dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We are grateful to the  ESA  spacecraft operations team for supporting and planning the scientific observations during BepiColombo cruise and orbital mission. A. G. and L. I. thank  A. Di Ruscio  ( Sapienza University of Rome ) for his support in the numerical simulations of the MORE investigation. A. G. and L. I. acknowledge funding from the  Italian Space Agency (ASI)  grant  N. 2017-40-H. 0. T. V. H. was financially supported by the  Belgian Research Action through Interdisciplinary Networks (BRAIN. be2. 0 project STEM) and by the  Belgian PRODEX program  managed by the  European Space Agency  in collaboration with the  Belgian Federal Science Policy Office. D. H. was financially supported by the  German Ministerium für Wirtschaft und Energie  and the  German Zentrum für Luft-und Raumfahrt  under contract  50 QW 1501.
recovered a global estimation of the planetary ephemerides including other mission datasets (e. g. , Mars Reconnaissance Orbiter) with conservative a priori information MESSENGER BepiColombo MORE Estimated values Errors due to Imperi et al. (2018) De Marchi and ± 1 -σ planetary (1 -σ ) Cascioli (2020) ephemerides (1 -σ ) GM [km 3 s -2 ] 132712440042. 2565±0. 35 0. 87 0. 05 0. 08 J 2 (×10 -7 ) 2. 246±0. 02 0. 02 0. 01 0. 03 Ġ M /GM -6. 130±1. 47 3. 14 2. 8* 0. 92 (×10 -14 per year) η (×10 -5 ) -6. 646±7. 2 6. 24 0. 3 6. 9 β -1 (×10 -5 ) -1. 625±1. 8 1. 57 0. 1 1. 7 γ -1 (×10 -5 ) - - 0. 1 0. 1 α 1 (×10 -5 ) - - 0. 06 0. 034 α 2 (×10 -5 ) - - 0. 01 0. 007 * This solution is based on BepiColombo only. The combination of multiple datasets enables significant enhancements of Ġ M /GM estimate which is known as Nordtvedt equation.
In this context, this work presents for the first time a combination of omics data to determine the molecular mechanisms involved in rubber tree growth. For this task, we used a breeding population to infer QTLs using a GWAS approach. These results were incorporated into network analyzes based on RNA-Seq and enzymatic networks. By using this multiomics framework, our study supplies important cues on the interconnection of the metabolic mechanisms of rubber tree growth, providing novel growth-associated genes for future research on increasing Hevea production.
Phenotypic Analyses As the main characteristic evaluated in rubber tree genetic breeding  (Rao and Kole, 2016) , SD was measured in the selected population during the first 4 years of genotype development. Each plant was individually phenotyped (in centimeters) at a height of 50 cm from the soil in two seasons with contrasting average rainfall (low precipitation and high precipitation), which are considered in Hevea studies as contrasting environments  (Chanroj et al. , 2017; Souza et al. , 2019). The variance caused by the genotypic effects was estimated using the best linear unbiased predictor (BLUP) with the breedR package in R  (Munõz and Sanchez, 2017). The linear mixed model was as follows: y X X X X Z Z B R W M G g w b r w m g = + + + + + + + m s e 2 , where y is the vector of the phenotypic measures; m is the trait mean; and X B , X R , X W , and X M are the incidence matrices for the fixed effects of blocks (b), replicates (r), water levels (w) and month of the measurement (m), respectively.
Genotypic Analyses The extraction of genomic DNA was performed according to  Souza et al. (2013)  and  Conson et al. (2018). Genotyping-bysequencing (GBS) libraries were prepared from genomic DNA using the method proposed by  Elshire et al. (2011). Initially, the genomic DNA of each sample was digested using the methylationsensitive enzyme EcoT22I to reduce the genomic complexity. The resulting fragments of each sample were linked to specific barcodes and combined in pools. These fragments were amplified by PCR and sequenced. Sequencing of the PR255 x RRIM217 population was performed using the Illumina HiSeq platform, and sequencing of the GT1 x RRIM701 and GT1 x PB235 populations was performed with the GAIIx platform (Illumina Inc. , San Diego, CA, United States). Processing of the GBS data from both experiments was carried out at the same time.
