dataesr/scientific-paragraphs-categorization huggingface dataset:
At m/z 200 and at a resolving power of 70,000, survey scans were obtained. The resolving power of the higher energy dissociation spectra was configured to be 17,500 at m/z 200, the normalized energy of collision to be 30 eV, the width of the isolation to be 2 m/z, and the underfill ratio, which is defined as the target value's minimum percentage that may be achieved during the maximum filling period, to be 0. 1%. The equipment was operated in activated peptide recognition mode. The MS data have been deposited in the Proteo-meXchange Consortium via the PRIDE partner repository with the dataset identifier PXD018845  118. Data analysis MASCOT 2. 2 was implanted into Proteome Discoverer 1. 4 against UniProt Vitis vinifera (20180122, 54701 proteins), and the decoy database was employed to search the MS/MS spectra.
The MS was performed with positive ion mode, and the parameters were as follows: A complete MS1 scan was achieved at 70,000 resolving power (at 200 m/z), the target ACG was 3. 0 × 10 -6 , and the maximum ion injection duration was 250 ms. After the complete MS scans, 20 parallel reaction monitoring scans with 35,000 resolution (at 200 m/z), AGC value of 3. 0 × 10 -6 and maximum injection duration of 200 ms were performed. The target peptides were then separated using a 2 Th window. In an HCD collision cell, ions were activated or dissociated at a normalized collision energy of 27. The Skyline method (MacCoss Lab, University of Washington) was used to analyze the raw data  120. In this analysis, the signal intensities of each greatly changed protein's single peptide sequences were quantified and normalized to a standard reference.
Acknowledgements This work was supported by the  National Key Research and Development Program of China  ( 2018YFD1000300 ,  2019YFD1002500 ) and  National Natural Science Foundation of China  ( 31601716 , No. 31972374 ). We thank  Professor Steven van Nocker  ( MSU Department of Horticulture ) for critical and valuable suggestions on the manuscript. We thank  Shanghai Applied   Protein Technology Co. Ltd. for help with iTRAQ sequencing and PRM verification.
Metabolomic Analysis Metabolites produced by Bs29784 in vitro were analyzed together with blank samples. In vivo metabolite production was determined using intestinal digesta from chickens receiving either non-supplemented feed or feed supplemented with Bs29784. Therefore, intestinal content of the jejunum, ileum or cecum was freeze-dried for 24 h. To 100 mg of freeze-dried material, 2 mL of ice-cold methanol (80:20) was added, vortexed and centrifuged (9000 rpm, 10 min), after which the supernatant was filtered using a PVDF filter (0. 45 µm × 25 mm diameter) and used at a 1:3 dilution. Xcalibur 3. 0 software (Thermo Fisher Scientific, San José, CA, USA) was employed for targeted data processing, whereby compounds were identified based on their m/z-value, C-isotope profile, and retention time relative to that of the internal standard.
16S rRNA Gene Amplicon Sequencing The V3-V4 hypervariable region of the 16s rRNA gene was amplified by using the gene-specific primers S-D-Bact-0341-b-S-17 and S-D-Bact-0785-a-A-21  [27]. The PCR amplifications were performed as described by  Aguirre et al. (2019)   [24]. CleanNGS beads (CleanNA, Gouda, The Netherlands) were used to purify PCR products. The DNA concentration of the final barcoded libraries was measured with a Quantus fluorimeter and Quantifluor dsDNA system (Promega, Madison, WI, USA). The libraries were combined to an equimolar 5 nM pool and sequenced with 30% PhiX spike-in using the Illumina MiSeq v3 technology (2 × 300 bp, paired-end) at the Oklahoma Medical Research center (Oklahoma City, OK, USA). Demultiplexing of the amplicon dataset and deletion of the barcodes was done by the sequencing provider.
Quality of the raw sequence data was evaluated using the FastQC quality control tool (Babraham Bioinformatics, Cambridge, UK), followed by an initial quality filtering with Trimmomatic v0. 38  [28]. Reads with an average quality per base below 15 were cut using a four-base sliding window and reads with a minimum length below 200 bp were discarded. The paired-end sequences were assembled and primers were removed using PANDAseq [29], with a quality threshold of 0. 9 and length cut-off values for the merged sequences between 390 and 430 bp. Chimeric sequences were removed using UCHIME  [30]. Open-reference operational taxonomic unit (OTU) picking was performed at 97% sequence similarity using USEARCH (v6. 1) and converted to an OTU table  [31]. OTU taxonomy was assigned against the Silva database (v132, clustered at 97% identity)  [32]  using the PyNast algorithm with QIIME (v1.
