dataesr/scientific-paragraphs-categorization huggingface dataset:
S912 ESTRO 38 Material and Methods ARTISCAN Beam QA software is based on EpiDream method (Boutry et al, Med Phys 2017) converting EPID signal in air into absorbed dose in reference conditions. The product is developed to perform dose quality control of photon and electron beams using EPID. Our study was performed on 6 MV and 6 MV FFF photon beams from Elekta linac (Versa HD with iViewGT v3. 4. 1) and on 6 MV photon beam and 6 MeV electron beam from Varian linac (Clinac 2100C with EPID AS500). Intrinsic characteristics of the solution were first evaluated, including repeatability, reproducibility, dose linearity and dose consistency over the time. Then, we performed tests of our current internal QA program: daily X-Ray and electron output constancy, beam profiles constancy, wedge transmission factor constancy, monitor chamber linearity.
Results The reproducibility of 10 EPID measurements was 0. 09% for 6 MV (Elekta). Dose linearity for photon and electron beams for the Varian linac was better than 1% for MUs higher than 20 with EPID solution. From 3 to 15 MUs, dose linearity was between 1. 3% and 2. 4% for photon beam and 0. 7% and 1. 9% for electron beam EPID solution. A 4 months analysis showed a dose deviation for a square field between +0. 05% and -1. 25% for 6 MV beam and between +0. 39% and -1% for 6 MV FFF beam (Elekta). For the daily output constancy on Elekta linac, the mean difference obtained for 32 QAs between EPID dosimetry and ionization chamber was -0. 1% (σ=0. 3%) for 6 MV and -0. 6% (σ=0. 3%) for 6 MV FFF beam. Beam profiles obtained with ARTISCAN Beam QA software and the StarCheck were plotted for 6 MV, 6 MV FFF and 6 MeV beams in Fig 1.
Seventeen proteins predicted to be GPI-anchored were identified in the B. microti proteome (Table  2 ). Most of these proteins coincide with the predictions of  Cornillot et al. (2016) , who obtained a list of 19 predicted GPI-APs. Two of the proteins found in the present study (GenBank Accession numbers XP_012647776. 1 and XP_012650569. 1) were not identified earlier, which brings the total of predicted GPI-APs to 21. Interestingly, the genes encoding two of the identified GPI-APs (BmGPI12 and BmGPI13) are among the most expressed in both mice and hamsters  (Silva et al. , 2016). Similarly, in B. bovis, the gene encoding GPI-anchored Merozoite Surface Antigen-1 is one of the most actively transcribed in the merozoite stage  (Pedroni et al. , 2013) , highlighting the importance of GPI-APs for membrane structure and/or function.
was washed in 70% ethanol, air dried, and solubilized in 200 l of Tris-EDTA (TE) buffer (pH 8). The genomes were sequenced using Illumina MiSeq technology by GATC (Mulhouse, France) and Biofidal (Vaulx-en-Velin, France). Libraries with 2 ϫ 300-bp and 2 ϫ 125-bp reads were constructed for Biofidal and GATC, respectively. The reads were processed using Unicycler v0. 4. 3  (13) , quality controls were assessed with FastQC and Trimmomatic, and contigs shorter than 200 bp were removed, resulting in genomes of 6. 6 Mb for N. asteroides ATCC 19247 T , 6. 3 Mb for N. cyriacigeorgica DSM 44484 T , 6. 5 Mb for N. cyriacigeorgica EML446, and 6. 8 Mb for N. cyriacigeorgica EML1456. The genomes were annotated on MicroScope  (14). The characteristics of the draft genomes are summarized in Table  1. These genome sequences provide valuable data to study the ecology, evolution, pathogenicity, phylogeny, and physiology of Nocardia cyriacigeorgica complex species.