SNPs were identified with TASSEL GBS 5 software  (Glaubitz et al. , 2014)  using the following parameters: (i) k-mer size of 64 bp; (ii) minimum read quality (Q) score of 20; and (iii) minimum locus depth of six reads. Reads were aligned with the rubber tree reference genome proposed by  Liu et al. (2020a)  using Bowtie2 version 2. 1 software  (Langmead and Salzberg, 2012)  with the very sensitive option. We only kept the biallelic markers selected with the VCFtools program  (Danecek et al. , 2011). Using snpReady software  (Granato and Fritsche-Neto, 2018) , SNPs with more than 20% missing data and minimum allele frequency (MAF) <0. 05 were filtered out. Imputation was performed using the k-nearest neighbor imputation (kNNI) algorithm  (Hastie et al. , 2001). LD estimations were calculated with the ldsep R package  (Gerard, 2020)  based on the squared Pearson correlation (R 2 ).
Genome-Wide Association Studies GWAS were performed using the Fixed and random model Circulating Probability Unification (FarmCPU) method implemented in the FarmCPU R package  (Liu et al. , 2016). This method tests the association of markers as fixed and random effects in a mixed linear model in separate steps  (Liu et al. , 2016). The kinship matrix and the first two principal components (PC1 and PC2) from a principal component analysis (PCA) were used as covariables in the mixed linear model to control the effects caused by the population structure  (Challa and Neelapu, 2018). The significance threshold used for the association mapping was calculated based on 30 SD permutations and a 95% quantile value. Additionally, we expanded the set of putatively associated markers through LD. Considering a minimum R 2 of 0.
The 24 samples (three replicates per sample at each time) were randomly pooled (four samples per pool) and grouped using the TruSeq Paired-End Reads Cluster Kit on the cBot platform (Illumina Inc. , San Diego, CA, United States). The cDNA libraries were posteriorly sequenced on the Illumina Genome platform Analyzer IIx with a TruSeq kit with 36 cycles (Illumina, San Diego, CA, United States) for 72 bp paired-end reads. RNA-Seq barcodes were removed from FastQ files using Fastx-Tookit, 1 and raw reads were filtered using the program NGS QC Toolkit 2. 3  (Trivedi et al. , 2014) , keeping only sequences with a minimum Q-score of 20 across at least 70% of the sequence length. The filtered sequences were combined with bark reads  (Mantello et al. , 2014)  and mapped to the reference genome of H.
brasiliensis  (Tang et al. , 2016)  using the HISAT2 aligner  (Kim et al. , 2015). The alignment was ordered and assembled using SAMtools  (Li et al. , 2009)  and Trinity  (Grabherr et al. , 2011)  software, respectively. Hevea brasiliensis scaffolds  (Tang et al. , 2016)  were submitted for ab initio annotation using the Maker-P  (Campbell et al. , 2014)  tool. The Trinity assembled transcripts and the Maker-P annotations were combined with nonredundant H. brasiliensis ESTs in the NCBI database (August 2016) and used as a database for aligning 1 http://hannonlab. cshl. edu/fastx_toolkit/index. html assemblies against the H. brasiliensis genome  (Tang et al. , 2016)  with the PASA v2. 0 pipeline  (Haas et al. , 2003)  after removing redundant alternate splicing data. The obtained transcripts were filtered with a minimum size of 500 bp and evidence of transcription; we excluded sequences that were only predicted by ab initio genome annotation and with high identity for nonplant transcripts.
To estimate the physical position of these sequences across Hevea chromosomes, we performed comparative alignments of these transcripts against the H. brasiliensis genome proposed by  Liu et al. (2020a)  using BLASTn  (Johnson et al. , 2008). The annotation of these transcripts was performed using the Trinotate v3. 2. 1 program  (Haas, 2015)  and SwissProt database (downloaded in February 2021;  Boeckmann et al. , 2003).
Gene-Associated Markers The analysis of candidate genes in QTL regions was performed based on transcript annotations. Candidate genes for the phenotypic variation of GWAS-discovered SNPs were considered by using the first transcripts positioned in the upstream and downstream regions of these markers. In addition to the SNPs significantly associated with the phenotype discovered by the GWAS, which we will call snpsGWAS here, we also searched for candidate genes in the neighboring snpsLD. The GO terms associated with these annotations (snpsGWAS and snpsLD) were investigated using REVIGO  (Supek et al. , 2011). The genomic regions of the phenotypically associated SNPs discovered in this work were compared with the QTLs discovered by  Conson et al. (2018)  from the mapping population GT1 x RRIM701.
