dataesr/scientific-paragraphs-categorization huggingface dataset:
However, it is not possible to represent retroaction loops in Crop2ML as it is done in Record with coupled models. In order to illustrate Crop2ML concepts and transformation results, a phenology and an energy balance models are used. Phenology, the timing of crop development is the heart of most crop J o u r n a l P r e -p r o o f growth models and is an essential component of most crop modeling platforms. The energy balance model involves interconnected components that allows estimating canopy temperature, evapotranspiration, and heat transfer between the canopy and the air. These processes are implemented as BioMA standalone components  (Manceau & Martre, 2018)  of the wheat PBM SiriusQuality  (He et al. , 2012; Martre et al. , 2006). The two components were converted into Crop2ML packages, and then automatically translated into different languages and model components that conform to different PBM platforms.
Discussion The Crop2ML framework enables a user to exchange and reuse biophysical components between various PBM platforms through shared declarative specifications. The use of a minimal language to describe the model algorithm once and the transformation system facilitates reuse of models' components. ModelUnits and ModelComposite can be accessed and composed following a white box approach. Therefore, the Crop2ML approach greatly increases the ability of modelers to share their algorithms. The protocol will allow modelers to borrow components easily and will facilitate their intercomparison and improvement in different PBM platforms.
How does Crop2ML address model reuse compared to other initiatives? Some initiatives addressed model reuse by providing multi-scale and multi-language integrative frameworks such as Crops in silico  (Marshall-Colon et al. , 2017)  the Open Modeling Foundation OpenMI  (Buahin & Horsburgh, 2018). These frameworks can compose and simulate heterogeneous   2006 ) is similar to ours but it is limited to models where the dynamics of the modeled processes is represented by simple mathematical expressions without control structures, which does not match crop modeling context. Hucka et al. (2003)  used MathML  (Ausbrooks et al. , 2003)  to express interactions between variables through mathematical formalisms well defined in the systems biology community. This approach is similar to that of  Rizzoli et al.
Connecting Crop2ML to PBM platforms Given that Crop2ML datatypes do not handle complex data structures other than arrays and lists, some compromises or transformation should be made to the import-export process on the platform side with respect to handling other data structures used in platforms. As an example, BioMA provides the Dictionary data type that is a set of keys associated with values to represent either input or output variables. This data type is not handled by Crop2ML, and by most PBM platforms. As an The Crop2ML transformation system is designed to support the specificities of the target PBM platforms. However, the semantic of a Crop2ML model is based on shared concepts to describe at a high level a biophysical process by a discrete-time model. There is no semantic reason to support the description of each instance of the concepts.
, 2013). In addition, to facilitate the exchange and reuse of model components, semantic descriptions of model variables and parameters would facilitate the linking of crop models to plant phenomics data  (Neveu et al. , 2018). We were able to achieve fully-automatic export of Crop2ML model to several PBM platforms. The import process into Crop2ML is more mixed regarding the overall differences between PBM platforms. It is much easier to start with concepts shared and reused by PBM platforms than to start from divergent views of model representations to achieve a particular result. Some PBM platforms need to extend their concepts for model specification or to provide a rich model documentation in order to produce complete Crop2ML model specifications. This reveals the need of a good level of abstraction to represent a model in various PBM platforms.
Acknowledgements C. M. was supported through a PhD scholarship from the  French National Research Agency  under the Investments for the Future Program, referred as  ANR-16-CONV-0004  and  INRAE Divisions AgroEcoSystem  and  NUM. P. M. acknowledges the support of  INRAE Division AgroEcoSystem  through the  Modélisation du fonctionnemnet des Peuplements Cultivés (MFPC)  network. The authors thank  Dr. Loic Manceau  ( INRA, UMR LEPSE ) for discussions and its help to translate the wheat phenology BioMA component of SiriusQuality in Crop2ML.
3D Geometric Representation of the Cooked Peeled Shrimp To better understand the microwave heating of the shrimp, we limited the study to a single un-packed, cooked, and peeled shrimp. Considering that the 3D construction of the external contours is enough for good accuracy of the numerical model, the 3D representation of a shrimp was obtained in a stereolithography (STL) file from a model repository and imported into COMSOL Multiphysics software (v. 5. 5, COMSOL Inc. , Stockholm, Sweden)  [45]. To approximate the 3D model to real shrimp dimensions, we measured the external dimensions at six different locations (points I, II, III, IV, V, and VI) using a calliper with an accuracy of ±0. 03 mm, according to Figure  3. One tray of ready-to-eat shrimp (130 g of cooked and peeled shrimp) was purchased at a local market in Nantes (France).
