dataesr/scientific-paragraphs-categorization huggingface dataset:
Dietary intake database: The National Diet and Nutrition survey (NDNS) The NDNS uses 4-day food records to provide estimates of diet, nutrient intake, and nutritional status for a representative sample of the UK population aged 1. 5 years and older. The present pooled database was for three cycles of cross-sectional data collection (2008/09, 2009/10, 2010/11). For each year, participants were asked to record food and drinks consumed both at home and away from home in diaries during 4 days, including weekend days. Data collection methods remain constant from year to year allowing data to be combined across survey years. Children aged 12 years and over were encouraged to complete the diaries themselves, while for children below 12 years the parent or caregiver was asked to complete the diary.
The survey also included an interview to provide information on socio-demographic status and lifestyle  [18]. Heights and weights were measured, using a portable stadiometer and weighing scales, the details of which are provided elsewhere  [18]. Parents/guardians provided the written consent for taking measurements. Body Mass Index (BMI) values (kg/m 2 ) were calculated and compared to pediatric standards  [20]. The NDNS data were collected for a total sample of 3073 individuals, including 845 children aged 4-13 years. Ethical approval for NDNS was obtained from the Oxfordshire Research Ethics Committee. The consent form is available online  [21].
Plain water and beverage consumption Water and beverages were classified into these categories: tap water, bottled water, milks (including flavored), sodas (regular and diet), 100% fruit juices, hot beverages (coffee and tea), fruit drinks, sports and energy drinks, flavored waters, and liquid supplements for nutrition use. All other foods were categorized as solid food. Following past procedures  [5] , a distinction was made between the consumption of drinking water and beverages (in g/d) and the consumption of water from water, beverages, and foods (in ml/d). The NDNS food records for each respondent provided information on the amount in grams of each food and beverage consumed. The water content of beverages and the moisture content of foods were then established using a nutrient composition database  [24].
Analyses of water and beverage consumption by eating occasion and eating location were based on the mean total weight of plain water and water from beverages calculated in g/d. By contrast, the comparisons to EFSA total water guidelines were for ml of water content from plain water, beverages, and foods, calculated in ml/d. Data on the time of consumption were also provided in the NDNS. The time of consumption was assigned to one of 7 categories: 6 am-9 am, 9 am-12 pm, 12 pm-2 pm, 2 pm-5 pm, 5 pm-8 pm, 8 pm-10 pm and 10 pm-6 am. Time analyses were based on the entire sample; those children who consumed nothing during a given time period were assigned 0 g consumption. Information on the place of food or beverage consumption was also examined. The key places reported were "at home", "at school", "friends", "fast-food", "coffee and restaurant", "other", and "unspecified".
Tests of percentages of children failing to meet EFSA recommendations were based on non-parametric Chi square test. The estimated percent of children meeting the Dietary Reference Intake (DRI) for water represents the upper bound of the estimate, since the mean of 4 days' water intake may not represent the habitual intake of an individual. Total water intake per 1000 kcal and total energy intake from food groups were compared by gender and age. An additional multiple regression was conducted to examine the effect of IUC on water consumption, adjusting for covariates. The tests were conducted using the SAS statistical software package version 9•4 and the SURVEYREG, SURVEYMEANS and SURVEY-FREQ procedures. Total water intake were expressed as percent EFSA guideline (1600 ml/d for boys and girls 4-8 years; 2100 ml/d for boys 9-13 years and 1900 ml/d for girls 9-13 years).
Discussion The present study adds to the existing literature on water consumption in the UK  [8]  and allows for a direct comparison of children's water drinking habits in France and the US  [5, 27]. Those prior analyses were also conducted using nationally representative databases collected by the respective governments. Dietary intake methodologies were comparable. Whereas the French INCA data were based on 7-day food records, the British NDNS used 4-day food records. Our past studies, conducted in the US and in France  [5, 26] , used the same age groupings (4-13 years), the same beverage classification scheme, and also distinguished between tap and bottled water. Total water intakes were compared to adequate water intake by age group, as defined by EFSA and by the US Institute of Medicine  [5, 26].
