dataesr/scientific-paragraphs-categorization huggingface dataset:
We repeated all genotyping experiments to ensure that a sequence found in one individual was also detected, if present, in relatives and all other individuals in the population. We deposited MHC sequence data in GenBank (accession numbers DQ103715-DQ103732, DQ103734-DQ103746, EU693911-EU693914). We used two methods to differentiate functional MHC-DRB genes from nonfunctional pseudogenes. First, we reviewed all sequences for stop codons  [Abbott et al, 2006]. One sequence (Masp-DRB-6*0404) had a stop codon, so we removed this from the dataset for analysis. Next, we examined patterns of transcription using cDNA from a subset of seven mandrills representing all known Masp-DRB loci and lineages, and for whom mRNA samples were available  [Setchell et al. 2010 ]. We found that 15 ⁄ 16 of the mandrill MHC-DRB sequences identified in these animals were transcribed and, therefore, possibly functional (although we did not investigate whether the sequences were translated).
Each haplotype consisted of 1-4 sequences (mean 2. 4), and was present in 1-3 of the founder individuals (mean +/-SEM = 1. 5 +/-0. 2) and 1-75 of all individuals (mean +/-SEM = 17. 1 +/-4. 3). Each individual mandrill possessed 1-7 sequences, in two haplotypes; when we found only one haplotype in an individual we assumed the individual was homozygous for that haplotype. We detected no changes in MHC haplotype from parent to offspring in our dataset, suggesting that no major recombination occurred in our sample. We examined MHC sequence diversity in terms of the number of sequences in each haplotype. As MHC sequences may differ in nucleotide composition, but still share the same amino acid sequences due to the presence of silent substitutions, we calculated the number of amino acid differences between each pair of MHC sequences as an estimate of genetic dissimilarity  [Landry et al.
Statistical analyses We used a mixed model (in SPSS) including dyad identity as a random effect to compare parental MHC-dissimilarity (measured as the total number of different MHC sequences possessed by a mother x sire dyad) with the number of MHC sequences in the offspring (Hypothesis 1). To test whether selection resulted in more heterozygous offspring than expected by chance (Hypothesis 2), we compared the inheritance patterns of paternal haplotypes, given the known maternal haplotype, with the 50:50 expected from chance using binomial tests. To test whether selection occurred for more dissimilar combinations of haplotypes over similar combinations (Hypothesis 3) we compared MHCdiff and AAdiff for the maternal haplotype with each of the two alternative paternal haplotypes and tested for differences in similarity using Wilcoxon matched-paired tests.
1), sires (N = 15, range: 2-30, mean = 8. 0), and mother-sire dyads (N = 75, range: 1-6, mean = 1. 6) each contributed multiple offspring to the dataset, leading to the potential for pseudo-replication, if individuals or dyads show biased MHC transmission. With the exception of the initial mixed model, we were unable to control for this, as it was not possible to include parent or dyadic ID as a random factor in our analyses. Unfortunately, the relatively low numbers of offspring contributed by individual animals and the diversity of MHC haplotypes in the population meant that the occurrence of any particular MHC haplotype was too low to test for the transmission of particular MHC haplotypes with reasonable statistical power. maternal haplotype, that paternal haplotype was passed on in 11 cases, which did not differ significantly from chance (one-tailed binomial exact test: P = 0.
ACKNOWLEDGEMENTS We are grateful to the  Centre International de Recherches Médicales de Franceville, Gabon (CIRMF) ,  Dr. E. Jean Wickings ,  Dr. Delphine Verrier , and past and present staff of the  Primate Centre  for making this study possible. The CIRMF is financed by the  Gab onese government, TOTAL Gabon and the Ministère Français des Affaires Etrangères et Européennes. This study was funded by  Leverhulme Trust  project grant no. F/01576/B. We are grateful to several anonymous reviewers for their comments on previous versions of the manuscript, which improved it greatly.