Average external dimensions were obtained from the measurement of 10 cooked peeled shrimp before and after microwave heating (rectangular waveguide cavity at P in = 40 W and ∆t = 100 s). The original 3D model of the shrimp was scaled using COMSOL Multiphysics software (v. 5. 5, COMSOL, Stockholm, Sweden) to better fit the real dimensions of the shrimp.
3D Meshing Procedure and Numerical Resolution The simulation of the electromagnetic wave propagation (Equation (2)) and heat transfer (Equation (  3 )) was performed by COMSOL Multiphysics software (v. 5. 5, COMSOL, Stockholm, Sweden) using the Heat Transfer and Radiofrequency modules. Numerical simulations were performed in a Dell Precision T7810 workstation (Dell Inc. , Round Rock, TX, USA) with Intel Xeon processor at 2. 56 GHz and 256 GB of RAM (Intel Corporation, Santa Clara, CA, USA). Total computation times required for the rectangular and cylindrical applicators systems were around 2 min and 7 min, respectively. The tetrahedral element was used as the basic element for the volume mesh on the basis of previous works  [40, 45]. For the rectangular applicator, the mesh consisted of 6976 tetrahedral elements ("finer" size) with max.
Experimental Validation To check the validity of the numerical models, we performed the direct temperature measurements at selected locations during microwave heating (Figure  4 ) using fibre optic temperature sensors, and data were compared with the simulation results. For both applicators, the sample holder was neglected in the modelling approach, as its influence on the reflection coefficient was small (less than 2 %). To validate the mathematical model of the rectangular applicator, we purchased two trays of cooked and peeled shrimp (260 g) at a local market in Nantes (France). A single shrimp was selected, weighted, and heated by placing it in a holder structure at the centre of the rectangular applicator. To ensure the same sample position, we custom-made a PTFE holder structure comprised of a slab (84 × 41 × 11 mm) and case (41 × 41 × 11 mm).
Statistical Analysis To evaluate influence of the microwave heating on dimensions and mass of shrimp before and after treatment, the analysis of variance (one-way ANOVA) followed by Tukey's test (p ≤ 0. 01) were carried out with Minitab software (v. 19, Minitab Inc. , State College, PA, USA). Dielectric properties (ε and ε ) at 2450 MHz were calculated by linear interpolation of adjacent points and correlated with temperature (3 • C ≤ T ≤ 95 • C) using the multiple regression tool of Excel software (v. 2016, Microsoft, Redmond, WA, USA). Similarly, empirical correlation of thermo-physical properties (ρ, C p , and k) with temperature were adjusted. Electrical conductivity (σ) was calculated on the basis of dielectric loss factor (ε ) at 2450 MHz, as described in Equation (  6 ), where ω = 2π f is the angular frequency of the electromagnetic waves (rad/s) and ε 0 is the permittivity of free space (8.
selho Nacional de Desenvolvimento Científico e Tecnológico-CNPq (grants 459177/2014-1 and 311868/2018-5); and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-CAPES (finance code 001). The datasets generated for this study are available on request to the corresponding author. The authors declare no conflict of interest. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Conflicts of Interest: Nomenclature C p specific heat capacity (J/kg• • C) c → speed of light (m/s) E electric field vector (V/m) E 0 incident electric field amplitude (V/m) f → frequency (Hz) H magnetic field vector (A/m) h heat transfer coefficient (W/m 2 • • C) k thermal conductivity (W/m• • C) m. m mass mass flow rate (g) (g/s) N → n number of data points unit vector normal to the boundary surface (-) (-) P microwave power (W) Q volumetric heat (W/m 3 ) RSME root mean square error ( • C 2 ) T temperature ( • C) t time (s) V volume (m 3 ) Greek Symbols ε relative complex permittivity (-) ε relative dielectric constant (-) ε relative dielectric loss factor (-) ε 0 permittivity of free space (F/m) µ r relative magnetic permeability (H/m) ρ material density (kg/m 3 ) σ electrical conductivity (S/m) ω angular frequency (rad/s) Subscripts abs absorbed exp experimental ext external ambient air gen generated in input pred predicted w water.
