dataesr/scientific-paragraphs-categorization huggingface dataset:
Genes that were differentially regulated by >1. 5-fold in the epidermis of FSPCre-Pparb/d -/-mice enriched the GO of lipid metabolism, molecular transport, small molecule biochemistry, cell death and survival, and cellular movement, ranked according to their P-values (a lower P-value indicates a stronger association to the indicated GO) (Fig. 4a ). A total of 80 unique gene transcripts were responsible for the top three enriched GOs in the epidermis. Among these 80 genes, 85% of them (68 genes: 37 upregulated, 31 downregulated) were lipid-Fig. 3  Mouse models of SSc recapitulate the fibroproliferative subset of human SSc. a Microarray datasets from FSPCre-Pparb/d -/-mice were hierarchically clustered with microarray datasets from Tsk1/2 heterozygous mutant mice, bleomycin-induced fibrosis mice, SGVHD mice, and human SSc.
Each mouse model or human disease cluster is represented by a unique color. Microarray datasets for other mouse models of SSc and human SSc were downloaded from the NCBI GEO database. FSPCre-Pparb/d -/-mice (yellow box) uniquely recapitulated the expression pattern of 113 genes in human fibroproliferative SSc, i. e. , 113 genes that were downregulated in human fibroproliferative SSc were similarly downregulated in FSPCre-Pparb/d -/-mice. Bar charts represent the gene ontologies (GO) associated with the 113 gene set, ranked by -log(P-value). Arrows represent GOs that are highly relevant to SSc. b Tsk1/2 heterozygous mutant, bleomycin-induced fibrosis, bleomycin-induced fibrosis and SGVHD mice recapitulated the expression pattern of 110 genes in human fibroproliferative SSc, i. e. , 110 genes that were upregulated in human fibroproliferative SSc were similarly upregulated in previous mouse models of scleroderma.
Fibroblast PPARβ/δ regulates Lrg1 to modulate TGFβ1 signaling The interrogation of our microarray datasets suggested that dysregulated TGFβ1 signaling plays a central role in the profibrotic phenotype of FSPCre-Pparb/d -/-mice (Fig. S4 ). Indeed, FSPCre-Pparb/d -/-mice exhibited an enhanced activation of TGFβRII and SMAD3 via phosphorylation compared to Pparb/d fl/fl mice (Fig. 5a ). Of interest is the diminished expression of LRG1 in the dermis of FSPCre-Pparb/d -/-mice (Fig. 5b, S4b ). LRG1 modulates TGFβ1 signaling in a tissue-specific manner and has been implicated to play a role in vascularization  36, 37. Thus, the role of LRG1 is aligned with the dysfunctional microvascularization and dysregulated TGFβ1 signaling observed in SSc patients. PPARβ/δ agonist GW501516 (GW) increased LRG1 mRNA and protein levels, which were attenuated either by PPARβ/δ antagonist GSK0660 (GSK) or an inverse agonist compound 10h (10h) in normal primary human fibroblasts (Fig.
Histological, immunohistochemical, and immunofluorescence staining Tissues were processed, sectioned and stained as previously described  65. PicroSirius Red staining was performed as recommended by the manufacturer (ab150681, Abcam, Cambridge, United Kingdom). Colorimetric images were captured using Axio Scan. Z1 with Plan Apochromat 20×/0. 8 objective (Carl Zeiss, Germany). Fluorescence images were captured using the LSM 710 confocal microscope with the Zeiss EC Plan-NEOFLUAR 20×/0. 5 NA objective. Analyses were performed with ZEN 2012 Light Edition software (Carl Zeiss, Germany).
Lucifer yellow permeability assay Lucifer yellow dye (1 mg/mL; Sigma-Aldrich, USA) was topically applied on the epidermis for 1 h. Excess Lucifer yellow was washed away with 1 × PBS, and the skin tissues were cryofrozen with OCT tissue freezing medium (Leica, USA). Tissues were cryosectioned at 8 μm and mounted with Hoechst 33,342 dye (Life Technologies, USA). Images were taken using the LSM 710 confocal microscope with the Zeiss EC Plan-NEOFLUAR 20×/0. 5 NA objective. Analyses were performed with ZEN 2012 Light Edition software (Carl Zeiss, Germany).
