dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2019). Transcriptomic datasets from young, male and female Amborella flower buds have also recently been generated  (Käfer et al. , 2022). Here, we contribute the first detailed transcriptomic study of Amborella female flower development, performed using Laser Assisted Microdissection (LAM). We use the data obtained to address the two following questions. Firstly, can transcriptomic data be used to identify entire genetic modules whose expression in plant tissues has been conserved over deep evolutionary time? Secondly, to what extent can transcriptomic datasets, generated in independent studies, be usefully compared in plant evo-devo analyses? As a pilot study, we have applied Weighted Gene Coexpression Network Analysis (WGCNA;  Langfelder & Horvath, 2008)  to our own and published transcriptomic datasets from Amborella, and to comparable published datasets from the model angiosperm Arabidopsis thaliana.
Laser-Assisted Microdissection Amborella female flower buds were fixed, embedded, sectioned and subjected to LAM procedures on a PALM Micro-Beam system (Zeiss) as described by  Sakai et al. (2018). Approximately ten tissue sections were combined for each sample. RNA was extracted from these using an Arcturus Pico RNA Extraction Kit (Thermo Fisher Scientific). RNA integrity was assessed using a 2100 BioAnalyzer (Agilent Technologies) and samples showing RNA Integrity Numbers (RINs) of >7. 0 were processed for library construction. Library construction, sequencing, read mapping and data normalization cDNA syntheses were performed from Amborella RNA samples using the SMARTer Ultra Low Input RNA Kit for Sequencing (Clontech) and libraries were prepared according to Illumina DNA Sample Preparation kit instructions.
Libraries were checked for quality using a 2100 Bioanalyzer and pooled in groups of five using a barcoding system to be processed in three runs of Hi-seq2000 (Illumina) paired-end high-throughput sequencing. Subsequent steps were performed on the data obtained from these sequencing runs, on datasets from Amborella leaves, roots and mature tepals  (Flores-Tornero et al. , 2019) , and on datasets from young Amborella flower buds  (Käfer et al. , 2022). Read quality was assessed using FastQC (https://www. bioinformatics. babraham. ac. uk/projects/fastqc/ ). Adapters, sequencing artifacts and low quality regions of reads were trimmed using Cutadapt v1. 16  (Martin, 2011). SNPs between the reference genome and reads were identified using STAR mapper v2. 7. 3a  (Dobin et al. , 2013)  and reads were then mapped to the Amborella V1 complete genome sequence (Amborella Genome Project, 2013) using HISAT2 v2.
2. 1  (Kim et al. , 2019)  and the previously identified SNPs. Splicing sites were first identified by mapping all reads, and then used for per-sample mapping. Raw readsper-gene were counted using FeatureCounts v1. 5. 1  (Liao et al. , 2014). Subsequent steps were performed on the Amborella read-count data obtained as described above, and on published read-count data obtained in duplicate from Arabidopsis flower and vegetative tissues  (Klepikova et al. , 2016). Arabidopsis datasets were chosen to be broadly equivalent to the Amborella datasets included in our analyses, including leaf, root and flower tissues, with particular emphasis on female reproductive organs. Data from both species were normalized using DEseq2 (Love  et al. , 2014)  and subjected to batch-effect removal using limma  (Ritchie et al.
Gene expression clustering Gene clustering was performed in parallel on Amborella and Arabidopsis datasets using the WGCNA package  (Langfelder & Horvath, 2008). All datasets were used in clustering analyses, except for five male and four female Amborella flower-bud datasets, and one dataset from each of two Amborella laser-microdissected sampletypes, as these did not group with the majority of the corresponding replicates during the WGCNA data-cleaning procedures (Supplementary Figs  S1-S2 ). Clustering was performed using the WGCNA automated method using default settings, except that a signed (instead of the default unsigned) network topology was chosen, and the maximum block size parameter was set to 30 000, i. e. greater than the number of genes under analysis in each species. Gene expression modules of a minimum size of 30 genes were generated from dendrograms at a cut-height of 0.
