dataesr/scientific-paragraphs-categorization huggingface dataset:
Minimum peptide length was set to 7 aa. Maximum false discovery rates were set to 0. 01 at PSM, peptide and protein levels. Intensity-based absolute quantification (iBAQ)  [48]  values were calculated from MS intensities of unique + razor peptides. Proteins identified in the reverse and potential contaminants databases as well as proteins only identified by site were discarded from the final list of identified proteins. For each analysis, iBAQ values were normalized by the sum of iBAQ values in the analyzed sample  [49]. Only proteins identified with a minimum of two unique + razor peptides in one sample were considered.
Acknowledgements This work was partially supported by the  French National Research Agency   ANR-14-CE14-0023-01 ,  Institut Français de Bioinformatique  ( ANR-11-INSB-0013 ), the  Fondation Bettencourt Schueller  ( OTP51251 ) and the  Provence-Alpes-Côte -d'Azur région (2010 12125). Proteomic experiments were partly supported by the  ProFi grant  ( ANR-10-INBS-08-01 ). We thank  Laurence De Marchi  and  Daouda Traore  for providing the soil samples and  Olivier Poirot  for helpful discussions. We thank  Dr Elsa Garcin  for reading and improving the manuscript. We also thank  Dr A. Kosta  at the IMM imagery platform and  F. Richard  and  A. Aouane  for their expert assistance on the IBDM imaging platform and  Dr N. Brouilly  for data acquisition that allowed producing the Supplementary movie.
Data availability The annotated genomic sequences determined for this work have been deposited in the Genbank/EMBL/DDBJ database under the following accession numbers: M. vitis: MG807319, M. australiensis: MG807320, M. maliensis: MK978772, Z. vitis: MG807318, mvtv: MG807316 and matv, MG807317. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD009037  [75].
Developing high yielding rice varieties that are tolerant to drought stress is crucial for the sustainable livelihood of rice farmers in rainfed rice cropping ecosystems. Genomic selection (GS) promises to be an effective breeding option for these complex traits. We evaluated the effectiveness of two rather new options in the implementation of GS: trait and environment-specific marker selection and the use of multi-environment prediction models. A reference population of 280 rainfed lowland accessions endowed with 215k SNP markers data was phenotyped under a favorable and two managed drought environments. Trait-specific SNP subsets (28k) were selected for each trait under each environment, using results of GWAS performed with the complete genotype dataset. Performances of single-environment and multi-environment genomic prediction models were compared using kernel regression based methods (GBLUP and RKHS) under two cross validation scenarios: availability (CV2) or not (CV1) of phenotypic data for the validation set, in one of the environments.
Here, we report the results of our investigations on the effectiveness of trait-specific marker selection and of multi-environment prediction models in improving the PA of genomic predictions for drought tolerance in rice. We disposed of a large genotypic dataset of 215 k SNP for the reference population (280 accessions) of the rainfed-lowland rice breeding program of the International Rice Research Institute (IRRI), and of phenotypic data of the same population under one normal and two managed drought growth conditions. Subsets of trait and environment-specific markers (28k SNP) were selected for each trait under each of the three growth environments, using results of GWAS performed with the complete genotypic dataset. Performances of single-environment and multi-environment genomic prediction models were compared using kernel regression based methods (GBLUP and RKHS).
Phenotyping and analysis of phenotypic data Field experiments. The field experiments took place at the experimental station of the International Rice Research Institute (IRRI; 14. 18˚N, 121. 25˚E), in the framework of the research activity of Dr. Arvind Kumar, in charge of lowland rice breeding at IRRI, the Philippines. Three independent experiments were conducted in the 2015 dry season, to evaluate the performances of the 280 accessions under three managed environments, E1, E2 and E3. E1 corresponded to the standard lowland rice cultivation, without stress, i. e. transplanting crop establishment in puddled soil and continuous flooding (anaerobic conditions) throughout the crop cycle. E2 corresponded to standard lowland rice cultivation associated with application of drought stress at the reproductive stage (see below).
