dataesr/scientific-paragraphs-categorization huggingface dataset:
The value of the breaking point and the values of slope and standard deviation of the A bi-linear regression model was fitted (blue dotted line) for grain yield using the segmented R package  [7]  with default settings. The value of the breaking point and the values of slope and standard deviation of the residuals (RSD) are indicated for each period. Data were from the Agreste database (https://agreste. agriculture. gouv. fr/agreste-web/accueil/ (accessed on 31 October 2021)). Thus, there is a need to speed up genetic progress for yield potential as well as to improve tolerance to biotic and abiotic stresses, which are expected to increase in frequency and intensity as a consequence of climate change. New resources and methods that will help deliver improved varieties within shorter selection cycles (e.
Assembling a New Pre-Breeding Panel for the European Breeding Programs At the beginning of the BREEDWHEAT project, an elite panel (BWP2 panel) was assembled to represent the diversity of cultivated winter wheat in France. The panel was composed of 220 European elite varieties of winter wheat released by the different breeders between the mid-1970s and the early 2010s, 89% of which have been mostly released in France since 2000. This panel was extensively used for genome wide association studies on several agronomic traits (see below,  [17] [18] [19] [20] [21] 61] ). It was decided to select a complementary winter wheat pre-breeding panel representing the worldwide diversity and optimized for association genetics. Out of the 4506 accessions previously characterized, 1340 accessions were referenced as winter type on passport data and were adapted to French growing conditions (based on comparison to standard varieties) regarding plant height and heading dates.
The objective was to design a panel of about 500 accessions for association studies. Two sampling strategies were investigated with the aim to minimize future spurious associations due to structure, and as far as possible with a limited allelic diversity reduction. Both the maximum length sub-tree (MLS) sampling strategy and minimal SD subset (MSDS) sampling strategy were implemented in DARwin software  [62]. The MSDS sample was finally chosen based on lower global LD values and more diverse geographical origins of the accessions. Indeed, the MSDS strategy tries to find a sub-sample that shows the smallest disequilibrium. The procedure is a stepwise algorithm, removing at each step the unit with the greatest contribution to the general disequilibrium between all pairs of loci. This new panel (BWP3 panel) sampled a much larger diversity than the elite panel, as shown with the principal coordinate analysis based on the TaBW280K SNP array (Figure  3 ).
(F) Geographical projection of the first axis of the PCoA for 2210 modern varieties. Biology 2021, 10, x 2 of 30 tree (MLS) sampling strategy and minimal SD subset (MSDS) sampling strategy were implemented in DARwin software  [62]. The MSDS sample was finally chosen based on lower global LD values and more diverse geographical origins of the accessions. Indeed, the MSDS strategy tries to find a sub-sample that shows the smallest disequilibrium. The procedure is a stepwise algorithm, removing at each step the unit with the greatest contribution to the general disequilibrium between all pairs of loci. This new panel (BWP3 panel) sampled a much larger diversity than the elite panel, as shown with the principal coordinate analysis based on the TaBW280K SNP array (Figure  3 ). Finally, set to a size of 450 accessions, this panel was consequently used for association studies.
This detailed analysis of grain subproteomes provides information on how wheat grain storage protein composition can be managed in low-level fertilization conditions. To complete this work, the response to N and S nutrition of albumin-globulin proteome of the grain in development was re-analyzed to establish relationships between the storage proteins and all quantified and identified the albumins-globulins (n = 352) present in all samples  [73]. Both datasets were integrated using methods implemented in Mixomics to find candidate albumins-globulins related to seed storage synthesis. This approach was completed by linkage mapping to identify the candidate albumins-globulins statistically associated with storage proteins. These integrative approaches highlighted 18 albumins-globulins, out of which three were statistically validated by association genetics.
Crop Modeling In the coming decades, plants are expected to be exposed to highly contrasting growth conditions and agricultural practices. In response to climatic and crop management changes, plants will shift their phenotype, which may affect their agronomic performances. Anticipating the plastic responses of plants is crucial to adapt their management and identify future varieties to be grown or bred. Given the large number of varieties and environments to be tested, computational plant modeling could help researchers and breeders identify efficient genotypes for given environments. Such plant models could build upon the large field trials and genotyping data that were recently collected in the BREEDWHEAT program  [18, 21] , allowing to decompose the genotype-by-environment interactions (G × E).
