dataesr/scientific-paragraphs-categorization huggingface dataset:
These samples are analyzed with optimized protocols following a traditional, culture dependent approach and a culture independent, omics-based approach. In this way, a comprehensive description of the microbiota will be obtained. The presence/absence of pathogenic microorganisms may be then correlated with particular characteristics of the microbiota in the samples, the distribution in time or space. Further, routes of contamination within the processing plant may be identified. Such type of information is critical in adopting a preventive approach that is based on knowledge and data within a particular production but may also be integrated into a refined risk assessment for infant formulae. The second objective is to investigate the behavior of L. monocytogenes, chosen as a model foodborne pathogen, under in vitro conditions that mimic the food production process.
Conclusions Infant microbial food safety is of extreme importance. Significant interventions that aim at improving microbial food safety have been consolidated in the last 20 years. The HACCP approach, GHP, specific guidelines for producers of infant formulae have helped in making this type of products reach a high standard of safety. Nevertheless, contamination by dangerous foodborne pathogens remains a potential threat to public health and outbreaks of disease due to contaminated infant formulae are still occasionally reported. To tackle this safety issue, omics tools are important in identifying contamination routes, highlighting microbial interactions influencing pathogenic microorganisms, understanding their behavior under food production conditions. Generating omics data that could be integrated into risk assessment is the purpose of the SAFFI project with the overarching aim of further improving infant food microbial safety.
GC-FID Analysis GC-FID analyses were carried out using a Clarus 500 Perkin Elmer (PerkinElmer, Courtaboeuf, France) system equipped with a FID and two fused-silica capillary columns (50 m × 0. 22 mm, film thickness 0. 25 μm), BP-1 (polydimethylsiloxane) and BP-20 (polyethylene glycol). The oven temperature was programmed from 60 °C to 220 °C at 2 °C/min and then held isothermal at 220 °C for 20 min; injector temperature: 250 °C; detec-tor temperature: 250 °C; carrier gas: H 2 (0. 8 mL/min); split: 1/60; injected volume: 0. 5 μL. The relative proportions of the essential oil constituents were expressed as percentages obtained by peak-area normalization, without using correcting factors. Retention indices (RI) were determined relative to the retention times of a series of n-alkanes with linear interpolation (Target Compounds software from Perkin Elmer).
Cdl 2 was assigned to the lithological subunit A2 which was similarly dated as Early Cenomanian  40. A new male specimen of Antodicranomyia azari is reported in amber from the Archingeay deposit (Arc 1), which already yielded the type material of this species described in 2007  23. And the male of a new species is reported from the Cadeuil deposit (Cdl 2). All specimens newly reported herein are deposited in the Geological Department and Museum of the University of Rennes (IGR), France. The specimens were examined using a Nikon SMZ 1500 stereomicroscope equipped with a Nikon DS-Fi1 camera. The measurements were taken with NIS-Elements D 3. 0 software. The length of head was measured as the length of the head capsule. Measurements of the discal cell were taken from the proximal to distal ends of the d-cell; measurements of the vein M 3 were taken from the point of connection of vein m-m with vein M 3 to the margin of wing; the length of genitalia was measured from the posterior margin of tergite IX to the apex of gonocoxite.
Acknowledgements We warmly thank  Mr. Eric Dépré  ( Aigrefeuille d' Aunis ) for the donation of the type specimen from Cadeuil. Financial support for field studies and collection of Charentese amber was provided by  French National Research Agency  grant  BLAN07-1-184190  (project  AMBRACE  to  D. Néraudeau ,  University of Rennes ). This research was supported by the project of the  National Science Centre, Poland , Grant No. 2016/23/B/NZ8/ 00936.
An increasing amount of such prior information is available, ranging from publicly available trait mapping information to functional or structural annotations of the genome  [12]. By appropriately including these heterogeneous and complex data, it is hoped that causal mutations could be more readily identified and prioritized, thus potentially improving model predictions and interpretability. Such prior biological information can be collected on the same individuals used for genomic prediction, or on an independent population (e. g. , from publicly available databases, such as FAANG "Functional Annotation of Animal Genome"  [12] ). Several methods have been introduced for the former case, such as GTBLUP  [13]  and GTiBLUP  [14] , although such fully coupled datasets remain rare. However, prior biological information from independent sources are much more widely accessible, and such information can be used to assign variants to annotation categories.