ACKNOWLEDGMENTS Florian Vautrin  held a doctoral fellowship from the  region Auvergne-Rhône-Alpes. The LABGeM ( CEA/Genoscope  and  CNRS UMR8030 ), the  France Génomique , and  The French Institute of Bioinformatics  (funded as part of the  Investissement d'Avenir program  managed by the  Agence Nationale pour la Recherche , contracts  ANR-10-INBS-09 ,  ANR-11-INBS-0013 ) are all thanked for their support to the MicroScope annotation platform. We thank the  Infiltron  project  ANR-17-CE04-0010-03. We thank  Gonzalo Bermejo Miranda  for his English grammar revision and help in the review of the manuscript.
Quantitative Real-Time PCR (RT-qPCR) Quantitative real-time PCR (RT-qPCR) was accomplished on the Applied Biosystems 7500 Real-Time PCR system (Applied Biosystems, Foster, CA, USA) using SYBR ® Premix Ex Taq™ (Tli RNaseH Plus) (Takara, Dalian, China) to analyze the expression level of CHS1 either from N1 to N4 and adults, as well as the nymphs after 12, 24, 48, and 72 h after treatment with RNAi. Primers for the CHS1 were designed based on the conserved sequence of soybean aphid, Aphis glycines Matsumura (Hemiptera: Aphididae) CH1 gene (GenBank Accession No. JQ246352. 1). All primers were synthesized using PRIMER 3. 0 (http://bioinfo. ut. ee/primer3-0. 4. 0/) (Table  1 ). The reaction of qRT-PCR was performed in a 20 µL volume of a mixture, which contained 10 µL of SYBR ® Premix Ex Taq, 7.
8 µL ddH 2 O, 0. 4 µL of ROX, 0. 4 µL of each primer, and 1 µL of the cDNA. The qRT-PCR conditions were 95 • C for 30 s, followed by 40 cycles of 95 • C for 5 s and 60 • C for 34 s, and then one dissociation step cycle of 95 • C for 15 s, 60 • C for 1 min, 95 • C for 30 s, and 60 • C for 15 s. The qRT-PCR analysis was repeated in triplicate. The standard curve was established with serial dilutions of cDNA (1, 1/10, 1/100, 1/1000, 1/10,000, and 1/100,000) to check the amplification efficiencies and cycle threshold (Ct). Quantification of gene transcriptions was conducted using the 2 -∆∆Ct method  [45]. EF1α (GenBank Accession No. EU019874. 1) and β-ACT (GenBank Accession No. KF018928. 1) were used as the internal control  [46].
ACKNOWLEDGMENTS We thank  Julien Gronnier  for his membrane lipid template and  Alexandre Martinière ,  Matthieu Platre  for critical comments on the manuscript. T. O. is funded by the  German Research Foundation (DFG)  under  Germany's Excellence Strategy  ( CIBSS -EXC-2189 -Project ID 39093984 ). Y. J. is funded by the  French National Research Agency ANR caLIPSO  ( ANR-18-CE13-0025-02 ) and  ANR   STAYING-TIGHT  ( ANR-18-CE13-0016-02 ).
aureus, of which little is known. This study was undertaken to evaluate a data set of 3410 S. aureus isolates, taken from milk samples with a somatic cell count of > 400 000 cells/mL from commercial dairy herds. These isolates were tested for antimicrobial susceptibility using the Kirby Bauer method for ampicillin, cloxacillin, penicillin G, clindamycin, oxy-tetracycline, cephalexin, cefuroxime and tylosin. The samples were from 830 dairy herds, out of the estimated 2000 commercial dairy herds in South Africa. All the antibiotics tested, except for cephalosporins, showed a predicted prevalence of resistance of above 50% in most provinces, which is a concern. The lowest prevalence of resistance to the majority of the categories of antibiotics tested was present in KwaZulu-Natal during spring.
An augmented Dickey-Fuller test (ADF test) was used to confirm stationary residues compared to the null hypothesis which was that the time series had a unit root. Further apparent relationships between season and province on the prevalence of antibiotic resistance of all S. aureus isolates were tested with a general linear mixed model (GLMM) ('glmer' within 'lme4' package with R software © version 3. 3. 3) using a logit link-function. This model allowed the random effect from the different herds and from the repetition of data collection over time, to be taken into account. This type of GLMM takes into account this random effect when comparing the different provinces and the different seasons as well as the interactions between seasons and provinces. The existence of interaction between provinces and seasons was checked by using logistic regression.
