dataesr/scientific-paragraphs-categorization huggingface dataset:
Materials and Methods Ethics Statement. All tissues sampled for the experiments were collected on pigs bred for another project (ANR-09-GENM-005-01, 2010-2015). The experiment authorization number for the experimental farm GenESI (Genetics, testing and innovative systems experimental unit) is A-17-661. The procedures performed in this study and the treatment of animals complied with European Union legislation (Directive 2010/63/EU) and French legislation in the Midi-Pyrénées Region of France (Decree 2001-464). The ethical committee of the Midi-Pyrénées Regional Council approved the experimental design (authorization MP/01/01/01/11). All the foetuses used in this study were males and were obtained by caesarean. Microarray data description and pre-processing. Expression data were obtained from skeletal muscle for two foetal gestational ages (90 and 110 days of gestation) associated with four foetal genotypes (two extreme breeds for mortality at birth -Large White (LW) and Meishan (MS)-and two reciprocal crosses -MSxLW and LWxMS).
The final dataset consisted of 44,368 probes for 61 samples under eight different conditions (four genotypes at two gestational ages). A precise description of the experimental design and data collection can be found in Voillet et al. 21. Normalized expression data (log2-transformed) and sample information are available in NCBI (GEO accession number GSE56301). Missing values were imputed with k-NN (R package "impute" function, with k = 3). Gene annotation was updated (nblast/NCBI July 2017, Sscrofa10. 2) and the 40,847 annotated probes were found to correspond to 13,855 unique genes. For each gene, the probe with the highest average correlation with the other probes associated with the same gene was selected to serve as a representative in further statistical analyses. Network inference. Networks were inferred using Gaussian Graphical Models (GGMs  28  ) from n = 61 samples.
All simulations were performed with the free statistical software R (https://cran. r-project. org). The inference was performed using our own scripts (available at https://github. com/tuxette/internet3D) and the graphs were displayed and analysed using the R package igraph (Csardi and Nepusz)  56. Network mining and clustering. Nodes of importance to the network structure were obtained by computing the degree and the betweenness centrality measurement for every node. Node clustering was performed by applying the Louvain algorithm  57  , which performs fast approximate optimization of the modularity  58. All clusterings were found to be significant using the permutation test described in Montastier et al. 59  by generating 500 random networks with the same degree distribution (all clusterings were found to have a modularity larger than that obtained on the 500 random networks, p-value < 0.
The analysis was performed using the Overrepresentation Enrichment Analysis (ORA) method, selecting non-redundant Biological Processes (BPs). The final network was analysed through the use of Ingenuity Pathway Analysis version 01-12 (updated on March 31 st , 2018). Ingenuity Pathway Analysis (IPA, Ingenuity Systems; QIAGEN, Inc. , Valencia, CA, USA, https://analysis. ingenuity. com/pa) contains a large bibliographic database (Ingenuity Pathways Knowledge Base) with various molecular relationships already identified between two genes (protein-protein interaction, ligand-receptor regulation, enzymatic modification, transcriptional expression regulation, etc. ). The obtained network is a graphic representation of the molecular relationships between molecules. All edges are supported by at least one reference from the literature, or from canonical information stored in the Ingenuity Pathways Knowledge Base.
The obtained networks were improved for representation using Path Designer. Nodes are displayed using various shapes that represent the functional class of the gene product. The Functional Analysis identified the biological functions, the canonical pathways and the upstream regulators that were the most relevant to the dataset. Molecules from the dataset that were associated with biological functions, canonical pathways or upstream regulators in the Ingenuity Knowledge Base were considered for the analysis. Fisher's exact test was used to calculate a right-tailed p-value determining the probability that each function and pathway assigned to that dataset is due to chance alone. The networks proposed by IPA were cleaned (some nodes/genes were discarded) in order to keep only the genes necessary to connect the co-expressed genes.
Acknowledgements This project received financial support from  French National Agency of Research  ( PORCINET grant   ANR-09-GENM005 ), from  INRA Animal Genetics Division  ( Internet3D  project) and from  INRA GenPhySE (Génétique, Physiologie et Systèmes d'élevage) laboratory (Toulouse). The PhD fellowship of  Maria Marti-Marimon  is supported by the  French Ministry of National Education. The PhD fellowship of  Valentin Voillet  was supported by the  INRA Animal Genetics Division , the  INRA Animal Physiology and Livestock Systems  and the Occitanie region. The funders had no role in the study design, analyses, results interpretation and decision to publish. The authors would also like to thank  Yvon Billon  and collaborators ( INRA experimental unit GenESI  ( UE1372 )) for providing animals, and the T.
