dataesr/scientific-paragraphs-categorization huggingface dataset:
34, 39, 40  This approach must be adapted to every spin system and model of motions, and has been difficult to validate experimentally so far. Two-field NMR relaxation measurements offer the unique ability to measure accurate relaxation rates at a low field (0. 33 T), which can be used to verify, validate, or calibrate the analysis of high-resolution relaxometry datasets. Accurate relaxation rates measured at low field demonstrate the systematic deviation of highresolution relaxometry measurements and validate our previously reported analysis. Monoexponential decays were obtained for both longitudinal and rotating-frame relaxation measurements (Figure  2  and Figure  S3-S6 ). As anticipated in our analysis of carbon-13 longitudinal relaxation at low-field by high-resolution relaxometry,  39  relaxometry rates are systematically lower than rates measured on the two-field spectrometer by 13% on average (Fig.
2. a). This confirms that relaxation rates recorded without control of cross-relaxation pathways are biased. Importantly, the accurate measurements of relaxation at low field validate our analysis of relaxometry datasets. The ICARUS analysis of relaxometry rates accounts for the expected systematic deviation of decay rates from true relaxation rates by computing a relative correction in a simulation of the full experiment. For the first time, we are able to compare corrected relaxation rates obtained by relaxometry and accurate relaxation rates obtained with the two-field NMR spectrometer. The comparison is very favorable (Fig. 2. a) and demonstrates the reliability of the ICARUS analysis. 34, 39, 40   The two-field NMR spectrometer gives the unprecedented ability to measure transverse relaxation rates at magnetic fields incompatible with high-resolution NMR of macromolecules.
Transverse and longitudinal relaxation rates were found to be very similar (Fig. 2. b), which is expected since we are at the edge of the so-called "extreme narrowing" regime. The ability to measure transverse relaxation rates at such low fields opens the possibility to access transverse relaxation rates in systems where such measurements are impaired by large chemical exchange contributions at high field. Relaxation rates at low magnetic field display a high information content about dynamics and are complementary to high-field datasets. We recently investigated the dynamics of isoleucine side chains based on the measurements of high-field relaxation and longitudinal relaxation by relaxometry of d1 carbon-13 nuclei, covering magnetic fields from 0. 33 T to 22. 3 T. 39  Up to five parameters of local motions could be obtained for each d1 methyl group: a correlation time for the rotation of the methyl group around the CC axis, tmet; an order parameter Sf 2 and associated correlation time tf for fast motions of the CC axis; an order parameter Ss 2 and associated correlation time ts for slow motions of the CC axis.
4. c) as well as Ile36 (Fig. S7 ), in a manner similar to the use of relaxation rates at 20 magnetic fields (Fig. 4. a). This suggests that a combination of standard high-field relaxation rates with accurate relaxation rates measured at a carefully chosen low field can be sufficient to describe quantitatively dynamics over a wide range of time-scales (from few ps to few ns). We have developed a new class of NMR experiments to measure accurately transverse and longitudinal relaxation rates at low field while preserving the resolution of high-field NMR on a two-field NMR spectrometer. These measurements are an essential tool to refine and validate the analysis of high-resolution relaxometry datasets, which we have shown for the determination of sidechain motions of Ubiquitin. We also demonstrate that accurate relaxation measurements at a single low field can be sufficient to characterize quantitatively motions in the pico-and nanosecond ranges.
), for scalable analytics. It will use monitoring and profiling information for taking decisions based on trade-offs related to cost, data constraints, efficiency and resources availability. During the project, EUXDAT will be in contact with scientific communities, in order to identify new trends and datasets, for guiding the evolution of the e-Infrastructure. The final result of the project will be an integrated e-Infrastructure which will encourage end users to create new applications for sustainable development. Kuska and Mahlein  [1]  have identified the usefulness in the use of sensors, mainly optical, to precisely detect plant diseases. They also acknowledge the need to address the challenges to be resolved in the implementation of such methods for more efficient plant disease protection.
