dataesr/scientific-paragraphs-categorization huggingface dataset:
Limitations Although these observations represent a complete database concerning a large number of accessions which have been phenotyped on more than 30 traits, they contain missing data left blank as many phenotyping data on tree crop species. Those missing data are notably due to particular climatic conditions some years. Then, regarding climate data from the SENuRA observation site, they are not as accurate as the other two because temperature and rainfall data are not given daily but with monthly average. There is also an imbalance in the amount of data for 'Franquette' and 'Lara' accessions. Indeed, these two cultivars are usually used as controls for experimentation in France. In that respect, more data are available for controls. Finally, the dates of observation ("Campaign" column in Table  1 , Data file 1) are heterogeneous.
When an accession has been introduced in orchards at the Prunus/Juglans Genetic Resources Center in year n, it has been usually observed in years n + 1, n + 2 and n + 3. However, the year n of introduction can vary a lot due to the long process of germplasm establishment. The "Portail Data INRA" repository is mainly a landing page which hosts the entire dataset. But for more details, readers have the possibility to manage the data with a pleasant interface (e. g. to choose a particular accession) using the "link to data" available on the page. This link brings to the URGI Plant and Fungi platform hosted by the Unité de Recherche Génomique Info (URGI) which is a research unit in genomics and bioinformatics at INRA, dedicated to plants and crop parasites. This platform also provides the passport descriptors such as the country of origin or the pedigree for each accession.
Regarding the context of global competition and climate change, a new French research project called INNOV'noyer started in 2017 in cooperation between the Centre Technique Interprofessionnel des Fruits et Légumes (Ctifl-Fruit and Vegetable Interprofessional Technical Centre) and the INRA Bordeaux-Aquitaine. The objective being to start a new breeding program using marker-assisted selection, and to provide a larger choice of walnut cultivars better adapted to this context. In this way, these data represent a valuable aid for the project and for any researcher who wants to obtain information regarding phenology of a major tree crop species [3]. To our knowledge, these data also provide the most extensive dataset available publicly Open Access BMC Research Notes *Correspondence: elisabeth. dirlewanger@inra.
Acknowledgements First, our special thanks are addressed to  Eric Germain  and  Francis Delort , former heads of the walnut breeding program at  INRA of Bordeaux. We also gratefully thank the entire staff of the arboriculture experimental unit of Toulenne and that of the two French walnut experimental stations of Creysse and SENuRA for their work during many years to characterize the accessions. Finally, we thank  Peter Prince  ( Lab technician, Ctifl ) for English language revision of Objective part.
tab Tabular data file (. tab) Portail Data INRA repository (URGI Plant and Fungi Dataverse, INRA's Walnut Genetic Resources Observation) (https ://doi. org/10. 15454 /58SYJ V) Data file 5 WATO-Walnut-Trait-Ontology. csv Comma separated values file (. csv) Portail Data INRA repository (LovINRA, Walnut Trait Ontology) (https ://doi. org/10. 15454 /AV5RT 2) [7].
Fatty acid composition of lipids Fatty acids composition was determined using method ISO  5508 (1990). In brief, fatty acids (FAs) in above lipid extract were converted to fatty acid methyl esters (FAMEs) before analysis by shaking a solution of 60 mg of oil in 3 mL of hexane with 0. 3 mL of 2 N methanolic potassium hydroxide. FMAEs were analyzed by gas chromatography (Varian CP-3800, Varian Inc. Middelburg, The Netherlands) equipped with a flame ionization detector (FID). The column used was a CP-Wax 52CB (30 Â 0. 25 mm i. d. ; Varian Inc. ). The carrier gas was helium, and the total gas flow rate was 1 mL/min. The initial column temperature was 170 °C, the final temperature 230 °C, and the gradient was 4 °C/min. Injector and detector temperature were 230 °C. Data were processed using Varian Star Workstation v 6.
Evolution of cell wall components and soluble sugars As already mentioned, the pulp is currently used as feed for cattle. Thus the chemical composition of hexane-extracted argan fruit pulp over the ripening period is given in Table  3. Regarding fibers analyzed according to  Van Soest et al. (1991) , one notes in Table  3  that NDF (neutral detergent fibers or whole lignocellulosic fibers) does not exceed 31%, meaning that fibers and already presented hexane-extractible amount about 40% or less; thus other components make the main contribution, and some of them will be discussed later on. NDF content drops from 30. 9 to 19. 1% over the period, with no evolution between May and June samples. Other measured data -ADF (acid detergent fibers) and ADL (acid detergent lignin)follow the same pattern with time.
