dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS We thank  Dr. Eleonora Cesari  and  Livia Pellegrini  for technical assistance. This work was supported by grants from  Telethon  ( GGP 12189 ;  GGP 14095 ),  Associazione Italiana Ricerca sul Cancro (AIRC ;  IG14581 ), and  Ministry of Health ''Ricerca Corrente ''  and ''5x1000 Anno 2014'' to  Fondazione Santa Lucia.
Genotypes are accessible via the WIDDE repository (http://widde. toulouse. inra. fr/widde/). We compared these newly generated data with published genotypes of some European and Asian cattle breeds, Bali cattle and gaur (Table  1 ). The combined data set comprises 37,429 SNPs. Using PLINK  68  , we removed SNPs with call rates <90% or with minor allele frequencies <0. 001 and discarded individuals with 10% missing genotypes. The resulting data set contained 36,872 SNPs and 736 animals from 44 populations representing taurine cattle, zebu, and three species related to cattle (Bos javanicus -banteng, Bos gruniens -yak and, Bos frontalis -gayal). Gaur (Bos gaurus)  69  was used instead of gayal in f4 analysis (see below) of the species composition of Chinese cattle because of the indicine zebu introgression into gayal.
Population genetic analysis. We used PLINK  68  to calculate the observed homozygosity for each population. Three complementary methods were used to analyze the genetic diversity among populations. First, a Principal Component Analysis (PCA) was carried out to investigate the pattern of genetic differentiation among populations and individuals using the R package SNPRelate 70 , which performs eigen-decomposition of the genetic covariance matrix to compute the eigenvalues and eigenvectors. Second, population structure was evaluated by unsupervised and supervised model-based hierarchical clustering implemented in the Admixture software  71. The results were visualized using the program Distruct  72. Third, a NeighbourNet network was constructed using Reynold's distances between populations using Splitstree 4.
A 445-bp mtDNA control region was amplified and sequenced as described previously (GenBank accession codes KY682307-KY682687)  20. A total of 381 newly generated sequences were analyzed together with published sequences (Supplementary Tables  S2  and S3 ). Haplotype diversity of the segment 16,023-16,262 (numbering according to GenBank accession no. V00654) was computed using the software DnaSP  75. Y-chromosomal genotyping was carried out for 140 samples (Table  S2 ) with the protocol described by Bonfiglio et al. (2012)  76  , which differentiates Y1 (dominant in north Europe) and Y2 (dominant in other taurine cattle) and Y3 (indicine cattle) type Y chromosome  77. Genome-scan for adaptive differentiation and association with environmental and morphological covariables. Whole genome-scans for adaptive differentiation and association with population-specific co-variables were performed with BayPass 2.
28  57. Similarly, the posterior means of the two hyperparameters a and b for the Beta distribution of across population allele frequencies obtained on the POD (a = b = 1. 02) were almost equal to the ones obtained in the original data (a = b = 1. 00). Taken together, these sanity checks indicated that the POD faithfully mimics the real data set, allowing us to define a 0. 1% significance threshold on the XtX statistics (XtX = 32. 3) to identify genomic regions harboring footprints of selection. We collected values for six environmental covariables, i. e. average temperature, average relative humidity, sunshine, average air pressure, wind speed, and precipitation from China Meteorological Administration (http:// data. cma. cn/). Values for 10 morphological covariables, i. e. male body weight, female body weight, male height, female height, male body length, female body length, male heart girth, female heart girth, male fore-shank circumference, and female fore-shank circumference were provided for 17 out of the 20 studied indigenous breeds in National Commission of Animal Genetics Resources 3 , but these data were not available for the populations LZ, BN and HH.
Acknowledgements This work was supported by  National Natural Scientific Foundation of China  ( 31561143010 ,  31272418 ) and  China Agricultural Research System  ( CARS-37 ). Xi Wang  was supported by  Shanxi Scientific and Technological Development Programs  (20140311018-1, 20120311021-1). The authors thank  Dr. Hideyuki Mannen  ( Kobe University, Japan ),  Dr. Albano Beja-Pereira  ( Universidade do Porto, Portugal ) and  Hartati Hartati (Indonesian Agency for Agricultural Research and Development, Ministry of Agriculture ) for providing detailed information on their data sets.