For this analysis, the sequences underlying the QTLs  (Conson et al. , 2018)  were aligned to the reference genome of  Liu et al. (2020a)  using BLASTn. Alignments with identity above 90% and with the largest coverage area were selected (minimum e-value of e-10). Based on the position of this alignment in relation to the reference genome of  Liu et al. (2020a) , a representation of the 18 chromosomes of H. brasiliensis was made using snpsGWAS, snpsLD and QTLs  (Conson et al. , 2018)  using the MapChart program v. 2. 2  (Voorrips, 2002).
Coexpression Networks For modeling coexpression networks, we used RNA-Seq count data grouped into transcript clusters through PASA v2. 0 software  (Haas et al. , 2003). Only transcripts with at least 10 counts per million (CPM) were retained and normalized with a quantile-based approach implemented in the edgeR package in R  (Robinson et al. , 2010). Weighted gene correlation analysis (WGCNA) was performed using the WGCNA R package  (Langfelder and Horvath, 2008)  together with Pearson correlation coefficients. A soft thresholding power β-value was estimated for fitting the network into a scale-free topology, and a topological overlap measure (TOM) for each gene pair was used for building a dissimilarity matrix and for performing unweighted pair group method with arithmetic mean (UPGMA) hierarchical clustering.
The best clustering scheme was defined using a variable height pruning technique implemented in the Dynamic Tree Cut R package  (Langfelder et al. , 2008). The groups containing genes associated with snpsGWAS were used to model a specific coexpression network using the igraph R package  (Csardi and Nepusz, 2006)  with Pearson correlation coefficients (minimum R value of 0. 5), where we calculated the hub scores for each gene considering Kleinberg's hub centrality scores  (Kleinberg, 1999).
Metabolic Network Modeling From the annotations performed for genes surrounding the snpsGWAS and the snpsLD, we retrieved the enzyme commission (EC) numbers and investigated the related metabolic pathways using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database  (Kanehisa and Goto, 2000). All the H. brasiliensis metabolic pathways with enzymes related to snpsGWAS and snpsLD were retrieved and used to model a metabolic network using BioPython v. 1. 78 (Cock et al. , 2009). From the created network, we evaluate the following topological properties: (i) degree  (Barabási and Oltvai, 2004 ), (ii) betweenness centrality  (Brandes, 2001) , (iii) stress  (Brandes, 2001) , (iv) short path length value  (Watts and Strogatz, 1998) , and (v) neighborhood connectivity  (Maslov and Sneppen, 2002) , using Cytoscape v3.
By employing the TASSEL pipeline, we produced 363,641 tags, which were aligned with the Hevea reference genome, producing an alignment rate of ~84. 78%. We identified a total of 107,466 SNPs, which were filtered, resulting in a total of 30,266 high-quality markers (~28. 16%), with an imputation rate of ~6. 74%. This filtered SNP dataset was used for PCA, with 18. 33 and 2. 61% of the variance explained by the first two main components, respectively (Supplementary Figure  2 ). Although high LD decay was observed (Supplementary Figure  3A ), we also assessed the LD decay rate only in the regions containing transposable elements (TEs; Supplementary Figure  3B ), which was higher.
RNA-Seq Analyses A total of ~530 million and ~633 million paired-end (PE) reads were obtained for the RRIM600 and GT1 genotypes, respectively. After quality filtering, we obtained ~933 million PE reads for assembling the transcripts through Trinity software. We identified 104,738 transcripts ranging from 500 to 22,333 bp (average transcript size of 1,874 bp and N50 of 2,369 bp) that were related to 49,304 genes. In total, 82,629 transcripts (78. 89%) could be annotated using the Swiss-Prot database. We were able to associate Gene Ontology (GO) categories with 81,095 transcripts (77. 42%) and metabolic pathways from the KEGG database with 74,668 transcripts (71. 29%). A total of 11,150 different proteins could be associated with the estimated set of genes for rubber trees, with a high incidence of TEs; the retrovirus-related Pol polyprotein from transposon RE1 (RE1) (4.
