dataesr/scientific-paragraphs-categorization huggingface dataset:
Based on our model, a 5-start genomic fiber containing the complete Mimivirus genome  (1, 196 ,989 bp) should be composed of ~95,000 GMC-oxidoreductases subunits (~93,000 for a 6-start). This gives a molecular ratio of 12. 5 dsDNA base pairs per GMCoxidoreductase subunit, which would be more than experimentally measured. However, in the cryo-EM dataset, there are at most 50% of genomic fibers containing the DNA genome (mostly Cl1), while some DNA strands can be observed attached to the relaxed genomic fiber Cl3 but the vast majority of released DNA was lost during purification of the genomic fiber. Applying an estimated loss of 50 % to the total DNA compared to the measured values, we obtain a ratio, which is in the same order of magnitude of the ones measured in the purified genomic fiber samples.
9 μm, Dr. Maisch) using a 60-min gradient for fiber preparations and a 140-min gradient for virion. MS and MS/MS data were acquired using Xcalibur (Thermo Scientific). Peptides and proteins were identified using Mascot (version 2. 7. 0) through concomitant searches against Mimivirus reunion, classical contaminant databases (homemade) and the corresponding reversed databases. The Proline software (75)  was used to filter the results: conservation of rank 1 peptides, peptide score ≥ 25, peptide length ≥ 6, peptide-spectrum-match identification false discovery rate < 1% as calculated on scores by employing the reverse database strategy, and minimum of 1 specific peptide per identified protein group. Proline was then used to perform a compilation and MS1based quantification of the identified protein groups.
Results Taxonomy: differences between species. One-way ANOVA showed a significant effect of taxonomy on size in the whole dataset and the adults subsample (Mean sq = 0. 13, F = 94. 43 p ≪ 0. 001 and Mean sq = 0. 06, F = 52,15 p ≪ 0. 001, Supplementary Table  2 ), and paired comparisons revealed size differences between C. foxi and C. grandiceps and all others (Supplementary Table  3 ; Fig. 2 ). The smaller species form a size gradient rather than discontinuous distributions. All extreme size values (biggest and smallest species) correspond to the highly sympatric westernmost African lineages, with the smallest species being C. theresae, and the largest C. grandiceps (Figs. 1, 2a, b ). The PCA plot depicting the first two axes (Fig. 2c , PC1 = 33. 2% of total variance, PC2 = 19. 05% of total variance) shows high overlap between the different lineages, highlighting the difficulties regarding species identification.
PC1 describes shape changes mostly visible in the rostrum to brain case ratio, negative values being associated with a rostrum elongation and broadening, an increased rostrum to brain case ratio, and a rounded brain case. PC2 describes changes in overall skull width and brain case roundness, with species at the extreme positive of the axes presenting broader skulls, and rounded brain cases. The MANOVA showed a significant effect of taxonomy on shape PCs in the whole dataset and the adults subsample (Table  1 : R 2 = 0. 24, F = 22. 22, p = 0. 001 and R 2 = 0. 24, F = 11. 89, p = 0. 001 respectively), and paired comparisons showed significant shape differences between all species except for C. cf. grandiceps and C. theresae (Supplementary Table  4 ). Despite the  Arrows represent non-significant size differences.
In the C. poensis complex, the last common ancestor of the West African lineages probably occurred in the Guinean Rain forest  15  , and the forest dwellers in our dataset show in average larger skull sizes than the open landscape inhabitants. Small body size may have been selected in certain lineages or populations as it may increase fitness by facilitating predator evasion, regardless of the environment  42. Small body size may also arise as a consequence of population density in certain areas  43. Considering the relatively strong effect of size on shape for certain species, and the knowledge that allometry can serve as an easily accessible source of variation, it may have represented a very useful tool in the rapid diversification of the group, under stressful situations of competition for food resources and predator evasion.
turba and C. similiturba, and the critically endangered C. wimmeri endemic to Ivory Coast 47. Specimens originally described as C. grandiceps were divided into two lineages, C. grandiceps and a monophyletic group of smaller individuals temporarily named C. cf. grandiceps while awaiting formal description. Details regarding this choice may be found in SM1 (methods section). The analyses including taxonomy as a factor focused on the 7 remaining species (species with N ≥ 9), 6 of which are distributed in West Africa. In total, 4 datasets were generated: (1)  all specimens of all species (N 1 = 433), (  2 ) adults of all species (N 2 = 233), (3) all specimens of species for which N ≥ 9 (N 3 = 428) and (4) adults of species for which N ≥ 9 (N 4 = 221). The choice of examined lineages and their individual count per lineage can be found in SM1 (methods section and Table  1 ), and the specific individuals included in each subset can be found in SM2.
