dataesr/scientific-paragraphs-categorization huggingface dataset:
2. The model was trained with a learning rate of 0. 0003 and a momentum of 0. 90. The model was first pretrained on the Common Objects in Context (COCO) dataset to provide the starting point. The COCO dataset  [31]  contains 0. 33 million images with 1. 5 million of object instances belonging to 80 object categories. More details on the Faster-RCNN used could be found in Madec et al. [20]. The pretrained model was then fine-tuned over the calibration image extracts. It identified and localized stems using a bounding box associated with a confidence score varying between 0. 0 and 1. 0. The trained model was finally applied to all the image extracts available. When identified stem bounding boxes were overlapping, a minimum of 0. 75 overlap fraction was used to eliminate one of the overlapping bounding boxes.
Statistical Analysis. Both Gréoux and Clermont datasets were randomly split into 2/3 for model calibration and 1/3 for validation. A first global training (called here Cgc) was investigated by pooling the calibration datasets of Gréoux and Clermont sites. The same was done for the validation datasets Vgc. The performances of this global calibration (Cgc) were also evaluated on both the Gréoux (Vg) and Clermont (Vc) validation datasets. Then, a cross-validation was also investigated to better evaluate the robustness of the classification: the calibration was completed on the Gréoux (Cg) or Clermont (Cc) calibration datasets and validated on the Gréoux (Vg) and Clermont (Vc) validation datasets. Table  3  presents the several cases considered. A detected stem bounding box (i. e. , with a score >0.
Stems Are Accurately Identified Using the Faster-RCNN Model. To evaluate the robustness of the RCNN model, it was calibrated on the Gréoux (Cg), Clermont (Cc), or both datasets (Cgc). Performances computed over the validation datasets were very good with 0. 91 < precision< 0. 96 and 0. 93 < recall < 0. 97 (Table  4 ). Precision and recall were well balanced with a small bias: -0. 03 < bias<0. 01. The results showed that the classification accuracy of stem identification was very high based on the precision and recall values over the same experiments (Table  4 ). The robustness of the classification was further investigated by comparing the precision and recall values computed over the validation datasets coming from the other experiments. Results show that the classification evaluated over the same experiment used to calibrate the model was always performing the best (Table  4 ).
The classification performances decreased significantly when the model calibrated over a single experiment was validated on the other experiment. This may be explained both by the limited sample size of the calibration dataset and also by the specific features associated with each experiment, including the spatial resolution (Tables  2  and 3 ). However, when the calibration was completed over the pooled experiments (Cgc), the precision and recall values decreased only slightly when evaluated over each individual experiment (Vg or Vc) (Table  5 ). The model captured the key information common to the two experiments to provide a consistent stem identification. It confirmed the efficiency and robustness of the Faster-RCNN method.
Stem Density Is Accurately Estimated. The consequences of the identification performances of the Faster-RCNN model discussed previously were evaluated in terms of plant density at the image extract level. For the sake of consistency, several calibration and validation datasets were considered to further evaluate the robustness of the model. Results showed RRMSE values ranging from 6. 08% to 9. 19%. Calibrating over the pooled datasets (Cgc, Table  5 ) provided the best performances with RRMSE lower than 7%. A slight degradation of the performances was observed when calibrating over a single dataset. Calibrating over the Gréoux dataset provided the worst performances when validated over the Clermont dataset (RRMSE=9. 2%) because of the smaller sample size and variation in the cutting height and inclination of the stems during harvest between Gréoux and Clermont sites.
In the following, we have used the Faster-RCNN trained over the pooled Gréoux and Clermont calibration datasets (Cgc) that provided more robust performances. When considering the calibration over the pooled dataset (Cgc) that provided the overall best performances, very small biases were observed with points closely distributed around the 1:1 line (Figure  4 ). The scatter around the 1:1 line appeared to be relatively independent of the stem density (Figure  4 ).