The volcanic data have been extracted and quantified considering available digital elevation models (largely taken from GeoMapApp; http://www. geomapapp. org), satellite images available on Google Earth (https://www. google. com/earth/); geological maps from the Geological Research and Development Centre, Bandung (usually at the 1:100,000 scale, plus more detailed maps on selected volcanoes at the 1:25,000 and 1:50,000 scale). The selection of the Quaternary volcanoes has been made merging the data from the Smithsonian Global Volcanism Program (SGVP; http://volcano. si. edu) and the information contained in the detailed study of  Sieh and Natawidjaja (2000) ; the latter includes 13 more volcanoes with respect to the SGVP database, inferred to be <100,000 years old. Both the elongation of the volcanic edifice and the alignment of the flank vents within have been used to try to define the preferred structural control of the volcano, in order to try to relate this to the known structural trends in Sumatra.
The structural field data, seldom reported in the literature, allow better characterizing the deformation pattern of the GSF. The faults have been kinematically characterized, using slip data obtained from the identification and measurement of the slickenlines on the fault planes. Measurements involved the determination of the pitch and possible sense of motion. The latter was determined considering the presence and orientation of micro-and mesoindicators on the fault plane (e. g. ,  Fossen, 2010). The slip data are derived from the pitch of the slickenlines, that is the angle which a fabric makes to the strike direction. Here we use the convention that pitch values range from -90 • to +90 • ; these correspond to pure strike-slip motions, whereas pitches = 0 • correspond to pure dip-slip motions.
This distribution implies that other mechanisms, in addition to the slab depth and the location of the GSF, may determine the location of the volcanoes in Sumatra. In order to understand more in detail the distribution of the volcanoes with regard to the location of the GSF, we first extract the volcanoes lying above a shallower or deeper slab (i. e. , slab depth z ≤ 115 km or >145 km, respectively, which represent the 20 th and 80 th percentiles of the distribution of the depth of the slab in the dataset); the location of these volcanoes may be partly controlled by factors different from the slab depth, such as a local weakness caused by the GSF (Figure  4A ). We then compare the distribution of the distances of these volcanoes from the GSF to the distance of all the other volcanoes from the GSF, considering that, in case the position of the former volcanoes is GSF-controlled, their distance should be significantly smaller than the one of the latter group.
These data have been collected at 7 outcrops and in 3 trenches, the latter all along the GSF, within Neogene to Holocene deposits (volcano sedimentary, alluvial fans and colluvial deposits, sandstones). A first compressional event was characterized by a ∼N-S trending maximum horizontal compression, associated with a subvertical (contractional domain; plot F; Figure  10 ) or a subhorizontal minimum compression (strike-slip domain; plot G; Figure  10 ). In both cases, predominant NNW-SSE striking dextral faults are activated. This compressional kinematics was followed by two extension directions: an arc-normal extension, mainly activating NW-SE striking normal faults (plot H; Figure  10 ), followed by    Carey, 1979)  assumes that the slip represented by the striation occurs in the direction of the resolved shear.
Deviatoric principal stress axes: σ 1 , σ 2 , σ 3 , are the compressional, intermediate and extensional deviatoric axes, respectively. The age of the faulted formation is also reported. The exact location for the deviatoric principal stress axes could not be determined in (D) because of the relatively few (five) and clustered (four out the five) data. an arc-oblique (WNW-ESE striking) extension, mainly activating NNW-SSE dextral transtensive faults (plot I; Figure  10 ). As the fault slip data of the latter event are seen both at the contact and within the Holocene deposits, the NNW-SSE transtensive dextral faults correspond to the present-day kinematics. Most of the collected faults in the southernmost part of the GSF have a NNW-SSE strike (Figure  11C ), and are associated to both dextral and normal motions (Figure  11D ); however, the highest peak in the pitch distribution is given by a moderately positive dip-slip motion (Figure  11D ); as most faults have a NE plunge, this peak reflects the previously mentioned dextral transtensive kinematics.
