dataesr/scientific-paragraphs-categorization huggingface dataset:
To that end, specific image processing was developed using a custom-written MATLAB script (The MathWorks, Natick, USA). Local stretch. The centers of all follicles were identified and labelled (see Supplementary Material) to define a network at each deformation step and calculate the local deformation tensor. To that end, we performed a Delaunay triangulation and calculated for each triangle (Supplementary Fig. S3 ) the deformation tensor relative to the initial triangle in the non-stretched state. This deformation tensor exhibited three independent components λ xx , λ yy and the sliding angle ω representing respectively the stretch ratios in x and y direction and the shear in the ROI. Thus, at each deformation step, we obtained three maps of deformation (Supplementary Fig. S3 ). Finally, the averages of each tensor component provided a measure of the local deformation in the ROI, which was compared to the global stretch ratio applied to the skin sample (Fig.
Acknowledgements We thank  Nadine Aguilera  for breeding mice and maintaining transgenic strains (PBES,  SFR Biosciences-Gerland, Lyon, France ),  Isabelle Lamarre-Jouenne  for technical support in skin handling,  Xavier Solinas ,  Jean-Marc Sintès ,  Vincent de Greef  for technical implementation of the setup and  Etienne Decencière  for stimulating discussions. This work was supported by grants from  Ecole Polytechnique  (interdisciplinary project), from  Agence Nationale de la Recherche  ( ANR-13-BS09-0004-02  and  ANR-10-INBS-04 ) and from  Association Française du Syndrome Ehlers-Danlos (AFSED).
It was equipped with a 8. 8 mm thin single-crystal scintillator (Tb-doped Lu 2 SiO 5 ), a 10Â objective (0. 3 numerical aperture), a 2Â eye-piece and the ESRF custom made CCD camera FReLoN (type: A7899), operating with a nominal effective pixel size of 0. 7 mm  [20]. The exposure time was set to 0. 2 s for each of 1000 projection images while rotating the sample through 1808. Pad morphology data were reconstructed using the ESRF software package PyHST_2  [21] , which includes a phase-retrieval using Paganin's approach  [22]. For data visualization and segmentation, ZIBAmira software (Zuse Institute, Berlin, Germany; FEI Visualization Science Group, Burlington MA, USA) was used.
The corresponding vertical displacement of the tarsus distal side (substrate displacement) was calculated by measuring the angle between the two leg segments from digital images of the camera connected via a C-mount to a microscope with long working distance. A series of rotation angles was used to avoid perspective errors. The maximum angle value of 458 was defined by the geometry of the aluminium wedge. The angular deflection values were chosen according to previously reported values well within the physiological working range of the slits  [16, 17]. The tarsus was thus deflected against the metatarsus in a stepwise manner, and mCT datasets were obtained at each stationary position after 1 h relaxation time for each step. For tomography measurements, the sample was rotated around the horizontal axis while keeping the distance between the leg sample and the wedge constant.
Scanning acoustic microscopy SAM measurements were performed using KSISAM2000 by Kra ¨mer Scientific Instruments (Herborn, Germany), and data were collected using software MATSAM custom made by the Q-BAM Laboratory (University of Halle-Wittenberg, Germany). Temperature in the room was maintained at 218C. Two spider leg samples had been previously embedded in MMA. After polishing (removing one half of the metatarsus) the sample surface represented the sagittal plane of the pad. The SAM analysis of this surface was performed under deionized water using 400 Hz and 820 Hz, respectively.
The substrate vertical displacement was calculated from the distance adjusted between the Tomo-press mounting tools (fixing the metatarsus on the one side and the aluminium wedge on the other side). The pad displacement and deformation values were extracted from the mCT data analysis. deflection angle between tarsus and metatarsus (8 While the distal part of the pad is dominated by green channel fluorescence and contains an internal inner region with only weak fluorescence the dorsal part and the 'appendix' are predominantly showing auto-fluorescence in the red channel. Interestingly, a pronounced dark-red region in the inner part of the distal pad region is observed. Such strongly sclerotized internal cuticle is not common in arthropods, except in structures like muscle attachment sites or sclerites  [1].
