dataesr/scientific-paragraphs-categorization huggingface dataset:
Monthly Distribution of P. papatasi in the North African Region Data collected on the seasonal P. papatasi dynamic depicted bi-to tri-modal activity from March to November (Figure  4 ). They point to a higher P. papatasi density during the hot period in the Moroccan sites. In the Algerian sites studied, El Honda recorded a monomodal activity, with the maximum activity in August. In M'Sila, a bi-modal distribution with a maximal peak in June is recorded. Sidi Bouzid, Tunisia, displays seasonal activity with a density peak in September. The two sites studied in Libya display a bi-modal distribution with maximal density in June and September. These results show P. papatasi density peaks primarily during the hot season in the North African region. a bi-modal distribution with maximal density in June and September.
There are great laps in time between sandfly density and ZCL incidence with differences between region countries. P. papatasi displays a bi-modal pattern of activity, with the highest activity in June and September, while maximum ZCL cases are recorded in November and January (Figure  6 ). Data collected from the North African countries included in this study display a bi-modal distribution of P. papatasi in June and September; concomitantly, the peak of ZCL cases was recorded in November (Figure  7 ). Overall, in the North African region, two to four months laps are recorded between lesions and scars reminiscent of L. major infection appearance and peak density of P. papatasi. Such a lap was also recorded in Afghanistan (Mazar-e Sharif) with an incubation period of 8-12 weeks. In Germany, the incubation period for ZCL was reported to be seven weeks  [3].
major infection appearance and peak density of P. papatasi. Such a lap was also recorded in Afghanistan (Mazar-e Sharif) with an incubation period of 8-12 weeks. In Germany, the incubation period for ZCL was reported to be seven weeks  [3]. Data collected from the North African countries included in this study display a bimodal distribution of P. papatasi in June and September; concomitantly, the peak of ZCL cases was recorded in November (Figure  7 ). Overall, in the North African region, two to four months laps are recorded between lesions and scars reminiscent of L. major infection appearance and peak density of P. papatasi. Such a lap was also recorded in Afghanistan (Mazar-e Sharif) with an incubation period of 8-12 weeks. In Germany, the incubation period for ZCL was reported to be seven weeks  [3].
Strengths and Limitations North Africa belongs to one of the most impacted geographic areas by ZCL. In this paper, for the first time, the seasonal incidence of L. major infection (ZCL) and the activity of its primary proven vector (P. papatasi) were investigated at the North African geographic scale. Using data from the literature, we explore associations between disease incidence, vector activity, and climatic factors to delineate underlying factors playing a role in the ZCL seasonal dynamic and its spread. Nevertheless, the nature of the data we analyzed, dispersed in terms of time and geographic coverage, as well as the differences in time steps between data, particularly those concerning P. papatasi dynamic, limits the strength of our analyses and, therefore, the conclusions raised by the study.
Climatic data Climatic data for each experiment were recorded using a weather station installed at one end of the field. The incident global solar radiation (either GS1 dome solarimeter, Delta-TDevices, Cambridge, UK, or LI-200 Pyranometer, Li-Cor, Lincoln, NE, USA), air temperature at 2 m above the ground level (HMP45C, Vaisala, Helsinki, Finland), and soil temperature at a depth of 2 cm (T thermocouples, PyroControle, Vaulx en Velin, France) were measured at 1 min intervals, averaged on an hourly basis, and stored in data loggers (CR1000, Campbell Scientific, Logan, UT, USA). In the aerobic experiment, soil water tension at a depth of 15 cm was recorded daily using three tensiometers (Jet Fill 2725, Soil Moisture Equipment Corp. , Goleta, CA, USA).
About 8. 7% of the SP gene set (96 genes) was expressed only in some of the different hosts, of which 28% were not detected in the Asian wheat line 'Chinese Spring' (CS). A total of 35 genes were identified in only one host of which 43% were specific to the FHB susceptible wheat cultivar 'Recital' (REC). A total of six newly characterized gene accessions, not present in the PH-1 reference genome, were detected, including three genes expressed in all the hosts. In the PathoV data set, 1016 SP genes (87. 4%) among the expressed SP genes were expressed in all strains independently of their aggressiveness; they form a strain-independent SP gene set (Figure  1B ). About 5. 5% of the PathoV SP gene set (64 genes) was expressed only by some of the different strains. A total of 82 genes were identified in only one strain and nearly 45% of them were found in the most aggressive strain MDC_Fg1.