35. The R-codes used for these WGCNA data cleaning and clustering steps are given in Supplementary Protocol S1. The Amborella and Arabidopsis modules generated using WGCNA were processed into return-separated lists of gene-identifiers using Perl-Code-1 (Supplementary Protocol S1), and these were then converted to orthogroup identifiers using Perl-Code-2 (Supplementary Protocol S1), which also takes as input a mapping file in. csv format, derived from the gene-orthology relationships obtained using PHYTOZOME (Supplementary Tab. S1). Orthogroup members in Amborella and Arabidopsis modules were then compared to provide input for a series of 234 hypergeometric tests to determine the significance of all possible inter-species module intersections (for 13 Amborella x 18 Arabidopsis modules). Lists of orthogroups, generated using Perl-Code-2, were first compared between species using Perl-Code-3 (SI Methods), which lists and counts the Amborella orthogroup members present in each module intersection (= sample successes, q).
Gene annotation data were added to these lists for the first-listed Arabidopsis gene in each orthogroup from V9 of the Arabidopsis genome, obtained from TAIR (https://www. arabidopsis. org/). GO-term gene-function analyses were then performed on these lists using PANTHER, accessed via TAIR. As a complement to GO-term analyses, gene annotations were also searched semantically using a custom list of search terms (Perl-Code-7). The full list of Arabidopsis genes from the nine most highly conserved module intersections was also searched using Perl-Code-8 (SI Methods) for a list of 92 genes of particular interest, known to control aspects of female reproductive development.
S2). These data, which constitute the first detailed transcriptomic study of Amborella female flower buds, add considerably to the transcriptomic resources available from this species, the probable sister to all other living angiosperms. Comparisons between Amborella and Arabidopsis identify nine major geneexpression modules conserved since the MRCA of living flowering plants Gene-clustering analyses using WGCNA from normalized read counts (Supplementary Tabs S3 and S4) yielded totals of 19 and 16 expression modules (AtrME1-13 and AthME1-19) from Amborella and Arabidopsis transcriptomic datasets, respectively (Supplementary Figs S3 and S4, and Tab. S5). We compared the orthogroup-content of these modules pairwise between species (Supplementary Tab. S6) and tested the significance of all possible module intersections using the hypergeometric test (Supplementary Tab.
S7). The nine most significant of these intersections, which we term Conserved Modules A to I (CM-A to -I), contain between 205 and 1324 orthogroups, with p-values ranging from 5. 97x10 -13 to 1. 50x10 -89 (Tab. 1). We then used the WGCNA package to obtain eigengene values that characterized the expression of the 13 Amborella and 18 Arabidopsis modules identified (Supplementary Tab. S8). Module eigengene values are defined as the first rightsingular vector of the standardized module expression data  (Langfelder and Horvath, 2007) , and can take positive or negative values representing highest-to-lowest relative expression levels in each experimental sample or replicate. Effectively, the We compiled lists of the Arabidopsis and Amborella genes in each highly significant module intersection, together with their orthogroup identifiers (Supplementary Tab.
S9). We then performed GO-term enrichment analyses on the Arabidopsis genes from these conserved modules, the results of which are shown in Supplementary Tab. S10. Lists of tissue-/process-specific GO-terms were extracted from these data by searching using the key-words listed in Supplementary Tab. S10. The results of this analysis, shown in Fig. 4 , indicate clear tissue-related specializations for seven of the nine ancient modules identified, in good agreement with the tissues in which these modules were putatively upregulated in the MRCA of living angiosperms (Fig. 3 ). Accordingly, over-represented genes in CM-A and -H appear to function principally in photosynthesis, in agreement with the strong upregulation of these two modules in leaf tissues. CM-C, and -D appear to make the most significant contribution to reproductive processes, while these two modules, together with CM-I, also show the greatest levels of enrichment for genes involved in reproductive development.
