dataesr/scientific-paragraphs-categorization huggingface dataset:
GN-2 (real surplus = 0. 12) GN-3 (real surplus = 4. 92) 3. 27 This puts the observer (and the researcher who has to deal with the data) in an uncomfortable position: a diminution of the agnatic cousin marriage surplus due to improving memory (for example by repeated field or archive research) does not by itself indicate that the formerly observed surplus was just an illusion. It only means that there is a marked agnatic observer bias -but this does not exclude that there is nevertheless a real surplus of agnatic cousin marriages, and that the observed surplus will again start to rise as memory improves further. This combination -high agnatic bias in the context of real agnatic marriage preferenceprobably characterizes the "Ebrei" dataset. On the one hand, we know that informants' recall rates are low and agnatic observer immobility is high (since the observer is a 17 th century notary who never leaves his village, and the informants are men who are asked their parents, wives, and eventually their wife's fathers).
But on the other hand, we also know (from other sources on matrimonial norms and from the inspection of patronyms) that agnatic marriages are really preferred. The genealogical data cannot, by themselves, validate or invalidate the hypothesis of agnatic marriage preferences. Reducing the dataset to an unbiased and well-explored subset (for instance, by restraining the matrimonial circuit census on spouses whose grandparents are all known) may be the only solution.
Acknowledgements This work has been partially supported by the  French National Agency of Research (ANR)  through grant " SIMPA "  ANR-09-SYSC-013-02. The paper has benefited from regular interactions with the members of the research group  TIP (Traitement Informatique de la Parenté , http://www. kintip. net). We are particularly grateful to  Camille Roth ,  Telmo Menezes  and  Michael Gasperoni  for their many valuable comments on earlier versions of this article, as well as to  Anne Garcia-Fernandez  for her assistance with the diagrams. All errors, of course, are ours.
Development of the questionnaire Separate questionnaires were developed for collecting data from pig farmers and hunters. The questionnaire investigated the six different drivers of behaviour specified in the theory of planned behaviour  (Ajzen, 1985) : beliefs in the effectiveness of the behaviour, beliefs in the consequences of the behaviour, social pressure, motivation to comply with social norms, beliefs in being able to achieve the behaviour and factors inhibiting ability to achieve behaviour. Both questionnaires were developed in English, tested by two hunters and two pig husbandry experts known by the authors to check for potential problems with interpretation of questions, and subsequently translated into the three relevant languages by two natives from each of the three study areas.
The final web-based versions were developed and made available online using the SurveyMonkey â software (available at: www. surveymonkey. com). For both questionnaires, the introduction section contained the informed consent information and explained the purpose of the questionnaire survey. It was also specified explicitly that the responses would be treated anonymously. All questions were closed or semiclosed, and the questionnaires were estimated to take less than 15 min to complete. To assess attitudes and beliefs, participants were asked to answer several questions using a Likert scale with five categories (for example 'very often', 'often', 'sometimes', 'rarely', 'never' or 'I strongly agree', 'I agree', 'I disagree', 'I strongly disagree', 'I have no opinion'). The questionnaire for pig farmers was structured into four sections comprising 36 items in total.
The first section collected data on the farm background (including farm age, herd size, production type, etc. ). For this section, appropriate response options were defined based on the opinion of co-authors with knowledge about pig farming systems (Germany: HN; Bulgaria: PP; Russian Federation: AG, DK). The second section established farmers' perception of ASF. The third section investigated motivations for reporting suspected cases, and the fourth section investigated reasons for not reporting. The questionnaire for hunters was structured into three sections comprising 46 items in total. The first section collected data on hunter background (including frequency of hunting, species hunted, perceived role in wildlife surveillance, etc. ). For this section, appropriate response options were defined based on the opinion of co-authors with knowledge about hunting practices (Germany: SB; Bulgaria: PP; Russian Federation: AG, DK).
Data collection The survey was administered to a convenience sample of pig farmers and hunters. In early 2013, the electronic links to the questionnaires were distributed in each of the selected study areas to (i) pig farmers' associations, (ii) selected large pig veterinary practices and (iii) hunters' associations, with the request to forward them to their members or clients. Before distributing the questionnaire, information seminars were held in Germany at meetings of the pig farmers' associations to describe the purpose of the study. Responses to the questionnaires were gathered from the three countries over a period of 8 weeks.