To this end, we propose the BayesRC+ model, with a cumulative mixture prior distribution on SNP effects. As the proposed BayesRCπ and BayesRC+ models incorporate biological information in different ways, their performance is likely to be highly dependent on the underlying genetic architecture of the studied traits, the construction of annotation categories, and the biological relevance of the prior information. However, given the potential difficulty in defining annotation categories in practice, both models must be robust to the inclusion of noisy or irrelevant annotations. To evaluate our models, we simulated data with various genetic architectures and annotation configurations. In the same perspective, we applied both methods to real pig data in conjunction with annotations constructed from a public database, again varying the way annotation categories were constructed.
Gibbs sampling As the full posterior distributions for all models described above are intractable, model parameters are estimated with a Gibbs sampler, using the posterior mean of their estimations. Algorithm implementation details for BayesR were previously described by Kemper et al. [16]  and Moser et al. [9]  and were used as a base to implement BayesRC, BayesRCπ and BayesRC+. Broad steps of the algorithms for each are shown in pseudocode Additional file 1: Algorithms 1-4. Concretely for BayesRCπ , within a given iteration of the Gibbs sampler SNPs are assigned to the annotation category with probability proportional to its conditional likelihood given the current estimates of other model parameters. Note that this step is analogous to that in the standard BayesR algorithm of assigning SNPs to one of the four effect classes, based on a conditional likelihood calculation given the current estimates of model parameters.
BayesRCO package on Github We propose the BayesRCO (BayesRC for Overlapping annotations) software, which implements five different Bayesian genomic prediction models, including three state-of-the art approaches (BayesCπ  [7] , BayesR, and BayesRC) and our two novel algorithms, BayesRCπ and BayesRC+. The implementation of our two new models builds on that of the bayesR software found at https:// github. com/ synth eke/ bayesR  [9]. Since BayesR can be seen as a special case of BayesRC with a single annotation category, and BayesRC as a special case of BayesRCπ and BayesRC+ using non-overlapping annotations, the BayesRCO algorithm is divided into three independent modules: BayesCπ , BayesRCπ and BayesRC+.
Phenotype simulation To simulate phenotypes y for the n = 2605 bulls, we used the linear model in Equation (  1 ), with parameters set as follows. For each simulated dataset, we randomly sampled a set of SNPs among those with a MAF ≥ 0. 15 ; by focusing on frequent variants, we sought to reduce the impact of extreme MAFs on genomic prediction  [17]. For selected SNPs, the corresponding effect β i for selected SNP i was set as follows: where k ∈ {k small , k medium , k large } corresponds to the proportion of the total additive vari- ance σ 2 g for a given effect size class (described below), MAF represents the frequency of the alternative allele in the population, and u i is drawn from a discrete Uniform{-1, 1} distribution to allow non-null effects to take on positive or negative values. For remaining (unselected) SNPs, β i was set to 0.
Data description and pre-processing A set of n = 634 and n = 664 animals (from 60 and 70 Large-White sows, respectively) from a population of 75% Large-White × 25% Creole crossbred pigs were raised in a temperate or tropical environment  [18]. These offspring were descendants of a common batch of 10 boars that were themselves crossbred 50% Large-White × 50% Creole. A vari- ety of traits were measured in this experiment using a common recording protocol in the two environments; trait measurements were pre-corrected for environment, age and sex effects. In this paper, we focus in particular on back fat thickness (BFT) and average daily weight gain (ADG), both measured at 23 weeks. For these traits, a total of n = 1147 and n = 1146 animals were respectively phenotyped. Animals were genotyped with the Illu- mina Porcine 60k BeadChip array.
Strategies for constructing annotations from pigQTLdb Animal QTLdb (https:// www. anima lgeno me. org/ QTLdb) groups together curated results from genotype-phenotype association studies in several livestock species  [20]. Cross-experiment QTL data from PigQTLdb (Release 45; SS11. 1) for traits relevant to pig production were downloaded for eleven trait sub-hierarchy categories (anatomy, behavioral, blood parameters, conformation, fatness, fatty acid content, feed conversion, fowth, immune capacity, litter traits, reproductive organs). An additional "other" category was created for markers not included in PigQTLdb. The potential utility of annotations depends on several factors, including their quality, relevance to the trait considered, LD around annotated mutations, and concordance in genotyping and annotation density (e.