LL 95 % and UL 95 % are the lower limit of the 95% confidence interval and the upper limit of the 95% confidence interval, respectively. To calculate the prevalence of resistance for each season, province or interactions and their confidence intervals with an accepted error of 5% the results of the GLMM could not be used directly. Indeed, the formula applied on the results of the 'glmer' function to obtain the respective prevalence of resistance for all seasons, provinces or for all the potential interactions does not take into consideration the random effect. Therefore, the prediction of level of resistance was used, using the GLMM as a model applied to the complete data set based on the assumption that the data set was truly a good representative sample of the population of interest. The 'predict' function of the 'lme4' package adapted for fitted mixed-effect models was used to calculate the prevalence of resistance per province, per season or per province within season.
Statistical analyses Data are presented as means ± standard error. The weekly values for testosterone concentrations were analysed using an ANOVA with time as a repeated measure and the experimental treatment of the males as the main factor. The Tukey test was used to detect weekly differences between groups. The variables expressed as percentages -does with ovulations, those expressing oestrous behaviour and having ovulations, fecundity and fertility -were analysed using the Fisher-Freeman-Halton exact probability test for multiple group comparisons, and the Fisher exact probability test for two-group comparisons as required. Ovulation rates and prolificacy were compared using the Mann-Whitney U test. Productivity and the dates of ovulation, and ovulation with oestrous behaviour were compared using a one-way ANOVA with the male treatment as a fixed effect.
Fast approximate numerical method The quantity of interest is the regression of on , which can be approximated as follows: (1) Run a GBLUP model with additive effect. (2) Extract residuals ̂ and GEBVs ̂ from the output. (3) For each SNP marker : This approximate method is very fast, but ignores the uncertainty in the estimation of ̂ and ̂, and therefore it will be accurate for informative datasetsi. e. , for moderate to highly heritable traits or large datasets. It may be used for a fast screening followed by a REML analysis  (Jannink, 2007; Crawford et al. , 2017)  for a subset. Where y are quantitative phenotypes of interest, is the fixed effects vector (e. g. herd-sex-year contemporary group), is a design matrix relating records to fixed effects, is a diagonal matrix whose diagonal contains , the coding of the different genotypes at locus i, and k i is the buffering effect of locus i, expressed as a regression of phenotype on the product gene content at i multiplied the additive polygenic effect.
Analytical implementation We implemented the analyses using shell scripts to manipulate the data, Wombat  (Meyer, 2007)  for the REML analyses (see APPENDIX 1), and our own programs for the fast A c c e p t e d M a n u s c r i p t approximate analyses. Scripts and source code are available from the corresponding author upon request. The GWAS was performed in a high-performance cluster computer (Genotoul Toulouse bioinfo platform), in which the epistatic effect of each marker (k i ) was estimated separately from each other. Wall clock computing time was approximately 4 days for the exact analysis run in parallel, and a few minutes for the fast approximate one.
For each population, six of these SNPs, including three of each pattern (positive to negative, and negative to positive), are listed in Table  1. Table  1  also lists the effect of a SNP in the coding regions of PLAG1 (pleomorphic adenoma gene 1), a well-known locus affecting stature in humans  (Gudbjartsson et al. 2008) , as well as growth and fertility traits in cattle  (Fortes et al. , 2013; Karim et al. , 2011). The SNP on PLAG1 was found to be significant only in the GWAS of the middle bins (BIN3 and BIN2 in BB and TC, respectively) as well as the GWAS of the whole dataset in both populations. Among the genes listed in Table  1  we highlight LRIG3 (Leucine-rich repeats and immunoglobulin-line domains protein 3), a body size-related gene found to be under positive  selection in a study of five bovine breeds including Brahman  (Xu et al.
