dataesr/scientific-paragraphs-categorization huggingface dataset:
CLIMEX model We used CLIMEX (Hearne Scientific Software, version 4. 02, Australia,  Kriticos et al. 2015)  to model the climate suitability patterns for T. absoluta. The "Compare Location (one species)" module was employed for estimating the potential distribution of T. absoluta. The Ecoclimatic Index (EI), which ranges from 0 to 100, is used to describe the overall climatic suitability of a given location for a species to persist. An EI value of 100 represents ideal conditions for survival, whereas an EI value of 0 indicates unsuitable conditions for survival. The EI value is determined by the annual growth index (GI A ) and the stress index (SI): EI = GI*SI (1) Each of the indices used to calculate annual growth and stress are based on functions that describe the species positive or negative response to temperature and moisture estimated from either laboratory experiments or from fitting the species distribution or fitting to phenology data  (Zalucki & Van Klinken 2006).
Climatic data and scenarios To build the model, we used year-round occurrence data for T. absoluta populations in open fields in South America (Supplementary Fig. 1 ) to estimate parameters (Supplementary Table  1 ); Mediterranean data was used for model validation (Supplementary Fig. 1b ). The CliMond CM30_1995H_V2 climatic dataset interpolated at 30 arc minute (~50 km) resolution was used  (Kriticos et al. 2015). This dataset has long-term monthly climate means centered on the year 1995 for precipitation, maximum temperature, minimum temperature, and relative humidity at 9:00 and 15:00 hours. Future climate scenarios were extracted from the Climate futures databases (CliMond) for twenty-year averages centered on 2080. The CMIP5 Global Climate Data were derived using the Representative Carbon Pathways (RCP) emission scenarios  (Kriticos et al.
Our simulation of the distribution and phenology of T. absoluta is based on an ENM (Ecological niche model) to describe the species response to climate. A correlative SDM (species distribution model) would likely fit the occurrence data very well but be precisely wrong if the occurrences in areas with harsh winters are taken at face value. As two recent examples for Bemisia tabaci show, when using SDMs, it is not even sufficient to filter records on the basis that they have been observed in the field  (Ramos et al. 2018). In these cases, field records from extremely cold regions (adjacent to APFs) were included in the model training data, and consequently the potential for establishment in cold environments was significantly overestimated. The known distribution of T. absoluta is still limited in China  (Zhang et al.
, 2012). Despite its signi cance for food security in Africa, pearl millet yields generally remain very low, averaging less than 1 ton/ha ( FAOSTAT Database, 2014,  Wrigley et al. , 2015]. However, the genetic potential of pearl millet, which can be utilized to enhance its tolerance to abiotic stresses such as water stress and improve yield, has yet to be fully exploited  (Haussmann et al. , 2012). Encouraging prospects exist for leveraging the available sequenced genome  (Varshney et al. , 2017)  and the identi cation of yield-associated quantitative trait loci (QTLs)  (Yadav et al. , 2002; Tharanya et al. , 2018]  offering promising avenues for further improvement  (Yadav et al. , 2021). Pearl millet, despite its immense potential for climate change adaptation in African and Indian agriculture, is often considered an orphan crop with a smaller scienti c community compared to other cereals  (Mace et al.
Correlation between metabarcoding and metabonomics datasets Co-inertia analysis of active microbial OTUs abundance and the chemical space covered in the root and RAS compartments of each PM line revealed a correlation and covariation between these two datasets, in uenced by the aggregation ratio (Fig. 4 ). The rst two components explained approximately 40% of the total variance. In the root compartment, components 1 and 2 accounted for 25. 7% and 16. 7% of the variance, while in the RAS compartment, they accounted for 20. 4% and 19. 8% of the variance. Notably, there was a clear separation between the HAL lines and the other two lines (IAL and LAL), which exhibited closer proximity to each other in the root compartment than in the RAS (Fig. 4A-C ). To further explore this correlation, we conducted co-inertia analysis of the main OTUs and the chemical space categorized as CHO, CHNO, and CHOS compounds for the different PM lines in the two compartments (Fig.
