dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2010). These modules would then calculate the changes in LAI and crown volume. The LAI is non-linearly correlated with the height of the trees and the canopy closure (not shown) and needs to be transformed into a solar-zenith-angle-dependent effective LAI to represent the structure of the canopy in the 1-D canopy transfer model. Different approaches have been proposed to relate LAI to effective LAI  (Haverd et al. , 2012; Ni-Meister et al. , 2010). The 1-D radiation transfer used in this study  (Pinty et al. , 2006)  is computationally fast and requires only a small number of input parameters, whereas global effective scattering parameters are provided by the JRC-TIP package  (Pinty et al. , 2011a). Applying the model adjustments discussed above to an Earth system model would give a tool that could be applied to produce a comprehensive assessment of the effect of forest management on climate.
The dominant tree cover was 21 m tall with a high canopy closure. The oak stand was situated at Fontainebleau in France (coded FR-Fon; 48. 48 • N, 2. 78 • E). It consists mainly of Quercus petraea L. In 2006 the stand density was 1134 trees per hectare, of which 234 were Quercus petraea and 900 were Carpinus betulus. The average canopy height of Quercus petraea was about 27 m, whereas the Carpinus betulus were about 10 to 20 m tall; stand age (in 2013) was about 150 years. For all validation sites, stand-level albedo was observed from in situ incoming and outgoing shortwave radiometric measurements and recorded in the IMECC database, with the exception of EE-Jär. From this database years was only selected when outgoing and incoming shortwave radiation were recorded. Albedo was calculated as the ratio of downward and upward radiation as observed with two-way pyranometers (Kipp and Zonen, Delft, The Netherlands).
Acknowledgements. We would like to thank  Miina Rautiainen  for useful comments on an earlier draft of the manuscript. J. Otto  and  S. Luyssaert  were funded through  ERC starting  grant  242564  and received additional funding through  FWO-Vlaanderen  and  COST ES0805 TERRABITES. Edited by:  G. Wohlfahrt The publication of this article is financed by  CNRS-INSU.
4. Cyclic stability of residual stress in AA 5083-H111 hammered surface and their consequences on fatigue strength A numerical procedure has been developed, using ABAQUS software, to predict residual stress redistribution and work hardening modification under cyclic loading. The organization chart of the proposed procedure, given in Fig. 5 , requires initial work hardening and residual stress profiles, cyclic material behaviour law, and applied cyclic loading conditions. At least two experimental stabilized residual stress profiles are needed to validate the numerical procedure. Using multi-axial criteria, accounting for residual stress and work hardening, the developed procedure predicts suitably the fatigue life. The application of this procedure on the as machined and hammered AA 5083-H111, leads to the following results:  The stabilized residual stress was satisfactory predicted by taking into account the initial work hardening with respect to the experimental results (Fig.
The sgEvolver simulator  (Darling et al. 2004 (Darling et al. , 2010) )  is used to generate simulated genome alignments, although it lacks an explicit model for sequence translocation or mobile element evolution. EvolSimulator is a genome simulator, but it has a somewhat simple model of evolution and a focus on ecological parameters  (Beiko and Charlebois 2007). Another option, the ALF simulator  (Dalquen et al. 2012) , models gene and neutral DNA evolution. For this study we used the EVOLVER software, which can simulate fullsized, multichromosome genome evolution in forward time  (Edgar et al. 2009). EVOLVER models an explicitly haploid genome and lacks a population model; its framework and expert-curated extensive parameter set are intended to produce ''reference-like'' genomes, i. e. , haploid genomes.
The third approach addresses alignments by statistical measures. For global MSA there are several options, e. g. , the T-Coffee CORE/TCS index  (Notredame and Abergel 2003; Chang et al. 2014) , Heads or Tails (HoT)  (Landan and Graur 2008) , GUIDANCE  (Penn et al. 2010a,b) , and StatSigMA-w  (Chen and Tompa 2010). For this work, we expand on the probabilistic sampling-based alignment reliability (PSAR)  (Kim and Ma 2011)  method, which samples pairwise suboptimal alignments to assess the reliability of MSAs. Statistical measures are attractive because they can be used with the complete alignments of real sequences. However, without a gold standard to compare against, they are only a proxy to a true assessment of accuracy. The final category of common assessment methods addresses how well a program generates alignments for a given computational task.
