dataesr/scientific-paragraphs-categorization huggingface dataset:
Respective classifiers are available as supplementary files in Tables  S4A  and S5. For validation purposes, we used a) respective calculated thresholds, and b) the proportion determined in the test cohort. All p-values were adjusted for multiple testing using Benjamini-Hochberg correction in order to control the false discovery rate. A prognostic gene signature for overall survival and event-free survival was developed using a L 1 penalized Cox PH regression model with tenfold cross-validation for tuning the shrinkage parameter lambda. Internal validation was done with leave-oneout cross-validation based on an additional outer loop. External validation of prognostic transcripts and signatures was carried out on two publicly available data sets  [46]  (GSE24080) from the MAQC-II project. Multivariable Cox PH regression was used to adjust for clinic-pathological covariates.
Of these, 60 showed a significantly higher, 20 a lower expression in myeloma compared to normal plasma-cell samples (P#. 05). Only 7 of the significantly upregulated genes are related to glycan degradation vs. 55 to glycome synthesis, indicating an increase in the proglycosylation part of the glycome. The same holds true if gene families and subfamilies according to the KEGG database are analyzed by Goeman's global test between malignant and normal plasma cells as well as myeloma cell lines (Table  1 , Table  S3 ). Ranked by the degree of significance in differential expression, the glycosylphosphatidyl-inositol (GPI)anchor pathway and the glycosaminoglycan heparan-sulfate (GAG-HS) pathway were identified as the top two pathways, in both cases showing a larger number of higher vs. lower expressed genes in GAG-HS pathway, 3 genes involved in the biosynthesis of the GAG basic structure and chain elongation were significantly higher expressed, two HS sulfotransferases responsible for sulfatation of the GAG-HS at various carbons of the GAG disaccharide unit were significantly lower and one higher expressed (Figure  2 ).
To investigate whether diminished PIG-M expression impacts cell surface expressed GPIanchored receptors, we analyzed KMS-12-BM and OPM-2 for the expression of complement regulatory proteins CD55 (Decay Accelerating Factor) and CD59  [48, 49]. Dependence of their cell surface expression by GPI-anchoring was verified by phospholipase C (PLC) treatment prior to antibody staining. On both cell lines CD55 and CD59 were susceptible to PLC treatment as their expression was significantly diminished after enzymatic cleavage (Fig. 4F ). As expected, CD55 and CD59 expression was considerably lower on PIG-M low KMS-12-BM cells as compared to PIG-M hi OPM-2 (CD55: mean fluorescence intensities 278 and 624, respectively; CD59: mean fluorescence intensities 398 and 1185, respectively). CD56 (NCAM), although described as being occasionally expressed as GPI-anchored variant, was only present as the membrane-spanning type on these myeloma cell lines (data not shown).
Acknowledgments We thank  Veronique Pantesco ,  Maria Do ¨rner ,  Sonu Kumar , and  Giovanni Mastrogiulio  for technical assistance. Microarray data were obtained at the  Microarray Core Facility of IRB (Montpellier, France , http://irb. montp. inserm. fr/en/index. php?page = Plateau&IdEquipe = 6). The manuscript is accompanied by supplemental data.
osug. fr/ ∼ picard/dmrtml/). This model initially developed at Laboratoire de Glaciologie and Géophysique de l'Environment in Grenoble, France, for perennial snowpack  (Brucker et al. , 2010 (Brucker et al. , , 2011a) )  and improved for seasonal snow on soil  (Roy et al. , 2013)  and on superimposed ice  (Dupont et al. , 2012) , is now suitable for modeling the microwave emission in a wide range of snowy continental environments. It is also designed to be extensible and uses standard For-tran90, which allows efficient computations with different types of computers and operating systems and facilitates the embedding in other models or assimilation schemes. A convenient user interface is provided by the optional bindings with the Python language. This paper is a comprehensive scientific reference for DMRT-ML users.
An empirical correction of this problem has been recently proposed  (Dierking et al. , 2012). Even though it could be accurate enough, it has not the theoretical grounds that characterize the DMRT-ML theory and makes one of its merits. Further improvements are needed. The main characteristics of the DMRT-ML implementation are the availability as an open source software, the efficiency of the computation due to the use of Fortran90 and LAPACK library, the wide range of cryospheric environments that can be modeled without any change of the source code (dry and wet snowpack, seasonal snow over soil, seasonal snow over bubbly ice, frozen lakes, perennial snow, etc. ). In addition, modeling sea ice will be possible in the near future with the implementation of the dielectric constants of salted snow and water.