38 [0. 74; 9. 09]). Finally, hunters who do not usually submit samples were most likely (although the difference was not statistically significant neither) to believe that a lack of awareness of the possibility to test hunted wild boar was a reason for not reporting it (OR = 1/0. 57 = 1. 75 [0. 87; 3. 57] ). No interaction between the study area and the other significant variables could be detected within our dataset.
Discussion The objective of this study was to investigate the factors associated with the attitude of farmers and hunters towards reporting suspected ASF cases in different parts of central and Eastern Europe. It is important to be aware that the study design, data collection method and sampling approach result in several limitations for this dataset which will be discussed in more detail below. The results of the analyses therefore need to be interpreted very cautiously. The models developed in this study do not explicitly identify causal relationships between the different variables and the outcome of interest; they only show statistical associations between beliefs and behaviours. We still believe that given the lack of information about the reporting behaviours and associated drivers the findings presented here provide a basis for informing the design of more effective communication strategies aimed at increasing the likelihood of reporting.
Acknowledgements We thank  Milen Georgiev  and  Svetlana Buzdugan  for their support with the translation of questionnaires. We also thank  Dr Pablo Alarcon  for the useful discussions about the principles of decision-making processes. We are also very grateful to  William Gilbert  for improving the readability of this paper. Finally, we would like to thank two anonymous reviewers who provided comments that improved significantly the manuscript. This work has been carried out as part of the  European Union  funded project  ASFORCE  ( KBBE. 2012. 1. 3-02 ).
05) discriminating features. Ion clusters whose intensities were most elevated in the high consumers were annotated using online and inhouse databases and their identities checked using commercial standards and MS-MS fragmentation. The best discriminants, and thus potential markers of coffee consumption, were the glucuronide of the diterpenoid atractyligenin, the diketopiperazine cyclo(isoleucyl-prolyl), and the alkaloid trigonelline. Some caffeine metabolites, such as 1methylxanthine, were also among the discriminants, however caffeine may be consumed from other sources and its metabolism is subject to inter-individual variation. Receiver operating characteristics curve analysis showed that the biomarkers identified could be used effectively in combination for increased sensitivity and specificity.
The accurate assessment of coffee consumption in observational studies, as with other foods of dietary importance, is problematic. In the largescale epidemiological studies needed to characterize the links between diet and health, intake data are collected by dietary questionnaires, which rely on accurate self-reporting by participants. Despite advances in data collection techniques, bias remain a problem, and burdensome and expensive protocols using multiple 24 h dietary recalls and/or validated thorough food frequency questionnaires (FFQs) are necessary to obtain accurate assessment of food intake. A potential alternative to these assessment methods is the use of biomarkers, which if well-validated could reliably reflect the recent and/or habitual consumption of a food or dietary pattern of interest  [14].
Data pre-processing and statistical analysis Full-scan UPLC-QTof-MS data for the 260 subjects were extracted and processed using XCMS software  [19]. To correct for drift between different series, ion intensities were normalized using data from quality controls consisting of pooled study urine samples  [20]. The datasets obtained for positive and negative modes were merged and then filtered to remove any ions that did not appear in more than 25% of the samples of at least one group (low or high F&V consumers). For the present study, statistical analysis was then carried out on a reduced dataset comprising the data matrix of the 19 low-and 20 high coffee consumers. Data were log transformed and an orthogonal signal correction (OSC)  [21]  filter with Pareto scaling was applied to all remaining ions to reduce variability not associated with the diet effect.
Both univariate and multivariate statistical approaches were used. On the OSC filtered dataset, a one-way ANOVA (R software) was performed on each ion to search for differences in intensities between the selected low and high coffee consumers, applying a Benjamini-Hochberg (BH)  [22]  p-value correction to reduce the risk of false positive discovery. All ions with p-value,0. 05 were considered statistically significant. In parallel, a partial least squares discriminant analysis (PLS-DA) was performed on all detected ions (SIMCA-P+ software, version 13. 0, Umetrics AB, Umea, Sweden). The predictive ability of the PLS model was assessed by cross validation (Q 2 cum) and permutation test (n = 100; plot and CV ANOVA). Variable Importance Projection (VIP) values were obtained as indicators of importance of each ion in the discrimination.