2008; Choquet et al. 2009b ). Accordingly, MECMR models offer an ideal framework to obtain robust estimates of pair fidelity when there is ambiguity of state assessment. In this paper, we present a flexible MECMR modeling framework to obtain robust estimates of pair fidelity in wild populations. We based the conceptual development of our modeling approach on the pair bond between the members of monogamous breeding pairs (where females and males exclusively associate together at the breeding site) because this is one of the most studied dyadic interactions in behavioral, population, and evolutionary ecology. Also, the way that data on pair memberships is collected is similar to the way in which information on other dyadic associations in wild are obtained. To demonstrate the utility of our approach, we apply it to 30 years of data from a wild population of socially monogamous great tits Parus major Linnaeus, to estimate annual survival and fidelity rates and investigate patterns of pair fidelity in the population.
Application of the MECMR to great tit breeding data To demonstrate the utility of this MECMR modeling framework for estimating pair fidelity rates, we applied the model to 30 years of breeding records  from a long-term monitored population of great tits breeding in nest boxes. Around 1020 nest boxes are distributed at variable densities within the c. 385 ha mixed deciduous woodland of Wytham Woods, Oxfordshire, U. K. During the synchronous breeding season (April-May), nest boxes were visited on a weekly basis to collect data on breeding attempts. Individuals were captured between days 6 and 14 of the nestling phase either within the nest box by hand or using traps, or with mist-nets in front of the box. All captured individuals and all nestlings that survived to day 15 were marked with unique metal rings.
Evaluating model performance on the simulated capture histories We evaluated the ability of our new modeling framework to estimate state-dependent survival and fidelity rates, under varying recapture rates using simulated capture histories. We also compared the accuracy of the pair fidelity rates obtained by our new model to those obtained by the traditional method (which ignores imperfect detectability and capture heterogeneity and where only definitive cases of fidelity and partner change can be considered). We simulated three separate capture history datasets consisting of 6400 capture histories, over 10 sampling occasions, under state-dependent but varying recapture rates (recapture probabilities of birds in the state "AD" for three simulations were 0. 5, 0. 7, and 0. 8, while the recapture rate for birds in state "AS" was 0.
9). The values of all other parameters were chosen to reflect the values obtained from our great tit dataset (ignoring temporal variation), and were kept the same in all three simulations (φ AS = 0. 6, φ AD = 0. 4; w AS = 0. 4; w AD = 0. 3; c AS and c AD = 0. 73; c AS/AD = 0. 13, l = 0. 92). To estimate parameters using the MECMR modeling framework, we ran a model in which survival and transition rates were dependent on the state of departure, with recapture rates dependent on the state of arrival. To calculate pair fidelity rates using the traditional method, we applied the following equation: w TRAD ¼ f =ðf þ chÞ (9) where f is the number of times event 1 occurred (definite case of pair fidelity) and ch is the number of times events 2 and 4 occurred (definite cases of partner change). To calculate pair fidelity rates of birds breeding with the same partner as in t À 1 we applied equation 9 to birds that were recorded in event 1 at t and in events 1, 2, or 4 at t + 1.
Results The results of the GOF tests revealed no detectable lack of fit of the CJS model in either dataset (females: v 2 = 80. 10, df = 91, P = 0. 786; males: v 2 = 76. 14, df = 99, P = 0. 957), indicating that the general assumptions of the CJS model were reasonably met for both datasets. In addition there was no evidence for overdispersion in either dataset (females: ĉ = 0. 88; males: ĉ = 0. 88). For both sexes, there was strong support for models in which recapture rates were constant over the study period but varied by state (i. e. , pair status, Tables  1  and 2 ). Individuals that were faithful to the previous year's partner were more likely to be recaptured than were individuals that had changed partners (recapture rate: faithful females = 0. 72, CI = 0. 69-0. 76, changed partner females = 0.
e. , the probability of transitioning from state "AS" to state "AS" was higher than the probability of transitioning from state "AD" to "AS," Fig. 2C  and D ). Application of the the MECMR to the three simulated datasets (with different recapture rates of birds in the "AD" state), revealed that our model correctly estimated (with very little bias) all of the input parameters (Table  3 ). More importantly, the MECMR model estimated pair fidelity rates far more accurately than did the traditional method in each of the three datasets, and this was particularly evident in the first simulated dataset where recapture was lowest (Table  3 ).
