dataesr/scientific-paragraphs-categorization huggingface dataset:
Simulation and Analysis Tools All network simulations of the conductance-based model were written in python (http://www. python. org) using PyNN  (Davison et al. , 2008)  (http://neuralensemble. org/trac/PyNN) as an interface to the simulation environment NEST  (Morrison et al. , 2005; Eppler et al. , 2008)  (http://www. nest-initiative. org). The dynamic equations were integrated at a fixed temporal resolution of 0. 1 ms. Simulation management was performed using the python package NeuroTools (https://github. com/ NeuralEnsemble/NeuroTools). Data was analyzed in python using the scientific libraries SciPy (http://www. scipy. org) and NumPy (http://www. numpy. org/) and visualized using the plotting library Matplotlib (http://matplotlib. org/) or in Matlab (MathWorks).
ACKNOWLEDGMENTS We thank  Arvind Kumar ,  Frederic Chavane ,  Clemens Boucsein ,  Henriette Walz ,  Dirk Jancke ,  Moritz Helias  and  Jan Antolik  for helpful discussions. We thank  Jianzhong Jin ,  Reza Lashgari  for help during the LGN experiments. We thank  Andrew Davison  for help during the implementation of the model and  Jochen Eppler  for continues support with the simulator NEST. JK and AA were supported by the  German Federal Ministry of Education and Research (BMBF  Grant  01GQ0420  to  Bernstein Center for Computational Neuroscience Freiburg ) and the 6th  Research Framework FET Program  of the  European Union  (Grant  15879-FACETS  to  University of Freiburg ). LP, CM, YF, and GM were supported by the 6th and 7th  Research Framework FET Program  of the  European Union  (Grant  15879-FACETS  and  BrainScales 269921  to INT and UNIC).
Data sources Our samples combined geo-referenced presence-only data from the Global Biodiversity Information Facility (Gbif, http://www. gbif. org/), five French botanical institutes (Conservatoires Botaniques Nationaux) and additional observation data kindly provided by M. W. van Slageren (pers. com. ; details in the S1 Table and S1 Appendix; complete dataset in the S1 File). Data were cleaned by excluding records that were derived from a different country than stated by the collector or outside the documented range of the species (Diva-gis, http://www. diva-gis. org/; Google Earth, http://www. google. com/intl/fr/earth/). The modeling was performed at a coarse-grained scale in order to buffer both uncertainties on point coordinates and differences in sampling intensity across regions. Accordingly, we kept a single occurrence record per 30 arc min grid cell, covering at best the documented Old World range of each species  [7].
These gridded variables for current and future climatic conditions were directly accessed via the WorldClim data portal and downloaded at the 10 and 30 arc min spatial resolution (http://www. worldclim. org/; variables are described in the S2 Appendix). Current climate raw data were interpolations from weather station data spanning the 1950 to 2000 period  [43]. Future IPPC 5 climate data for 2050 (2041-2060 averages) were derived from General Circulation Models. The calibration and downscaling method is Available http://www. worldclim. org/. For future climate, the central trends of climate models were preferred over those derived from single models. Therefore, grid data were averaged over 16 models for which simulations were consistently available for two chosen Representative Concentration Pathways (RCPs listed in S2 Table ).
Evaluating the models The absolute area under the ROC curve (AUC) value was previously found to be a poor criterion for evaluating ENM models, especially with presence-only data  [48]. The significance of the association between occurrence data and climatic variables was therefore assessed using the one-tailed randomization test previously proposed by Raes & Steege  [48]. To perform this test, a thousand pseudo-samples of the same size as the real dataset were randomly sampled in each species background grid using a custom R script (R v3. 0. 3; http://www. r-project. org/, {raster} and {base} packages). The AUC value of each random pseudo-sample was then estimated by MaxEnt using the same settings as for the selected model. This provided a null empirical distribution of AUC values associated with each particular combination of background and sample size.
g. see  [104] ). Since anthropogenic land-use and land-cover change (LULCC) affects climate through both emissions and albedo, projected gridded trajectories of LULCC represent a new mandatory dataset for the four selected IPCC5 Integrated Assessment Modeling (IAM) teams (RCP modelers). However, these projections are associated with a great level of uncertainty  [105]. Indeed, various scenarios with very different regional changes in LULCC and industrial activity may be consistent with the very same RCP  [101, 102] , and cropland use projections are very sensitive to assumptions such as increases in crop yield, changes in diet, or how agricultural technology and intensification is applied  [44, [106] [107]. At present, choosing which IAM model/projection to use is unfortunately inseparable from the choice of scenario  [108].
