dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS Parts of LIME have been developed to address research requirements in projects funded by the  Research Council of Norway (RCN)  through the  Petromaks and Petromaks 2 programs. The following grants are acknowledged:  153264  ( VOG [Virtual Outcrop Geology ]; with  Statoil ASA) ,  163316  ( Carbonate Reservoir Geomodels [IRIS (International Research Institute of Stavanger)]) ,  176132  ( Paleokarst Reservoirs [Uni Research CIPR ]), 193059 (EUSA; with  FORCE Sedimentology and Stratigraphy Group) ,  234152  ( Trias North [University of Oslo ]; with  Deutsche Erdoel AG, Edison, Lundin ,  Statoil , and Tullow),  234111  ( VOM2MPS  [ Uni Research CIPR] ; with  FORCE Sedimentology and Stratigraphy Group), as well as SkatteFUNN (RCN)  project  266740. In addition, the  SAFARI  project consortium (http://safaridb.
Using genome-wide single nucleotide polymorphism (SNP) discovery in greater yam (Discorea alata L. ), 4,593 good quality SNPs were identified in 40 accessions. One hundred ninety six of these SNPs were selected to represent the overall dataset and used to design a competitive allele specific PCR array (KASPar). This array was validated on 141 accessions from the Tropical Plants Biological Resources Centre (CRB-PT) and CIRAD collections that encompass worldwide D. alata diversity. Overall, 129 SNPs were successfully converted as cost-effective genotyping tools. The results showed that the ploidy levels of accessions could be accurately estimated using this array. The rate of redundant accessions within the collections was high in agreement with the low genetic diversity of D. alata and its diversification by somatic clone selection.
The ligation reaction was achieved using T4 DNA ligase enzyme (New England Biolabs, Hitchin, UK) at 22°C for 1 hr, and the ligase was then inactivated, prior to sample pooling, by heating at 65°C for 20 min. Pooled samples were PCRamplified in a single tube. Single-end sequencing was performed on a paired-end lane of an Illumina HiSeq3000 (at the GeT-PlaGe platform, Toulouse, France). The Tassel 5. 2 pipeline  (Glaubitz et al. , 2014)  was used for SNP and indel calling. Sequence tags were aligned to D. alata contigs (http://www. ebi. ac. uk/ena/data/view/PRJEB10904) using Bowtie2 v2. 2. 6  (Langmead & Salzberg, 2012). Accessions with more than 70% missing data were removed. Vcf filtering was performed using Vcftools 0. 1. 14 (Danecek et al. , 2011 ; option: --minDP 8, --maf 0. 1, --max-missing 0.
| KASPar assay development and validation Genotyping-by-sequencing (GBS) produced more than 344 million reads resulting in 521,918 sequence tags out of which 207,810 (39. 82%) aligned exactly once on D. alata contigs. The remaining reads aligned at multiple locations (25. 18%) or did not align to any contig (35%). From these sequence tags, SNP calling produced a raw vcf file of 158,695 SNPs. This raw vcf file was then filtered resulting in a dataset of 40 accessions (Appendix A), and 4,593 good quality SNPs out of which 3,879 (84%) SNPs were mapped by BLAST on the D. rotundata reference genome. The KASPar assay was then developed by selecting 192 SNPs representative of SNPs mapped along the D. rotundata reference sequence, and they were tested on 141 accessions. Among the 192 SNPs, 26 (13%) SNPs failed as they did not produce any amplification signal.
From the remaining 166 SNPs (87%), 129 SNPs (Appendix C) with less than 20% missing data and a minor allele frequency of over 5% were retained as high-quality SNPs. This final dataset (129 SNPs × 141 accessions) contained an overall missing data rate of only 0. 5% with a maximum of 3% missing data per accession. The 129 validated KASPar SNPs were distributed on all linkage groups used to construct the D. rotundata reference genome (Figure  1 ). Their distribution was not homogeneous along chromosomes as their position was planned to be representative of that of the initial set of 3,879 mapped SNPs and not equally spaced.