, N j ) of the fractional factorial design; μ v h is the grand mean; α v h,f (j) is the main effect of parameter p f for parameter combination j; β v h,f (j)k(j) is the interaction effect between parameters p f and p k (k = f ) for parameter combination j; and v h,j is the residual. Each main effect α v h,f (j) can take two values, according to the level of parameter p f in combination j: α v h,f + or α v h,f -. Similarly, each two-way interaction effect can take four values: β v h,f + k + , β v h,f + k -, β v h,f -k + , β v h,f -k -. The fractional factorial design determines the parameter combinations needed to estimate all main effects and two-way interactions. It is obtained using R package planor 2 and consists of N j = 2 12 parameter combinations, yielding as many simulations. According to the sparsity-of-effects principle, a system is usually dominated by main effects and low order interactions, so neglecting third-order and higher interactions can still provide good estimates.
(  14 ). Finally, a total generalised sensitivity index is calculated for each parameter p f as the sum of the total sensitivity indices on each component, weighted by the inertia of the component: tGSI f = N c c=1 w c tSI c f. ( 16 ) We use the multisensi R package 3 for this analysis. In what follows, we show how Principal Process Analysis can help with the analysis of complex biological models. We apply the approach to a model of the circadian clock developed in  [17, 19] , which we describe in the following section.
Modifications of structuring H-bonding were the major cause in the thigh domain although one mutation gave mRNA decay. In contrast, short-range changes induced in calf-1 appeared minor suggesting long-range effects. All result in severe to total loss of aIIbb3 in platelets. The absence of mutations within a key Ca2+-binding loop in the genu led us to scan public databases; three potential single allele variants giving major structural changes were identified suggesting that this key region is not protected from genetic variation. Conclusions: It appears that the aIIb genu is the object of stringent quality control to prevent platelets from circulating with activated and extended integrin.
| Other cases We performed a literature research for missense variants affecting amino acids 471-769 extending from the base of the b-propeller, through the thigh to the upper calf-1 domain of human aIIb using PUBMED (https://www. ncbi. nlm. nih. gov/pubmed) (Keywords: Glanzmann thrombasthenia). Clinical and biological data were obtained retrospectively from the cited publications and are summarized and referenced in Table  S1. For a wider view we also researched missense variants from the Human Genome Mutation Database, the 1000 Genomes project, the United Kingdom 10K Whole Exome Sequencing project, and the National Heart, Lung and Blood Institute Exome Sequencing Project as assembled by  Buitrago et al. (2015)  and updated the information within control populations by searching ExAC (www.
| Nucleotide and amino acid numbering Human Genome Variation Society (HGVS) nomenclature for cDNA and protein is used throughout unless stated otherwise. For nucleotide numbering, the A nucleotide of the ATG start codon was designated +1 (cDNA ITGA2B Genbank accession number NM_000419. 3). For amino acid numbering, +1 starts with the initiating Met with signal peptide included. This is often followed by that of the mature protein (in parentheses), this involves subtracting 31 amino acids from the HGVS numbering of aIIb and 26 for b3. Amino acid numbering for the mature protein was previously used in the crystal structure of aIIbb3 and subsequent in silico modeling (see  Xiao et al. , 2004; Zhu et al. , 2013). Alamut Visual software version 2. 9 (Interactive Biosoftware, Rouen, France) was the primary source (1) for evaluation of nucleotide and amino acid conservation between species and within other integrin a subunits, (2) for estimating the physical-chemical difference between amino acids (Grantham score) and (3) for the pathogenicity of the newly substituted amino acid by SIFT (version 6.
| RESULTS 3. 1 | A novel mutation affecting the aIIb genu causing type I GT Sanger sequencing of DNA from the index case, a Belgium type I GT patient, identified a homozygous c. 1772A>C transversion (NC_000017. 11:g. 44379795T>G) affecting exon 18 of ITGA2B. It is a rare allele, featuring in one report on public databases, rs778608263 with a minor allele frequency (MAF) G = 0. 000008354 (ExAC). This variant gives a p. Asp591Ala substitution (D560A for mature aIIb). Asp591 is highly conserved within vertebrates and is retained in all human integrin a-subunits (Figs  S1  and S2 ). The physical and chemical deviation between Asp and Ala is large (Grantham score: 126) and the mutation is described as "deleterious" by SIFT, "probably damaging" by PolyPhen and "disease causing" by Mutation Taster (Alamut Visual).