This is typically the assessment made by a biologist in choosing an alignment program, i. e. , how well does it perform in practice, according to intuition or analysis? Unfortunately, these assessments, often being one-offs, rarely make it into the literature and are difficult if not impossible to generalize from because these assessments are made for the purposes of a given analysis. Notably for WGAs,  Bradley et al. (2009)  assessed how much alignment methods influenced de novo ncRNA predictions and  Margulies et al. (2007)  analyzed the effect of different WGAs on the prediction of conserved elements. There have been relatively few independent or community organized assessments of WGA pipelines. Notably, as part of the ENCODE Pilot Project  (Margulies et al. 2007 ), four pipelines were assessed across a substantial number of regions, and Chen and Tompa later compared those alignments using the StatSigMA-w tool  (Chen and Tompa 2010).
Competition organization and submissions The initial data sets were released in December 2011 and teams were given until February 2012 to submit their entries. The initial simulated data sets included the truths and information on where to obtain (and optionally contribute to) the analysis software. As in the Assemblathons, none of the teams had access to the data sets until their initial release. The Alignathon received 35 submissions, 13 for the primate simulation, 13 for the mammal simulation, and nine for the fly data set (Table  3 ; Supplemental Tables  S2, S3 , and S4). The pipelines that were used to generate the alignments represent those used by genome browsers to generate their WGAs: VISTA-LAGAN for the VISTA Browser  (Frazer et al. 2004; Dubchak et al. 2009) ; MULTIZ for the UCSC Genome Browser  (Miller et al.
In particular, the EPO and Pecan submissions both use the Pecan program  (Paten et al. 2008; Paten et al. 2009)  to align sets of  F-score value. The horizontal line is, respectively, the overall precision, recall, or F-score value; the upward triangle with a vertical line is the regional precision, recall, or F-score mean value, 6 the regional standard deviation; the downward triangle with a vertical line is the PSAR-precision, coverage, or pseudo F-score mean value, 6 standard deviation for values that were computed using regional subalignments. syntenic sequences, and the Cactus program  (Paten et al. 2011)  uses the same pairwise-HMM to generate much of its multiple alignment as Pecan, but these submissions were relatively different from one another.
The interchromosomal step allows chromosome fusions, fissions, segment copying, segment movement, reciprocal translocations, and nonreciprocal translocations. Additionally, EVOLVER keeps a separate mobile element library that can insert mobile element DNA into the simulated genome; this library is itself also undergoing simulated evolution. EVOLVER logs all evolutionary events that take place during a cycle and keeps track of the relationships between residues in the parent and child genomes. EVOLVER as distributed is only capable of performing a single cycle of evolution. In order to run the arbitrary phylogenies necessary for this project, we used the evolverSimControl and evolverInfileGeneration tools available at https://github. com/ dentearl/evolverSimControl and https://github. com/dentearl/ evolverInfileGeneration/, respectively.
These extra tools, along with mafJoin (https://github. com/dentearl/mafJoin/) were used to construct MAF files containing the entire simulated true evolutionary relationships of all of the genomes: leaves, internal nodes, and the root. As in the Assemblathon project, we initiated the simulation using a subset of the well-annotated human genome, hg19/GRCh37. Complete chromosome sequences for chromosomes 20, 21, and 22 along with annotations for those chromosomes from the UCSC Genome Browser tracks mgcGenes, knownGene, knownGeneOld5, cpgIslandExt, and ensGene, were obtained from the UCSC Golden Path download site. The tool suite evolverInfileGeneration was used to take the raw data and make it into an EVOLVER infile data set. This starting data set was then put through the EVOLVER simulator for a distance of 1.
Regional alignments and PSAR To accommodate PSAR, which processes global MSAs in which the alignment is represented as a 2D matrix where the aligned sequences, interspersed with gaps, are the rows and the columns represent the equivalence classes of aligned bases, we constructed subalignments of sampled regions. Regions were randomly sampled by picking intervals of a chosen reference genome (for the flies D. melanogaster, dm3, and for the simulations, simHuman). For each of the three test sets, regional intervals were selected by sampling five different starting values from a discrete uniform distribution (0, g À 1 À 500,000), where g is the total length of the reference genome and 500,000 is the length of the interval. Sampled values were then mapped back to individual chromosomes. All alignments containing any positions of the reference within these intervals were extracted from the submitted alignments.
