dataesr/scientific-paragraphs-categorization huggingface dataset:
MATERIALS AND METHODS Strains, culture conditions, fluorescence staining, microscopy, and image processing Strains used in this study are described in Supplemental Material, Table  S1  in File S2. Sake yeast strains and a control strain (BY4743) were cultured at 25°in YPD medium containing 1% (w/v) Bacto yeast extract (BD Biosciences, CA), 2% (w/v) Bacto peptone (BD Biosciences), and 2% (w/v) glucose. Cells at log phase were fixed and triply stained with FITC-conjugated concanavalin A (FITC-ConA) (Sigma, St. Louis, MO) for the cell wall, rhodamine-phalloidin (rh-ph) (Invitrogen Corp. ) for the actin cytoskeleton, and 4',6-diamidino-2-phenylindole (DAPI) (Sigma) for nuclear DNA, as described previously  (Ohya et al. 2005). Fluorescent microscopy images of the cells were acquired using an Axio Imager microscope equipped with a 6100 ECplan-Neofluar lens (Carl Zeiss, Germany), a CoolSNAP HQ cooled-CCD camera (Roper Scientific Photometrics, Tucson, AZ), and AxioVision software (Carl Zeiss).
Microscopy images of the cells were analyzed with CalMorph (ver. 1. 3) image processing software designed for diploids  (Yvert et al. 2013). We obtained the morphological data of the 501 traits from the single-cell data. Descriptions of each trait were presented previously  (Ohya et al. 2005). The CalMorph user manual is available at the Saccharomyces cerevisiae Morphological Database (SCMD; http://yeast. gi. k. u-tokyo. ac. jp/datamine/).
For sake and natural yeast isolates, we used the morphological data of 27 sake yeast strains (n = 5) cultured in YPD medium, 36 natural yeast strains (n = 5) cultured in SD medium, and 45 replicates of BY4743 cultured in YPD (n = 11) and in SD (n = 34) to compare the morphological phenotypes between sake and natural yeast isolates. The morphological data of 36 natural yeast isolates (n = 5) and BY4743 (n = 34) were acquired previously  (Yvert et al. 2013). Categories of the strains and the media were incorporated as explanatory variables in the linear model. Representative values of each strain, effects of medium, and dispersion values of experimental error in each trait were estimated with the linear model by maximum likelihood estimation. Z values of each strain from BY4743 in all traits were estimated using the Wald test, after subtracting the effects of the medium with dispersion values, and used for PCA.
For PCA in Figure  2  and Figure  3 , a single Z value was calculated for each strain in each trait from replicated values in each strain. For PCA in Figure  6  and Figure  7 , the Z value was calculated for each replicated value in each strain. For awa mutants and their parents, we employed morphological data of six awa mutants (K601, K701, K901, K1001, K1401, and K1501) and five parental strains (K6, K7, K9, K10, and K14). Categories of the strains and the awa mutants were incorporated as the explanatory variables into the linear model. We selected 56 of the 501 traits to have the minimum Akaike information criterion (AIC) among the other models with or without the explanatory variables of the strains, the medium, and/or the interaction term among them. Then, 21 out of the 56 traits were fitted to the selected model by the likelihood ratio test at P , 0.
05 after Bonferroni correction. Finally, of the 21 traits, seven (Figure  S4  in File S1) were detected to have significant effects on the awa mutants at P , 0. 05 after the Bonferroni correction by the Wald test. To statistically test the morphological differences among the sake yeast strains, and between K1001 and K1701 strains, we conducted one-way ANOVA of the GLM for each trait. Categories of the strains were incorporated as the explanatory variable into the linear model. For the sake yeast strains, we employed morphological data of 27 sake yeast strains (n = 5) and 11 replicates of BY4743 cultured in YPD. Z values of each sake strain from BY4743 in all traits were estimated using the Wald test and used for PCA. For the K1001 and K1701 strains, we employed five replicated morphological data sets for each strain.