Acknowledgements. This work was supported by the  French Programme National de Télédétection Spatiale , the  Centre National d' Étude Spatiale , the  Programme Internationale de Collaboration Scientifique (CNRS) between Grenoble's and Sherbrooke's universities  and the  Canadian Natural Sciences and Engineering Research Council. The manuscript was written in part during a visit at Sherbrooke university supported by the grant CMIRA 2012 ExploraPro,  Region Rhone-Alpes. We thank  S. Morin  for helpful comments and the  DMRT-ML user community  for bug reports and suggestions through the  DMRT-ML mailing-list. Edited by:  K. Gierens The publication of this article is financed by  CNRS-INSU.
Sequence Analysis Tools The CODDLE software (Codons Optimized to Discover Deleterious Lesions, http://www. proweb. org/coddle/) was used to identify regions of the target gene in which G/C to A/T transitions are most likely to result in deleterious effects on the protein. The PARSESNP software (Project Aligned Related Sequences and Evaluate SNPs, http://www. proweb. org/ parsesnp/) was used to illustrate the distribution of mutations within the gene and to indicate the nature of each single mutation. The SIFT software (Sorting Intolerant from Tolerant, http://sift. jcvi. org/www/SIFT_seq_submit2. html) was used to predict the impact of the mutation on the protein. Multiple sequence alignment of full-length protein sequences was performed with ClustalW software (http://www. ebi. ac. uk/Tools/clustalw2).
Phylogenetic Tree and 3D-structure Prediction Protein sequences of COMT from several vascular plants were identified by protein blast (http://www. ncbi. nlm. nih. gov/and http://www. greenphyl. org) and aligned with Clustawl2 (pairwise alignment) at (http://www. ebi. ac. uk/Tools/msa/clustalw2). Phylogenetic analysis was performed with TreeTop (http://www. genebee. msu. su/services/phtree_reduced. html). Cluster algorithm was used and 100 bootstrap were done. Final construction of tree was made with TREX (Treeview Newick Viewer option) at http://www. trex. uqam. ca/. Bootstrap values are expressed in percentages and placed at nodes of the trees. The 3D-structure of the BdCOMT6 protein was predicted from the crystallized ryegrass enzyme  [24]  with the molecular modeling program Geno3D (http://geno3d-pbil.
This work was funded by the European  FP7 project nu 211982 ''RENEWALL' ' and the  Agence Nationale de   la Recherche (ANR)  projects  ANR-08-KBBE-004 ''CELLWALL''  and  ANR-10-BLAN-1528  ''PHENOWALL''. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Psyllid samples used in molecular procedures Twelve C. pruni samples (i. e. , insects from the same locality) used in this study, mostly from France, are described in  Sauvion et al. (2007)  (Table  1 , Figure  1 ). These samples constitute 310 psyllids (males or females) genotyped at nine microsatellite loci and previously assigned to groups referred to as A and B (Table  1 ). We aimed to increase our dataset with 14 additional samples in order to cover the known distribution area of C. pruni to the greatest extent possible  [14]. The individuals of seven of these 14 samples were genotyped: three samples from France and four samples from Serbia, Italy, Germany and Spain (samples 13 to 19, Table  1 ). All microsatellite genotypes were assigned to a group using the Bayesian admixture procedure described in Sauvion et al.
Country codes are defined in ISO 3166-1. individuals from group A from Montgamé (sample 12). Two highly divergent clades strictly correspond to previously characterized microsatellite genotype clusters, so that each cluster is monophyletic at that locus (Figure  3 ). Sequence divergence within a clade is low in comparison to divergence between clades. Sequences are available under Genbank accession numbers KF305120 to KF305171. See Table  1  for details on each location. Plants marked with an asterisk (conifers) are used by psyllids for overwintering and not for reproduction.