The ability to write the Laurent expansion of a given Feynman integral in such a form has important implications: • Numerical evaluation. The representation allows for an immediate numerical evaluation in the Euclidean region, at least in principle, without further preparations. • Analytical evaluation. All resulting parametric integrals are themselves Feynman integrals and are therefore well-adapted to direct analytical integration techniques. Let us motivate in particular the second point, which was the original trigger for the development of the method and is what distinguishes it from the conventional sector decomposition approach. In brief, HyperInt  [14]  is a computer algebra package for the analytical evaluation of parametric integrals which can be expressed in terms of multiple polylogarithms and, in particular, Feynman integrals which happen to be linearly reducible.
If all involved integrals happen to live in the same dimension, the problem can be solved immediately using any standard IBP reduction program. In this work, we made use of both Reduze 2 and LiteRed to carry out our IBP reductions. At the present time, the latter program is particularly convenient to use for our purposes since it already ships with built-in support for dimension shifts. In any case, it is conceptually straightforward to perform the necessary IBP reductions even when using a program without native support for integrals which inhabit different spacetime dimensions. Suppose we have an IBP reduction to some basis B d in hand for a fixed number of dimensions, d. Using a dimension shift relation such as the one given in Eq. (  2   = ÃT B d = A T M -1 d+2δ b -2 • • • M -1 d B d.
Acknowledgements The research of RMS is supported in part by the  ERC Advanced Grant EFT4LHC  of the  European Research Council , the  Cluster of Excellence Precision Physics, Fundamental Interactions and Structure of Matter  ( PRISMA-EXC 1098 ). EP is supported by  ERC  grant  257638. Our figures were generated using Jaxodraw [48], based on AxoDraw [49].
Daily rainfall data In Kenya and North Tanzania, we selected 36 rain gauges having at most 25% missing entries from 1961 to 2001 (Fig. 1a ). Overall there are 5. 9 % of entries missing with 21 stations having fewer than 365 missing days. The mean annual cycle (Fig. 1b ) shows two maxima associated with the March-May Long rains (LR) and the October-December Short rains (SR). There are rather large spatial variations in mean rainfall with drier areas in the northeast and some lowland areas in the south, while rainfall is higher along the Indian Ocean coast and over the highlands of Western and Central Kenya. The bimodal annual cycle tends to vanish westward with noticeable rainfall during boreal summer. In North Cameroon, we selected 33 rain gauges having a maximum of 15% missing entries from 1970 to 2001.
2 ). In wetter districts, the correlation between rainfall and maize yields is weaker (not shown). In Eastern Africa, the onset date of the rainy season is usually considered as the climatic information most awaited by farmers  (Recha et al. , 2008; Rao et al. , 2011; Orlove et al. , 2010; Speranza et al. , 2008). We evaluated the significance of onset for farmers through a 2-year survey along the eastern slopes of Mt Kenya. Mwongera et al. (2014)  randomly sampled farmers at three altitudes (750, 950, and 1100 m), recording sowing dates of all major crops during two SR seasons  (2009 and 2010)  and two LR seasons (2010 and 2011, Table  1 ). Observed daily rainfall is unavailable for these seasons and locations. We used proxy rainfall estimates from the second version of the RFE dataset on a 0.
Acknowledgements This study has been funded by grant # ANR-08-VMCS-001  under the project  PICREVAT. In Argentina , the research leading to these results has received funding from the  European Community 's  Seventh Framework Program  ( FP7/2007-2013 ) under grant # 212492 , and from  ANR  in the frame of the program  INTERRA/SYSTERRA  (grant # ANR-09-STRA-04 ). We thank  Mr Joseph Oyuga  from  FEWS NET Kenya. Lastly, we thank  Mea Halperin  for his help in proofreading and correcting of the final paper.
Validation of the feasibility of physical map construction from chromosome-specific BAC libraries (http://olomouc. ueb. cas. cz/dna-libraries/cereals) has set the pace for this ultimate goal; currently, physical mapping of other chromosome-specific BAC libraries are underway (www. wheatgenome. org/Projects). While the first physical maps of the wheat genome relied on the FingerPrintedContig (FPC) software  [31]  for the assembly of the BAC clones, the recently developed Linear Topology Contig (LTC) software has been suggested to build longer and fewer contigs, thus improving the map quality  [32]. Indeed, in our previous study on 1AL chromosome, LTC generated an assembly of 583 contigs with the N50 contig size, which is considered as a quality measure of the physical map, of 1,166 kb, whereas FPC generated assembly consisted of 1,180 contigs with an N50 contig size of 460 kb  [15].