Bellisle et al. [13]  had previously noted that French children consumed about 80% of the total daily fluid intake at home  [13]. However, data on the place of consumption ought to be interpreted cautiously, given many instances of places identified as other or unspecified. Total water consumption from water, beverages, and food moisture in the UK was estimated at 1338 ml/d. Of this 901 ml was provided by water and beverages and 437 ml/d by moisture from foods. Total water consumption in France for the same age group was estimated at 1324 ml/da wholly remarkable level of agreement. Of this, 801 ml was provided by beverages and 524 ml by moisture from food. Total water consumption in the US was estimated at 1580 ml. Of this, 1136 ml was provided by water and beverages and 447 ml by food moisture.
The study had limitations. First, the NDNS data, based on self-report, are subject to random inaccuracies and to systematic reporting biases. The proxy recall for younger children may be an additional source of bias. The 4 days of food records impose a burden on respondents and data quality can be variable. The NDNS database is the standard source of information about dietary intake in the UK and is comparable in scope and importance to the National Health and Nutrition Examination Survey in the US and the INCA 2 database in France.
The samples, dissolved in 0. 05 M HNO 3 , were introduce with a stable introduction system (SIS: double Scott/cyclonic spray chamber) coupled with a low flow PFA nebulizer (50 mL min À1 ). Time integrations for blanks were 80 s, while samples and standards took 320 s. The zinc isotope ratios were measured relative to an in-house Zn single element standard from MERCK (Lot #9953) (denoted as Zn UNB ) and expressed in d-notation as below: d 66 Znð‰Þ ¼ 66 Zn. 64 Zn sample 66 Zn= 64 Zn standard À 1 The d 68 Zn values were calculated using the ratio R( 68 Zn/ 64 Zn) to test for mass dependency and absence of interferences (Fig. 2 ). The data in this paper are reported relative to the Johnson Matthey 3e0749-L (JMC) reference material, which was previously calibrated against our Zn UnB reference material (DZn JMC- UnB ¼ þ0.
Statistical analysis Comparison of zinc concentrations and isotope compositions among the different sampling sites were verified by statistical tests using the SPSS ® software v. 18. 0 (IBM ® , Armonk, NY, USA). The normality distribution of data was verified using Kolmogor-oveSmirnov and Shapiro-Wilk tests, while Levene's test was applied to check the homogeneity of variances (confidence level of 95%). One-way ANOVA and Student test t were applied to data normally distributed. If data deviate from the normal distribution or fail in Levene's test, the Welch ANOVA was used instead of a oneway ANOVA.
Zinc isotope signatures of SPM samples in the fluvial-estuarine system The dataset for the zinc isotope compositions and concentrations of filtered water and SPM is shown in Table  1. The d 66 Zn JMC values of the SPM samples varied between þ0. 15 and þ 0. 95‰ (Table  1 , Figs. 2  and 3 ). The SPM collected around the old metallurgic zone (Saco do Engenho, Fig. 1 ) displays the highest zinc concentrations (325e2339 mg g À1 ) and the heaviest isotope compositions (þ0. 87 to þ0. 95‰) in the Sepetiba bay. These d 66 Zn JMC values fall within the range of the isotopic signatures associated to metallurgic wastes and effluents reported in previous studies which vary mostly between þ0. 5 and þ 1. 49‰  (Sivry et al. , 2008; Juillot et al. , 2011; Yin et al. , 2016). These isotopic signatures reflect an isotopic record of the contamination by the old electroplating wastes that took place along the last five decades.
Acknowledgements The authors acknowledge the financial support and grants provided by  CNPq (Brazilian Research Council , grant numbers:  161944/2012-4  and  211238/2014-7 ),  FAPERJ (Rio de Janeiro State Research Foundation ; project No. E-26/111. 403/2014 ),the  LMI-OCE  and the 7th European  Community Framework Programme (NIDYFICS, n 318123 ) and CLIM  AMAZON  project ( European Science Foundation ).
Pesticide Selection The complete list of pesticides (n = 338) included in the study is shown in Table  S1 , while the list of oxon metabolites of organophosphorus pesticides (n = 15) is provided in Table  S2. The selection of pesticides was driven by retaining the main members of various major chemical classes of pesticides, i. e. , the organochlorines (n = 30), the organophosphorus compounds (n = 99), the pyrethroids (n = 42), the carbamates (n = 74), and the triazines (n = 31); miscellaneous pesticides (n = 62) belonging to various additional classes of pesticides, such as the neonicotinoids, were also included. Most if not all of these chemicals are included in pesticide and/or chemical data bases, such as the Hazardous Substance Data Bank (HSDB), a Toxicology data file on the U. S. National Library of Medicine's (NLM) Toxicology Data Network (TOXNET) (Bethesda, MD, USA), which is freely available online  [27].