The ability of satellite data to monitor crop diseases and pests has also been displayed by the work of Yuan et al. [5]. A combination of remote sensing data has been used, with different resolutions each (Worldview 2 and Landsat 8), to calculate a combination of vegetation indices and environmental characteristics. The above managed to accurately reflect the regional spatial distribution of crop diseases and pests. For this concept, a monitoring system will be designed and implemented based on Sentinel-2 image analysis algorithms in order to monitor the condition of olive crops and detect anomalies in their physiology. Anomalies, as deviations from normal growth, will be further investigated by hyperspectral data and other data sources and attributed to relevant factors such as biotic or abiotic stresses (disease, pests).
Methodology Carotenoid reflectance index 2 (CRI2) is an index that provides information concerning plant stress levels related to deprivation  [6]. This is due to an increase in carotenoids concentration relatively to chlorophyll concentration decrease in plant tissues, which can consequently be expressed as reflectance differentiations in the electromagnetic spectrum. A model was built using ArcMap 10. 5 tools. To create and test the model (and the resulting script in Python) a sample image (S-2 L2A), including the study area was downloaded from the Copernicus open access hub (https://scihub. copernicus. eu/dhus/#/home), and a subset of the image was processed. The index is calculated by the following equation: which based on Sentinel-2's band reflectance ranges is better represented by bands 2 and 5:.
DISCUSSION It can be observed that the level of detail in the CRI2 map is enough to highlight a certain number of fields for further investigation by experts. Also, due to the continuous supply of imagery data from Sentinel-2, a time series of estimations can be utilized to monitor and assess the progress of stress levels in the fields. A limitation of this service could be that the apparent stress levels are not specific to a single source, such as a pathogen or abiotic stress, but rather display the deterioration of plant health. Also, in order to properly apply the model, the field borders of olives are a prerequisite to produce field-specific results. There are many occasions that such a database has errors in crop type or, for some cases, it may not exist at all. Although the model was created using ArcGIS tools in arc.
py, it was later modified to work in python using only open source modules for the computations, achieving the same results. This was done, in order to be compatible with the Jupyter notebook environment, which is used for the development of services for mundi. (https://mundiwebservices. com/solutions/overview) A future aim for this service is to use data from olive fields and provide a more accurate and specific index. Also, further testing of the currently-evolving e-infrastructure with similar vegetation indices models, can enhance its ability to provide cloud processing as an essential need for the end users.
CONCLUSIONS A methodology to utilize Sentinel-2 imagery data to assess olive water stress levels was demonstrated in this work. This was achieved by calculating the CRI2 index, which is used as an indicator of the mean stress levels of a given olive field. A model was created that completes this set of calculations in ArcGIS environment and was modified in python to only utilize open source modules. This makes it compatible with Jupyter Notebook and possible to run in the e-Infrastructure being designed in the EUXDAT project. This kind of service can prove useful for advisors and scientists alike, as it paves the way for a platform which can be used both as an early warning system, as well as to expand into new applications.
Plant self-incompatibility (SI) is a genetic system that prevents selfing and enforces outcrossing. Because of strong balancing selection, the genes encoding SI are predicted to maintain extraordinary high levels of polymorphism, both in terms of the number of S-alleles that segregate in SI species and in terms of nucleotide sequence divergence among distinct S-allelic lines. However, because of these two combined features, documenting polymorphism of these genes also presents important methodological challenges that have so far largely prevented the comprehensive analysis of complete allelic series in natural populations, and also precluded the obtention of complete genic sequences for many S-alleles. Here, we present a novel methodological approach based on a computationally optimized comparison of short Illumina sequencing reads from genomic DNA to a database of known nucleotide sequences of the extracellular domain of SRK (eSRK).
By examining mapping patterns along the reference sequences, we obtain highly reliable predictions of S-genotypes from individuals collected in natural populations of Arabidopsis halleri. Furthermore, using a de novo assembly approach of the filtered short reads, we obtain full length sequences of eSRK even when the initial sequence in the database was only partial, and we discover new SRK alleles that were not initially present in the database. When including those new alleles in the reference database, we were able to resolve the complete diploid SI genotypes of all individuals. Beyond the specific case of Brassicaceae S-alleles, our approach can be readily applied to other polymorphic loci, given reference allelic sequences are available.