2005). Regardless of the underlying mechanisms, the existence of capture heterogeneity will bias estimates of pair fidelity obtained by methods that do not account for it, and argues against the use of traditional methods of estimating pair fidelity in wild populations. A key advantage of the MECMR modeling framework developed in this study is that both fidelity and survival rates can be obtained in a single analysis and variation Estimates of the recapture rates (p), survival rates (φ), and fidelity rates (w) were obtained for birds breeding with the same partner as in the previous year (state "AS") and those breeding with a different partner to the last year (state "AD"). Datasets were simulated using the following values (φ AS = 0. 6, φ AD = 0. 4; w AS = 0. 4; w AD = 0. 3; p AS = 0. 9, c AS and c AD = 0.
73; c AS/AD = 0. 13, l = 0. 92; see text for details) and with p AD set to 0. 5 (Dataset 1), 0. 7 (Dataset 2), or 0. 8 (Dataset 3). Figure  2. Survival rates (AE95% CI) for (A) adult female and (B) adult male great tits by pair status (estimates obtained from best supported model for each sex, see Tables  1  and 2 ) and pair fidelity rates (AE95% CI) for adult (C) female and (D) male great tits (estimates obtained from best supported model for both sexes, see Tables  1  and 2 ). Filled symbols show rates for individuals that remained with their previous partner (state "AS") while open symbols show rates for individuals that changed partners (state "AD"). in these vital rates can be modeled as a function of pair status. These features allowed us to test hypotheses regarding the dynamics and fitness benefits of pair fidelity in the studied population.
Yeast two-hybrid methods The coding sequence for amino acids 1-544 of Moesin (GenBank gi: 386764061) was PCR-amplified, cloned into pB27 as a C-terminal fusion to LexA, and checked by sequencing the entire insert. The library was screened as previously described  (Formstecher et al. , 2005) , except as noted. Sixty-five million clones (sixfold the complexity of the library) were screened, and 227 His + colonies were selected on a medium lacking tryptophan, leucine, and histidine, supplemented with 5 mM 3-aminotriazole. Sequences of positive clones were used to identify the corresponding interacting proteins in the GenBank database (National Center for Biotechnology Information) using a fully automated procedure.
ACKNOWLEDGMENTS We thank  C. Berry  for assistance with Minos element excision screening,  L. Zeng  for technical assistance, and  P. Vanderzalm  for imaging assistance. We thank  M. Nahm  and  S. Lee  for the pAc-dRich-GFP construct and  E. Chen ,  M. Mlodzik ,  J. Settleman ,  G. Longmore ,  D. Bilder , the  Bloomington Drosophila Stock Center , and the  NIG (Japan)  for providing fly stocks, and the DSHB,  E. Laufer ,  H. Bellen ,  R. Holmgren , and  D. Duncan  for providing antibodies. Thanks to  M. Glotzer ,  I. Rebay , and  G. Marques  for comments on the manuscript and the  Fehon and Rebay labs  for helpful suggestions on this work. This work was funded by grant  GM087588  from the  National Institutes of Health  to R. F. and by a  GenHomme Network Grant  ( 02490-6088 ) to Hybrigenics.
The recent development of Next Generation Sequencing (NGS) technologies and the creation of a molecular biology platform in New Caledonia  (Majorel-Loulergue et al. , 2009) , allowed us to initiate the sequencing of the P. gabriellae leaf transcriptome using Roche GS-FLX titanium technology. We obtained more than 500,000 reads that are publicly available at the European Nucleotide Archive under accession number ERP001334. The de novo assembly of the sequence reads generated approximately 30,000 contigs that represented the first EST data set for P. gabriellae. We are currently using these original sequences to identify putative Ni transporters from the NRAMP, IREG/FPN, YSL, ZIP/IRT families  (Verbruggen et al. , 2009; Krämer, 2010; Rascio and Navari-Izzo, 2011) , that might be involved in nickel tolerance and hyperaccumulation in leaves  (Merlot et al.
ACKNOWLEDGMENTS Because of space limitation, we were not able to cite all studies that would have been relevant to this topic, but we would like to collectively acknowledge researchers from the  University of New Caledonia (UNC) , the  neo-Caledonian Agronomic Institute (IAC) ,  IRD ,  CNRS , or from other institutions and also volunteers that are acting in New Caledonia to better describe, understand, valorize and protect its peculiar flora. We would like to especially thank  Laurent L'Huillier  ( IAC ) for funding some recent metal analyses,  Laure Barrabé  and  Nadia Robert  for unpublished information and the picture presented in Figure 2, and  Fondis Bioritech (Guyancourt, France)  for the use of the hand-held XRF instrument. The costs for this publication were supported by funding from  CNRS.