Hierarchal cluster analysis Hierarchal cluster analysis (HCA) in Figure  3C  and Figure  S3  in File S1 was performed using the hclust function in the default package of R. Dissimilarity was calculated by subtracting the correlation coefficient R from one, where R was calculated from the PC scores of six PCs (60% CCR) from PCA for 27 sake strains and BY4743. The dendrogram in Figure  3C  was generated from the average linkage of the dissimilarity using the plot. phylo function in ape of the R package  (Paradis et al. 2004 ). The significance of each cluster in Figure  S3  in File S1 was estimated from the approximately unbiased probability value (AU P-value) using the pvclust function in the pvclust package of R  (Suzuki and Shimodaira 2006).
2005). We cultured cells of each strain in a rich medium as five biological replicates; fixed cells with formaldehyde; and stained the cell wall, nuclear DNA, and actin with FITC-ConA, DAPI, and rh-ph, respectively. Images of at least 200 cells per culture were acquired using fluorescent microscopy and analyzed with CalMorph to quantify 501 traits reflecting cell size, shape, orientation, and intracellular organization. Altogether, morphological data of. 1000 cells were acquired for each strain, allowing the statistical analysis of morphological differences among the sake strains.
Morphological diversity of sake yeast strains To understand the morphological diversity of the sake yeast strains, we compared the morphological variation of sake yeast strains with that of natural S. cerevisiae isolates. To make this comparison, we first identified morphological differences of the yeast strains from the standard strain (BY4743) for each parameter and normalized the morphological value by applying a GLM as described previously  (Yang et al. 2014). We used the high-dimensional morphological data of 36 typical natural yeast isolates previously published  (Yvert et al. 2013 ) as a reference. This set of isolates was selected from various geographical and ecological origins. These strains belong to a panel that was previously used to explore the genetic diversity of the species  (Schacherer et al.
Morphological traits of sake yeast differentiated by geographical origin and period of distribution To determine whether morphological features are specific to geographical origin, we mapped the sake strains in their own degenerated orthogonal phenotypic space (Figure  3A ). For this purpose, we performed PCA only with the sake yeast data set to capture the most prominent intraspecies morphological variations. The first two components, PC1 and PC2, were explained by 38% of the variance in total and correlated with cell size and cell elongation, respectively (Figure S2 in File S1 and Table  S3  in File S2). Sake strains from Kyushu (gray dot) and Tohoku (yellow dots) were plotted with a low PC1 score, while those from Kansai (violet dots) were plotted with a high PC1 score (Figure  3A ). Sake strains from Kyushu and Tohoku were all assigned to the K7 lineage, while sake strains from Kansai were distributed by the Meiji era, and were classified as old sake yeast.
Based on the morphological database of nonessential deletion mutants  (Ohya et al. 2005) , impairment of 23 genes results in similar phenotypes to those observed in old sake yeast strains (large cell size and high ratio of budded cells). The gene ontology (GO) terms "cellular response to stress (GO:0033554)" and "double-strand break repair (GO:0006302)" are enriched significantly (Fisher's exact test, P , 0. 05 after Bonferroni correction) among these genes, suggesting that the genes required for DNA repair and the stress response are impaired in the old sake strains. It is interesting to note that the stress response genes SWI4 and SWI6, whose mutants show a similar phenotype to the old strains, are involved in alcohol tolerance  (Teixeira et al. 2009; Yoshikawa et al. 2009 ). Therefore, it is possible that a loss of function of these genes occurred in old sake strains, resulting in potential disadvantages during sake brewing.
This idea is supported by the potentially deleterious mutation in SWI6 (G280R) that was found in many old sake strains (A. Friedrich and J. Schacherer, unpublished results). An alternative possibility is that the K7-lineage strains have common loss-of-function mutations responsible for the morphological changes. The deletion of VPS36 produced a similar morphology to the K7-lineage strains in the morphological database  (Ohya et al. 2005 ) and a higher fermentation rate (D. Watanabe, unpublished results). Thus, SNPs in these genes may be responsible for the morphological phenotypes and high fermentation abilities of K7-lineage strains. Since the K7-group yeast strains produce flavored sake, it is interesting to wonder whether there is a direct relationship between morphology and fermentation properties.