9. 1) default parameters  [33]. OTUs with a total abundance below 0. 01% of the total sequences were discarded  [34]. Potential contaminant chloroplastic and mitochondrial OTUs were removed from the dataset, resulting in an average of approximately 76,080 reads per sample, with a minimum of 25,725. Alpha rarefaction curves were generated using the QIIME "alpha_rarefaction. py" script and a subsampling depth of 25,000 reads was selected.
Metabolic Function Prediction of the Microbial Communities Functional genes (KEGG orthologues, KOs) were inferred from the 16S amplicon sequencing data using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt), as previously described  [24, 35]. The resulting KEGG orthologues were further summarized into functional modules based on the Gut-specific Metabolic Modules (GMM) database using GoMixer (Raes Lab)  [36, 37]. The contribution of various taxa to different GMMs was computed with the script "metagenome_contributions. py".
Differences in alpha diversity were assessed using a Wilcoxon's rank sum test. Beta diversity was calculated using Bray-Curtis distance. Differences in beta diversity were examined by permutational analysis of variance (Permanova) using the adonis function from the vegan package  [39]. Differences in relative abundance at the phylum and family level were assessed using the two-sided Welch t-test from the mt wrapper in phyloseq, with the p-value adjusted for multiple hypothesis testing using the Benjamini-Hochberg method. The DESeq2 algorithm was applied to identify differentially abundant genera or functional modules between the control and Bs29784 group [40]. Significant differences were obtained using a Wald test followed by a Benjamini-Hochberg multiple hypothesis correction. For all tests, an adjusted p-value (q-value) ≤0.
05 was considered significant. Biologically relevant differences in functional modules between the birds fed a control diet or Bs29784-supplemented diet were selected using a Log2 fold change (Log2FC) > 2 and q-value < 0. 1. The association of microbial abundances (at family, genus or OTU level), with hypoxanthine and nicotinic acid levels measured in the intestinal content, were analyzed using the multivariate analysis by linear models (MaAsLin2) R package. MaAsLin2 analysis was performed separately on the ileal and cecal samples, while controlling for treatment covariates  [41].
0001), were selected for evaluation in the in vivo samples. The total number of bacteria, as well as the number of Bacillus spp. in the jejunum, ileum and cecum were determined using qPCR. Supplementation of the diet with the probiotic B. subtilis strain Bs29784 did not introduce alterations in the total bacterial load (data not shown), but significantly increased the number of Bacillus spp. in the ileum (p = 0. 005), jejunum (p = 0. 008), and cecum (p = 0. 014) (Figure  1A-C ). To further assess whether this increase in Bacillus spp. was reflected in an increase in Bs29784 metabolites, the levels of hypoxanthine and nicotinic acid were determined. Overall, broilers fed a Bs29784-containing diet showed higher levels of hypoxanthine and nicotinic acid in the intestinal content. The increase in hypoxanthine was most pronounced in the ileum (p = 0.
For genomic selection, prediction models are first trained with phenotypic and genomic data of a training population. In a second step, the models predict the performance of breeding material based on the genomic data alone. These genomic estimated breeding values are then used to make selections among the breeding material, thus increasing the breeding efficiency and genetic gain. Several studies have assessed genomic prediction accuracy for apple quantitative traits related to fruit quality and phenology  22, 23, 29, [32] [33] [34] [35] [36]. Genomic selection can double genetic gain, as demonstrated by yield traits in dairy cattle  37  , but the accuracy of genomic prediction for yield traits in apple has not been studied. Analyses of genomic datasets beyond 100K SNPs have been limited to flowering and harvest time (GWAS and genomic prediction)  26, 36  , fruit firmness and skin color (GWAS)  28, 38.