Analysis of deep sequencing data Reads from Next-Generation Sequencing (NGS) were trimmed by Trim Galore! (Galaxy v0. 4. 2) in order to remove Illumina small RNA adapters and were aligned to the S. cerevisiae sacCer3 reference genome by Hisat (Galaxy v2. 0. 3, k = 5). The aligned reads were filtered for MapQ >= 30 by BamFilter (Galaxy v0. 0. 2) and sorted with Samtools (Galaxy v2. 0). The genomic regions corresponding to the aligned reads were intersected with the regions of known yeast mRNA coding sequences (Bedtools v2. 25. 0; CDS intervals, positions of stop and start codons of SGD genes were downloaded from UCSC Genome Browser as a gtf file). For each aligned read corresponding to an mRNA CDS, the middle position of the corresponding genomic interval was calculated using R (RStudio v0.
99. 896 with R v3. 3. 2). The distance between the middle positions and either the translation start site or the position of the stop codon of the corresponding mRNA was calculated. The distribution of obtained distances from the translation start site or the stop codon were visualized on a density plot using ggplot2 (v2. 2. 0). Code for distance calculation and plots is published on https://github. com/jadybea/Rio1p.
ACKNOWLEDGEMENTS With thank  Katrin Karbstein  for the gift of antibodies,  Marlène Faubladier  for plasmid construction,  Natacha Pérébaskine  and  Sébastien Fribourg  for the gift of purified proteins,  Stéphanie Balor  and  Vanessa Soldan  ( METI platform, Toulouse ) for technical support in electron microscopy,  Yves Romeo  and  Jean-Paul Gélugne  for helpful discussions.
The therapeutic use of RhoA inhibitors (RhoAi) has been experimentally tested in spinal cord injury (SCI). In order to decipher the underlying molecular mechanisms involved in such a process, an in vitro neuroproteomicsystems biology platform was developed in which the pan-proteomic profile of the dorsal root ganglia (DRG) cell line ND7/23 DRG was assessed in a large array of culture conditions using RhoAi and/or conditioned media obtained from SCI ex vivo derived spinal cord slices. A fine mapping of the spatio-temporal molecular events of the RhoAi treatment in SCI was performed. The data obtained allow a better understanding of regeneration/degeneration induced above and below the lesion site. Results notably showed a time-dependent alteration of the transcription factors profile along with the synthesis of growth cone-related factors (receptors, ligands, and signaling pathways) in RhoAi treated DRG cells.
For protein extraction was performed from SCI tissues from control rats (n ϭ 3) and rats 12 h after SCI treated with RhoAi ϩ FK506 (n ϭ 3) or not treated (n ϭ 3). For the collection of the conditioned media, control rats (no balloon inflation, n ϭ 6), rats 12 h post-injury (n ϭ 6) and rats 3 days post-injury (n ϭ 6) were sacrificed. For the behavioral experiments, 5 rats received saline and 5 rats received RhoAi ϩ FK506. Statistical analysis: For the proteomic statistical analysis of conditioned media, as a criterion of significance, we applied an ANOVA significance threshold of p Ͻ 0. 05, and heat maps were generated. Normalization was achieved using a Z-score with a matrix access by rows. Obtained data from tissue analyses and behavioral testing were reported as mean Ϯ S. E. Mean values among different experimental groups were statistically compared by one-way ANOVA and Tukey's post hock tests using Graph pad PRISM software.
The chromatography system was coupled to a Thermo Scientific Q-exactive mass spectrometer programmed to acquire in a data-dependent mode Top 10 most intense ion method. The survey scans were done at a resolving power of 70,000 FWHM (m/z 400), in positive mode and using an AGC target of 3e6. Default charge state was set at 2, unassigned and ϩ1 charge states were rejected and dynamic exclusion was enabled for 25 s. The scan range was set to 300-1600 m/z. For ddMS 2 , the scan range was between 200 -2000 m/z, 1 microscan was acquired at 17,500 FWHM and an isolation window of 4. 0 m/z was used. MS Data Analysis of T0, T30 min, T1 h and T4 h Protein Extract After RhoAi Treatment-Tandem mass spectra were processed with Thermo Scientific Proteome Discoverer software version 1. 4. Spectra were searched against UniprotKB/Swiss-Prot (version January 2016) filtered with Rattus norvegicus (31093 sequences) taxonomy using the SEQUEST HT algorithm (version 1.