This method is primarily based on two parameters: (1) the lipophilicity of chemicals, evaluated from partition-coefficient (P) by a LogP value calculated according to the Wildman-Crippen method (WLogP), and (2) their polarity, determined by a calculated topological polar surface area (tPSA) value. A prior analysis of human absorption and brain permeation databases for drugs has allowed us to delineate two ellipsoidal regions (one for intestine permeation and one for brain permeation, which are not mutually exclusive) on the graph WLogP versus tPSA  [21]. The compounds included in these ellipses are predicted to display high intestinal absorption, i. e. , the fraction absorbed (Fa) is ≥0. 30, with Fa equal to the amount of absorbed chemical/amount of ingested chemical, or brain permeation, i.
e. , the brain-blood concentration ratio is ≥1 (the LogBB, defined as the logarithm of the ratio of the drug concentration in the brain versus that in the blood, is therefore ≥0)  [20]. Such predictions have been previously performed with good accuracy (around 90%) for drugs  [21]. The ellipsoidal regions defining the intestine and brain permeation were drawn as initially reported in  [21]  using the software R (R Foundation for Statistical Computing, Vienna, Austria)  [31] ); the specific R code used is shown in Appendix A.
It should however be kept in mind that each physicochemical parameter, when separately considered, may fail to unambiguously predict intestine permeation, thus highlighting the relevance of combining two parameters as is done in the BOILED-Egg method. Finally, the in vitro measurement of transport across human intestinal Caco-2 cells for some pesticides, such as the neonicotinoids acetamiprid and imidacloprid, have revealed high apparent permeability coefficients  [75, 76]  consistent with in vivo absorption with 100% efficiency, and thus with the in silico prediction of high absorption for these two pesticides (Table  S1 ). The prediction of brain permeation for pesticides cannot, unfortunately, be validated by human experimental pharmacokinetics data, owing to the quasi-absence of such data in the scientific literature and pesticide database.
Ten millilitres of 6 N HCl was added to the dried pellets (insoluble collagen) and, for total collagen measurement, to 50 mg of freeze-dried muscle and then hydrolysed overnight at 105°C. After incubation with activated charcoal (Norit A), they were filtered (OOB filter papers) and then diluted (5Â in dH 2 O). OH-proline content was determined according to the procedure of  Woessner (1961). Optical densities were read at 557 nm. OH-proline was chosen as the standard to compare results and a standard curve was plotted to determine OH-proline concentrations. The data are represented as means of triplicates and expressed in micrograms of hydroxyproline per milligram of dry matter.
Results: In the present study, a bioinformatic approach was used on 72 plant PAEs from 16 species among 611 plant PAEs available in plant genomic databases. An overview of plant PAE proteins, particularly Arabidopsis thaliana PAEs, based on phylogeny analysis, protein motif identification and modeled 3D structure is presented. A phylogenetic tree analysis using protein sequences clustered the plant PAEs into five clades. AtPAEs clustered in four clades in the plant kingdom PAE tree while they formed three clades when a phylogenetic tree was performed only on Arabidopsis proteins, due to isoform AtPAE9. Primitive plants that display a smaller number of PAEs clustered into two clades, while in higher plants, the presence of multiple members of PAE genes indicated a diversification of AtPAEs.
The GalA residues can be acetylated at positions O-2 or O-3 in both HG and RG-I  [6] [7] [8]. The degree of acetylation can be regulated by pectin acetylesterase (E. C. 3. 1. 1. 6; PAE) which cleave the acetylester bond from pectin  [5, 9]. Plant PAEs belong to family CE13 of carbohydrate esterases in the CAZy database  [10]. PAEs have also been identified in bacteria and fungi where they belong to family CE12  [10] [11] [12] [13] [14] [15]. CE12 and CE13 are classified as SGNH family proteins (Pfam accession number: CL0264). The 12 families in this superfamily are characterized by four conserved residues: S, G, N and H. There is also an aspartic residue that appears to be involved in the catalytic site  [16, 17]. To date, few studies have reported the biochemical properties and physiological function of PAE  [5, 9, [18] [19] [20] [21].
