dataesr/scientific-paragraphs-categorization huggingface dataset:
We used BWA-MEM version 0. 7. 17  2  to map the resequencing reads to the white lupin reference genome. Picard tools (https://github. com/broadinstitute/picard/issues) have been used to detect and remove PCR and Optical duplicates. We then used GATK 4. 0  3  HaplotypeCaller tool to call variants. This identified ca. 300,000 SNPs without filtering the data. All the SNPs are evenly distributed on the 25 chromosomes and contigs. We generated a VCF file with this information, available in the white lupin Genome Browser. Assembly of chloroplast genome. A de novo assembly protocol was used to assemble both cytoplasmic genomes. The chloroplast genome was generated using NOVOPlasty 3. 2  4  , by using the aforementioned Illumina reads, after adapterremoving step. The assembled chloroplast genome (plastome) resulted in a single circularized contig of 151,915 bp.
After dehydration in an ethanol series, 4′,6-diamidino-2-phenylindole (DAPI) in Vectashield (Vector Laboratories, http://www. vectorlabs. com) was applied. Microscopic images were recorded using a Zeiss Axiovert 200M microscope equipped with a Zeiss AxioCam CCD. Images were analyzed using the ZEN software (Carl Zeiss GmbH). Primer and oligo-probes information is presented in Supplementary Note 2.
LalbCENH3-ChIP and ChIP-seq analysis. Chromatin immunoprecipitation experiments were done with Abcam ChIP Kit -Plants (ab117137) following the manufacturers instructions. First, 1 g of young L. albus cv. AMIGA leaves were collected and cross-linked with formaldehyde 1% for 15 min on ice. Leaves were then ground in liquid nitrogen and sonicated using a Diagenode Sonicator. Sonicated chromatin-DNA ranging from 200-1000 bp was immunoprecipitated using anti-LalbCENH3. Immunoprecipitated DNA and, as a control, an input chromatin DNA samples (3-7ng for each sample) were sent for ChIPseq at BGI. The original ChIPseq sample data are available at White Lupin Genome Website (http://www. whitelupin. fr). To identify repeats associated with CENH3-containing chromatin, reads from the ChIPseq experiment obtained by sequencing DNA from isolated chromatin prior to (the input control sample) and after immunoprecipitation with the CENH3 antibody (the ChIP sample) were separately mapped to the repeat clusters.
The mapping was based on read similarities to contigs representing individual clusters, using BLASTn (  22 ) with parameters "-m 8 -b 1 -e 1e-20 -W 9 -r 2 -q -3 -G 5 -E 2 -F F" and custom Perl scripts for parsing the results. Each read was mapped to a maximum of one cluster, based on its best similarity detected among the contigs. Ratio of ChIP/input reads assigned to individual clusters was then used to identify repeats enriched in the ChIP sample as compared to the input. White lupin diversity analysis. We selected 14 white lupin accessions, covering a wide range of diverse material, to evaluate a broader range of the genetic diversity and determine population structure and linkage disequilibrium. More information about these accessions can be found in Supplementary Note 3.
Data generation with short-reads technology. Young leaves of 30 plants were used to extract genomic DNA of each accession using the QIAGEN Genomic-tip 100/G kit following the supplier's recommendations. The accessions were sequenced using Illumina technology using paired-end 2x150 bp short-reads. It was generated a total of 310. 95 Gb of data with average sequencing depth of 45. 99x (Supplementary Note Table  6 ). Mapping and SNP detection. Cutadapt 1 was used to remove Illumina Truseq adapters from the sequencing data and to remove bases with a quality score lower than 30, in both 5' and 3' end of the reads. Reads with a length lower than 35 were discarded. We then used BWA-MEM version 0. 7. 17  2  to map the resequencing reads from all 15 genotypes to the white lupin reference genome. PCR and Optical duplicates have been detected and removed using Picard Tools.
3  10. A principal component analysis (PCA) was also performed in R  11  (http://www. R-project. org/) function "prcomp". A Bayesian model-based clustering method implemented with STRUCTURE v2. 3. 4  12, 13  was used to investigate the population structure using all the filtered SNPs. The program was run 10 times for each K value, ranging from 1 to 5, with a 1,000 burn-in time and 1,000 iterations. The optimal K value was determined based on the ΔK from the Structure Harvester v0. 6. 94  14  program, through Evanno's test  15. Long-read sequencing and de novo assembly of GRAECUS and P27174. Sequencing. Longread sequencing was realized using Oxford Nanopore technology, using a GridION 18. 04. 1-0, with a software Minknow 1. 10. 24-1 at platform at Get-PlaGe core facility (INRA, Toulouse, France).