4. 1. 14). The search was performed choosing trypsin as the enzyme with one missed cleavage allowed. Precursor mass tolerance was 10 ppm, and fragment mass tolerance was 0. 1 Da. N-terminal acetylation; and cysteine carbamidomethylation; methionine oxidation were set as variable modifications and cysteine carbamidomethylation as fixed modification. Peptide validation was performed with the Percolator algorithm by filtering based on a q-value below 0. 01, which corresponds to a false discovery rate (FDR) of 1%. Proteins were identified with a minimum of 2 peptides with at least one unique peptide per protein. The data sets used for analysis and the annotated MS/MS spectra were deposited at the ProteomeXchange Consortium (http://proteomecentral. proteomexchange. org) via the PRIDE partner repository with the data set identifier PXD004639 (for review: Username: reviewer60033@ ebi.
ac. uk Password: 7O08FxXe). MS Data Analysis of Protein Extract and Secretome After SCI-CM With or Without RhoAi Treatment-All the MS data were processed with MaxQuant (version 1. 5. 6. 5) (19) using the Andromeda (20) search engine. Secretome and protein extract from ND7/23 cell line were processed in two different files. Proteins were identified by searching MS and MS/MS data against Decoy version of the complete proteome for Rattusnorvegicus of the UniProt database (21) (Release June 2014, 33,675 entries) combined with 262 commonly detected contaminants. Trypsin specificity was used for the digestion mode with N-terminal acetylation and methionine oxidation selected as the variable. Carbarmidomethylation of cysteines was set as a fixed modification, with up to two missed cleavages. For MS spectra, an initial mass accuracy of 6 ppm was selected, with a minimum of 2 peptides and at least 1 unique peptide per protein, and the MS/MS tolerance was set to 20 ppm for HCD data.
For identification, the FDR at the peptide spectrum matches (PSMs) and protein level was set to 0. 01. Relative, label-free quantification of proteins was performed using the MaxLFQ algorithm (  22 ) integrated into MaxQuant with the default parameters. The data sets, the Perseus result files used for analysis and the annotated MS/MS spectra were deposited at the ProteomeXchange Consortium (23) (http://proteomecentral. proteomexchange. org) via the PRIDE partner repository  (24)  with the data set identifier PXD004639 (for review: Username: reviewer 60033@ebi. ac. uk Password: 7O08FxXe) for cellular extracts and secretomes. Analysis of the proteins identified was performed using Perseus software (http://www. perseus-framework. org/) (version 1. 5. 6. 0). The file containing the information from identification was used with hits to the reverse database, and proteins only identified with modified peptides and potential contaminants were removed.
0, http://www. pantherdb. org) and STRING (version 9. 1, http://string-db. org). Subnetwork Enrichment Pathway Analyses and Statistical Testing-The Elsevier's Pathway Studio version 9. 0 (Ariadne Genomics/ Elsevier) was used to deduce relationships among differentially expressed proteomics protein candidates using the Ariadne ResNet database  (25, 26). "Subnetwork Enrichment Analysis" (SNEA) algorithm was selected to extract statistically significant altered biological and functional pathways pertaining to each identified set of protein hits (C1, R1, L after RhoA inhibitor treatment sets). SNEA utilizes Fisher's statistical test used to determine if there are nonrandomized associations between two categorical variables organized by specific relationship. SNEA starts by creating a central "seed" from all relevant entities in the database, and retrieving associated entities based on their relationship with the "seed" (i.
e. binding partners, expression targets, protein modification targets, regulation). The algorithm compares the sub-network distribution to the background distribution using one-sided Mann-Whitney U-Test, and calculates a p value indicating the statistical significance of difference between two distributions. In our analysis, "GenBank" ID and gene symbols from each set were imported to the software to form an experimental data set. For the reconstruction of networks of pathways, biological processes and molecular function were evaluated for each single protein hit and its associated targets (networks and pathways)  (27, 28). Integrated Venn diagram analysis was performed using "the InteractiVenn"; a web-based tool for the analysis of complex data sets. Behavioral Testing-Animals were evaluated using Basso, Beattie, and Bresnahan (BBB) open-field test to assess motor function after SCI at day 0, 7, 14, 21, 28, 35, 42 and 49 days post injury.
