dataesr/scientific-paragraphs-categorization huggingface dataset:
thaliana  [27]. Two-thirds of the plants presented disease symptoms (Fig  1B ). While most diseased plants presented few symptoms (disease index = 1, n = 1,476, 39. 5%, Dataset 1 in S1 Dataset), a nonnegligible fraction of diseased plants presented severe symptoms (disease index > = 5, n = 407, 10. 9%, Dataset 1 in S1 Dataset) (Fig  1B ). Because resources involved in plant development may not be further available for plant immunity, we investigated this trade-off  [41, 42]  by testing whether plant development affected disease resistance. In line with the growth-immunity trade-off, disease index was significantly and positively correlated with plant size (i. e. maximal rosette diameter used as a proxy) scored before the onset of winter (Table  1  and S2 Fig). However, variation in rosette size explained on average only 2.
thaliana  [46] , total seed production of each plant scored for disease index was approximated by measuring total silique length  [24]. Based on genotypic values (Dataset 2 in S1 Dataset), we first calculated relative fecundity and standardized disease index in each micro-habitat for which disease index was significantly heritable. We found standardized S estimates to be negative across micro-habitats, indicating that accessions presenting severe symptoms produced on average fewer seeds than accessions with few symptoms (Fig  3 ). In addition, S estimates were significantly different among the three soil types (S3 Table ), with S estimates in soil C being between 2. 3 and 7. 1 higher than S estimates in soils A and B (Fig  3 ). The presence of P. annua did not significantly affect S estimates (S3 Table  and Fig 3).
The efficiency of this combination was demonstrated in the TOU-A population, with the fine mapping (in particular, the detection of QTLs with small effects) and the cloning of four of the QTLs associated with quantitative disease resistance to the bacterial pathogen Ralstonia solanacearum  [48]. Natural genetic variation for disease index was highly polygenic, with the detection of between 13 and 73 QTLs depending on microhabitat (Fig  4 ), for a total of 195 detected QTLs overlapping with 548 unique candidate genes (Dataset 3 in S1 Dataset). The genetic architecture was highly variable among the micro-habitats, with most candidate genes being specific to a particular micro-habitat (Fig  5 ). The main exception concerns 20 candidate genes that were common both in absence and in presence of P.
annua; 'bolting' measures the effect of being bolted on the day plants were scored for disease symptoms; 'diameter' accounts for developmental effects approximated by maximal rosette dimeter before the onset of winter; and ε is the residual term. Inference was performed using ReML estimation, using the PROC MIXED procedure in SAS v. 9. 4 (SAS Institute Inc. , Cary, North Carolina, USA). All factors were treated as fixed effects, except the term 'acc', which was treated as a random effect. For fixed effects, terms were tested over their appropriate denominators for calculating F values. Significance of the random effects was determined by likelihood ratio tests of the model with and without these effects. Best linear unbiased estimates (BLUEs) of disease index were obtained for each accession in each of the six micro-habitats by running the following mixed model (PROC MIXED procedure in SAS v.
Genotypic selection analysis The extent of natural selection on disease resistance was measured by the selection differential S  [43, 44] , within each of the five micro-habitats for which disease index was significantly heritable. Based on BLUEs, we first calculated in each micro-habitat relative fecundity as the fecundity estimate divided by the mean fecundity estimate, and disease index standardized to a mean of zero and a standard deviation of one  [58]. The following analysis of covariance (ANCOVA) was then run in each micro-habitat (PROC GLM procedure in SAS v. 9. 4), with the S estimate corresponding to the regression slope value: relative fitness d ¼m relative fitness þ disease index d þ ε d ðModel 3Þ To test whether the fecundity-disease index relationship was affected by the soil type and/or the presence/absence of P.
Genome-Wide Association mapping combined with a local score approach Although the effects of population structure on phenotype-SNP associations were demonstrated to be limited in the TOU-A population  [26, 32] , we nonetheless run GWA mapping using a mixed model implemented in the software EMMAX (Efficient Mixed-Model Association eXpedited)  [71]. To control for the effect of population structure, we included as a covariate an identity-by-state kinship matrix K. This kinship matrix was based on 1,902,592 SNPs identified among the 195 accessions of the TOU-A population  [24]. Because rare alleles may increase the rate of false positives  [63, 72] , we only considered SNPs with a minor allele relative frequency (MARF) > 7%, a MARF value above which the p value distribution obtained from the EMMAX mixed model is not dependent on MARF values in the TOU-A population  [24].