Study design UX023-CL304 (clinicaltrials. gov identifier NCT02537431) is an ongoing, phase 3, single-arm, multicenter trial investigating the efficacy of burosumab in improving osteomalacia in adults with XLH. Subjects were treated with burosumab, 1. 0 mg/kg (rounded to the nearest 10 mg) administered subcutaneously every 4 weeks (Q4W) for at least 96 weeks. Transiliac crest bone biopsies-horizontal full-thickness biopsies of the ilium from a site 2 cm dorsal of the anterior superior iliac spine-were obtained at baseline and week 48 using a needle with an inner diameter of at least 5 mm; the protocol recommended the use of a Bordier needle. Subjects received two 3-day courses of tetracycline-HCl (or demeclocycline-HCl) 20 and 8 days before each biopsy. The use of tetracycline allowed for dynamic histomorphometric analysis, in which unstained sections were mounted unstained for florescence microscopy as described by Glorieux and colleagues.
(14) For structural morphometric analysis, additional sections of the biopsy were also stained using Masson Goldner Trichrome as described. (14) Biopsies were qualitatively analyzed in real-time to determine if osteomalacia was present by an experienced histomorphometrist (FR). If the baseline biopsy did not reveal osteomalacia in a given subject, they continued treatment but did not undergo a second biopsy at week 48. After week 48, all subjects continued treatment for an additional 48-week extension period, for a total duration of 96 weeks. This report summarizes data through week 48.
Statistical analysis Statistical analyses were conducted in SAS version 9. 4 or higher (SAS Institute, Cary, NC, USA). Descriptive summaries for efficacy and safety endpoints were provided. Continuous variables were summarized by number of subjects, mean, standard deviation (SD), median, minimum, and maximum. Categorical data were summarized as the total number or percentage of subjects. Histomorphometric endpoints were analyzed using a two-sided t test. If the normal assumption was not met, a sign test for median was used. The 95% CI and p value were provided. For other selected endpoints, the least squares (LS) mean and standard error (SE) for the change from baseline to week 48 were provided using the generalized estimating equation (GEE) repeatedmeasures analysis, including time as the categorical variable adjusted for baseline measurement in the model with compound symmetry covariance structure.
However, subjects in the present study received burosumab for only 48 weeks, whereas the mean duration of treatment in Sullivan and colleagues  (6)  was more than four times longer. Although conventional therapy may improve osteomalacia, it remains unclear whether the benefits of such therapy outweigh the potential risks. Sullivan and colleagues  (6)  reported hypercalciuria and tertiary hyperparathyroidism. These events, as well as nephrocalcinosis, are known risks associated with conventional therapy. (2, 24) In contrast, in the present study, we observed no changes in serum calcium or PTH concentrations, urine calcium excretion, nor renal nephrocalcinosis scores and no incidents of hyperphosphatemia. (21)  Improvements in phosphorus metabolism, fracture healing, markers of bone remodeling, and patient-reported outcomes in the present study were consistent with observations in a larger phase 3 placebo-controlled study investigating the efficacy and safety of burosumab in adults with XLH (https://clinicaltrials.
gov/ct2/show/NCT02526160). (12, 13)  In both studies, mean fasting serum phosphorus levels increased rapidly and all values determined at the mid-point of the dose interval were above the lower limit of normal (2. 5 mg/dL). Consistent with the high rate of fracture healing in the larger phase 3 study, two of the four active pseudofractures identified at baseline in the current study were completely healed after 12 weeks of burosumab treatment. In both studies, increases in serum P1NP (LS mean increase: current study 77%, previous phase 3 study 56%) and serum CTx (36%, 29%) also suggest that burosumab increases bone remodeling and formation. Our study is not without limitations. Because of the invasive nature of a transiliac crest biopsy, a control arm was not included in the study design, and baseline values served as a reference for improvement.
