dataesr/scientific-paragraphs-categorization huggingface dataset:
0. 1) (44) , which is an open source statistical analysis environment. The raw quantitative data were converted into a "MsnSet" class object from the R package MSnbase  (44 and 45). The MsnSet class is derived from the "eSet" class and mimics the "ExpressionSet" class used for microarray data. An "MSnSet" object contains quantified expression data for MS proteomic data as well as the experimental meta-data, and allows the analysis of quantitative MS-based data using advanced statistical tools implemented in R, and particularly that of the pRoloc R package  (46)  (http://www. bioconductor. org/packages/2. 12/bioc/html/pRoloc. html), which takes as input objects of the MSbase classes. Raw quantitative data were subjected to some pre-processing steps (Fig. 1 ), including missing value imputation, log-transformation and sample normalization in order to render it compliant with the basic standards of good quality data (completeness, consistency and accuracy).
Note that within the whole pipe-line dedicated to the quantitative data analysis, all type of replicates (biological or technical) for a given group (e. g. the BBY group) have been treated equally. Missing Value Imputation-Quantitative proteomics data are well known for high rates of missing values. This fact requires deeper consideration when trying imputing them. In the present study, 1295 proteins were identified in four groups of samples (thylakoids, stromalamellae, margins, and BBY). However, not all proteins were identified in each individual group. For such proteins, the quantitation algorithm associated a NA (nonattributed) value to all samples where that protein was not identified. The actual missing values (MVs) were recorded for proteins that had been identified in at least one sample in a particular group, and they were also assigned with NAs.
The MVs imputation strategy consisted of replacing the censored MVs by the minimum value observed, while the MCAR MVs they were imputed using a nearest neighbor strategy  (51) , which was implemented as a routine preprocessing in MSnset (45) (Supplemental Material). Data Normalization-For the 24 samples corresponding to the 4 biological groups (thylakoids, BBY, margins and stroma-lamellae), the distributions of the log-transformed abundances were examined in order to investigate about the presence of technical bias. For each of the 4 groups of samples, quantiles normalization (52) was performed after the imputation of missing values implemented in the MSnSet R package (Supplemental Material). Statistical Analysis of Differential Abundance-We employed the R package LIMMA  (53)  which implements a robust method for differentially abundance assignment widely used for microarray data, based on linear protein-wise models as well as an empirical Bayesian model for robust estimates of protein-wise standard errors.
As the MSnSet class mimics the "ExpressionSet" class used for microarray data, and as this latter is used as input object for LIMMA, it appears that any proteomics dataset converted into the MSnSet format is compliant with a LIMMA use. The method in LIMMA is based on a modified version of Student t test (called moderated t test), where the standard errors are reduced toward a common value using a simple Bayesian model. This means that, when estimating the variable-wise standard error, the model borrows the information from the other genes (or in this case, proteins), which leads to more robust estimates. As a consequence, the model is well-suited to assess differentially abundance for datasets with few samples. In a recent comparison study on the significance testing for small microarray experiments  (54) , LIMMA has been found as being the method that balances the best the number of true positives and false positives.
Working with such relative abundance values will homogenize low and high abundant proteins, providing that proteins more abundant in BBY with respect to stroma-lamellae will not be sensitive to the actual protein abundance. As a consequence, a number of 255 proteins, which were not identified in the thylakoids samples, were not considered. Cluster analysis was performed using PAM (Partition Around Menoids) (56), a particular implementation of the k-medoids algorithm. k-medoid is one of the numerous variations of the extremely well-known k-means algorithm. This latter is implemented in the R package pRoloc (46) (http://www. bioconductor. org/packages/2. 12/ bioc/html/pRoloc. html), as well as numerous more sophisticated algorithms, such as kernel k-means, spectral clustering (57) (by mapping to the corresponding R package (58)), or hyperspherical clustering (59) (by direct implementation).
