dataesr/scientific-paragraphs-categorization huggingface dataset:
DATA AVAILABILITY STATEMENT The vcf file will be available in the Banana Genome Hub (https://banana-genome-hub. southgreen. fr/gigwa). Chromosome painting results will be available at https://gemo. southgreen. fr/. The vcf file, allele ratio profiles, and chromosome painting results will also be available from https://banana-genome-hub. southgreen. fr/ in the Download section. The scripts were added to the vcfhunter toolbox (https://github. com/SouthGreenPlatform/VcfHunter). The Illumina reads are available under Bioprojects PRJEB58004 and PRJEB28077.
Colour Measurements Colour measurements were carried out on fresh samples for cutting, on six fish, at the three locations specified in Figure  1. The total sample number was 126 over the whole of the kinetic. Each sample was measured with 3 consecutive acquisitions, the values of which were averaged. Colours were measured based on the CIELab colour space system using a chroma meter (Konica Minolta CM-2500d, Tokyo, Japan) after calibration with a white standard. The trichromatic parameters L*, a*, and b* were calculated against CIE standard illuminant D65. The values were transferred directly to Excel using Spectra-Magic NX software version 3. 31 (Konica Minolta CM-2500d, Tokyo, Japan).
Colour Measurements Colour measurements were carried out on fresh samples for cutting, on six fish, at the three locations specified in Figure  1. The total sample number was 126 over the whole of the kinetic. Each sample was measured with 3 consecutive acquisitions, the values of which were averaged. Colours were measured based on the CIELab colour space system using a chroma meter (Konica Minolta CM-2500d, Tokyo, Japan) after calibration with a white standard. The trichromatic parameters L*, a*, and b* were calculated against CIE standard illuminant D65. The values were transferred directly to Excel using SpectraMagic NX software version 3. 31 (Konica Minolta CM-2500d, Tokyo, Japan).
Images were acquired at magnifications of ×3000, ×8000, ×12,000, and ×25,000. Sarcomere lengths were quantified using Image J software v. 1. 53r for Windows 10. At least 50 sarcomeres per fish were measured for D0, D5, D7, D9, and D15, taking care to make the measurements in different muscle fibres. For each sample, a mosaic was taken with the ×4 lens; then, images were taken with the ×10 and ×20 lenses to show any alterations in the samples in more detail.
Machine Learning Analyses To visualise how spectral data are distributed in a low dimensional space, we used a t-SNE analysis  [17]. Perplexity was adjusted to 50. A SVM model was computed to predict the 7 postmortem times from the sole spectral data (n = 805). The radial basis function (RBF) kernel was considered, and a 10-fold cross-validation was applied to train the model. The output of the model is a classification matrix showing the correct percentage of samples against the true value. Additionally, t-SNE and SVM models were performed in the Orange data mining software version 3. 33  [18]. Then, to identify biomarkers of freshness or advanced postmortem time, we computed a linear discriminant analysis (DA) from PC components calculated on the same spectral data. The model assumed that each group had a normal distribution.
The membership probabilities of each observation in a given group were determined using Mahalanobis distance (the distance of an observation from the mean of a group divided by the standard deviation along the direction vector). The F1 score was calculated from the sum of each principal component's contribution, which is the product between the canonical scores and the weight associated with each PCA dimension. Here, the F1 score reflects the relative contribution of the wavelength in discriminating one or the other group, which helped us identify biomarkers attributed to freshness or D7 postmortem time. These analyses were performed in Jmp 11. 0 (JMP, SAS Institute, Cary, NC, USA).
Consistent with this hypothesis, our data point to a good correlation between pH variations and L* values over a 15-day time course (n = 18 per time point, Sup. dataset), whereas Rammouz and colleagues  [20]  found no significant relationship between pH variations and luminance in trout. Overall, the biochemical parameters measured showed little metabolic change in trout muscle during the 15-day postmortem time, at least in terms of protein proteolysis and lipid oxidation. This result is at least partially explained by the high activity of the trout before slaughter, and or our rigorous storage conditions at 0 • C.
