dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2009; Sigourney et al. , 2012; Schofield et al. , 2013). They are not usually applied to back-calculation computation, although they are easy to implement using R (see for example  Gelman and Hill, 2007 ; package lmer source, http://r-forge. r-project. org/projects/lme4/) or WinBUGS/OpenBUGS  (Lunn et al. , 2000 or Ntzoufras, 2009 ; http://www. mrc-bsu. cam. ac. uk/bugs/winbugs/contents. shtml) Here, a low-density endangered fish species Zingel asper (L. ) on which little data is available was used to assess the performance of a classic linear model and hierarchical models in both frequentist and Bayesian formulations. Given the short lifespan of this species nonlinear and age effects were ignored. Growth was supposed homogenous within a cohort and variable between cohorts The models are presented and we identify conditions where the Bayesian hierarchical approach is more accurate.
In contrast, each cohort has its own variability and the term b j represents the discrepancy between cohort j and mean population curve. For all cohorts, a common unspecified variance-covariance matrix, D, models the variability of and the dependencies between latent variables (b oj , b 1j , b 2j ). The model was estimated using R package lme4 (function lmer). The program is detailed in the supporting file. In order to improve our estimation, Z asper a priori biological information was included in our third model using a Bayesian approach. Model 3 Lf ij = (β 0pop + b 0j ) + (β 1pop + b 1j ) Ls ij + (β 2pop + b 2j ) Ls 2 ij + ε ij (3. 1) With ε ij ∼ iid N (0, σ 2 ) independent of b j = (b 0j , b 1j , b 2j ) ∼ iid N 3 (0, D) (3. 2) β pop ∼ N 3 (m, V), σ 2 ∼ IG( shape, rate) and D ∼ IW(d, R -1 ) (3.
ACKNOWLEDGMENTS This research was supported in part by grants awarded by the  Science Foundation of Ireland  ( SSPC2 12/RC/2275 ,  13-TIDA-B2625 ,  07/IN. 1/B948 , 12/TIDA/B2411 ,  12/TIDA/B2405 ,  09/RFP/BMT 2350 ); the  Department of Agriculture , Fisheries, and  Food  ( DAFF11/F/009  MabS,  FIRM/RSF/CoFoRD ;  FIRM 08/RDC/629 ); the  Environmental Protection Agency  ( EPA 2008-PhD/S-2 ), the  Irish Research Council for Science, Engineering, and Technology  ( PD/2011/2414 ;  RS/2010/2413 ), the  European Commission  ( FP7-PEOPLE-2013-ITN ,  607786 ;  OCEAN2012 ,  287589 ;  FP7-KBBE-2012-6 ,  CP-TP 311975 ;  FP7-KBBE-2012-6 ,  CP-TP-312184; Marie Curie 256596 ); and the  Marine Institute  (Beaufort award  C2CRA 2007/082 );  Teagasc  (Walsh Fellowship 2013), and the  Health Research Board  ( HRA/2009/146 ).
Extended Data Extended Data Figure  1. Genome assembly and evolutionary rates a, Genome assembly and annotation. b, Genome-wide dN/dS. Rates are calculated from 20 resampled sets of 200 orthologous genes. Gene annotations from interspecies projections (see Methods in Supplementary Information) were excluded from the data set. Top left, map of Africa showing lakes in which cichlid fish have radiated. Right, the five sequenced species: Pundamilia nyererei (endemic of Lake Victoria); Neolamprologus brichardi (endemic of Lake Tanganyika); Metriaclima zebra (endemic of Lake Malawi); Oreochromis niloticus (from rivers across northern Africa); Astatotilapia burtoni (from rivers connected to Lake Tanganyika). Major ecotypes are shown from each lake: a, pelagic zooplanktivore; b, rock-dwelling algaescraper; c, paedophage (absent from Lake Tanganyika); d, scale eater; e, snail crusher; f, reef-dwelling planktivore; g, lobe-lipped insect eater; h, pelagic piscivore; i, ancestral river-dweller also found in lakes (absent from Lake Tanganyika).