The Stem Density Is a Good Proxy of the Ear Density. The stem density estimated with the Faster-RCNN model calibrated over the Cgc dataset was compared to the ear counted visually at the ground level. Both quantities were evaluated on different samples, expected however to represent the average microplot value. Results showed that the estimated stem density based on the Faster-RCNN model was very consistent (Figure  5 ) with the measured ear density at the Gréoux (Table  6 ) and Clermont (Table  6 ) experimental sites. The scatter between ear and stem densities appeared to increase with the density: this was obvious between the Gréoux (250<density<550) and Clermont (400<density<800) sites. Part of the larger scatter observed over the Clermont site might come from a smaller sample size for the ear density visual counting (1.
The Biovolume Is a Good Proxy of the Above-Ground Biomass. A total of 37 microplots from the Gréoux dataset were used to relate the measured AGB with the ear density and the four structural traits derived from high-throughput measurements: stem density, stem basal area computed as the product of the average stem diameter and the stem density, plant height, and biovolume computed as the product of the basal area and plant height. Results show that all these traits are strongly correlated with AGB (Figure  7  and Table  7 ). The best relationship is however obtained using the biovolume that combines the three main original traits: stem density and average stem diameter that are combined into the basal area; and plant height. Note that these traits are relatively independent: stem density and plant height are loosely correlated (Figure  6 , r 2 =0.
16); plant height and basal area are also loosely correlated (r 2 =0. 15). Because the dataset used was limited, the predictive performances of the relationships observed between the AGB and the five traits were evaluated using a leave-one-out crossvalidation method  (Jin et al. , 2018). The best determination coefficients were observed consistently for the biovolume (Table  7 ) with a relative error of 5. 8%, i. e. , within the order of magnitude of the accuracy with which AGB was measured. To further improve the predictive model, we used all the five traits together within a multiple linear regression model. Marginal improvement of the model performances was observed (Figure  7  and Table  7 ). This may be explained by the strong relationships between the five traits used, as well as the decrease in the degree of freedom induced by the increase of the number of coefficients to be adjusted (six coefficients instead of two needed when using only the biovolume).
Acknowledgments This study was supported by " Programme d'Investissement d' Avenir "  PHENOME  ( ANR-11-INBS-012 ) with participation of FranceAgriMer and " Fonds de Soutien à l'Obtention Végétale ". We thank also the people from Gréoux and Clermont who participated in the experiments. The work was completed within the  UMT-CAPTE  funded by the  French Ministry of Agriculture.
Raxml and Phylobayes analyses are based on an amino acid dataset of 117,782 positions from 286 genes from 12 species. The Astral analyses are based on a 1125-nucleotide dataset of 478,617 positions from the 6 Glossina (full trees are in Additional file 2: Figure  S2A-C ). The values at nodes represent the bootstrap supports and posterior probabilities from the maximum likelihood and Bayesian analyses, respectively (Bootstrap/posterior probability). c Molecular phylogeny derived from whole mitochondrial genome sequences. The analysis was performed using the maximum likelihood method with MEGA 6. 0 composition, length, and GC content. The use of HRM on these variable regions successfully resolved the differences between test samples consisting of different tsetse species as well as individuals with different haplotypes or from different populations (Additional file 2: Figure  S4 ).
Many of the Glossina-specific genes are not currently associated with GO annotations as they lack characterized homologs in other species. As such, these sequences were not included in this analysis. However, ~60% of the genes within the combined Glossina gene repertoire are associated with GO annotations, which allowed for the analysis of a sizable proportion of the dataset (Additional file 1: Table  S7 , Additional file 5). Glossina genus universal and specific genes are enriched in genes coding for proteases and odorant-binding proteins The orthology groups containing genes specific and universal to the Glossina genus are enriched in odorantbinding and serine-type endopeptidase activities. The universality of these genes within Glossina and their absence from the other surveyed Dipteran species suggest they are currently associated with tsetse-specific adaptations.
The ontology categories with the most significant p values across all six species represent proteolysis-associated genes (serine-type endopeptidase activity (GO: 0004252) and proteolysis (GO:0006508)). This category encompasses 92 Glossina-specific proteases with predicted serine-type endopeptidase activity. The abundance of this category may be an adaptation to the protein-rich blood-specific diet of both male and female flies. A similar expansion of serine proteases is associated with blood-feeding in mosquitoes, and the presence of an equivalent expansion in tsetse may represent an example of convergent evolution  [47]. This class of peptidases is also associated with critical functions in immunity, development, and reproduction in Diptera  [48] [49] [50] [51]. Homology analysis of these proteases by BLAST against an insect-specific subset of the NCBI NR database reveals that most bear the closest homology to chymotrypsins and trypsin proteases in other Brachyceran Fig.