Accordingly, the members of the >5000 orthogroups (Tab. 1) contained in these nine intersections can be considered to have conserved major aspects of their expression profiles in both the Amborella and Arabidopsis lineages since the MRCA of living angiosperms. These genes include around a third of a sample of 61 genes known to be involved in the control of female reproductive development in Arabidopsis (Tab. 2, Supplementary Tab. S11). These results represent an initial proof-of-concept, giving a positive answer to the first of the questions we asked at the outset of this work concerning the feasibility of comparing gene-expression clustering data over vast phylogenetic distances. A combination of GO-term and custom semantic analyses showed clear functional specializations for seven of the conserved modules identified, while CM-E and -F appeared to show somewhat less-specific roles and expression patterns (Figs 3 and 5, Supplementary Tab.
1, Fig. 3 , Supplementary Tab. S6), have been highly expressed in female reproductive tissues since the MRCA of living angiosperms. Analyses of genome structure indicate the Amborella genome to have undergone no whole genome duplication since the At-ε whi h d before the radiation of living angiosperms  (Jiao et al. , 2011). In the Arabidopsis genome, by contrast, there is evidence of one genome triplication (At-γ) w g i i (A - α A -β) h h k Th i arge-scale duplication events in the Arabidopsis lineage inevitably render gene-orthology relationships with Amborella more complex than with a much more closely related species, such as another member of Brassicaceae. Nonetheless, the use of a genome-wide analysis of orthology relationships has permitted, in the present study, a meaningful comparison of global transcriptome data between species whose lineages diverged at the dawn of the angiosperm clade.
How useful are datasets derived from independent transcriptomic analyses? Clearly, it is very convenient for practitioners of evo-devo to be able to use published datasets from other workers, in addition to their own novel data. Accordingly, the second question we asked at the outset of this study concerned the usefulness of comparing datasets from different sources, which by definition were not generated as part of a unified experimental design. The answer to this question, from the results presented here, seems to be that such datasets can be usefully employed, at least if care is taken to match tissues and developmental stages as closely as possible. Several of the nine most highly significant module-intersections identified in our study showed deviations from a simple 1:1 relationship between species.
For example, the development of female reproductive organs such as carpels and ovules clearly integrates genes from several ancient modules, some of which are also highly expressed in vegetative organs (Tabs 1 and 2, Fig. 3 ). Although many plant transcriptomes have now been assembled  (Leebens-Mack et al. , 2019) , the availability of large-scale gene-expression datasets that focus on distinct organs, tissues and developmental stages continues to lag considerably behind in all but a few model species. It is likely, however, with techniques such as the LAM method employed in the present work, and also the very promising singlecell RNA-sequencing technologies (e. g. Ryu et al. , 2019) , that detailed large-scale expression data will soon become available from a much wider range of plant species.
Acknowledgements We are grateful to  Gildas Gâteblé  and  Bruno Fogliani  ( IAC, St Michel, New Caledonia ) for generously supplying plant material and to  Marc Robinson-Rechavi  ( University of Lausanne ) for advice on the comparison of clustering data. We thank the  PSMN (Pôle Scientifique de Modélisation Numérique) of ENS-Lyon  for the use of computing resources. This paper is dedicated to the memory of  Michael W. Frohlich  ( Royal Botanic Gardens, Kew ), a pioneer in Plant Evo-Devo and an exceptional friend and colleague, who passed away suddenly in December 2021.
Data availability RNA-seq datasets generated in this work are available from the NCBI Sequence Read Archive (SRA) under BioProject ID PRJNA788337 (http://www. ncbi. nlm. nih. gov/bioproject/788337 ). Downloaded from https://academic. oup. com/jxb/advance-article/doi/10. 1093/jxb/erac044/6522798 by Bibliotheque Diderot De Lyon -Ens De Lyon user on 11 February 2022.
Indeed, the G function can be highly nonlinear notably for high-frequency bands F for which the frequencies have to be considered. A surrogate model is then not adapted, and the risk of misestimation of the EMC probability is not negligible. For this purpose, if the number of iterations becomes larger than n switch = 250, this means that the surrogate model is not able to mimic the G function well at the distance d, and we propose then to switch to Algorithm A1, taking into account calls to the G function already performed during Algorithm A2. The general scheme of Algorithm A2 is available in Figure  2. The block indicating "call to the G function" implies the launch of the CRIPTE computer code simulation to evaluate the induced currents for a given configuration of cable bundle installation parameters (distance, heights, length).