These mechanisms can also be an important starting point to develop novel approaches, such as species-specific dsRNA-based pest control strategies. So far, a genome of this species has not been published. Here, we assembled and annotated a de novo, chromosome-level scaffolded genome of D. radicum using PacBio and Hi-C Illumina sequencing. We used three different approaches to annotate the genome; Cufflinks, which uses transcript assembly; GeMoMa, which is homology-based, and BRAKER, for additional prediction of genes not covered by the first two methods. Generated RNASeq data of all four life stages (eggs, larvae, pupae, adults) and two relevant stress factors (heat stress in adults, plant toxin stress in larvae), allowed us to validate predicted genes and to identify specific gene families which were expressed in each of the life stages.
PacBio data processing Raw PacBio reads in bam file format were converted into fasta files by using samtools (version 1. 3. 1)  (Li et al. , 2009)  as part of the SMRT link software (version 5. 1. 0, https://www. pacb. com/support/softwaredownloads/). Extracted raw PacBio reads (6,539,960 reads) were checked for potential contaminations with prokaryotic DNA by applying EukRep (version 0. 6. 2)  (West, Probst, Grigoriev, Thomas, & Banfield, 2018)  with default parameter settings. Only reads classified as eukaryotic (4,454,601 reads) were used for the de novo genome assembly.
Chromosome-scale scaffolding Hi-C Illumina reads were aligned to the purged assembly with the Juicer pipeline incorporating juicer_tools (version 1. 22. 01)  (Durand et al. , 2016 ), "-s DpnII" and a restriction site file (generated with the generate_site_positions. py script contained in juicer) provided by "-z" option. The sequences of the purged assembly were scaffolded with the Juicer output on Hi-C read alignments into chromosome-scale super-scaffolds applying the 3D-DNA genome assembler (version 18011)  (Dudchenko et al. , 2017)  with the additional setting of "--splitter-coarse-stringency 30 --gap-size 100". This resulted in the final genome assembly from D. radicum.
Phylogeny -Comparative genomics based on BUSCOs Phylogenetic analyses were done with BuscoOrthoPhylo (https://github. com/PlantDr430/BuscoOrthoPhylo) which is a wrapper script to concatenate and align protein sequences, and to construct a phylogenetic tree based on single-copy BUSCO genes. BUSCOs of the endopterygota_odb10. 2019-11-20 gene set, consisting of 2124 genes, were used as the basis for the analysis. In the initial phase complete single-copy BUSCO genes which were shared by 10 selected species (Table  1 ), were computed. Protein sequences of the shared genes were extracted and concatenated for each species. MAFFT aligner (version 7. 475)  (Katoh, Misawa, Kuma, & Miyata, 2002)  was run on concatenated FASTA file(s) and finally RAxML (version 8. 2. 12)  (Stamatakis, 2014)  with "-rx_p_sub PROTGAMMAWAG" as model and "-b 100" bootstrap steps was used to reconstruct the phylogenetic tree.
For the adult stage, we collected 18 fully developed random adults. Nine individuals were exposed to 35°C  (Michaud, Marin, Westwood, & Tanguay, 1997)  for 2 hours, whereas the control adults were kept under normal conditions. We pooled three adults for one replicate, resulting in three replicates for control and elevated temperature treatment. All samples were surface sterilized using the same procedure as described for the adult flies. We in the genome with GeMoMa and default parameters, separately for each species and by incorporating mapped RNASeq data for refining intron boundaries. The resulting nine gene annotation sets were filtered and merged using the GeMoMaAnnotationFilter (GAF) with "f="start=='M' and stop=='*'" atf=''". extracted Only transcripts of genes starting with the start codon "M(ethionine)" and ending with a stop codon "*" were considered and all isoforms were retained.
Transcriptome assembly -RNA-Seq-based gene predictions To assemble one transcriptome per life stage and condition, we merged the read mappings (bam files) of the three replicates per condition and life stage. For the transcriptome assembly of the mapped RNASeq data, we used Cufflinks (version 2. 2. 1)  (Trapnell et al. , 2010). Initially, soft-clipped read mappings were clipped, and assembled to six transcriptomes using Cufflinks with default parameters and "-fr-firstrand". Predicted transcripts were filtered for proper start and end of protein coding transcripts by applying the GAF with "f="start=='M' and stop=='*'" atf=''". Finally, UTR annotations were predicted and added using GeneAnnotationFinalizer with "u=YES rename=NO".