As methods to automatically assess the right number of clusters are still under development for the next version of pRoloc, this point was tackled as described in Supplemental_Material. Curated Annotation of Proteins-Information about identified proteins were retrieved and compiled from the literature (http://www. ncbi. nlm. nih. gov/pubmed) using AGI numbers or protein names and screening for recently published data, from UniProtKB (http:// www. uniprot. org/), TAIR (http://www. arabidopsis. org/), PPDB (60) (http://ppdb. tc. cornell. edu/), AT_Chloro (14) (http://www. grenoble. prabi. fr/at_chloro/), SUBA3 (61) (http://suba. plantenergy. uwa. edu. au/) and FIG. 1. Pipeline for data analysis.
An Updated Repertoire of Chloroplast and Thylakoid Proteins-The present study allowed the identification of 1295 proteins (supplemental Table  S1 ). We first evaluated the coverage of the chloroplast thylakoid proteome by combining present data with earlier large-scale analyses targeted to the chloroplast. To this aim, we compared proteins identified during this work with those previously reported by Ferro et al. (14)  (AT_CHLORO database) and Zybailov et al. (15) , that is, two extensive studies, performed at the whole chloroplast level, where similar numbers of proteins were identified (supplemental Fig. 3 ). These previous chloroplast data and present results were collated to obtain a total of 2103 nonredundant proteins. The overlap of the present study with the other data shows that the AT_CHLORO database contained more than 300 proteins that were not identified during the study of  Zybailov et al.
Referring to MapMan classes  (62) , we also removed proteins that belong to classes for which proteins are acknowledged to be localized either in other chloroplast compartments, that is, stroma and envelope, or in other subcellular compartments. These MapMan classes are the following ones: Calvin Cycle, DNA, envelope transporters, glycolysis, mitochondria electron transport, mitochondria transporters, protein aa (amino acid) activation, protein synthesis (ribosomal proteins), RNA processing, RNA transcription, RNA. RNA binding, starch metabolism and TCA (tricarboxylic acid cycle proteins). Proteins that belong to the class "PS/light reaction" were labeled as thylakoid proteins. We also labeled as thylakoid proteins those that were identified in the thylakoid with a percentage Ն 50% of occurrence in our AT_CHLORO database  (14).
To classify these proteins, we first used targeting prediction tools (ChloroP and TargetP) and database annotations (SUBA, PPDB) for the analysis of their subcellular and subplastidial localization. We also screened the literature and other databases (TAIR, MapManBin, Uni-ProtKB), to extract information about the available protein descriptions and functions (supplemental Table  S5 , rank 1-63). Out of these 218 proteins, 63 were predicted to be targeted to plastids using both ChloroP and TargetP targeting prediction tools, most of them (50 proteins) being thus also annotated as putative plastid proteins in the SUBA database. We thus considered that these 63 proteins were indeed good candidates for new chloroplast and thylakoid components. We then carefully analyzed the remaining 155 proteins and selected an additional set of 40 proteins that could also be associated to the list of these new chloroplast components, these proteins being predicted to contain a chloroplast transit peptide using either ChloroP or TargetP, or already classified as plastid protein in SUBA or, for very few proteins, experimentally associated to the chloroplast in more targeted studies (supplemental Table  S5 , rank  64 -103).
The protein names, their function or presence in a specific protein complex were carefully determined (see Materials and Methods section) as well as their subcellular and subchloroplastic localizations that were deduced from the screening of the literature, using previously known published information (e. g. from the AT_CHLORO and the PPDB databases) or from predictions using bioinformatics tools (e. g. ChloroP). After removing 12 proteins that are wellknown major components of the plasma membrane, the tonoplast, the peroxisome or the mitochondria (supplemental Table  S6 , ranks 504 -515), proteins that were previously associated to the stroma or the envelope compartments (supplemental Table  S6 , ranks 307-475), proteins from cluster 7 (except At2g34420; supplemental Table S6, ranks 475-503) and additional stromal ribosomal proteins (supplemental Table  S6 , ranks 295-306) 294 proteins were identified as being differentially distributed in BBY and stromalamellae fractions.
Six additional transporters were identified, like the phosphatetriose phosphate (TPT, At5g46110) and the oxoglutarate/ malate translocators (DiT1, At5g12860, and DiT2-1, At5g64290), that were already known to be associated with the chloroplast envelope. In the AT_CHLORO database, they have been associated mainly with the envelope but also with the thylakoid membranes. The phosphate and triose phosphate and oxoglutarate and malate translocators with the protein OEP16 -1 (At2g28900) were all found in BBY and stroma-lamellae fractions with enrichment in the latter subfraction. This specific enrichment is consistent with a contamination by envelope membranes which is higher in stromalamellae than in BBY fraction, the envelope membrane having similar density compared with the stroma-lamellae's one.