This approach successfully identified a diversity of recombinant S-haplotypes  (Tsuchimatsu et al. 2017 ), but it is unclear whether its success was contingent on the low allelic diversity (only three divergent S-alleles) in the selfing A. thaliana and how it can be extended to species with functional SI, i. e. characterized by a large number of highly divergent S-locus allelic lines. A similar approach has been used to identify the Slocus genotype of individuals from Malus domestica, based on individual shotgun sequencing data and a database of 35 previously sequenced S-RNase alleles  (De Franceschi et al. 2018 ). The approach was successful but involves manual fine-tuning and requires the availability of an exhaustive database of reference sequences, which in many species will not be available.
Here, we present a novel methodological framework building upon the approach of  Tsuchimatsu et al. (2017) , using data from individual shotgun sequencing in order to genotype individuals at the Slocus based on the availability of a large database of known S-allele reference sequences. The method uses two complementary approaches (see Materials and Methods for detailed information): (1) a mapping approach which attempts to map sequence reads successively on each of the reference sequences and reports a set of custom statistics to identify the best hits based on quantitative thresholds (Fig. 1A ); and (2) a de novo assembly approach for the discovery of novel S-allele sequences and phylogenetic reconstruction to place the obtained assembled contigs within the phylogeny of S-allele reference sequences (Fig.
1B ). We produced optimized pipelines to perform both analyses based on a dataset of FASTQ read sequences and a set of reference sequences. We applied the method to a published dataset consisting of 56 A. halleri subsp. gemmifera individuals for which  Kubota et al. (2015)  obtained genomic Illumina resequencing reads. We show that the method allows exhaustive genotyping of heterozygous as well as homozygous individuals, providing the first accurate evaluation of the frequency distribution of S-alleles in a natural population of selfincompatible Brassicaceae. Although the method has been designed and tested for studying S-allele diversity in Brassicaceae, it can be applied to any type of highly polymorphic locus, given a sample of reference allelic sequences is available.
Database structure, curation and transformation into a k-mer library The first step of our approach was to construct a database of publicly available nucleotide sequence variants of the gene of interest. In the present case, we started from divergent SRK alleles in A. halleri based on published studies  (Castric and Vekemans 2007; Castric et al. 2008; Goubet et al. 2012 ). To compare mapping properties in a controlled manner and establish thresholds for genotyping, we also retrieved single-copy low diversity paralogous sequences (Aly3, Aly7, Aly8, Aly9, Aly10, Aly14, AlLal2) from the draft A. halleri subspecies gemmifera genome  (Briskine et al. 2017). All reference sequences in the database were trimmed to include only the extracellular domain using AhSRK03 (KJ772380. 1) as a reference. We then developed a dedicated algorithm (kmerRefFilter) to produce an exhaustive library of k-mers present in the set of reference sequences (size 20-mers), from which duplicates and low-complexity k-mers (using a Shannon Entropy threshold of 0.
Short reads preparation and filtering We then retrieved raw FASTQ Illumina sequence data published by  Kubota et al. (2015)  on 56 A. halleri subsp. gemmifera individuals from natural populations in Japan and checked their quality with FastQC (v0. 11. 4). To reduce the dataset and decrease computation time, we filtered these raw reads using the k-mer library obtained above. Paired reads were kept only if at least one k-mer matches exactly on either the forward or the reverse read sequence. This results in a drastic reduction of the number of reads (0. 03% on average), allowing very efficient processing of downstream steps.