Receiver Operating Characteristics (ROC) curves, widely considered to be the most objective and statistically valid method for the evaluation of biomarker performance, were constructed using the ROCCET web-based tool  [23]. As with the previous statistical analysis, data used for ROC analyses were those filtered with OSC. The 20 high and 19 low-coffee consumers selected for the study were used as the training set and 10 high and 10 lowcoffee consumers were selected among the validation population as the hold-out set. Both individual and multiple biomarker ROC curves were built using the support vector machines algorithm. The average of predicted class probabilities of each sample and the average predicted accuracy were then calculated across 100 crossvalidations, giving the respective confusion matrices for the training and hold-out sets.
Identification of discriminant ions Ions with ANOVA p-value less than 0. 05, VIP greater than 1 and a mean intensity ratio high consumers/low consumers greater than 1 (OSC filtered values) were retained for further investigation. The list of discriminants was first grouped into clusters according to LC retention time (RT), and correlation between fragments was confirmed by a visual check of the corresponding extracted ion chromatograms (EIC) of the UPLC-QTof analysis and comparison of their distribution across the whole dataset. To obtain accurate masses, molecular formulae, and additional structural information, a small number of representative samples were also analyzed using a high resolution LTQ Orbitrap Velos TM hybrid mass spectrometer (Thermo Fisher Scientific, San Jose, CA) operating in various modes (i.
e. full scan and collision induced dissociation fragmentation) using mass resolution from 7500 to 30000. Chromatographic separation was achieved with a RSLC Ultimate 3000 liquid chromatography module (Dionex), using the same conditions as for the UPLC-QTof-MS analysis. The ions of interest were retrieved in the LTQ-Orbitrap full scan chromatograms to obtain accurate masses for these signals, which generally allowed the determination of molecular formulae. Online databases such as the Human Metabolome Database (HMDB; www. hmdb. ca), KNApSAcK (http://kanaya. naist. jp/ knapsack_jsp/top. html), Dictionary of Natural Products (http:// dnp. chemnetbase. com) and Metlin (http://metlin. scripps. edu) were queried to hypothesize identities. In addition, a customized in-house database on coffee phytochemical metabolites was used, comprising all phytochemicals reported in coffee according to Phenol-Explorer (http://www.
phenol-explorer. eu), Duke's Phytochemical and Ethnobotanical Database (http://www. ars-grin. gov/duke), the Dictionary of Natural Products, KNApSAcK and a literature survey, as well as their known metabolites reported in the literature. Additional likely human metabolites of coffee constituents were also included, as predicted in silico by the software Meteor-Nexus (v. 13. 0. 0, Lhasa Ltd, Leeds, UK). If a hypothesis was within 1 mDa of a chemically and biologically plausible coffee-derived metabolite, the commercial standard was acquired to perform the final confirmation of identity. If this was not available, fragmentation was performed on the LTQ-Orbitrap and the resulting spectra examined and compared to those available in online databases or literature. In silico prediction of the mass fragmentation of the candidate structures was also performed using Mass Frontier TM software (Thermo Fisher Scientific, San Jose, CA).
Results The 20 high coffee consumers reported a median intake of 290 mL/d (range: 183-540 mL/d), whereas all low consumers reported zero habitual consumption in all questionnaires. No significant differences in sex, age, season of urine sampling or BMI were observed between the two groups (Chi-squared p-value = 0. 408, 0. 886, 0. 069 and 0. 869, respectively). After data pre-processing, 932 and 179 ions were detected in urine analyzed in positive and negative modes, respectively. Data were compared using both univariate and multivariate statistics. ANOVA with false positive discovery BH correction showed that 119 and 13 ions in positive and negative modes respectively had significantly different intensities (p-value,0. 05) in the groups of low and high coffee consumers. All significant ions except one (m/z 258.