Analysis of neuropsychological data Neuropsychological data were analyzed with SAS 9. 1 software (SAS Institute, Cary, NC, USA). OCD patients and HC were compared using the Wilcoxon signed-rank test for continuous variables, and McNemar's test for categorical variables. For continuous variables, we report the means (6 SD), and for categorical variables, we supply the number of patients in each category (and the corresponding percentage).
Analysis of PET data PET data were analyzed using SPM2 software (Wellcome Dept of Cognitive Neurology, London, UK) implemented in MATLAB, Version 7 (Mathworks Inc. , Sherborn, MA, USA). Statistical parametric maps are spatially extended statistical processes that are used to characterize specific regional effects in imaging data. SPM combines the general linear model (to create the statistical map) with Gaussian field theory in order to draw statistical inferences about regional effects  [28]. All the participants' images were first realigned and spatially normalized into standard stereotactic space according to MNI space. An affine transformation was performed to determine the 12 optimum parameters for registering the brain images onto the template, and the subtle differences between the transformed image and the template were then removed using a nonlinear registration method.
This study was supported by a grant from the  French ''Programme Hospitalier de Recherche Clinique '' (PHRC) and by a grant from the '' Fondation Pierre Deniker ''. This study was partially supported by the '' Unitede Recherche Clinique Intersectorielle en Psychiatrie'' du Centre Hospitalier Henri Laborit , which received financial support from the  Agence Regionale de la Santede Poitou-Charentes. N. Jaafari  was supported by the financial support, ''bourse chercheur invite'', of the  Region Poitou-Charentes. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Statistical analyses A square root transformation was conducted on Time 1 Weight-based restriction to improve normality. When transformations were insufficient, non-parametric statis-tics and robust methods were used. Data reduction was conducted using principal component analysis. A BMIz change score was computed by subtracting Time 2 BMIz scores from Time 1 values. Relationships between maternal feeding practices and BMIz change, as well as child eating behaviors were explored with correlational analyses at each time point. Prospective relationships between (1) maternal feeding practices at Time 1 and child eating behaviors at Time 2; and (2) between child eating behaviors at Time 1 and maternal feeding practices at Time 2, were investigated using hierarchical multiple regression analyses.
As an important proportion of adult patients was shown not to maintain their serological and virological response, treatment up to HBsAg clearance could be a safer choice for patients with histological evidence of severe fibrosis (C2)  [170]. Patients should be monitored after discontinuation because of the possibility of post-treatment flares (B1). Patients who do not undergo HBeAg seroconversion on treatment, the rare children with HBeAg-negative chronic hepatitis and cirrhotic patients need long-term treatment with NA (B1). Tenofovir or entecavir, if allowed by the age, are the first choice (A1). Long-term efficacy and safety data in adults support such a strategy, but no data are available for adolescents as yet  [173] [174] [175] [176]. During long-term treatment with NA, HBV DNA levels should be monitored every 3 months, as HBV DNA reduction to undetectable level is of paramount importance to avoid resistance (B1).
A total of 4809 (DNA) and 3872 (RNA) different taxa were identified using BLAST-searches, revealing a highly diverse microbial community at all depths (Table  S2 ). The taxonomic composition based on all rRNA genes (5,923 sequences) using BLASTn-searches against the SILVA database is shown in Figure  3A  and based on the metagenomes (1,882,842 sequences, excluding all rRNA genes) and the metatranscriptome (421,528 sequences, excluding all rRNAs) using BLASTx-searches against the non-redundant database of NCBI in Figure  3B  and 3C. A large percentage of the sequences found in both the metagenomes and metatranscriptomes had no significant match against the nonredundant database of NCBI. On average, 49% of the sequences remained unidentified, which is comparable to other studies that utilized high-throughput sequencing technologies in marine habitats  [37, 40, [58] [59] [60].