Predicting Postmortem Day Using Spectral Data and Machine Learning In addition to the above methods, we also explored the applicability of infrared spectroscopy combined with machine learning to predict the postmortem days from spectral data (Figure  4 ), and also to identify specific molecular bonds (i. e. , biomarkers) associated with the freshness (Day 0) or with increased postmortem aging (Figure  5 ). 1 ). Table  1. Molecular bonds were identified from the classification model of fresh muscles (D0) over more advanced postmortem time (D7). Biological compounds associated with these spectral ranges were identified from the literature. A number of the biomarkers found for D7 were also identified at D15 in a separate model comparing D0 to D15 (Figure  S3 ). 1 ).
Peaks that helped identify D7, such as 1490 and 1670 cm -1 (Table  1 ), also contributed to identifying the aging state at D15 (Figure  S4 , Table  S1 ). We thus conclude that the disappearance of nucleic acids and an increase in fatty acids in the spectral signatures of postmortem tissues may potentially be useful predictors of the advancement of postmortem aging. We explored how well biomarkers of postmortem aging could correlate with the biochemical parameters shown in Figure  2. For this analysis, we randomly divided our spectral dataset into three so as to match the number of physicochemical measurements, thus enabling us to calculate correlations (Supplementary Excel File). Interestingly the protein solubility values shown in Figure  2C  correlated negatively at a significant level (p ≤ 0.
Cell Morphology and Organisation of Endocarp Virtual sections highlighted the spatial and temporal variability in the cell shape of the endocarp (Figures  3  and 4 ). To more globally explore the structure of the endocarp, its surface was isolated using the Crop3D software for caryopses at two stages of development: at 150 • DAA and at 250 • DAA in the lobe region. Before 150 • DAA, the mesocarp was not disintegrated enough to allow for a segmentation of the endocarp. At 250 • DAA, 3D reconstructions were performed at different positions along the caryopsis (Figure  10A ). They highlighted the surface of the endocarp cross cell layer (Figure  10B-G ). The surface was not flat, and the cross cells appeared as patches of continuous and elongated cells arranged in parallel with the ends located at approximately similar positions and forming ridges.
In the dorsal region of the caryopsis, below the cross cells layer, tube cells were present. Their 3D structure and arrangement were obtained using the Crop3D software at different longitudinal positions (Figure  11A ). In stages prior to 150 • DAA, the tube cells were not clearly distinguished in the X-ray images in positions other than the upper part of the caryopsis because no sufficient voids were present in the cell layer (Figure  3B ). The 3D representations highlighted the fact that tube cells were elongated and orientated in the same direction. Their orientation axis was perpendicular to that of the cross cells, both cell layers forming a 3D grid (Figure  11E-H ). At 150 • DAA, tube cells were contiguous, although spaces were present between the cells at the top (Figure  11B, E ). At the bottom and at the top (Figure  11B, D ), the tube cells were mostly adherent to the seed, while in the middle part, the tube cells did not systematically adhere to the seed (Figure  11C ).
The 3D reconstruction of individual cells was carried out using the Free-D software to visualize the arrangement of these top endocarp cells compared to the underneath seed compartment (Figure  12C, D ). At the bottom of the caryopsis, several layers of endocarp and structures forming "extensions" from the seed were also visible (Figure  12E ). The 3D reconstruction of individual cells showed that the cells that appeared as "extensions" were cells with an elongation axis that followed the curvature of the seed, while cells in the next layers had an elongation axis corresponding to the caryopsis longitudinal axis (yellow and brown) (Figure  12F ). of the caryopsis because no sufficient voids were present in the cell layer (Figure  3B ). The 3D representations highlighted the fact that tube cells were elongated and orientated in the same direction.
Other imaging techniques can provide complementary information. For example, magnetic resonance imaging allows for quantifying water fluxes within the organ. Microscopy or mass-spectroscopy imaging can describe the chemical composition within tissue slices. Moreover, the local mechanical properties of tissues can be measured using atomic-force microscopy. The joint analysis of the information provided by different techniques represents a methodological challenge. Correlative imaging approaches and image registration methods represent a first technical solution to combine different observations from the same sample  [46] [47] [48]. An additional difficulty comes from the fact that imaging data are often acquired on different samples. In that case, the information provided by an individual image has to be interpreted as a sample measurement representative from a whole population.