The sequencing of several plant genomes has significantly promoted the identification and characterization of plant genes. Several portals and databases of genes, protein sequences and functions, including Phytozome, Uniprot, PlantCAZyme, and eFP Browser, can be used to investigate the PAE gene family in the plant kingdom  [23] [24] [25] [26]. One way to have a better understanding of the structure and function of a protein family, including Arabidopsis, is to use comparative genomics and phylogenetic approaches  [21]. In our study, a comprehensive analysis of plant PAEs, and particularly that of Arabidopsis, was undertaken using bioinformatic approaches. A phylogenetic tree using Arabidopsis PAE protein sequences was generated. Three clades of Arabidopsis PAEs were identified. The 3D homology of one Arabidopsis PAE from each cluster was modeled to investigate the diverging branches.
Phylogenetic tree of plant PAE proteins In order to study plant PAEs in a broader evolutionary context, a phylogenetic tree was generated from 72 plant PAEs using the full-length sequence of the proteins without their signal peptides. The sequences of putative PAEs were retrieved from the Phytozome, PlantCAZyme and Uniprot databases  [24] [25] [26]. The 611 putative plant PAEs available in these databases were from 37 diverse genomes. Of these 611, 72 plant PAEs from 4 clades (Moss, Spike moss, Dicots, Monocots) and 16 species, including Arabidopsis, maize, rice, Physcomitrella, Selaginella, Sorghum, Brachypodium, flax, poplar, Medicago, litchi, lettuce, castor bean and cacao tree, were selected (Additional files 1 and 2). These data were chosen using different criteria, including extensive sequence annotation of the PAE protein of some genomes, availability of full-length protein sequence, model species as well as relevant literature on plant PAE gene function  [5, [20] [21] [22].
In all species, PAEs are encoded by a multigene family, except for Physcomitrella, in which only one PAE has been annotated according to the Phytozome database. The number of PAE genes in lower plants appears lesser (1-7) than in higher plants. The average number of PAEs in higher plants (between 10 and 11) is independent of the size of the genome (Additional file 1). The highest number of PAE isoforms was mostly found in dicots, including apple  (27) , soybean  (23) , oil seed rape  (22) , and yellow monkey flower  (20). However, 24 putative PAEs were annotated in Switchgrass, a monocot, while 11 and 12 putative isoforms were annotated in Sorghum and Brachypodium, respectively (Additional file 1). We cannot exclude that the low numbers of PAE genes in some genomes could be related to the incomplete sequenced genome or the incomplete annotation sequence of some genomes, including cucumber, castor bean, lettuce and flax, which might be an issue.
The evolutionary history was inferred by using the Maximum Likelihood method based on the JTT matrix-based model  [71]. The tree with the highest log likelihood (-22,303. 4018) is shown. Initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using a JTT model, and then selecting the topology with superior log likelihood value. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. The analysis involved 72 amino acid sequences. All positions with less than 95% site coverage were eliminated. That is, fewer than 5% alignment gaps, missing data, and ambiguous bases were allowed at any position. There were a total of 341 positions in the final dataset.
We constructed a phylogenetic tree for the 12 AtPAEs, in a similar way to the one obtained with the plant PAEs (Fig. 2a ). A BLOSUM80 score matrix was used since AtPAEs are homolog proteins. The twelve Arabidopsis PAEs were grouped into three clades, instead of the four clades observed in the plant kingdom tree. As observed Fig. 2  Phylogenetic analysis and specific conserved sequence motifs in the Arabidopsis thaliana PAE protein sequences. a The tree shows three distinct groups of Arabidopsis PAEs. The tree was generated by neighbor-joining distance analysis of PAE protein sequences using the Muscle program in Mega 7. 0 software  [27]. Protein sequence alignment was achieved by BLOSUM80. The sequences used are full-length proteins without their signal peptide. b The sequence logo was made using WebLogo 3  [34].
Based on these data and one selected gene per clade, AtPAE4, AtPAE8 and AtPAE10 were chosen. For this purpose, a search for structural templates was carried out. There is no plant PAE structure available in the PDB database. The 3D structure of one Aspergillus aculeatus rhamnogalacturonan esterase (AacRGAE) has been solved (PDB code: 1PP4)  [28]. This fungus PAE belongs to the CE12 family. When one Arabidopsis AtPAE8 was threaded with AacRGAE using the FUGUE algorithm, the structure was severely distorted using the RGAE template, although the root mean square deviation (RMSD) and the template modeling score (TM-score) values were 1. 1 Å and 0. 55, respectively (Additional file 6). In addition, sequence structure comparison threaded with FUGUE gave an amino acid sequence identity of 17%, which is low.