, 2018). We were able to identify SNPs with small distances from QTL regions (Figure  3 ). To infer the associations between the set of SNPs (snpsGWAS and snpsLD) and expressed genomic regions, we performed comparative alignments of the transcripts assembled to the rubber tree chromosomes. SNPs were assigned to the first genes that were downstream and upstream of their location with an average distance of 7 kbp (Supplementary Table  2 ). Among the snpsLD, genes related to the transcription of important proteins involved in different stresses were found, such as TNT 1-94, receptor-like protein EIX2, integrin-linked protein kinase 1, U1 small nuclear ribonucleoprotein 70 kDa, histidine-containing phosphotransfer protein 2, rhomboid-like protein 14, and mitochondrial and threonine-protein kinase STN7.
All these genes formed a unique interaction network with weaker interactions connecting the found groups, which putatively represents the direct and indirect molecular associations with the SD phenotype. For the analysis of all reactions triggered by the genomic regions associated with GWAS, we evaluated this set of 1,528 genes for related GO terms (Figure  6 ). From the biological process category, we found new GO terms not associated with the genes related to snpsGWAS and snpsLD. These GO terms included defense response, positive regulation of transcription, cell wall organization, photosynthesis, cell division, mitotic cell cycle phase transition, carbon fixation, cell population proliferation, asymmetric cell division, and stomatal closure. Regarding the genes found in these modules, as also observed in the general transcriptome profile, we observed a predominance of genes related to the protein retrovirus-related Pol polyprotein from transposon 17.
Metabolic Networks Due to the clear absence of functional annotations, all the genes identified in the coexpressed modules with a known enzymatic activity relatedness were used for modeling a metabolic network using the KEGG database. In this structure, each enzyme corresponds to a node, and their connections are based on metabolic interactions. Nineteen genes were related to 19 different enzymes present in 28 metabolic pathways (Supplementary Table  4 ). All these reactions were joined into a unique network structure containing 405 nodes (enzymes) and 1,311 edges (average number of 5. 338 neighbors and diameter of 22 nodes; Figure  7A ; Supplementary Figure  6 ), representing a diverse cascade of mechanisms with putative associations with plant growth. Network topology measurements were performed to identify the most important enzymes in the modeled mechanisms.
4. 1. 4) (18 connections) FIGURE 5 | Coexpression network containing the SNP gene modules discovered by GWAS. Yellow shows the genes annotated for the snpsGWAS, blue shows the genes annotated for the snpsLD and gray shows the genes identified in the modules. The highlighted genes with a red border represent the 10 hubs with the most connectivity, while the size of the nodes shows the number of connected genes. and malate dehydrogenase (oxaloacetate-decarboxylating) (NADP+) (ec: 1. 1. 1. 40) (15 connections). Among the 17 hubs, pyruvate kinase (ec: 2. 7. 1. 40) also presented high values for other centrality measures (betweenness and stress). Additionally, the enzyme threonine synthase (ec: 4. 2. 3. 1) showed the highest short path length value (14. 30) and the highest eccentricity value (  22 ), and the glucuronokinase enzyme (ec: 2.
, 2012). Additionally, for going further in the establishment of putative genes related to QTLs, we integrated these results with co-expression network analyses based on RNA-Seq data. This approach has been shown to be effective in several species  (Calabrese et al. , 2017; Schaefer et al. , 2018; Yan et al. , 2020) , but with a restricted use in non-model plant species. Different from establishing a genomic window surrounding these markers and performing comparative alignments against plant databases  (Chanroj et al. , 2017; García-Fernández et al. , 2021) , we used an assembled transcriptome for the association of the snpsGWAS. This step was performed mainly because of the absence of available data for several neglected species in public databases  (Schaefer et al. , 2018) , such as H. brasiliensis.
, 2015; Sathik et al. , 2018; Mantello et al. , 2019; Deng et al. , 2018; Ding et al. , 2020). Such studies in rubber trees are still incipient and have not yet been coupled with breeding strategies for the genetic improvement of the species. Starting from RNA-Seq-based data, we could associate our GWAS results with expression profiles from important Hevea genotypes, incorporating our results into a complete set of molecular interactions estimated through the WGCNA approach. Using this strategy, we can infer biological functions for genes present in the same network module, as these genes probably exert correlated functions  (Childs et al. , 2011). This is the first initiative that proposes the integration of GWAS and coexpression networks in rubber trees to identify genes with great potential to be used in MAS.
