dataesr/scientific-paragraphs-categorization huggingface dataset:
Cooking methods are often indicated in or can be deduced from dietary questionnaires, and these data should be considered when estimating intake. A detailed subclass-by-subclass account of polyphenol losses due to processing has already been published  [10]. The aim here was to analyze the standardized Phenol-Explorer food processing dataset to describe the effects of processing in more quantitative terms, facilitating the comparison of different processes and allowing decisions to be more easily taken on the adjustment of polyphenol content of processed foods. In particular, we wish to clarify the influence of different variables upon polyphenol losses due to processing, and search for rules that could be used to adjust for processing losses where only the content of the raw food is known and no corresponding RF is available.
langual. org) and the USDA National Nutrient Database for Standard Reference (http://www. ars. usda. gov/ba/bhnrc/ndl). Where no exact match existed for both food and process, the closest possible yield factor was used. Individual RFs were calculated before being aggregated and averaged to obtain a single value for each combination of polyphenol, food, process, and analytical method. All polyphenol contents before and after processing, yield factors, and published data sources are retrievable from the Phenol-Explorer website.
Treatment of different analytical methods Publications collated for the database used different methods of analysis to determine polyphenol loss due to processing. To compare data, it is important to distinguish between the quantitation of well-defined polyphenols native to foods, such as quercetin 4 -O-glucoside, and grouped compounds such as quercetin derivatives or total anthocyanins. Those publications that measured well-defined compounds used HPLC with no prior hydrolysis of polyphenol glycosides or esters. Compounds studied using this method were usually conjugates, since aglycones are not normally native to foods. Other publications used a hydrolysis step prior to analysis to release polyphenol aglycones from sugar and ester moieties, effectively grouping all derivatives of one aglycone before determining changes due to processing.
Statistical analysis RFs for well-defined and grouped compounds were pooled separately and median and interquartile ranges (IQRs) calculated for the most commonly studied process, foods, and polyphenols. Specific examples for which enough data were available were then chosen for more detailed analysis. These consisted of the polyphenols caffeic acid (CA), 5-caffeoylquinic acid (5-CQA), quercetin and quercetin 3-Orutinoside (Q3Rut), the foods black bean and potato, and the domestic process boiling. Statistical analyses and data visualizations were performed using Microsoft Excel and R open-source statistical software.
Food processing data collated in Phenol-Explorer The 143 original publications used produced 4296 individual RFs. Automatic aggregation of these individual values by food, polyphenol, process, and analytical method generated a new set of 1253 RFs, of which around half were averages of multiple values (Supporting Information Fig. 1 ). The term RF will henceforth refer to the values from this new dataset only unless otherwise stated. Vegetables, fruits, and seeds were most frequently studied, particularly potato, tofu, tomato, and bean. Boiling was the most studied process (n = 230), although fresh and frozen storage together accounted for a quarter of all data (n = 316; Fig. 1 ). Domestic cooking was most often studied for vegetables and seeds, whereas storage and industrial processing were more studied for fruits and beverages (Fig.
5-CQA losses often varied more with food than with process and, for some foods, losses due to different cooking methods were similar (Fig. 5A ). Zucchini, bean, carrot, and broccoli were most sensitive to 5-CQA loss. CA was, in most cases, measured after hydrolysis of food extracts, and therefore may represent total CQAs. Differences in compound retention between foods were similar to those seen for 5-CQA (Fig. 5B ). The free CA content of peeled potato, globe artichoke, and eggplant increased upon cooking, presumably due to a simultaneous hydrolysis of different CQA esters. These data suggest that these hydroxycinnamates are sensitive to all domestic cooking methods and that their food source influences this sensitivity more than the process to which it is subjected.
Relative sensitivity of different polyphenols according to their structures Storage processes excluded, the median RF for total phenolics was 0. 74 (n = 163), corresponding to an overall loss of polyphenols. In terms of individual polyphenols, most data were available on the flavonols quercetin, kaempferol, and Q3Rut and the hydroxycinnamates 5-CQA, caffeic, ferulic, sinapic, p-coumaric, gallic and protocatechuic acids (Table  3 ). RFs for well-defined compounds (those measured without hydrolysis of extracts) were highly variable (IQR > 1). RFs for grouped compounds were less variable and median RFs were usually 0. 5 or lower. Contents of 5-CQA, e. g. were reduced substantially overall by processing (median RF = 0. 38, IQR = 0. 4). Losses of protocatechuic and vanillic acids, for which variability was also low, were also extensive (median RF = 0.