Parsing Secretome Gene Sets towards Effectome Gene Sets That Are Regulated along the Infection Progress Among the DEGs that proved to be SP genes, those demonstrating fluctuating transcription during the interaction are supposed to be essential components of the infection progress (as effectors are defined  [32, 35] ). With this regard, we established two effectomes in both HostV and PathoV data sets with, respectively, 476 and 761 accessions,.
Parsing Secretome Gene Sets towards Effectome Gene Sets That Are Regulated along the Infection Progress Among the DEGs that proved to be SP genes, those demonstrating fluctuating transcription during the interaction are supposed to be essential components of the infection progress (as effectors are defined  [32, 35] ). With this regard, we established two effectomes in both HostV and PathoV data sets with, respectively, 476 and 761 accessions, by selecting only SP genes with varying expression levels during the infection progress (Supplementary Table  S3A ,B). In the HostV data set, the aggressive strain MDC_Fg1 displayed 91% (433 genes) of its effectome in all the infected wheat genotypes (Figure  3A ). Genes found in only a subset of hosts accounted for 35 genes including two newly identified genes absent from the PH-1 genome while only eight effector genes were host-specific.
Predicted Functions of the Core Effectome Are Highly Diverse As a whole, 140 Pfam and 66 GO terms characterizing 230 and 126 genes, respectively, were found in the core effectome (Supplementary Table  S4 ). The gene set was significantly enriched in two GO Biological Process terms: 'carbohydrate metabolic process' and 'proteolysis' with 35 and 15 annotated genes, respectively. At the molecular function level, the core effectome was enriched in 'metallocarboxypeptidase activity', 'hydrolase activity, hydrolyzing O-glycosyl compounds', 'polygalacturonase activity', 'hydrolase activity', 'alpha-L-arabinofuranosidase activity' and 'flavin adenine dinucleotide binding' functions. Finally, the core effectome was enriched in the 'extracellular region' as GO Cellular Component term. The search for peptidases in MEROPS database and CAZymes in dbCAN database yielded 38 and 98 genes of the core effectome genes, including 4 and 16 EffectorP-predicted effectors, respectively.
0  [88]. Then, we performed a mapping of each assembled genome on the PH-1 reference genome using Minimap2 v2. 12  [89]. Contigs unmapped and larger than 1 kb in size have been recovered. Additionally, redundant contigs have been removed with Cd-hit v4. 6. 7  [90]. In this way, we completed the PH-1 reference genome and represented a reference pangenome. On the other hand, to create the GFF/GTF annotation files of the reference assembled pangenome, we used the MAKER2 v2. 31. 10 annotation pipeline  [91]  with a combination of evidence-based methods (transcriptome data and homology with known proteomes) and ab initio gene prediction with SNAP v2013-02-16  [92]  and Augustus v3. 2. 1  [93]. The repeating elements of the assembled pangenome were masked using the library of all the repeated elements from the Repbase database (1 February 2017 update)  [94]  using RepeatMasker v4.
0. 7  [95]. This F. graminearum pangenome is composed of 17,647 protein coding genes, adding 3502 genes to the GCA_900044135. 1 assembly of the reference strain PH-1. The putative pansecretome of F. graminearum was generated from the panproteome using SignalP v5. 0b software  [96]  for conventionally secreted proteins and using ApoplastP v1. 0. 1  [97]  for unconventionally apoplastic secreted proteins. This added 437 putative secreted proteins to the reference strain PH-1 and achieved a total of 2339 proteins, including 1656 conventionally secreted proteins. Localizer v1. 0. 4  [98]  was used on protein mature sequences generated by SignalP v5. 0b to predict the protein's subcellular localization within the host. Gene Ontology (GO) and Protein family (Pfam) annotations were generated with Interproscan v5.
52-86. 0 [99]. To identify putative genes related to pathogenicity, Blastp  [100]  searches were performed against the Pathogen-Host Interaction database v4-10 (PHI-base) [101,102] and against MEROPS v12. 1 for peptidases and their inhibitors  [103]. Carbohydrate-Active Enzymes (CAZymes) screening was performed with HMMER v3. 1b2  [104]  against the dbCAN HMM profile database v9. 0  [105]. Secreted proteins with additional effector features, i. e. , small cysteine rich proteins, were predicted with EffectorP2. 0 software  [53].
Data Cleaning Step Raw reads were trimmed for adapters and low quality bases (phred score < 20) with TrimGalore v0. 6. 5  [108]. Noncalled bases and polyA tails were trimmed from reads with homemade Perl scripts. Then, low complexity (compression size < 65%) and short (size < 60 nucleotides) reads were discarded from the fastq files using homemade Perl scripts. Sample files were decontaminated by mapping with STAR v2. 7. 1. a [109]  against a homemade database of potential contaminants composed of 1649 viral genomes, 9267 bacterial genomes and the human genome. The genomes were downloaded from the NCBI Reference Sequence Database  [110]. Best whole genome assembly available for each organism or clade without gaps and unlocalized scaffolds were used while genomes with more than ten successive ambiguous bases were discarded.