Final genome annotation and completeness evaluation We ran GeMoMa`s GeMoMaAnnotationFilter (GAF) with "f='' atf='' tr=true aat=true" to integrate the predicted gene models from all three applied approaches, the homology-based, the RNASeq-based and ab initio gene prediction approach, and yield a master gene annotation file for the D. radicum genome. As gene-related features we include mRNA, CDS, five_prime_UTR, and three_prime_UTR specificities in the annotation file and several attributes that gave additional information on the predicted transcripts and can be used for user-specific filtering. We evaluated the completeness of the final set of protein-coding genes with BUSCO v4 as we did for the evaluation of the completeness of the genome (section 2. 4. 5), but set "-m proteins".
Functional annotation Predicted D. radicum protein sequences were subjected to PANNZER2 (Protein ANNotation with Z-scoRE)  (Törönen, Medlar, & Holm, 2018) , which predicts functional descriptions and GO classes. Additionally, extracted protein sequences were subjected to InterProScan (version 5. 45-80. 0. )  (Blum et al. , 2020; Jones et al. , 2014)  and scanned for information on protein family and domains in all member data bases (-appl CDD, HAMAP, PANTHER, Pfam, PIRSF, PRINTS, ProDom, PROSITEPATTERNS, SMART, TIGRFAM, Gene3D, SFLD, SUPERFAMILY, MobiDBLite) and for GO-or pathway annotation ("-goterms -iprlookup -pa"). GO terms annotated for transcripts with PANNZER2 and InterProScan were merged. Additionally, to get a functional annotation per gene, we merged the annotations of all respective transcripts.
Analysis of life cycle data We extracted the sequences of all annotated transcripts and quantified their abundances with kallisto (version 0. 46. 1,  (Bray, Pimentel, Melsted, & Pachter, 2016) ) with "-b 100" bootstraps and "-rf-stranded". The abundances were imported into the statistical framework R (version 3. 6. 2) (R Core Team, 2020) for further analyses using the R package tximport (1. 14. 2)  (Soneson, Love, & Robinson, 2015). Using tximport transcript-level, estimates for abundances were summarized for further gene-level analyses. We denoted a gene as expressed if it had a TPM (transcript per million) value ≥ 1 in at least one of the 18 transcriptome samples. We refer to this set of genes as the "data set of expressed genes". We called a gene present in a life stage or condition if it occurred in at least one replicate.
This aggregation resulted in a matrix with six columns (four life stages, two conditions). These six sets were analyzed for life stage and condition-specific gene expression and also for intersections with the R package UpSetR (1. 4. 0)  (Gehlenborg, 2019). We performed Gene Ontology (GO) analyses of pre-defined gene sets using R (version 4. 0. 4) with the latest version of the R package topGO (2. 42. 0)  (Alexa & Rahnenfuhrer, 2020)  with GO. db (3. 12. 1)  (Carlson, Falcon, Pages, & Li, 2020). We used Fisher's exact test to identify over-represented GO terms. Raw p-values were corrected for multiple testing using the method proposed by Benjamini and Yekutieli  (Benjamini & Yekutieli, 2001)  implemented in p. adjust contained in the basic R package stats. To get an indication of which processes were active, we aggregated single significant GO-terms (adjusted p value < 0.
For condition-specific GO analysis, we additionally determined the genes exclusively expressed in the stressed condition of the larval and adult stage, respectively. Again, we also extended the stress-specific genes sets by the respective intersection gene set. We clustered samples and genes contained in the data of expressed genes using the R package umap (0. 2. 7. 0)  (Konopka, 2020). UMAP (uniform manifold approximation and projection) is a technique to reduce dimensions and bring similar data vectors, samples (columns) or genes (rows) in close proximity. In our analyses we projected the data vectors in both cases in a two-dimensional space and tested different values for the size of the neighborhood (n_neighbors) and the minimal distance (min_dist) between data points (either samples or genes).
Process of genome annotation and evaluation We sequenced the transcriptomes of all four life stages (eggs, larvae, pupae, and adults) of D. radicum, and included two stress factors (heat stress on adults and plant toxin on larvae) that are relevant for the survival of D. radicum to support the prediction of a comprehensive set of protein-coding genes in the D. radicum genome. Our homology-based protein-coding gene prediction with GeMoMa relied on nine already sequenced and annotated genomes of phylogenetically related species, herbivore species sharing the same host plant range or common pests on crop plants or stored grains (Table  1 ). We predicted 19,343 proteincoding genes comprising 46,286 transcripts (Table  3 ) having a homologue in at least one of the nine selected species. As a complementary approach, we assembled the transcriptomes of all life stages from egg to adult, plus adults and larvae subjected to two stage-related stress factors using Cufflinks.