However, this requires deep sequencing and usually an assumption of ploidy levels present in the dataset  (Bourke, Voorrips, Visser, & Maliepaard, 2018). Application in collection management may nevertheless not require allelic dosage assessment at each locus. Our aim was thus to develop a tool for estimating ploidy levels and not variations in copy number. Moreover, the results showed that ploidy levels for each accession can be accurately deduced from the percentage of "polypoid-like" genotypes on overall heterozygous genotypes. Regarding the overlapping distributions of this ratio (Figure  2 ), the only risk is to confuse triploids and tetraploids estimated at 3%. Consequently, ploidy level assessment is possible and fairly accurate for D. alata using the KASPar assay developed in this study.
dataset included 129 SNPs validated on 141 accessions corresponding to 43 unique redundancy groups. The resuming of the 141 accessions to 43 unique redundancy groups was related to the narrow D. alata genetic diversity, above all in polyploid germplasm (i. e. , triploids and tetraploids) already identified in previous studies. For example, using DarT markers, a low varietal richness was revealed by Vandenbroucke et al. (2016), who studied 80 landraces from six different Vanuatu islands and differentiated only seven unique genotypes. Using isozyme markers, Lebot et al. (1998) studied 269 worldwide distributed cultivars and concluded that the genetic diversity of the most widespread cultivars was narrow.
Numerous studies have reported the use of multi-spectral and multi-temporal remote sensing images to map irrigated crops. Such maps are useful for water management. The recent availability of optical and radar image time series such as the Sentinel data offers new opportunities to map land cover with high spatial and temporal resolutions. Early identification of irrigated crops is of major importance for irrigation scheduling, but the cloud coverage might significantly reduce the number of available optical images, making crop identification difficult. SAR image time series such as those provided by Sentinel-1 offer the possibility of improving early crop mapping. This paper studies the impact of the Sentinel-1 images when used jointly with optical imagery (Landsat8) and a digital elevation model of the Shuttle Radar Topography Mission (SRTM).
Reference Dataset The reference dataset was established through three field campaigns carried out at different stages of the growing season: after sowing (May 2015), at flowering (July 2015), and during senescence (September 2015). Irrigated maize crops were identified by the presence of irrigation equipment or from information supplied directly by the farmer. The dataset was composed of 317 fields (114 irrigated maize and 203 non-irrigated fields: 171 growing maize and 32 sunflower) (Table  1 ). The irrigated plots covered more restricted elevations (Figure  2 ), and the mean size of the fields concerned was larger than for non-irrigated crops. The two irrigation systems most frequently used in our region are pitons and sprinkler systems. The plots equipped with piton systems are often larger and located on flat areas.
The reference dataset was established through three field campaigns carried out at different stages of the growing season: after sowing (May 2015), at flowering (July 2015), and during senescence (September 2015). Irrigated maize crops were identified by the presence of irrigation equipment or from information supplied directly by the farmer. The dataset was composed of 317 fields (114 irrigated maize and 203 non-irrigated fields: 171 growing maize and 32 sunflower) (Table  1 ). The irrigated plots covered more restricted elevations (Figure  2 ), and the mean size of the fields concerned was larger than for non-irrigated crops. The two irrigation systems most frequently used in our region are pitons and sprinkler systems. The plots equipped with piton systems are often larger and located on flat areas.
Incremental Classification We employed an incremental processing scheme to evaluate the usefulness of each date of acquisition and of each sensor for performing the seasonal classifications. For this purpose, we generated a supervised classification every time that a new image acquisition was available, using the previously available imagery. Every new acquisition led to a new classification. A fully automated processing chain based on OpenCv 2. 4 and GDAL 1. 11 libraries was developed for this study. The classifier used was the Random Forest algorithm. The number of trees was set to 100, and the maximum depth to 25. Temporal gap filling of the cloudy pixels was performed with the gap-filling module of the Iota2 software  [21]. The method relies on a linear interpolation of the cloudy pixels using the previous and following cloud-free dates.
The Landsat 8 images used in the classification process were thus gapfilled, providing one image every 16 days. Every classification was evaluated using the reference dataset, which was randomly split into two parts: 50% of the ground truth polygons were used for training the classification and 50% were used for the validation. The global performance of each classification was evaluated using the Kappa coefficient (k)  [56] , and the performances of each class were evaluated using the F-score (Equation (  1 )), the precision (Equation (  2 )), and the recall metrics (Equation (  3 )).