Image Preprocessing Image data generated by the Psiche beamline consisted in binary data files in a raw format, with intensity coded into 32-bits floating point values. Raw data files were converted to 3D 8-bits grayscale images, with the intensity re-calibrated between 0 and 255, and saved as 3D Tiff images. For each 3D image, three 2D slices corresponding to the middle slice along each of the X, Y, and Z direction were generated to provide a quick overview of the caryopsis shape. Preprocessing was performed with the ImageJ/Fiji software  [54].
Alignment To facilitate further analysis, each caryopsis was aligned along its main axes. First, the crease tip was identified from segmented cross-sections of the caryopsis. The series of 3D positions were used to align the crease axis with the vertical axis. In the second step, the caryopsis was rotated around the vertical axis such that the two lobes were orientated in the y-axis direction. Finally, each caryopsis was centered within the image, using the same image size for all the caryopses of the same stage. The same alignment procedure was applied to binary images and grayscale images. The estimation of the transform parameters was performed within Matlab (the Mathworks, Natick, MA, USA). The 3D aligned images were generated using in-house software tailored to process large 3D images (https://github.
Visualization The ImageJ/Fiji software  [54]  was used for the interactive visualization of 3D images, either using slice-by-slice display, or using the 3D Viewer plugin. To facilitate the interpretation of 3D structures, a specific plugin for ImageJ was designed that allowed for the generation of a 3D scale bar with various locations and orientations (https: //github. com/ijtools/ScaleBar3d, accessed on 21 February 2023).
Three-D Visualization of the Organization of the Inner Tissues Several tissues and cell layers within the caryopsis structure are located inside or between other bright tissues, making it difficult to assess their 3D structure and organization. For this, we developed a semi-interactive software tool (Crop3D) that allows us to crop from a 3D image a region corresponding only to the desired tissues. The user is able to select representative slices and manually extract the 2D contour corresponding to the structure of interest. The contours are merged together with help of Fuchs' algorithm  [55]  to generate a 3D polygonal mesh that surrounds the structure. Finally, the result image is generated by using the intensity of the original image for voxels located inside the mesh, and zero for voxels located outside.
Visualization of the 3D Epicarp (RotCrop3D) The 3D visualization of epicarp cells from high-resolution 3D images is made difficult by the variations in the orientation of the epicarp, the size of the images, and the potential presence of other structures (tape and surrounding tissues). To overcome these problems, we developed the RotCrop3D plugin for the ImageJ/Fiji software. RotCrop3D allows for cropping and resampling a portion of a 3D image using arbitrary orientation. The user can select the size of the result image, the center of the box, and the orientation. The orientation can be chosen from three rotation angles. It can also be evaluated automatically by computing the local gradient around the center point and using its direction for the z-axis. The result is a new 3D image, where the epicarp is roughly parallel to the XY plane, making the visualization of epicarp cells and stomata much more straightforward.
Acknowledgments: We thank the greenhouse teams of IE Végépole from  INRAE Clermont-Ferrand  for valuable assistance in plant preparation and care. Eric Badel ,  Fabienne Guillon ,  Andrew King , and  Camille Rivard  are acknowledged for their contribution to the image acquisition, described in Legland et al. , 2022. We greatly acknowledge  Yuka Kuroda  for her careful revision of the English grammar and syntax.
Methods Data were collected from individuals living in villages located near Sibiti, the capital town of the Lékoumou Division in the Republic of Congo. These individuals were examined as part of a placebo-controlled clinical trial evaluating the safety and effect of levamisole on L. loa MFD  [9]. This trial consisted of measuring baseline participants' MFD a few days before treatment and monitoring their MFD on Day 2 (D2), D7 and approximately D30 (from D29 to D31) after treatment with levamisole or placebo. Individuals were first examined in October 2019 to identify those meeting the inclusion criteria for the trial, which was supposed to start on March 2020. Due to the COVID-19 pandemic, the trial had to be postponed, and a second screening survey had to be organized between January and March 2021.