Statistical analysis Data analysis was carried out in R software version 3. 4. 1 (R Core Team, 2017). Analysis of variance (α = 0. 05) was used to determine whether grain density class and cultivar had a significant effect on SGD, elemental analyses and starch analyses. Where a significant effect was indicated, a post-hoc Tukey's Honestly Significant Difference (HSD) (α = 0. 05) test was conducted to determine which samples differed from one another. This is indicated by different letters in the results table. A stepwise linear regression was performed in R using the 'olsrr' package to determine which variables significantly contributed to predicting SGD and therefore should be included in the equation  (Hebbali, 2018). The response variable was SGD, and the dependent variables were: nitrogen, carbon, total starch, amylose, and B granule volume.
Independent variables were selected based on p-value, the threshold for a variable to enter the equation was P < 0. 1 and to exclude a variable from the equation was P > 0. 3 in accordance with the default setting on the 'olsrr' package  (Hebbali, 2018). The correlation between measured grain density and calculated grain density was determined using Pearson's product-moment in the R package "corrplot"  (Wei and Simko, 2016).
The aim of this equation was to better understand the basis of barley SGD and therefore this component of SW. To test both the accuracy and efficacy of this equation at predicting density for samples from different origins, the equation was applied to an independent grain sample for validation. The equation generally under-estimated density for samples with a higher measured density, and the gradient of this was not parallel with the original dataset. However, a model was then built from the original dataset using N alone to predict density, and when applied to this validation dataset, improved predictions were made. Different environmental conditions are known to have large effects on starch granule composition, functionality and proportions. The grains used to build the model came from Docking, Norfolk, whereas the validation set came from a bulk of harvests from across Scotland.
Acknowledgements The authors thank  Dr David Seung  of the  John Innes Centre  for assisting with starch granule analysis and allowing us to use his particle size analyser for the work outlined. Without David this important part of the work would not have been possible. The authors also thank the  AHDB  (project number:  21130047 ) for both the financial support of this work and the provision of samples. We also acknowledge funding from the  Scottish Government's Rural and Environmental Science and Analytical Services (RESAS) division.
Physicochemical and Texture Analysis Fat content by Gerber-Van Gulik [11], moisture  [12]  (method 926. 08, AOAC 1995), protein  [12]  (935 method, AOAC 1995), ash  [12]  (method 935. 42, AOAC 1995) and NaCl  [12]  (method 935. 43, AOAC 1995) were determined as well as pH with a potentiometer HI 9230 (Hanna Instruments, Limena, Italy). A textural profile analysis (TPA) was performed using a TA-Xt2i texture analyzer (Stable Micro Systems, Surrey, UK) and Texture Expert 7. 15 H software (Stable Micro Systems, Surrey, UK) with a load cell of 5 kg. Measurements were performed in triplicate and hardness, adhesiveness, cohesiveness, springiness and chewiness were calculated from the double bite curves.
Strains Identification and Time Temperature Gel Electrophoresis (TTGE) The PCR extracts from bacterial isolates were sequenced by GATC-biotech company (Konstanz, Germany). Partial sequences were compared to those in the GenBank database using the BLAST program. Cheese PCR products were subjected to a TTGE analysis using a Dcode universal mutation detection system (Bio-Rad Laboratories, Hercules, CA, USA) following the method described previously  [14].
Statistical Analysis A completely randomized design (DCA) was performed for physicochemical data, color, texture and rheology variables (p < 0. 05). Averages were compared by the least significant difference test (LSD). For the descriptive analysis a randomized complete block design (RCB) with a split plot arrangement was used, while for the acceptability tests, a RCB was performed. The least significant difference method was used for comparison of means. Principal components analysis (PCA) was applied to obtain preference maps. The statistical calculations were performed using the statistical software SAS ® version 9. 1 (SAS Institute, Inc. , Cary, NC, USA), along with The Unscrambler ® version 9. 2 (CAMO PROCESS AS, Oslo, Norway).
MS data handing software (Xcalibur Qualbrowser and QuantBrowser, Thermo Electron Corp) was used to search for metabolites and their appropriate m/z values and perform semiquantitative analysis of target compounds. All peaks were checked for m/z value, retention time and fragmentation products for identification purposes. Based on an initial qualitative analysis of the dataset a targeted list of compounds was selected for semi-quantification using total areas of selected ion chromatograms. Principal Component Analysis (PCA) was used to summarise broad scale variation among hydrolysed and non-hydrolysed samples in both positive and negative modes. Analysis of variance was performed to compare high salt vs. low salt in berry diets individually for hydrolysed and non-hydrolysed datasets as well as ESI positive and ESI negative datasets.