Data acquisition. A recent study focusing on Crocidura species from Sulawesi showed that skull size, rostral length and skull width are useful criteria for species discrimination  48. All three are captured in the complete outline of the skull in dorsal view, using skull centroid size as a proxy for overall size. Due to the small size, the fragile nature, and the high likeliness of Crocidura specimens, using the outlines allowed the inclusion of more individuals while capturing the variability in overall skull shape. In addition, the use of an automated procedure avoided the biases caused by manual landmark placement  49. 433 genotyped individuals of the C. poensis species complex were photographed using a NIKON D5600 and a 60 mm AF-S Micro NIKKOR lens. The individuals and their information are displayed in SM2.
Environmental variables. Due to the wide geographic distribution of the complex, the existence of a latitudinal, longitudinal, and climatic gradient was investigated in the skull morphology of the global species pool. The 19 climatic variables were extracted from the GPS coordinates using the WordClim2 raster database (https:// www. world clim. org/, Ref. 54  ) with a 2. 5 arc-minute resolution and the st_within function from the sf package  55. Since many of the climatic variables are highly correlated, a PCA was run separately on the datasets of numerous specimens and numerous adults subsample (N 3 and N 4 ), using prcomp in the stats package, to extract synthetic uncorrelated climatic variables. In order to account for spatial autocorrelation caused by the geographic distribution of our samples, principal coordinates of neighbor matrices (PCNMs) were calculated with the vegan package 56 using the truncated geographic distance matrix  57, 58.
These PCNMs correspond to spatial filters representing geographic distance. A forward selection was performed to select only the climate PCs and spatial filters significantly correlated with shape and size, using forward. sel from the adespatial package  59. Collection habitat was simplified into broad categories depending on vegetation density. All habitats and their simplifications can be found in SM2. Allometry and age effect. The effect of taxonomy and age were first tested, both on size and on shape using ANOVA and MANOVA (aov, from stats; procD. lm, from geomorph). Due to the existence of a significant interaction term between taxonomy and age on shape, all subsequent analyses were run on the entire dataset as well as on adult specimens separately, to verify that any detected effect would remain significant regardless of age.
In addition, even though size and shape are extracted as separate components from the GPA, the shape component still contains some size information  6. Size-free shape coordinates (residuals of a multivariate regression of shape on size) could not be retained due to an interaction term between taxonomy and size, witness to the absence of a common allometric slope between all species, and size was kept as a covariate  6. After size was regressed against shape with a species grouping, the allometric slopes of the different species were compared in the numerous specimens dataset (N 3 ; pairwise function, RRPP package  60  ). The significance of size on shape was tested to estimate the strength of allometry in each individual species from N 3 , by subsetting the raw outlines and running the morphometrics protocol on each species data set separately.
Pooled species samples. The absence of a significant interaction term between taxonomy and sex both for size and shape (p > 0. 05) justified sex pooling. The effects of taxonomy, habitat, latitude, and longitude were tested separately on size and on shape, to avoid confusion from multiple interaction terms. As mentioned above, the effects were tested both on the whole data set and the adult subsamples (N 1 through N 4 depending on whether taxonomy was considered as a predictor), to control for age effect (size: Supplementary Table  2  & shape: Table  1 ). Pairwise differences between species were tested on the datasets of numerous specimens and numerous adults subsample (N 3 and N 4 ). Significant size differences were examined using a post-hoc t-test with Bonferroni correction to account for multiple testing (pairwise_t_test from the rstatix package 61 ; Supplementary Table  3 ).