Based on a custom script  [74] , we retrieved all candidate genes underlying QTLs by selecting all genes inside the QTL regions as well as the first gene upstream and the first gene downstream of these QTL regions (Dataset 3 in S1 Dataset). The TAIR 10 database (https://www. arabidopsis. org/) was used as our reference. The number of candidate genes that were either specific to a single micro-habitat or common between several micro-habitats were illustrated by UpSet plots using the package UpSetR implemented under the R environment  [75]. plot illustrating the number of candidate genes that were either specific to a single micro-habitat (i. e. single black dots) or common between two micro-habitats (i. e. black dots connected by a solid line). 'w/o': absence of P. annua, 'w. ': presence of P.
Bat echolocation call recording and identification We sampled bats acoustically at all 57 trees between June and August 2017 using Song Meter SM2BAT+ recorders (sampling rate: 384 kHz) connected to SMX-U1 omnidirectional ultrasonic microphones (Wildlife Acoustics, Concord, USA). At each tree, we attached the recorder around the bare tree trunk or a branch at 1. 8 m height and installed a data logger (RC-5; accuracy: 0. 5 • C; Elitech, London, UK) to monitor temperature at night every 15 min. Recording was triggered automatically when sounds in the frequency range 12-192 kHz with a signal-to-noise-ratio level ≥12 dB were detected, and continued for 15 s. Sampling took place during one full night, from 30 min before sunset to 30 min after sunrise, and only when meteorological conditions were suitable for bats to forage, i.
Landscape analysis We extracted landscape characteristics around each isolated tree at different spatial scales. We created five buffers (100, 250, 500, 1000, 2000 m radii) around the trees using ArcGIS Desktop v10. Larger spatial scales were not considered to minimise the likelihood of landscapes overlapping as most trees were located <2 km apart (mean: 1. 6 km; range: 1. 0-3. 1 km), even though overlapping landscapes do not themselves represent a violation of independence  (Zuckerberg et al. , 2020). While the smaller spatial scales allow us to describe the environment close to the trees, the larger ones represent the mean daily foraging movement of many bat species present in the study area  (Laforge et al. , 2021). Using land use data compiled at 10 m spatial resolution for a previous study  (Tournant et al.
, 2013) , we quantified within each buffer the (i) landscape composition using the proportion of forest (which was negatively correlated with the proportion of grassland across the five spatial scales, |r| > 0. 7), (ii) landscape configuration using the edge density (i. e. total length of all edge segments between multiple habitat patches in the buffer divided by the buffer area, m/ha) because many bat species use habitat patch edges to forage and commute, and (iii) landscape compositional heterogeneity using Shannon's diversity index calculated across all the reclassified habitat types (n = 10; Appendix S3). These variables were calculated with the "landscapemetrics" R-package (Appendix S4). We finally calculated the distance between trees and the nearest woody habitat patch (i. e. forests and hedgerows only) and used this metric as a proxy of habitat connectivity that is easily interpretable by land managers  (Frey-Ehrenbold et al.
Statistical analysis We conducted a series of generalized linear mixed-effect models (GLMMs; "glmmTMB" package) to assess the effects of tree and landscape attributes on bat taxon-specific activity. The response variable was the number of bat passes per night for species or group of species. Because bat activity is count data, GLMMs were fitted with a negative binomial error distribution due to over-dispersion and coupled with a logit link function. Variables related to tree characteristics, tree connectivity, and landscape context were included as fixed effects and tree category was considered as random effect. We also added mean temperature at night and Julian day as covariates. All fixed variables were continuous and scaled (mean = 1; SD = 0) to enable direct comparisons. All analyses were conducted in R version 3.
e. mean temperature at night and Julian day). We examined collinearity among predictors present within the full models using the Spearman's correlation coefficient and the variance inflation factors (VIFs) and results did not highlight collinearity issues (|r| < 0. 7, VIF < 3). We conducted model selection based on the full models. We generated all possible models ("MuMIn" package) and ranked the most parsimonious ones based on AICc  (Burnham and Anderson, 2002). We conducted a model-averaged procedure of most parsimonious models (i. e. those with ΔAICc < 2), accounting for model selection uncertainties  (Grueber et al. , 2011)  and report the full model average estimates. The significance of the effects was evaluated using 95% confidence intervals  (Nakagawa and Cuthill, 2007). When a significant interaction between TreM type diversity and landscape variable was found, we undertook a spotlight analysis to explore the nature of the interaction effect using the "emmeans" package.