Hydroxyproline assay Hydroxyproline content in tissues was determined as previously described  67. Trans-4-hydroxy-L-proline (0-300 µg/mL) was used to generate a standard graph. Absorbance was measured at 550 nm using a SpectraMax M2e Multi-Mode Microplate Reader and SoftMax Pro Microplate Data Acquisition & Analysis Software (Molecular Devices, USA). The value of unknown hydroxyproline was determined from the standard graph. The weight of each respective wound biopsy was used for normalization.
Gene expression microarray cDNA synthesis was performed the Applause WT-Amp ST System (NuGEN Technologies, USA). The resulting cDNA was purified with MinElute Reaction Cleanup Kit (Qiagen, USA), followed by fragmentation and labeling using the Encore Biotin Module (NuGEN Technologies, USA). The fragmented and labeled cDNA were mixed with a hybridization master mix (Affymetrix, USA) in accordance to the manufacturer's protocol, and 90 μL of the resultant mixture were injected into the GeneChip® Mouse Gene 1. 0 ST Array gene chips (Affymetrix, USA). The scanned data was collected for analyses using the Partek Genomics Suite v6. 6 (Partek Inc. , USA). Differences in gene expression between FSPCre-Pparb/ d -/-and wild-type samples were identified using the ANOVA function of the Partek Genomics Suite.
Microarray datasets from FSPCre-Pparb/d -/-mice were subsequently hierarchically clustered with microarray datasets from tight skin (Tsk) 1 or 2 heterozygous mutant mice (GSE71999), bleomycin-induced fibrosis mice (GSE71999), sclerodermatous graft versus host disease (SGVHD) mice (GSE24410), and human samples of SSc (PMC2481301, GSE9285), downloaded from NCBI Gene Expression Omnibus (GEO) database. We focused our analysis on an intrinsic SSc gene set comprising 995 genes that was previously reported 33 (GSE9285). Gene ontology (GO) analysis of gene subsets was performed using the Ingenuity Pathway Analysis software (Ingenuity Systems Inc. , USA). The microarray protocols and data have been deposited in NCBI's Gene Expression Omnibus (GEO) database accession number GSE71419.
Acknowledgements This work is supported by the  Singapore Ministry of Education  under its  Singapore Ministry of Academic Research Fund Tier 2  ( MOE2010-T2-2-009 ) to NST; ( MOE2012-T2-1-014 ) to SC, ( MOE2014-T2-1-036 ) to XW and WW, ( MOE2015-T1-001-034 ) to WW and NST; and  A*STAR-NHG-NTU   Skin Research Grant  ( SRG/14003 ) to NST;  Lee Kong Chian School of Medicine, Nanyang Technological University (NTU) Start-Up Grant , the  Région Midi-Pyrénées  through the  Chaire d'Excellence Pierre de Fermat  and the  Bonizzi-Theler-Stiftung  to WW. The authors are thankful to the Director,  CSIR-NEIST, Jorhat  for his support and  SERB-Department of Science & Technology (SERB-DST) ,  Government of India  for providing the Ramanujan Fellowship ( SB/S2/RJN-087/ 2014 ) to  Dr. Mintu Pal. We wish to thank  Pierre Chambon  and  Daniel Metzger  for their collaboration in generating the floxed PPARβ/δ mice.
1 ). Clinical data indicate that TOSV can cause meningitis, meningoencephalitis or encephalitis. Clinically, there is no marker to distinguish TOSV from other causes. Laboratory-based diagnosis using direct or indirect methods are therefore the only possibility to identify TOSV. Interestingly, encephalitis is frequently seen in central nervous system infections caused by TOSV, although the outcome is favourable  [58] [59] [60] [61]. Detection of TOSV RNA using real-time molecular techniques is now the reference standard. In the Mediterranean area, an estimated 250 million people are exposed to TOSV, with seroprevalence rates higher than 40% in certain regions (Fig. 1 ). In France, >8 million people are at risk of TOSV infection. TOSV is the most prevalent arthropodborne virus in Europe. However, there are only 295 references using the keyword 'Toscana virus' in the National Center for Biotechnology Information PubMed database for a virus that was discovered almost 50 years ago.