Forms were compared two by two to test for statistically significant discrimination between species using the pairwise function (Supplementary Table  4 ). The relative contribution of taxonomy, climate (all relevant PCs) and geography (represented by the relevant spatial filters) was calculated using variation partitioning (Ref. 62 ; varpart in the vegan package) both for the whole dataset of numerous specimens and the numerous adults subsample (N 3 and N 4 ). The results are displayed with a schematic Venn Diagram representing the percentage of variation explained by each of the examined factors (Fig. 5 ). In order to calculate the covariance between shape and climate, and visualize the climatic gradient in skull shape, a two-block partial least square  63  was run on shape data and scaled climatic variables for the complete dataset using a combination of tw2.
Acknowledgements This work was supported by the " Action Transversale du Muséum 2017 " ( CROCIDURA  project), the " Projet fédérateur du département Origines et Evolution 2020 " ( COLLCROC  project). Molecular lab work was performed at the " Service de Systématique Moléculaire " ( UMS 2700 2AD ,  Muséum national d'Histoire naturelle, Paris, France ). Pictures were taken on a macrophotography stand at the  MNHN , for both local and exterior specimens (on loan). IV was funded by a  doctoral fellowship  of the doctoral school ED227. Our thanks go to the field collectors responsible for acquiring the specimens included in this study:  Alain-Didier Missoup ,  Elisabeth Fichet-Calvet ,  Emilie Lecompte  and  Vladimir Aniskin. The map was drawn in Procreate 5. 2. 6 (https:// procr eate. art/) and vectorized with Adobe Illustrator 24.
Data analysis Differences on the AP between T. cruzi-infected (I) and non-infected (NI) insects were explored in the overall population, within each sex, within each population (i. e. , sylvatic, domestic, and laboratory-reared), and within each sex within each population using univariate and multivariate analyzes. Means and standard deviations of abundance were calculated for each type of sensilla (chemoreceptors: BR, TH, TK, and mechanoreceptors: BA) and antennal segment (pedicel, agellum 1 and agellum 2). As original data and their transformations were not normally distributed using Shapiro-Wilk tests [78], Kruskal-Wallis nonparametric tests were used for univariate analyses, followed by pairwise comparisons  [79]. Data were analyzed with the MINITAB Statistical Software, version 17 (Minitab Inc.
Temperature dependence of the viscosity analysis The viscosity of the solvent was measured as the solvent contributes to the viscosity of the sample. The viscosity of the solutions as a function of the concentration must cross the viscosity axis at the solvent viscosity. Although the viscosity of water is reported in literature, its value by the instrument used in this study is unknown because it may depend on the constant of the geometry (Eq. (  5 )) used to perform the measurement. Moreover, the viscosity of the solvent gives an insight on the precision of the instrument while measuring the viscosity of the sample at concentrations approaching zero. The raw data of the temperature dependence of the viscosity of the solvent (demineralized water with 200-ppm NaN 3 ) is shown in Fig. 1. The values of the viscosity of water by DHR3 rheometer using Couette geometry and the value reported by  Korson, Drost-Hansen, & Millero, 1969 (Korson et al.
The values of B 2 and T c were examined as function of the polysaccharide concentration to show the influence of the polysaccharide on the solution resistance to thermal flow. The experimental data of the concentration dependence of the viscosity for various temperatures were analyzed using Huggins and Kraemer function to obtain the C* and the intrinsic viscosity [η]  (Chronakis et al. , 2000). The C* and [η] were evaluated as function of the temperature and the results used to discuss the thermal stability of the polysaccharide in solution.
Acknowledgement We thank  Vincent VERDOOT  for his technical assistance on the Rheometer Instruments. The authors thank  Pr. Marguerite Rinaudo  for providing us with the kappa-Carrageenan product. We thank  Laurine Buon  and  Eric Bayma from Cermav  for their kind assistance on the product composition. This work was financially supported by  I-MEP2 PhD  graduate school. The  Laboratoire Rhéologie et Procédés (LRP)  is part of the  LabEx Tec 21 (Investissements d'Avenir -grant agreement n •   ANR-11-LABX-0030 ) and of the  PolyNat Carnot Institut Investissements d'Avenir -grant agreement  (n •  ANR-11-CARN-030-01 ).