Acknowledgements We are very grateful to  Josiane Peyre  for her valuable technical assistance, and the following individuals for their contributions of specimens of C. pruni and other psyllid species:  Olivier Lachenaud  ( INRA-Montpellier, France ) for providing C. bidens, C. melanoneura and C. notata from France;  Philippe Robert  ( INH-Angers, France ) for providing C. pyri from France;  Snezana Tanaskovic  ( Faculty of Agronomy, Cacak, Serbia ) for providing C. pruni from Serbia;  Luigi Carraro  ( Dipartemento di Biologica Applicata, Udine, Italy ) and  Rosemarie Tedeschi (Di. Va. P. R. A. Entomologia e Zoologia applicate all'Ambiente, Univ. Torino ) For providing C. pruni from Italy;  Barbara Jarausch  ( RLP AgroScience GmbH, AlPlanta, Neustadt, Germany ) for providing C. pruni from Germany;  Patrik Kehrli  ( Station de Recherche Agroscope Changins-Wädenswil ACW, Nyon, Switzerland ) for providing C.
Seven patients (Patients A3-A7, U1 and U2; three probands and four affected siblings) had SLC52A2 mutations found through exome sequencing. The nature and location of each SLC52A2 mutation are shown in Fig. 2A. Seven different missense mutations and one premature stop mutation were identified. The SLC52A2 mutations p. Gly306Arg (c. 916G 4 A) and p. Leu339Pro (c. 1016T 4 C) have been reported previously  (Haack et al. , 2012; Johnson et al. , 2012). The other six mutations reported here are novel. All mutations were predicted as not tolerated by the SIFT prediction program and predicted as probably damaging by PolyPhen2, except for p. Ala284Asp (c. 851C 4 A), which was predicted as possibly damaging. All mutations except the nonsense mutation p. Gln234X (c. 700C 4 T) (only conserved in the Gorilla gorilla and RFVT1) and the p.
Ala284Asp (c. 851C 4 A) mutation (not conserved in Danio rerio) alter amino acids evolutionarily conserved from humans to D. rerio and are also conserved in RFVT1 and RFVT3 (Fig. 2B ). Patients A1, A2, A5, A6, A7 and L1, and the two affected members of the original Lebanese BVVL family  (Megarbane et al. , 2000)  were found to be homozygous for the p. Gly306Arg mutation and at all 11 single nucleotide polymorphisms studied, indicating that the mutation arose on a common haplotype within the Lebanese population as a founder mutation (data available on request). Variant Server (http://evs. gs. washington. edu/EVS/), single nucleotide polymorphism database (dbSNP) (http://www. ncbi. nlm. nih. gov/ snp) and 1000 Genomes Project (http://www. 1000genomes. org/) databases were screened for the identified mutations.
(B) Structural conservation of relevant amino acid residues in RFVT2 across species and in RFVT1 and RFVT3. Dark blue, medium blue and light blue colours correspond to amino acids conserved in 56, 55 or 53 of 7 sequences, respectively. Conservation among species of the affected amino acid residues was determined using Ensembl to retrieve the sequences and Clustal Omega software  (Thompson et al. , 1994)  for multiple sequence alignment. The Ensembl protein IDs for the RFVT2 orthologous sequences reported are ENSGGOP00000028056, ENSSSCP00000028741, ENSMUSP00000023220 and ENSDARP00000045674.
Acknowledgements We are indebted to the patients and their families who have made this work possible and have allowed us to share their stories;  Andrew Singleton, Ph. D  and  Janel Johnson, Ph. D ,  National Institute on Aging, National Institutes of Health, Bethesda, MD, USA , for their collaborative work in identifying the SLC52A2 gene via exome sequencing and  Michael Groves, Ph. D. and  Bart Wagner ,  B. Sc. for assistance with sural nerve electron microscopy and morphometry.
Datasets Two datasets were used. The first one was used to setup the segmentation procedure for the quantification of symptoms by image analysis. It features images of leaflets of P. vulgaris cultivar (cv. ) Flavert plants inoculated with the strain Xff CFBP4834-R or water. In this first dataset, the imaged leaflets were not detached from the plant to monitor the development of symptoms during 11 days after inoculation (dai). The same leaflets were imaged first at 1 dai then every day between 4 and 11 dai to monitor the development of the symptoms. For each leaflet, the symptomatic area was delimited either by: i) thresholding based on expert visual observations, ii), thresholding based on modeling pixel-wise F v /F mdistributions as mixtures of Gaussian distributions or iii) thresholding based on the probability of misclassification of a healthy pixel followed by a subsequent clustering of diseased pixels to describe the various stages of alteration of plant tissues.