7 (C 26 H 34 O 11 ) and 11. 4 min (C 26 H 32 O 10 ) were found to be glucuronide conjugates with very similar fragmentation spectra. The most plausible hypothesis for the cluster eluting at 11. 4 min was a glucuronide of a kahweol metabolite produced by oxidation of a primary alcohol. Oxidation was the most likely biotransformation predicted by Meteor software for kahweol. The cluster eluting at 9. 7 min might be a hydrated analogue that lost a water moiety during ionisation. Hippuric acid and its 3-hydroxy derivative were also correlated with coffee consumption. The ratio of mean intensity in high consumers/mean intensity in low consumers (Table  1 ) varied from 1. 4 for 3-hydroxyhippuric acid to 17. 5 for atractyligenin glucuronide, with values around 4-5 for cyclo(isoleucyl-prolyl), kahweol oxide glucuronide, dimethylxanthine glucuronide and trigonelline.
The trigonelline metabolite N-methylpyridinium, also reported as a marker of coffee intake  [27] , was not among the discriminants in the present study. Masses corresponding to cafestol and kahweol, the well-known coffee diterpenoids, were not observed among the discriminants, although a feature at [M+H] + 329 was tentatively identified as a kahweol oxide glucuronide. The metabolism of cafestol and kahweol in humans is poorly documented. An intervention study in ileostomy volunteers showed that both are well absorbed in the small intestine but little is excreted as conjugates of glucuronic acid or sulfate in urine, suggesting other metabolic routes such as oxidation  [30]. Oxidation of the primary alcohol of kahweol with subsequent glucuronidation of this alcohol was the first biotransformation predicted by Meteor software, supporting our tentative identification.
All possible dietary and metabolic origins must be carefully checked for any proposed biomarker. Biomarker validation is a laborious process requiring doseresponse and pharmacokinetics studies, attention to specificity and association with intake in various populations with different ethnic and dietary backgrounds, and consideration of the main factors affecting the relationship between the biomarker concentration and the quantity of food consumed. Our three new biomarkers were closely correlated with reported coffee intake for 260 subjects from the same SU. VI. MAX2 cohort, despite the semi-quantitative measurement of biomarker intensity. The correlations could be even more accurate if intake data were collected at the same time as urine samples in which the biomarker is quantified. Coffee is an ideal foodstuff for which to search for biomarkers of consumption.
Kinetic parameters were calculated from the initial rate activities of the eight purified SAPB enzymes using YLY and FAAF as synthetic peptide substrates at different concentrations, ranging from 0. 2 mM to 50 mM. Michaelis-Menten constant (K m ) and maximal reaction velocity (V max ) values were calculated by Lineweaver-Burk plots using Hyper32 software. The pH and temperature values used in this study were adjusted to the optimum conditions for each SAPB enzyme as reported elsewhere  [11]. Enzymatic activities were determined on each substrate according to standard conditions. For the natural substrate, the complete hydrolysis of a protein unit was defined as the amount of enzyme that produced 1 mg of amino-acid equivalent to tyrosine per min, in absorbance at 660 nm, under the experimental conditions described.
Bioinformatics and Homology Modeling The automated comparative protein structure homology modeling server, SWISS-MODEL (http://www. expasy. org/ swissmod/), was used to generate the three-dimensional model of SAPB enzymes based on the crystal structure of subtilisin E (PDB-code 1SCJ). The Deep View Swiss-PDB Viewer software from the EXPASY server (http://www. expasy. org/spdbv) and PyMOL v0. 99 (http://www. pymol. org) were used to visualize and analyse the three-dimensional model.
