dataesr/scientific-paragraphs-categorization huggingface dataset:
DNA concentrations were estimated with the NanoDrop 1000 spectrophotometer (ThermoFisher Scientific, Illkirch Graffenstaden, France). We amplified an mtDNA fragment of 432 bp from the control region (CR1), following  Gaubert et al. (2016). PCR products were visualized on 1. 5% agarose gel and sent for bidirectional sequencing at Genoscreen (https://www. genoscreen. fr/en; Lille, France) and Macrogen Europe (https://dna. macrogen-europe. com/en; Amsterdam, The Netherlands). Sequences were aligned manually with BioEdit v7. 2. 5  (Hall, 1999) , and deposited in Genbank under accession numbers OP897333 -OP897461. We amplified 20 microsatellite markers developed from the genome of WBP using four PCR multiplexes after  Aguillon et al. (2020). Six loci (PT_276641, PT_353755, PT_378852, PT_1162028, PT_1753627 and PT_1849728) from the original multiplexes were not considered in our analysis, because of a significant proportion of amplification failures likely due to fluorescent dye degradation (for each locus, > 43% of missing data across all individuals).
Phylogenetic clustering We evaluated the clustering of our CR1 sequences (N = 108) from West Africa relative to the six WBP mtDNA lineages  (Gaubert et al. , 2016)  through a distance tree analysis including all the CR1 sequences available in Genbank at the time of drafting the manuscript (N = 101). Phylogenetic tree reconstruction was performed in MEGA 11  (Tamura, Stecher & Kumar, 2021)  using Neighbor-Joining (NJ), 500 bootstrap replicates and Kimura 2-parameter model  (Kimura, 1980).
Genetic diversity Geneious v 9. 0. 5  (Kearse et al. , 2012)  and the Microsatellites plugin (https://www. geneious. com/features/microsatellite-genotyping/) were used for allele scoring and genotype extraction. After excluding the six deficient loci (see above), we obtained a final dataset of 116 samples genotyped at 14 loci with at most 21% missing data per genotype (Appendix Table  1 ). Validation of our 14 selected microsatellite loci was performed on a subset of "best" 24 samples with ≥80% genotyping success, taken from the whole study zone (as preliminary results showed a lack of nuclear genetic structure between WAfr and Gha; see below). Detection for the presence of null alleles, assuming population at equilibrium, was performed with Microcheker v 2. 2. 3  (Peakall & Smouse, 2012).
Discriminative power of microsatellites markers We used GenAlEx to evaluate the discriminative power of our microsatellite markers in our total dataset (N = 116) by calculating the number of identical genotypes among samples with the Multi-locus tagging option (Matches sub-option). We used the psex() function from the R package poppr (with method = multiple;  Kamvar et al. , 2014)  to assess the probability of encountering the same genotype more than once by chance. We ran Gimlet v1. 3. 3  (Valière, 2002)  to calculate values of unbiased identity probability (uPI) and sibling identity probability (PIsibs).
Acknowledgments KJG and SGB received funding from the  Ministère de l'Enseignement Supérieur et de la Recherche Scientifique (MESRS) of Côte d'Ivoire , as part of the " Trace-Brousse " project financed by the  Franco-Ivorian Cooperation  within the framework of the  Debt Reduction-Development Contracts (C2Ds ) managed by the  Institut de Recherche pour le Développement (IRD). KJG received a PhD scholarship from " Appui à la Modernisation et à la Réforme des Universités et Grandes Ecoles de Côte d'Ivoire"  ( AMRUGE-CI_N°2 ). Field research  and sampling were conducted under the authority of  Ministère des Eaux et Forêts of Côte d'Ivoire , through  Direction Générale des Forêts et de la Faune and Direction de la Faune et des Ressources Cynégétiques. PANGO-GO  ( ANR   ANR-17-CE02-0001 ) and  BUSHRISK  ( FCT IC&DT 02/SAICT/2017 -n° 032130 ) supported the lab work.
Fluorescently labeled images were acquired using a Zeiss LSM980 confocal microscope, equipped with an Airyscan2 module. To ensure consistency when comparing samples within a given experiment, identical scanning parameters were applied to all conditions in each independent experiment. Confocal Z-stacks and projections were processed and adjusted for brightness and contrast in ImageJ, using K. Terretaz's visualization toolset (https://github. com/kwolbachia/Visualization_toolset). To count the total number of cells in hatchlings and adults, Imaris software (Oxford instrument) was employed. Nuclei were highlighted using custom thresholding to allow for quantification. Quantifications of BODIPY-stained storage cells and EdU + cells were performed manually in ImageJ. In all instances, z-stacks encompassing the entire depth of the animals were utilized.