5% moisture of five plants harvested at physiological maturity. Analysis of phenotypic data. The drought stress applied in E2 and E3 experiments proved to be too severe for a number of accessions that did not reach the reproductive stage or did not produce any grain. The three traits could consequently only be measured properly in 230 accessions in E2 and in 226 accessions in E3, of which 204 were common to the two experiments. Consequently, data analyses were run for 280 and 204 accessions in E1 and for 204 accessions in the E2 and E3. The phenotypic data were analyzed using the PROC MIXED procedure of SAS v9. 0 (SAS Institute Inc. , Cary NC, USA). For each experiment and trait, the following model was used to adjust phenotypic measurements: Y ijk ¼ m þ C j þ b k þ N i � a ik þ ε ijk where Y ijk is the phenotype of either the i th accession or the j th check in the k th block, μ the overall mean, C j the checks effect considered as fixed, ß k the block effect considered as random, N i a covariate that amounted one for accessions and zero for checks entrees in each block, α ik the random effect of the i th accession in the k th block and ε ijk is the residual considered as random.
Genotyping and analysis of genotypic data Genotypic data. The genotypic data for the 280 accessions were obtained from the International Rice Informatics Consortium database for the 3,000 rice genomes project (http://iric. irri. org/). It corresponded to the 962k Core-SNP dataset. Filtering of the 962k loci for missing data (threshold of � 20%), the frequency of the minor allele (MAF, threshold of � 2%), and for the rate of heterozygosity (Ho, threshold of � 5%), resulted in a working set of 215,242 SNP. The average rate of missing data per locus for the 280 accessions was 4. 1%. The rate of missing data per accession ranged from 1. 5 to 17. 3%. Heterozygoty (Ho) per accession was 0. 2 to 10. 5, average 1. 0%. The average minor allele frequency was 14. 6%. The missing data were imputed using Beagle v4.
0  [47]. The final matrix of 280 accessions and 215,242 SNP is hereafter referred to as the "215k SNP" marker set. The SNP x accession matrix after imputation is available for download at http://tropgenedb. cirad. fr/tropgene/JSP/interface. jsp?module=RICE as GS-Ruse datasets "GS-Ruse_IRRI-Reference-Population_150K" and "GS-Ruse_IRRI-Reference-Population_28K". Characterization of population structure and linkage disequilibrium. The population structure was investigated with 2,859 SNP with no missing data, using the sNMF software  [48]. Once the likely number of subpopulations was determined, each accession was assigned to one of those subpopulations, if the proportion of its inferred ancestry derived from a subpopulation was above 0. 80%. Otherwise, the accession was considered as admixed.
A distancebased clustering method was also implemented using the same genotypic data: First pairwise dissymmetry between genotypes was computed using simple matching information and an unweighted neighbor-joining tree was built using Darwin software v6  [49]. Then the tree topology was visualized using FigTree v1. 4. 3. [50]. Pairwise linkage disequilibrium (LD) between SNP loci was calculated at the level of the individual chromosome, using the working genotypic datasets and the r 2 estimator proposed for non-phased genotypic data  [51]. Marker selection for genomic prediction. Taking advantage of the large number of markers available, two marker-selection approaches were tested, with the aim of (i) identifying the minimum marker density while maintaining the highest possible PA of genomic predictions and (ii) of analyzing the performances of trait-specific marker selection.
In the first approach, five LD thresholds (r 2 � 0. 25, r 2 � 0. 50, r 2 � 0. 75, r 2 � 0. 90, and r 2 � 1) combined with three MAF thresholds (� 2%, � 5% and � 25%) were considered based on the following procedure. The complete pairwise r 2 matrix for each chromosome was computed on the 215k SNP dataset. Then for each chromosome, single loci or clusters of loci with pairwise LD with other loci below the threshold were identified. All singletons (LD with other loci below the given threshold) were kept. For the clusters of loci, one locus with the fewest missing data before imputation and the highest MAF was randomly selected to represent the cluster. Lastly, the three thresholds of MAF were applied to each of the five levels of LD, leading to 15 genotypic matrices, the smallest composed of 2,859 SNP (S2 Table ).