Amino acids engaged in H-bonds (dotted lines) are represented as sticks with C atoms in white, N atoms in blue, O atoms in red and S atoms in orange. In (a) the black arrow indicates the position of Ile596; for Ile596Thr numbers in red are interatom distances in Angstroms, while a red dotted line represents a potential H-bond introduced by the presence of Thr596. In (b) the Ca2+ is represented as a gray sphere and the dotted lines coordination links to nearby amino acids. Models were obtained using the PyMol Molecular Graphics System, version 1. 3 and the 3fcs pdb file and 23 (calf-1) nonsynonymous missense variants of unknown pathogenicity were present. A current assessment of the aa471-769 sequence in the ExAC database (ITGA2B, 21/08/2017) revealed 97 missense variants. This increased to 134 when Ensembl was consulted; a list that now includes presumed heterozygous expression of eight of the 15 variants causative of GT when homozygous or part of compound heterozygosity.
A clearly defined struc- ture of the aIIb genu is a Ca2+ loop with two Cys (Cys633 and Cys639) involved in a disulfide bridge and four amino acids engaging in Ca2+ coordination (Cys633, Asp636, Val638, and Glu673) (Figure  1b ). At the present time, none of these amino acids are mutated in a GT patient. However, on performing a database search we found missense variants for 3 of them on the Ensembl database: Cys633Ser (rs1126555; no frequency data), Asp636Asn (rs749873100; MAF 0. 00004) and Glu673Lys (rs368974006; MAF 0. 00002/8). Although no biological or clinical information associated with these mutations is available, at least Cys633Ser is stated as being probably damaging by Polyphen, and our in silico modeling shows that Cys633Ser and Glu673Lys have a deep impact on aIIb structure (Figure  1b ): the first by disrupting the disulfide bridge, and the second by affecting Ca2+-binding.
To determine whether this region was protected from genetic variation we researched various databases and highlighted three rare variants: Cys633Ser (rs1126555), Asp636Asn (rs749873100), and Glu673Lys (rs368974006) (Ensembl database). Modeling of these residues showed that Cys633-Ser and Glu673Lys indeed do have a deep structural impact: the first by disrupting a disulfide bridge, and the second by pushing away the Ca2+ atom by steric encumbrance. The presence of these potentially damaging mutations presumed to exist as very rare single alleles in a control population is compatible with the fact that heterozygotes for GT generally do not bleed  (George et al. , 1990). It would also be consistent with a constantly changing ITGA2B and ITGB3 mutation repertoire in the general population  (Buitrago et al.
, 2014) , the effects of climate on species distributions  (Elith et al. , 2006) , the outcome of species interactions over large spatial scales  (Gomez-Aparicio et al. , 2011; Alexander et al. , 2015) , and the distribution of disturbances such as fire  (Krawchuk et al. , 2009). Ecological science is therefore dependent on climate databases covering both time and space. The two climate databases most widely used in ecology to date are the WorldClim  (Hijmans et al. , 2005)  and CRU TS datasets  (Harris et al. , 2014) , which differ in their quality of data over time and space. WorldClim includes climatic means at high resolution (30 arc sec or ∼1 × 1 km) over a reference  period (e. g. , 1960-1990) , which precludes any study of climate variation as well as the study of recent climate change.
CRU TS provides yearly climate data (from 1901 to the present day) at 0. 5 • longitude by 0. 5 • latitude resolution (∼50 × 50 km), a low spatial resolution that limits our understanding of spatio-temporal processes at fine scales. To fill this gap between spatial and temporal climate databases in order to study ecological processes, we present EuMedClim, a new climate dataset that provides high spatial and temporal resolution (30 arc sec including monthly, seasonal and yearly time steps) of gridded climatologies for the years 1901-2014 across Europe and the Mediterranean Basin. We used an anomaly approach to interpolate spatial yearly climate data of CRU TS (version 3. 23) using the climate surfaces of WorldClim (version 1. 4) at 1 km resolution. Evaluation of our downscaled data against individual weather station data show good agreement between EuMedClim and original databases (EuMedClim vs.