Using a QTL-GBLUP (genomic best linear unbiased prediction) model in an independent dataset, the authors found that the correlation between predicted and observed GS30 and GS55 ranged from 0. 37 to 0. 71 and 0. 74 to 0. 94, respectively. GWAS for GDDpv and GDDp also showed the major impact of photoperiod sensitivity genes (Ppd-D1, Ppd-B1) on the earliness of the tested cultivars. Finally, simulations were performed for every marker-based GDDpv × GDDp combinations (n = 50,451), in order to identify the ideotypes maximizing climatic risk avoidance. They found strong interactions between the tested genotypes, sowing dates, and climate zones. For instance, early genotypes and late sowing should be preferred in oceanic regions with high Mediterranean influence (southwest of France). In contrast, the optimal growth period was overall shorter in semi-continental locations where early genotypes and early sowings should be avoided because of the high frequency of late frosts.
A very high spatial resolution is mandatory when estimating organ level traits because they first need to be identified, requiring a ground sampling distance at least smaller than one fifth the typical organ dimension. Deep learning methods based on convolutional neural networks are now very efficient for segmentation and plant or organ identification and counting. They are currently assessed to score symptoms of diseases targeting specific organs. However, the main drawback of such machine learning methods is the difficulty in obtaining a robust model. It therefore requires a large and diverse training dataset populated by many acquisition sessions with possible differences in ground sampling distance, illumination conditions, growth stages, and background. However, for most of the traits considered in Table  2 , the methods that we developed showed a higher broad sense heritability compared to the traditional phenotyping methods.
The BREEDWHEAT Genomic Selection software, BWGS  [120]  (Figure  5 ), is available as an easy-to-use, standalone R package, with default parameters adapted to the commonly used size of datasets. It includes two methods for missing data imputation, four methods for selecting marker subsets (random, LD-based, GWAS-based), and 14 methods for GEBV prediction (from historical GBLUP to Bayesian radial neural networks). Additionally, two methods are proposed for sampling training subsets: random (useful for teaching purpose) and optimization, according to Rincent et al. [121]. A first function enables carrying out random cross-validation to select the best parameterization and the most predictive method, and a second function to carry out the prediction of GEBV in the target set of lines after designing the best model on the training set.
The source code of BWGS R functions as well as example files and notes are freely available on https://forgemia. inra. fr/umr-gdec/bwgs (accessed on 16 December 2021), and also from the CRAN (https://cran. r-project. org/ (accessed on 16 December 2021)). Figure  6  illustrates the predictive ability of the 14 prediction methods obtained for grain yield by cross-validation on a training population of 760 breeding lines with historical trial data from 2000 to 2014 (unbalanced data). BWGS was primarily developed to estimate single trait GEBV. However, wheat breeding requires simultaneous improvement of several traits, which are often correlated to each other. This is why we also tested methods for multi-trait genomic selection and traitassisted genomic selection  [122]. In this study, an extension of the optimization algorithm from Rincent et al.
[121]  was proposed, and a case study was carried out on bread making quality using data from a real breeding program. Results were presented considering the respective cost of genotyping and phenotyping for both the main and the secondary traits. Such economic parameterization was also used in a virtual selection program using a novel algorithm, whose parameters were co-constructed with BREEDWHEAT breeders. This software enables the breeder to compare realistic breeding schemes of similar cost, with or without a step of genomic pre-selection  [123, 124]. BWGS was primarily developed to estimate single trait GEBV. However, wheat breeding requires simultaneous improvement of several traits, which are often correlated to each other. This is why we also tested methods for multi-trait genomic selection and trait-assisted  We also addressed the endless question of predicting G × E interaction.
Genetic Resources Data Integration The data of 5232 accessions managed by the INRAE Biological Resource Center were integrated in GnpIS. The passport data and the related phenotyping data can be displayed using the FAIDARE (FAIr Data-finder for Agricultural REsearch) web interface. FAIDARE allows one to find data using free text, controlled vocabularies, and identifiers of key scientific resources such as genetic material or phenotyping and environmental traits. It indexes databases around the globe and offers data preview of all those datasets with relevant ontology annotation when available with a link back to the original database.