The expression of the genes was normalised to the expression of glycerinaldehyde-3-phosphatedehydrogenase (GAPDH) as housekeeping gene as per the 2 -ΔΔCT method  [49]. The stability of house-keeping gene in expression across all the samples was validated in a preliminary test by comparison with three alternative house-keeping genes, coding for two ubiquitins (UBI5, UBI10), and one actin (ACT11) as described in  [50]. To minimise the technical variation between samples and measurements in the qPCR assay, we adopted a strategy, where all samples were run on the same plate and also the reference gene was always measured in the same run with the genes of interest. For each treatment, three biological replicates were conducted, each in 2-3 technical replicates, to obtain robust results. For accession numbers, description of gene functions as well as primer sequences of the genes measured in this study see Supplementary Table  S1.
Statistical analysis We conducted the statistical analysis using the addin Real Statistics Resource Pack (http:// www. realstati stics. com/ free-downl oad/ real-stati stics-resou rce-pack/) for Excel. We used a homoscedastic, twotailed t-test to test differences in disease severity and fungal growth between the JA-deficient mutants and the Nihonmasari wild type, where significant differences (P < 0. 05) are indicated by *, and highly significant difference (P < 0. 01) with **. For the multiple comparison of different transcript levels between any pair of treatments during compatible and incompatible interactions, we employed a One-Way ANOVA followed by Duncan's test using the R package Agricolae, where significant differences (P < 0. 05) are indicated by different letters above each bar. In the case of histology and transcript analysis, we conducted three biological replicates (in technical triplicates).
Introduction The acquisition of genes through horizontal gene transfer (HGT) is mostly observed in prokaryotes, where they play a significant role in adaptive evolution (e. g. antibiotic resistance). To a lesser degree, cases of HGT have also been observed in eukaryotes with important consequences in the biology of the organism  [1]. The increase of new genomes being sequenced and the prediction of new gene sets, represents an opportunity to detect additional HGT cases and to characterize more precisely the possible donors. To sustain these needs, high-throughput yet robust HGT detection methods are required. One method to predict potential HGTs is to calculate the difference in similarity using BLAST  [2]  (or other sequence similarity search software) between phylogenetically closely related and distant species.
AvP extracts all the information needed to produce input files to perform phylogenetic reconstruction, evaluate HGTs from the phylogenetic trees, and combine multiple other external information for additional support (e. g. gff3 annotation file, transcript quantification file). Our method does not rely on an explicit reference species tree and only uses a simplified take on the species phylogeny, according to the organism tested. This allows for a rapid phylogenetic detection of HGTs that can then be used as input for more sophisticated analyses.
Software description AvP performs automatic detection of HGT candidates within a phylogenetic framework. The pipeline comprises two major steps: (i) prepare, and (ii) detect, and three optional steps: (iii) classify, (iv) evaluate, and (v) hgt_local_score (Fig  1 ). Although the pipeline has been extensively tested with protein datasets, it should work at the DNA level, including non-coding sequences (see GitHub documentation). For the rest of the article we assume a protein dataset. AvP requires three primary files, (i) a fasta file containing the proteins of the species being studied, (ii) a tabular results file of similarity search (e. g. BLAST or DIAMOND  [9] ) against a protein database, and (iii) an AI features file. Furthermore, the user must provide two config files, one with information on the taxonomic ingroup in the study (defining which group of species is considered closely related and which group is distantly related) and one defining multiple software parameters.
AvP prepare The software selects proteins for downstream analyses based on any combination of the metrics AI, outg_pct, and AHS (described below). Then, the software collects all protein sequences corresponding to significant hits from the database based on the tabular results file of the homology search and groups the query species sequences based on the percentage of shared hits (by default 70%) using single linkage clustering. Alternatively, the user can specify a file containing user-generated groups of queries and hits (e. g. from OrthoFinder  [10]  or protein domain analysis). For each group, a fasta file is created containing the query species sequences and their respective database hits. Each file is then aligned using MAFFT  [11]  with an option for alignment trimming with trimAl  [12].