Gene Transcriptional Analysis Data from the whole-genome transcriptome analyses of two biological replicates of maize plants exposed to three consecutive dehydration stresses (amounting to six whole-genome RNA seq analyses) were used for the network correlation analyses. These data were previously published in  Ding et al. (2014). Genes that in the first stress (S1) and under repeated stresses (S2, S3) were similarly expressed were categorized as non-memory; genes that in S2 or S3 produced transcripts at significantly different levels from the levels produced in S1 were categorized as memory, as described in detail below. Bioinformatics analysis of transcriptome sequencing data of watered (W), S1, and S2 samples was performed for two biological replicates per sample. The distribution of raw and mapped reads for the two samples is available in  Ding et al.
Classification of Stress Memory Response Types According to our operational definition, a memory response implies that changes in gene expression or in a physiological parameter induced by repeated dehydration stresses (S2 or S3) are significantly different from responses displayed in the first stress (S1). Whole transcriptome data were from W, S1, and S3, whereas physiological data were collected at W, S1, R1, and S2. Transcript analysis of selected genes showed similar expression responses in S2 and S3, and so we considered these stages to be comparable for the transcript and physiological data. Among the 3740 memory genes in Z. mays, a subgroup of 2924 genes did not exhibit altered expression in S1 compared to the initial, non-stressed expression in W, but significantly changed (increased or decreased) expression in subsequent stresses.
Co-expression Network and Gene Ontology Analyses Co-expression network analysis was used to identify genes with coordinated transcriptional responses (modules). Among the total of 4986 Z. mays genes exhibiting memory and nonmemory responses to repeated dehydration stress  (Ding et al. , 2014) , 4710 genes displaying sufficiently high variation (range of FPKM-normalized read counts across a total of six samples for W, S1, and S2 with two biological replicates in each of the three treatments for a given gene >0. 05) were identified, and their values were used to construct a correlation matrix using the R package, WGCNA  (Langfelder and Horvath, 2008). Dynamic tree cutting was adopted to identify modules with minModuleSize of 30  (Langfelder and Horvath, 2007). Eigengenes were used to cluster all identified modules using Average Hierarchical Clustering analyses  (Langfelder and Horvath, 2007).
Pairwise distances between modules were calculated from the correlation between eigengenes for each module as an estimate of similarity. A distance of 0. 2 was used as a cut-off to group modules into meta-modules. The correlation coefficients used to construct the co-expression network could be noisy due to our sample size of six for the transcriptomic data, although it has been demonstrated that overall the quality of the transcriptomic data set was good enough to generate reliable results, as shown in  Ding et al. (2014). To increase the signal-to-noise ratio and reduce the false positive rate, during network construction, we used a high threshold to fill out genes with low expression levels or high fluctuations, and only genes with high correlation were considered to belong to the same module.
This high threshold for data analysis reduced the false positives significantly and facilitates obtaining robust results. Gene ontology (GO)  (Ashburner et al. , 2000; Bard and Rhee, 2004)  and term enrichment analyses using AgriGO  (Du et al. , 2010)  were performed to identify putative functions of dehydration stress memory genes in the network analysis. Genes sharing the same GO term are considered to be involved in similar biological processes, allowing us to identify the putative functions represented in each module. Statistical analysis was performed using Fisher's exact test with subsequent adjustment for multiple testing by calculating the false discovery rate (FDR) as implemented in AgriGO. The minimum number of mapping entries was set to 5, and the list of genes from the maize genome AGPv3 release 5a database was used as a reference 1.
Analysis of Physiological Parameters and Correlation Between Transcriptional and Physiological Dehydration Stress Responses To quantify changes in physiological parameters across dehydration treatments, we used Student's t-tests. Chlorophyll concentrations were analyzed using a linear mixed-effect model, with experiment date as a random intercept, and differences due to treatment analyzed using type III tests of fixed effects with degrees of freedom determined by the Satterthwaite method as implemented in R statistical software (R Core Development Team, 2011) using the packages lme4  (Bates et al. , 2014)  and lmerTest  (Kuznetsova et al. , 2014). The novel component of our study is that the previously published transcriptomic data  (Ding et al. , 2014)  were integrated with the detailed plant physiological data using bioinformatic and gene expression network analyses in order to investigate how changes in gene expression correlated with changes in specific physiological processes during response to repeated drought stress.