The expansion includes 2223 OGs and encompasses 4948 genes between G. palpalis and G. fuscipes. Homology-based analysis of these genes by comparison against the NCBI NR database revealed significant (e value < 1 × 10 -10 ) results for 603 of the genes. Within this subset of genes, ~5% represent bacterial contamination from tsetse's obligate endosymbiont Wigglesworthia. Sequences homologous to another well-known bacterial symbiont Spiroplasma were found exclusively in G. fuscipes. This agrees with previous observations of Spiroplasma infection of colonized and field-collected G. fuscipes flies  [56]. Four genes bear homology to viral sequences (GPPI051037/GFUI045295 and GPPI016422/ GFUI028200). These sequences are homologous to genes from Ichnoviruses. These symbiotic viruses are transmitted by parasitic Ichneumonid wasps with their eggs to suppress the immune system of host insects  [57].
Genome completeness analysis (BUSCO and CEGMA analyses) Quality of the genome assembly and training of the ab initio predictors used in the gene prediction pipeline was determined using the diptera_odb9 database which represents 25 Dipteran species and contains a total of 2799 BUSCO (Benchmarking Universal Single-Copy Orthologs) genes derived from the OrthoDB v9 dataset  [114]  (Table  2 ).
Identification of horizontal gene transfer events All genome sequence files for G. pallidipes, G. palpalis, G. fuscipes, G. austeni, and G. brevipalpis used for the wholegenome assembly were also introduced into a custom pipeline for the identification of putative horizontal gene transfer (HGT) events between Wolbachia and tsetse. Wolbachia sequences were filtered out from WGS reads using a combination of MIRA  [130]  and NextGenMap  [131]  mapping approaches. The reference sequences used were wMel (AE017196), wRi (CP001391), wBm (AE017321), wGmm (AWUH01000000), wHa (NC_ 021089), wNo (NC_021084), wOo (NC_018267), wPip (NC_010981), and the chromosomal insertions A and B in G. morsitants morsitans. All filtered putative Wolbachiaspecific sequences were further examined using blast and custom-made databases.
Whole-genome pairwise alignment We generated all possible pairwise alignments between the six Glossina species (including G. m. morsitans) and an outgroup, M. domestica, using the Ensembl Compara software pipeline  [122]. LASTZ  [132]  was used to create pairwise alignments, which were then joined to create "nets" representing the best alignment with respect to a reference genome  [133]. G. m. morsitans was always used as the reference for any alignment of which it was a member; otherwise, the reference genome was randomly assigned. Coverage statistics and configuration parameters for all alignments are available at https:// www. vectorbase. org/compara_analyses. html.
Glossina phylogeny prediction We identified orthologous genes across the six Glossina species and six outgroups (M. domestica, D. melanogaster, D. ananassae, D. grimshawi, L. longipalpis, and A. gambiae) by employing a reciprocal-best-hit (RBH) approach in which G. m. morsitans was used as a focal species. We identified 286 orthologs with a clear reciprocal relationship among the 12 species. All orthologs were aligned individually using MAFFT  [134]  and concatenated in a super-alignment of 478. 617 nucleotide positions. The nucleotide alignment was translated in the corresponding amino acids and passed through Gblocks  [135]  (imposing "half allowed gap positions" and leaving the remaining parameters at default) to obtain a dataset of 117. 783 amino acid positions. This dataset was used for a maximum likelihood analysis in RAxML  [136]  employing the LG+G+F model of replacement and for a Bayesian analysis using Phylobayes  [137, 138]  employing the heterogeneous CAT+G model of replacement.