Co-inertia analysis of root and RAS omics datasets Co-inertia analysis of the microbial populations and the speci c metabolic compounds datasets from the root and RAS compartments revealed a correlation between the microbial populations and speci c metabolic compounds in the PM lines, as depicted in the 3D plots (Fig. 4 ). The separation of the correlated omics data sets of each PM line and their alignment with the soil aggregation ratios further supported the link between plant-microbiota interactions and soil aggregation. Similar approaches combining metabonomics and metagenomics have been employed in various plant species, including A. thaliana  (Huang et   (Gotthardt et al. , 2016) , poplar  (Kaling et al. , 2018)  highlighting the role of root exudation in shaping the root/rhizosphere-associated microbiota and their collective impact on soil aggregation.
10. 0)  (Bolyen et al. , 2019). The sequences were demultiplexed, denoised and chimeras were removed using DADA2  (Callahan et al. , 2016). The sequences were then aligned using MAFFT  (Katoh et al. , 2013)  and used to construct a phylogeny using FastTree (Price et al. , 2009). Taxonomy was assigned using a Naïve Bayes classi er trained on the GreenGenes 16S rRNA gene database (version 13_8) and the Unite ITS database (version 7). 8,213 features of 16S rRNA gene were generated from 212,986 reads from 28 samples (and with C1 sample ltered the numbers were 7945; 205,924 and 27 respectively). 1 412 features of ITS were generated from 780,825 reads from 28 samples (and with C1 sample ltered the numbers were 1355; 735,565 and 27 respectively). After generating the OTUs/sample abundance matrix, Metadata le and Phylogenetic tree from Qiime 2 pipeline, they were uploaded to the MicrobiomeAnalyst server (Dhariwa et al.
For beta-diversity, a Bray-Curtis dissimilarity was used to measure the distance between each pair of samples. This explicit comparison of microbial communities (pairwise) based on their composition was tested using permutational multivariate analysis of variance (PERMANOVA; 999 permutations) and plotted by the nonmetric multidimensional scaling (NMDS). Linear discriminant analysis (LDA) coupled with the LDA effect size (LEfSe) technique was performed using LEfSe module for huttenhower lab galaxy  (Segata et al. , 2011). The structure of abundance dataset was modi ed using R software to t LEfSe module le format. The signi cance of Kruskal-Wallis and pairwise Wilcoxon tests were examined at the level of 0. 05, and the threshold of LDA score was 2. Microbial network analysis was performed by using the co-occurrence correlation of 16S rRNA gene and ITS abundance in root and RAS for each PM line.
Pearson correlation test was used to have the p-values and then corrected them by ltering with a p-value of 0. 05 as threshold  (Junker et al. , 2011)  resulting in a minimal correlation coe cient of 0. 8. The resulting network topology and structure were computed using igraph package (Csardi et al. , 2006) on R software and described by a set of indices  (Newman, 2003). Extraction of hubs for each network by calculating the number of degree and the betweenness was then performed. The analysis of microbial network was completed by studying the topological roles of the nodes (including hubs) in the four PM lines by calculating the among and within-module connectivity parameters Pi and Zi, respectively  (Guimerà & Nunes Amaral, 2005). Finally, in order to investigate the relation and interaction between metabarcoding and metabonomics datasets, a co-inertia analysis was performed with each metabonomic sample associated with its metabarcoding sample.
Funding French Agence Nationale de la Recherche  ( ANR RootAdapt ANR17-CE20-0022-01 ) of data and materials Raw DNA les and associated metadata were deposited in the  US National Center for Biotechnology Information (NCBI)  sequence read archive under accession number  SAMN30841935. Raw data about 16S rRNA gene and ITS metabarcoding are stored in the FigShare platform (https:// gshare. com/account/items/23635608/edit), and Raw data about metabonomic analyses are also stored in the FigShare platform (https:// gshare. com/s/4dbf0ab5f7c6b2c9d41a). The remaining data that support the ndings of this study are provided in Supplementary Data.
( A) and (C) 3D plots depicting the CIA of metabarcoding (represented by circular markers) and metabonomics (represented by square markers) data sets in the root and RAS compartments of the four PM lines (L200 in green, L3 in orange, L253 in purple, and L132 in blue). The lines connect the position of samples in the metabarcoding dataset with the corresponding position in the metabonomics dataset. (B).