The dand e-proteobacterial sequences were even more abundant within the rRNA gene dataset (Figure  3A ) when compared to the metagenomes (Figure  3B ), probably due to the lack of representatives genomes for these groups. The metatranscriptomes showed a more variable picture of the microbial community (Figure  3C ). In surface waters eukaryotic sequences formed the largest identifiable group (5%), while at all other depths c-proteobacteria were dominating (13. 3-30. 5%). In sulfidic waters e-proteobacterial transcripts were further identified in relatively high, but also more variable proportions (5. 0-13. 2%), when compared to the metagenomes. Notably, 'other bacteria', summarizing 19 bacterial phyla, were present at all depth, but never exceeded 5. 8% of the sequences, in both the metagenome and the metatranscriptome datasets.
Metabolic activity and functional diversity of the microbial community General activity patterns. To assess the functional diversity of the microbial community in detail, we used three different approaches to investigate our sequence data. The BLASTsearches were supplemented by scans of our sequences with profile hidden Markov models of the ModEnzA Enzyme Commission (EC) groups  [69]  and of the Pfam protein families  [70]. Furthermore, we recruited the DNA and RNA sequences onto the genomes of the five organisms most often recognized by our BLAST-searches, SUP05, R. magnifica, V. okutanii, Sulfurovum and D. autotrophicum  [41, [64] [65] [66] [67]. For these genome recruitments, we calculated the expression-ratio, a measure of the enrichment of selected transcripts over the corresponding genes, normalized to the total pool of all protein-coding sequences (Figure  5  and S4).
D. autotrophicum, one of the most abundant organism we identified in our sequence dataset, is a metabolically versatile SO  4 22  reducing marine d-proteobacterium, which can completely oxidize organic carbon compounds to CO 2 , but is also capable of growing autotrophically on hydrogen (H 2 )  [67]. The genome recruitment plots of D. autotrophicum (Figure  S4B ) show regions of the genome mostly related to energy metabolism and nutrient cycling. High expression-ratios for key sulfur metabolizing enzymes like the dissimilatory sulfite reductase (dsrABD), the adenylylsulfate reductase (aprAB) and the sulfate adenylyltransferase (sat2) suggest that D. autotrophicum could have been reducing SO 4 22 and thus may have contributed to the formation of H 2 S at our sampling site. However, since many sulfur cycling proteins (e.
1 mM O 2 ), our flux calculations indicate that the larger part of the H 2 S was probably oxidized anaerobically with NO x. Nitrogen cycling. To shed light on the nitrogen cycling carried out by the microbial community, we measured potential rates of various nitrogen transformation processes using 15 NO 3 2 , 15 NO 2 2 , 15 N 2 O or 15 NH 4 + incubations and compared them with corresponding abundances of functional genes and transcripts (the sum of BLAST-hits, EC number-and Pfam-assignments) involved in their turnover (Figure  7 ). Reduction of NO 3 2 to NO 2 2 was active throughout the anoxic and sulfidic zones, with the highest rates measured at 40 m (2500 nmol N l 21 d 21 ; Figure  7B ). In comparison, the much lower rate measured at 30 m (150 nmol N l 21 d 21 ) might have been caused by limitations in reductants, as suggested by the much lower H 2 S concentrations at that depth.
Sequence annotation pipeline The sequence data was organized and analyzed with the Meta2Pro annotation pipeline  [96]. All raw sequences were clustered using Cd-hit  [97]  with a sequence identity threshold of 98% and a word length of 8, delivering about 1,581,637 (DNA) and 592,711 (RNA) cluster representative sequences in total. The rRNA genes and rRNAs in these cluster representatives were identified by a BLASTn-search  [98]  against the SILVA database [99] including both prokaryotic and eukaryotic small and large ribosomal subunit sequences with a bit score cut off of 86. The bit score cut off of 86 as described earlier  [100]  was validated using a simulation exercise. All sequences deposited in the SILVA database (477,749 sequences) were randomly fragmented into one million sequences to simulate a pyrosequencing run.
The generated fragments had a normal length distribution with mean and standard deviation values similar to those from our own dataset (mean: 420 base pairs and standard deviation: 150 base pairs). This simulated dataset was Cd-hit-clustered (as described above) and BLAST-searches against the SILVA database itself were carried out. All queries which hit themselves in the database (e. g. the sequence of origin of the query) or hit a sequence belonging to the same taxonomic lineage (allowing a mismatch of up to two taxonomic levels) were considered true positives, while those fragments that hit sequences from other taxonomic lineages were false positives. The bit score distributions for the true and false positives were binned and a threshold sweep was used to calculate the sensitivity and specificity value at each bit score threshold.