Mapping and Assignation Genome and annotation files of Triticum aestivum v1. 1  [113]  and F. graminearum (pangenome) were combined into a host-pathogen genome. This combined genome contains 269,428 (high confidence and low confidence genes) wheat genes and 17,647 F. graminearum genes. The cleaned RNA-seq files were mapped against this combined genome with STAR v2. 7. 1a. For each species of the pathosystem, gene-level counts were generated with featureCounts software from the subread v2. 0. 1 package  [114]. Only uniquely mapped read pairs were considered at the read count step.
Expression Pattern Characterization For the following analyses, raw count values were normalized using the regularized logarithm transformation (rlog) implemented in the DESeq2 package  [120]  before being z-score transformed. To assess the capacity of the selected genes to discriminate the samples according to the host or strain × time point combinations and their relevance to describe the infection stages, we performed Partial Least Squares Discriminant Analysis (PLS-DA) using the R package mixOmics  [121]  with 10 components and default parameters for the other options. Heatmaps representing gene expression patterns were made using the Pheatmap package  [122]. The Hierarchical Ascendant Clustering (HAC) of genes and samples was performed with the ward. D2 agglomeration method  [123]  applied on the Euclidean distance matrices.
Acknowledgments: The authors thank  Richard Blanc  for its valuable help in the plant cultivation,  Florence Cambon  and  Olivier Soudière  for their assistance in RNA extraction and fungal spore production, as well as  Hélène Rimbert  for helpful discussions on methodology and cluster use. The authors are also grateful to  Jérôme Salse  for coordinating WheatOmics project. Computer calculation and storage facilities were provided by the  Mésocentre Clermont-Auvergne University  and by the TGCC infrastructure of the CEA.
Funding: This research and PhD grant (F. R. ) were funded by the  Region Auvergne-Rhône-Alpes  and the  FEDER  supports. This work is supported by  France Génomique  ( WheatOMICS  project) and the  Agence Nationale de la Recherche of the French government  ( NewMyco  project,  ANR-15-CE21-0010 ). The authors also acknowledge the support received from the  Agence Nationale de la Recherche  of.
DISCUSSION Interpreting the Long-Term Declines in Radar Signal. We quantified the long-term vulnerability of intact rainforests to repeated droughts, detected by persistent trends of declining radar signal in tropical canopies in nearly decades. Several hypotheses can be proposed to explain the observed long-term trends in radar signal (SI Appendix, Sections 12-17). The declines do not appear to be related to the processing of the radar data (SI Appendix, Figs. S2  and S3 ), to data acquisition time or polarization mode (SI Appendix, Fig. S17 ), or to filtering due to heavy rain events (SI Appendix, Fig. S18 ). We verified that these declines were not caused by direct anthropogenic effects since nonforested areas and degraded or deforested pixels were conservatively masked (SI Appendix, Figs.
ACKNOWLEDGMENTS. We gratefully acknowledge help and discussions with the following colleagues:  A. G. Dai ,  T. R. Feldpausch ,  Y. Jiang ,  O. L. Phillips ,  C. Chen ,  L. Xu ,  Y. Yang , and  L. M. Zhou. We warmly thank the three anonymous reviewers for their insightful comments. This study was supported by an  Investissement d'Avenir grant  managed by the  Agence Nationale de la Recherche  (CEBA, ref. ANR-10-LABX-25-01 ;  TULIP, ref. ANR-10-LABX-0041 ;  ANAEE-France :  ANR-11-INBS-0001 ), and by the  National Natural Science Foundation of China  (grant no. 31988102 ). This research was also supported by a  Centre National d' Etudes Spatiales (CNES) postdoctoral fellowship  to S. T. , the  CNES-BIOMASS  pluriannual project, and the  European Space Agency (ESA) Climate Change Initiative (CCI)   Biomass  project (contract no.