Discussion RFs were collected for Phenol-Explorer for a wide range of processes, foods, and polyphenols. Generally, availability of data for given processes, foods, or polyphenols reflected importance to human diets. For example, the large volume of data on 5-CQA and CA corresponds to the high intake of these phenolic acids, at least in Western diets  [41]. In some cases, however, this was not true; e. g. data on polyphenol retention due to domestic cooking was abundant, but those on industrial processing such as canning and drying were much more limited, particularly given the importance to all diets of these common operations. In addition, industrial processing is made more complex by the combination of unit operations, such as where freezing of vegetables is preceded by blanching to denature enzymes  [6] , and published data were most often from laboratory simulations of industrial processes.
However, median RFs for the most frequently studied grouped compounds were typically 0. 5 or less, indicating that all subclasses of dietary polyphenols may be degraded by food processing, although RFs within subclasses were highly variable. These variations may be explained by hydroxylation pattern and bound sugars, as these determine molecular size, polarity, and solubility. The high RFs often observed for simple phenolic acids show that they often form upon degradation of more complex polyphenols. These data should prove to be useful for refining estimations of intake in observational studies on polyphenols and health  [44] , and RFs should be employed in the building of food composition tables wherever experimentally determined polyphenol contents of processed foods are not available. In conclusion, this analysis of Phenol-Explorer data leads us to recommend that only published RFs matching food, process, and polyphenol be used when adjusting the polyphenol contents of raw foods to account for processing.
These included models where a was set to 1. 0, such that survival time was exponentially distributed with a constant hazard function of k. Models were fitted using the nonlinear platform of JMP â version 9. 0. 2. Full details are described in Appendix S1. The probability females would take a blood meal in the six cages of Experiment I was analysed with a binomial split-plot model using the lme4 package of R (http:// r-forge. r-project. org/projects/lme4/). Whole plots were cages where males were either infected or uninfected, with the subplot being female infection status within cages. The volume of blood taken by females in these cages was analysed in the same split-plot design with a mixed model analysis of variance (ANOVA) using JMP; this model was also used to analyse female fecundity.
Effect of infection on reproductive success The fitness costs experienced by mosquitoes targeted in a control program will determine the strength of selection for them to evolve resistance against the control measure. The delay until resistance begins to evolve, as well as the rate and magnitude of its evolution will be proportional to the selection pressures involved  (Georghiou 1972). A measure of the strength of selection can be estimated for control measures involving pathogens by comparing the relative fitness of infected and uninfected mosquitoes, such that C ¼ 1 À w infected w uninfected where C is the coefficient of selection for resistance to evolve and w is mosquito fitness. Our data are not sufficient to estimate the overall fitness of either infected or uninfected mosquitoes, but provide information on some important components of fitness.
When the prevalence of disease is low in the human population, the probability a female contracts an agent of human disease increases to a greater extent as the number of meals she takes increases. However, the overall probability of taking an infected blood meal remains low. We estimated the potential influence of altered bloodfeeding behaviour and reduced survival on the relative probability of an adult female mosquito infected with V. culicis would contract a human pathogen in her first gonotrophic cycle and survive a period equal to the EIP of the dengue virus using data from this study (see Appendix S3 for full details). This probability generally increased as the prevalence of dengue in the human population decreased and as the probability of females surviving a blood-feeding event increased.
Acknowledgements We thank anonymous reviewers for their comments on an earlier version of the manuscript. V. S. was financed through a contract from the  Centre National de la Recherche Scientifique (CNRS), France , a Fellowship from the  Ministerio de Educaci on de la Rep ublica Argentina  and the  French Embassy in Argentine , and a Fellowship from the  Consejo Nacional de Investigaciones Cient ıficas y T ecnicas (CONICET) ,  Argentina. P. A. and Y. M. thank  CNRS  and  IRD  for support. This paper is dedicated to the memory of  Dr. Jaroslav Weiser  (1920Weiser ( -2012)).