The last three transporters, IEP18 (At5g62720), BASS2 (At2g26900), and NHD1 (At3g19490) were not identified in the BBY fraction. Because, the function of IEP18 is still unknown, we cannot conclude about the relevance of its localization both in chloroplast envelope and in the stroma-lamellae. Characterization of an Arabidopsis bass2 mutant suggested that this protein is a sodium-dependent pyruvate transporter of the chloroplast envelope  (102). In this study, the authors also suggested that sodium influx could be balanced by the sodium: proton antiporter NHD1. This transporter, previously identified both in chloroplast and plasma membrane proteomes  (15, 101) , was recently characterized as a sodium exporter of the chloroplast envelope  (103). In the AT_CHLORO database, BASS2 was found associated with both envelope and thylakoid membranes, although NDH1 was not detected.
Thus, beside their characterized function as transporters of the chloroplast envelope, we cannot exclude a dual localization in subchloroplast compartment with a physiological function that remains to be determined in the thylakoids. One question that we wanted to address through identification of the stroma-lamellae proteome concerns the controversy about envelope localization of some transporters. Indeed, both the ATP/ADP carrier TaaC (At5g01500) and Na ϩ -dependent phosphate transporter Pht4;1 (ANTR1, At2g29650) were found exclusively associated to the envelope in the AT_CHLORO database (14) although they were described as thylakoids transporters  (104, 105). Moreover, none of these two proteins were identified in proteomic investigations dedicated to the study of membrane or lumen thylakoid (supplemental Table  S4 ).
* This study received financial support from the  French National Research Agency  ( ANR-2010-GENOM-BTV-002-01   Chloro-Types ;  ANR-10-LABEX-04   GRAL Labex, Grenoble Alliance for Integrated Structural Cell Biology ;  ANR-10-INBS-08   ProFI (Proteomics French Infrastructure)  and from  EU   FP7 program  ( Prime-XS  project, Contract no. 262067 ) and the  Marie Curie Initial Training Network Accliphot  ( FP7-PEOPLE-2012-ITN , grant agreement number  316427 ). □ S This article contains supplemental Figs. S1 to S9 and Tables  S1  to S7.
The three other tanks were managed independently from the tank with the array of samplers. They were prepared in the same way (same soil, same temperature, and same soil water content). For each tank, a unique soil ceramic sampler  (Gaudin 1991 ) was attached to a 2 g-USG and placed at 15 cm depth. Soil solution sampling was practiced 4 days later in order to obtain the ammonia concentration, noted c 4. The mean value of c 4 was used for calculation of D, the diffusion coefficient for ammonia (see data analysis below). Ammonia was measured by an indophenol-blue method and urea by a diacetyl-thiosemicarbazide method. Field experiments (Table  1 ) The classical geometry of urea application was practiced in Arivo experiment: USG was placed at 7-10 cm depth in the center of a 0. 16-m 2 square, between four rice transplants in 20 cm 9 20 cm geometry.
Data analysis The diffusion coefficient for ammonia was calculated from the mean ammonia concentration measured at 4 days after USG deep placement (this concentration is noted c 4 ) at the placement site. Its expression was derived from Eq. 2: D ¼ ½1=ð4 pÞ½ðQ=8Hc 4 Þ 2=3 ð4Þ In the lab, the mean value of c 4 was used for calculation of D [Eq. 4], for prediction [with Eq. 1] of c g (r, t) at r = 0-2. 5-5-7. 5 cm and its comparison with the experimental values [c at (r, t) = (0,8)-(0,14)-(0,28)-(0,56)-(5,14)-(5,28)-(5,56)]. In the fields, the c 4 values (from measurements at four days) were used for calculation of the diffusion coefficient for each site [Eq. 4], and then for prediction [with Eq. 2] of c g (0, t) corresponding to 2 or 4 g-USG, in situation without rice transplants. Root ammonia uptake was not noticed until the ammonia concentration attained a threshold of 10 mmol L -1.