Acknowledgements We would like to thank the  Broad Institute Genomics Platform  for sequencing of the 5 cichlid genomes and transcriptomes. Sequencing, assembly, annotation and analysis by  Broad Institute  were supported by grants from the  National Human Genome Research Institute (NHGRI). Genome evolution, duplication and TE analysis, ILS and ancient variant analyses were also supported by  Swiss National Science Foundation  grant  PBLAP3-142774  awarded to D. B. and by  University of Oxford Nuffield Department of Medicine Prize Studentship  to  Y. I. L. TE  and copy number variation analyses were supported by the  German Science Foundation (DFG) , and advanced grant  29700  ("GenAdap") by the  European Research Council (ERC). CNE analysis and zebrafish functional assays were supported by the  Biomedical Research Council of A*STAR ,  Singapore.
Instead, we calculated genome-wide sequence identity between all annotated genes for each group of species involved (see Methods) and compared these values to those of the transferred LTR-RTs (Table  1 ; Fig. 2 ). Distributions of pairwise sequence identities between the closest gene homologs were always unimodal (Fig. 2 ). We concluded that sequence identity at the peak of the distribution should be a good indicator of overall ge-nomic divergence. Peak values were always lower than 90% (our threshold for the detection of LTR-RT HTTs). Finally, we checked phylogenetic incongruence for the 32 HTTs. Sequences homologous to horizontally transferred elements were identified in the NCBI nucleotide databases, excluding the species involved in the transfer, and a tree built including these sequences.
Identification and characterization of HTTs Genome sequences from 40 angiosperm species were downloaded mainly from Phytozome v9. 0 (http://www. phytozome. net/) (the complete list with all sources is given in Supplemental Table  1 ). Fullsize LTR-RT elements were identified in these genomes (by in silico analysis using LTRharvest prediction software, http://www. zbh. unihamburg. de/?id=206)  (Ellinghaus et al. 2008). Default parameters were used except for the following: -xdrop 37 -motif tgca -motifmis 1 -minlenltr 100 -maxlenltr 3000 -mintsd 2. A total of approximately 300,000 copies were obtained and merged into one multi-FASTA database. To detect HTT candidates, we used a clustering strategy that we described previously for LTR-RT family classification  (El Baidouri and Panaud 2013). This method, based on an allagainst-all comparison of LTR-RT sequences, was used to retrieve elements sharing high sequence identity (>90% within monocots or dicots and >85% between monocots and dicots because of the greater divergence time between these classes) between different species.
As a first step, a nucleotide BLAST (all against all) of these elements was performed using the following parameters: -r 2 (reward for nucleotide match), -e 1e-20 (E-value), -F F (Filter = false), and -m 8 (for tabular output). The second step consisted of clustering sequences (based on the BLAST results) using SiLiX software (http://lbbe. univ-lyon1. fr/SiLiX) (Miele et al. 2001 ) in order to define highly similar LTR-RT copies. About 124,000 distinct clusters were obtained. The vast majority of these clusters contain several copies of the same LTR-RT family from a single species. This is what is expected in the case of a vertical TE transmission. However, 32 clusters contain LTR-RT elements that share high sequence similarity and belong to at least two different species. All potential candidates were validated by checking that the LTR-RT sequences were located on large contigs and not on isolated, short sequences in genome assemblies, and that high sequence identity was limited to the elements themselves-to eliminate possible contamination or annotation errors.
Estimation of genomic distances Gene identities were computed following two methods. (1) For BG HTTs, a set of 20 genes  (Zhang et al. 2012 ) was used to determine an average K s value for each species pair involved in the transfer. K a /K s were computed using K a /K s calculator software (Table  1 ; Supplemental Table  2 ;  Zhang et al. 2006). (2) For the BO and BC HTTs between more distantly related species, in which K s values are subject to caution due to potential saturation of sites, full genome comparisons of the complete gene sets were performed using a BLASTN homology search procedure (Fig. 2 ). For each pair of species involved in HTT, a nucleotide BLAST was performed (BLAST 2. 2. 26+) using all coding DNA sequence (CDS) of one species as a query against all CDS of the other species, with the default parameters.