The Landsat 8 images used in the classification process were thus gapfilled, providing one image every 16 days. Every classification was evaluated using the reference dataset, which was randomly split into two parts: 50% of the ground truth polygons were used for training the classification and 50% were used for the validation. The global performance of each classification was evaluated using the Kappa coefficient (k)  [56] , and the performances of each class were evaluated using the F-score (Equation (1)), the precision (Equation (  2 )), and the recall metrics (Equation (  3 )). Fscore = 2 × (Pi × Ri) Pi + Ri (1 As in the study by Inglada et al. [31] , the random split was done 10 times in order to perform 10 classifications. This allowed the average and confidence intervals for the Kappa, Fscore, and recall to be computed.
The orientation of orthographic views has been discussed as early as 1962  [1]  and it is solvable when at least three views are considered. The factorization method by Tomasi and Kanade  [14]  provides a simple method for pose estimation of orthographic image streams and it can also be used to calibrate perspective images. The other constraint of the method is that it requires full visibility: only points visible in all views can be exploited. In this article we aim to show the advantages of using the orthographic projection model for pose estimation of perspective images. We investigate the types of scenes where the orthographic model outperforms a perspective approach and test the robustness of the method for degenerate scenes. Moreover, we provide a Matlab implementation and a corresponding web demo that estimates the pose of three views from enough matching points.
Implementation The pose estimation of perspective cameras based on the scaled-orthographic method is implemented by the Matlab function OrthographicPoseEstimation. For this method, the input data are the matching points throughout M ≥ 3 views and the calibration parameters for each camera (calibration matrix K with the focal length and principal point). The output are the two possible orientations in variables Sol1 and Sol2 that contain a rotation matrix R and translation vector t for each view and also matrices R, S, T from the Tomasi and Kanade factorization (S contains the 3D reconstruction of the matching points passed as input). The general pipeline for pose estimation from images to a refined solution would be the following (see Algorithm 4): Matching between pairs: For each pair of images the matching features should be found.
The optimization is carried out by the function BundleAdjustment using the Levenberg-Marquardt algorithm  [5]  (already implemented in Matlab). After using the two possible initializations, the solution with lower reprojection error at the end of the optimization is chosen as the correct final pose estimation. Algorithm 4: Pose estimation pipeline Input: Images I 1 ,. , I M , M ≥ 3, focal length f. // Only for M = 3 in our code Output: Rotations R i and translation T i (i = 1. M ), 3D point coordinates in matrix S. 1 For each pair of images {I i , I j }, compute matching points (x k i , x k j ) // SIFT matches 2 Extract tracks of points W = (x j i ), i = 1. M , j = 1. N 3 Apply AC-RANSAC. Each sample of 4 tracks yields two hypotheses of structure and motion. 4  Reestimate structure and motion from all inlier tracks (yielding again two solutions).
To do the conversion, it is necessary to know the size of a pixel in millimeters, which can be computed by dividing the CCD width W (in millimeters) by the horizontal number of pixels w. The value of W is not part of the EXIF information, but the camera model is part of EXIF. From the latter, the sensor size can be looked up in a database, such as  [2]. Finally, original photographs are often too large for the demo system to answer in an acceptable time, so a zoom-out of factor z may be applied. Finally, the conversion follows the formula f = z • w W • f mm. ( 18 ) 5 Experiments To study the robustness and performance of this method, we evaluated its results when applied to different scenes, comparing them to other algorithms based on the perspective model. Synthetic data was used to test the sensibility to different parameters such as noise, focal length and configuration of the space points and cameras.
Real Data We have evaluated the orthographic method along with the perspective method based on the fundamental matrix on several real databases. Again, we test the two methods on triplets of images and compare the errors and the failures of each method. We also show the results for the solutions after applying a bundle adjustment (BA) step to both methods. In these real experiments, we chose to use a small sample of 50 correspondences on the bundle adjustment in order to improve the speed of the optimization. To select the inlier tracks of each evaluated triplet, we use an a contrario RANSAC adapted to each method. In the case of the fundamental matrix, the RANSAC method is applied to each pair of views separately and the common inliers in the three pairs are selected. In Tables 1-4 we show, for each dataset, several statistics averaged over all evaluated triplets: the resulting reprojection error (e repr ), rotation and translation errors (e rot and e trans ), the percentage of correspondences selected as inliers, the percentage of valid cases (VC) as described in Section 5.