It was documented by a transition in the log-linear representation of ammonia concentration versus time. The diffusion phase was described by a concave curve as for the theoretical prediction of Eq. 2. It was followed by a first-order disappearance of ammonia  (Gaudin 1991) , corresponding to root ammonia uptake and ammonia assimilation. The rate constant of ammonia uptake was obtained by linear regression of the Napierian logarithm of ammonia concentration with time. Its confidence interval (at the 95 % level) was visualized with a Statview software.
Summary and conclusion The objective of this study was to understand how different was ammonia diffusion under different placement geometries of urea in paddy soils and how the results of urea supergranules (in terms of paddy yields) in various geometries could reflect balance or imbalance in the source-sink relationship. A large part of these experiments were performed in farmers' fields where use of USG has been promoted by a FAO program. The collected data were interpreted with a model of ammonia diffusion. The following information were drawn from the kinetics of ammonia disappearance: time and rate of ammonia withdrawal under 2 g-and 4 g-USG treatments. A modified pattern of rice transplanting was used for one field situation in order to keep constant the N application rate (58 kg ha -1 ) in the 4 g-USG treatment.
Affymetrix Array Hybridization RNA hybridization (two biological and two technical repetitions) was performed using Affymetrix Sunflower Gene WT Chip  [14, 15]  at the affymetrix platform at INRA-URGV, Evry, France. RNA samples were checked for their integrity on a bioanalyzer (Agilent Technologies, Waldbroon, Germany). One hundred ng of total RNA were used for biotin-labelled cRNA synthesis using GenechipH WT cDNA synthesis and amplification kit (Affymetrix, Santa Clara, CA) following the manufacturer's instructions. The raw and normalized microarray data are available in the database (AFFY_Dormance_totalRNA_Sunflower, Gagnot et al. [16] ) and the Gene Expression Omnibus (GEO) repository of the National Center for Biotechnology Information (NCBI), accession number GSE23046  [17].
Real-time Quantitative RT-PCR Total RNA (4 mg) was treated with DNase I (Sigma), reverse transcribed with Revertaid Reverse Transcriptase (Fermentas) in a 25 ml reaction volume and amplified with Mastercycler ep Realplex (Eppendorf) using 5 ml of 30-fold diluted cDNA solution. Primers were designed with primer3 software and their respective sequences are shown in Table  1. Real-time PCRs were performed with the MaximaTM SYBR Green qPCR Master Mix (Fermentas) and 0. 23 mM of each primer (Fermentas) in a 15 ml reaction. Cycle thresholds (Cts) were calculated using the Realplex 2. 0 software (Eppendorf). For each plate and each gene, a standard curve made with dilutions of cDNA pools was used to calculate the reaction efficiencies, and the relative expression was calculated according to Hellemans et al.
Data collection The database included two sets of data. The first one included quantitative data obtained from the young trees in our experimental orchard. Data were collected to analyze tree growth, branching and fruiting at tree and branch levels, across the four years following planting, i. e. 2010, 2011, 2012, and depending on the variable 2013. The weight of fruit per tree was collected in 2011 and 2012. Fruit production in 2010 was not considered due to the low amount of fruit per tree and the large variability between trees for a same cultivar. Trunk circumference at 20 cm above the grafting point was measured in spring 2011, 2012 and 2013. In winter 2010-2011, three branches per cultivar and per block, directly inserted on the first annual growth of the trunk (2009), representative of each cultivar, were analyzed.