LibreOffice is a powerful office suiteits clean interface and feature-rich tools help you unleash your creativity and enhance your productivity. LibreOffice includes several applications that make it the most powerful Free and Open Source office suite on the market: Writer (word processing), Calc (spreadsheets), Impress (presentations), Draw (vector graphics and flowcharts), Base (databases), and Math (formula editing) Octave https://www. gnu. org/software/octave/ GNU Octave is a high-level interpreted language, primarily intended for numerical computations. It provides capabilities for the numerical solution of linear and nonlinear problems, and for performing other numerical experiments. It also provides extensive graphics capabilities for data visualization and manipulation. Octave is normally used through its interactive command line interface, but it can also be used to write non-interactive programs.
The Octave language is quite similar to Matlab so that most programs are easily portable. Scilab http://www. scilab. org/en Scilab is free and open source software for numerical computation providing a powerful computing environment for engineering and scientific applications. Scilab includes hundreds of mathematical functions. It has a high level programming language allowing access to advanced data structures, 2-D and 3-D graphical functions. A large number of functionalities is included in Scilab. http://www. abinit. org/ ABINIT is a package whose main program allows one to find the total energy, charge density and electronic structure of systems made of electrons and nuclei (molecules and periodic solids) within Density Functional Theory (DFT), using pseudopotentials and a planewave or wavelet basis… learn more in the internet site! https://csc.
fi/web/elmer/elmer Elmer is an open source multiphysical simulation software mainly developed by CSC -IT Center for Science (CSC). Elmer development was started 1995 in collaboration with Finnish Universities, research institutes and industry. After its open source publication in 2005, the use and development of Elmer has become international. Elmer includes physical models of fluid dynamics, structural mechanics, electromagnetics, heat transfer and acoustics, for example. These are described by partial differential equations which Elmer solves by the Finite Element Method (FEM). SciDAVis http://scidavis. sourceforge. net/ SciDAVis is a free application for Scientific Data Analysis and Visualization.
The MS data acquisitions were performed using the Xcalibur 2. 1 software. Full MS scans were acquired at a high resolution (FWMH 30,000) using an Orbitrap analyser (m/z: 400-2000), while the CID spectra were recorded for the five most intense ions in the linear LTQ traps. The LC-MS/MS spectrum files corresponding to the 180 samples were processed via the X!Tandem pipeline, available at http://pappso. inra. fr/bioinfo/xtandempipeline, with the X!Tandem version TORNADO. Some replicates were randomly selected for technical repetitions as a verification of the lack of technical bias during LC-MS/MS process. No significant difference was observed between any repeats. Protein identification was achieved by comparing the MS data to both the UniProt Knowledgebase restricted to B. distachyon (http://www.
uniprot. org/, October 2013) and a contaminant database, including human keratins and trypsin. Enzymatic cleavage was declared as a tryptic digestion with one possible miscleavage event. The fixed modifications of Cys residues by iodoacetamide and the possible oxidation of Met residues were considered. Precursor mass and fragment mass tolerance were set at 5 ppm and 0. 5 Da, respectively. The results from the X!Tandem analysis were filtered according to a peptide e-value below 0. 01 in the X!Tandem pipeline. Proteins were identified using at least two specific peptides.
Functional Annotation Bioinformatics analysis of the identified proteins was performed using the ProtAnnDB tool (www. polebio. lrsv. upstlse. fr/ProtAnnDB/) [27]. The sub-cellular localization of the proteins was predicted using different software programs: TargetP (http://www. cbs. dtu. dk/services/TargetP/), Predotar (https://urgi. versailles. inra. fr/predotar/predotar. html), SignalP (http://www. cbs. dtu. dk/ services/SignalP/), Phobius (http://phobius. sbc. su. se/) and TMHMM (http://www. cbs. dtu. dk/ services/TMHMM-2. 0/). The selection of the CWPs corresponded to the intersection results obtained with these above software programs. The functional domains or motifs were predicted using PROSITE (http://prosite. expasy. org/), Pfam (http://pfam. xfam. org/), and InterProScan (http://www.