Supplementary videos S1 and S2 were assembled using Imaris and ImageJ software, respectively. Tardigrade schematics and figure compilation were generated using Adobe Illustrator. To determine statistical significance, normality of all datasets was first assessed using the Shapiro-Wilk test. Two-tailed Student's t-test was conducted for the dataset concerning total number of storage cells (Fig. 2D ). For all other comparisons, Mann-Whitney U nonparametric tests were employed for two-way comparisons, and the Kruskal-Wallis test was used for statistical analyses in Fig. 4C. Fisher's exact test was selected for the association assessment, based on a 2 × 2 contingency table. Statistical significance for all quantitative comparisons is represented as ****, where p-value ≤ 0. 0001. Graphs and statistical analyses were performed using GraphPad Prism 9.
ACKNOWLEDGEMENTS We thank  Simon Galas  and  Myriam Richaud  for providing us with H. exemplaris and Chlorococcum samples, and for giving us advice on tardigrade manipulation. We also thank  Benjamin Lacroix  for helping us to establish the model by sharing his equipment, and  Kseniya Samardak  and  Sylvain Kumanski  for their help with tardigrade husbandry. We are grateful to  Kevin Terretaz  for his valuable tips and macros on ImageJ. We also express gratitude to the CRBM direction for its support. We acknowledge the imaging facility MRI, member of the  France-BioImaging  national infrastructure supported by the  French National Research Agency  ( ANR-10-INBS-04 ,  «Investments  for the future»). This research was supported by the  I-SITE MUSE (Montpellier Université d'Excellence).
The completeness, protein coding capacity, repeat structure and sequence variation are discussed in supplementary information (Supplementary Figs. 1 2 3 4 5 6 and Supplementary Tables  1 2 3 4 ) providing the basis for comparisons. The new v10 reference assembly spans 1,448. 4 Mb and is substantially more complete than the previous v9 sequence 18 , assigning 219. 2 Mb more sequence to chromosomes (Supplementary Table  1 ). The v10 assembly is also far more contiguous, with half of the sequence contained in 32 contigs longer than 14. 6 Mb (vs. 71. 0 kb in v9). The assembly captures 99. 6% of known coding sequence (Supplementary Table  2 , Supplementary Note 2). We found that the fragmented quality of earlier assemblies was due in part to the fact that 68. 3 Mb (4. 71%) of the genome was not sampled by the 8× redundant Sanger dideoxy whole-genome shotgun dataset  33  (Supplementary Fig.
Additional chromosome-scale frog genomes To assess the evolution of chromosome structure across a diverse set of frogs, we generated chromosome-scale genome assemblies for three new emerging model species, including the Zaire dwarf clawed frog Hymenochirus boettgeri (a member of the family Pipidae along with Xenopus spp. ) and two neobratrachians: the Puerto Rican coquí Eleutherodactylus coqui (family Eleutherodactylidae) and the túngara frog Engystomops pustulosus (family Leptodactylidae). These chromosome-scale draft genomes were primarily assembled from short-read datasets and chromatin conformation capture (HiC) data (Supplementary Data 1, Supplementary Table  5 , Supplementary Note 3). To further expand the scope of our comparisons, we also updated the assemblies of two recently published frog genomes: the African bullfrog Pyxicephalus adspersus  21  from the neobatrachian family Pyxicephalidae, and the Ailao moustache toad Leptobrachium (Vibrissaphora) ailaonicum  22  from the family Megophryidae (Supplementary Fig.
9. 0 (Supplementary Note 2). The assembly was polished with Arrow 131 , Pilon 132 v1. 23, and then FreeBayes 133 (v1. 1. 0-54-g49413aa) with ILEC (map4cns; https://bitbucket. org/rokhsar-lab/map4cns). The genome was annotated with the DOE-JGI Integrated Gene Call pipeline 134 (IGC) using transcript assemblies (TAs) generated with Trinity 135,136 v2. 5. 1 from multiple developmental stages and tissues (Supplementary Data 1, Supplementary Note 2). RepeatModeler 137 v1. 0. 11 was run on all frog species. The frog and ancestral repeat libraries from RepBase 138 v23. 12 were combined with the repeats consensuses identified by Repeat Modeler. The merged repeat library was used to annotate repeats of all frogs with RepeatMasker 139 v4. 0. 7 (Supplementary Notes 2 and 3).
