because of the initial population bottleneck followed by genetic drift during the first generations, it is expected that in these isolates the frequencies of some genetic variants may have changed compared to those of the founding pool. Some variants probably disappeared, thus limiting heterogeneity, while others may have become more frequent, which could facilitate their identification.
Overall, changes in allele frequency are larger for rare variants than common ones. Thus, allele frequencies in isolated populations and large populations are expected to differ the most at rare variants. Therefore, rare variants with increased allele frequencies in isolated populations could be more easily identified in genetic studies of traits with a Mendelian or complex inheritance. Thus, we focused our attention on variants which have an increased allele frequency in the Cilento isolates compared to a general Italian population. Among those, we searched for variants of particular interest, such as those having a functional impact on rare diseases. Furthermore, we performed different population genetics analyses to better characterize these populations as genetic isolates. Finally, we tested the utility of using exome data as a local reference panel to improve the accuracy of imputation and potentially increase the power of further genetic studies on human traits in these populations.
Results Description of Cilento exome sequencing. The analysis of the whole-exome data on the Cilento populations revealed high-performance sequencing, with an aligned read mean depth on target regions of 75X. We found 347,684 variants, 43. 8% of which are rare (Minor Allele Frequency, MAF ≤ 1%) and 23. 6% have MAF between 1% and 5%, giving a total of 67. 4% having low frequency (MAF ≤ 5%). 20. 7% of all variants identified in Cilento are singletons (Minor Allele Count, MAC = 1). We grouped sequence variants into four functional impact categories, in order of decreasing severity: 1) HIGH (Loss of Function), including stop-gain or -loss variants, frameshift variants, splice donor or acceptor variants and initiator codon variants; 2) MODERATE, including missense variants and in-frame insertions and deletions; 3) LOW, including synonymous variants, stop retained variants, incomplete terminal codon variants and splice-region variants; and 4) MODIFIER, including intronic and intergenic variants, 5′ and 3′ UTR variants, regulatory region and transcription factor binding site (TFBS) variants, miRNA variants and non-coding exon variants.
Cilento novel variants. Comparing the Cilento exome data with five largely used reference datasets (see Methods), we found that 39,946 variants, corresponding to 11. 5% of all the identified variants, are novel, while 88. 5% were found in at least one reference database and 10. 9% were shared with all the five databases (Supplementary Table  2 ). The percentage of novel variants in each frequency class decreases as MAF increases (as presented in Fig. 2  and in Supplementary Table  2 ). In accordance with the capacity of WES in identifying new rare variants  2, 10  , 87. 3% of Cilento novel variants have a MAF below 1% while the remaining 12. 7% is distributed in the MAF classes above 1%. However, we also identified 19 novel variants (0. 047%) with a MAF > 5%. According to their impact, 70.
2% of the novel variants were included in the MODIFIER class, 9. 9% in the LOW, 18. 1% in the MODERATE, and 1. 8% (716 variants) in the HIGH class (Supplementary Table  2 ). We observed a statistical significant enrichment of Cilento novel variants in the HIGH and MODIFIER categories and a depletion in the MODERATE and LOW classes compared to variants shared with the other databases (Supplementary Table  3  and Fig. 3 ). Overall, these differences were more significant for those variants belonging to the lower MAF classes. In detail, for all variants with MAF < 2% we observed a significant difference in proportion in all impact classes, while for variants with MAF 2-5% we observed only a significant enrichment and depletion in the MODIFIER and the LOW classes respectively.
To discern whether the genes containing the variants showing AF increases in each isolate are located in specific pathways, an over-representation analysis was performed using the ConsensusPathDB (CPDB) program  22. Because of the small sample size of the Cilento isolates (Cardile in particular), we only considered variants with a fold increase in frequency >5 respect to TSI or variants monomorphic in TSI that have a MAF ≥ 0. 0223 in Cilento villages (corresponding to a fold increase of 5 for a singleton in TSI). This MAF threshold ensures that variants analysed have at least MAC = 3 in Cardile and MAC = 5 in Gioi and Campora. According to these criteria, 9,126 variants in Campora, 6,882 in Gioi and 11,512 in Cardile (only 354 in common between the three sets), were used for the analysis. These variants mapped to 8,442, 6,710, and 9,979 genes respectively (of which 2,087 are in common).
Improvement of Imputation. Two imputation strategies were compared on the Cilento dataset on chromosome 10: firstly using the latest release of the 1KG_Ph3 and secondly using a combination of the 1KG_Ph3 and a local reference panel of haplotypes created by phasing the Cilento WES data (we denote these two imputation strategies as '1KG_Ph3' and '1KG_Ph3 + WES'). Over the entirety of chromosome 10, 3,921,562 and 3,923,571 variants were imputed by the strategies 1KG_Ph3 and 1KG_Ph3 + WES respectively. Indeed, including this WES local reference panel will allow the imputation of the previously discussed novel variants of Cilento. We compared imputation quality scores for variants that were neither imputed as monomorphic or present on either of the two genotyping arrays used in Cilento; our comparison involved 2,321,569 variants including 7,545 exonic variants present in the WES panel.