1. 4 and the number of iterations of the bundle adjustment. Reims. This dataset is made out of a total of 55 images all acquired with a long focal length (f = 400mm) and it covers the main interior wall of the cathedral in Reims, France (see Figure  9 ). We tested the 200 triplets of images with the most correspondences and we compared the results to an "artificial" ground truth, the solution given by the SfM realized by MicMac  [9]  with not only the long focal length images but also with 16 other images with shorter focal length (7 with f = 100mm and 9 with f = 50mm) to stabilize the process. The internal calibration and distortion on the images was estimated by the same MicMac process and used in our computations. For this particular dataset, most of the scenes are quasi planar and the cameras are aligned.
We can see in Table  1  that while both methods have problems to estimate the translations, the orthographic model gives a better estimate according to the lower number of mean iterations needed to reach the minimum and the higher percentage of pose estimation success. EPFL fountain-P11  [13]. In juxtaposition to the previous case, we tested our algorithm in a short focal length dataset to evaluate its robustness. The fountain-P11 dataset is made out of a total of 10 images taken with a 35mm equivalent focal length of 32. 5mm and has an available ground truth. We tested 70 of the possible triplets and the results are shown in Table  2. The orthographic  method does not manage to get similar results to the perspective method; moreover, it has much higher reprojection and angular errors. However, the orthographic model is able to give as much as 91.
43% of valid results, meaning that, even if the perspective method is the obvious choice for this kind of scenes, the orthographic method would still be a viable option to get a first pose estimation to proceed to bundle adjustment. e Statue. To show the accuracy of the orthographic method for long focal lengths, we acquired a new set of images with a focal of length approximately 1000mm. The images cover the face of a statue (Figure  10 ) positioned in the middle of one of the fountains of Château de Champs-sur-Marne, France. The dataset consists of a set of 58 images and we tested 200 possible triplets. We can see in Table  3  how the mean reprojection error for the perspective-based method is extremely high while for the orthographic model it is just a little above 1 pixel in the initial pose estimation.
Example As an example, we show the results for one triplet of images from the Statue dataset. In Figure  11  the images are shown along with the 982 tracks between them. The RANSAC based on the orthographic   model gives a big enough set of inliers for the pose estimation but leaves out many true tracks. However, the method manages to correctly label the true outliers (notice on the right of the third image in Figure  11  a set of points that were marked as a correspondence but are clearly not visible in the other two images). For this triplet of images, the solution found by the orthographic method is much closer to the final refined solution than the initial pose given by the perspective method. We can see this quantitatively, in the number of iterations needed to reach the minimum in Table  4 , where the iterations taken by the perspective solution are much higher.
Liver cancer is a highly heterogeneous disease in terms of etiology, tissue and cellular morphology, tumor molecular characteristics, microenvironment composition and prognosis. Several studies, based on tumor gene-expression profiling (GEP) data, have dissected the molecular heterogeneity of liver cancer. They resulted in various tools, either delineating homogeneous tumor subtypes or calculating molecular scores of prognostic or biological functions. Here, we present MS. liverK, an easy-to-use R package providing a comprehensive implementation of these tools, for research use.
Introduction Primary liver cancer is the third most deadly cancer worldwide, with Hepatocellular carcinomas (HCC) accounting for 85%-90% of the cases. The molecular heterogeneity of liver cancers has motivated numerous transcriptome-based studies, yielding gene-signatures and algorithms for molecular subtyping, prognostic prediction and functional scores computation. The use of these tools is of great interest for researchers dedicated to liver cancer study, but is very limited due to their dispersion. Here, we introduce MS. liverK (Molecular Signatures in Liver Cancer), an R package reimplementing these tools. MS. liverK takes as input a (log2 scale) transcriptome matrix of liver cancer samples, either microarray-or RNA-Seq-derived. It also includes graphical functions, allowing to easily visualize the outputs.