Selection of case studies This research project had the goal of understanding the CUMAs' contribution to the development of agroecological practices. Our research was based on the study of six CUMAs (see Table  1 ) and used an analytical framework constructed according to an iterative process. Case studies were identified with the assistance of the National CUMA Federation, and were intentionally chosen to include a range both of geographies (different parts of France) and of farming systems. In addition, the six case-study CUMAs were selected because their cooperative activities supported two types of practices: (1) the introduction of forage legumes into the farming system; and/or (2) a shift toward conservation agriculture with reduced herbicide use (see Box 2). FNCUMA data show that shared investments in equipment specific to these two types of practices have increased in recent years.
| Spatial data Based on aerial photographs of the study site, we manually digitised homogeneous habitat polygons (in ArcView GIS 3. 3, Esri). Each polygon was assigned to a habitat type (e. g. woodland, hedgerow, meadow or crop) determined annually by field observations. Most of the genotyped deer (n = 304/334) were equipped with GPS collars to monitor individual movement-related behaviours. Baseline and intensive monitoring were programmed to record a GPS fix every 6 h (see SI for exceptions) and every 10 min over a 24-h period once per month, respectively, for approximately 1 year. We removed aberrant GPS fixes and all locations prior to dispersal according to the procedure detailed in the SI.
Genomics for RADseq data and Genotoul bioinformatics platform Toulouse Midi-pyrénées for providing help, computing and storage of RADseq data. They also thank Anne Charmantier, Céline Teplitsky, Luis Miguel-Chevin, Pascal Marrot, Jeffrey Carbillet, Alastair Wilson and Jon Brommer for helpful discussions on the methodology and the interpretation of the results. They acknowledge Pim Edelaar, Gabriel Munar Delgado and two anonymous referees who provided constructive feedback on a previous version that greatly improved the manuscript. This project was financially supported by the French Institute for Agricultural and environmental Research (INRAE), the UK Natural Environment Research Council (NERC), the Biomolecular Analysis Facility at the University of Edinburgh (NBAF901), the 'PATCH' RPDOC ANR project (ANR-12-PDOC-0017-01) to Cécile Vanpé, and the 'Mov-It' ANR project (ANR-16-CE02-0010-02) to Mark Hewison and Nicolas Morellet, the Centre National de la Recherche Scientifique (CNRS) core funding to Benoit Pujol, the EU within the framework of the Marie-Curie FP7 COFUND People Programme through the award of an Agreenskills fellowship to Erwan Quéméré and the Federal University of Toulouse through the PhD scholarship of Laura Gervais (IDEX grant 'GENEMOV' to Benoit Pujol and Erwan Quéméré).
Funding information " PATCH" RPDOC ANR, Grant/Award Number :  ANR-12-PDOC-0017-01 ;  Mov-it ANR, Grant/Award Number :  ANR-16-CE02-0010-02 ;  NERC Environmental Bioinformatics Centre , Grant/Award Number:  NBAF901 ;  Université Toulouse III -Paul Sabatier , Grant/Award Number: IDEX-GENEMOV.
The expression of the main enzymes involved in lipogenesis (FASN, FADS1, SCD) was surprisingly over-expressed in the pHu-line (data not shown). 1 H-NMR metabolic profile of pHu-and pHu+ egg yolk aqueous phase. The metabolic profile of the yolk aqueous phase was analysed by 1 H-NMR spectroscopy at E0 to obtain a snapshot of the initial yolk composition in both lines and at E10 to appreciate the evolution of this composition during embryo development due to nutrient utilisation and enzymatic pre-digestion. Metabolites identified in egg yolk are composed of amino acids and derivatives (leucine, isoleucine, alanine, valine, phenylalanine, methionine, lysine, glutamine, glycine, histidine, tyrosine, threonine, glutamate, pyroglutamate, aspartate, asparagine, betaine, 2-oxoisocaproate and indoxylsulphate, which results from the degradation of tryptophan), sugars and derivatives (glucose, mannose, myoinositol), fatty acid-associated metabolites (choline, 3-hydroxybutyrate), energy-associated metabolites (fumarate, citrate, lactate), and pantothenate, also called vitamin B5 (Fig.
3 ). 1 H-NMR metabolic profile of pHu+ and pHu-amniotic fluid. Metabolomics analysis by  1  H-NMR spectroscopy of the amniotic fluid was performed at 10 days of incubation (E10), i. e. , before embryo metabolism intensifies. PLS-DA was carried out on the 1 H-NMR spectral data and the model obtained had a high cross-validated predictive ability (Q 2 ) of 0. 605 and an overall proportion of the variation in Y explained by the model (R 2 Y) of 0. 754. The reliability of this model was assessed by CV-ANOVA, which gave a P-value equal to 0. 00011. The PLS-DA model from the 1 H-NMR spectrum provided clear discrimination of the pHu+ and pHulines (Fig. 4A ). The corresponding contribution plot generated by SIMCA+ software was used to identify the metabolites of the amniotic liquid that permitted discrimination between lines.