Genotyping, Phenotyping, and GWAS Data Integration GnpIS core-DB is a hybrid database composed by a relational database (PostgreSQL) that allows for a high level of data integration though well curated pivot data and a NoSQL database (ElasticSearch), which enables fast searchable capabilities. All the SNPs of the Axiom TaBW410K chip (including the TaBW280K first published markers)  [14]  and the corresponding genetic map were integrated in GnpIS. SNP positions on the reference genome and their flanking sequences are available as well as a custom download in a VCF format. Phenotyping data from panel BWP2 (29 trials since 2011), panel BWP3 (11 trials since 2016), and AB-QTL (20 trials since 2017) were integrated in GnpIS following the same trait ontology  [136] , which allows one to search for a variable across all of the BREEDWHEAT phenotyping experiments with download capabilities in standard formats such as MIAPPE and ISA-TAB.
This package was disseminated through national and international training sessions. The objective was also to develop a centralized repository suited to the data generated within BREEDWHEAT and following the FAIR principles. It was specifically designed to manage and link different data types: genomics data, germplasm description, genotyping data, genetic maps, ontologies, phenotyping data, and the results of association studies. The information system was successfully implemented with the data and a dedicated webpage was set up to easily access them. Further works on traits of interest are needed in many areas. Recent future climatic scenarios  [137]  do not predict a change in the trend of increased constraints for crops cultivated in Northern Europe. Average temperature increase will lead to accelerated development rates and shorter growth cycle.
Acknowledgments: The authors are grateful to all members of the  BREEDWHEAT community  from the different institutes and companies:  UMR GDEC (INRAE-UCA ),  UMR GAEL (INRAE-UPMF), UPR BIA (INRAE) ,  US EPGV (INRAE) ,  UMR GQE (INRAE-UPS-CNRS-AgroParisTech) ,  URGI (INRAE) ,  UMR BIOGER (INRAE-AgroParisTech) ,  UME ECOSYS (INRAE-AgroParisTech) ,  UMR Agronomie (INRAE-AgroParisTech) ,  UMR SADAPT (INRAE-AgroParisTech) ,  UMR BFP (INRAE-UB I&II) ,  UPR CNRGV (INRAE) ,  UMR EMMAH (INRAE-Université d'Avignon) ,  LIMOS (UCA) ,  GEVES, ARVALIS Institut du végétal, Biogemma, AgriObtentions, BASF, Caussade Semences ,  Florimond Desprez ,  Limagrain-Europe ,  KWS-Momont, RAGT ,  Secobra Recherche , and  Syngenta. We also thank  INRAE UE GCIE Mons and UE PHACC Clermont-Ferrand. They have participated, over 10 years,.
Funding: This research was part of the Investment for the Future PIA  BREEDWHEAT  project funded by the  French Research National Agency  ( ANR-10-BTBR-03 ),  FranceAgriMer  ( 2013-0544 ), and the  French  fund to support breeding research ( FSOV-2012D ). Experiments benefited from the platforms developed within the  PIA   PHENOME  project funded by the  French Research National Agency  ( ANR-11-INBS-012 ).
Numerical tools AlgId and AlgIso We present in the following the numerical tools AlgId and AlgIso that we developed to help us identifying the algebras of our solutions. These tools are however built for a broader use. The main input of the code AlgId is a set of structure constants of a Lie algebra, as well as its dimension. The first part of the code computes the CS, DS, the Killing form eigenvalues, and its signature. In a second part, these results are compared to a data base of 6d algebras, namely the 16 of table 1 and the 9 of table 2 (without 6 u(1)), and the code indicates any match. These outputs should help identifying the initial algebra. This code is meant to work not only for structure constants taking integer or round values, as e. g. in classification tables, but also when having numerical values as those obtained in our solutions.
This method to determine the linear independence is actually implemented every time a new vector is added to M v : if it is found linearly independent, the dimension of the ideal is increased by one; if the vector is not independent, then it is not added to M v. This is done recursively until one has tested all vectors obtained by all brackets defining the ideal. One obtains in this manner the dimension of the ideal, and builds this way the CS and DS of the algebra considered. The rest of AlgId is straightforward, so let us now present AlgIso. That code aims at finding an isomorphism between two algebras that are specified as input. If one of the algebra depends on parameters, as e. g. some of the tabulated ones, these parameters are automatically turned into variables and the code also searches for appropriate values.