Also in these conditions we observed a significant LC3-II reduction (Po0. 01, data not shown) and a significant increase of a-synuclein levels ( þ 54% for the 14 kDa a-synuclein, and þ 62% for the 70 kDa a-synuclein, Po0. 01 in both cases; Figures  3j-l ), strongly supporting the assumption that a defective autophagy leads to the accumulation of a-synuclein aggregates. Alpha-synuclein as an autophagy-related marker in primary human T lymphocytes. Monitoring autophagy in vivo or in organs is a poorly developed investigation area at present. 18 To verify the potential value of a-synuclein as an autophagy-related marker in freshly isolated T lymphocytes, we examined these cells for LC3-II and the monomeric form of a-synuclein by western blot, and evaluated the possible correlation between these two parameters.
Informed consent was obtained from all participants and the local ethic committee approved the study. Cell purification and cell cultures. Peripheral blood mononuclear cells were isolated by Ficoll-Hypaque density-gradient centrifugation. Cells were cultured in RPMI-1640 (Gibco BRL, Grand Island, NY, USA) medium with 10% FBS (Thermo Scientific, Waltham, MA, USA), supplemented with 2 mM glutamine (EuroClone, Pero, Milan, Italy) and 50 mg/ml gentamycin (EuroClone). Immunomagnetic negative selection kit from Miltenyi (Miltenyi Biotec, Bergisch-Gladbach, Germany) was used to purify CD3 þ T lymphocytes from peripheral blood mononuclear cells. Purity of isolated cells, assessed by flow cytometer, reached routinely at least 97%. For serum-starvation experiments, cells were cultured in RPMI-1640 medium supplemented with 1% FBS for 4 h (time chosen on the basis of preliminary time course experiments for 2-24 h; data not shown).
Flow cytometry. Intracellular phenotyping of purified T cells was performed as previously described. 37 Briefly, cells were fixed with 4% paraformaldehyde, permeabilized with FACS permeabilizing solution (BD BioSciences, San Jose, CA, USA; 340973), washed and stained. Alpha-synuclein expression was detected using anti-a-synuclein (1 mg Â 10 6 cells, Sigma, St. Louis, MO, USA; S5566) monoclonal antibody (mAb) and FITC-conjugated anti-mouse IgG (Fab specific, Sigma, F2653) as a secondary antibody. An appropriate isotype control antibody (Sigma, M5409) was used. Acquisition was performed on a FACSCalibur (BD BioSciences) and data were analyzed using the CellQuest Pro software (BD BioSciences). SDS-PAGE and western blot. Purified T lymphocytes were lysed in RIPA buffer (100 mM Tris-HCl pH 8, 150 mM NaCl, 1% Triton X-100, 1 mM MgCl 2 ) in the presence of complete protease-inhibitor mixture.
Only cells displaying intact mitochondria were taken into account. At least 50 different cells were evaluated. Statistical analysis. Results were analyzed on SPSS Inc. (Chicago, IL, USA; version 14. 0). The Mann-Whitney unpaired test was used to compare quantitative variables in different groups. Spearman's rank correlation coefficient was applied for calculation of the correlation between parallel variables in single samples. Linear regression analysis was used to display a best fit line to the data. For morphometric analyses, the values found in control and specific siRNA-transfected cells were analyzed by using paired t-test. P valueso0. 05 were considered as significant.
EMPIRICAL EXAMPLE To illustrate our approach, we applied it to a clade that satisfied two conditions: (1) a reasonably sized and sampled phylogeny is available, and (2) some independent estimates of the duration of speciation are known from the literature. Because literature on this topic is scarce, the second condition is the most stringent. One study that meets this condition is a relatively recent and in-depth paper by  Curnoe et al. (2006)  on the duration of speciation in primates. The first condition is also satisfied for primates because  Fabre et al. (2009)  provide a primate phylogeny with 273 tips and 103 missing species. The data we used are deposited in Dryad (datadryad. org/resource/doi:10. 5061/dryad. 8492,particularly datadryad. org/bitstream/handle/10255/dryad. 8494/PRIMATES-C-hronogram_273_taxa.