Accordingly, the more recently published 1BL physical map constructed by the FPC software was integrated with the LTC-generated assembly  [16]. In this study, we present the physical map of chromosome 5DS, entirely constructed by the LTC software. As the first physical map of the D-genome, the physical map of the 5DS chromosome of bread wheat expands the opportunities to study wheat genome evolution and domestication, and develop molecular markers and tools for gene cloning and genomics assisted breeding.
Of this library, 26,112 BAC clones with an average insert size of 143 kb, giving approximately 12. 5x coverage of the arm, were fingerprinted using SNaPshot™ High-Information Content Fingerprinting (HICF) procedure  [35]. Good-quality fingerprints were obtained for 21,656 clones (82 % of the clones that were fingerprinted) and used to construct the physical map of 5DS. A robust tool for constructing physical maps from fingerprinted BAC libraries has been the FingerPrinted-Contig (FPC) software, which was also adopted by the International Wheat Genome Sequencing Consortium (IWGSC) for physical mapping of wheat chromosomes. As an alternative to FPC, Linear Topological Contig (LTC) software has been introduced recently and reported to build fewer and longer contigs  [32]. LTC also enables evaluation of the contig topology; disruptions of the linear chromosome structure, indicating problematic clone overlaps that can then be corrected or avoided.
Initially, both software programs, namely FPC and LTC, were separately implemented in the physical map construction. The resulting preliminary maps were compared to determine the most reliable one to work on further. Although the estimated coverage of the chromosome arm by FPC-constructed map was greater than LTC-constructed map (78 % versus 68 %), the number of contigs in LTC-constructed map was considerably less than that of FPC, while the N50 contig size was almost twice as large (Table  1 ). Additionally, the largest contig size with the LTC-map was markedly higher than that of FPC-map (6,649 kb versus 4,053 kb). Consequently, LTC-constructed preliminary map was used for further analyses. The minimum tiling path (MTP) originally picked by LTC in the LTC-constructed map was found to contain several buried clones.
Thus, MTP clones were reselected using FPC on the LTC-constructed map, which resulted in the inclusion of longer clones in the MTP. The MTP clone overlaps were also tested using LTC. Since LTC software requires a significance cut-off of 10 -15 to build overlapping clones, any clone overlaps significant at a less stringent cut-off can be considered as unreliable. For contigs with accurate ordering of clones, such low quality overlaps may result from missing bands in the fingerprints, which was the case for the contigs in the LTC-map that were reported to contain unreliable clone overlaps. Therefore, all clone overlaps that were significant only at a cut-off of 10 -14 or above were reinforced by adding 210 additional clones that covered the same overlap region, to avoid any gaps at the sequence level.
6a ). However, there were also striking differences between the two genome zippers, involving groups of genes. The region of orthologous genes delineated by Bradi4g00450-Bradi4g00790 and mapped to the 0. 78-1. 00 deletion bin in our genome zipper, was located closer to the 0-0. 63 deletion bin in the genome zipper constructed by IWGSC. Intriguingly, Bradi4g05880, previously located to the 0-0. 63 bin in our genome zipper, was relocated to the distal deletion bin 0. 78-1. 00 based on the gene order information from the Ae. tauschii physical map (Fig. 6a ). Thus, it can be concluded that the genome zippers demonstrate the virtual gene orders to some extent but are highly dependent on the datasets used to construct them. Additionally, three Brachypodium orthologs (Bradi4g00980, Bradi4g02450, Bradi4g06000) appeared to be duplicated as suggested by the ordering of contigs sharing homology with these genes.
Sizes of the boxes do not necessarily reflect contig sizes, rather these indicate the number of orthologous matches for a given contig. Visualization is performed on Matlab Triticeae genomes, several small scale rearrangements are likely to occur through evolution  [40]. The rearranged 5DS genome zipper integrated with the physical mapping data was also compared with binmapped ESTs and SSRs, the consensus SSR map of the 5DS chromosome (http://wheat. pw. usda. gov/GG3/ maps-short) and genetically mapped COS markers (Fig. 7 ). The previously bin-mapped ESTs and SSRs were generally consistent with our map, except for a few EST markers matching syntenic genes or contigs that were positioned in a different deletion bin, suggesting small-scale rearrangements. Curiously, the region of the chromosome arm delineated by Bradi4g02900-Bradi4g03750 (Fig.
5x coverage of the chromosome arm, were fingerprinted using SNaPshot™ High-Information Content Fingerprinting (HICF) procedure  [35]. The fingerprints were then processed to remove bands stemming from either the vector or the host gDNA, bands resulting from partial digestion or star activity, unreliable bands of unexpected sizes and background noise using the FingerPrint Background removal (FPB) software  [49]. True bands in the range of 50-500 bp were analyzed by GenoProfiler software  [50]  to eliminate cross-contaminations and negative controls.