Assembly and annotation of five pipanuran genomes. Contigs were assembled with Supernova 122 v2. 0. 1 (E. pustulosus and H. boettgeri) or Meraculous 140,141 v2. 2. 4 (E. coqui). For E. coqui, residual haplotypic redundancy was removed using custom scripts (https://github. com/abmudd/Assembly) prior to scaffolding with SSPACE 127 (v3. 0). E. pustulosus and H. boettgeri contigs were ordered and oriented using MUMmer  125  (v3. 23) alignments to PBEC-polished (map4cns commit dd89f52; https://bitbucket. org/rokhsarlab/map4cns) DBG2OLC 123 (commit 1f7e752) hybrid contigs (Supplementary Note 3). All three assemblies were scaffolded further with linked-reads and Scaff10X (v2. 1; https://sourceforge. net/projects/phusion2/files/scaff10x).
E. pustulosus and H. boettgeri chromosome-scale scaffolds were constructed with Dovetail Genomics HiC via the HiRise scaffolder  142  , followed by manual curation in JuiceBox  [128] [129] [130]  v1. 9. 0. Due to the fragmented nature of the E. coqui assembly, initial chromosome-scale scaffolds were first constructed by synteny with E. pustulosus, then refined in JuiceBox  [128] [129] [130]  v1. 9. 0. Gaps in the E. pustulosus and H. boettgeri assemblies bridged by PacBio reads were resized using custom scripts (pbGapLen; https://bitbucket. org/bredeson/artisanal) and filled with PBJelly 143 (PBSuite v15. 8. 24). These two assemblies were polished with FreeBayes and ILEC (map4cns commit dd89f52; https://bitbucket. org/rokhsar-lab/map4cns). A final round of gapfilling was then performed on the three assemblies using Platanus 144 (v1.
2. 1). Previously published L. ailaonicum  23  (GCA_018994145. 1) and P. adspersus  21  (GCA_004786255. 1) assemblies were manually corrected in JuiceBox  128-130 (v1. 11. 08) using their respective HiC and Chicago data (Supplementary Data 1). Gaps in the corrected P. adspersus scaffolds were resized with PacBio reads (as described above) and filled using Platanus 144 (v1. 2. 1) with published TruSeq Illumina data obtained from NCBI (PRJNA439445). As described elsewhere 145 , all assemblies were screened for contaminants prior to scaffolding, and only final scaffolds and contigs longer than 1 kb were retained for downstream analyses. More details on assembly procedures can be found in (Supplementary Note 3). Genomic repeats in all five species were annotated with RepeatMasker 137,139 (v4. 0.
7 and v4. 0. 9) using the repeat library generated above. Protein-coding genes were annotated for E. coqui, E. pustulosus, H. boettgeri, and P. adspersus using the IGC  134  pipeline with homology and transcript evidence. For each respective species, newly generated RNA-seq data were combined with public H. boettgeri  20  (BioProject PRJNA306175) and P. adspersus  21.
Centromeres, satellites, and pericentromeric repeats. Tandem repeats were called using Tandem Repeat Finder 57 (trf genome. fa 2 5 7 80 10 50 2000 -l 6 -d -ngs). To identify tandem repeats enriched in pericentromeric and subtelomeric regions, we extracted the monomer sequences of all tandem repeats overlapping the region of interest. A database of non-redundant monomers was created by making a dimer database. Dimers were clustered with BlastClust 154 v2. 2. 26 (-S 75 -p F -L 0. 45 -b F -W 10). A non-redundant monomer database was created using the most common monomer size from each cluster. The non-redundant sequences were mapped to the genome with BLASTN 155 (-outfmt 6 -evalue 1e3). The enriched monomeric sequences in centromeres and subtelomeres were identified by selecting the highest normalized rations of tandem sequence footprints in the region of interest over the remaining portions of the genome.
Genetic variation. Reads were aligned with BWA-MEM 156 (v0. 7. 17-r1188) and alignments were processed using SAMtools 157 (v1. 9-93-g0ca96a4), keeping only properly paired reads (samtools view -f3 -F3852) for variant calling. Variants were called with FreeBayes 133 (v1. 1. 0-54-g49413aa; --standardfilters --genotype-qualities --strict-vcf --report-monomorphic). Only bi-allelic SNPs with depth within mode ± 1. 78SDs were retained. An allele-balance filter [0. 3-0. 7] for heterozygous genotypes was also applied. Segmental heterozygosity/homozygosity were estimated using windows of 500 kb with 50-kb step using BEDtools 158 (v2. 28. 0) for pooled samples or snvrate 159 (v2. 0; https://bitbucket. org/rokhsar-lab/wgs-analysis). For more detail, see Supplementary Note 2.