AvP detect There are two options available for phylogenetic inference within AvP: (i) FastTree  [13] , and (ii) IQ-TREE  [14]. The defaults for these programs are [-gamma -lg] for FastTree and [-mset WAG,LG,JTT -AICc -mrate E,I,G,R] for IQ-TREE. The user can change the IQ-TREE parameters in the config file. These two approaches vary in time and compute requirements, and consequently in tree reconstruction accuracy  [15]. Alternatively, the software can utilise usergenerated phylogenetic trees using the alignment files created with AvP prepare with any program that can produce a valid Newick tree format file. By default, AvP does not impose a branch support threshold. However, the user can define a support threshold in the config file under which branches collapse into polytomies. Each phylogenetic tree is then processed (midpoint rooting) and each query sequence is classified into one of the following three categories: HGT candidate (✓), Complex topology (?), No evidence for HGT (X).
AHS: A new contamination-aware metric The two metrics that are widely used (AI and h) utilise only the best Ingroup and Donor hit from the BLAST output. This poses several potential issues and AI in particular can be 0 if both hits have e-value of 0, although they can differ in similarity. The h metric resolves this problem by using the bitscore instead of the e-value. However, both of these metrics are sensitive to taxonomically misclassified or contaminating sequences in databases as they only rely on the best hits. For instance, if the best hit is wrongly assigned a Donor taxid in the database, these metrics will erroneously detect a candidate HGT. In the opposite, a wrongly assigned Ingroup taxid in the database would necessarily result in no HGT detection if it is the best hit. A different approach to try to circumvent this issue is to aggregate all the bitscores of Donor and Ingroup sequences and then perform the calculation of h.
HGT pipeline We tested our pipeline using the predicted protein set for the tardigrade species Hypsibius exemplaris (previously named H. dujardini)  [17]. We used the database NCBI nr instead of SwissProt+TrEMBL libraries, used in the original publication, and selected candidates with AI > 30 instead of h ST > 30 (HGT Index), while the phylogenetic inference was performed with FastTree instead of RAxML  [18]. The final selection was 401 proteins (386 genes) compared to 463 proteins (463 genes), and based on the phylogenetic trees, we detected a total of 379 candidate HGTs (95%) instead of 357 (77%). Overall, 342 candidate HGTs were common to AvP and the previously published analysis, the ones not identified by our pipeline having an AI below 30. We then evaluated the candidate HGTs by comparing the likelihoods of the original HGT-supporting trees to those of constrained trees in which tardigrade and other metazoan proteins were forced to form a monophyletic group.
Equally likely topologies were observed for 27 proteins bringing the total number of strongly supported candidate HGTs to 352 (1. 7% of the total proteins present in the genome). To assess the effect of using different databases, we performed two more searches against SwissProt (SP) and Uniref90 (UR). A total of 196 / 333 / 401 proteins were selected when using SP / UR / NR resulting in 127 / 292 / 352 candidate HGTs after alternative topology tests (AvP evaluate). Hence, depending on the sampling of the sequence diversity present in the sequence database, the number of detectable candidate HGT varies considerably. In the original publication describing Alien Index (AI)  [3] , the authors considered AI > 45 to be a good indication of foreign origin while genes with 0 < AI < 45 were designated intermediate.
However, this AI threshold value was originally defined on one single species only, the bdelloid rotifer, and further analyses on plant-parasitic nematodes have shown that an AI > 45 might be too stringent, leaving several true positives undetectable  [19]. Here, we calculated the F1 score Eq (3) for all N with AI > 0 in H. exemplaris to decide the optimal threshold between precision and sensitivity. We found that selecting genes with AI > 10 represented an optimal balance between sensitivity and precision  (Fig 4). Therefore, we propose to perform AvP with AI > 0 with FastTree option to minimize the risk of missing HGT cases and utilise the scripts provided to calculate the F1 score and based on that, decide the optimal AI threshold (which is 10 for tardigrade example) for more sophisticated and time-consuming phylogenetic analyses.