Preliminary map construction A total of 21,656 good-quality fingerprints were used to construct the preliminary physical map of 5DS. Initially, two software programs, FingerPrintedContig (FPC)  [31]  and Linear Topology Contig (LTC)  [32] , were separately used to construct physical map of 5DS. For both preliminary assemblies, previously established and optimized parameters for FPC  [13, 15]  and LTC  [15, 32]  were used, which are summarized below. For the FPC assembly, the previously described methodology was followed  [13, 15]. Briefly, the initial assembly of the BAC clones was performed at a high stringency with a Sulston Score probability cut-off of 1e -75 which was then incrementally increased at six steps to 1e -45 , adding singleton clones to the ends of the existing highconfidence contigs and merging contig ends at each step.
Minimum tiling path and BAC pooling The minimum tiling path (MTP) was generated separately by FPC and LTC software from the respective assemblies. Upon the observation that the MTP selected by the LTC software included several buried clones, an alternative MTP of 2155 clones was selected via FPC with the parameters described previously  [15] , and were then tested by the LTC to confirm the clone overlaps. As the LTC program builds initial net of clone overlaps at a Sulston Score probability cut-off of 10 -15 , any overlaps that are only significant at cut-offs above 10 -15 are considered unreliable. Within a contig, if the clone orders are correct but the clone overlaps are unreliable, the contig is deemed reliable, however, gaps may remain at the sequence level. In order to present an adequate source for reference sequencing and avoid such gaps at the sequence level, clone overlaps marked as 'unreliable' by LTC is reinforced with additional clones, as follows: For overlaps that were significant only at a cut-off of 10 -14 or above, 210 additional clones covering the same overlap were picked manually.
Marker design, selection and MTP pool screening In order to anchor contigs of the 5DS physical map with molecular markers, 1. 34x coverage 454 Roche reads originating from 5DS  [25]  were utilized to design Insertion Site-Based Polymorphism (ISBP) and Simple Sequence Repeat (SSR) markers using IsbpFinder. pl and Isbp-Sort. pl scripts and SciRoKo program, respectively  [51, 52]  (Additional file 3). A total of 16,727 high-confidence ISBP markers, which contain a unique non-repetitive sequence flanking the junction site of a repetitive element, were defined, of which 99 high-confidence ISBP markers were tested on MTP pools. Additionally, MTP pools were screened by genetically mapped molecular markers to integrate 5DS physical map with the available genetic maps. For this purpose, 2 gene based markers (Pina-D1, Pinb-D1), 23 Simple Sequence Repeat (SSR) markers and 63 Expressed Sequence Tag (EST) markers were retrieved from GrainGenes database 2.
The final design was comprised of 1,370 probes for genetically mapped genes/markers, 34,980 probes derived from conserved gene reads, and 25,600 probes for ISBP markers designed from 5DS survey sequences. For the microarray hybridization, MTP pools were labeled with Cy3 and Cy5 using the SureTag DNA Labeling Kit (Agilent Technologies, Cat. No. 5190-3400) following the manufacturer's instructions. Labeled pools were hybridized to the probes in pairs in a dye-swap design. Hybridization and wash steps were performed as indicated by the manufacturer, and the arrays were scanned with NimbleGen MS 200 microarray scanner (Roche NimbleGen, Inc. ) at 2 nm resolution with autogain. Agilent Feature Extraction Software (v. 11. 5. 1. 1) was used to extract fluorescence data from the scanned images. Data normalization and deconvolution was performed using custom R scripts independently for row, column and plate pools, as previously described  [15, 36].
Contig elongation and contig ordering Using the 'Elongation' feature of the LTC program reported recently  [18] , contig ends from the preliminary map were manually tested for possible overlaps with other contigs at a less stringent cutoff of 10 -15. In order to locate these contigs and supercontigs into the deletion bins along 5DS, molecular markers assigned to MTP clones through PCR or microarray hybridization were used. Since ISBP markers designed from the 1. 34x coverage 5DS survey sequence reads lacked a genetic location, those ISBP markers that were mapped by PCR were also screened on homozygous deletion lines of 5DS-2 and 5DS-5, which are 0. 78 and 0. 67 of the full length chromosome arm, respectively. The contigs within each deletion bin were ordered based on the order of orthologous sequences on the recently published physical map of the Aegilops tauschii genome  [55].
