dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2009; Miller et al. , 2008) , (B) an iterative minimization of the elevation difference residuals  (Berthier et al. , 2007; Rodriguez et al. , 2006) , (C) an analytical solution for translation only based upon terrain slope and aspect  (KŠŠb, 2005a; Nuth and KŠŠb, 2011)  and (D) one semi-manual approach based upon terrain slope  (VanLooy and Foster, 2008). The first algorithm minimises the sum of squares of the Euclidean distances between two surfaces while the other algorithms rely on the minimisation of elevation difference residuals over assumed stable terrain. The strategy for analyzing the results was to visualize the elevation change grids in grayscale to determine whether some misalignment remained (Òfalse hillshadeÓ) and to compute the vector sum of three or more co-registration vectors between the datasets that should form a perfect triangle.
The Òfalse-hillshadeÓ of the DEM differences for 4 out of 6 contributions displayed only small hints of the terrain suggesting that the co-registration was successful and misalignment properly removed. In two cases, the terrain was clearly visible despite proper triangulation residuals. This occurred after co-registration in one software, and final differencing in another software, specifically from a nearest neighbour resampling that is default within the ÔRaster Calculator ToolÕ of the ESRI ArcGIS software. In some of the contributions using the same algorithms (not shown here), the triangulated residuals resulted in magnitudes of similar order to the pixel and half pixel sizes of the DEMs. We attribute this artifact to the variable definition of the pixel corner vs. centre in the various software products, so it occurs specifically when switching software products within a single processing chain.
1) where α is terrain slope, ψ is terrain aspect, and the parameters a and b are the magnitude and direction, respectively, of the co-registration vector and c is the mean bias between the DEMs divided by the mean slope of the selected terrain. The implementation for minimisation is not strictly defined, and we use a robust linear least squares method (implemented in Matlab) to determine unknown a, b and c. Since the solution to this analytical relationship is solved using a non-analytical surface (i. e. the terrain), the first solution may not be the final solution and iteration of the process is required to arrive at an ultimate solution. Typically 1-2 additional iterations are required (depending upon the minimisation routine); we stop the process when improvement of the standard deviation is less than 2%.
e. the DP-RT-RepAlt, the RP-RT-RepAlt and the XO-RepAlt algorithms. The last three columns report the absolute differences between the average values of the three algorithms and the average values of the airborne dataset, whose trends are also visualised in Fig. 7  as a function of the elevation. They show a similar tendency when elevation becomes greater than 300 m. For elevations below this value, a dissimilarity between the absolute differences can be noted when radar altimeter data are used, indicating that radar performs better in regions with moderate slope than in regions with high slopes like the ice margin. Fig. 8  shows the scatter-plots between the two repeat track algorithms used, i. e. the DP-RT-RepAlt and the RP-RT-RepAlt, against the crossover method, i. e the XO-RepAlt. In both cases the RMSE is more than 1 m/yr and the correlation coefficient assumes a small value close to zero indicating that the there is no correlation between the two methods.
, 2008 and 2011 ). Here we focus on Landsat data with 15 m spatial resolution (ETM+ pan), due mainly to the large spatial coverage of one scene, the huge archive available and the perspective of future missions with similar characteristics (e. g. Landsat 8, Sentinel-2). Data acquired by Envisat ASAR (C-Band), ALOS PALSAR (L-Band) and TerraSAR-X (X-Band) data were considered as microwave imagery for algorithm testing. (image signal-to-noise ratio). Ahn and Howat (2011)  applied normalised cross-correlation (NCC) to a large number of input image versions (e. g. channels, gradients, filtered versions, principle components) and a range of template sizes, to find the most probable displacement based on the resulting stack. Though computationally very expensive, this approach is locally adaptive in many geometric and radiometric respects.