Acknowledgments The authors wish to express their gratitude to  Dr. I. Hssairi ,  Mr. A. Zitoun ,  Mrs I. Rekik ,  Mrs N. Ayadi , and  Mr. F. Ben Kahlaoui  ( UVRR-CBS ) for their technical assistance and valuable help during the preparation of this work. We extend our thanks to  Pr. H. Belghith  and  Pr. A. Gargouri  ( LVBPPE-CBS ) for their constructive discussions and suggestions. Many thanks are also owed to  Pr. Z. Fakhfakh ,  Pr. A. Kallel , and  Mrs S. Damek  ( USCR-MEB/03-FSS ) for their help with the scanning electron microscopy. We acknowledge our industrial partner  Mr. M. E. Moussa  and  Mr. M. S. Bchir  from  ''SO. SA. CUIR'' leather tannery (M'Saken, Sousse, Tunisia ) for granting us access to their technical facility to perform our semi-industrial scale tests on animal hides using the SAPB enzymes.
The history of a domesticated marine macroalga is studied using archaeological, phylogeographic and population genetic tools. Phylogeographic and population genetic analyses demonstrated that the cultivated red alga Gracilaria chilensis colonised the Chilean coast from New Zealand. Combining archaeological observations with phylogeographic data provided evidence that exchanges between New Zealand and Chile have occurred at least before the Holocene, likely at the end of the Last Glacial Maximum (LGM) and we suggest that migration probably occurred via rafting. Furthermore, the remarkably low microsatellite diversity found in the Chilean populations compared to those in New Zealand is consistent with a recent genetic bottleneck as a result of over-exploitation of natural populations and/or the process of domestication.
Genotyping and Sequencing DNA extraction followed  [25]  and PCR amplification of the five microsatellite loci and allele size scoring were performed according to  [26]. Products were run on 6. 5% polyacrylamide denaturing gels in a LI-COR DNA sequencer model 4200TM (LI-COR, Lincoln, NE, USA). A total of 93 individuals from New Zealand and 108 individuals from Chile were sequenced for the Internal Transcribed Spacer 2 (ITS2, operon of nuclear ribosomal genes). Eleven individuals (three from Chile and six from New Zealand) were sequenced for the ribulose 1,5-bisphosphate carboxylase/oxygenase gene (rbcL, plastid) and compared with two sequences of G. chilensis from Chile already deposited in GenBank: AY049396 from Coquimbo  [27]  and DQ095784 from Lachangua  [28]. The ITS2 PCR reactions and sequencing followed the protocol described in  [25].
The rbcL amplifications were carried out following the procedures described by  [14]. Sequences of 481 bp of ITS2 (GENBANK N ˚HQ998639 to HQ998839) and of 641 bp of rbcL (GENBANK N ˚HQ998840 to HQ998848) were obtained with an ABI 3100 Sequencer (Applied Biosystem, Foster City, CA). Sequences were edited using CHROMAS  [29]  and multiple sequence alignments were constructed with BIOEDIT  [30].
We calculated allele accumulation over sampling size for both Chile and New Zealand (including Chatham Island) by jackknife resampling (100 times), using the package PopGenKit v1. 0  [35]  implemented in R v3. 0. 3 (http://cran. r-project. org/). We applied sample size intervals of one unit, varying from one to a maximum of 410 individuals for Chile and a maximum of 187 individuals for New Zealand. A median-joining network of ITS2 ribotypes was constructed for both the New Zealand and Chilean data sets using NETWORK v 4. 5  [36]. To compare population structure in New Zealand and Chilean populations using microsatellites, we employed two different methods. First we used a Bayesian, model-based genetic admixture analysis implemented in STRUCTURE  [37]. Individuals were combined into one dataset for analysis, without any a priori population assignments and admixture was allowed.
The programme, then, determines the minimum set of edges (connections) that sufficiently explain the among-population covariance structure of all of the populations. The network of population connections can then be analysed by various post hoc analyses. POPULATION GRAPH was implemented on the web (http://dyerlab. bio. vcu. edu/wiki/index. php/) using the population genetic dataset. A test for distinct clustering of New Zealand and Chilean populations was conducted a posteriori using the methods outlined by Dyer and Nason  [40]. To detect bottlenecks, we used the graphical method developed by Luikart et al  [41]  by grouping alleles of the five microsatellite loci into 10 allele's frequency classes. Luikart et al. [41]  argued that due to the probabilistic reduction of rare alleles, bottlenecks might cause a characteristic mode-shift distortion in the typical L-shape distribution of allele frequencies, i.