( 7 ) This probability density attains a maximum at τ opt = max 0, 1 D ln (D -ϕ) (D + ϕ). ( 8 ) The density, mode and any quantile, or moment (including the mean) can be computed with the package PBD, available on CRAN and on Dryad (datadryad. org/resource/doi: 10. 5061/dryad. js88n), together with the simulated data and R code. With the estimated parameter values, we ran 1000 simulations to obtain a bootstrap estimate of the error in our estimated mean duration of speciation. It also provided a way to assess the goodness-of-fit of the model by comparing simulated lineagethrough-time plots with the observed one.
Currently, phylogenetic trees are usually built using a standard, nonprotracted birth-death model, or even a simple Yule model as the prior for the species tree. Ideally, the protracted speciation model should be implemented as a species tree prior in phylogenetic reconstruction software package such as BEAST  (Drummond and Rambaut 2007) , MrBayes  (Huelsenbeck and Ronquist 2001; Ronquist and Huelsenbeck 2003) , or RevBayes  (Höhna et al. 2013) , and protracted speciation parameter values can then be directly taken from the Bayesian posterior distribution of these parameters when reconstructing the phylogeny from sequence alignments. This implementation is not trivial, however, because considering that speciation is protracted not just affects the species tree prior, but also the probability distribution of the gene tree when we have multiple samples per species.
Experimental data The data we used are from a prospective randomized study previously published  [15]. Briefly, 29 piglets from a single farm were included 4 weeks after birth and were put in individual cages throughout the study and randomly assigned (9:10:10) to once-aday oral treatment with placebo, ciprofloxacin 1. 5 or 15 mg/kg/ day for 5 days (D1 to D5). Ciprofloxacin concentrations and counts of total and ciprofloxacin resistant enterobacteria were measured in fecal samples at pre-dose on D1, D3 and D5 and on D7, D9, D12, D16 and D27. A microbiological assay that measures the ability of the antibiotic to inhibit the growth of Bacillus subtilis strain ATCC6633 was used to determine fecal concentrations of free (active) ciprofloxacin. Total and fluoroquinolones resistant enterobacteria were counted by plating serial dilutions of the feces on Drigalski agar without or with 2 mg/L of ciprofloxacin, respectively  [15].
Therefore, all parameters can be written as h i ~he g i where g i ~N(0,v i 2 ). Parameter variability was fixed to 0 in case of low estimated value and high relative standard error (RSE). We assumed combined error model for fecal ciprofloxacin concentrations (parameterized in s CIP add and s CIP prop for additive and proportional error, respectively), and constant error model for log 10 of resistant and total counts of enterobacteria, noted s RES add and s TOT add , respectively. Data were analyzed using MONOLIX 4. 2. 0 (www. lixoft. eu), a software devoted to maximum likelihood estimation of parameters in NLMEM, based on the SAEM algorithm  [35]. The code for the model implemented in MONOLIX can be found in the supporting information. Details on the fitting method and the likelihood expression in kinetic models defined by ordinary differential equations can be found in  [36].
Sequence Statistics, Annotation, and Functional Classification of Genes Tetra-and pentanucleotide frequencies were computed for each fosmid nucleotide sequence with the TETRA package  (Teeling, Waldmann, et al. 2004). Subsequently, z scores data values derived from the frequency matrix were used to conduct principal component analysis (PCA)  (Raychaudhuri et al. 2000)  using the MeV program  (Saeed et al. 2003). Each contig was individually processed for gene identification and annotation as follows. We identified all open-reading frames (ORFs) ! 30 amino acids (aa) using the bacterial, archaeal, and plant plastid code (transl_table=11, see http://www. ncbi. nlm. nih. gov/Taxonomy/taxonomyhome. htm l/index. cgi?chapter=cgencodes, last accessed June 25, 2014). In parallel, candidate coding DNA sequences (CDSs) were defined using Prokaryotic Dynamic Programming Genefinding Algorithm (version 2.