Functional annotation Overall, 77. 1 % (62,418) of the genes were functionally annotated with at least one GO-term and/or protein family or domain information (Figure  4b , Figure  S2 , Table  S9 ), including 71. 15 % (42,244) of the only ab initio predicted genes. Focusing on the expressed genes by using our in-house whole life stage RNASeq data, we found a reasonable number of 30,492 genes (37. 64 %) having an estimated expression of ≥ 1 transcript per million (TPM) (Figure  4c ). A high number of genes was predicted by BRAKER only, but most of these genes were not expressed under our conditions, although the number of expressed genes is higher than in the other sets (Figure  4c ). From the set of expressed genes, 50 % (15,270) were functionally annotated with at least one GO-term (Figure  4d ).
Acknowledgements We thank the  Long Read Team of the DRESDEN-concept Genome Center, DFG NGS Competence Center , part of the  Center for Molecular and Cellular Bioengineering (CMCB) ,  Technische Universität Dresden  and the  MPI-CBG , especially  Sylke Winkler  for their great support and the collaboration. Dominik Jakob  is acknowledged for his assistance with the insect culture. Great thanks to  Denis Tagu  and  Fabrice Legeai  ( INRAE, Rennes, France ),  Denis Poinsot  ( University of Rennes, France ) and  Ekaterina Shelest  ( BIU, iDiv, Leipzig, Germany ) for their helpful advice and encouragement in earlier stages of this project. We also greatly thank  Jens Keilwagen  ( JKI, Quedlinburg ) for valuable discussions on gene prediction and support on GeMoMa.
://github. com/TransDecoder/TransDecoder). Finally, predicted transcripts were filtered for a proper start and end of protein coding transcripts and retaining the UTR annotations by applying the GAF with parameters "f="start=='M' and stop=='*'" atf='' aat=true tf=true". 2. 7. 4 Ab initio gene predictionAdditionally, we aimed to predict genes not covered by the homology-based and the transcriptomebased approach, due to a lack of homology or because of no or low expression under the specific conditions of the sampled life stages. To obtain such ab initio gene predictions, we ran RepeatMasker (version 4. 1. 0, http://www. repeatmasker. org) with RMBlast (version 2. 10. 0, The resulting six transcriptomes were subjected to Cuffmerge, which is part of the Cufflinks toolbox, to generate a single master transcriptome.
While Cufflinks assembled transcripts with exon annotation, missing coding regions and UTRs were identified with TransDecoder (version 5. 5. 0, httpshttp://www. repeatmasker. org/RMBlast. html) and "-species insecta -gff -xsmall" to find and mask repetitive sequences annotated for insects in the RepeatMasker repeat database. For ab initio prediction of protein-coding genes on the masked genome sequences, we ran BRAKER (version 1. 9) (Brůna, Hoff, Lomsadze, Stanke, & Borodovsky, 2021;  Katharina J. Hoff, Lange, Lomsadze, Borodovsky, & Stanke,     2015; Katharina J Hoff, Lomsadze, Borodovsky, & Stanke, 2019) , which combines GeneMark (version 4. 59_lic) (Lomsadze, Burns, & Borodovsky, 2014)   and AUGUSTUS (version 3. 4. 0) (Stanke, Schöffmann, Morgenstern, & Waack, 2006)   with "--gff3 --softmasking" and provided the mapped RNASeq data as hints for initial training of gene models and gene predictions.
The dry DNA was dissolved in 30 µl of 10 mM Tris HCl pH 8. 0 and purified using a GeneClean Turbo kit. Ten-µl aliquots of purified DNA extracts were resolved by electrophoresis in a 0. 8% agarose gel together with dilutions of calf thymus DNA (Bio-Rad). The gels were stained with ethidium bromide, photographed under a camera, and the staining intensities were measured with Bio-1D ++ software (Vilber-Lourmat, Marne-la-Vallée, France). The DNA concentrations were calculated using a standard curve of 25-150 ng of calf thymus DNA plotted versus the staining intensity. DNAs were stored at -20°C until use.