Cyathostomins are ubiquitous equine nematodes. Infection can result in larval cyathostominosis due to mass larval emergence. Although faecal egg count (FEC) tests provide estimates of egg shedding, these correlate poorly with burden and provide no information on mucosal/luminal larvae. Previous studies describe a serum IgG(T)-based ELISA (CT3) that exhibits utility for detection of mucosal/luminal cyathostomins. Here, this ELISA is optimised/validated for commercial application using sera from horses for which burden data were available. Optimisation included addition of total IgG-based calibrators to provide standard curves for quantification of antigen-specific IgG(T) used to generate a CT3-specific 'serum score' for each horse. Validation dataset results were then used to assess the optimised test's performance and select serum score cut-off values for diagnosis of burdens above 1000, 5000 and 10,000 cyathostomins.
Following four washes with 200 ll of wash buffer, 100 ll of Invitrogen ELISA TMB Stabilized Chromogen substrate (SB02, Thermo Fisher Scientific, USA) were added and incubated in the dark at 25 °C for 10 min. The reaction was stopped with 100 ll of 1 M hydrochloric acid and absorbance (O. D. ) read at 450 nm. For calculation of a 'serum score' for each sample, the mean absorbance reading for each calibrator and sample was determined and the average background control value subtracted. Using the normalised calibrator data, a standard curve (linear model) was produced, and the slope and intercept calculated. A serum score was then calculated using the equation: ðNormalised sample absorbance mean À InterceptÞ=Slope This optimised test protocol was then applied in validation studies using sera from necropsied horses for which cyathostomin burden data were available and in studies assessing CT3-specific IgG(T) in live populations based in geographically distinct regions.
75-14. 23 (Supplementary Table  S2 ). The 88 positive sera used were from several populations of infected horses for which cyathostomin burden data were available. A summary of these populations and the associated references for each study are detailed in Supplementary Table  S1. All serum samples were stored at À20 °C in small aliquots to minimise freeze/thaw cycles. Most of the validation sample set (110/112) was tested in at least two independent ELISA experiments; however, due to limited sample volume, two samples were tested (in duplicate) in a single experiment.
Statistical analyses Statistical analyses were performed using Prism version 7. 00 (GraphPad Software, USA) and SPSS version 26. 0 software (IBM Corporation, USA). CT3 serum scores and TWB (i. e. , the total of luminal larval and adult and mucosal larval stage cyathostomins estimated in each animal) threshold levels determined for each of the 112 horses in the validation study were analysed. Three different TWB threshold levels (1,000, 5,000, 10,000 cyathostomins) were chosen to distinguish between positives (those above the TWB threshold) and negatives (those below the TWB threshold). The diagnostic performance of the CT3 serum score to predict cyathostomin TWB levels as positive or negative using these thresholds was determined as sensitivity, specificity, positive and negative likelihood ratios, and positive and negative predictive values, and summarised by ROC curve analysis, including determination of the AUC.
Development of logistic regression models to convert measured CT3 serum scores to the probability that cyathostomin total worm burden exceeds selected threshold levels Using SPSS, univariate logistic regression models  (Hosmer et al. , 2013)  were developed to relate CT3 serum scores to the natural logarithm of the odds that an individual would have a TWB above or below a given threshold. The models were based on TWB threshold levels ranging from 0-10,000 mucosal and luminal cyathostomins and serum scores measured for the 112 horses described above. Based on the results of the ROC curve analysis that indicated diagnostic performance was excellent up to10,000 cyathostomins, various TWB thresholds up to this level were assessed (Supplementary Table  S4 ). The models contained the following terms: Logit ¼ constant þ ðcoefficient x serum scoreÞ where: logit = natural logarithm of the odds that TWB exceeds the threshold The relationship between logit and probability can be expressed as follows: logit ¼ lnðodds TWB exceeds thresholdÞ ¼ lnðp=ð1 À pÞÞ where p ¼ probability that TWB exceeds threshold Applying the models to the serum score measured for each horse produced logit values.