• The susceptibility level of all conductors of bundle B was adjusted in F to obtain an optimised segregation distance d opt between both bundles of about 15 cm. This use-case is reasonably balanced in terms of the representativity of real industrial cases and the computing resources needed to calculate the G function defined in Equation  (1). Consequently, the objective is to compute d opt , for heights of bundles varying between 1. 5 cm and 20 cm and a bundle length varying between 6 m and 15 m. This optimum distance should ensure that the probability to be susceptible is lower than α = 0. 05. In order to demonstrate the mathematical challenge of this use-case, the current induced on one elementary conductor of bundle B and computed by the CRIPTE computer code  [4]  is plotted versus frequency in Figure  4  for two very close installation configurations:  Figure  4  clearly illustrates the fact that below the resonance frequencies, the current response varies slowly with the installation parameters (distance, heights, and length), and the strategy of optimisation, including the surrogate model, will easily converge.
Rain-on-grid simulations for the modeling of 2D unsteady flows in response to precipitation input are implemented in Hydrologic Engineering Center's River Analysis System (HEC-RAS) software by means of a finite volume method and a sparse parallel linear solver running on a unique processor. Obviously, this may fail due to memory shortage when the discretization yields too large linear systems. Such simulations are good candidates for the design of partition and domain decomposition methods for the modeling of very large catchments by means of hydrological sub-units. Thinking in load-balanced computations, the proposed area-balanced partition method is based on the flow accumulation information. Implemented in HEC-RAS, the domain decomposition method comprises 2D-1D models combining 2D sub-unit meshes to 1D channels for extracting outflow data from upstream units and passing the information to downstream units.
Two computing strategies may be implemented to balance the computational load over a multi-core computer. On the one hand, a sparse parallel solver may be implemented as in the HEC-RAS software  (Brunner 2016)  to split the linear system into small blocks in order to distribute them on different cores. This approach is particularly well suited to a shared memory multicore processor. On the other hand, a domain decomposition method (DDM) may be set up on a partition of the catchment. GIS tools are used to split the watershed into a number of hydrological sub-units  (Apostolopoulos & Georgakakos 1997; Vivoni et al. 2011). The PDE problem is decomposed into a number of boundary value sub-problems accordingly. A DDM then organizes the solution by gathering the solutions computed for the sub-units and by setting relevant boundary conditions at the interfaces.
Stream-based methods Provided with a DEM and an outlet, GIS software tools such as ArcGis, QGIS, or TopoToolbox  (Schwanghart & Kuhn 2010)  allow for the delineation of the corresponding watershed and stream network through the calculation of a flow direction and a flow accumulation. As an illustration, the flow accumulation raster related to the case study DEM is plotted in Figure  1. It records the number of cells, and, thus, the area drained by each of the DEM cells, outlining the stream network. The latter comprises a number of connection points called confluences and a number of links called river reaches. Based on the confluence points, the basic watershed partition offered by GIS tools allows to delineate sub-basins. When the stream network presents nearby confluences, this partition method may create very small sub-basins.
AREA-BALANCED DOMAIN DECOMPOSITION METHOD DEM analysis and sub-unit delineation GIS are precious tools to support hydrological modeling. Software like ArcGIS or QGIS provides graphical user interfaces and programming interfaces with Python. In contrast to this, TopoToolbox  (Schwanghart & Kuhn 2010 ) is a set of Matlab functions dedicated to DEM analysis, with a special focus on hydrology. An overview of the respective performance of these tools is proposed in  Schwanghart & Scherler (2014). Among them, we choose TopoToolbox because it is integrated in a scientific computation framework with good efficiency. TopoToolbox is an object-oriented toolbox provided with a user guide that describes the methods that are necessary to our partition tool. As figured out in listing 1, the DEM is read as a grid object.