DATA GENERATION Input Datasets WorldClim version 1. 4  (Hijmans et al. , 2005)  provides global grids of 1960-1990 average climate for monthly precipitation, mean, minimum and maximum daily temperatures, and 19 derived bioclimatic variables at 30 arc sec resolution (http:// worldclim. org/version1). The grids were produced by spline surface fitting spatial interpolation using data on latitude, longitude and elevation from weather station  (Hijmans et al. , 2005). CRU TS version 3. 23  (Harris et al. , 2014)  provides global climate grids at 0. 5 × 0. 5 • resolution of monthly precipitation, mean, minimum and maximum daily temperatures for the years 1901-2014 (http://data. ceda. ac. uk/badc/cru/data/cru_ts/cru_ts_ 3. 23/). The CRU dataset was produced by spatial interpolation of yearly climate anomalies of weather station data relative to  1961 -1990 means (Mitchell and Jones, 2005).
EuMedClim Anomaly Approach From CRU and Worldclim Climate Databases The fine 30 ′ resolution WorldClim data were used to adjust the coarse spatial 0. 5 • resolution of CRU time series data following an anomaly approach  (Zimmermann et al. , 2013; Fréjaville and Curt, 2015; Moreno and Hasenauer, 2015). This approach combines the temporal information of CRU with the spatial information of WorldClim, by using climate grids derived from Wordclim as a reference for the spatial variation and yearly climate from 1901 to 2014 derived from CRU as a reference for temporal variation. The final resolution of EuMedClim is 30 arc sec resolution (∼1 km) for yearly climate grids from 1901 to 2014. The spatial window of EuMedClim includes Europe and the Mediterranean basin (extent: -20 • to 60 • longitude E; 20 • to 72 • latitude N).
The particular procedure for the anomaly method is as follows: we first averaged yearly data from CRU over the period  (same reference period as WorldClim data) and then calculated the differences (anomalies) between CRU and WorldClim average values over this reference period. CRU average values were resampled at the WorldClim resolution (30 arc sec) using bilinear interpolation (we named this dataset the CRU interpolated reference period) for comparison of both databases. Finally, anomalies between the two datasets were added to the CRU yearly data to build the EuMedClim dataset at 30 arc sec resolution for the years 1901-2014. This approach uses the existing high-resolution grids (i. e. , WorldClim) to interpolate coarse-resolution grids (i. e. , CRU) with resampling techniques (bilinear interpolation), instead of using complex regression models with topography and geographical parameters that are generally used for the interpolation of irregularly distributed weather station data (e.
g. ,  Hijmans et al. , 2005; Feidas et al. , 2014). We computed and applied the anomalies in different ways depending on the data characteristics. We used the difference method  (Zimmermann et al. , 2013)  to downscale each temperature and water balance variable k: EuMedClim t,k,i = CRU t,k,i -(CRU ref ,k,i -WorldClim k,i ), where CRU t,k,i is the CRU interpolated value at cell i for the year t, and CRU ref ,k,i -WorldClim k,i is the anomaly (difference) at cell i between CRU and WorldClim mean values of k over the reference period. The yearly grids of each variable k derived from CRU data were prior resampled (bilinear interpolation) to the resolution of WorldClim (30 arc sec). Similarly, we used the ratio of change method  (Zimmermann et al. , 2013)  to downscale each precipitation and potential evapotranspiration variable k (that comprise only positive values): EuMedClim t,k,i = (CRU t,k,i + 1) × (WorldClim k,i + 0.
001)/(CRU ref ,k,i + 1), where (CRU ref ,k,i + 1)/(WorldClim k,i + 0. 001) is the anomaly (ratio) at cell i between CRU and WorldClim mean values of k over the reference period. An increment of 1 mm precipitation (or evapotranspiration) was added to both yearly and reference-period CRU data to avoid null values in cases where CRU ref ,k,i is null (no precipitation over the reference period at a given location). Similarly, an epsilon increment (0. 001) was applied to WorldClim data to avoid any infinite values that could result if WorldClim k,i is null. Finally, downscaled precipitation and evapotranspiration values in EuMedClim were limited to values ≥0.