Missing values between stages were not replaced, as the study was conducted separately on proteins shared among the three stages and between two stages. PCA was performed using the ade4 package (Version 1. 16-2, available at http://pbil. univ-lyon. fr/ADE-4) to highlight the variable correlations according to the stages. One-way ANOVAs were used to determine the proteins with a significant developmental stage effect. Their abundances were considered to be significantly different for p-values below 0. 001. This dataset of differently abundant proteins was further used with hclust and the gplots package to build heat maps (version 2. 14. 1, available at https://cran. r-project. org, the Comprehensive R Archive Network). We extrapolated the preferential stages of protein clusters according to their abundance from the three stages heat map and its row flanking dendrogram by fixing the value of 1.
Proteomic Analysis To account for biological variability, the proteins were extracted from five independent biological replicates per stage to be analysed by LC-MS/MS after 1D-E separation. Only proteins identified with at least two specific peptides present within the same extract were retained. A dataset of 1122 proteins was deduced from this data analysis and considered for further analyses. Only proteins with a predicted signal peptide that lacked a C-terminus endoplasmic retention signal (KDEL or HDEL) were conserved for further analysis and considered as CWPs. Among the 1122 proteins identified, approximately 40% fulfilled these criteria, leading to a total of 466 CWPs, with all developmental stages taken into account (Supplementary Table  S1 ). A list of these proteins and the corresponding LC-MS/MS data are available online in the WallProtDB database (http://www.
Distribution among the Three Developmental Stages Among the 466 non-redundant CWPs identified, 435, 418 and 412 were found at 9, 13 and 19 DAF, respectively (Figure  3 ; Supplementary Table  S1 ), with 367 CWPs identified at all three stages. Forty CWPs were found at 9 and 13 DAF, seven at 13 and 19 DAF, and 18 at 9 and 19 DAF. Only 34 CWPs were identified in a single stage (10 at 9 DAF, four at 13 DAF and 20 at 19 DAF). Their relative amounts were found to be close to the median value of the whole protein dataset in these stages, meaning that they were above a potential detection threshold. These CWPs belonged to different functional classes, such as proteases, miscellaneous proteins, PACs or proteins related to lipid metabolism (Supplementary Table  S1 ). When focusing on the number of CWPs in a given functional class, there were only few differences according to the stage (Supplementary Table  S2 ).
distachyon  [15] [16] [17]. Today, the global cell wall proteome of B. distachyon contains 585 CWPs, with 253 CWPs specifically found in the cell wall proteome of grain (data from this analysis and  [16] ), and 233 found both in the grain and in vegetative organs (Supplementary Figure  S6 )  [15, 17]. To our knowledge, this is the largest cell wall proteome from a monocot plant species, not far behind that of the dicot model plant A. thaliana which comprises 710 CWPs according to the WallProtDB database. An increasing amount of lyophilised cell wall material was obtained during the time course of the development of grains, i. e. , from 9 to 19 DAF. This is consistent with the thickening of cell walls during grain development, most probably in relation to the accumulation of mixed-linked β-glucan that acts as both a storage component and a structural molecule.
Moreover, bridging information from different single cross maps has allowed high density consensus maps to be built based on several thousand markers. These have improved genome coverage, proposed a validation of marker ordering, and reduced large gaps due partly to the absence of polymorphism between parents  [18] [19] [20]. Single nuclear polymorphisms (SNPs) in coding and noncoding sequences have recently become favorable markers for building high-density genetic maps thanks for their high abundance in the genome  [16, 21]. Rapid advances in sequencing capabilities and dramatic cost reductions have facilitated genome-wide discovery of SNPs, even for polyploid species such as wheat  [22]. The reduction of genomic complexity via techniques such as RNAseq sequencing can help generate large SNP databases for bread wheat  [23]  and durum wheat  [24].