Gene expression of digestive enzymes and nutrient transporters in the yolk sac membrane of the developing pHu-and pHu+ embryos. The gene expression profiles of digestive enzymes and nutrient transporters were measured in the yolk sac membrane (YSM) at E10, E14 and E17 during embryonic development in both pHu+ and pHu-lines to follow its functionality during the second half of embryonic development. The YSM expressed digestive enzymes such as APN, which cleaves neutral and basic amino acids from the N-terminal end of peptides  16. Its mRNA expression was stable over time (E10 to E17) and was not altered by genetic line (Fig. 5 ). The expression of several transporters of peptides and amino acids, i. e. CAT1, EAAT, PepT1, LAT1 and gLAT2 was determined at the mRNA level (Fig. 5 ). No line or age effect was evidenced for CAT1 (data not shown).
1 H-NMR processing and analysis. The 1 H spectra were performed on a Bruker 600 MHz cryoprobe spectrometer. The integration of the NMR peaks is proportional to the concentration of metabolites but also to the number of protons that resonate under the peak. The 1 H-NMR spectra were reduced into consecutive and non-overlapping spectral regions (buckets). Each bucket represents a variable. This allows generating a data matrix that can be analysed by multivariate statistics. Beforehand, steps involving phasing, correction of the baseline and alignment of the chemical shifts were performed. The area of the buckets for the amniotic fluid was normalised according to the total area of the spectrum, whereas for the lyophilised egg yolk, the normalisation was carried out on the reference because of the presence or not of some peaks according to age.
When the residuals were not normally distributed and/or variances were not homogenous between groups, data were analysed with the non-parametric Kruskal-Wallis test (n comparisons) or by the Mann-Whitney test (two comparisons). A level of significance of 5% (p ≤ 0. 05) was adopted for all tests. In 1 H-NMR spectroscopy, a principal component analysis (PCA) with unsupervised analysis, followed by supervised and discriminant analysis (PLS-DA), were performed using MetaboAnalyst (https:// www. metab oanal yst. ca/) and SIMCA software (SIMCA 13. 0. 3 software (Umetrics, Umeå, Sweden). The overall quality of the model was appreciated by the R 2 Y, defined as the proportion of variance in Y explained by the predictive component of the model and Q 2 the predictive ability of the model classes. The higher R 2 Y and Q 2 , the better the separation between the pHu+ and pHu-lines or stages.
Acknowledgements The authors thank the staff of PEAT  INRAE Poultry Experimental Facility  (2018, https:// doi. org/ 10. 15454/1. 55723 26250 88729 2E12 ) for producing and rearing animals, and the technicians of the BOA unit ( INRAE, UMR BOA, Nouzilly, France ) for measurements on birds, sampling and laboratory analyses. We thank  Magali Chessé ,  Nelly Bernardet  and  Mylène Da Silva  ( INRAE, University of Tours, Nouzilly, France ) for their help with collecting egg samples. This work was supported by the  INRAE "Animal Physiology and Livestock Systems" Division  [ ACI_PHASE2017_INOVE ]. This article has been proofread by PRS (Proof-reading-Service. com).
Sequencing A SMRTbell sequencing library was generated from the HMW extraction of MO_IP_504 by the Exeter Sequencing Service. This was sequenced on 3 SMRT cells on a Sequel I instrument to generate 24. 0 Gb of Pacbio continuous long-read (CLR) data. A chromium 10Â library was prepared from the HMW extraction at the Cancer Research UK Cambridge Institute, UK. This library was sequenced by Edinburgh Genomics (EG) on a single HiSeqX lane, generating 25. 3 Gb of paired-end reads after processing with Long Ranger v2. 2. 2  (Marks et al. 2019). However, the weighted mean molecule size of these data (12. 86 kb) limited its use for scaffolding of the Pacbio assembly and the reads were therefore only used for polishing. Hereafter, these data are simply referred to as Illumina WGS. In addition, tissue from MO_IP_500 was used for chromatin conformation capture (HiC) sequencing.
The HiC reaction was performed using an Arima HiC kit, following the manufacturer's instructions for flash-frozen animal tissue. An NEBNext Ultra II library, prepared by EG, was sequenced on an Illumina MiSeq, generating 4. 7 Gb of paired-end reads. Paired-end RNA-seq data were generated for individual MO_IP_504. To obtain tissue for RNA extraction, the adult individual was divided bilaterally (including all parts of the body: head, thorax, and abdomen). For further details on RNA extraction, see  Ebdon et al. (2021).