Comparison of PA of genomic predictions obtained with each of these matrices, using phenotypic data from E1 environment, (see below and results section) identified the matrix with 28,091 SNP (r 2 � 0. 5 and MAF � 5%), hereafter referred to as 28k SNP dataset, as the best compromise. In the second approach, the selection criterion was the degree of association of the SNP loci with the target phenotypic trait, as measured by genome-wide association analysis (GWAS). Taking the results of the first selection into account, marker selection was performed as follows: (i) the 215k SNP dataset was filtered for MAF � 5%. (ii) The resulting set of 148,916 SNP was used for GWAS, for each trait, under each of the three environments (E1, E2 and E3), using the standard mixed linear model (MLM) under Tassel v5.
2. 48  [52]. MLM uses a genotype based kinship matrix (K) jointly with population structure (Q). We computed K and Q using the dataset of 2,859 SNP corresponding to the LD and MAF thresholds of r 2 � 0. 25 and MAF � 20% respectively. Q corresponded to the estimate of the individual ancestry coefficients for the most likely number of subpopulations. (iii) For each trait, under each drought management, 28,091 SNP with the smallest P-value of trait-marker association were extracted to serve in genomic prediction experiments. All GWAS experiments were implemented with accessions that were not included in the corresponding model training process for the genomic prediction experiments, i. e. with 80% of the total number of accessions. The objective was to avoid the overfitting of the training model.
Statistical methods for genomic prediction Single environment models. Two kernel regression models were used to predict GEBV in each drought stress experiment: the genomic best linear unbiased prediction (GBLUP) and the reproducing kernel Hilbert spaces regressions, RKHS,  [53]. The implementation procedures of the two models is detailed in  [42]. Briefly the GBLUP method that hypothesizes a strictly additive determinism of the genetic effects  [54]  was implemented using the genomic matrix G = M � M' (M being the genotypic matrix) and the Expectation-Maximization convergence algorithm with the R package KRMM  [55]. The RKHS method that captures more complex genetic determinism  [56]  was also implemented using the KRMM. Multi-environment models. To predict the GEBV with data from the three environments (E1, E2 and E3), hereafter referred to as multi-environment prediction, we used the statistical models described above with extensions to include environmental effects.
In the extended GBLUP model, the effects of environments and markers are separated into two components: the main effect of the markers for all the environments and the effect of markers for each individual environment  [38]. For RKHS, we used the extended models incorporating G×E corresponding to the "Empirical Bayesian-Genotype × Environment Interaction Model" proposed in  [39]. Implementation of the models. Analyses were performed in the R 3. 4. 2 environment  [57]  with the R packages BGLR 1. 0. 5  [58]  and MTM 1. 0. 0  [59]. For both packages, 25,000 iterations for the Gibbs sampler were used after 5,000 burn-ins were removed. The analyses were supported by the CIRAD-UMR AGAP HPC Data Center of the South Green Bioinformatics platform (http://www. southgreen. fr/). Assessing predictive ability of genomic prediction.
Genotypic characteristics of the population Within the 215k genotypic dataset, the average marker density along the 12 chromosomes was one marker every 1. 95 kb. However, the SNP loci were unevenly distributed (S1 Fig). The 28k genotypic dataset represented an average marker density of one marker every 13. 7 kb. However, 426 pairs of adjacent loci had distances above 100 kb, the largest distance being 427 kb. The distribution of MAF was skewed toward low frequencies in both 215k and 28k datasets. The MAF was below 10% for 50% and 37% of loci in 215k and 28k datasets, respectively. The average LD was rather low, 0. 123 and 0. 063 in the 215k and 28k datasets, respectively. The decay of LD along physical distance is presented in the (S2 Fig). For between-marker distances of 0 to 25 kb, the average r 2 value was 0.