ACKNOWLEDGMENTS We thank  S. Morinaga  for providing technical assistance and members of the  Laboratory of Signal Transduction  for helpful discussions. This work is supported by  Grants-in-Aid for Scientific Research  from the  Ministry of Education, Culture, Sports, Science and Technology, Japan  ( 15H04402  to Y. O. ); an annual research fund from the  National Research Institute of Brewing, Japan  (to T. A. ); and  Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research  ( JP26882019  to S. O. ).
end-cycle sow carcass condemnation in a French slaughterhouse Pierre-Yves Decaudin 1 , Didier Raboisson 2 and Agnès Waret-Szkuta 2 * 1 GMC VETO, ZA Les Gouvernaux, Chabeuil, France, 2 IHAP, Université de Toulouse, INRA, ENVT, Toulouse, France Surveillance at an abattoir allows all animals or carcasses that present a potential public health risk to be withdrawn from the human food chain. Whole-carcass condemnation results in important economic losses, not only for the producer but also for other participants in the meat industry. Access to the personal electronic database of an abattoir in France enabled us to run logistic regression models to investigate the risk factors for whole-carcass condemnation of end-cycle sows in that abattoir. When end-cycle sows that were slaughtered and eviscerated between 22 June 2015 and 8 December 2015 (185 days) were considered (n = 19,866), the results highlighted the importance of the total theoretical time off feed, which represents the fasting period from leaving the farm of origin to the time of slaughter (including transportation and waiting time at the abattoir).
Each 10-h increase in the theoretical time off feed was associated with a 31% greater likelihood of whole-carcass condemnation [odd ratio (OR) = 1. 31, CI 95% (1. 27; 1. 34)], and a 10 kg increase in carcass weight before refrigeration was associated with a 23% lower likelihood of carcass condemnation [OR = 0. 77, CI 95% (0. 75; 0. 78)]. The results also indicate the importance of the producer group that the farmer belonged to (P < 0. 01). A relatively small number of variables was available in the actual database to study the relevant risk factors for whole-carcass condemnation associated with or without diseases at the farm of origin. This derives partly from the fact that traceability at the abattoir in France is done per batch rather than individually for pigs; further, limited information is available at the farm level.
An investigation of the reasons for whole-carcass condemnation could have been informative; however, it was not feasible in a reasonable timeframe because these data were not saved in a database in a systematic way. Some of the difficulties encountered in this study should soon be alleviated by using the meat inspection information system software for collecting livestock meat inspection data. Implemented recently by the French ministry of agriculture, this new tool should allow for broader perspectives in swine surveillance. the use of such data for swine is the lack of data availability and heterogeneity, which depend on the system available in abattoirs both in France and internationally  (3). When high-quality data are routinely collected, they can serve as a basis for epidemiological studies.
Data The main study population consisted of end-cycle sows slaughtered between 22 June 2015 and 08 December 2015 (dataset 1, 185 days, n = 22,609), for which information on the status of evisceration (yes/no) was available. This criterion was selected to perform the main analysis and investigate carcass weight as a potential risk factor for condemnation. Therefore, only sows with a known eviscerated carcass weight before refrigeration were kept in dataset 1 (n = 19,866). It included end-cycle sows with carcasses that had been considered healthy at the time of veterinary inspection (n = 19,517) and end-cycle sows that were eviscerated but wholly condemned (n = 349), with up to three reasons for condemnation considered. The animals were provided by 22 producer groups from 47 departments (France is divided into 97 departments) and were transported by 17 different transport operators.
The database included the date and hour of the last meal the sow ate, the date and hour of sow departure from the farm, the date and hour of arrival at the abattoir, and the date and hour of slaughter. A second dataset includes all end-cycle sows that were (i) slaughtered between 14 January 2015 and 6 December 2015 and were (ii) wholly condemned or not condemned (dataset 2, n = 44,426). It included dataset 1, but evisceration status was not completely available for all sows in dataset 2. The weight after evisceration was not available in dataset 2. Several variables were generated. The variable duration of transport resulted from the difference between the date and hour of arrival at the abattoir and the date and hour of departure from the farm. The variable waiting time at the abattoir (wta) resulted from the difference between the date and hour of arrival at the abattoir and the date and hour of slaughter.