For general nuclear staining 4 -6-diaminidino-2-phenylindol (DAPI) (1: 200) was added to the final secondary antibody solutions. Finally, sections were mounted and cover slipped with Vectashield mounting medium (Vector Laboratories). Quantification Analysis-Immunohistochemically stained sections were analyzed using confocal microscope (Leica DM1500) and quantification was performed by ImageJ software. Five sections per animal were analyzed for each staining in rostral, lesion and caudal segments. For synaptophysin quantification analysis, images were first transformed into monochromatic 8-bit images and then threshold was adjusted to optimal value. Synaptophysin positivity was evaluated as a percentage of black pixels in overall image (value 0 -255, where 0 ϭ white pixels, 255 ϭ black pixels).
1A, 1B ). These results established the ability to block the MEMO1-RHOA-DIAPH1 signaling pathway and stimulate neurogenesis using such RhoAi. Impact of RhoAi Treatment on ND7/23 DRGs Proteome-In order to identify proteins involved with RhoAi the proteomic approach was then performed with ND7/23 DRGs cell lines incubated with CM-SCI collected from L, R1, and C1 segments in presence or absence of RhoA inhibitor, using identical scenario as in experiments for neurite outgrowth evaluation (Fig. 1C ). Secretomes collected in each condition have been processed by shotgun analyses. Proteins with an abundance that was significantly different among the samples were determined according to the MaxQuant and Perseus software. As a criterion of significance, we applied an ANOVA significance threshold of p Ͻ 0.
05, and heat maps were generated (Fig. 1C , supplemental Data S1). Heat maps were performed and hierarchical clustering indicated two main branches i. e. one for Control branch (CTB)(DMEM conditions with or without RhoAi) and the second one is related to Conditioned medium branch (CMB) (L, R1, or C1 conditions with or without RhoAi). This branch is then sub-divided into two sub-branches: lesion on one side and R1 or C1 on the other side (Fig. 1Ca ). From these data, clear clusters could be retrieved between the two branches. By contrast, in the CMB, only one main cluster allowed to differentiate all media in presence or absence of Rhoi (a yellow boxed area). A zoom of this cluster is presented in Fig. 1Cb  and the ibaq quantitative values in Table  I. Main proteins found in this cluster that could be sorted according to their over-expressed intensities are in the following order: immunoglobulins (IgG chains light and heavy), AKT proteins (AKT1, AKT2, and AKT3), BMP1, syntaxin 12, serpin 3, GMP ganglioside activator, meosin, hemopexin, protein VSP26b, 14-3-3 protein theta, and protein disulfide isomerase.
Proteomic Investigation of the Time Course of RhoAi Treatment on ND7/23 DRGs Cells Line-To investigate the time course of molecular events induced by RhoA inhibitor on ND27/23 DRG cell line, we performed a kinetic proteomic study on cell extracts obtained at 30 min, 1h or 4 h posttreatment (supplemental Data S4). 2389 proteins were overall identified (supplemental Data S4A and S5) Global networks were generated from specific proteins identified in each conditions (supplemental Data S4). Compared with T0 where it can be observed that proteins are integrated in 4 overexpressed clusters (mRNA degradation, mitotic spindle checkpoint, ER to Golgi transport and vesicular trafficking) (data not shown), at 30 min after treatment, several specific cellular events occurred including chromatin condensation, cellular stress response, anchorage independent growth and malignant transformation (supplemental Data S4Ba).
05, were generated (Fig. 5A , supplemental Data S6). Heat maps were performed and hierarchical clustering indicated two main branches i. e. one for SCI and the second one is related to treated animal with RhoA Inhibitor and control (without lesion). This branch is then subdivided between RhoA inhibitor treatment in function of the considered segment (Rostral, Lesion and Caudal) on one side and control in the other side (Fig. 5A ). From these data, clear clusters could be retrieved between the two branches. Clusters 1 and 2 are specifically found in Rostral and Caudal segments after SCI. Cluster 1 integrates many subexpressed proteins in SCI segments (Fig. 5A , supplemental Data S6). By contrast, cluster 2 constitutes the one of overexpressed proteins in SCI. Such proteins are in control and treated animals sub-expressed in caudal and lesion segments.