Impact of annotation scenarios on prediction accuracy We calculated the Pearson correlation between simulated and estimated phenotypes in the validation data for each model in each simulation scenario (annotation configuration, heritability, large QTL effect size). Results were averaged across the 50 simulated datasets for each setting. As a baseline, we consider the results for BayesR, which ignores annotation information. Hence, we tested the difference of correlation for each of the following pairs of models: {BayesRC vs BayesR}, {BayesRCπ vs BayesR} and {BayesRC+ vs BayesR}. In the case of h 2 = 0. 2 , average (± sd) BayesR prediction accuracy was 0. 211 (± 0. 050), 0. 224 (± 0. 053) and 0. 234 (± 0. 054) for k large of 1%, 2. 5% and 5%. For h 2 = 0. 5 , these values were respectively 0.
2 , k large = 5% in scenario A). Moreover, a positive average gain is observed in all scenarios with BayesRC for sufficiently large QTLs and/or heritability. We next looked at whether a better use of overlapping annotations could improve prediction accuracy. Overall, the differences in prediction between BayesRC and BayesRCπ are slight, corresponding to an average gain or loss of about 0. 1 correlation points depending on the scenario and setting. At most, BayesRCπ led to a 1-point gain in correlation, for one dataset with h 2 = 0. 2 , k large = 5% and scenario A. The small differences here can likely be explained by the construction of annotation sets, where the random downsampling of annotations for BayesRC still tends to categorize multi-annotated markers in an enriched annotation, a favorable situation for BayesRC; as such markers are already well-ranked by BayesRC, the impact of BayesRCπ will be limited.
In Fig. 2B  we show the densities of log posterior variance by assigned annotation category (via a MAP rule for the PAIP) for one representative dataset (scenario A, h 2 = 0. 5 , k large = 1% ). The distributions of log V i are distinct for each category, though that of unannotated SNPs is clearly separated from those of the moderately and strongly enriched annotation categories. This seems to be consistent with the simulated distributions of annotations in scenario A, for which strong and medium QTLs were found in both annotations; larger differences in annotation enrichments may lead to a greater effect on PAIP values. For example, moving from k large = 1% to k large = 5% , the share of σ 2 g contributed by large QTLs in the strongly and moderately enriched annotations increases respectively from 5% to 25%, and from 2% to 10%.
Impact of directly modeling multi-annotated markers versus down-sampling annotations BayesRCπ shares greater similarity to BayesRC than BayesRC+, so its impact on prediction accuracy and marker variance estimation may potentially be more limited. However, assigning multi-annotated markers to a single annotation during data pre-processing, as we have done for BayesRC in this work, can have a negative effect on marker variance estimation. For example, inadvertently assigning a large-effect QTL to an uninformative annotation may lead to an underestimation of its effect. In Fig. 4  ( h 2 = 0. 5 , k large = 1% , scenario D), we show the difference in estimated posterior variance for BayesRCπ and BayesRC+ on the full set of large QTLs (250 across simulated datasets) with respect to BayesRC. We recall that in this scenario, all large QTLs were multi-annotated (systematically for the two strongly enriched annotations, often for moderately enriched annotations, and rarely for weak or unenriched annotations).
Fuzzy and hard expanded windows for annotation construction The "fuzzy" strategy we have explored here is essentially best adapted to BayesRCπ , as it allows for ambiguity in annotation assignment by design. In the case of ADG, this annotation strategy provided good predictions. To give additional insight into the behavior of BayesRCπ for "fuzzy" annotations, we provide an illustration for ADG in Additional file 1: Fig. S5  of the posterior variance of markers, averaged over the ten cross-validation datasets, according to their relative position (zoomed in between 8000 and 12000 for For each annotation, we represent the proportion of markers assigned to the medium and large SNP effect size class, average on the 10-fold datasets. The two panels shows the results for the two studied trait, ADG and BFT.