Briefly, DNA was used to prepare a library with the ONT It was used the Ligation Sequencing Kit 1D (sqk-lsk109). The DNA was sequenced using a single ONT MinION R9. 4 flowcell (FLO-MIN106) for 48h. Base-calling was performed using Albacore 2. 1. 10-1. This produced 1,280,206 sequences for GRAECUS, corresponding to 12. 45 Gb of data with a N50 length of 13. 6 Kb (27. 6 x of sequencing depth). For the accession P27174 this produced a total of 1,738,579 reads corresponding to 14. 59 Gb of data with N50 length of 11. 8 Kb (32. 36 x of sequencing depth). De novo assembly of GRAECUS and P27174. The de novo assembly of the two genotypes were performed using CANU  16. For P27174-4, two round of correction have been made prior to the assembly step, using the parameters correctedErrorRate=0. 16 anc corMaxEvidenceErate=0.
15. For GRAECUS, only one round of correction have been made, using minOverlapLength=400, correctedErrorRate=0. 16  and corMaxEvidenceErate=0. 15. The Illumina pairedend data described in 3. 1 were used to polish two times the two genome assemblies using Pilon  17. BUSCO v 3. 0. 0 18 was run on the set of predicted transcripts. The assessment software detected for GRAECUS 96. 8% of complete gene models (1142 complete single copy and 188 duplicated respectively) plus 9 additional fragmented gene models. For P27174 97. 8% of complete gene models (1125 complete single copy and 220 duplicated respectively) plus 4 additional fragmented gene models. Structural variation of these two accession were performed in Assemblytics software  19  based on whole genomes alignments generated with MUMmer  20.
Illumina TruSeq adapter sequences have also been removed. The resulting reads shorter than 35 pb have been discarded. The quality checked RNA-seq reads were then mapped on white lupin reference genome using Hisat2 23 software. Transcripts were assembled and quantified using Stringtie software. Gene counts were extracted and imported in the R package DESeq2 24. These counts have been normalized according to the size factor computed by DESeq2. For small RNA sequencing, 24 independent root RNA-seq libraries were constructed using NEXTflex™ Small RNA-Seq kit according to the manufacturer's protocol. All small RNA libraries were sequenced on an Illumina NextSeq 500 sequencing platform, using a single-end, 75 nt read metric instrument according to the manufacturer's instructions. A total of 460,506,072 reads of 75 nt were sequenced.
ACKNOWLEDGMENTS This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  ( Starting Grant LUPINROOTS -grant  agreement No  637420  to B. P. ). We acknowledge  Lucie Combes-Soia  and  Valerie Labas (Physiologie de la Reproduction et des Comportements ,  UMR85 ,  INRA ,  CNRS ,  Université de Tours, France) ,  Olivier Bouchez  ( GeT-PlaGe ,  INRA Auzeville, Castellan Toulosan, France ), and  Charles Poncet  and  Veronique Gautier  (Genotyping Platform Gentyane, Clermont-Ferrand, France). This work was partly performed in the LRSV and LIPM, which belong to the  TULIP LABEX  ( ANR-10-LABX-41 ). This work was also partly performed in IPS2, which belong to the  SPS LABEX  ( ANR-10-LABX-40 ). We also thank the  VILLUM Foundation  for the Young Investigator grant awarded to F.
BreezoMeter uses the CAMS pollen predictions and big data analysis to provide a continuous current condition pollen index (https://breezometer. com/products/pollen-api). However, its validation is not yet available. Air Matters broadcasts pollen and air quality data in most countries of the world (https://air-matters. com). The Copernicus Atmosphere Monitoring Service (CAMS), implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) on behalf of the European Union, provides forecasts for birch, olive and grass pollen to allow allergy sufferers in the EU to take preventive measures days before exposure. (https://atmosphere. copernicus. eu/news-and-media/news/cams-helping-allergy-sufferers). CAMS supports a range of smartphone applications designed to limit exposure to such allergens.