In support of the first Tropospheric Ozone Assessment Report (TOAR) a relational database of global surface ozone observations has been developed and populated with hourly measurement data and enhanced metadata. A comprehensive suite of ozone data products including standard statistics, health and vegetation impact metrics, and trend information, are made available through a common data portal and a web interface. These data form the basis of the TOAR analyses focusing on human health, vegetation, and climate relevant ozone issues, which are part of this special feature. Cooperation among many data centers and individual researchers worldwide made it possible to build the world's largest collection of in-situ hourly surface ozone data covering the period from 1970 to 2015. By combining the data from almost 10,000 measurement sites around the world with global metadata information, new analyses of surface ozone have become possible, such as the first globally consistent characterisations of measurement sites as either urban or rural/remote.
Furthermore, there is inadequate data coverage in many regions of the world, combined with inhomogeneous data quality and metadata information, data access, and language issues. Prior attempts to summarize the global distribution of tropospheric ozone and its trends (e. g. Cooper et al. , 2014; Sofen et al. , 2016a)  were limited to readily accessible data from large networks maintained by the World Meteorological Organisation, and North American, and European institutions, which introduced substantial geographical bias in the analyses. In the framework of TOAR the most comprehensive database possible of global surface ozone observations has been established at Forschungszentrum Jülich in Germany. The database contains surface ozone data sets with hourly time resolution collected from all accessible data sources worldwide, including regional or national air quality monitoring networks, multi-national programmes, and individual researchers' data.
These data and the associated metadata that describe measurement sites and instrumentation have been augmented with several pieces of information from global gridded data sets. As a result, the TOAR database contains the world's largest collection of surface ozone observations in homogeneous form and allows for consistent analyses across all networks and in many world regions. While the TOAR database includes measurements from several hitherto inaccessible sources, a certain analysis bias remains due to the much denser observation networks in the Northern hemisphere mid-latitudes than anywhere else. The TOAR database constitutes the foundation of all major analyses of surface ozone distributions and trends throughout the TOAR special feature. In particular TOAR-Health  (Fleming et al. , 2017, this issue) , TOAR-Vegetation  (Mills et al.
, 2017, this issue), and TOAR-Climate (Gaudel et al. , 2017, this issue)  draw heavily on data and data products from the database described in this article. In spite of its value for tropospheric ozone research, we would like to emphasize, however, that the TOAR database is not a primary data archive and has no intention to replace or substitute any existing data center for environmental observations, nor does it have any obligation to maintain updated data records or inform countries or other legal entities in legally binding form. Establishing the TOAR data archive has only been possible through cooperation with many officially endorsed data archives, and these remain the primary repositories for the vast majority of ozone observations that are now accessible through TOAR. TOAR provides a variety of ozone metrics based on hourly observations, but not the hourly observations themselves.
This article describes the TOAR database and the ozone data products (including standard statistics as well as metrics relevant for assessing health, vegetation, and climate impacts, and trend statistics) that have been generated from hourly averages of continuous surface observations. It further demonstrates new possibilities of surface ozone analyses that have become possible through linking ozone data sets with global metadata, and it highlights the necessity and problems of a posteriori data quality assessment. The article structure is as follows: Section 2 summarizes the data sets that have been identified and made available for the TOAR database. Section 3 describes the different methods for public access to the TOAR database and the surface ozone data products. Section 4 details the procedures that have been applied to harmonize the ozone data and metadata in the TOAR database, including the extended metadata that were added from several global, gridded data sets.
Section 5 discusses ozone quality control issues. Section 6 presents the ozone metrics data sets and demonstrates the enhanced analysis potential made possible through this work. We discuss ozone changes with (station) altitude, regional ozone differences, and seasonal cycles of ozone in different latitude bands. Finally, section 7 presents conclusions including commentary on the current state of the global surface ozone observation network and recommendations for its future development. The paper is accompanied by detailed technical documentation on: the TOAR ozone metrics and metrics data products (Supplemental Material 1), and the Jülich Open Web Interface (JOIN; Supplemental Material 2). All TOAR surface ozone data products including also standard graphics and software, are available at the PANGAEA data publishing portal (https://doi.