JHEP08(2022)109 g ij = M 2 p                     3 2ρ 2 0 0 0 0 0 2 τ 2 0 0 0 0 0 15 2σ 2 1 - 9 2σ 1 σ 2 - 9 2σ 1 σ 3 0 0 - 9 2σ 1 σ 2 27 2σ 2 2 - 9 2σ 2 σ 3 0 0 - 9 2σ 1 σ 3 - 9 2σ 2 σ 3 27 2σ 2 3                    . (3. 15) The data for the other cases encountered in our solutions is given in appendix C; we refer to the code MSSSp for further cases.
Numerical tool MaxSymSolSpec (MSSSp) The computation of the scalar potential and the field space metric, as described in section 3. 1 and 3. 2, has been automatized in the numerical tool MaxSymSolSpec (MSSSp) that we have developed. The code first takes as input the list of source sets. From this data, the fields (ρ, τ, σ I ) can be defined. A first task is to determine a set of independent fields, and remove the redundant ones. The user can specify a complete list of redundant fields, based for instance on section 3. 2 and appendix C. Otherwise, the code determines such a list by itself. To that end, the field space metric is computed and its rank is checked, row after row, allowing to identify redundant fields. Once a set of independent fields is identified, a proper field space metric is computed, as well as the scalar potential V.
The remainder of cells occurred in areas of low entropy, each often containing cells from just a single patient (Fig. 1A, B ). We next sought to cluster the cells in a manner that recapitulated the two dominant areas of tumor cells with moderate entropy as noted in the UMAP projection. To accomplish this, we first applied PhenoGraph (PG)  7  to define phenotypically distinct clusters among B-cells across the full dataset which yielded 78 separate PG clusters (Fig. 1C  and Supplementary Fig 2). While this level of clustering segregated individual populations of tumor cells with low entropy from one another well, the two dominant areas of moderate entropy appeared to be over-clustered. We thus applied hierarchical clustering of the 78 PG-level clusters to obtain 19 meta-clusters, or MCs (Fig.
Integration of B-and T-cell datasets We next assessed co-occurrence of B with T cell populations using 107 samples for which both B-cell and T-cell CyTOF data were available (73 FL and 34 rLN) and plotted the results in a force-directed map (Fig. 5A ). Normal B-cells and various naïve and CM T-cell subsets formed a dense community that largely excluded tumoral B-cell clusters. The most populated B-cell PG cluster B01 (and major PG cluster in MC-A) significantly co-occurred with terminally differentiated (CD57 + ) subsets of Th1 and CD8EM T-cells (clusters T37 and T86, respectively; highlighted in Fig. 4A ). The second most populated B-cell PG cluster B02 (and major PG cluster in MC-B) significantly cooccurred with a CD57-, cytokine-rich subset of Tfh T-cells (cluster T10; highlighted in Fig.
Interestingly, types A vs. B vs. NOS showed significantly different risks of transformation (Fig. 7A ) with type B showing the highest cumulative risk. Outcomes of individual MC types within the NOS group (i. e. , C, D, E, F, etc. ) were varied but difficult to assess due to limited numbers of these cases (Supplementary Fig. 19 ). We also looked at intratumoral phenotypic diversity among malignant B-cells (measured as entropy; Fig. 3C ) and found it to be significantly  correlated with risk of transformation (Supplementary Fig. 20 ). In multivariate analyses with FLIPI group  29, 30  , both MC type B and entropy remained significant (Fig. 7B ). As noted above, panel sequencing did not reveal any significant mutational associations with type B tumors, and this dataset was similarly non-informative for intratumoral entropy.