Phylogenetic incongruences between horizontally transferred LTR-RTs and species trees For each transferred element, the reverse transcriptase domain was used as a query for a homology search against the NCBI nr nucleotide database using the BLASTN algorithm and excluding the species involved in the transfer. All sequences, including queries, were aligned using Muscle, and a phylogenetic tree was built using a maximum likelihood method and 100 bootstrap replicates. Alignments and phylogenetic analyses were performed using SeaView software on a LINUX platform.
Pairwise full genome comparisons Validation of selected comparisons was carried out a posteriori by whole-genome comparisons. The genomic sequences of the species implicated in inter-order and inter-class HTTs were split into small fragments of 1 kbp using splitter software from the EMBOSS package (http://emboss. bioinformatics. nl/cgi-bin/emboss/help/ splitter). A BLAST2seq search was performed using the sequence fragments of the two species implicated in an HTT event as query and subject. All genomic regions that produced significant BLAST hits (sequence identity >90% and HSP length >200 bp) were retained for further analysis. Nucleotide BLASTN and protein BLASTX searches for highly similar regions were performed against the NCBI nr databases (http://www. ncbi. nlm. nih. gov), and hits are reported in Supplemental Table  3.
Acknowledgments This work was supported by the  University of Perpignan ,  CNRS , and  IRD ; a PhD fellowship from the  CNRS  and  Region Languedoc-Rousillon  to M. E. B. ; an  Agence Nationale de la Recherche  grant (AZORIZ) to N. P. ; and a  Labex  grant from the  Agropolis Fondation  ( RETROCROP 1202-041 ) to M. M. and O. P. We thank  N. Billotte ,  A. D'Hont ,  E. Guiderdoni ,  C. Roux ,  P. This , and  J. Wood  for providing DNA material; and  M. A. Grandbastien  and  T. Wicker  for their useful comments on the manuscript.
Species and focal traits Investigating evolutionary processes resulting from natural selection requires the use of data sets where phenotypes and relatedness are collected from populations in their natural environment. In this situation, estimating accurate G matrices necessitates long-term datasets with multigenerational pedigrees. We focused on four morphological traits that are most commonly measured in adult birds: wing length, tarsus length, body mass and bill length. Populations were hence selected based on the availability of a pedigree and the minimum number of morphological traits needed. We gathered 10 data sets representing seven bird species from three continents (Table  1 ): red-billed gull (Chroicocephalus scopulinus,  [21] ), great reed warbler (Acrocephalus arundinaceus,  [22] ), barn swallow (Hirundo rustica, two populations,  [23] ), blue tit (Cyanistes caeruleus, three populations,  [24] ), collared flycatcher (Ficedula albicollis,  [25] ), Savannah sparrow (Passerculus sandwichensis,  [26] ) and house sparrow (Passer domesticus,  [27] ).
Acknowledgments We thank  J. Hadfield  for statistical advice,  R. Julliard  for fruitful discussion and  M. Morrissey ,  Loeske Kruuk  and  Fabrice Eroukhmanoff  for helpful comments on a previous draft of the manuscript. We gratefully acknowledge  CC-IN2P3  for providing a significant amount of the computing resources needed for this work. Thanks to all the people who helped obtain field data, especially  J. Schroeder  for the house sparrow project,  A. Barbosa ,  N. Cade  ´e,  J. Cuervo ,  L. Garamszegi ,  D. Gil ,  I. G. Hermosell ,  F. Mateos ,  S. Merino ,  J. Moreno ,  C. Navarro  and  P. Ninni  for the barn swallow project,  J. Blondel ,  P. Perret ,  M. Lambrechts  and  D. Garant  for the blue tit project,  S. Bensch ,  M. A ˚kesson ,  B. Nielsen ,  H. Westerdahl , and  Kvismare Bird Observatory  for the great reed warbler project, and numerous Bowdoin undergraduates on the Savannah sparrow project.