Chromatin conformations and higher-order interactions. HiC read pairs were mapped with Juicer 128 (v1. 5. 6) and observed counts were extracted at 1 Mb resolution with JuicerTools. Centromeres were estimated manually in JuiceBox  129  and refined with Centurion 99 v0. 1. 0-3-g985439c using ICE-balanced MapQ0 matrices (https://bitbucket. org/rokhsar-lab/xentr10/src/master/hic). Rabl chromatin structure was visualized with PCA from Knight-Ruiz 166 -balanced MapQ30 matrices, and significance estimated by permutation testing using custom R scripts. Rabl constraint between p-and q-arms was measured as the sum of square distances (SSD) in PC1-PC2 dimensions, calculated between non-overlapping bins traveling sequentially away from the centromere. Inter-/intra-chromosomal contact enrichment analyses were quantified from MapQ30 matrices using c 2 tests in R 167 v3.
(BioProject PRJNA439445) data, and unpublished E. coqui data (stages 7, 10, and 13 hindlimb [Harvard University]; stage 9-10 tail fin skin [French National Center for Scientific Research]). Transcript assemblies used as input to IGC were assembled with Trinity 135,136 (v2. 5. 1) and filtered using the heuristics described in Supplementary Note 3.
Acknowledgements We thank  Karen Lundy  and the  Functional Genomics Laboratory at the University of California Berkeley  for running quality control on extracted DNA and RNA and for preparing Illumina shortinsert libraries;  Oanh Nguyen  and the  DNA Technologies and Expression Analysis Cores at the University of California Davis Genome Center  for preparing and sequencing PacBio libraries;  Dovetail Genomics  for providing the HiC library preparation kit, running quality control on HiC libraries, and preparing and sequencing HiC libraries;  Shana McDevitt  and the  Vincent J. Coates Genomics Sequencing Laboratory at the University of California Berkeley  for sequencing HiC and Illumina short-insert libraries;  Shengqiang Shu  for advice on the use of the IGC annotation pipeline. We thank  Rick Elinson  for providing E.
Data Availability The assemblies, annotations, and raw data are deposited in NCBI for v10 X. tropicalis (BioProjects PRJNA577946 and PRJNA726269), E. coqui (BioProject PRJNA578591), E. pustulosus (BioProject PRJNA578590), H. boettgeri (BioProject PRJNA578589), L. ailaonicum (BioProject PRJNA578588), and P. adspersus (BioProject PRJNA578592).
Image analysis was done using the IPSO Phen software v1. 20. 3. 17 (https://github. com/tpmpinra/ipso_phen)  (56) , resulting in a total of 56 parameters measured, including 37 measures of morphology, e. g. total area, hull, width, height. Additional parameters linked to colorimetry, including mean and standard deviation for all channels in various color spaces (RGB, LAB and HSV) were also recorded. As D. sansibarensis vines tended to grow in irregular patterns, no morphological parameters could be reliably analyzed except for total leaf area, which was calculated as the median of leaf area extracted of images from all 6 angles. Chlorophyll content was estimated through RGB values of plant images as described by Liang and colleagues  (55). Plants were automatically watered daily and fertilized at the beginning and once mid-experiment.
Review of the literature on quality traits in cereal and arable production Related papers published between 2002 and 2022 were retrieved from the ISI Web of Knowledge in the Core Collection Database for a systematic literature review. In all the relevant studies conducted so far, the focus of this review is on the utilization of RS technology at the field scale for predicting grain quality. The review does not take into account the research that involves the use of instruments such as Raman spectroscopy or near-infrared spectrometers in laboratory settings for predicting grain quality. Several keywords were used, including 'spectral index', 'vegetation index', 'remote sensing', 'grain quality', 'grain protein', 'gluten', 'harvest index' and 'starch'. A total of 106 articles were eventually included in the analysis.
Retrieving crop-quality-related traits from remote sensing data Different remote sensing information can be acquired using various sensors (Section 4), and three approaches to crop quality estimation have been discussed in Section 5. One of the key processes is retrieving crop quality variables or critical agriculture parameters, such as LNC, PNC, LAI and NNI, from remote sensing data. For this purpose, various algorithms have been developed along with numerical algorithms and computer techniques (Fig. 5 ). The mathematical expression of crop agronomic parameters based on spectral bands, vegetation indices or radar information is the initial inversion method and a method with a relatively wide range of applications  [2, 13]. VIs have been widely used to monitor critical agriculture parameters at different growth stages  [107].