Pre-processing for SAR and optical sensors In the pre-processing step data import and quality checks (e. g. missing line detection for SAR data) are performed. The most crucial step is the accurate co-registration of the data to be matched (see section 3). Offsets were measured using the same algorithms as in the main processing (though often with different parameterisations, e. g. with a reduced sampling to decrease the computational effort) or using another algorithm and form the base of coregistration. A glacier mask and a DEM can be optionally employed to limit the search over stable ground and to compensate for the stereo offsets relevant for the range offset field, respectively. The slave (or search) image can be then either transformed to the geometry of the master (or reference) image or (e.
g. polynomial) transformation parameters can be computed and applied to the matching results without transforming the images. The coregistration transformation has the advantage to make offset-tracking and other usages of the images easier, but increases the computational time and storage requirements and may introduce loss of information, even if this is marginal for SAR data. A generic optical offset-tracking procedure for large-scale and frequent operational usage will have to be based on orthorectified data, as some data are, or will be only available in orthorectified form (Landsat, Sentinel-2) or because orthorectification within the tracking procedure will be difficult, e. g. due to instable sensor geometry  (Leprince et al. 2007 ). As a result, the displacements are not necessarily the highest accuracy level theoretically achievable, but will rather be contaminated by propagated DEM errors, errors in the sensor model and sensor orientation, such as jitter  (Scherler et al.
Main Processing of satellite SAR data The NCC algorithm is simple and robust and can also be applied to SAR data. Offsets are measured using rectangular windows that are m 1 x m 2 (range x azimuth) pixels in dimension at a set of positions in the scene. The locations may be uniformly distributed over the image frame but for deformation mapping at specific regions (i. e. glaciers) also can be selected for dense sampling. After global co-registration, the residual offsets should not be larger than a small fraction of the patch size that will be used for measuring the offset field. Typical values for m 1 and m 2 are in the range of 64 to 256 pixels depending on the noise level, sensor resolution and specific application. Generally these sizes correspond to 300 -1000 m on the ground. Corresponding data patches are extracted from each single-look complex image (SLC) and are typically over-sampled by a factor of 2 or more using FFT interpolation to substantially improve the accuracy  (Werner et al.
Elevation change from DEM differencing The first step towards automated DEM differencing would be the implementation of a universal co-registration algorithm. All of the here presented round-robin-tested automated approaches performed with similar accuracies but different efficiencies. In terms of universal application, we propose a co-registration algorithm based upon terrain slope and aspect that requires only 2-3 optimazation iterations and is applicable with non-continuous elevation data, for example, from ICESat. Other lessons learned from the round robin are that various software products have different importing routines for the same file format (i. e. Geotiff) affecting the pixel definition (pixel centre vs. corner) and leading to large co-registration errors (typically half a pixel size) if an algorithm implementation requires switching the software.
Elevation changes from altimetry In regard to the comparison between the elevation changes derived with satellite altimetry and the airborne measurements performed over the Austfonna ice cap, the absolute differences obtained using the DP-RT-RepAlt, the RP-RT-RepAlt and the XO-RepAlt showed similar trends when the elevation became greater than 300 m. It appeared that due to the large footprint of the radar altimeter, it was not properly measuring the elevation at the margin of the ice cap where surface slopes are high. In addition, the repeat-track method applied to laser altimeter data provided estimates of ice cap elevation change with greater accuracy than the cross-over method applied to radar altimeter (Table  5 ). Table  7 summarises.
Glacier velocity Results for glacier velocity measurement using satellite optical data suggested that no individual matching method clearly outperformed all other methods investigated under all circumstances, but rather that a set of, for instance, two methods Ð e. g. the NCC and the CCF-O Ð should be combined depending on the image conditions and the glacier characteristics. Results from the SAR round robin algorithm intercomparison demonstrated that, for the provided datasets and with temporal baselines >11 days, the normalized cross-correlation of chips in amplitude SAR images performed better compared to phase-based SAR methods (SAR interferometry and multiple-aperture interferometry, see Fig. 9 ), in particular regarding its wider application to different glaciers and SAR data. Accuracy of offset-tracking using high to very-high resolution SAR data with a time interval of one orbital cycle are about 10 m/yr, similar to the accuracy for medium-resolution optical satellite imagery (e.