Material The Dic2 x Silur mapping population (DS) used in this study consisted of 135 F 6 recombinant inbreed lines (RILs) derived from a cross between an emmer wheat accession (Triticum turgidum ssp dicoccum) named Dic2 and the durum elite Silur variety. A durum wheat de novo assembly of the transcriptome (DWr) composed of 80,691 contigs was available from a past study  [38]  (S1 File). Briefly, RNAs of 172 lines of an Evolutionary Pre-breeding pOpulation (EPO) were sequenced and assembled. Homeosplitter software (http://bioweb. supagro. inra. fr/homeoSplitter) was applied on this de novo assembly to unravel homeologous copies that were initially erroneously merged in a single chimeric contig  [38]. Read alignments on this reference led to 84,710 high quality SNPs  [24]. Transcripts of the bread wheat chromosome survey sequence for cv.
Chinese Spring (BWr) generated by IWGSC and provided by the Ensembl database (http://plants. ensembl. org/ Triticum_aestivum) was used here for comparison purposes  [1, 37]. All BWr contigs were attributed to a specific chromosome. The physical positions for chromosome 3B are available  [39]  and proxies for physical positions within other chromosomes were estimated using population sequencing (POPSEQ) data generated by Chapman et al. [40]  (Ensembl release 28). An alternative estimation of the genetic positions of these contigs is also available from the IWGSC genome zipper  [37]. DWr contigs were blasted against BWr. It gave us a putative assignment of DWr contigs to bread wheat chromosomes and provided putative physical  [40]  and putative genetic positions  [37]  of the DWr contigs and consequently of the carried SNPs.
Initial polymorphism detection RNA was extracted and purified for the two parents of the DS population and 18,899 SNPs were detected using the protocol of  [24]. In brief, sequencing was carried out using the Illumina mRNA-Seq paired-end indexed protocol on an HiSeq2000. Thirty-eight and 41 million read pairs were produced for Dic2 and Silur (resp. ) and preprocessed with Cutadapt  [41]  to remove adaptor sequences, trim read extremities with low quality scores (parameter -q 20) and keep reads with a minimum length of 35 bp. We first mapped the cleaned reads on DWr using BWA  [42]  while allowing 3 errors (-n 3 in the aln step). We then used Picard tools (http:// broadinstitute. github. io/picard/) to remove PCR and optical duplicates  [43]. Remaining unmapped reads (23 million [64%] and 15 million [39%] for Dic2 and Silur resp.
Bait design Baits are 120 bp long sequences surrounding a targeted SNP. They were designed on single exons since baits spreading over multiple exons would not have complementary matches on the genome and would thus not efficiently capture the targeted sequence. Exon frontiers available from the BWr annotation  [1]  were used to annotate regions of interest for SNPs detected in DWr using a blast followed by exonerate via the TriAnnot pipeline  [45]. Exon frontiers were detected for 59,922 of the DWr contigs (74%). We considered only SNPs located in exons longer than 120 bp to be able to design single exon baits and we targeted only one SNP per contig to minimize the target redundancy. This strong filter reduced the number of targeted SNPs from 18,899 to 4,352. To complete this set of specific Dic2 x Silur polymorphisms, 1,888 supplementary SNPs were chosen within the EPO dataset, with the same quality filters.
SNP calling on captured sequences Captured DNA was sequenced on an Illumina HiSeq3000, which gave paired reads of about 150 bp each. Reads were preprocessed and cleaned according to the protocol used for initial polymorphism detection. Mapping and genotype calling were done using whole DWr and targeted BWr contigs. For targeted SNPs, as we were confident that polymorphism did exist, genotype calling was accepted with as few as two reads per individual. Recovered SNPs constituted the expected recovered SNP dataset (ER-SNP). Read2SNP identified new SNPs in loci homeologous or paralogous to the targeted one. A threshold of four reads per individual was used to call a genotype for these untargeted SNPs. These SNPs were likely in low expressed genes and were thus not detected in the preliminary transcriptomic analysis of the DS population parents.
They accounted for the bonus SNP dataset (bonus SNP) that complemented the ER-SNP. Finally, all SNPs were filtered according to three criteria so as to keep only highly reliable SNPs: i) a low number of heterozygotes controlled by an Fis value above 0. 8, as 1. 5% heterozygosity was expected after six successive selfing generations ii) at least 100 out of the 135 available RILs genotyped, and iii) balanced frequencies with a minimum expected heterozygosity (Nei's He,  [47] ) of 0. 34 so as to avoid segregation distortion, which is undesirable for genetic map building. This cleaning process led to the ER-clean-SNP and bonus-clean-SNP dataset.