Briefly, the flow rate was 0. 4 mL/min, and the gradient elution was as follows: 0-10 min, 5%-35%B; 10-10. 10 min, 35%-80%B; 10. 10-11 min, 80%B isocratic; 11-11. 10 min, 80%-5%B; 11. 10-12. 50 min, 5% B isocratic. Regarding the analysis of the rest of the phenolic metabolites, the mobile phase was 0. 2% acetic acid as solvent A and acetonitrile as solvent B, and the flow rate was 0. 4 mL/min. The gradient elution is as follows: 0-5 min, 5%-10 B%; 5-10 min, 10%-12. 4%B; 10-18 min, 12. 4%-28%B; 18-23 min, 28%-100%B; 23-25. 5 min, 100%B; 25. 5-27 min, 100%-5%B; and 27-30 min, 5%B. The injection volume was 2. 5 µL. The AcQuity UPLC TM system was coupled to a PDA detector, and a triple quadrupole detector (TQD TM ) mass spectrometer (Waters). The software used was MassLynx 4. 1. Ionization was performed by electrospray (ESI) in the positive mode for the analysis of anthocyanins, and in negative mode for the remaining phenolic compounds.
(Poly)Phenols Content in Diets Food matrices can interact with (poly)phenols altering their bioavailability. To ensure that the differences observed in polyphenol metabolism of the animals, are not related with food matrix effects, we assessed whether the high-salt content in the diets had an influence in the (poly)phenol content extracted from the berries or on the release of (poly)phenols from the food matrix after digestion. The analysis of intact, non-hydrolysed LS, LSB, HS and HSB diet extracts by HPLC-MS revealed a great complexity and diversity of compounds particularly in the diets supplemented with berries (Supplementary Tables  S4  and S5 , and supplementary Figure  S1 ). To further analyse the impact of salt in the (poly)phenol composition derived from berries, and to maximise the analysis of the differences observed among the samples, the HPLC-MS data was processed by means of Principal Component Analysis (PCA, Supplementary Figure  S2 ).
All the diets clustered according to the presence or absence of berries in the diet (Supplementary Figure  S2A , PC 1, 92. 67%). LS and HS diet samples without berries are clustered together (Supplementary Figure  S2A ), which demonstrate that the presence of berries in the diet clearly is the main determinant that distinguish the diets. No effect of the salt is evidenced in (poly)phenol composition. Analysis of variance revealed that there were no significant differences (p < 0. 05) between non-hydrolysed berry diets containing low salt and non-hydrolysed berry diets containing high-salt diets in both ESI positive and negative mode datasets (Supplementary Tables  S4  and S5 ). The content of anthocyanins in our berry mixture was evaluated since it is described as the major (poly)phenol class represented in the mixture (Supplementary Table  S6 ).
Analysis of Aglycones after Enzyme Hydrolysis of Glycosides In order to understand whether the presence of the salt affects the release of the aglycones from the food matrix, and in a certain way, to predict possible effects that occurred during the gastro-intestinal digestion of the Dahl/SS rats, we decided to analyse diets after enzymatic digestion. Extracts from the diets (LS, LSB, HS and HSB) and the berry mixture were incubated with enzymes from Aspergillus niger: hesperidinase containing α-l-rhamnosidase and β-d-glucosidase activities and cellulase containing endo-1,4-β-d-glycosidic activity. We used the same strategy as for the non-hydrolysed samples, samples were analysed by HPLC-MS, and data was processed by means of a PCA. Supplementary Figure  S2B  shows, again, that the presence of berries in the diets is responsible for the differences between the samples (PC 1 88.
Metabolic Fate of (Poly)Phenols We assessed the metabolic fate of (poly)phenols after the chronic ingestion of the berry-enriched diet for 9 weeks and evaluated the persistence of these metabolites in the organism. Moreover, we aimed to understand whether the metabolic fate of (poly)phenols was affected by the high blood pressure induced by the salt enriched diet. The experiment was not designed as a multi-component pharmacokinetic study, and rats were fasting overnight, approximately for 16 h. With this approach, we were not able to identify any (poly)phenol metabolites in heart, liver, and plasma, indicating that they do not persist in these organs and fluids after 16 h (data not shown). Food intake was monitored, and no differences were observed between animals with the different diets.