2003; Flatt 2005) , maintenance of genetic variance in selected populations previously under stabilizing selection  (Gimelfarb 1989) , and opposite signs of GWAS associations in different populations  (Huang et al. 2012). A simulated dataset would assist understanding the implications of the analytical approach presented here, but we argue about the difficulty of simulating a dataset in a realistic fashion. Jannink (2007) simulated a trait with a genetic variance of 60 and a phenotypic variance of 100. An epistatic variance of 20 was generated with the marked QTL interacted with nine other unlinked loci based on a compound epistatic network proposed in the context of plant breeding  (Cooper et al. 2002). Further research is warranted to understand how and if this approach could work to simulate buffering epistasis by which a set of QTLs have opposing effects on the extremes of the additive polygenic value and in the presence of selection.
9737 0. 5433 3. 0404 0. 9272 -0. 3234 3. 5297 ARS-BFGL-NGS-100083 8 11,133,497 0 SCARA3 0. 9528 -0. 5113 3. 4369 0. 6920 0. 1975 4. 1983 BovineHD0800014800 8 49,143,207 36,501 ALDH1A1 0. 9820 0. 7474 3. 5992 0. 7831 0. 2002 3. 9915 BovineHD0800015819 8 52,427,816 8,480 PCSK5 0. 0452 -0. 4416 3. 3439 0. 2349 -0. 1963 3. 6686 Hapmap48089-BTA-81187 8 52,524,895 0 GCNT1 0. 9403 0. 3760 3. 0638 0. 6829 0. 1562 3. 2041 BovineHD1000026980 10 92,312,649 0 TSHR 0. 6054 0. 2160 3. 5761 0. 4686 -0. 1712 4. 0605 BovineHD1000026985 10 92,331,573 0 TSHR 0. 3946 -0. 2140 3. 5761 0. 5317 0. 1779 4. 1064 BovineHD1000030828 10 92,335,071 0 TSHR 0. 4216 -0. 2017 3. 2507 0. 7002 0. 1623 3. 2973 BovineHD1200025989 12 84,830,432 15,785 5S_rRNA 0. 7141 0. 2066 3. 2041 0. 6998 -0. 1603 3. 0872 BovineHD1400011237 14 37,055,691 0 ENSBTAG00000002076 0.
1033 -0. 3042 3. 0404 0. 5288 -0. 1442 3. 0872 BovineHD1400011280 14 37,211,716 0 ENSBTAG00000050514 0. 9095 0. 3733 3. 9915 0. 6543 0. 2204 5. 9710 BovineHD1400013234 14 44,638,868 5,792 PMP2 0. 6080 0. 1951 3. 0169 0. 6512 -0. 1791 3. 9225 BovineHD1600016214 16 56,665,443 0 TNR 0. 1409 0. 2618 3. 0872 0. 6231 -0. 1635 3. 6917 BovineHD1700009805 17 35,070,459 0 IL21 0. 9377 0. 4452 3. 7841 0. 7129 -0. 1633 3. 2973 BovineHD1700009807 17 35,079,284 8,519 IL21 0. 0623 -0. 4429 3. 7610 0. 2859 0. 1659 3. 2274 BovineHD1700009808 17 35,083,771 13,006 IL21 0. 0623 -0. 4429 3. 7610 0. 2859 0. 1659 3. 2274 BovineHD1700009811 17 35,094,499 23,734 IL21 0. 9377 0. 4452 3. 7841 0. 7131 -0. 1626 3. 2740 BovineHD1900012991 19 45,742,542 18,774 ENSBTAG00000053203 0. 2399 0. 2629 3. 5297 0. 3921 -0. 1908 4.
SlZnT2 Sequence Analysis The ZIP transporter SlZRT2 was previously identified in the genome of S. luteus UH-Slu-Lm8-n1 v2. 0  (Coninx et al. , 2017b). The corresponding amino acid sequence was further investigated in silico. Transmembrane domains (TMDs) were predicted by the topology prediction program TMHMM2. 0  (Krogh et al. , 2001). Amino acid sequence similarities were calculated with Sequence Manipulation Suite version 2  (Stothard, 2000). A subcellular localization prediction was performed with "ProtComp v. 9. 0. Identifying sub-cellular location (Animals and Fungi)" from Softberry 1. SlZRT2 and ScZRT2  (Eide, 1996)  were aligned with Multiple Alignment using Fast Fourier Transform version 7 (MAFFT)  (Katoh and Standley, 2013).