Modeling Methods and software The variables of interest of dataset 1 and dataset 2 were compared with a Wilcoxon test to detect any difference in the two datasets. Similarly, a Wilcoxon non-parametric test was performed to compare the characteristics of sows that were or were not condemned. The condemnation rate per department was mapped with ArcGis 10. 4. Then, univariate logistic models for the outcome variable "carcass condemnation" (0 or 1) were run for each of the explanatory variables. The explanatory variables were either categorical, including producer group (21 classes), department (27 classes), and transport operator (17 classes), or quantitative and continuous, including carcass weight (W), wta, and the three fasting-period variables. The level of significance for the statistical tests was set at 0.
Descriptive analysis Descriptive statistics are provided for dataset 1 since it was the focus of the main analysis (Table  1 ; Figure  1 ). No significant changes were observed in the descriptive statistics for data set 2. The percentage of total condemnation was of 1. 76% (dataset 1). The median W among producer groups was uniformly distributed and ranged between 150 and 170 kg. The producer group with a median W of 102 kg was not included in the final analysis to avoid introducing bias. The difference in the median W between end-cycle sows that were not condemned (162. 2 kg) and those that were wholly condemned (139. 9 kg) was significant (P < 2. 2 × 10 -16 ). No particular spatial pattern in departmental condemnation rates was observed (Figure  2 ). The analysis of the reasons for carcass condemnation for end-cycle sows with W < 140 kg (n = 151) showed that the first three reasons for whole-carcass condemnation were non-specific abscesses in multiple locations, arthritis, and pleurisy.
improvement of Data recording in slaughterhouses A relatively small number of variables was available in the actual database to study the relevant risk factors for whole-carcass condemnation associated with diseases at the farm of origin. This derives partly from the facts that traceability at an abattoir in France is per batch of pigs rather per than individually and that limited slaughterhouse information is available at the farm level. It would have been interesting to have data on sow parity, age, or reason for withdrawal at the farm level. In addition, it could have been useful to have information related to the sanitary status of the farm of origin, mortality rate at the farm, and the type of farm (farrowing or farrowing to weaning or farrowing to fattening). The structure of the dataset did not allow to identify a subpopulation to build the model and another one to validate the proposed model, since it would have introduced biases related for instance to the spatial distribution of the department of origin and to the different times before slaughter.
An investigation of the reasons for whole-carcass condemnation could have been informative, but this was not feasible in a reasonable timeframe because the data were not saved in a database in a systematic way. A number of possible combinations of reasons for condemnation are likely because up to three different reasons can be cited in different orders, although one or two reasons may occasionally be sufficient. However, the first analysis on the subset of sows weighing less than 140 kg suggests that the reasons for condemnation in the abattoir studied here are similar to the major reasons found for swine carcass condemnation in all French abattoirs  (27)  but are different from those in Portugal; for example, see Garcia-Diez and Coelho  (11). This is probably due to different geographical locations, variable climatic conditions, different rearing systems at the farm level, and different sanitary statuses of the animals  (10).
Abscesses, arthritis, and pleurisy are lesions that develop over moderate to long time periods and that do not appear to be compatible with time spent at the abattoir. Refining this analysis to include all end-cycle sows available in the database could possibly allow a better interpretation of the time-related risk factors. Indeed, depending on the major lesions recorded and the delay in their appearance, the timeframe, or component of the production chain to focus on could be more appropriately identified (farm, transport, or abattoir) to optimize resource allocation and gain useful and reliable information. The results presented here constitute a foundation for discussions among the different actors in the pork production chain, with some descriptive results that differ from those that were expected in relation to quality recommendations or legal frameworks.
Quality Filtering and Taxonomic Classification of Sequencing Data Illumina MiSeq reads were analyzed using in-house pipelines (Richard Christen; described in  Boissière et al. , 2012; Hartmann et al. , 2012; Gimonneau et al. , 2014; Massana et al. , 2015). Briefly, Silva 119 NR (analyses performed in 2014) was used as the reference database for taxonomic identification  (Quast et al. , 2012). An in silico extraction of Silva amplicons using forward: 5 ′ -CCTACGGGNGGCWGCAG-3 ′ and reverse: 5 ′ -GACTACHVG GGTHTCTAATCC-3 ′ primers, followed by an analysis by length/number of amplicons, yielded the following results: 1-50/0; 51-100/0; 101-150/2;  151-200/1; 201-250/1; 251-300/11; 301-350/40; 351-400/21,669; 401-450/448,575; 451-500/450; 501-550/192; 551-600/1,198; 601-650/0. This demonstrates that most amplicons were between 350 and 450 nucleotides in length.