145 and 0. 103 in the 215k and 28k datasets, respectively. These r 2 dropped to half their initial level at a mean distance between markers of around 600 kb and 250 kb, in the 215k and 28k datasets, respectively. Analysis of population structure did not provide a clear-cut indication on the number of sub-populations. We opted for five subpopulations, as the estimate of individual ancestry coefficient derived with K = 5 provided the most congruent assignment of the accessions with the group assignment provided by the International Rice Informatics Consortium (S1 Table ). Four subpopulations corresponded to the four subgroups of the indica group and the fifth assembled accessions from aus group. The distance-based analysis of diversity also confirmed the assignment of the accessions to indica and aus groups as well as the presence of subgroups within the indica group (S3 Fig).
25 led to significantly higher PA than the ones with r 2 > 0. 25, except for DTF. In the latter case, the difference in PA between r 2 � 0. 25 and r 2 � 0. 5 was not significant (S3B Table ). Given these results, and the fact that the next steps of our study dealt with phenotypic data from three environments, we took the conservative decision of using the 28k SNP dataset (r 2 � 0. 50, MAF � 5%).
In other words, GWAS performed with 224 accessions (the size of the training set for GWAS in the population of 280 accessions) is more informative than GWAS performed with 163 accessions, the size of the training set in the population of 204 accessions. This is in agreement with the well-known positive relationship between the size of the population and the power of GWAS to accurately evaluate the effect of each marker  [63]. These gains of PA with GWAS-derived marker, despite the limited size of our training populations, suggest that more substantial gains of PA could be achieved if more consolidated QTL information was available. Such consolidated QTL information can be built from the large number of publicly available QTL database and SNPs detected in different linkage mapping and GWAS experiments.
However, the development of medicanes is influenced by additional factors like the presence of baroclinic instability or a cold cutoff low in the upper atmospheric layers. It is likely that climate change will also affect these factors. Romero and Emanuel (2013)  and  Cavicchia et al. (2014b)  both showed that the intensity of medicanes is projected to increase, while their frequency will decrease. Ensemble sensitivity studies, such as those presented here, are thus a valuable tool for offering insights into how the life cycles of medicanes may differ in a warmer climate. Code and data availability. Information about the availability of the COSMO-CLM source code can be found at https://www. clm-community. eu (last access: 14 April 2019). The simulation data generated as part of this work have been archived at the DKRZ World Data Center for Climate (https://www.
Acknowledgements. The computational resources were made available by the  German Climate Computing Center (DKRZ). The authors would like to acknowledge the  European Union  for funding this research through an  Erasmus+ scholarship. Robin Noyelle  would like to thank  Ingo Kirchner ,  Stefan Pfahl  and  Edoardo Mazza  for their fruitful comments. Review statement. This paper was edited by  Vassiliki Kotroni  and reviewed by two anonymous referees.
This paper describes a preliminary experiment concerning the use of advanced Artificial Intelligence/Knowledge Representation techniques to improve the present formalization/digitization procedures of Cultural Heritage assets-with reference, in particular, to all types of Cultural Heritage "iconographic" entities. In this context, in agreement with the recent proposal to characterize the digital description of Cultural Heritage items making use of the notion of "Cultural Heritage Digital Twin", we are mainly concerned with the possibility to consider not only the external, "physical", aspects of these iconographic items but also the "message" they convey in a more or less explicit way. For our experiment, some aspects of the Mona Lisa painting by Leonardo da Vinci have been formalized, along with their context, making use of NKRL, the "Narrative Knowledge Representation Language".
(see Section 2. 1 below), the "Narrative Knowledge Representation Language", NKRL  [4]  has been chosen for representing the Mona Lisa twin and its immaterial component in particular. The paper is organized as follows. Section 2 supplies the general background of this work, introducing first a short state of the art about the use of digitalized procedures in a Cultural Heritage context and recalling then the main features of NKRL. Section 3 is the main Section of the paper, showing how NKRL could be used to represent appropriately, in computer suitable form, a sample of those "hypotheses"  [3]  that have flourished about the origin and the meaning of "La Gioconda". A short conclusion, Section 4, closes the paper.