, 2008) , EPCAM  (Yamashita et al. , 2008)  and hypoxia  (van Malenstein et al. , 2010). MS. liverK computes all the corresponding scores. We applied MS. liverK to the TCGA LIHC RNA-seq dataset (n=286). The graphical output shows a striking concordance across the various subtyping systems, the prognostic score and the functional scores (Figure  1 ). This observation is expected, given that molecular subtypes should represent homogeneous types of tumors with specific biological and prognostic characteristics.
Software implementation and use The package has been entirely written using R language. It includes pre-packaged data from Gene Expression Omnibus (GEO), accession number GSE20238  (Mínguez et al. , 2011) , to be used as example data. A vignette describing the use of the package is available on GitHub (https://github. com/FPetitprez/MS. liverK/blob/master/vignettes/vignette. pdf). On a standard computer, the computation of all subtypes and scores takes a couple of minutes.
Molecular dynamics simulations In this study, we employ molecular dynamics (MD) simulations using the GROMACS 4. 0. 5 programme package  (Hess et al. , 2008). For efficiency reason, we investigated the applicability of the MARTINI coarse-grained (CG) force field  (Arnarez et al. , 2015; de Jong et al. , 2013; Hsu et al. , 2017; Monticelli et al. , 2008; Periole et al. , 2009) , which is about 500-fold faster than the full-atomistic GROMOS  (May et al. , 2014). In addition, we modified the elastic network that is used in MARTINI based on our starting conformations, including only elastic network constraints that differed less than 1Å between the I O and O O states. This allows transition between both states, while maintaining protein structure stability. This composite scheme will henceforward be referred to as ConsDYN (CON-Served DYNamics).
Analysis of channel dynamics Essential dynamics analysis was performed on the GLUT1 and GLUT3 simulations using built-in analysis tools on GROMACS. To allow this comparison between these two homologous proteins, and allow for focusing on overall motions of the channel region, we selected the structurally conserved helical segments, as summarized in Supporting Table  S2 , available as extended data  (Feenstra, 2019a). Then, the covariance and eigenvalue calculation was performed on the ensemble of both wild-type systems, using the full atomistic (AT), coarse-grained (CG) and ConsDYN simulations. To analyse the channel dynamics from the ConsDYN, we defined several order parameters as previously proposed by  Nagarathinam et al. (2018) , by measuring distances between adjacent TM helices, at the intracellular (in)-and extracellular (out)sides of the protein (see Figure  1B, C ).
Prediction of mutation impact For each of the mutants of GLUT1 and GLUT3 considered here, Table  1  lists the predicted impact upon mutation obtained from SIFT  (Sim et al. , 2012)  and PolyPhen-2  (Adzhubei et al. , 2010). Most mutations are classified as likely pathogenic by both methods, with the exception of GLUT1 R93Q, and GLUT3 R91C and R91H. However, these methods are trained on the dbSNP database which also includes these known mutations, so this should be no surprise. Moreover, these predictions do not allow us to gain any insights into the mechanism by which these mutations may affect transporter function.
d β (x, y), m = 1,. , M s. t. G(x, y) ≤ 0 g k (x, y) ≤ 0, k = 1,. , K d β (x, y) ≥ β t (11) Where, d β (x, y) denotes the distance between the optimal solutions and the design points and β t is the reliability cible. The optimization problem is solved using the multi-objective genetic algorithms (MOGA) tool in MATLAB software (gamultiobj).
Deterministic model The theoretical formulation described above was implemented in Matlab. Tables  2  and 3  show, respectively, the natural frequencies of the acoustic cavity and one aluminium plate. It is clear that the theoretical formulation gives accurate results with the finite element analysis using Ansys in the frequency rang 0 -400Hz. It can be noted that a total of 4 acoustic modes and 2 structural have been found in the frequency band 0 -400Hz. It can be said that the frequency peaks of the calculated results coincide with the resonance frequencies of the acoustic box and the aluminum plate. The magnitude of the acoustic pressure inside the cavity is presented in figure  5. The validation of the proposed analytical formulation of the modified coupled acoustic-structural system has been verified and then we can process with the MORBDO problem in order to obtain the optimum reliable design.