However, in that case a slightly different classification of loss of function variants was used including only the stop-gained (nonsense) or splice site-disrupting (splice donor or acceptor) variants. By comparing the Cilento sequences with data from five public databases we have identified many novel, Cilento specific variants, representing more than 11% of the total set of variations found in Cilento. This was similar to the percentage observed in an exome-sequencing study on an isolated population from the Dalmatian island of Vis (9%; where five databases were explored)  2  and lower than the percentage reported in a whole-exome sequencing study of a French-Canadian founder population (20%; but here only the 1000 genome database was explored)  4. As already found in other studies on isolated populations 2,10 , novel variants were enriched in rarity and functional relevance, which is expected as these variants have not fully undergone purifying selection due to the relatively recent foundation of these isolates.
Sequence reads were aligned with the Burrows-Wheeler Aligner (BWA, v0. 5. 10) algorithm on to the GRCh37 human assembly. The paired-read alignments were sorted and stored in BAM format using samtools (v0. 1. 18). Duplicates were marked with Picard (v1. 72), and local re-alignment and Base Quality Score Recalibration were carried out using Genome Analysis Toolkit (GATK, v1. 5-9, https://software. broadinstitute. org/gatk/)  42. After data pre-processing, a multi-sample variant-calling was performed using Samtools mpileup (v0. 1. 19). GATK Unified Genotyper (v1. 5-32-g2761da9) was used to recall the sites discovered by samtools and to perform the Variant Quality Score Recalibration (VQSR). Single Nucleotide Variants (SNVs) and indels were filtered by minimum VQSLOD score corresponding to the truth sensitivity threshold of 99.
5% and 95% respectively. Genotypes with a depth (DP) < 4 and/or a genotype quality (GQ) < 60 for that particular call, were set as null genotypes. Called variants were annotated with dbSNP137 rsIDs and 1000 Genomes super population allele frequencies that were extracted from the final 1000 Genomes Phase 1 integrated (v3) callset. Functional annotations were called with the Ensembl Variant Effect Predictor (http://www. ensembl. org/info/ docs/tools/vep/index. html)  43  against Ensembl 75, which provided genes and transcripts affected by the variants, location, coding consequence predictions and SIFT, PolyPhen and Condel annotations as well as GERP conservation scores. The most deleterious effect, according to severity estimated by Ensembl (https://www. ensembl. org/info/genome/variation/prediction/predicted_data.
All variants failing QC steps were removed. All subsequent analyses were performed using clean post-QCed datasets. Per-variant QC steps were performed using VCFtools (v0. 1. 12b). The post-QC Cilento exome-sequence dataset contained 245 (93 from Campora, 94 from Gioi and 58 from Cardile) individuals (45. 7% males) and 347,684 variants. Ti/Tv ratio. To assess the probability of false positives we evaluated aggregate transition-transversion (Ti/Tv) ratio for all variants that passed QC. Average Ti/Tv ratio for targeted regions over all subjects and all locations was 2. 57, in accordance with the value expected in such studies.
Also, sequence variants were classified into four functional impact categories, in order of decreasing severity: HIGH (Loss of Function), MODERATE, LOW, and MODIFIER, following functional annotation groups as reported by Ensembl (https://www. ensembl. org/info/genome/variation/prediction/predicted_data. html). In order to identify novel variants not reported in the reference datasets, we compared the Cilento exome data with five databases: HapMap3 release 2 (http://www. hapmap. org), 1KG_Ph3 (http://www. internationalgenome. org/), Haplotype Reference Consortium (HRC) release 1. 1 (http://www. haplotype-reference-consortium. org/), dbSNP142 (https://www. ncbi. nlm. nih. gov/projects/SNP/), and ExAC release 0. 3. 1 (http://exac. broadinstitute. org/). To check if a Cilento variant was shared with a reference database we matched chromosome, position and both the alleles (including multiallelic variants in reference database).
Functional enrichment of novel variants. To test for functional enrichment of Cilento novel variants, we divided all variants in two groups: those found in at least one of the five reference database (SHARED) and those private to Cilento (NOVEL). Variants were then grouped by MAF and impact categories, as described above. Two sided tests based on asymptotic normal distributions were used to compare the proportion between the SHARED and NOVEL datasets for each MAF and Impact category. A Fisher exact test was used instead for the categories containing variant counts <5. Fold enrichment was calculated with the following equation: = E V t V t / / M i N M i N M S M i S M , , , where E M i , is the fold enrichment of impact i in M MAF category; V N M i , and V S M i , are the number of variants in M MAF category with impact i in the NOVEL and the SHARED dataset respectively; t N M and t S M are the total number of variants in the M MAF category in the NOVEL and the SHARED dataset respectively.
Rare disease-causing variants. Variants increased in allele frequency in the isolates were searched for in ClinVar (Human Variation of Clinical Significance) database (ftp://ftp. ncbi. nih. gov/pub/clinvar/vcf_GRCh37/ clinvar_20170530. vcf. gz) to identify those responsible for rare genetic diseases. Chromosome, position and alleles were used to match the variants increased in allele frequency in Cilento with those included in the ClinVar. Only those variants indicated as Pathogenic in ClinVar and reported in the OrphaNet database (www. orpha. net) because responsible for rare conditions, were selected. Direct genotyping of the rs72653706 variant on the overall population sample (Campora N = 750, Gioi N = 720, Cardile N = 544) and of the rs201680145 variant on the Cardile population sample (N = 564) was performed by TaqMan assay (Bio-Rad, USA).