After antibody staining was completed, cells were fixed with methanol and stained with Cell-ID Intercalator-Ir dye (Fluidigm cat#201192) and prepared for CyTOF acquisition according to the manufacturer's protocols. Cells were acquired on a CyTOF2 instrument (Fluidigm) equipped with Super Sampler (Victorian Airship). Typically, ~50,000 live B-or T-cell events per sample were obtained. CyTOF data pre-processing FCS files from each acquisition batch were concatenated using CyTOF Software (v6. 0. 626, Fludigm) and normalized with spiked-in EQ Four Element Calibration beads (Fludigm cat#201078) using Normalizer (v0. 3)  45  from MATLAB (v8. 6). Bead-normalized FCS files were then imported to MATLAB-based Single-cell Debarcoder (https://github. com/zunderlab/single-cell-debarcoder)  46  which generates sampleassigned FCS files with two debarcoding parameters (separation and mahalanobis distances).
Dimensional reduction We chose UMAP dimensional reduction tool 6 from umap-learn (v0. 3-v0. 4) package to visualize single-cell data with the following settings: minimal distance (md) =0. 4 and nearest neighbor number (nn) = 30. t-SNE was also used in some analyses, mostly for QC assessment. We used the Barnes-Hut Stochastic Neighbor Embedding (bh-SNE implementation)  49  available in Rtsne (v0. 15) R package. Markers used for dimensional reduction and clustering analyses are indicated in Supplementary Data 9. We replaced the two Ig light chain markers, kappa and lambda, with a single anonymized Ig light chain marker, "KL", using the higher value from either kappa or lambda.
Unsupervised clustering of B-cell data To accommodate computational limitations, typically ~5000 non-T cell (CD3-negative) events were randomly subsetted from each patient sample, then concatenated into a single data matrix containing ~900,000 total cell events. We then performed clustering using the graph-based algorithm PhenoGraph (v1. 5. 2; Python package) on the same data matrix as for dimensional reduction with nn = 100, which was used previously on a dataset of similar size  50. To minimize stochasticity of the clustering algorithm, we first performed 25 iterations of PhenoGraph clustering. We then then applied the Adjusted Rand Index (ARI)  51  and Normalized Mutual Information (NMI)  52  measures to generate pair-wise similarity scores and plotted the resulting 25 sets of 24 values each.
We defined the consensus cluster assignment as the set of results with highest average ARI and NMI scores (ARI: 0. 96, NMI: 0. 95). ARI and NMI scores were calculated using functions from aricode (v1. 0. 0) R packages. To reduce noise from rare cell events, we retained only those cells that were assigned to Phenograph clusters containing at least 1% of total non-T cells in each respective sample. Following Phenograph clustering, all output clusters containing verifiable CD19+, CD20+, and/or CD22 + B-cells were meta-clustered by hierarchical clustering using the hclust function in R. We then selected 19 meta-clusters as optimal based on the gap statistic  8.
Inter-sample entropy calculation We used Shannon entropy to help identify phenotypically similar cells across samples. For a dataset with n = 191 samples, a K-NN graph (K = n -1) was constructed to find the K nearest neighbors, followed by calculating the proportion (p m ) of each sample among the K nearest neighbors. Then we calculated the inter-sample entropy score for each cell as - P 190 m = 1 p m Á logðp m Þ.
05). Additional adjustment for multiple testing was not performed as the numbers of possible cluster pairs and total samples did not together exceed those modeled using simulated random data to assess for Type I errors in the originally reported method  55. The ForceAtlas algorithm in Gephi (v0. 9. 2) was then used to graph significant positive and negative associations between nodes. Since ForceAtlas does not accept negative values, association scores were transformed using the exponential function, e. To assess mutual exclusivity of MC-A and MC-B type cells within individual samples, we examined all available data events up to 50,000 non-T cells per sample. We applied the 1% threshold for presence/ absence as above but calculated as a fraction of total malignant B-cells within the sample.
Immunohistochemistry Formalin-fixed, paraffin-embedded (FFPE) serial whole tissue sections (4 um thickness) from diagnostic FL biopsies were stained with antibodies against CD3 (polyclonal, Dako cat#GA50361-2), CD57 (clone TB01, Dako cat#GA64761-2), or PD1 (clone NAT105, Cell Marque cat#315 M) after antigen retrieval at 97C with high pH for 20 min on a Dako Omnis automated slide-staining system. Whole slide images were acquired using a MoticEasyScan Pro digital slide scanner (x40 magnification, standard mode) and viewed using Aperio ImageScope (v12. 4. 3. 5008) software.