Gene Expression Analysis in S. luteus The effect of the extracellular Zn concentration on the expression of SlZRT2 was analyzed in the isolate UH-Slu-P4 via reverse transcription quantitative polymerase chain reaction (RT-qPCR). The experimental setup and data analysis were performed as described by  Coninx et al. (2017b). For the amplification of SlZRT2, a gene-specific primer pair was designed using Primer3web version 4. 1. 0 (Rozen and Skaletsky, 2000)  (forward primer: 5 TTCTACGCTCTCACTCGAAG 3 ; reverse primer: 5 CGGTGAACTGTATGACTGGA 3 , primer efficiency = 96. 9%). Expression data were normalized with the reference genes TUB1, ACT1, AM085296, AM085296, and GR97562  (Ruytinx et al. , 2016). Reference gene expression stability was confirmed within the current experimental setup using GeNorm  (Vandesompele et al.
, 2002). The geometric mean of the relative expression levels of the reference genes was calculated and applied as a normalization factor. Gene expression data were expressed relative to the sample with the highest expression level via the formula 2 -Ct. Mean values of the biological replicates (n = 4) were calculated, rescaled to the 20 µM Zn (control) condition within each time point, and log2 transformed. To assess differences in SlZRT2 expression levels, a two-way analysis of variance (ANOVA) followed by a Tukey's HSD test was performed in "R" version 3. 2. 2 (R Core Team, 2012). The transcript profiles of SlZRT1 and SlZRT2 were analyzed in free living mycelium (FML) UH-Slu-Lm8-n1  (Kohler et al. , 2015)  and in symbiotic S. luteus-Pinus sylvestris ECM root tips. Expression data were obtained from the GSE63947 expression data set, which is published  (Kohler et al.
SlZRT1 and SlZRT2 Transcript Levels in Free-Living S. luteus Mycelium and Mycorrhizal Root Tips To assess whether SlZRT1 and SlZRT2 are differentially expressed in S. luteus FLM and ECM root tips, RNA-Seq data of these morphological structures were analyzed. Indeed, transcript levels  of both ZIP genes are significantly higher in the ECM root tips of the S. luteus-P. sylvestris association, when compared to the in vitro FLM (Table  1 ; p values <0. 05). A sevenfold increase in the expression of SlZRT1 was observed in ECM root tips. This change in gene activity was less pronounced for SlZRT2, for which the transcript levels were 2. 3 times higher in the ECM root tips.
FUNDING Funding was provided by the  Research Foundation Flanders  ( FWO  project  G079213N ). LC holds a  Flanders Innovation & Entrepreneurships Ph. D. fellowship  ( IWT  project  141461 ). Her research visit at  INRA Grand Est Nancy  was funded by the  Laboratory of Excellence Advanced Research on the Biology of Tree and Forest Ecosystems (ARBRE ; grant No. ANR-11-LABX-0002-01 ).
Comparing fruit species therefore appears as a valuable way to get a better understanding of metabolism. On the one hand, the comparison of results of studies about species of different botanical families allows pointing the drivers of sugar or organic acid accumulation but this kind of comparison is often hampered by heterogeneous analysis approaches applied in each study and incomplete dataset. On the other hand, cross-species studies remain rare but have brought new insights into key aspects of primary metabolism regulation. In addition, new tools for multi-species comparisons are currently emerging, including meta-analyses or re-use of shared metabolic or genomic data, and comparative metabolic flux or process-based modeling. All these approaches contribute to the identification of the metabolic factors that influence fruit growth and quality, in order to adjust their levels with breeding or cultural practices, with respect to improving fruit traits.