A fragment of 825 bp was amplified with the primers BarS (forward 5′-GTTATGCCTGTAATGATTG-3′)  (A ´lvarez-Presas et al. , 2011)  and COIR (reverse 5′-CCWGTYARMCCHCCWAYAGTAAA-3′)  (Lazaro et al. , 2009) , following  Mateos et al. (2013). PCR products were purified and sequenced in both directions on a 3730xl DNA Analyzer 96-capillary sequencer (Applied Biosystems, Foster City, California, United States). Results of both analyses were concatenated to obtain a COI sequence of 909 bp in length (designated in this text as 'long sequence'). Sequences were edited using CodonCode Aligner software (CodonCode Corporation, Dedham, MA, USA), compared to the GenBank database content using BLAST and deposited in GenBank under accession number MG655587-MG655618. For several specimens only 'short' sequences were obtained (Table  2 ).
Molecular identification of sampled specimens Sequences were obtained from specimens belonging to five species (Table  2 ), including three named species, B. kewense (specimens from 13 localities, 17 sequences including replicates), D. multilineatum (specimens from four localities, eight sequences including replicates), B. vagum (specimens from three localities, five sequences including replicates) and two unnamed species, Diversibipalium 'black' (one specimen from one locality, one sequence), and Diversibipalium 'blue' (specimens from two localities, six sequences including replicates). A tree (Fig. 2 ) was constructed from an analysis of our new COI sequences and sequences from GenBank. Both NJ and ML trees showed comparable topologies, but the bootstrap values of branches, in both trees, were contrasted: 100% for all branches representing species, and very low for upper nodes.
We thus considered that the trees were informative for showing the genetic identity of all specimens within a species, but not for inferring relationships between taxa. Thus, no further comment about interspecies relationships are given in the rest of this text; in that we follow the general principles of COI barcoding  (Hebert & Gregory, 2005 ): 'we emphasize that DNA barcodes do not aim to recover phylogenetic relationships; they seek instead to identify known species and to aid the discovery of new ones. ' We remarked, but do not comment, probable misidentification of certain sequences deposited in GenBank, such as Novibipalium venosum or the 'D. multilineatum' (HM346600). Each of the three named species belonged to a clade with high (100%) bootstrap support (Fig. 2 ). For B. kewense, the clade includes GenBank sequences from Spain, Azores Islands, and Cuba; our 13 new sequences (excluding replicates) are from seven localities in metropolitan France, three overseas French territories (Guadeloupe, Martinique, French Guiana), and two other countries, Monaco, and Portugal.
All COI sequences were strictly identical. For D. multilineatum, the clade includes GenBank sequences from Italy and France (sequence from specimen MNHN JL177, already published by  Mazza et al. , 2016) , and our six new sequences (excluding replicates) are from three localities in metropolitan France. All COI sequences were strictly identical. For B. vagum, no sequence was found in GenBank. Our five new sequences are from one overseas French territory (Guadeloupe) and two other countries, Montserrat (West Indies) and Florida, USA. All COI sequences were strictly identical. For Diversibipalium 'black' from Metropolitan France and Diversibipalium 'blue' from Mayotte, each sequence was found to have no close match in GenBank sequences or our new sequences, suggesting that they each belong to a species which has never been sequenced for COI gene.
Note: There was a total of 857 positions in the final dataset. incomplete black transverse neck band, interrupted dorsally by a small median gap, and ventrally by the creeping sole. The ventral headplate is a greyish colour with a light ochre margin. The ventral surface (Fig. 6 ) is a light ochre colour, with a distinct off-white creeping sole, delineated by paired, narrow, longitudinal diffuse grey-violet stripes beginning at the ventral termination of the collar, and extending the entire body length. In Fig. 7 , we present evidence of predation on an unidentified native European earthworm, and in Figs. 8 and 9 evidence of reproduction by scissiparity where the shed fragment is immediately motile but does not possess the characteristic hammershaped head.
, 2017; Chaabane et al. , 2016)  and various triclads (A ´lvarez-Presas & Riutort, 2014) including land planarians (family Geoplanidae) (A ´lvarez-  Presas et al. , 2011 Presas et al. , , 2012 Presas et al. , , 2014)). The present study shows that COI short sequences, easily obtained from almost all specimens, have inter-specific distances of 10. 9-21. 2% (Table  3 ). These interspecific distances are high enough to differentiate species of bipaliines, especially in the absence of intra-species variation. Long sequences provide even higher inter-specific distances, ranging 15. 9-25. 9% (Table  4 ) but these are less easily obtained, and the database includes only four species. Of course, it might be objected that the current database (seven species with short sequences) is extremely limited in comparison to the number of species described in the bipaliines-more than 160  (Winsor, 1983a).