All analyses were performed using STATA software version 15. 1. The protocol of the trial (ClinicalTrials. gov Identifier: NCT04049630) during which the data analyzed in this article was collected was approved by the Committee on Ethics in Health Sciences Research of the Republic of Congo (no. 226/MRSIT/IRSSA/CERSSA), and an Administrative Authorization (no. 469/MSP/CAB/ UCPP-19) was released by the Ministry of Health and Population of the Republic of Congo (MHPRC)  [9]. Samples and data were collected by personnel for the MHPRC and before each step (screening surveys and inclusion in the clinical trial proper); each participant was given an information notice in French and in the local language (Lingala) and asked to sign an informed consent form (in both languages).
Correlation Since antioxidant compounds are the strongest and final indicator to separate in some genotypes and cultivars of potato, several multivariate analyses were performed on the data matrix of genotypes and cultivars to evaluate the antioxidant compounds of genotypes and cultivars of potato and establish the range of responses detectable in this potato collection. Based on cluster analysis, clusters of some genotypes and cultivars of potato could be identified largest distance on the dendrogram (Figure  1 ). Examination of antioxidant compounds in genotypes and cultivars of potato data obtained from the heatmap showed that the highest TSF was related to genotype 68, which is consistent with the average comparison results (Table  1 ). Also, the result showed that the highest TPC was related to genotype 58 (Figure  1 ), which is not consistent with the average comparison results (Table  1 ).
According to the result of the Pearson correlation coefficient (PCC) in antioxidant compounds of some genotypes and cultivars of Potato (Table  2 ), the highest Pearson correlation coefficient (PCC) between TPC was related to FRAP. Also, the lowest correlation in antioxidant compounds was associated with TSS and TAA. The result showed that the highest correlation value occurs between DPPH and TFC (0. 947). potato collection. Based on cluster analysis, clusters of some genotypes and cultivars of potato could be identified largest distance on the dendrogram (Figure  1 ). Examination of antioxidant compounds in genotypes and cultivars of potato data obtained from the heatmap showed that the highest TSF was related to genotype 68, which is consistent with the average comparison results (Table  1 ).
Statistical Analysis Data were statistically analyzed according to a 1-way ANOVA test. The significant differences were then evaluated using Duncan's posthoc test with a significance level of p < 0. 05. Pearson correlation coefficients were calculated for all the analyzed genotypes. Data from chemical profile and antioxidant capacity were also analyzed using a PCA (principal component analysis), and the results were graphically processed to highlight the contribution of each variable respectively (analyzed compounds) in the samples' differentiation. All computations were analyzed by SAS 9 software (SAS Institute, Cary, NC, USA).
Similarly, 12 QTL associated with oil content were identified using 292 accession numbers  [78] , and 253 loci controlling oil content, protein content, oleic to linoleic acid ratio, and fatty acid composition were identified  [77, 80]  using 120 and 250 genotypes, respectively. Among all the genotypes that have been used as a mapping population, more than 50% are from core collections. QTL that have been mapped for important quantitative traits, particularly utilizing a biparental approach, have been discussed in the next sections.
A large number of software implementing the above methods are used in peanut, including R/qtl, QTL Cartographer, and ICIM Mapping  [15, 28, 30, 33, 34]. From most studies reviewed here, QTL mapping technique accuracy depends on several factors, including the statistical method's capacity to locate and estimate the genetic effect of the QTL, the type and size of the mapping population, the genetic and heritability of the trait, the number and contribution of each QTL to the total variance, their interactions and their distribution over the genome. In addition, the ploidy coupled with mixed meiotic behavior is not yet considered in QTL detection and may affect the accuracy of QTL mapping in peanut. Along with these accuracy factors, QTL analysis has limitations like other techniques. Some of these limitations include the inability to detect all loci, the number of QTL detected, their precise position, and their effects are subject to statistical error.
Quantification of Protein Expression Relative quantities of recombinant proteins were assessed using Bio-Rad image lab 6. 1. 0 tools. The software was used to quantify the protein bands of interest by selecting them. The adjusted band volumes, subtracting the background, were calculated by the software. The values calculated in the data analysis table were used to determine the ratios for the desired proteins using a cellular biotinylated protein of approximately 130 kDa.