Conclusions and Perspectives We have described methods and algorithms for deriving three glacier related products (area, elevation change, flow velocity) from a variety of space-borne sensor types (optical and microwave imagers or altimeters). They generally provide complementary information and are thus particularly useful for glaciological research when combined. The algorithms presented were selected for data production in the Glaciers_cci project after careful evaluation and comparison with alternative methods. In regard to a more general data processing workflow, all methods selected have also product-specific peculiarities. For the glacier area product the mapping algorithm is very simple (band ratio with threshold) and the manual editing of wrong classifications (debris cover) in the post-processing stage drives the accuracy of the final product.
For ice velocities the major differences between different processing schemes may also stem from different implementations, pre-and post-processing steps, which should thus be carefully selected. Velocity measurements from repeat satellite optical and SAR sensors have a large potential to be accomplished automatically further arriving at robust global-scale products. More detailed information on algorithms, work flows and product generation is available from the documents on the Glaciers_cci website (http://www. esa-glaciers-cci. org/). With the recently launched (Cryosat-2, TanDEM-X, Landsat 8) or planned (Sentinels 1 and 2) satellite missions and the commitment to free data distribution by space agencies, the contribution of spaceborne sensors to glacier monitoring will play an increasing role in the future.
A genetic map was constructed using 2145 markers among which 1479 could be positioned on 28 different linkage groups. The sex-averaged linkage map spanned a total of 3057 cM with an average marker spacing of 2. 1 cM. With the exception of a few regions, the marker order was the same in Japanese Quail and the chicken, which confirmed a well conserved synteny between the two species. The linkage analyses performed using QTLMAP software revealed a total of 45 QTLs related either to behavioral (23) or production (  22 ) traits. The most numerous QTLs (15) concerned social motivation traits. Interestingly, our results pinpointed putative pleiotropic regions which controlled emotional reactivity and body-weight of birds (on CJA5 and CJA8) or their social motivation and the onset of egg laying (on CJA19).
A quality filter was applied (mapping quality > 30, read depth > 10), and SNPs resulting from transversion or found close to another SNP (in the 120 flanking base pairs) were excluded. Only SNPs with an allele frequency of 0 in one pool and 1 in the other pool were then selected, corresponding to line-specific SNPs in the 20 individuals sequenced. After Illumina scoring, final selection was made in order to optimize the SNP distribution on the genome with the chicken genetic map as the basis. Genotyping was performed with the iSelect method from Illumina. Genotype calling was carried out with the GenomeStudio software (GenomeStudio V2010. 1, Illumina). Genotypes were filtered for call rate (>0. 85), call frequency (>0. 50) and cluster dispersion. Markers with an unexpected deviation from Hardy-Weinberg equilibrium (p < 10 -6 ) were removed with PLINK  [30].
Map construction The genetic map was constructed using CriMap version 2. 504 software  [31]. First the PREPARE function was used to verify the genotyping data for incompatibilities with Mendelian inheritance, and errors were rechecked and data were excluded when necessary. The TWOPOINT function was used with a LOD score threshold of 50. 0 in order to create the linkage groups. Markers belonging to the same linkage groups were then analyzed with two iterations using the BUILD and FLIPS functions. In the first step, the BUILD option was used with a LOD score threshold model of 6. 0, and the order of loci generated was examined with the FLIPS and CHROMPIC option. In the second step, the BUILD option was used taking a LOD score of 3. 0 on the previously generated order as a basis in order to add the loci excluded during the first analysis.
QTL detection QTLs were detected by QTLMAP software (version 0. 9. 6) developed for outbred populations and applied to a mixture of half and full sib families  [32]. The methodology used was the linkage analysis (LA) with a heteroskedastic model assuming non-equal residual (within sire) variances. No assumptions were made about the fixation of the QTL alleles in the founder populations and different QTL effects were estimated separately for each family analyzed. In the present study, we performed two complementary analyses. The first, modeling only sire QTLs, allowed precise estimation of the QTL effect (because of the high number of offspring per sire) but on a limited number of parents. The second, including also dam QTLs estimated from at least 20 offspring, was expected to detect more QTLs by increasing the potential number of heterozygous parents.