Another recent proposal of extension of CIDOC CRM concerns the implementation of an ontological tool called VIR (Visual Representation)  [11] -see also https://ncarboni. github. io/vir/. VIR expands the key entities and properties of CIDOC CRM by introducing seven new classes (e. g. , "Iconographical Atom") and 20 new relationships (e. g. , "portray/is portrayed") corresponding to the needs of the visual and art historical community. According to the authors, the resulting model is able to provide a clear distinction between "denotation" and "signification" of a Cultural Heritage element, permitting, in particular, the definition of diverse denotative criteria for the same representation. Taking as an example the representation of a widely known iconographical character, Saint George, the VIR-supported ontological/graphical representations allow us to follow the evolution in time of the set of attributes illustrating this character.
The evident solution to this problem is that of making use, instead of binary models, of n-ary ones where a given predicate can be associated with multiple arguments-for example, the n-ary "purchase" relation/property concerns events where at least a seller, a buyer, a good, a price, and a timestamp are involved. Unfortunately, it is not so evident that an n-ary solution should be used, given the associated implementation difficulties. To give only an example, a common misunderstanding consists in asserting that the use of specific n-ary knowledge representation structures is not necessary given that any n-ary relationship can be simply reduced to a set of binary relationships. This is in principle true, and this sort of decomposition can be also useful for, e. g. , dealing with very practical problems like storing efficiently n-ary relationships into standard databases.
Using NKRL in the Context of the Cultural Heritage Domain NKRL is both a "structured n-ary" (see below) knowledge representation language and a fully implemented  [4] , computer science environment built up thanks to several European projects. It has been successfully used in many different narrative-based domains like terrorism news, analysis of industrial incidents, sentiment analysis, conceptual IoT, etc. The most important NKRL features are highlighted below. From an ontological point of view, the most striking characteristic of the language concerns the addition of an "ontology of elementary events" to the usual ontology of concepts. In NKRL this last-called "HClass", hierarchy of classes-presents some interesting aspects, with respect in particular to the modelling of difficult notions like colour and substance  [4] , pp.
For example, determiners of the "location" type can be associated through the colon operator, ":", with the arguments of the predicate introduced by the SUBJ, OBJ, SOURCE and BENF functional roles. Another important category of determiners associated, in this case, to a full template or predicative occurrence concerns some constants called "modulators", for example, the temporal modulators (begin, end, obs(erve)). These last, associated with the determiners date-1 and date-2, are of a particular importance in the framework of the temporal representation system of NKRL; a recent paper on this subject is  [14]. Eventually, "querying/reasoning" in NKRL ranges from the direct questioning of NKRL knowledge bases making use of "search patterns" (NKRL equivalents of natural language queries) to high-level inference procedures  [15].
A First (Quite Simple) Result Table  1  supplies the NKRL representation of a fragment of the whole formal encoding of the Mona Lisa picture, which is presented here, for illustrative purposes, under the form of an independent "narrative". This last could be conceived as a didactic description, in a pure documentary style, of basic information about this picture. The formal representation into NKRL terms presented in this table and in the following ones is basically a "manual" type of encoding facilitated, however, by the use of a powerful editor included in the standard NKRL software; see in this context  [4]. In Table  1 , gio1. c1 and gio1. c3 denote "binding occurrences" (see  [4] , pp. 91-98), different then from the "predicative" ones like gio1. c2, gio1. c5, gio1. c6 etc. , which represent elementary events and are structured according to the general syntax of Equation (  1 ).
These could be then used to create some (preliminary and naive) associations between the two classes of elements on the basis of the (assumed) reactions of Louvre's visitors in face of them. Assuming now that the Table  4  data have been introduced in an NKRL knowledge base about the Mona Lisa painting, we could ask, following John F. Asmus' line of reasoning, whether it could be possible to retrieve in this base some information about the existence of relationships between the right part of the landscape (LANDSCAPE_1) and the Mona Lisa's portrait. In an NKRL context, the direct questioning of a knowledge base is implemented by means of a "search pattern" pi-as already stated, a partially instantiated template-that tries to match/unify information in the base thanks to a Filtering Unification Module (FUM)  [4] , pp.