A population of 269 diverse apple accessions from across the globe and 265 progeny from 27 parental combinations originating in recent European breeding programs constitutes our apple reference population (apple REFPOP)  36. The apple REFPOP has a high-density genomic dataset of 303K SNPs and was deemed suitable for the application of genomics-assisted breeding  36. Combined with extensive phenotypic information, the apple REFPOP provides the groundwork for marker-assisted and genomic selection across contrasting European environments. Hence, 30 traits related to productivity, tree vigor, phenology, and fruit quality were measured in the apple REFPOP during up to three years and at up to six locations with various climatic conditions of Europe (Belgium, France, Italy, Poland, Spain, and Switzerland).
Phenotypic data analysis The accession and progeny groups of the apple REFPOP were evaluated for 30 quantitative traits at up to six locations. The measurements for ten traits were collected at one location, while the remaining 20 traits were available from at least two locations (three traits were measured in two locations, three traits in four locations, eleven traits in five locations and three traits in six locations, Supplementary Table  1 ). Most traits (25) were assessed during three seasons while five traits were measured during two seasons (Supplementary Table  1 ). Accounting for environmental effects in the phenotypic data, BLUPs of traits (best linear unbiased prediction of random effects of genotypes, see Equation  1 ) were produced across all locations and separately for each location.
Migicovsky et al. 22  reported an overlap between associations with harvest time and fruit firmness on chromosome 3 falling within the coding region of NAC18. 1. The authors hypothesized that the lack of associations on other chromosomes was likely due to low SNP density around the causal loci (the study used a GBS-derived 8K SNP dataset). The larger number of associations reported here might be a result of the high SNP density (303K SNPs) deployed in GWAS, however, not all previously reported loci were re-discovered. The SNPs associated with harvest date and fruit firmness on chromosome 10 were further apart (~6 Mb). For harvest date, one of the associations on chromosome 10 was stable across locations and several associations were location specific. However, the association on chromosome 10 with fruit firmness was found for the Italian location only.
Random forest is known to assign higher importance to correlated predictor variables (here the markers) in the tree building process  60  , which may have contributed to the particularly high prediction accuracy found for red over color with random forest. The prediction accuracy for red over color reached ~0. 4 in several former prediction studies  22, 23, 29, 34  and was approximately doubled in our work, which demonstrated the potential of the current study design for accurate genomic predictions. For harvest date, the currently reported prediction accuracy of 0. 78 was only slightly higher than the accuracy of 0. 75 obtained with the initial apple REFPOP dataset measured during one year  36  , but these accuracies showed a considerable improvement over other accuracies of approximately 0.
Conclusion This study laid the groundwork for marker-assisted and genomic selection across European environments for 30 quantitative apple traits. The apple REFPOP experimental design facilitated identification of a multitude of novel and known marker-trait associations. Our multi-environment trial provided accurate genomics-estimated breeding values for apple genotypes under various environmental conditions. Limited G×E detected in this work suggested consistent performance of genotypes across different European environments for most studied traits. Utilizing our dataset, more efficient selection of traits related to yield may lead to higher productivity and increased genetic gain in the future  37. Improved fruit quality would appeal to consumers and tree phenology could be synchronized with current and future climates to secure production.
Genotyping A high-density genome-wide SNP marker dataset was produced as reported by Jung et al. 36. Briefly, SNPs from two overlapping SNP arrays of different resolution, (i) the Illumina Infinium® 20K SNP genotyping array  20  and (ii) the Affymetrix Axiom® Apple 480K SNP genotyping array  21  , were curated and then joined applying imputation with Beagle 4. 0 66 using the recently inferred pedigrees  4. Nonpolymorphic markers were removed to obtain a set of 303,148 biallelic SNPs. Positions of SNPs were based on the apple reference genome obtained from the doubled haploid GDDH13 (v1. 1)  16.
Genome-wide association studies The Bayesian-information and linkage-disequilibrium iteratively nested keyway (BLINK)  70  implemented in the R package GAPIT 3. 0 71 was applied using the genomic matrix , an  ×  matrix for a population of size  = 534 genotypes (i. e. , accessions and progeny) with  = 303,148 markers, with acrosslocation BLUPs (across-location GWAS) or location-specific BLUPs (location-specific GWAS) as the response. BLINK was used with two principal components and the minor allele frequency threshold was set to 0. 05. Marker-trait associations were identified as significant for p-values falling below a Bonferroni-corrected significance threshold  * =   ⁄ with  = 0. 05 (- () > 6. 74). The proportion of phenotypic variance explained by each significantly associated SNP was assessed with a coefficient of determination ( ).