However, the current database includes most invasive world-wide species, inter-specific distances are high, and intra-specific variation was almost inexistent for most species. For these reasons, we believe that identification of common invasive species of bipaliine flatworms can reliably be done from COI barcoding. Barcoding can be done from a very small worm, immature, or even a fragment. Moreover, COI barcoding can probably alert scientists to the presence of species not previously sequenced, if a sequence different from those reported in the present study is found. The fact that some bipaliines do not reproduce sexually outside their native habitat or tropical and subtropical climates, but only by scissiparity  (Winsor, 1983a) , is probably one reason explaining why no variability was found in specimens, since specimens are clones, and no or very few mutations can happen.
ACKNOWLEDGEMENTS A  complete French translation of the article is available as Supplemental File / Une traduction francaise integrale de l'article est disponible comme fichier supplementaire. Des vers geants chez moi   !Plathelminthes (Plathelminthes, Geoplanidae, Bipalium spp. , Diversibipalium spp. ) en France metropolitaine et dans les territoires francais d'outre-mer. We thank all the citizens who participated in the survey; those who sent specimens are particularly thanked. Names of citizens, and sometimes scientists, who provided photographs and/or specimens are indicated in Tables 2, 5-8 and in the Supplemental Information. We apologize for not mentioning the names of citizens who kindly.
The PFS time will always be derived from imagery assessment dates, not from visit dates. The primary analysis will be based on investigator-recorded assessments. If a new anticancer treatment or surgery is used before the data cutoff and if there is no documentation of progressive disease before the start of the new anticancer treatment, then the PFS is censored at the last adequate tumor assessment before the new anticancer treatment started, regardless of whether there was a progressive disease or death after the start of the new anticancer treatment. The co-primary endpoint is quality of life.
Responsibilities of the sponsor The sponsor of this study has responsibilities to Health Authorities to take all reasonable steps to ensure the proper conduct of the study as regards ethics, protocol adherence, integrity, and validity of the data recorded on the eCRF. During monitoring visits, the following points will be scrutinized with the investigator: patient informed consent, patient recruitment and follow-up, patient compliance to the study treatment, study treatment accountability, concomitant therapy use, AE documentation and reporting, and quality of data.
Electronic case report form Study data for each randomized subject will be entered into an eCRF (Clinsight © Web application) by site personnel using a secure, validated web-based electronic data capture (EDC) application. Coordination center will have view-only access to all data upon entry in the EDC application. Selected data for subjects who are not randomized will be collected in the EDC. Instances of missing, discrepant, or uninterpretable data will be queried with the investigator for resolution. Any changes to study data will be made to the eCRF and documented in an audit trail, which will be maintained within the clinical database.
Instruments This present paper draws on data collected within the REFINEMENT project using the REMAST tool (Refinement Mapping Services Tool). This tool has five main sections (Table  1 ), with the focus on the MHSI (Mental Health Services Inventory) used to classify and describe mental health services in the eight study areas. The core of the MHSI is represented by DESDE-LTC, which provides most of the information needed to complete this inventory. The feasibility, reliability and validity of DESDE-LTC has previously been described  (Salvador-Carulla et al. , 2000 , 2011a , 2011b , 2013; Gutierrez-Colosía et al. , 2017).
Data analysis All MTCs in the study areas were analysed separately according to whether they provided core health or other care. Table  2  illustrates DESDE-LTC codes included in each of the two groups. Both at the overall level and for each study area, the total number of MTCs providing health and other care and the number of MTCs per type of care and target group were computed, as well as the percentage of core health care and other care services as a share of total MTCs. In each study area, the rate of MTCs classified as core health care and other care per 100 000 adult population was also calculated. Services were considered 'general health' if they could be used by other users alongside mental health service users, whereas services with 'mental health' as target group were those providing care exclusively to people with mental health problems.
Data collection procedure Specific training was provided on the use of the DESDE-LTC classification system to the researchers of the REFINEMENT project responsible for data gathering and codification. For the collection of local information in every study area, researchers consulted available databases and health agencies and established direct contact with services. Particularly, in Girona (Spain) and Helsinki and Uusimaa (Finland), there were formal agreements with the Departments of Health. Data were gathered into an ad hoc database, which was completed in August 2012, and further revised and updated to create a final database in July 2013. Detailed information on the procedure is provided in  Salvador-Carulla et al. (2015). other study areas, it was not possible to map them due to a lack of information.