Acknowledgments: The authors thank  Pedro Oliveira  ( Instituto Nacional de Investigação Agrária, Oeiras, Portugal ) for providing Vaccinium spp. , Rubus spp. and R. idaeus,  Jana De Bodt  (Centrum for microbial ecology and technology- FBE-Ghent University ) for the SCFA analysis, and  Sébastien Massart  from  DNAVision  for the microbiota analysis.
Albumins and globulins (AGs) of wheat endosperm represent about 20% of total grain proteins. Some of these physiologically active proteins can influence the synthesis of storage proteins (SPs) (gliadins and glutenins) and consequently, rheological properties of wheat flour and processing. To identify such AGs, data, (published by  Bonnot et al. , 2017)  concerning abundance in 352 AGs and in the different seed SPs during grain filling and in response to different nitrogen (N) and sulfur (S) supply, were integrated with mixOmics R package. Relationships between AGs and SPs were first unraveled using the unsupervised method sparse Partial Least Square, also known as Projection to Latent Structure (sPLS). Then, data were integrated using a supervised approach taking into account the nutrition and the grain developmental stage.
We used the block. splda procedure also referred to as DIABLO (Data Integration Analysis for Biomarker discovery using Latent variable approaches for Omics studies). These approaches led to the identification of discriminant and highly correlated features from the two datasets (AGs and SPs) which are not necessarily differentially expressed during seed development or in response to N or S supply. Eighteen AGs were correlated with the quantity of SPs per grain. A statistical validation of these proteins by genetic association analysis confirmed that 5 out of this AG set were robust candidate proteins able to modulate the seed SP synthesis. In conclusion, this latter result confirmed that the integrative strategy is an adequate way to reduce the number of potentially relevant AGs for further functional validation.
, 1999; Flaete et al. , 2005; Bonnot et al. , 2017). In contrast to this, the effects of S on the synthesis and accumulation of HMW and LMW glutenins are opposite. Therefore, S deficiency decreases the total amount of polymeric proteins because LMW are the major components of glutenins. Changes were brought on by an imbalance of S-modified protein composition which is associated with alterations of dough rheology  (Zhao et al. , 1999). S fertilization improved all major parameters of baking quality in wheat by increasing the content of S-containing amino acids  (Järvan et al. , 2008). In summary, N and S supplies together influence the N to S ratio in the grain leading to significant changes in SP composition at maturity  (Bonnot et al. , 2017). In order to gain insights into the contribution of AGs to grain storage composition, we analyzed the large-scale proteome dataset for Einkorn (Triticum monococcum ssp.
monococcum) described in  Bonnot et al. (2017). Einkorn is an ancestral wheat whose diploid genome is a sister to bread wheat (T. aestivum) genome A  (Marcussen et al. , 2014)  and for which genomic resources are available, making it a suitable species in which to explore proteome responses to N and S nutrition. We used the integrative methods implemented in the R package MixOmics  (Lê Cao et al. , 2016)  and put forward by  Duruflé et al. (2018)  that take into account all quantified proteins and not only those differentially expressed as in classical omics analyses. We identified 18 important AGs. We provided a statistical validation of five of them in bread wheat by association analysis and seven of these identified AGs have already been identified as potential candidates in a network analysis based on association rule discovery  (Bonnot et al.
Protein Identification and Quantification The mass spectrometry proteomic data were first published in  Bonnot et al. (2017)  and have been deposited to the ProteomeXchange Consortium  (Deutsch et al. , 2017)  via the PRIDE  (Vizcano et al. , 2016)  partner repository with the dataset identifier PXD006058. Proteins were identified by matching peptides using the Uniprot protein database version 2014_07 limited to the Triticum genus using X!Tandem 1 and X!TandemPipeline  (Langella et al. , 2017)  as described in  Bonnot et al. (2017). Functional classification was established on the basis of gene ontology (GO) information rules provided by Uniprot  (Ashburner et al. , 2000). Proteins that had no 1 www. thegpm. org/tandem functional annotation or GO information were analyzed with Blast2GO (version 3.