A Mobile app Rating Scale (MARS) instrument (  48 ) -available in Australia -has been used to assess the mHealth apps for AR (Tan et al, in revision). A 'patient empowerment index through mobile technology' was recently designed and used to evaluate AR in order to support patients choosing an mHealth app and physicians recommending it  (42). Most but not all mHealth apps which include clinical data are available in English and some are available in over 15 languages (Table  2 ). The majority of mHealth apps are freely available on iOS and Android. However, very few have provided clinical data supporting their validity. mHealth apps for AR include: • Self-monitoring.
d-Examples of apps • AllergyMonitor® (http://www. tpsproduction. com/) AllergyMonitor ® is an app that has been translated into 10 languages. Its target is to improve allergy diagnosis by matching trajectories of symptom-medication scores and pollen counts  (26, 28, 46, 53, 54). Furthermore, it enhances shared decision-making by fostering the exchange of information between users and their health care professionals. The latter can access and view their patients´ recorded data in a backoffice, which allows the management of patients´ clinical data and gives a structured overview on the individual disease management. The data recorded by the user then complete the set of information being integrated in the back-office in real-time. As users also register the intake of their prescribed medication, this feature allows the monitoring of compliance which has been shown to effectively increase the adherence to treatment with nasal corticosteroids and sublingual allergen-specific immunotherapy.
CDSs can enhance decision-making through the use of the following tools: (i) computerized alerts and reminders to health care providers and patients, (ii) clinical guidelines, (iii) focused patient data reports and (iv) diagnostic support (68). Many apps used in AR provide patients with some help for AR control. However, for this, they should be labelled as CE2A in the EU. Apps that do not provide help can be registered as CE1. An electronic CDSS (eCDSS) based on MASK now exists in AR and is in the process of validation  (69). It is not clear whether other tools have been validated as they have not been published. CDSSs may also be very useful in stratification strategies and in reporting outcomes in clinical trials such as in Allergen Immunotherapy (https://www. ncbi. nlm. nih. gov/pubmed/30955224).
5-Potential of mHealth apps for allergy phenotype discovery Conventionally, phenotypic studies have relied on traditional observational designs. Apps provide a new source of information on daily symptoms and the opportunity to discover new phenotypes. Few studies have been published concerning allergy phenotypes assessed with an app. A prospective analysis has compared six disease severity scores for AR against pollen counts  (53). Many different and incomparable symptom (medication) scores are used to assess AR control. Disease severity scores for seasonal AR evaluated by an internet-based platform provide similar results at population level but are heterogeneous in individual patients. Multimorbidity in allergic airway diseases is well known (  70 ), but no data has ever existed regarding how multimorbidity impacts the daily dynamics of specific symptoms, including severity and work.
1) and a minimum log fold-change (logFC) of 1 (Additional file 3). RNA-Seq results were validated by qPCR analysis (Additional file 4) for the P. viticola induced genes VvPR10. 1, VvPR5, VvROMT and VvSTS1. The results demonstrated a strong significant correlation (r = 0. 95-0. 99) between qPCR and RNA-Seq data (Additional file 4). Among those 2612 differentially expressed (DE) genes, 34 were found to encode stilbene synthase proteins (Fig. 6a ). Of these 34 VvSTS genes, 26 were identified as being more highly induced in leaf tissues undergoing a successful Rpv3-1-mediated defense (Rpv3-1/ avrRpv3 + ) and include the VvSTS genes shown to be upregulated by qPCR in Fig. 4. Even though most of the individual VvSTS genes are statistically significantly induced by infection in all samples, a significant difference was found when comparing the gene expression of all 34 VvSTS genes (Fig.
Histochemical studies Aniline blue staining was used to monitor P. viticola mycelium development according to Hood and Shew  [75]. Leaf discs were inoculated with zoospore suspensions as described above. Samples were collected at 24, 48 and 72 hpi and documented with an epifluorescence microscope (ZEISS Axio Scope. A1; Kübler HXP-120C lighting device; Filter set: Zeiss 05; software AxioVision Rel. 4. 8). Programmed cell death was studied by trypan blue staining at 24, 28, 32 and 48 hpi as described in Feechan et al. [76]. For a photographic record of leaf disc tissues a ZEISS Axio Lab. A1 microscope with a Zeiss Axiocam MRc camera and Zen blue software were used.