Comparison of top ranked SNPs by estimated posterior variance Incorporating biological information in genomic prediction models has the potential to improve their interpretability by better prioritizing informative markers. In a similar spirit to genome-wide association studies (GWAS), where markers are prioritized by p-value from a univariate test of association, we can use the estimated posterior variances from the Bayesian models to rank markers. To evaluate the similarities and differences in top-ranked SNPs for each method, we selected for each the top 100 markers according to the average (across 10 cross-validation datasets) estimated posterior variances. Results are shown in Fig. 7  for ADG and "hard" pigQTLdb annotations (a setting with good prediction results) for BayesRC, BayesRCπ and BayesRC+; BayesR, without annotations, is also included for reference.
Models integrating annotations, and in particular BayesRCπ and BayesRC+, are particularly good at prioritizing medium and large QTLs according to their estimated posterior variances. BayesRCπ and BayesRC+, in addition to BayesCπ , BayesR and BayesRC, have been implemented in an open-source software package called BayesRCO. Many strategies for constructing annotations are possible, and we compared several approaches based on extended hard or fuzzy windows around known pigQTLdb hits. In future work, a promising approach would be to further extend our models to fully account for continuous, rather than categorical, annotations.
Funding This work is part of the  GENE-SWitCH  project that has received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme  under the grant agreement n o 817998. This work also benefited from the clustering activities organized with the  BovReg  project, part of the  European Union 's  Horizon 2020 Research and Innovation Programme  under the grant agreement n o 815668. The financial support of the  French National Agency of Research (ANR PigHeaT ,  ANR-12-ADAP-0015 ) is also gratefully acknowledged.
5% ethephon every month since the 2 nd years of tapping (harvesting system noted S/2 d3 ET2. 5% 12/y). Three healthy trees, with no dry cut, with homogeneous girth as possible were selected as replications for each clone except for PB 260 which consisted of four replicates. Latex samples collection for physiological and antioxidant quantification was performed in May 2021, three weeks after stimulation. The result can be accessed in Supplementary data 1.
Experiment 2: effect of harvesting systems Ten-year-old trees from clones IRR 118 and BPM 24 were used in this experiment. Prior to the experiment, all trees were under the S/2 d3 (without stimulation since open tapping) tapping system in the B0-1 panel. Twenty healthy trees were selected for each clone. Four harvesting systems (S/2 d6, S/2 d3, S/2 d1, and S/2 d3 ET2. 5% 12/y) were compared using five trees as replicate for each treatment. Latex samples collection was performed in April 2021, four months after treatments were applied. The data is presented in Supplementary data 2. 2. 1. 3. Experiment 3: effect of TPD in five recommended rubber clones Given all trees can have a slight dry cut length, DCL was observed on five recommended clones i. e. IRR 118, BPM 24, IRR 112, RRIC 100, and PB 260 tapped with the harvesting system S/2 d3 ET 2.
Data analysis Statistical analysis was carried out using the XLSTAT program (Addinsoft Inc. , New York, USA). Interaction between Clones and Harvesting system (2nd experiment) was analysed through 3-ways or 2-ways analysis of variance (ANOVA). For these analyses, pairwise comparison of harvesting systems for each clone was performed by Bonferroni method with α ¼ 0. 05/(n ((n-1)/2)), n being the number of treatments. To study the effect of clonal variation on the antioxidants and the effect of tapping frequency, ethephon stimulation, and DCL severity (1st and 3rd experiment), a one-way ANOVA followed by Tukey HSD test with α ¼ 0. 05 was implemented. A supervised Linear Discriminant Analysis with DCL severity as dependent categorical variable was carried out involving yield, latex flow, latex diagnosis, antioxidants, and cofactors parameters to find combinations of these parameters that could explain occurrence of TPD.
RSH level is prone to genetic and environmental factors but RSH remains a good physiological marker of latex metabolism if supported by reference data RSH is one of the LD parameters defined as the total sulfhydryl compounds in latex. RSH can protect subcellular organelles by trapping   Chrestin, 1985). This parameter is used to estimate the antioxidant capability to overcome oxidative stress due to the harvesting system in latex. The RSH content ranges from 0. 5 to 0. 9 mM in latex  (Jacob et al. , 1984)  and can reach up to 2. 2 mM, according to  Chrestin (1985). In this study, the observation of five recommended rubber clones  (IRR 118, BPM 24, IRR 112, RRIC 100, and PB 260)  showed a clonal variation with the value ranging from 0. 21 mM to 0. 73 mM. In wheat, the inherent genetic variation in endogenous antioxidants content is suggested due to the variation in biosynthesis and utilization pathways  (Roy et al.