A second dataset featured images of leaflets of five bean cultivars (cvs. Flavert, Michelet, Pike, Caprice and Wonder) inoculated with Xff CFBP4834-R. The image analysis procedure previously calibrated on the first dataset was applied to images belonging to this second dataset for evaluating the resistance of these commercial bean cultivars to Xff CFBP4834-R. In this second dataset, leaflets were detached from inoculated plants just before imaging to ease and speed up the image acquisition.
Significant symptom development began at 7 dai on leaflets inoculated with Xff CFBP4834-R (p-value < 0. 01). From 9 dai on, the symptoms were predominantly composed by wilted tissues (Figure  2A ). Moreover, as in this first dataset we monitored the evolution of symptoms on the same leaflets over time, we could assess shrinking of leaflets induced by the pathogen. Shrinking corresponds to the difference between the maximum size of the leaflet and its current size at the observation timepoint. Shrinking of leaflets may represent up to 10% of the total leaflet area (Figure  2 ). It mostly corresponds to necrotic tissues. However, some shrinking may also originate from natural aging of leaflets as shrinking was also detected on mock-inoculated leaflets.
In practice, using the R package MCLUST  [44] , the pixel-wise F v /F m -distributions were first fitted to Gaussian mixture models having from 1 to 4 components: one cluster should group pixels representing healthy tissues and three clusters should group pixels representing the various stages of the symptom development. The model best supported by the data is then determined using BIC criteria and lastly corrected by the ridgeline unimodal method  [42, 43]. For each pixel in the image, a probability of membership to each cluster is estimated. Clusters gathering less than 1% of the pixels were considered as not significant. Pixels initially attributed to these non-significant clusters were assigned according to their second better probability of membership. On each image of mock-inoculated leaflets, pixel-wise F v / F m -distribution could be modeled as one single cluster per image.
Evaluation of the resistance of commercial bean cultivars to Xff CFBP4834-R Five bean cultivars (cvs. Flavert, Michelet, Pike, Wonder and Caprice) were inoculated with the strain Xff CFBP4834-R or water. Symptoms were quantified by our image analysis procedure. The Expert-based thresholding approach was not used as the thresholds were defined from a training set of images only on cv. Flavert but not on the other cultivars. Instead, the total amount of diseased areas was determined using probability-based thresholds. Then, using a clustering approach (that does not require training datasets), the various stages of the alteration of plant tissues were discriminated and quantified. F v /F m images were taken on detached leaflets at 7 and 11 dai and the amounts of diseased tissues on leaflets were calculated (Figure  5 ).
The procedure presented in this study was automated under R and the R script is available at http://lisa. univ-angers. fr/PHENOTIC/telechargements. html. Running our procedure on the 1080 images of our dataset, two minutes only are needed for the Expert-and Probabilitybased thresholding analyses. The use of MCLUST  [44]  to discriminate various stages of alteration of plant tissues increases up to one hour the calculation time, which remains much faster than rating disease severity by visual observations.
A clustering approach applied on the diseased areas allows the quantification of each stage of the alteration of plant tissues. The segmentation approach developed in this study was automated using R, and the script is available at http://lisa. univ-angers. fr/PHENOTIC/telechargements. html. Such a development of automated segmentation approach speeds up the assessment of disease severity on plants. It may reveal a significant improvement for high throughput testing of the plant resistance to pathogens during breeding.
Thresholding based on modeling pixel-wise F v /F m distributions The R package MCLUST  [44]  was run to select the number of Gaussian distributions that compose the Gaussian mixture model best supported by the data and to estimate the mean, variance and weight in the mixture distribution of each cluster. For each pixel, a probability of membership to each cluster is also returned. Gaussian mixture models including from 1 to 4 clusters with unequal variance were fitted to each leaflet pixelwise F v /F m -distribution. The clusters representing less than 1% of the pixel-wise F v /F m -distribution were suppressed as they were considered as artifactual clusters. Pixels initially attributed to these non-significant clusters were assigned according to their second better probability of membership.