Validation of the optimised CT3 ELISA in a commercial setting using serum from horses with enumerated cyathostomin burdens Analysis of the CT3 ELISA validation dataset demonstrated high ROC-AUC values for cut-off thresholds up to, and including, 10,000 mucosal and luminal cyathostomins (Supplementary Fig. S1 ; Table  1 ; ROC-AUC range 0. 910-0. 956). At higher cyathostomin thresholds, ROC-AUC values were less than 0. 9, the level accepted as excellent diagnostic utility  (Swets, 1988). To investigate serum score thresholds to use in the test's application in equine practice, values up to serum score 50. 0 were assessed from the ROC curve coordinates by examining the trade-off of diagnostic sensitivity against specificity. Serum scores in the maximal zone of the Youden index (i. e. , J = sensitivity + specificity -1) were interrogated and values selected in this region for TWB threshold cut-offs of 1,000 mucosal/luminal cyathostomins (serum score cut-off: 14.
Measurement of CT3-specific IgG(T) in mares and foals over 480 days To provide information on the dynamics of serum IgG(T) in naturally infected populations subjected to anthelmintic treatment and to provide data to inform timing of CT3 ELISA testing posttreatment, a time course study was undertaken in a mare and foal population based at INRA. CT3 serum scores obtained for these mares and foals over time are depicted in Supplementary Fig. S2. In all foals, CT3-specific IgG(T) was not detected before suckling and, by the third day, foals demonstrated CT3-reactive antibody in sera after consuming colostrum. In most mare/foal pairs, CT3specific IgG(T) levels in foals after suckling aligned with those measured in sera taken at the same timepoint from the respective dam. After acquisition of maternal IgG(T), in all cases, CT3-specific IgG(T) dropped in foal sera over the next 30-60 days, followed by an increase, indicating foals were ingesting grass infected with cyathostomin larvae.
, 2020). Beyond the 10,000 TWB threshold, ROC-AUC values were < 0. 9, possibly associated with the previously observed plateau in cyathostomin-specific serum IgG(T) after repeated infections  (Dowdall et al. , 2004; Mitchell et al, 2016). Direct comparison of CT3-specific IgG(T) with TWB across the 112 horse validation dataset demonstrated no significant linear correlation, as in previous studies  (Tzelos et al. , 2020). This may be associated with the fact that serum score values over 50. 0 are outside the linear range of the ELISA, as determined by the test calibration curve, and are likely to have confounded correla- tions between cyathostomin counts and antigen-specific IgG(T) measured across the cohort. ROC-AUC values of < 0. 9 were obtained when CT3-specific IgG(T) and mucosal larval counts were compared, as identified previously for the research version of the test  (Tzelos et al.
The analysis of the German Yard dataset also indicated that age and FEC contribute to the odds of a negative serum score at the 14. 37 cut-off; these factors should be taken into consideration when selecting horses to be tested, and in interpreting the results of the CT3 ELISA. The results indicate that the CT3 ELISA can be used as a tool by veterinarians to engage horse owners to implement more sustainable control practices. Where recent FEC test results in the group have been low (i. e. , 0 -<200 EPG) and excellent pasture management is practiced, veterinarians can choose to advise no anthelmintic treatment, or they can apply the CT3 test to individuals to assess levels of cyathostomin infection, and use the result to inform the treatment decision. In these cases, when selecting the TWB treatment threshold to apply (1,000, 5,000, or 10,000 cyathostomins), clinical evaluation should be undertaken, together with an assessment of the level of infection risk based on knowledge of the yard management practices.
Acknowledgements The technical transfer component of these studies was supported by the  Horse Trust, Bucks, UK (Registered Charity :  231748 , Grant number:  G3017 ). The Austin Davis co-authors on this manuscript are employed by the company that provides the CT3 ELISA as a diagnostic service and so have a financial interest in the results of this study. Animal experiments on the INRAE Welsh ponies were approved by the  Ministry of Higher Education and Research  and the  Val de Loire ethics  committee, (France, CEEA VdL committee number n°19) under the APAFIS#240 license. Procedures were performed in accordance with the European Council Directive (2010/63/UE) and French laws (Articles  R214-87  to  R214-137  of the Rural Code and decree n°2013-118 dated February 1, 2013, published on February 7, 2013).