Gohet and collaborators developed a clonal latex metabolic typology based on latex diagnosis  (Gohet et al. , 2019). This tool could be tested to better understand the context of evolution of RSH content. In this present study, both studied clones showed a significant increase in RSH in response to high tapping frequency (d1). In response to ethephon stimulation, RSH significantly increased for clone IRR 118 while it remained stable for clone BPM 24. These data were collected four months after the application of harvesting system treatments, which can be considered as a short-term or temporary stress. The increase in RSH was associated with greater latex metabolic activity associated with high Pi. Chrestin (1985)  suggested that a high RSH concentration might result from more active glutathione synthesis as a compensation of the oxidative processes driven by tapping and stimulation.
Statistical Analyses of Field Survey Data The data from the 2019 survey were processed and analysed using the R 4. 2. 1 statistical software  [22]. Virus indexing results were extracted from the complete dataset (Supplementary Table  S1 ), used to generate a matrix summarising the number of positive samples for the viruses and virus genera searched in the 18 sampled plots and normalised by scaling with ranked subsampling using the 'SRS 0. 2. 3' package  [23]. The crop-related data (area, weed management, use of pesticides and origin of seeds) were encoded into an 18 × 4 matrix using the following unordered factor levels: Basse-Terre/Grande-Terre/Marie-Galante (variable "Area"), manual/mechanical/mulching ("Weed management"), yes/no ("Use of pesticides") and own/external ("Seed supply"). The putative relationships between the crop-related factors and virus abundances were investigated through generalised linear latent variable models using the 'gllvm 1.
Molecular Diversity of Macluraviruses The molecular diversity of yam macluraviruses in Guadeloupe was addressed for the first time in this work. To this end, degenerate primers YamMac4F and YamMac5R were used  [12]  for the amplification of a 292 bp region corresponding to positions 7885-8177 in the genome of yam chlorotic necrosis virus (YCNV-YJish; GenBank accession number MG755240), encompassing the 3 end of the sequence encoding the coat protein (CP) and the beginning of the 3 untranslated region (3 -UTR). A total of 28 D. alata and 2 D. rotundata samples collected in 2019, and 13 D. alata samples collected in 2020, respectively, were indexed positive (Supplementary Table  S1 ). Nine amplification products originating from D. alata and two from D. rotundata collected in 2019 were selected because they reflected the diversity of the plots where macluraviruses were detected.
The second latent variable showed a weaker correlation with CoV1 (positive) and DMaV (negative). The correlation matrix identified two groups of viruses that showed positive within-group correlations (general co-occurrence): YaV1, YMMV and YMV, on one hand, and DMaV, macluraraviruses and potexviruses on the other hand. The viruses from the latter group were not only among the least prevalent overall but had also never been detected in three out of the four field plots of Marie-Galante (Figure  1B ; Supplementary Table  S1 ). The molecular diversity of yam macluraviruses in Guadeloupe was addressed for the first time in this work. To this end, degenerate primers YamMac4F and YamMac5R were used  [12]  for the amplification of a 292 bp region corresponding to positions 7885-8177 in the genome of yam chlorotic necrosis virus (YCNV-YJish; GenBank accession number MG755240), encompassing the 3′ end of the sequence encoding the coat protein (CP) and the beginning of the 3′ untranslated region (3′-UTR).
A total of 28 D. alata and 2 D. rotundata samples collected in 2019, and 13 D. alata samples collected in 2020, respectively, were indexed positive (Supplementary Table  S1 ). Nine amplification products originating from D. alata and two from D. rotundata collected in 2019 were selected because they reflected the diversity of the plots where macluraviruses were detected. Selected amplification products were cloned and sequenced (Supplementary Table  S1 ). Phylogenetic analyses performed on the coding part of the sequences (131 nt in size) showed that these sequences were 94. 6-100% similar to each other and 81. 6-85. 5% similar to sequences from YCNV-Kerala  [26]  and YCNV-YJish  [27] , respectively (Figure  4 ). They were also 90. 8-93. 1% similar to one EST sequence generated from a sample originating from Nigeria  [28].