Acknowledgments We would like to thank the  Howard Hughes Medical Institute, Dr. and  Mrs. Gordon Ringold , the  NIH  (grant  2U41 HG002371-13 ), and the  NHGRI/NIH  (grant  5U01HG004695 ) for providing funding. We would like to thank the Genome 10K organizers for providing a venue to discuss an early version of these findings. J. M. is supported by  NIH  grant  R21HG006464 ,  NIH  grant  R01HG007352 ,  NSF  grant  1054309 , and  NSF  grant  1262575. M. H. is supported by  NIH  grant  R15HG005913. C. K. , J. -M. C. , I. E. , and C. N. were supported by the  Spanish Ministry of Economy and Competitiveness  (grant no. BFU2011-28575 ); the  Secretariat of Universities and Research, Department of Economy and Knowledge of the Government of Catalonia  ( 2009 SGR 951 ); the '' la Caixa ''  International Fellowship Programme  for a predoctoral fellowship at the  CRG  (to J.
-M. C. ); the  European Research Council  ( ERC-2008-AdG  no. 232947  to J. -M. C. ); and the  Spanish Ministry of Economy and Competitiveness , '' Centro de Excelencia Severo Ochoa   2013-2017 ,''  SEV-2012-0208. This work was supported by the  Wellcome Trust  (grant no. WT095908 ) and the  European Molecular Biology Laboratory. The research leading to these results has received funding from the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. 222664  (' 'Quantomics''). This publication reflects only the authors' views and the European Community is not liable for any use that may be made of the information contained herein. We thank three anonymous referees who provided a great deal of helpful feedback and suggestions.
Results: We propose new algorithms that process high-throughput sequencing (HTS) data to extract unnamed V(D)J junctions and gather them into clones for quantification. This analysis is based on a seed heuristic and is fast and scalable because in the first phase, no alignment is performed with germline database sequences. The algorithms were applied to TRγ HTS data from a patient with acute lymphoblastic leukemia, and also on data simulating hypermutations. Our methods identified the main clone, as well as additional clones that were not identified with standard protocols.
Software for V(D)J recombination analysis. The international ImMunoGeneTics information system (IMGT®) has developed several tools for the in-depth analysis of V(D)J recombinations  [8] [9] [10] [11] [12]. Many software focuses on V(D)J segmentation, identifying the V, D, and J regions in a sequence. The available V(D)J segmenters perform sequence alignments against full germline databases (JoinSolver  [13] , V-QUEST  [9] , HighV-QUEST  [11] ), possibly with some alignment heuristic (  [14] , IgBlast  [15] ), models such as hidden Markov models (HMMs) (iHMMune-align  [16] , SoDA2  [17] ), or maximumlikelihood-based techniques (VDJSolver  [18] ). A short benchmark of some of these tools has been published  [19] , but there is the need for more complete and independent evaluation. V(D)J analysis of high-throughput sequencing data.
Again, generic clustering tools cannot be used, because sequences with very small differences can be derived from different clones, especially if these differences occur in N-diversity regions. A solution is to cluster sequences taking advantage of the V(D)J segmentation. On immunoglobulin heavy chains, Chen et al proposed a clustering based on the results of iHMMune-align, implemented in the Clon-alRelate software  [37]. The clustering is based on a Levenshtein distance between CDR3 sequences that further takes into account the VJ assignation produced by iHMMune-align. The complete method has a quadratic time complexity in the input size. In another study, Laserson et al followed the dynamics of the immune response after vaccination, by partitioning the reads on the VJ recombinations (obtained with IMGT/V-QUEST), and by using a sequence-based clustering  [38].