Synteny analysis The synteny analysis was derived from whole-genome alignments performed as follows using tools from the UCSC Genome Browser  [146]. The LASTZ software package (version 1. 02. 00) generated the initial pairwise sequence alignments with the following parameters: E = 30, H = 2000, K = 3000, L = 2200, O = 400, and the default substitution matrix. From these alignments, Kent's toolbox (version 349)  [146]  was used to generate chain and nets (higher-level abstractions of pairwise sequence alignments) with the following parameters: -verbose = 0, -minScore = 3000, and -linearGap = medium. The multiple alignment format (MAF) files were built with MULTIZ for TBA package (version 01. 21. 09)  [147] , using the chains and nets, along with the phylogenetic relationships and distances between species.
Using the MAF files, pairwise homologous synteny blocks (HSBs) were automatically defined using the SyntenyTracker software  [148]. Briefly, the SyntenyTacker software defines an HSB as a set of two or more consecutive orthologous markers in homologous regions of the two genomes, such that no other defined HSB is within the region bordered by these markers. There are two exceptions to this rule: the first involves single orthologous markers not otherwise defined within HSBs, and the second involves two consecutive singleton markers separated by a distance less than the resolution threshold (10 kb for this analysis). As the 10-kb blocks were too small for visualization in Circos  [149] , they were aggregated into larger 250-kb histogram blocks, where each 250 kb Circos block shows the fraction of sequence identified as syntenic for a particular species when aligned to D.
Orthology and paralogy inference and analysis Phylogenetic trees were inferred with the Ensembl Compara "GeneTrees" pipeline  [122]  using all species from the VectorBase database of arthropod disease vectors  [129]. The trees include 33 non-Glossina species, such as D. melanogaster, which act as outgroup comparators. All analyses are based on the VectorBase April 2016 version of the phylogenetic trees. Representative proteins from all genes were clustered and aligned, and trees showing orthologs and paralogs were inferred with respect to the NCBI taxonomy tree (http://www. ncbi. nlm. nih. gov/taxonomy). The 15,038 predicted gene trees containing Glossina sequences were parsed to quantify the trees based on their constituent species. Raw tree files (Additional file 3) were parsed using a custom PERL script which is accessible via Github (https://github.
com/attardog/Comp_ Genomics_Scipts/releases/latest) to determine gene counts for representative Dipteran species for each gene tree  [150]. Count data were imported into Excel and filtered using pivot tables to categorize orthology groups based upon species constitution (Additional file 4). The orthology groups were broken into cohorts based on the phylogenetic composition of species within each group. The Glossina containing orthology groups were categorized as follows: common to Diptera (including the Nematocera sub-order), Brachycera sub-order-specific, Glossina genus-specific, Glossina sub-genus-specific (Morsitans and Palpalis), or Glossina species-specific. Each category is sub-divided into two groups, universal groups that contain representative sequences from all species within the phylogenetic category or partial orthology groups containing sequences from some but not all members of the phylogenetic category.
Gene Ontology analysis Gene-associated GO terms were obtained from the Vec-torBase annotation database via the BioMart interface. Genes from Glossina genus and sub-genus-specific orthology groups were isolated and tested for enrichment of GO terms. Analysis for GO terms for enrichment was performed with the R package "topGO. " The enriched genes were separated into species-specific lists compared against the entirety of predicted protein-coding genes from the respective species. Significance of enrichment was determined using Fisher's exact test (Additional file 1: Table  S7 , and Additional file 5).
Identification and analysis of gene expansions/ contractions Gene trees containing orthologs/paralogs representing each of the six Glossina species were analyzed to identify sub-genus-associated gene expansions/contractions. Gene trees were considered for analysis if the variance in the number of orthologs/paralogs between the six species was greater than 2. Variable gene trees were tested for phylogenetic significance relative to the predicted Glossina phylogeny using the CAFE software package  [151]  to reject potentially inaccurate variance predictions due to erroneous gene annotations. Gene trees with a CAFE score of < 0. 05 were considered significant (Additional file 1: Table  S8 , Additional file 7, Additional file 8). Sequences from gene trees satisfying the variance and CAFE thresholds were extracted with a custom PERL script (https://github.
com/attardog/Comp_Genomics_ Scipts/releases/latest) and analyzed by BLASTP analysis  [124]  against an insect-specific subset of the NCBI NR database. Gene trees were annotated with the most common description associated with the top BLAST hits of its constituent sequences. Gene trees were subjected to PCA analysis in R using the FactoMineR and Factoextra packages using species-specific gene counts as input data. The results were plotted and annotated with their associated BLAST-derived descriptions.