60, see http://prodigal. ornl. gov/, last accessed June 25, 2014)  (Hyatt et al. 2010). The two sets were thereafter matched, followed by automated annotation and CDS prediction corrections as follows. First, all ORFs were submitted to similarity search using BLASTP  (Altschul et al. 1997)  against the RefSeq_protein nonredundant database (GenBank, spanning 11/11-04/13 versions), SWISSPROT release 57. 11, the clusters of orthologous group (COG) databases (COG + KOG, seven eukaryotic genomes), and KEGG pathways database (Kanehisa Laboratories, Release 2012-11-12, see ftp://ftp. bioinformatics. jp/). Motif searches were performed in the conserved domain databases (CDD):  CDD. v. 2. 17, Pfam. v. 23. 0, SMART. v5. 1, COG. v. 1. 0, KOG. v. 1. 0, TIGR. v. 8. 0, and PRK. v. 4. 0 using RPS-BLAST  (Marchler-Bauer et al.
2005). Predicted CDS having matches in the RefSeq database with an e value 1 eÀ10 were validated as genes. Among these, CDS matching orphan RefSeq genes (i. e. , hypothetical proteins) were examined to determine whether they matched a COG functional category or contained any known motif in CDD databases. In such cases, the accepted annotation was switched to that of the relevant match, provided their BLASTP and RPS-BLAST e values remained below the 1 eÀ05 threshold. Small (<100 aa) orphan genes and predicted genes overlapping structural RNA genes were stripped off our gene list. Small ORFs ruled out in the CDS prediction step were checked for significant matches in RefSeq, COG, and CDD databases (with similar e value thresholds as above). These small ORF candidates were validated as genes provided that they did not overlap any other gene having high similarity in searched databases.
Taxonomic Affiliation of Archaeal Fosmids Genes from annotated fosmids were initially tagged according to the taxonomy of their best BLASTP hit in the RefSeq database. To class fosmids according to their most probable phylogenetic origin, the percent of genes affiliating to different broad taxonomic categories (Archaea, Bacteria, Eucarya, viruses, Crenarchaeota, Euryarchaeota, Thaumarchaeota, Nanoarchaeota, Korarchaeota, and unclassified archaea) was calculated for each fosmid and the resulting data matrix was processed with a quality cluster method (QT_Clust)  (Heyer et al. 1999)  with MeV (Multiexperiment Viewer; http://www. tm4. org/mev. html, last accessed June 25, 2014)  (Saeed et al. 2003). This provided a preliminary ascription of fosmids to different taxonomic groups. Subsequently, the affiliation of fosmids initially classed as archaeal was refined by phylogenetic analysis of all individual genes (see below).
Accessory or shell genes corresponded to gene clusters that were not present in all archaea, all the Thaumarchaeota, or all the Euryarchaeota reference genomes, genes having only 1-3 hits in the database or genes that lacked homologs in archaea but not in other life domains. This initial classification in broad categories was manually refined based on phylogenetic analyses. Phylogenetic Analyses, Refinement of Orthologous Clusters, and Identification of HGT Events Truncated genes at fosmid ends or sequences containing ambiguous positions (Ns) may escape the above criteria for automatic clustering and lead to a number of misclassifications and to the definition of a few erroneous clusters that need rectification. In addition, similarity-based analyses of cluster taxonomic affiliation are only indicative and need to be validated by proper phylogenetic analyses.
For this purpose, all the predicted ORF protein sequences in fosmids were included in a local genome database together with proteomes of 120 archaea (encompassing genomes, metagenomes, and environmental fosmids), 297 bacteria spanning the diversity of bacterial phyla, and 120 eukaryotes. We used an automated pipeline to reconstruct phylogenetic trees for each ortholog cluster and each nonclustered gene. Genes were compared (BLASTP) with our local database and aligned with their best BLASTP hits (maximum 250 hits per gene; e value exclusion threshold, 1 eÀ5) using MAFFT with default parameters  (Katoh et al. 2005). Each alignment was subsequently inspected, and misaligned sites or sites with more than 20% gaps were removed using BMGE  (Criscuolo and Gribaldo 2010). Phylogenetic trees were computed from the resulting alignments using FastTree with default parameters and an automatic choice of substitution model  (Price et al.