The difference between the sizes of the two proteins and a low pairwise amino acid similarity could corrupt the core structure of AtPAE8. The processed AtPAE8 protein contained 375 amino residues while the RGAE 3D structure contained only 233 residues. This result might highlight some structural differences between PAE from CE12 and CE13. Similar unfolded models were obtained when using AtPAE4 or AtPAE10 (data not shown). The structure of AtPAE8 was therefore predicted using LOMETS, a platform of several algorithms  [29]. All these servers found a consensus template, a human Notum that has a palmitoleoyl-protein carboxylesterase activity, (PDB code: 4UYU_A). Other good templates could be selected based on our criteria, which were templates from the same family with a similar function (Additional file 7).
Primary structure of AtPAEs When the protein sequence of each PAE was analyzed using the Pfam protein databases, the presence of a PAE domain (Pfam: PF03283) that corresponds to the catalytic part of the protein was revealed (Fig. 4a, b ). Based on other computational tools including TargetP, ARAMEMNON, and TMHMM for detecting signal peptides and transmembrane domains, nine AtPAEs showed the presence of a signal peptide sequence that was between 20 and 29 amino acid residues in length (Fig. 4a, b , Additional file 4). A signal peptide could not be identified (p-value < 0. 5) for three AtPAEs (AtPAE2, AtPAE4 and AtPAE5), (Fig. 4b ). No transmembrane domain was identified for AtPAEs, indicating that AtPAEs are unlikely to be membrane proteins. A glycosylphosphatidylinositol anchor was predicted for AtPAE10 and AtPAE12 (Additional file 4).
AtPAEs had a predicted molecular weight of 39-49 kDa (364-444 amino acids), (Additional file 4). The isoelectric point of the mature AtPAE forms appeared to be basic (8. 6-9. 4), except for AtPAE4, AtPAE5 and AtPAE6, which displayed a pI between 5. 7 and 6. 8, indicating a potential acidic pH of activity for these three AtPAEs. These data indicate that AtPAE4 and AtPAE5, which belong to clade 3, have some biochemical specificities that could be associated with a specific function. To assess whether such differences between isoforms exist in other species, and to determine the putative differences between PAEs from monocots and dicots, the primary structure analysis of the rice PAEs, a grass species, was carried out. OsPAEs displayed similar features compared to AtPAE proteins (Additional file 4).
Identification of conserved motifs in plant PAEs We then further investigated the presence of conserved specific motifs found in Arabidopsis and other plant PAEs. Using members of each clade of Arabidopsis PAE, we used the MultAlin and WebLogo 3 server to identify putative specific motifs  [34, 35]. The motifs ERqDTW-faxxS, TxxAKAVGrW and TSxRIxNKTIAE were specific to clades 1, 2 and 3, respectively (Fig. 2b ). The motifs were located at the C-terminus of the protein, thus providing a signature for a given clade. Using the Pfam protein databases, the specific motif of each clade did not match with any known domain. In an attempt to gain more insights into the structurefunction of plant PAEs, a multiple sequence alignment of the 72 selected plant PAE protein sequences without their signal peptide was performed using the MUSCLE algorithm  [36].
However, based on the literature data and structure alignments performed with SGNH superfamily proteins with known structures, we attempted to assign some functions to them. When all AtPAE proteins were structure-aligned with 4UYU, a superimposition of the active site (S, D, H) of 4UYU with identical amino acid residues of all the AtPAEs was observed, revealing a strong conservation of the catalytic site (Fig. 5 ). The catalytic triad S, D and H was located in the conserved GCSxG, NxayDxxQ and HCQ motifs found in AtPAE and all other plant kingdom PAEs. These motifs could not be found in PAEs from the CE12 family (Additional file 13). Indeed, when we structure-aligned some characterized bacterial PAEs, including paeY and yxiM, and fungal rhamnogalacturonan acetylesterase (AN2528, rhgT, yesY) with a fungal RGAE (AacRGAE, PDB code: 1PP4) using Expresso from T-Coffee, we identified the conserved GDSX and the DxxH motifs typical of esterases from CE12, which are known to accommodate the catalytic triad (Additional file 13)  [12, 28, 30, [37] [38] [39].