Functional annotations of conserved microarray probes Conserved read probes passing the statistical tests for microarray hybridization were annotated using Blas-t2GO tool  [42]. Initially, probe sequences were blasted against a local non-redundant Viridiplantae protein database using BLAST+ command line applications, v. 2. 2. 27  [56]  at an e-value of 10 -6. All blast hits were then mapped and annotated using the Blast2GO application with default settings for plants. Finally, multi-level charts were generated for Biological Process, Molecular Function and Cellular Component terms, individually.
Acknowledgements This research was supported by  TUBITAK  (Grant no. 111O665 ) and  Sabanci University. We are grateful to  Prof. B. S. Gill  ( Kansas State University, Manhattan, USA ) for the seeds of the double ditelosomic line 5D of bread wheat (Triticum aestivum) cv. Chinese Spring. We also thank  Dr. Ming-Cheng Luo  for providing marker data from Aegilops tauschii. Assistance of  Jarmila Číhalíková ,  Marie Kubaláková ,  Jan Vrána ,  Zdeňka Dubská ,  Romana Šperková  and  Jana Dostálová  with chromosome sorting and BAC library construction is greatly appreciated. BAA is supported by  Tubitak-BİDEB  scholarship. HŠ, JŠ and JD were supported by the  Czech Science Foundation  (award no. P501/12/G090 ) and by the grant  LO1204  from the  National Program of Sustainability I.
Introduction Regional Climate Models (RCMs) are sophisticated tools that allow representing physical processes in the atmosphere that are not yet resolved by the coarse resolution of Global Climate Models (GCMs)  (Giorgi, 2006; Feser et al, 2011). During the last decade, a huge effort has been made in order to adapt and apply these models to produce regional climate change scenarios in different regions worldwide. As a result, there is nowadays a number of comprehensive datasets developed in projects such as ENSEMBLES  (van der Linden and Mitchell, 2009)  and CORDEX  (Giorgi et al, 2009) , which also provide new opportunities for the intercomparison of different models, grid resolutions, boundary conditions, parameterizations (see e. g. Christensen et al, 1997; Jacob et al, 2007; Nikulin et al, 2011; García-Díez et al, 2013; Vautard et al, 2013)  and model domains  (Teichmann et al, 2013).
In order to ensure the area-averaged representativeness, the interpolation method was applied at an auxiliary 0. 01 • horizontal resolution grid and the final grids were obtained by averaging the results into the final resolution. For the Alps, the Alpine Precipitation Grid Dataset (APGD,  Isotta et al, 2013)  was used as observational reference. This data set was developed in the framework of EURO4M (European Reanalysis and Observations for Monitoring) for the period 1971-2008 and is a 5km resolution gridded product provided by MeteoSwiss. The interpolation procedure consists of local regression (precipitation-elevation regression on independent slopes model) and angular distance weighting. In this study, the APGD was conservatively remapped onto the rotated 0. 11 • and 0. 44 • RCM grid, therefore the APGD011/044 versions exactly match the EURO-CORDEX grids (0.
The authors are grateful to Prof. C. Schär for his helpful comments and E. van Meijgaard for making available the RACMO model data. We acknowledge the observational data providers. Calculations for WRF311F were made using the TGCC super computers under the GENCI time allocation GEN6877. The WRF331A from CRP-GL (now LIST) was funded by the Luxembourg National Research Fund (FNR) through grant FNR C09/SR/16 (CLIMPACT). The KNMI-RACMO2 simulations were supported by the Dutch Ministry of Infrastructure and the Environment. The CCLM and REMO simulations were supported by the Federal Ministry of Education and Research (BMBF) and performed under the Konsortial share at the German Climate Computing Centre (DKRZ). The CCLM simulations were furthermore supported by the Swiss National Supercomputing Centre (CSCS) under project ID s78.
Part of the SMHI contribution was carried out in the Swedish Mistra-SWECIA programme founded by Mistra (the Foundation for Strategic Environmental Research). This work is supported by CORWES (CGL2010-22158-C02) and EXTREMBLES (CGL2010-21869) projects funded by the Spanish R&D programme and the European COST ACTION VALUE (ES1102). A. C. thanks the Spanish Ministry of Economy and Competitiveness for the funding provided within the FPI programme (BES-2011-047612 and EEBB-I-13-06354.