Acknowledgments Acknowledgements We thank the landowners for granting permission to access their lands. We thank  Jean-Christophe Foltête  ( Université Bourgogne Franche-Comté ) for sharing land-use data and  Antoine Brin  ( EI de Purpan ) for his revision. We thank the associate editor and two anonymous reviewers who made valuable comments and suggestions that helped to improve an earlier version of the manuscript. This study was funded by the  Biotechnology and Biological Sciences Research Council  through the  South West Biosciences Doctoral Training Partnership  (grant number:  1576874 ).
Strategies Compared In order to avoid producing redundant sequences, for each patient included, one unique set of genomic data sequences will be produced by a unique sequencing platform (CNRGH, Evry, France). Analyses are then performed blindly by two independent mirror laboratories: one laboratory will analyze the genome with the parental inheritance information (GST), while the second will analyze only the 44 genes of the reference minimal strategy (44GPS), without parental inheritance information, as well as the proband genome (GSS) in a randomized selection of patients (Figure  2 ; Supplementary Table  S1 ). The results of the 44GPS will be withdrawn from the GS data for all patients, using a specific bioinformatics filtering procedure that mimics the results obtained using targeted capture analysis (exons ± 20 intronic bases, SNV and CNV analysis).
Fragile-X and CMA (included in the reference strategy) follow the routine care circuit, which is mainly independent of the GS circuit. GST analysis is performed following a harmonized, consensus protocol adopted by the six clinical laboratories involved in this study, and using a common web interface (Polyweb. fr, developed in-house at the Genetic diseases IMAGINE Institute, Paris, France). All genomic variations are called by standard callers (GATK and BWA) and are subsequently ranked using several factors to facilitate analysis (protein impact, inheritance model, ID known gene and control data bases, splice prediction effect. ) in the Polyweb interface. For the SNV/small indel, the scheme contains a first minimal filtration step (for SNV/indel, elimination of the variations with over 1,000 hits in GnomAD, or 5 hits at homozygous state).
In order to maximize the scientific rigor of the study and to alleviate the potential burden resulting from the interviews, whilst fostering discussions between the sociological and psychological sides, we chose to separate the interview procedures into two groups. For the psychological sub-study, 15 interviews with parents will be conducted at each assessment timepoint (T1, T2, T3), giving a total of 45 interviews. For the sociological sub-study, 15 interviews with parents will be conducted at each assessment timepoint (T1, T2, T3), giving a total of 45 interviews. A total of 15 interviews per assessment timepoint was chosen to achieve data saturation, a key concept of qualitative analysis, namely the point beyond which further interviews do not yield any new information  (Glaser and Strauss, 1967).
Variant Calling Whole genomic sequences are analyzed by two separate SV and SNV/indel pipelines developed and validated by the CNRGH sequencing platform and IMAGINE bioinformatics team. Briefly, the raw data will be produced as compressed FASTQ files generated from the. bcl files by the CNRGH sequencing platform. The sequences are aligned to the human reference genome GRCh37 using the Burrows-Wheeler Aligner BWA software  (Li and Durbin, 2010)  and made available as BAM files. Aligned sequences are sorted, cleaned and the PCR duplicates are marked using the Sambamba software  (Tarasov et al. , 2015)  in order to eliminate most of the NGS' well-known biases. A local realignment of the sequences around insertion and deletion sites and the base quality recalibration is performed using GATK  (McKenna et al.
, 2010). After sequence quality control and alignment of the reference genome, the CNRGH performs the variant calling on the entire genome for the Single Nucleotide Variants (SNV), small insertion/deletions (indels) and structural variants (including Copy Number Variant, CNV). SNV and indel calling are performed using the Haplotype Caller from GATK software in "bp resolution" mode to produce gVCF files. Imbalanced SV (CNV) detection > 1 kb is performed using three different softwares: Wisecondor  (Raman et al. , 2019) , Canvas  (Roller et al. , 2016)  and Manta  (Chen et al. , 2016). Balanced SV (translocation, inversion) detection is done using Manta software. Results are produced in the format of a VCF file to match the common file standard format in NGS analysis. These files are then collected by the IMAGINE Polyweb platform: additional combined TRIO gVCF analysis (genotypeGvcf) and CNV Wisecondor analysis will also be performed.