Immune gene and dN/dS analysis Orthology groups containing Drosophila genes associated with the GO term (GO:0002376) were queried from the dataset used in the orthology/paralogy analysis described above. Gene counts were visualized using the pHeatmap package in R (https://cran. r-project. org/web/ packages/pheatmap/index. html). The associated dendrograms are generated based on the similarity of the gene counts per family as determined by Pearson correlation. The dN/dS values were derived from the molecular evolution and selective pressure analysis described above.
RNA-seq data Total RNA was isolated for each of the six tsetse species from whole male and whole female (non-lactating and lactating) for RNA-seq library construction. Poly(A)+ RNA was isolated, then measured with an Agilent Bioanalyzer for quality. Samples were considered to be of high quality if they showed intact ribosomal RNA peaks and lacked signs of degradation. Samples passing quality control were used to generate non-normalized cDNA libraries with a modified version of the Nu-GEN Ovation® RNASeq System V2 (https:// www. nugen. com/products/ovation-rna-seq-system-v2). We sequenced each cDNA library (0. 125 lane) on an Illumina HiSeq 2000 instrument (~36 Gb per lane) at 100 bp in length. RNA-seq analyses were conducted based on methods described in Benoit et al. [80] , Rosendale et al.
[152] , and Scolari et al. [55]  with slight modifications. RNA-seq datasets were acquired from whole males, whole dry females, and whole lactating females. The SRA numbers for each of the libraries are listed in (Additional file 1: Table  S16 )  [153] [154] [155] [156] [157] [158] [159] [160] [161] [162] [163] [164] [165] [166] [167] [168] [169]. RNA-seq datasets were quality controlled using the FastQC (Babraham Bioinformatics) software package. Each set was trimmed/cleaned with CLC Genomics (Qiagen), and quality was re-assessed with FastQC. Each dataset was mapped to the predicted genes from each Glossina genome with CLC Genomics. Each read required at least 95% similarity over 50% of length with three mismatches allowed. Transcripts per million (TPM) was used as a proxy for gene expression.
Relative transcript abundance differences were determined as the TPM in one sample relative to the TPM of another dataset (e. g. , male/lactating female). A proportion-based statistical analysis  [170]  followed by Bonferroni correction at 0. 05 was used to identify genes with significant sexand stage-specific transcript enrichment. This stringent statistical analysis was used as only one replicate was available for each treatment. Enriched transcripts in lactating and dry transcriptomes from the species examined were compared to orthologous sequences in G. m. morsitans  [27]. Overlap was determined by comparison of the enrichment status of orthologous sequences in the Glossina species tested. The results of this analysis are visualized in a Venn diagram (http://bioinformatics. psb. ugent.
Cuticular protein analysis The predicted peptide sequences from each species were analyzed by BLASTp analysis  [124]  against characteristic sequence motifs derived from several families of cuticle proteins  [174]. Predicted cuticle proteins were further analyzed with CutProtFam-Pred, a cuticle protein prediction tool described in Ioannidou et al. [175] , to assign genes to specific families of cuticle proteins. To find the closest putative homolog to cuticle protein genes in Glossina, genes were searched (BLASTp) against Refseq protein database from the National Center for Biotechnology Information (NCBI). The protein sequences with the lowest e value were considered the closest putative homologs (Additional file 11).
For protein pairs with multiple DBDs, each DBD was aligned separately. From these alignments, we calculated the sequence identity of all DBD sequence pairs (i. e. , the percent of AA residues that are identical across all positions in the alignment). Using previously established sequence identity thresholds for each family  [176] , we mapped the predicted DNA binding specificities by simple transfer. For example, the DBD of the G. austeni GAUT024062-PA protein is identical to the DBD of D. melanogaster mirr (FBgn0014343). Since the DNA-binding specificity of mirr has already been experimentally determined, and the cutoff for Homeodomain family of TFs is 70%, we can infer that GAUT024062-PA will have the same binding specificity as mirr. All associated data can be found in Additional file 12.