Short reads alignment and procedure for genotype calling The pipeline then proceeds with unpaired alignment with end-to-end sensitive mode of the reduced set of paired reads onto each sequence of the reference database using Bowtie2 (v2. 2. 6)  (Langmead and Salzberg 2012). The resulting alignment files (in bam format) were analyzed using samtools (v1. 4)  (Li et al. 2009 ) to extract alignment metrics, specifically the error rate (number of mismatches divided by the number of mapped bases) reflecting the mean identity between the aligned reads and the reference sequence, and the coverage defined as the percentage of positions of the reference sequence covered by at least one read. Based on these two statistics, we aimed at identifying discriminatory criteria to distinguish positive from negative calls.
To achieve this, we observed the distributions of these statistics on the paralogous sequences, that by definition will be present in two copies in diploid individuals. We fitted simple probability density functions on these empirical distributions and integrated them to obtain cumulative distribution functions. For each individual, we then computed a genotyping score against each S-allele sequence in the reference database as the product of the probability of a positive call obtained from the distribution function of the error rate times the square root probability obtained from the distribution function of the coverage. We determined the threshold for positive calls based on the empirical distribution of genotyping scores from the paralogous sequences. S-locus genotype calls can either be automated based on this threshold, or determined visually by examination of detailed mapping patterns across the reference database.
Allele discovery by de novo assembly The genotyping procedure outlined above is based on alignment to a database of reference S-allele sequences. By definition, it is thus limited by how complete the database was to begin with. To overcome this limitation, we sought to develop a procedure to discover new S-alleles. We reasoned that de novo assembly of the filtered set of paired reads should potentially identify unknown S-alleles. To explore this possibility, we used the dipSPAdes assembler v3. 10. 1  (Safonova et al. 2015)  using the following parameters  (-k 21,41,81 and -careful)  to obtain a set of assembled contigs. Then the cap3 sequence assembly program v10/15/07  (Huang and Madan 1999)  was used with default parameters to generate consensus sequences from overlaps between forward or reverse contigs.
These consensus contigs were aligned against sequences of the SRK database using the local DNA alignment tool YASS v1. 14  (Noé and Kucherov 2005)  that allows alignment even in cases of distant similarity, as appropriate given the important divergence among SRK alleles. We retained all contigs aligned over at least 10% of a reference and not aligning against any paralog. Query and subject alignment positions were used for contigs orientation. As an internal validation of the assembled contigs, we examined how the paired reads mapped on them using Bowtie2 v2. 2. 6 (paired alignment with end-to-end sensitive mode)  (Langmead and Salzberg 2012). Samtools v1. 4  (Li et al. 2009 ) was used to extract several metrics, including coverage, mean read depth and depth across successive intervals (for detection of chimerism when depth levels vary sharply along the contig), insert average size, error rate (to detect cases where different S-alleles were assembled into a single hybrid contig), pairs with other orientation (used to evaluate contigs mis-assembly such as inversions), pairs on different contigs (to evaluate redundancy by detecting if paired reads align to multiple contigs).
These metrics can be used to curate the set of contigs visually and evaluate assembly quality. Finally, to compare the set of de novo contigs to SRK sequences in the starting reference database, we constructed a phylogenetic tree. Because of the procedure used for the initial filtering step of paired reads, the obtained contigs can be larger than the SRK sequences in the starting reference database and extend into the divergent 5'UTR and first intron. Hence, 5' and 3' ends were trimmed based on the largest pairwise YASS alignment. MUSCLE v3. 8. 31  (Edgar 2004a; Edgar 2004b ) was used for multiple alignment and PHYML  (Guindon et al. 2010)  was used for building a neighborjoining phylogenetic tree. The program outputs one tree per individual sample, as well as one global tree where all contigs in a collection can be compared to all others and to the SRK sequences in the database.
A powerful genotyping procedure We started from the heterogeneous database of all SRK sequences of A. halleri obtained from several published studies and deposited in NCBI. Collectively, this database contains 34 SRK allelic lines (Table  S1 ), but at this step it is unknown how much of the overall diversity present in the species this represents. Because they were obtained by different molecular methods, some of these sequences cover the complete extracellular domain (n=16, average length of the complete extracellular domain = 1300 bp), but the majority of them (n=18) are partial sequences (average length = 570 bp). We mapped the raw filtered Illumina reads of the 56 A. halleri individuals of  Kubota et al. (2015)  onto the paralogs of the reference database and observed a good fit of the empirical distribution of error rates with a normal distribution and of the distribution of coverage with an exponential distribution (Fig.