The random vector of residual effects followed a normal distribution  ~ (0,  ) with  ×  identity matrix  and the variance. Third, the Bayesian reproducing kernel Hilbert spaces regression (RKHS) was implemented using a multi-kernel approach  75   model applying a semi-parametric RKHS algorithm, with the random marker effects following  ~ (0,  ) with variance  and the model residuals assuming  ~ (0,  )  76. Fifth, a multivariate model with an unstructured covariance matrix of the random marker effect (here abbreviated as MTM. UN) was fitted for chosen pairs of traits using the Bayesian multivariate Gaussian model environment MTM (http://quantgen. github. io/MTM/vignette. html). The main BLUPs acted as the response , which was a vector of length  •  with  = 2 being the number of traits used in the model.
With all models, a five-fold cross-validation repeated five times was performed, generating 25 estimates of prediction accuracy. The folds were chosen randomly without replacement to mask phenotypes of 20% of the genotypes in each run. Prediction accuracy was estimated as a Pearson correlation coefficient between phenotypes of the masked genotypes and the predicted values for the same genotypes. The RF model was implemented in the R package ranger  77  , the models BayesCπ, RKHS and G-BLUP in the R package BGLR  78  and the MTM. UN model in the R package MTM (http://quantgen. github. io/MTM/vignette. html). Multi-environment genomic prediction. Two univariate multi-environment genomic prediction algorithms and one multivariate multi-environment algorithm were implemented, the response  being a vector of the adjusted phenotypic values of each genotype of length  ×  with  equal to the number of environments (nested factor of location and year).
The two univariate multi-environment models reported by Lopez-Cruz et al. 42  and implemented in the R package BGLR  78  were applied to explore the effects of genotypes, environments and their interaction in genomic prediction. Of the two models, the across-environment G-BLUP model (G-BLUP. E) assumed that marker effects were constant across environments. The random marker effects followed  ~ (0,    ) where   =  ⊗ , the  All three multi-environment genomic prediction models were applied with 12,000 iterations of the Gibbs sampler, a thinning of 5 and a burn-in of 2,000 discarded samples. The folds of a five-fold crossvalidation were chosen randomly without replacement. The cross-validation was repeated under two scenarios. In the first cross-validation scenario (CV1), the phenotypes of 20% of the genotypes were masked across all environments.
being an  ×  matrix of ones. The model residuals assumed  ~ (0,  ). Additionally to the constant effects of markers across environments as assumed in the previous model, the marker by environment interaction G-BLUP model (G-BLUP. E. G×E) allowed the marker effects to change across environments, i. e. , to borrow information across environments. The random marker effects were defined as  =   +   where   ~ (0,    ) and   ~ (0,   ) with  = 3 here for easier notation. The model residuals assumed  ~ (0,  ). Finally, a multivariate multi-environment factor-analytic model (here abbreviated as MTM. FA) using the Bayesian multivariate Gaussian model environment implemented in the R package MTM (http://quantgen. github. io/MTM/vignette. html) was fitted to the data. As in the previous two models, phenotypes of the same trait from multiple environments acted as response, although this model was originally designed to analyze multiple traits.
Data availability All SNP genotypic data generated with the 480K array used in this study have been deposited in the INRAe dataset archive (https://data. inrae. fr/) at https://doi. org/10. 15454/IOPGYF. All SNP genotypic data generated using the 20K array used in this study have been deposited in the INRAe dataset archive at https://doi. org/10. 15454/1ERHGX. The raw phenotypic data generated during the study are available in the INRAe dataset archive at (TBA upon acceptance).
Objectives: Achromobacter spp. are emerging pathogens in respiratory samples from cystic fibrosis patients. The current reference methods (nrdA-sequencing or multilocus sequence typing) can identify 18 species which are often misidentified by conventional techniques as A. xylosoxidans. A few studies have suggested that matrix-assisted laser desorption/ionizationetime-of-flight mass spectrometry (MALDI-TOF/MS) provides accurate identification of the genus but not of species. The aims of this study were (a) to generate a database for MALDI-TOF/MS Bruker including the 18 species, (b) to evaluate the suitability of the database for routine laboratory identification, and (c) to compare its performance with that of the currently available Bruker default database. Methods: A total of 205 isolates belonging to the 18 species identified by nrdA sequencing were used to build a local database.