Libraries for next generation sequencing were prepared using the NEBNext Ultra II Directional RNA Preparation Kit with NEBNext Dual Index Oligo's for Illumina and the NEBNext Poly A Selection Module (New England Biolabs) according to the manufacturer's instructions, at the Bioquant, CellNetworks Deep Sequencing Core Facility (Heidelberg, Germany). Single-end sequencing with a length of 75 bp for each read was run on an Illumina NextSeq 500 instrument at the Genomics Core Facility, EMBL (Heidelberg Germany). After sequencing, raw data were transferred to the Quantitative Biology Center (QBiC, https://portal. qbic. uni-tuebingen. de/portal/) at the University of Tübingen using an Aspera client.
Quality control, mapping, and differential expression analysis Initial steps from raw data quality control to mapping and eventually read counting was undertaken by QBiC on the High Performance cluster (HPC) of the University of Tübingen using a fully automated workflow written in Snakemake  [81]. The code is accessible here: https://github. com/qbicsoftware/rnaseq. This workflow utilizes the following software packages: FastQC (version 0. 11. 4) for initial raw data quality control, Cutadapt (version 1. 8. 3) for filtering reads containing matches to Illumina adapters, Tophat (version 2. 2. 3. 0) for mapping of filtered reads against the reference genome and HTseq-count (version 0. 6. 1p2) for counting. In the mapping step, reads were aligned to the Vitis vinifera reference genome PN40024  [82]  downloaded from Ensembl Plants (annotation release 38) in January 2018.
g. susceptible-infected (avrRpv3 + ) versus susceptible-control). The same approach allowed the extraction of more complex interaction terms such as [Rpv3-1_infected (avrRpv3 + ) versus Rpv3-1_control] versus [suscep-tible_infected (avrRpv3 + ) versus susceptible_control] in order to identify which genes respond to infection differently concerning different cultivars. The simple pairwise contrasts as well as more complex interaction terms were extracted from the same statistical model applied to the same dataset. Limma was also used to calculate empirical Bayes moderated p-values relative to a minimum required fold-change threshold which were adjusted for multiple testing by controlling the false discovery rate (FDR) ≤0. 1%  [83].
Acknowledgments We would like to thank  Claudio Moser ,  Giulia Malacarne  and  Silvia Vezzulli  ( Fondatione Edmund Mach ,  San Michele all'Adige, Italy)  for sharing unpublished information. We would like to thank  David Ibberson  of the  Heidelberg University Deep Sequencing Core Facility  for library production, sequencing and useful advice.
However, using such metrics is only possible if the validation data set is dense, in other words, if every pixel of the training area is labeled. Indeed, without this information, there is no way to split the validation data into geometric categories, or to extract the reference objects. Unfortunately, dense validation data is not available in most practical land cover problems. Indeed, there are many cases where training data is manually collected in the field, or comes from a combination of existing data bases, which are all incomplete, or for which certain classes are out of date. A small, dense validation set could be manually constructed, but this would limit the metric to a reduced region, and would be a very time-consuming process. Figure  1  illustrates the sparse reference data used later in the Experimental Section, which is similar to the database used in  [5]  for time series mapping over France.
Results for all 8 tiles in Section 6. The classifier used for the evaluation is a Random Forest  [11]  with 100 trees, and a maximal depth of 25. This region was chosen among the others as it covers a wide area, including large urban agglomerations as well as a variety of forests and agricultural lands. Figure  14a  shows a small area of this data set. Three spectral indices, namely the Normalized Differential Vegetation Index (NDVI), the Normalized Differential Water Index (NDWI) and Brightness, are also calculated from these images, which gives the full time series a total dimension of 489 features. A detailed description of the training data is also given in  [5]. In the selected region, the nomenclature contains 14 classes, which range from agricultural classes such as summer or winter crops, to natural covers like forests and water, as well as artificial surfaces, roads and urban areas, as shown in Figure  14b.
For the evaluation, the reference data is split into training and testing sets, at the polygon level, to avoid correlation between the two data sets, and to provide measurement of the generalization error, as is recommended by  [43]. Each data set is comprised of 15,000 samples per class, except for the Natural Grasslands class, where around 10,000 samples are selected, as there are fewer samples available for this class. The same number of samples was chosen for the testing sets. The benchmark classification is the pixel-based Random Forest classification  [11] , an example the of benchmark result is given in Figure  14b. The performance of the different methods is evaluated by the following criteria.