Bioinformatic Analyses Codon usage and the existence of potential rare codons were analysed using the codon usage calculator from the Sequence Manipulation Suite (https://www. bioinformatics. org/ sms2/codon_usage. html (accessed on 6 March 2023))  [28] , using 21,241 Culicoides sonorensis annotated CDS from VectorBase (https://vectorbase. org/common/downloads/release-61 /CsonorensisPIR-s-3/fasta/data/VectorBase-61_CsonorensisPIR-s-3_AnnotatedCDSs. fasta (accessed on 6 March 2023)). The GC content and identification of GC-rich regions in the viral nucleic acid sequences were analysed using a sliding window of 300 nucleotides and the GC content calculator (https://www. biologicscorp. com/tools/GCContent (ac-cessed on 6 March 2023)). RNA secondary structures and minimum free energy (MFE) were inferred using the 'RNAfold' programme, implemented in the 'ViennaRNA' package (version 2.
0)  [29]. Models of protein tertiary structures were investigated using Phyre2 (http://www. sbg. bio. ic. ac. uk/phyre2 (accessed on 27 January 2023)), and structures were visualised using ChimeraX software (version 1. 5)  [30]. Prediction of post-translational modifications in proteins was performed using musite (https://www. musite. net (accessed on 27 January 2023)).
Glycosylation in insect cells would add short chains of sugars  [31] [32] [33]  with a size of up to 3 kDa per site (up to 15 sugar moieties per site), thus potentially increasing the VP1 size by approximately 6 kDa. The site at 777-779 is only 11 amino acids downstream from the GDD motif (in the nucleotide triphosphate entry channel) of the polymerase, located close to the basal part on the outer surface of the polymerase (Figure  7 ). The NIT site is juxtaposing the NH 2 end of a VP3 monomer (J chain in PDB entry 6PO2) (Figures  7  and S8 ).
Glycosylation in insect cells would add short chains of sugars  [31] [32] [33]  with a size of up to 3 kDa per sit (up to 15 sugar moieties per site), thus potentially increasing the VP1 size by approxi mately 6 kDa. The site at 777-779 is only 11 amino acids downstream from the GDD moti (in the nucleotide triphosphate entry channel) of the polymerase, located close to the basa part on the outer surface of the polymerase (Figure  7 ). The NIT site is juxtaposing the NH end of a VP3 monomer (J chain in PDB entry 6PO2) (Figures  7  and S8 ). The amino acids surrounding the NIT site (EENITTLE) are fully conserved in at least some of the other atypical BTV serotypes including BTV-25, BTV-28, BTV-X-ITL2015 and ITL2021 (serotype 32). Figure  7  (panel D) shows the steric hindrance which a glycosyl chain of eight sugar moieties (Figure  S9 ) could engender thus possibly interfering with the anchoring of the amino terminal end of a VP3 subunit into a grove located at the lower outer surface of the VP1 and therefore potentially influencing both BTV particle assembly and replication.
Calculations To determine whether N, P, or N + P fertilization affected stem growth over time, we calculated the mean annual dbh as: Growth stem ¼ DBH t2 -DBH t1 ð Þ =t2 -t1, where DBH t2 and DBH t1 were the values measured values during the final (2020 for Paracou and 2019 for Nouragues) and first (2015) dendrometer readings, respectively. 8. 75% of the data were slightly negative values, which were reclassified and considered to be 0. Three outliers were excluded due to high values. Tree species were classified as either N-fixers or non-N-fixers following the classification provided by  Tedersoo et al. (2018). In the Nouragues and Paracou tree growth database, 181 individuals of 26 tree species were categorized as potentially N-fixers, and 333 individuals of 128 species were categorized as non-N-fixers.
Statistics analyses Data analyses were conducted using glmmTMB R  (Brooks et al. , 2017) , lme4  (Bates et al. , 2015)  and lmerTest  (Kuznetsova et al. , 2017)  packages in R, with ggplot  (Wickham, 2016)  and ggpubr used for visualization  (Kassambara, 2020). We evaluated the effects of fertilization on tree growth using generalized linear mixed models ("glmmTMB" function) to model Growth stem as a function of treatment (N, P, N + P, or control), N-fixer status, and their interaction as fixed effects, with topography (Top, Slope or Bottom), and site (Paracou, Nouragues) as crossed random effects (i. e. , "(1jSite)"). The Tweedie family linked to log distribution was used for null-inflated exponential-shaped datasets. We assessed whether fertilization affected the foliar elemental composition in comparison with control plots (2019 data) using mixed effect models ("lmer" function).