Main spectra profiles (MSPs) were created according to Bruker's recommendations for each isolate with the Biotyper software. Performance of the default Bruker database and ours for routine use were compared by testing 167 strains (including 38 isolates used from MSP creation) belonging to the 18 species identified by nrdA sequencing directly from colonies cultivated on various media. Results: Our new database accurately identified 99. 4% (166/167) of the isolates from the 18 species (score 2. 0) versus only 50. 9% (85/167) with the Bruker database. In the Bruker database 17. 3% of the isolates (29/167) were incorrectly identified as another species despite a score of 2. 0.
Introduction Achromobacter spp. are increasingly being reported in various infections in both immunocompromised and immunocompetent patients  [1e3]. They are emerging pathogens in cystic fibrosis (CF) patients [4e6] but their role in the decline of respiratory function is not clear  [6, 7]. Over the last decade, the taxonomy of the genus Achromobacter has been revised. Two current reference methods can be used to identify Achromobacter spp. to the species level: nrdA sequencing (765 bp fragment) or multilocus sequence typing (MLST/MLSA) (five-or seven-gene sequencing)  [8e10]. The only available database, developed by Spilker and Lipuma (https://pubmlst. org/ achromobacter/), provides online identification for 18 of the 21 described species: A. xylosoxidans  [11] , A. denitrificans  [12] , A.
Given that the intrinsic antimicrobial resistance and probably also virulence properties differ from one species to another, a rapid and accurate method of identification is urgently needed  [26]. For this purpose, mass spectrometry is a promising technique. However, the IVD-CE-certified databases currently available in France (VITEK® MS bioMerieux and microflex LT Bruker) harbour only two (A. xylosoxidans and A. denitrificans for VITEK® MS V3. 0) or six species (A. xylosoxidans, A. denitrificans, A. ruhlandii, A. piechaudii, A. insolitus and A. spanius for Bruker MS MBT-IVD-DB-7712). To date, no study has evaluated the performance of species identification by MS on isolates identified by a reference method. Therefore, we aimed to generate a local database by matrix-assisted laser desorption/ionizationetime-of-flight mass spectrometry (MALDI-TOF/MS) Bruker for routine identification of the 18 species of Achromobacter included in the pubMLST database (nrdA analysis) when the study started and to compare its performance with that of the currently available Bruker default database.
Bacterial isolates (Supplementary Material Tables S1, S2 and S3) In total 334 isolates belonging to 18 Achromobacter species were included. All had been previously identified to the species level by a reference method: sequencing 765 bp of the housekeeping gene nrdA  [10]  using the pubMLST database (https://pubmlst. org/) which contained 18 species. For each nrdA-765 allele not matching an allele listed in the database, we contacted T. Spilker to ensure a correct identification  [4, 23]. The number of strains and alleles by species are listed in the Supplementary Material Table  S3  (total of 92 alleles). Of 334 isolates, 192 were collected from the sputum of CF patients (96 from our CF centre, Dijon, 48 from nine other French centres, 25 from Belgium, nine from Denmark, and 14 from the USA), 71 from various samples from non-CF patients, and 53 from environmental samples (domestic, hospital, natural environments).
Generation of a local database (MSP creation) (Supplementary Material Table S1) A total of 205 Achromobacter isolates, including the 18 type strains, were used to generate the local database (MSP creation): A. aegrifaciens (n ¼ 9), A. agilis (n ¼ 1), A. animicus (n ¼ 5), A. anxifer (n ¼ 1), A. deleyi (n ¼ 3), A. denitrificans (n ¼ 3), A. dolens (n ¼ 5), A. insolitus (n ¼ 8), A. insuavis (n ¼ 17), A. kerstersii (n ¼ 1), A. marplatensis (n ¼ 10), A. mucicolens (n ¼ 12), A. pestifer (n ¼ 1), A. piechaudii (n ¼ 1), A. pulmonis (n ¼ 4), A. ruhlandii (n ¼ 8), A. spanius (n ¼ 9), A. xylosoxidans (n ¼ 101), and six isolates belonging to a new Achromobacter species not characterized to date (novel species according to MLST analysis and confirmed by Dr P. Vandamme, personal communication)  [4, 23]. All isolates were cultivated on MuellereHinton (MH) agar (Bio-Rad®) overnight at 37 C.