The extracted database is referenced below as the refseq. Each sequence identifier was reformatted to eight taxonomic fields as in PR2  (Guillou et al. , 2012)  making it easier to use a pipeline for analyses. In order to assemble paired-end reads, the software programs PEAR  (Zhang et al. , 2014)  and FLASH (Magoc and Salzberg, 2011) were both tested. PEAR using default parameters merged more pairs, and paired-end reads were therefore assembled and quality filtered (using Illumina quality scores) with PEAR. 94. 26% (3,622,233 reads) of the total reads (3,842,672 reads) were assembled; 0. 003% were discarded and 5. 734% were not assembled. Reads were then sorted by length using a dedicated python script, yielding the following results: 0-0/0; 1-50/3; 51-100/1,556; 101-150/1,435; 151-200/1,535; 201-250/1,578; 251-300/2,251; 301-350/9,401; 351-400/38,323; 401-450/132,487; 451-500/3,433,239; 501-550/381; 551-600/44; 601-650/0.
Reads shorter than 300 nucleotides (0. 23% out of the overall number of reads) were discarded, resulting in the extraction of 3,613,875 reads. The fasta file was then demultiplexed using a dedicated C++ program. Primer trimming was performed using CutAdapt v1. 8. 1  (Martin, 2011). Each file was dereplicated, sorted by decreasing abundance, chimera checked with UCHIME  (Edgar et al. , 2011)  and then clustered using Crunchclust  (Mondani et al. , 2011; Gimonneau et al. , 2014; Massana et al. , 2015; Tchioffo et al. , 2016;  available from https://github. com/dumaatravaie/crunchclust/ blob/master/Documentation; at a Levenstein distance of 5). After this clustering step, clusters that contained <2 reads were discarded as artifacts (see  Boissière et al. , 2012). All these steps resulted in 2,562,144 high-quality sequences contained in clusters with at least two reads (70.
Data and Statistical Analysis Statistical analyses were performed using the R package vegan. Rarefaction curves (Figure  S1 ) were performed prior to comparative analyses between infected and uninfected flies, and between the sampling sites. Significant differences in bacterial richness between the infected and uninfected flies, and between the three sampling sites were tested using nonparametric Kruskal-Wallis test. We used vegdist and hclust using single, average, and complete linkage methods for hierarchical clustering and then compared them for the presence of sub-trees. Nonmetric multidimensional scaling (nMDS) were generated using the R packages ggplot2 and phyloseq  (McMurdie and Holmes, 2013).
Novel Bacteria Taxa A phylogenetic tree was inferred to further investigate the potential dissimilarities between the bacteria sequences identified in this study and the sequences currently available in public databases, thereby illustrating the taxonomic relationships between the representative OTU sequences (Figures  S5A-D ). The most abundant OTU present in each of the 40 samples showed sequence similarities with Wigglesworthia sp. We identified 60 different genotypes of Wigglesworthia, similar to those from G. morsitans or G. brevipalpis. Depending on the sample, there were one to four genotypes per sample. However, most samples harbored one genotype, except those from Fontem that harbored two to four different genotypes in the same sample. Some OTU-representative sequences did not match known targets in the NCBI database (analyses performed in 2014) and had to be annotated manually.
At 72 h PB, the number of DEG was equally distributed between up-and down-regulated categories. As a first examination of the mechanical regulatory network, we considered for each time-point the transcripts presenting the higher amplitude of regulation (Additional file 2). At 0. 5 h PB, the 10 most highly upregulated genes encompassed genes already known to present a short-term response to mechanical stimuli, such as an ortholog of a XTH (Potri. 018G095200) [17] , or PtaZFP2  [23] , a poplar C2H2 TF (Potri. 001G235800). Strongly affected expression was also found for two other TF: one that was related to ethylene signalling (Potri. 004G220400) and the other that was an ortholog of RRTF (Potri. 009G101900), a redox-responsive member of the Ethylene Response Factor (ERF) TF subfamily  [24]. Interestingly, highly up-regulated genes also included homologs of the Late Embryogenesis Abundant (LEA) hydroxyproline-rich glycoprotein family (e.