Blocking oligonucleotides must be used during the hybridization phase of the adapters to avoid these unexpected technical captures and reduce the adapter size  [36, 46]. We believe that in our case, we initiated a genomic daisy-chaining process among the multitude of wheat DNA fragments carrying microsatellite sequences. Although we could not be certain that we identified the "guilty" baits, we removed them from the bait database provided in the supporting information (S5 File). Using a blocking agent made of identified microsatellites might also reduce this daisy-chain effect and improve the target capture efficiency. This huge presence of repetitive elements did not prevent capture of the targeted SNPs. Indeed, only 6% of the targeted positions were not covered at all. The median number of reads received per targeted contig per individual was 11.
4. Among the 6,240 targeted loci, 5,301 were thus recovered, but 768 of them designed on EPO were found to be monomorphic between Dic2 and Silur (Table  2 ). A hundred and sixty (160 / 6,240) targeted SNPs only presented heterozygous individuals and were discarded. The remaining 4,373 ER-SNPs were then filtered according to their (> 0. 8) Fis and (> 0. 34) Nei's He threshold, thus leaving 3,292 SNPs. From this set, 2,822 SNPs were genotyped for more than 100 individuals. These SNPs made up the ER-clean-SNP dataset. Finally, 45% of the total targeted SNPs were recovered. This success ratio would be as high as 65% if only polymorphic targeted SNPs based on Dic2 x Silur parents are considered. Many bonus SNPs were detected on positions that were not targeted by the baits. We identified 2,305 new SNPs passing the quality threshold filters.
This was especially interesting in our case since many SNPs on the 90K iSelect array were monomorphic on durum wheat even though 16 durum varieties were used to screen for polymorphisms  [6]. This array was recently used to build a high density consensus framework map  [19]  based on 10 populations. The number of recovered SNPs per mapping population increased from 2,567 (2. 9%) in elite x elite crosses to 10,911 (12. 1%) in highly polymorphic crosses involving the emmer wheat T. dicoccum (S3 Table ). Elite x elite populations are much harder to map due to their lack of polymorphism, which could explain the low density of the maps obtained with the 90K iSelect array. In our case, we targeted 6,240 existing SNPs in the parents and recovered 3790 SNPs (61%), including the bonus-SNP dataset. Roughly comparing the number of SNPs was not sufficient here.
The same search strings will be used on search engines and specialist websites as on databases. However, if search engines and specialist websites do not allow the same options (wildcards, quotation marks, etc. ) as publication databases, search strings will be modified using the database help in order to obtain the search string most similar to the original one. Final search strings used for each database, search engine and specialist website will be recorded in an Appendix, together with search dates. Searches in databases will be undertaken using english terms only, while searches in search engines and specialist websites will be performed either with English or French terms. No time or document type restrictions will be applied.
Study screening and inclusion criteria Scientific articles collected in databases will be assessed for inclusion at three successive levels: first on titles, second on abstracts and third on full-texts. At each stage, in case of uncertainty, articles will be retained for assessment at the following stage. Article eligibility, at the title screening stage, will be based on the list of selection criteria detailed in Table  3. These criteria encompass both the subject (ecology and related disciplines) and the population (all species of the temperate climatic zone 8 ) of the study. During title screening, studies will be classified into one of the following groups: vertebrates, invertebrates or flora/fungi. Next, each article found to be potentially relevant on the basis of its title will be judged for inclusion on the basis of its abstract.
Study quality assessment Following study inclusion assessment, the quality of included studies will be critically appraised. Based on assessments of their reliability and relevance, included studies will be categorized as having high, medium or low susceptibility to bias. Studies with a high susceptibility to bias will be excluded from the map and from the review. The exact criteria of study quality assessment will be developed as the review proceeds, but they will likely include study design type, coherence of the sampling design, duration between intervention and data sampling, sample size, randomization of sample selection, sampling replication, level of detail of the methodology, comparator relevance for our question, presence of measures of variation of outcomes and description of potential effect modifiers.