They were first described by measuring the length and the basal diameter of the first annual shoot extension (2010). In winter 2011-2012, the same data were collected on the second annual shoot extension (2011) of the side branches, and laterals which developed on all nodes of these two annual shoot extensions were characterized following the methodology developed by  Lauri et al. (1995). First, the type of laterals on 1-year-old wood (1YW, i. e. 2011 on the 2010 annual shoot, and 2012 on the 2011 annual shoot) was recorded. Laterals were classified into one of the three types: latent (L), vegetative (V), inflorescence (I). The number of each 1YW-lateral type, and the proportion of I over all functioning laterals (I + V) on the 2010 and 2011 annual shoots of the a For clusters in (a), the percentage of variance captured by the first dimension of the PCA calculated on the original variables composing this cluster is indicated at the first occurrence of the abbreviation of cluster name.
parent branch were calculated. Second, the type of laterals on 2-year-old wood (2YW) which developed in 2012 on the previously described 1YW-laterals branched on the 2010 annual shoot was recorded giving rise to two-year sequences. The number of each possible sequence combining lateral type on 1YW and 2YW on the 2010-annual shoot, namely LL, LV, II, IV, VI, VV, was also calculated (Table  2 ). The sequence II denoted the bourse-over-bourse. Extinction (E) only occurred following an I type giving rise to an IE sequence. The second set of data consisted of a quantitative rating of regular bearing of adult trees of the same cultivars, ranking from 0 (high alternate bearing) to 5 (high regular bearing). Data were collected from 10 independent experts, either technicians at experimental stations and in extension services or breeders that have good experience of at least one cultivar among the 24 studied in our experiment.
Data analyses The relationships among the 24 quantitative variables measured on the young trees and the expert rating of regular bearing of the adult trees, were investigated through a principal component analysis (PCA). However, working with all the original variables might entail bundles of several positively or negatively correlated variables, which may bias PCA analyses. To avoid the overweighting of these highly correlated variables our data analysis included three consecutive steps. First, a correlation table was calculated on all variables excluding the expert rating, and a clustering analysis was carried out on the sine 2 (i. e. 1 -R 2 ) distance matrix between the variables with the agglomeration method 'average'. The sine 2 was being used in order to account for the variable directions only.
e. without any impact on the construction of the principal components, to relate it to the variables investigated on the young trees. All analyses were performed using R statistical software version 3. 0. 2 (R Foundation for Statistical Computing, Vienna, http://www. rproject. org) with packages 'car' and 'cluster' to compute the correlation matrix and clustering analysis, respectively, and 'FactoMineR' to compute PCAs. In all cases threshold for statistical significance was set at P < 0. 01.
Conclusions The quantitative data collected on the young trees and expert rating were both done on cultivars grafted on a low-vigor rootstock, with almost no training and no pruning for young trees, and with quite similar training systems, i. e. central axis with free growing side branches, for adult trees. Our results showed that regular bearing at the adult stage was related to several vegetative and flowering traits on the young tree, among them a high level of the following three traits: bourse-over-bourse, spur extinction and percentage of flowering laterals among all functioning laterals on 1YW. They collectively constitute a syndrome of easyto-collect traits that can be quantified as soon as on 1YW of the first annual shoot growth of the side branches to predict with a certain safety the fruiting pattern of the adult tree.
Acknowledgements This study was partly funded by the  French Comité Technique des Productions de Semences (CTPS) ,  research programme  " Dispositifs expérimentaux en arboriculture fruitière permettant d'évaluer le comportement des variétés sous faible niveau d'intrants phytosanitaires. Mise au point méthodologique " ( 2009-2012 ), ref. C02-2009-fruitiers  BNI. The authors thank  E. Laget  for help in data collection and first analyses,  X. Bry  for advice on statistics, and  Karen Maguylo  for help on English language. The authors also acknowledge the contribution of each member of the panel of experts for the rating of regular bearing of the apple cultivars in orchards:  M. Bruyninckx ,  N. Drouzy ,  B. Hans-Joachim ,  B. Hucbourg ,  M. Lateur ,  C. E. Parveaud ,  G. Libourel ,  V. Mathieu ,  G.
Participants A total of 1,855 women with BC were included in this crosssectional study. All participants were registered on three cancer registries involving the following French administrative areas: Doubs, Loire-Atlantique, and Tarn. The inclusion criterion for the study was BC diagnosis in 2007. The exclusion criterion was male gender. Information collected by the BC registry came from several databases involving public and private pathology laboratories and hospitals, the regional French National Health System, and Comprehensive Cancer Centres. Because analyses in the present study focused on ER status, we excluded 148 women (8. 0%) who had unknown ER status. Therefore, full data were available for 1,707 (92?0%) women in the registry.