This strategy is implemented in an open-source software called Vidjil. Not computing the complete segmentation on each read allows huge time gains. Vidjil processes datasets with 100,000 reads in less than 1 minute on a laptop computer, including the de novo quantification of all the main clones. We also show that the predicted windows are specific enough for individual VJ recombinations to be safely clustered. They ensure a high-quality multiclonal analysis: We provide evidence for this quality on TRγ chains. We further test simulated data with additional mutations. Indeed, extracting such windows corresponds to what is done with conventional PCR primers specifically designed for one recombination. The method is independent of the number of reads, but the more reads that are sequenced, the lower the detection threshold will be.
Dataset preparation and sequencing Bone-marrow samples taken from a patient at diagnosis and after treatment were obtained from the tissue bank "Tumorothèque du Centre de Référence Régional en Cancérologie de Lille (CRRC)" which certified cell cryopreservation quality. Approval for this study was obtained from the Institutional Review Board of CHRU of Lille (CSTMT093) and was in accordance with the Declaration of Helsinki regarding the informed consent of patients. A written informed consent was obtained from the patient.
Algorithm overview To quantify the clonotype abundances starting from a set of reads, the method proceeds through the following two stages: • the ultrafast prediction of short zones called w-windows, which are regions of length w overlapping the third complementarity-determining region (CDR3); this prediction is based on substrings ("k-words"); • the identification and clusterization of the clones (relying solely on these w-windows), followed by a refined V(D)J segmentation on a representative read inside each clone. Note that the "sequence assignment" of  [14]  also used a step based on substrings. However, in that study, the authors eventually computed a full alignment of each gene to the corresponding germline database.
Ultrafast CDR3 prediction The purpose of this heuristic analysis is to extract from a read a sequence of length w, called the w-window, that overlaps the actual CDR3. Our goal is to center the w-window as much as possible on the junction region, predicting a window that also contains the 3' extremity of the V region and the 5' extremity of the J region. This analysis is performed in two steps. The first consists of indexing the germline V and J gene databases, and the second is performed on each read and extracts the wwindow using the information stored in the index. This analysis is very fast and scalable, because no alignment with germline sequences is required.
Indexing step This index is built once at runtime. It could be precomputed and loaded from disk when necessary. Because the germline databases are very small (a few hundred thousand base pairs, at most), it is not difficult to recompute them, and takes only a few seconds. The index is built on subsequences of length k, called "kwords". Every k-word from the germline genes is indexed with a specific label: either V (or J), when the k-word is unique to the V (or J) germline (possibly occurring in distinct sequences from the same germline), or ambiguous when the k-word is common to both V and J germline genes. The value of k is chosen so that such ambiguous words are very rare; by default, k is between 10 and 13, depending on the germline. For these small values of k, the index can be stored as a flat table of size 4 k.
Read clusterization using w-windows Clonal windows clusterization The prediction step extracts one w-window per read, at most. If there is no sequencing error, all the extracted wwindows for the same clone are strictly identical (Figure  2 , top). However, they may not be exactly centered on the actual V(D)J recombination if there are some variants compared with the germline database. The extracted w-windows are then sorted and counted. The relative abundance of each clonotype is then estimated using the number of reads with the same wwindow. The most abundant clones are kept for detailed analysis. V V V V V V k Predicted V × Substitution V V V V V J J J J J J J J J J J Predicted J Extracted window w V V V V V V V V V V V V V Predicted V × Substitution J J J J J J J J J J J Predicted J Extracted window < k V V V V V V V V V V V V V V V V V V V V V Predicted V × × Substitutions No predicted J V V V V V V V V V V V V V V V V V V V V Predicted V × J × Substitutions Predicted J Extracted window Figure  2  Heuristic finding a w-window on the forward strand from a scan of k-words in VJ recombinations.
Refined V(D)J segmentation The representative sequence identified for each clone can be segmented into V(D)J regions using any available segmenter  [9, 11, 13, [15] [16] [17] [18]. To give a first hint on the V(D)J segmentation, we also implemented a basic segmenter using dynamic programming against a database of germline genes. This segmentation runs, for each representative sequence of length , in O( r) time, where r is the size of the database of the germline gene. This segmentation is not at the core of the read clusterization and is provided only for convenience.