S1 , Fig. S2A ). We then mapped the filtered reads onto the S-alleles reference sequences and examined the mapping patterns (Fig. S2B ). A genotyping score of 10 -5 provided good discrimination power among S-alleles (Fig. S2B ), as no individual had scores above this threshold for more than two of the reference sequences in the database, as expected for diploid genotypes (Fig. 2 ). Strikingly, even when the mean sequencing depth was as low as e. g. X=5, our approach allowed proper discrimination (Fig. S3 ). Overall, at this stage we obtained 28 full S-locus genotypes (12 homozygotes and 16 heterozygotes), 24 potentially incomplete genotypes (a single allele identified, corresponding either to homozygous individuals or heterozygous individuals in which just a single allele could be determined), while no allele could be identified for the four remaining individuals (but see below).
Among the 34 SRK alleles present in the reference database, ten alleles were found within the analyzed dataset (AhSRK01, AhSRK02, AhSRK04, AhSRK12, AhSRK18, AhSRK19, AhSRK21, AhSRK26, AhSRK28, AhSRK-B). The large number of incomplete or missing genotypes strongly suggests that several S-alleles were absent from our reference dataset. This may partly be due to the strong divergence of the A. halleri subsp. gemmifera populations from the European A. halleri populations (Šrámková-Fuxová et al. 2017), as the latter were mostly used in previous screens of Sallele diversity  (Castric and Vekemans 2007). Hence, we used a de novo assembly procedure to attempt identifying these unknown alleles.
De novo assembly The de novo assembly procedure allowed us to obtain full-length sequences of the S-domain for several SRK alleles that were previously only partially sequenced  (Castric and Vekemans 2007). For instance, the reference sequence of AhSRK02 in GENBANK (EU075125. 1) was only 550 bp long, but we obtained 4 assembled contigs with high similarity to AhSRK02, two of which appear to cover the full-length S-domain, i. e. 1294 bp. (Fig. 3A ). The accuracy of the assembly is demonstrated by the strict identity of the two full-length sequences obtained (DRS032559 and DRS032547 in Fig. 3A ). Overall, among the ten identified SRK alleles, four were present in the database as partial sequences (AhSRK02, AhSRK18, AhSRK19, AhSRK26) and we were able to obtain full-length sequences of the S-domain for all of them.
Strikingly, the procedure also allowed us to recover sequences from alleles that were initially absent from the database, as illustrated in Fig. 3B. For instance, we obtained sequences of the same new allele (that we named temporarily AhNEW1) from three different individuals, which is close to but distinct from the previously known AhSRK22 allele (Fig. S4 ). By comparing those sequences with SRK sequences from the closely related species A. lyrata, we found that AhNEW1 is very similar to AlSRK09, which confirms that AhNEW1 and AhSRK22 are indeed distinct functional alleles, as trans-specific polymorphisms are very common at the S-locus (Fig. S4 ;  Castric et al. 2008). Overall, the de novo approach identified nine new SRK alleles (Table  S2 ): (1) five new alleles in A. halleri for which we found a trans-specific allele in A.
A complete allele frequency distribution. Using the extended database of reference sequences that was completed by including the nine additional SRK alleles identified by the de novo assembly procedure, we compared normalized depth coverage relative to paralogs for all positive calls. We found that relative read depth for individuals with a single S-allele called was similar to that of paralogs (relative read depth close to 1, consistent with these individuals being true homozygotes), and about twice that for individuals with two Salleles called, consistent with the latter being true heterozygotes (Fig. 2 , Fig. S5 , Fig. S6 ). Hence our approach provides a powerful way to distinguish homozygous from heterozygous individuals, and we now succeeded in obtaining full diploid SRK genotypes for all 56 individuals of A.