Further improvements could also include the detection of fine elements, in order to measure the degradation in such areas. It would be equally interesting to intersect the validation data with a buffer around each corner detected on a pixel-based classification result, to provide a class-specific metric of the deterioration in the corner areas. Moreover, it could be interesting to incorporate weights to the different corners, according to the classes that form them. For instance, the weights might be inversely proportional to the class frequencies, to balance the contribution of the different classes to the metric, in case of very unbalanced class proportions. The source code of the PBCM metric uses Orfeo ToolBox, and is freely accessible  [44].
5, 7. 5 and 10 μM of unlabeled 50S subunits were added to the reaction mix, which results in a 5-fold, 15 fold and 20-fold excess of unlabeled 50S. After completion of the synthesis reactions the diffusion properties were checked by fluorescence correlation spectroscopy and subsequently the solutions were diluted to a final concentration of labeled ribosomes in the order of a few picomolar for single-molecule measurements. Confocal fluorescence measurements and data acquisition. All experimental details of the performed measurements were described previously, for the DNA samples in refs. 40, 48 , for translation initiation with ribosomes in ref. 11  and for FRET-based biosensors (see Supplementary Note 3) in ref. 38. Briefly, all measurements were performed with a MicroTime200 (Picoquant, Berlin, Germany) equipped with pulsed laser diodes, an UPLSAPO 60X/1.
The related values for all presented measurements are given in the Supplementary Note Tables N1. 3-5. Here the error of the fractions of coincidence values is determined by the statistical error σ of the number of bursts as given by Poisson statistics with σ ¼ ffiffiffiffi N p (see also Supplementary Note 2). The data analysis was performed using self-written Matlab routines (Mathworks, Natick, MA, USA). OriginPro (9. 0. 0 G, 64 bit) was used to produce the graphical presentation of the obtained results. Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
2  and Dataset S1 A-E). Genes of the rDSR sulfur oxidation pathway and CBB carbon fixation pathways were highly expressed and underline the thioautotrophic role of Ca. R. santandreae [sum of 8 CBB cycle proteins: 4. 88 ± 1. 17% (mean ± SD) normalized spectral abundance factor (NSAF); sum of 14 rDSR pathway proteins: 7. 48 ± 0. 95% NSAF; n = 8 proteomes] (Fig. 2  and SI Appendix, Figs. S3 and S4 and Supplementary Note 2). CBB cycle genes including the ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) subunits cbbL and cbbS were among the top 10% of most highly represented genes in transcriptomes as well as proteomes (SI Appendix, Figs. S3  and S4 ). Microautoradiography corroborated the gene expression data, as signals for carbon fixation were limited to the symbiont-bearing trophosome region.
Without addition of an external electron donor supply, the Ca. R. santandreae symbionts fixed 9. 48 ± 10. 2 μmol C g -1 h -1 (maximum: 33 μmol C g -1 h -1 ) (SI Appendix, Figs. S5-S7 and Dataset S1F). These rates were in the range determined for other sulfur-oxidizing symbionts of, e. g. , Ifremeria nautilei (0. 7 μmol C g -1 h -1 ) and Riftia pachyptila (27 μmol C g -1 h -1 ) (32). The calculated fixation rates would be sufficient to sustain a doubling of the length of a Paracatenula sp. santandrea specimen in 10-34 d (Dataset S1F). Such growth rates, together with frequent asexual reproduction  (33, 34) , could explain their high abundances at the sampling site where they occurred in densities of 233-5,400 individuals per m 3 of sediment (1,809 ± 1,483; n = 40). The CBB cycle encoded in Ca.
S12  and Dataset S1 G and H). The Ca. R. santandreae symbiont can only take up selected peptides, but no sugars or fermentation end products that might be present in the sediments or originate from the host metabolism. Still, TCA cycle genes were highly transcribed, and the corresponding proteins were abundant, constituting a total of 3 ± 0. 49% NSAF (sum of seven proteins; n = 8 proteomes) of all measured proteins in the proteomes (Fig. 2  and SI Appendix, Figs. S3  and S4 ). We hypothesize that carbon fixation and the TCA cycle do not operate simultaneously in a single cell but are expressed in separate symbiont populations to avoid futile cycling. To replenish intermediates of the TCA cycle and to synthesize and convert acetyl-CoA into biomass, Ca. R. santandreae expresses the ethylmalonyl-CoA pathway and an incomplete 3hydroxypropionate bicycle (3-HPB) pathway.