Software The algorithms were implemented in C++ in an opensource software called Vidjil. The software can be downloaded from http://www. vidjil. org. The software takes as the input a set of reads and a database of germline genes. In all our experiments, we used the IMGT/GENE-DB database  [40]  downloaded from http://www. imgt. org. Vidjil outputs the list of w-windows detected and the most frequent clones. As explained above, the detection of w-windows is based on spaced k-mers extracted with seeds. By default the seed used for TRγ germline is #####-##### of weight 10. On this germline, there is no spaced k-word with this seed common to both V and J genes: There is thus very few chances to falsely discard reads. Depending on the receptor, there can be more overlap between k-mers of V and J genes.
In this case, or when there are more mutations or errors in the dataset, longer seeds should be used to improve the ratio of wwindows detected. By default, Vidjil uses a seed of weight 12 for TRβ and IgH and a seed of weight 13 for TRα. The user can also specify his own seed, or any value of k for a contiguous seed. Vidjil will output the 20 most abundant clones with their representative sequence and their refined V(D)J segmentation. It will not process clones with less than 10 reads. These parameters can be changed by the user. The user can also follow other clones, even if they are not among the most frequent ones, by specifying their w-window. The user can define the maximum number of substitutions, indels, and homopolymer errors that can be accepted between two similar windows. By default, we tolerate none.
Dataset The bone-marrow samples were obtained from a patient with B-ALL showing a TRγ rearrangement. The samples were taken at diagnosis (Diag) and at three follow-up points (Fu-1, Fu-2, and Fu-4, collected at 35, 122, and 207 days after diagnosis, respectively). A standard curve was established from serial dilutions of the diagnosis samples in a peripheral blood lymphocyte (PBL) solution mixed from five healthy donors, producing samples Scale-10 -2 , Scale-10 -3 , Scale-10 -4 , and Scale-10 -5. Those eight samples were sequenced as described in methods and can be accessed at http://www. vidjil. org/ data. In Additional file 1: Table  S1 , we provide statistics on these samples. We recall that on the TRγ chain, recombinations are in the VJ form. The number of reads differed for each dataset because the same coverage was not required for each of them for validation purpose.
Evaluation of the window prediction The window prediction phase is a heuristic that does not rely on dynamic programming and may therefore be less accurate than a more time-consuming algorithm. We assess the accuracy of the Vidjil heuristic on our datasets by comparing the location of the detected w-window with the ones predicted by IMGT/HighV-QUEST  [11]  and IgBlast  [15]. Our goal is not to assess the IMGT/HighV-QUEST and IgBlast software, but to verify that the Vidjil's heuristic is sufficiently accurate. Even if ClonalRelate  [37]  is of interest we could not compare to it since it builds upon results provided by iHMMuneAlign, that is specifically dedicated to immunoglobulin heavy chain analysis. We selected two datasets for this comparison: Diag, which contains high redundancy and a lower number of reads; and Scale-10 -5 , which is supposed to have much greater diversity.
IgBlast (version 1. 2. 0) was launched in its stand-alone version. We launched IgBlast using the TRγ germline database downloaded from IMGT/GENE-DB  [40]. The other parameters were left at the default settings. Only the "top segmentation" returned by IgBlast was kept, consisting of the top V and J gene matches. IMGT/HighV-QUEST was launched by specifying the organism (Human) and the locus (TRγ ); by specifying that the sequences originate from a single individual; and by allowing indels. The other parameters were left at the default settings. What was compared among these three tools was the position of the center of the window. IMGT/HighV-QUEST and IgBlast do not give this position, but it can be computed easily from the 3' position of the V region and the 5' position of the J region, which are given.
• The results for the actually sequenced dataset (see Figure  3 ) show that the center of the window predicted by Vidjil differed from those predicted by IMGT/HighV-QUEST and IgBlast by less than 10 positions in more than 97% of cases, and by less than 15 positions in about 99% of cases. Vidjil shows a greater concordance with IgBlast than with IMGT/HighV-QUEST. The reason may be that IMGT/HighV-QUEST is conceived for longer sequences. Our dataset may contain short sequences that Vidjil is also able to process. • As B cells are subject to somatic hypermutations, it is more difficult to segment their sequences. We can assess the robustness of the method against mutations by adding substitutions to our sequenced dataset. In the literature, estimates of the rate of sequence substitutions arising from somatic hypermutation are around 2%  [41, 42].