According to the study in  [21] , LTR-retrotransposons inserted into the introns of a gene can cause suppression of that gene. There is evidence suggesting that LTRretrotransposons can co-opt sequences from genes for their own purposes and vice versa  [3, 18]. Further, LTRretrotransposons make up a significant portion of the repeats at the centromeres of plants  [18, 22]. Tools capable of locating LTR-retrotransposons in a genome are therefore of great interest to researchers. Tools for locating TEs can be classified into six types: library-based, learning-based, signaturebased, comparative-genomics-based, de-novo-based, and pipelines of tools  [11, 23]. Detection of LTRretrotransposons currently are only available through library-based, signature-based, and pipeline tools. Library-based tools such as RepeatMasker  [24]  use a database of known TEs such as Repbase  [25]  and Dfam  [26]  to identify LTR-retrotransposons.
These genomes are plants with high TE content. Arabidopsis thaliana was specifically chosen for training because it is a model organism with a well-annotated genome. We utilized RepeatMasker  [24]  and a program called One Code To Find Them All  [37]  to locate Long Terminal Repeat (LTR) retrotransposon for our ground truth dataset. RepeatMasker is first used for locating LTRs and the internal parts of retrotransposons, using the Repbase database  [25]. We used the slow search parameter to increase the sensitivity of the search and provided the appropriate species name for each genome. The only exceptions to providing the species names were the species chosen for the cross-species evaluation (barring Oryza sativa japonica and Oryza sativa indica). These species were not specifically well annotated in the Repbase library.
• Mean Red score of the gap between the stretches. • Whether both stretches lie within the same repetitive region predicted by Red. We split our data into three sets: training, validation, and testing, consisting of 70%, 20%, and 10% of the stretches found in the four training genomes. We subtracted the mean and divided by the standard deviation of each feature (except the last feature because it is binary). The mean and the standard deviation were calculated on the training set. To determine the parameters of the model, a random search 10-fold cross validation was performed on 1,000 iterations. We trained the classifier with the parameters found by the random search and kept the model with the best F1 score on the validation set. We performed a final evaluation on the testing set. After determining that our model could achieve satisfactory results, we rescaled the data and trained the classifier on the entire dataset.
Identity Training Identity is a machine-learning-based tool designed for predicting pair-wise global identity scores efficiently  [36]. Identity takes a database of the sequences that will be compared later. Because Identity is an instance of self-supervised learning, it can generate its own labeled training data without the user's involvement. Candidate LTRs outputted by the detector module comprise a database that will be given to Identity. We train two instances of Identity. The first is trained with a focus on sequence pairs of 80-100% identity scores, and the second is trained with a focus on 60-100% identity scores. We refer to the first Identity instance as the standard Identity and the second one as the recent Identity because it is utilized for locating recently nested retrotransposons.
Evaluation Using our ground truth, we evaluated Look4LTRs alongside the following related tools: LTR Finder  [41] , LTRharvest  [27] , and LtrDetector  [29]. Recall that our ground truth is likely incomplete with possibly inaccurate boundaries. Therefore, the definitions of true positives and false positives needed to be modified to account for these two issues. A true positive is a predicted element that has an 80% reciprocal overlap with an LTR-retrotransposon in the ground truth. For false positives, we followed the method described in previous studies  [29, 39]. First, we locate all repetitive elements as reported by RepeatMasker and drop simple repeats and low-complexity regions as well as LTR elements -as described by RepeatMasker. This is our false positive dataset. A false positive is then defined as a predicted element whose two LTRs are overlapping (80% reciprocal) with two elements of the same family from the false positive dataset.
Expert manual confirmation A blind manual evaluation was performed on the results of Look4LTRs applied to the barley genome. This plant has a haploid genome size of about 5. 3 Gb distributed in seven chromosomes with a TE content of about 76% composed in majority of LTRretrotransposons  [43]. Sequences identified as single LTR-retrotransposons and recently nested insertions were investigated. For this, each sequence was compared to the Repbase database and to the NCBI nonredundant protein database using BlastX  [38]. Concerning single LTR-retrotransposons, six sequences were assessed. Three of them correspond to known LTR-retrotransposons (BARE1, BARE-2 and Sabrina) described in the barley. Two sequences could correspond to new families not described yet that contain a pol gene and two sequences likely corresponding to LTRs at each extremity, while the last sequence is a false positive not corresponding to any TE.