, 2014). However, tools are still required to take full advantage of the metabolomics datasets describing fruit composition that have been or will be, collected in repositories such as MetaboLights (Haug et al. , 2013 2 ) or the Metabolomics Workbench. 3 Although absolute quantitative data are easily reusable and comparable, this is not the gold standard for metabolomics data collected in these repositories, which are generally relative quantification datasets. Normalization methods for appropriate comparison of those data still need to be developed. The comparative analysis of transcriptomic profiles in varieties of climacteric and non-climacteric melon has highlighted differences, in particular for genes related to ethylene biosynthesis and signaling, but also in gene expression related to sugar metabolism.
For genomics, a computational pipeline has been proposed to identify metabolic enzymes, pathways and gene clusters for about 20 plant species from their sequenced genome including tomato, grapevine and papaya fruit species  (Schläpfer et al. , 2017). Metabolic pathway databases were generated for 22 species and metabolic gene clusters were identified from 18 species. These vast resources can be used to conduct comparative studies of metabolism regulation between species, with the challenge to decipher organ specificities. Recently, an ambitious study about the evolution of fruit ripening involving transcriptomics, accessible chromatin study and histone and DNA methylation profiling of 11 fruit species revealed three types of transcriptional feedback circuits controlling ethylenedependent fruit ripening  (Lü et al.
FUNDING LR was funded by the  French "Agence Nationale de la Recherche" (ANR)  through the  FRIMOUSS  project ( ANR-15-CE20-0009-01 ). JW was funded by  China Scholarship Council (CSC). We acknowledge the  FRIMOUSS, MetaboHUB  ( ANR-11-INBS-0010 ) and  PHENOME  ( ANR-11-INBS-0012 ) projects for further funding.
CONCLUSION To better understand the effects of environmental factors on grape quality, and to properly phenotype genetic resources, critical transitions in berry development require objective standards other than mean sugar concentration calculated from data pooled from a berry population. However, there is an inherent paradox in looking for precise stages of development in a nonsynchronous population, because it is necessary to account for the dynamic structure of the population  (Shahood et al. , 2019). Berry softening proved a much more pertinent indicator of the onset of ripening than colour change. Changes in berry firmness can be determined either manually or by using electronic devices, in a destructive or nondestructive way. During ripening, heterogeneity of berry development can be characterized by a combination of density sorting and quantification of metabolites.
This showed that 10% of the products were not correctly labeled, with the 3. 2% having a list of ingredients that did not match the recipe and allergens not being highlighted on 18% of products. Analysis for milk, egg, hazelnut, peanut, and gluten showed milk was the most common undeclared food allergen in confectionary products and bakery products, while chocolate products carrying PAL were found to contain levels of hazelnut (3. 1-18 500 ppm), milk (2. 7-8800 mg casein/kg), and peanuts (0. 7-42 500 ppm) that would cause an allergic reaction in 50% of the population allergic to that food  (8). The problems that allergens pose to the food industry are further emphasized by an analysis of the Rapid Alert System for Food and Feed database for the period 2011-2014 undertaken in the integrated approaches to food allergen and allergy management (iFAAM) project  (9).
This found that the unintended presence of milk (29%) was the primary cause of allergen recalls, followed by cereal containing gluten (16%), soy beans (9%), tree nuts (8%, including hazelnut and almond), egg (8%), and peanut (4%; 7). Cereals and bakery products (27%) were the most frequently reported allergen-recalled products, followed by ready-to-eat meals, confectionary, and "soups, broths, sauces, and condiments" occurring in 21, 9, and 7%, respectively. The definition of confectionary, cocoa, and bakery products caused difficulties for the analysis because some products, such as chocolate cookie and chocolate muffin mix, appeared to be listed under one category or the other arbitrarily. A further critical analysis of the FSA Food Alerts database from 2011 until 2014 indicated milk (24%) as the primary cause of allergen recalls in the United Kingdom, followed by cereal containing gluten (19%), tree nuts (8%), peanut (7%), and egg (5%).