Machine learning algorithm Machine learning methods, such as support vector machine (SVM), artificial neural network (ANN) and random forest (RF) analyses, have been widely used to integrate multi-source input variables, including the fusing of vIs and SAR/LiDAR-derived crop height, structural metrics  [77, 81]  or imagery textures  [78]. There is a linear correlation between grain yield and the spectral reflectance of winter wheat, as well as biomass. However, there is a nonlinear relationship between protein content and grain yield. This characteristic contributes to the higher accuracy of machine learning algorithms such as RF and ANN in predicting crop quality  [82]. Despite the excellent performance of deep learning algorithms, a well-trained network depends on a wealth of training datasets that are expensive to collect, and their generality for image quality has not been thoroughly tested.
The main canopy radiation transfer models include SAIL, NADIM, MCRM and DART. During the development of 2D and 3D radiative transfer models, great progress has been made in inverting LAI, chlorophyll, pigment and AGB. Physical-based methods that directly involve N have been rarely used, whereas plant nitrogen is directly related to crop quality attributes, such as GPC. The few studies that use radiative transfer models to analyse N generally rely on the correlation between leaf chlorophyll content and N  [49, 91]. To guarantee the transferability and robustness of monitoring different quality-related agronomic parameters, these mechanistic approaches are urgently needed. The powerful deductive capabilities of RTM models can be used to build large training datasets for machine learning models to infer parameter predictions that are not present in existing RTM models  [92].
The conversion of information to high spatialtemporal resolutions (downward conversion) is complex and cannot be easily programmed or applied in practice  [104]. Scale-up conversion, which converts information upward, is commonly used to solve the scale mismatch. Huang et al. combined phenological information with low spatial resolution remote sensing data and then adjusted the track of assimilation parameters generated by the crop growth model via the inversion of relatively accurate values from medium-and high-resolution images so as to improve the assimilation accuracy  [105]. Although the application of data assimilation systems in large areas continues to face many challenges, Huang et al. derived a 1 km daily AGB dataset of the main winter wheat producing areas in China based on this system  [80].
Acknowledgments. 55 References. 55 Acknowledgments This study was supported by the  National Natural Science Foundation of China  ( 42271396 ), the  Natural Science Foundation of Shandong Province  ( ZR2022MD017 ), the  Key R&D Project of Hebei Province  ( 22326406D ), and  The European Space Agency (ESA)  and  Ministry of Science and Technology of China (MOST) Dragon  ( 57457 ).
On-line carcass classification results are not only used to compensate farmers based on a payment grid but are often used by value chain stakeholders for other purposes, such as evaluating feeding practices and in breeding programs. However, as discussed earlier, the uncertainty of this assessment is quite high; therefore, caution must be exercised when using individual classification results or comparing methods. For example, the ZP method is widely used by small slaughter plants in many EU member states but with different equations. In a study by Font-i-Furnols et al. (  2016 ), large differences were found in the LMP obtained using different equations for the same dataset (up to almost five percentage points LMP), indicating a problem with low harmonization. Although accuracy is a problem when results from different member states are compared, reproducibility is important at the local level and depends on the operator, instrument, or other influencing factors.
The unrooted species tree inferred from STAG was then rooted using the STRIDE algorithm  (Emms & Kelly, 2017)  by identifying wellsupported gene duplication events. The resulting cladogram was visualized with R package ggtree (G. Yu et al. , 2017). To identify and visualize possible conflicting signals that would suggest recombination events and evolutionary relationships within the Xanthomonas sequence data, Multi-locus sequence analysis (MLSA) was carried out for 12 housekeeping genes fragments  (gyrB, gapA, lacF, gltA, fsuA, lepA, atpD, rpoD, glnA, efP, dnaK, and fyuA)  using autoMLSA2 (v0. 7. 1) (https://github. com/davised/automlsa2). The resultant splits. nex file was used in SplitsTree4 (v4. 17. 0)  (Huson et al. , 2008)  to develop a phylogenetic network. The possibility of recombination events was identified by the branches that form parallelograms  (Joseph & Forsythe, 2012).
Prediction of Mobile Genetic Elements To study the genomic differences driven by mobile genetic elements (MGE) in the genus Xanthomonas, we used Mobile Genetic Element Finder (MGEfinder) (v1. 0. 6)  (Durrant et al. , 2020). MGEfinder assembles the short reads and aligns them to a reference genome to find insertions  (Durrant et al. , 2020). Xanthomonas reads from this study and representative short reads were downloaded from NCBI's SRA database and trimmed using Trim Galore (v0. 6. 6) (https://github. com/FelixKrueger/TrimGalore). Reference genomes were indexed, and the cleaned reads were aligned with BWA-MEM (v0. 7. 17) (H. Li & Durbin, 2009). Target strains were assigned to pathogenic reference genomes according to their phylogenetic placement generating eight clusters (Table  S2 ). Each cluster contains a representative/type strain and the strains from this study.