The transcriptome used for annotation and construction of the coexpression network showed a large number of TEs, which are indeed present in large amounts in plant genomes  (Matsunaga et al. , 2015). In addition, these TEs were also found to be abundant in the selected functional modules, with TE 17. 6 and TNT 1-94 being the most prominent. These TEs have already been described as being involved in gene expression, responses to external stimuli and plant development  (Kashkush et al. , 2003; Matsunaga et al. , 2015; Traylor-Knowles et al. , 2017; Tran and Choi, 2020). In rubber trees, TEs may be related to the differential expression observed in some commercial clones, affecting important processes such as rubber production  (Wu et al. , 2020a). As pointed out by  Wang et al. (2020) , the identification of TEs associated with functional genes related to important characteristics suggest that they can be used as molecular markers in MAS, contributing significantly to the genetic improvement of woody trees.
FUNDING The authors gratefully acknowledge the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) for Ph. D. fellowship to FF (18/18985-7) and AA (2019/03232-6); the Coordenação de Aperfeiçoamento do Pessoal de Nível Superior (CAPES) for financial support (Computational Biology Program and CAPES-Agropolis Program); and the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for research fellowships to AS and PG.
BVPy is a python library to easily implement and study numerically Boundary Value Problems (BVPs) and Initial Boundary Value Problems (IBVPs) through the Finite Element Method (FEM). BVPy proposes an intuitive Application Programming Interface (API) to harness and combine the core functionalities of three powerful libraries: FEniCS  (Logg et al. , 2012)  provides the core data structures and solving algorithms; Gmsh (Geuzaine & Remacle, 2009) defines the domains and their meshing; and Meshio  (Schlömer et al. , 2020)  handles data reading and writing. Initially built in the context of developmental biology and morphomechanics, its purpose is to enable all users, even with little to none experience in FEM, to quickly and efficiently estimate the behavior of a wide variety of fields (scalars, vectors, tensors) on biologically relevant structures, inspired by biophysical and biochemical processes (morphogene patterning, active matter mechanics, active transports…).
State of the field Although an exhaustive inventory of the existing FEM solutions is far beyond the reach of this paper, it is worth mentioning a few existing alternatives; especially in the context of computational morphomechanics, in which BVPy has been developed. Some FEM-frameworks provide GUI such as Sofa  (Faure et al. , 2012) , FEBio  (Maas et al. , 2012)  or MorphoMechanX (see  (Sapala et al. , 2018)  for example of use) the yet-to-be published add-on to the MorphoGraphX software  (Reuille et al. , 2015). While such userfriendly solutions are accessible to a wide range of users; their "monolithic" construction reduces versatility and prevents more experienced users to tune them at will. Moreover, their code sources are not always freely available. Besides these GUI-based solutions, some "lighter" and more open frameworks, such as FREEFEM  (Hecht, 2012)  or FEniCS  (Logg et al.
, 2012)  are also available. Such solutions appear more versatile and opensource. But their use is usually restricted to people already familiar with the theory of Finite Elements. A gap currently exists between "closed," userfriendly, softwares on one hand and "open," technical frameworks on the other. BVPy attempts to fill this gap. Our strategy was to harness FEniCS richness into an intuitive and evolutive API. To that end, the library architecture maps the conceptual mathematical components of BVPs to abstract classes built on FEniCS and Gmsh core components (see the library general description below). From these abstract classes, experienced users can implement concrete classes, dedicated to their very specific need. Once implemented, these concrete classes can easily be instantiated and combined in a manner that do not require specific FEM-related skills (see the Example of use below).
Organization BVPy has been developed and organized as close as possible to the BVP and IBVP mathematical formulations given in Equation  1 and Equation  2. The library is built around the following key components, see Figure  1 : • Two main classes, named BVP and IBVP, that respectively encapsulate FEniCS implementations of Equation 1 & Equation 2. • Three main modules, bvpy. domains, bvpy. vforms and bvpy. boundary_conditions emulating the corresponding mathematical components of a bvp. • A bvpy. solvers module where a collection of FEniCS-based linear and non-linear solvers are available. Besides these main components, the bvpy. utils module gathers some useful "housekeeping" functions regrouped thematically into sub-modules, e. g. visu, io… See online documentation.
Example of use The following example is loosely inspired by  (Zhao et al. , 2020). The idea is to estimate the mechanical stress distribution within a 2D cross section of a pressurized plant tissue with heterogeneous rigidity. The goal here is to illustrate how parsimonious, intuitive and yet insightful, BVPy simulations can be. The recorded results can be processed by third-party software, e. g. Figure  2  shows the visualization of the corresponding stress field within the Paraview software. More examples are available in the tutorial section of the online documentation.