Biological Analysis The. vcf and. bam files are implemented in Polyweb software developed and previously validated by the IMAGINE bioinformatics platform. This software makes it possible to annotate, analyse and visualise all the genomic variations in two different web interfaces Polyviewer (for SNV, small indel, exonic deletion or duplication) and Polycyto (for balanced and unbalanced SV) of all human genes in trio or solo analysis. Moreover, a specific ID44 bioinformatic gene panel will make it possible to study variations from this gene list. Read variations are visualized using IGV software  (Robinson et al. , 2011).
1) SNV/indel analysis The polyviewer interface gives access to several annotations, such as patient and trio sequencing data (number of mutated and total reads), data from common free-access databases (GnomAD  (Karczewski et al. , 2020) , Clinvar  (Landrum et al. , 2020) , OMIM  (Amberger et al. , 2019) , GenCode  (Frankish et al. , 2019). ), licensed database HGMDpro and also internal databases (Déjà Vu), gene or protein predicted impact, splice prediction [SpliceAI  (Jaganathan et al. , 2019) ], and for trio analysis, inheritance status of the variation. Our objective is not to evaluate the value of GS for early detection of ID, but rather, to evaluate the risk/benefit of GS for the etiological diagnosis in already-identified ID. The internal database (Déjà Vu) contained over 20. 000 exomes, 50.
2) SV analysis The polycyto interface gives access to several annotations using AnnotSV software  (Geoffroy et al. , 2018) , DGV, OMIM and internal Déjà Vu databases. The internal SV database (Déjà Vu) contained at the beginning of the project 200 Novaseq sequenced genomes from non-ID patients. The number of genomes in Déjà Vu is now up to 2000 (mostly ID patients). Ranking of identified variations is based on calling quality and inheritance status. For balanced SV (translocation and inversion) a greater weight is given to variations whose break points are found in OMIM genes. After filtering CNV already detected at least 10 times in the Déjà Vu database, all detected CNV are analyzed using standard criteria [ACMG recommendations  (Riggs et al. , 2020) ]. For Déjà Vu count, two CNV are considered identical if they overlap over 75% of their reciprocal length.
Biological Interpretation Biological interpretation follows standard criteria [ACMG recommendations  (Richards et al. , 2015; Riggs et al. , 2020) ]. Briefly, balanced and imbalanced SV and SNV/Indel/Small exonic deletions, or duplications are all performed by two independent biologists. All variations are checked on. bam data and doubtful variations are confirmed using standard molecular analysis before biological analysis (less than 10 mutated reads for SNV, doubtful de novo status, doubtful deletion, duplication or translocation/inversion. ). For CNV (duplication, deletion), current cytogenetic analysis is performed based on DGV, inheritance mode, recurrency, and gene contents. For balanced SV, only variations disrupting a known ID gene are retained for interpretation. Small SNV/indel are analyzed following mendelian modes of transmission (de novo AD or X linked variations, transmitted AD variation in case of known incomplete penetrance or suspected parental mosaic, maternally X transmission in male patients, homozygous or compound heterozygous variations (SNV or CNV) in autosomal recessive hypothesis).
Medico-Economic Evaluation A cost-effectiveness analysis will be conducted over the estimated 9-12 months (maximum) required to perform the GS, interpret the data and return the results to the patient. Efficacy will correspond to the diagnostic yield of each of the three strategies being compared. In order to estimate costs from the perspective of the health service, patient management will be divided into three main periods of healthcare consumption (Figure  2 ). The costs in the medico-economic evaluation will be direct costs, corresponding to medical procedures carried out during period 2. They will include: 1) the costs of consultation with the clinical geneticist in the recruitment centers, 2) the costs of exams preceding the genetic analysis and inclusion in the DEFIDIAG project, and the cost associated with the first blood sample and its transport, 3) the costs associated with any new blood draws required; 4) the costs of genetic analyses, and 5) the costs of any additional and confirmatory tests.
Data Management Clinical and paraclinical data as well as the results of the genetic analyses carried out will be entered directly into the dedicated e-CRF by the investigators, helped by CRTs and by biologists and bioinformatics specialists in charge of the GS analyses. The patient diary specific to microcosting and patient follow-up are in paper or electronic format (forms independent of the e-CRF). Each patient is identified by a unique code including: the number of the recruiting center, the inclusion rank, the initials of the patient (first letter of surname and first letter of first name) and a code corresponding to his/her sex. The use of the CleanWEB software makes it possible to carry out checks for missing and incoherent data, and to generate queries immediately after data entry.