QTL comparisons between chicken and quail Each of our SNPs was attributed to a specific coordinate in the chicken genome through the first bwa alignment against the previous version of the genome (galGal3) and updated by blast analysis  [34]  of the 1000 bp flanking sequence of the SNP against galGal4  [35]. The Chicken QTL Database  [36, 37]  was used in order to assess the genetic environment in the chicken genome at the coordinates of the markers flanking the likeliest position of the QTL and at the coordinates of the markers flanking the confidence interval of the QTL. The results of these comparisons are shown in Additional file 1: Table  S1.
In order to confirm the similarity between QTLs observed in the quail and the chicken, we used the chicken QTL Database  [36, 37]. We first undertook these comparisons on widely studied traits such as body weight. In the present study, we detected several QTLs for body weight at a juvenile age (W17) and at a sexually mature age (W65). Interestingly, QTLs controlling these two traits co-localized on CJA1 (316 to 328 cM) and CJA5 (56 to 115 cM), suggesting the existence of regions controlling the overall growth of the animal. As shown in Additional file 1: Table  S1 , growth-related QTLs were discovered in the homologous regions of the chicken by several studies at a juvenile age but more rarely at a mature age. Similarly, body weight QTLs in the chicken were found in the homologous regions of W65 QTL on CJA3 (148-165 cM) and W17 QTL on CJA8 (37-60 cM).
600 µl of each supernatant from sample pretreatment was loaded followed by two wash steps: 200 µl of 5% (vol/vol) methanol and then 200 µl of 60% (vol/vol) methanol. Retained analytes were eluted and transferred to amber glass vials before liquid chromatography (LC)-mass spectrometry analysis. The LC-mass spectrometry/mass spectrometry system was comprised of an ultra-performance LC system (Acquity I-Class; Waters) coupled to a triple quadrupole mass spectrometer (Xevo TQ-S; Waters) operating in positive electrospray ionization mode and using MassLynx software version 4. 1 (Waters). 20 µl of each sample was injected into a 2. 1 × 50-mm ultra-performance LC ethylene-bridged hybrid phenyl column (Acquity; Waters) with 1. 7 µl maintained at 35°C. The data were independently verified by SAS Bone Metabolic Laboratories.
). Primary antibodies were extensively washed with 0. 1% PBS and Triton X-100 and incubated with appropriated secondary antibodies. Quantification of VDR + cells was performed on high magnification fluorescent images of studied regions of interest using ImageJ version 1. 44 software (National Institutes of Health) in at least three serial MS sections 100 µm apart from five MS and five control cases (Fig. 3 A ). 6-10 pictures were taken for each MS region of interest, including active chronic lesions, PPWM, as well as NAWM from nonneurological controls. The number of immunopositive cells per square millimeter was calculated.
Quantification For in vitro data obtained with OPC cultures, five randomly chosen areas of the coverslip were imaged per condition with the 20× objective (NA 0. 30; Ph 1), or 40× (NA 0. 50; Ph 1) for Duolink, using a fluorescence microscope (AxioVision Observer A1; Carl Zeiss) with a camera (Axio-Cam HRC; Carl Zeiss), and counting was done manually using the cell counter plugin in ImageJ. Organotypic cerebellar slices and in vivo CCP demyelination lesions were imaged using a confocal (SP5; Leica) and Advanced Fluorescence software, version 2. 7. 3. 9723 (Leica). In the case of cerebellar slices, four independent slices were analyzed per condition and per experiment. Five images of randomly chosen areas of each slice were imaged using the 40× objective (NA 1. 25) with 1. 5× digital zoom, 1,024 × 1,024 resolution, and 1.
75-2-µm thickness stacks. For analysis, a macro was created in ImageJ that measured the MBP-NFH colocalization area and the total NFH area. For CCP lesion images, the whole lesion of three sections was imaged per animal with the 20× objective (NA 0. 70), 1,024 × 1,024 resolution, and 1. 5-2-µm stacks. Using ImageJ software, the lesion area was delineated and measured, and the number of different cell types within the lesion was counted manually with the cell counter plugin. All the images were acquired at room temperature.