The clusters of the Gaussian mixture model selected were then merged if their fusion was unimodal according to the ridgeline unimodal method implemented in the R package fpc  [42, 43]. A threshold was calculated for each dai to discriminate between distributions characterizing the healthy tissues and distributions characterizing the diseased tissues. The means of the clusters found on mock-inoculated samples were averaged. Then, the confidence interval was calculated. A cluster displaying a mean inferior to the threshold was considered as characterizing diseased tissues.
Thresholding based on the probability of misclassification of a healthy pixel The probability based-thresholds were built based a pixelwise F v /F m -distribution resulting from the merging of the pixel-wise F v /F m -distributions of all the mock-inoculated leaflets. The thresholds corresponded to the 100-quantile, 500-quantile or 1000-quantile, i. e. the F v /F m values splitting 1/100, 1/500 and 1/1000 of the pixels of the distribution. The thresholds were determined for each dai and each cultivar to take into consideration the possible daily variations and the differences between cultivars. A pixel displaying a F v /F m value inferior to the threshold was considered as diseased. The total diseased proportion was then calculated for each leaflet. Then, a clustering method using the R package MCLUST  [44]  was performed on the diseased area detected by the Probability-based thresholds to segment the diseased area according to three stages of alteration of plant tissues.
Acknowledgements This work was supported by  Angers Loire Métropole ,  Conseil Général Département de Maine-et-Loire  and  Région Pays de la Loire  (Phenotic) and by  Institut National de la Recherche Agronomique  and  Région Pays de la Loire. We are grateful to  Valérie Grimault  at  Station Nationale Essais de Semences ,  Valérie Caffier ,  Carolyne Dürr  and  Philippe Simoneau  ( IRHS, Angers, France ) and  Laurent Noël  ( LIPM, Toulouse, France ) for helpful discussions. We thank  Marion Le Saux ,  Perrine Portier ,  Martial Briand  and  Géraldine Taghouti  at  French Collection of Plant-associated Bacteria (CFBP)  for providing the bacterial strain. We wish to thank  Elise Vandoeuvre at Vilmorin  for providing the seeds. We thank  Installations Expérimentales Mutualisées  ( INEM ) for growing the plants.
5. 1 Perspectives: In Situ Observations, Remote Sensing, Modelling and Synthesis The scope of SOLAS science depends on multidisciplinary and multi-scale approaches being applied to the complex problems and challenges within the field. Laboratory process studies and in situ lagrangian field experiments make substantial contributions to our understanding of the various biogeochemical processes and their feedbacks. Models are almost the only way to assess what are often complex problems and they rely on input from such studies. To truly represent the domain of SOLAS within Earth System models requires global-scale datasets of accurate measurements of relevant parameters. The most accurate data-based estimates of air-sea exchange processes require as much data as possible at large spatial and temporal scales in order to be able to validate and calibrate both model outputs and satellite data.
3. 8 % of the total aerosol mass, with aluminium, a tracer of mineral dust, at a slightly higher concentration of ca. 7 %  (Trapp et al. 2010; Carpenter et al. 2010). Total iron content reaches up to 33 μg m À3 in winter, and the soluble Fe content is between 0. 1 % and 15. 7 %  (Carpenter et al. 2010 ). Higher solubilities are measured at lower atmospheric dust concentrations, a ubiquitous feature among aerosol solubility datasets. Although the total aerosol mass is dominated by sea salt, in aerosol particles < 0. 14 μm diameter, non-seasalt components contribute about 80 % of the mass. These components include low-molecular-weight dicarboxylic acids (DCAs) and hydroxylated DCAs, methanesulphonic acid (MSA) and aliphatic amines. A bimodal size distribution for the DCA oxalic acid and coarse mode concentration maxima for the other DCAs are observed, as is typical for marine aerosols.
SOLAS/COST Data Synthesis Efforts The very nature of data collection at sea leads to relatively small-scale cruises of necessarily limited spatial/temporal scope. At an international level, the elevated resource requirements and organisational difficulties associated with large-scale research campaigns further impede coordinated data collection efforts. Considering this, it is unsurprising that oceanic data coverage is fragmentary. As set out within the Memorandum of Understanding for COST Action 735 and the aims of SOLAS Project Integration, a major Earth System Science challenge is to translate the findings of these research campaigns into large scale datasets and climatologies that help improve our understanding of global concentrations and air/sea fluxes. Data paucity calls for centralising all concentration measurements of relevant SOLAS parameters into one common database to secure their longevity.