Requests for corrections may also be generated by the CIC-EC1432 and sent to the recruiting center and/or the reference laboratory. The corrections will be made directly in the e-CRF by the investigators and/or the biologists, assisted by the CRTs. Histories of changes are systematically recorded. A data management plan, specific to the study, integrating centralized monitoring (enabling, for example, comparative monitoring of the distribution of subpopulations between centers and indicators of the quality of sample processing) was prepared before initiating the study in the participating centers.
ACKNOWLEDGMENTS The authors would like to thank the following clinical research technicians and genetic counsellors:  Akloul Linda ,  Baudier Marie-Pierre ,  Baurand Amandine ,  Bellengier Laurence Bernard Céline ,  Borel Myriam ,  Consolino Emilie ,  Crantelle Laura ,  De Nadai Narimène ,  Dorian Virginie ,  El Amrani Lamia ,  Faudet Anne ,  Fournier Chloé ,  Galivel-Voisine Annastasia ,  Gaudillat Léa ,  Gauthier Marjolaine ,  Glazunova Olga ,  Goetz Nathalie ,  Gonde Delphine ,  Gourves-Noizet Lena ,  Guegan Caroline ,  Guyon Laura ,  Haquet Emmanuelle ,  Kastner Claire ,  Khalil Mirna ,  Laurent Manon ,  Lextreyt Barbara ,  Mallet Audrey ,  Moukossi Caroline ,  Oumessoum Amina ,  Ngom Ndeye-Fatou ,  Pellen Anne-Sophie ,  Pelletier Valérie ,  Poirel Elisabeth ,  Prouteau Clément ,  Rubeck Coralie ,  Staraci Stéphanie ,  Sawka Caroline ,  Tempe Laurine ,  Wyrebski Antoine ,  Zordan Cécile.
FUNDING This work was supported by The  French Ministry of Health  in the framework of the  French initiative for genomic medicine (Plan France Médecine Génomique 2025 ;  PFMG 2025 ; https://www. aviesan. fr/mediatheque/fichiers/version-anglaise/actualites-en/genomic-medicine-france-2025-web). This work was supported by government funding from the  Agence Nationale de la Recherche  under the " Investissements d'avenir" program  ( ANR-10-IAHU-01 ).
RNA-seq Analysis Transcriptomic analysis was performed on AsPC-1/gem R or ASPC-1/pacli R cells isolated from xenografted tumors acquired in run02 experiment (see above). After dissociation, tumor cells were cultivated in RPMI medium supplemented with 10% (v/v) fetal calf serum, 100 µg/ml streptomycin, 100 U/ml penicillin, and 1% (v/v) ZellShield in the presence of PBS (control), or 1 µM OxA for 24 h. Total RNA was extracted using Trizol reagent (Fisher Scientific, Illkirch, France) and then treated with DNAse I RNAse free (Fisher Scientific, Illkirch, France) to eliminate genomic DNA. The integrity of the total RNA was verified by agarose gel electrophoresis and quantified using NanoDrop One (Thermo Scientific, Les Ullis, France). RNA-seq was performed on genomic platform [Genomic Paris Centre (IBENS), Paris, France], and sequencing data were managed with the modular and scalable workflow engine, Eoulsan 2.
5, which used DESeq 2 for normalization and differential analysis (IBENS, Paris, France). DESeq 2 files were analyzed with iDEP. 94  (22) , and the biological insight of mRNA significantly impacted by OxA was analyzed with WEB-based GEne SeT AnaLysis Toolkit (WebGeSTAT) packages using Kyoto Encyclopedia of Genes and Genomes (KEGG) database (https://www. genome. jp/kegg/). Heatmaps were generated with Morpheus (https://software. broadinstitute. org/morpheus).
Statistical Analysis Results were expressed as mean ± standard error of the mean (SEM). All data were analyzed with GraphPad Prism v8. 4. 3 software (GraphPad software, San Diego, CA, USA). The Student's t-test (two-tailed 95% confidence intervals) was used for comparison of two mean values. A normality test (Agostino-Pearson test) was applied to validate the use of an ANOVA with post-hoc Bonferroni's tests when groups of mean values were more than 2. In different figure legends, samples sizes were indicated, and statistical significance was defined as p<0. 05.