The sub-unit with the largest area is selected (imax) and its flow accumulation information is computed. A splitting point is determined by searching for the cell that drains half of that flow (coor(iSplitPt); see Figure  3 ). This is added to the set of outlet points defining the drainage basins. This process is iterated until the desired stopping criterion is met. This can be based on the number of sub-units and/or on the sub-unit areas. In listing 2, the process stops when all the sub-basins have an area less than a prescribed maxArea. Listing 2: Pseudo-code for the area-balanced partition method. The sub-watersheds are saved in the shapefile format using the shapewrite function of the Matlab Mapping toolbox such that it then can be used in the HEC-RAS simulations, for instance.
Confluence method unguided method Depending on the actual topography or its digitization, the watershed delineation may produce sub-units that present isolated cells, notably near to the outlet; see Figure  4. Defects with respect to the 4-neighbor connectivity may occur for any partition method or partition software. These are not appropriate to a finite element or a finite volume modeling. At the end of the partition process, a connectivity check may be performed to correct the decomposition by moving some of the outlet points upward. This can be achieved automatically by inspecting the properties of each sub-unit by means of the regionprops Matlab function, for instance. It is worth noticing that the user can also take them into account in a channel, as proposed in the 2D-1D modeling described hereafter.
Scientific computing aspects A 2D PDE solver is time-and memory-consuming, especially when the domain under study becomes large. This applies to the 2D unsteady rain-on-grid solver recently proposed within the HEC-RAS software. It processes linear systems by means of a sparse parallel solver to take advantage of a unique multi-core processor. This software is, thus, a perfect candidate to assess the benefits that could be brought by an additional partition of the watershed into hydrological sub-units. The impact of the improved balance in the sub-unit areas on the 2D PDE computation is assessed using HEC-RAS in Figure  11. For our first case study, the DDM workflow described in Figure  5  resulted in a speed-up of 2 when eight subunits of similar size are considered. Performances are a little lower with the confluence method.
CONCLUSIONS At a watershed scale, rain-on-grid simulations for the modeling of 2D unsteady flows in response to precipitation input are implemented in the HEC-RAS software by means of a finite volume method, a discretization of the topography, and a sparse parallel linear solver running on a unique processor. Obviously, this may fail due to memory shortage when the watershed of interest and the chosen discretization yield too large linear systems. Such large simulations are good candidates for the design of dedicated partitioning and domain decomposition methods for both the modeling of very large catchments and the reduction of the overall computational time. Thinking in a load-balanced domain decomposition method, we first propose an area-balanced partition method carried out by considering the flow accumulation information rather than the confluence points and/or the reach length constraint.
Second, the domain decomposition method is exemplified for the HEC-RAS software. Special attention has been brought to the set-up of 2D-1D models that combine 2D sub-unit meshes to 1D channels for the extraction of outflow data from upstream sub-units and their propagation to their downstream unit. The workflow is automated by means of the HEC-RAS Controller. For both case studies, the new partition and decomposition methods have demonstrated a better equilibrium for the subbasin areas. As a consequence and the computational load that is required for the solution of the related sub-problems, respectively, is balanced between the parts. As expected, the total computational time is substantially reduced when the domain decomposition method is used. Rain-on-grid simulations over the Saar watershed (1,747 km²) with a mesh size of 25 m cannot be carried out using HEC-RAS in a straightforward manner because these would yield 2,800,000 mesh elements, overflowing the 16 Gb of RAM memory.
Heliconius butterflies, a speciose genus of Müllerian mimics, represent a classic example of an adaptive radiation that includes a range of derived dietary, life history, physiological and neural traits. However, key lineages within the genus, and across the broader Heliconiini tribe, lack genomic resources, limiting our understanding of how adaptive and neutral processes shaped genome evolution during their radiation. Here, we generate highly contiguous genome assemblies for nine Heliconiini, 29 additional reference-assembled genomes, and improve 10 existing assemblies. Altogether, we provide a dataset of annotated genomes for a total of 63 species, including 58 species within the Heliconiini tribe. We use this extensive dataset to generate a robust and dated heliconiine phylogeny, describe major patterns of introgression, explore the evolution of genome architecture, and the genomic basis of key innovations in this enigmatic group, including an assessment of the evolution of putative regulatory regions at the Heliconius stem.