Online supplemental material Fig. S1  shows the effect of blocking VDR with the antagonist ZK159222 in the presence or absence of 9cRA, an RXR agonist. Fig. S2  shows the absence of effect of vitamin D in OPCs derived from MGCs exposed to normal serum in proliferation and differentiation. Table  S1  provides the clinical data of the MS patients and matching controls used for this study. Table  S2  shows the two independent measurements of vitamin D performed in the serum before and after charcoal treatment. Online supplemental material is available at http ://www. jcb. org /cgi /content /full /jcb. 201505119 /DC1. Additional data are available in the JCB DataViewer at http ://dx. doi. org /10. 1083 /jcb. 201505119. dv.
Acknowledgments We thank  Dr. E. May  and  Dr. A. Steinmeyer  for providing ZK159222, the  UK MS Tissue Bank  ( Prof. Richard Reynolds ,  Imperial College, London, England, UK ), and  C. Linington  for providing us with the MOG antibody. This work was supported by the  United Kingdom Multiple Sclerosis Society , the  Wellcome Trust ,  Sir David  and  Isobel Walker Trust , the  Medical Research Council , " Investissements d'avenir" Instituts Hospitalo-Universitaires A Institut du Cerveau et de la Moelle Epiniere , a  National Health and Medical Research Council   Early Career Fellowship (grant   628928 ), and  Obra Social "La Caixa. "The authors declare no competing financial interests. Submitted: 28 May 2015 Accepted: 27 October 2015.
thaliana. We assembled a dataset of 31 genotypes, including (i) five ecotypes with geographic ranges in the Northern hemisphere, (ii) 16 RILs generated by crossing two ecotypes from contrasting environments, (iii) five near isogenic lines (NILs) varying from wild-type only at loci previously identified to control LES variation and (iv) five knockout mutants, of which three have loss of function in candidate genes found within the above loci and two have reduced auxin sensitivity, causing vascular patterning defects. These genotypes have been extensively studied via molecular and phenotyping approaches  (El-Assal et al. 2001; Doyle et al. 2005; Keurentjes et al. 2007; Fu et al. 2009; Vasseur et al. 2012 ) and now provide a wide range of phenotypes with known genetic causes. We grew replicates of each genotype under controlled environmental conditions and used a high-throughput phenotyping system  (Granier et al.
Many vascular plant species have vastly different life histories than A. thaliana, so it is plausible that multiple genetic pathways are responsible for the strong observed convergence in LES with and across taxa. Data scale issues may also limit the generality of our conclusions. This study used a mixture of leaf-level measurements (VD) and whole-plant measurements (A m , N m , LDMC and LMA and LL), which may conflate the LES  (Wright et al. 2004 ) with the whole-plant economics spectrum  (Reich 2014). This could reduce or bias correlations: for example, our measurements of A m and LMA include leaves of different ages, while VD is measured for a single leaf for each individual. There may also be a biased relationship between average LL and flowering time, because of the non-linear increase in leaf number with plant size.
B. B. was supported by an NSF pre-doctoral fellowship. F. V. was funded by a CIFRE grant (ANRT, French Ministry of Research) supported by BAYER Crop Science (contract 0398/2009 -09 42 008). C. V. was supported by a Marie Curie International Outgoing Fellowship within the 7th European Community Framework Programme (Diversi-Traits project, no. 221060) and by the European Research Council (ERC-2014-StG-CONSTRAINTS grant). B. S. was funded by the Natural Sciences and Engineering Research Council of Canada. B. J. E. was supported by a NSF Advancing Theory in Biology award. D. V. was supported by EIT Climate-KIC program AgWaterBreed.
Data availability Datasheets and R code to replicate all analyses are available in Supporting Information. Pictures of leaf blades and petioles as well as raw biomass measurements are available at http://bioweb. supagro. inra. fr/phenopsis/ under experiment C3M13B  (Fabre et al. 2011). Images of chemically cleared leaves (greyscale) and hand-traced subregions (annotated with yellow for fully cleared regions; with red for veins) are available at http://clearedleavesdb. org under the collection name 'Arabidopsis thaliana genotypes'.