This project was funded by the  French ANR  (grant  ANR-08-JCJC-0041-01 & ANR-12-ADAP-0006 ), by the  OSU OREME , by grants from the  Swedish RC  (to BH and DH) and the  Spanish MEC  ( CGL2009-08976 ) to JB, FdeL and AM. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
At the same time, the majority of these countries are strongly dependent on imports, especially for cereals (except France and Turkey). Furthermore, the share of DES derived from cereals is still considerably high (Egypt 65%, Morocco 57%, Algeria 55%, Tunisia 51%, Libya 49%, Turkey 48%; Food and Agriculture Organization Corporate Statistical Database  [FAOSTAT]  2009, data available in November 2013). 5 This cereal-centered dependency can lead to a regional and national vulnerability. In particular, it occurs at the expense of middle-and low-income groups, and of the national government expenditure. For example, bread subsidies, amounting to US$2. 5 billion per year, were introduced in Egypt in 2008  (FAO, WFP, & IFAD, 2012). It is also necessary to consider food price volatility, in particular for cereals, as it affects consumers' capabilities to access food.
In this formula, X min and X max are, respectively, the lowest and highest values for any given indicator. The normalized value is transformed from a 0 to 1 value to make it directly comparable with other indicators. This means that the indicator with the highest raw data value will score 1, whereas the lowest will score 0. In the examples referred to, the indicator scores are normalized and then aggregated across categories to enable a comparison of broader concepts across countries. Normalization rebases the raw indicator data to a common unit, so that it can be aggregated. Finally, in our specific case the n particular vulnerabilities will be calculated following this formula: Another issue that has to be considered is the quantitative relevance, or weight, that is associated with the different components.
The author(s) disclosed receipt of following financial support for the research and/or authorship of this article:. Paolo Prosperi is a PhD student funded by a University of Catania scholarship and a Franco-Italian University complementary scholarship (Grant number: C2-19). Bruce Cogill and Thomas Allen's work was supported by The Daniel & Nina Carasso Foundation (Grant number: 00030240) and the Agriculture for Nutrition and Health Research Programme of the CGIAR. Notes 1. Data are not completely available for all Mediterranean countries. 2. This count involves overweight and underweight adults (age > 20 years) and overweight, underweight, stunted, and wasted children (age < 5 years) within the Mediterranean population. Available data at September 2013 were collected from World Health Organization, Global Database on Child Growth and Malnutrition and Global Database on Body Mass Index; United Nations (UN) Department of Economic and Social Affairs-Population Division.
3. In UN databases, many of the cited statistics are not assessed in several Mediterranean countries: Child stunting, wasting, and underweight are not assessed in Cyprus, France, Greece, Israel, Italy, Lebanon, Malta, Portugal, Slovenia, and Spain; child wasting is not assessed in Algeria, Croatia, Morocco, and Turkey. Child stunting is not assessed in Turkey; child overweight is not assessed in Croatia, Cyprus, France, Israel, Italy, Malta, Portugal, and Serbia; Adults underweight is assessed just in France, Italy, Jordan, Malta, Portugal, Spain, Macedonia, and Turkey. Adults overweight is assessed in all Mediterranean countries. 4. Except for the Occupied Palestinian Territories.
Determination of chemical denaturation midpoints (guanidine hydrochloride) Denaturation mixtures (150 l) were prepared in wells of a black 96-well plate (Greiner bio-one, Cat. No 655096) containing 50 mM sodium glutamate buffer, pH 7. 5 with 5 mM DTT, 5 mM MgCl 2 , 15% glycerol, increasing concentrations of guanidine hydrochloride (0 to 5 M) and protein at final concentrations of 0. 5 mg/ml (NS5) and 1. 1 mg/ml (NS5-Pol). Protein was added last. NS5 and NS5-Pol denaturation was followed by measuring fluorescence emission at 300-460 nm (excitation at 280 nm). Denaturation midpoints of NS5 and NS5-Pol were calculated by the Boltzmann equation using the GraphPad Prism software as in  (20).
Determination of steady-state catalytic parameters Initial velocity (v i ) values upon steady-state conditions were calculated as the product (P) formation with time during the linear phase of product formation: v i = d [P]/dt. Data sets obtained by the determination of v i values with increasing substrate concentrations were first fitted by non-linear regression to the Hill equation: v i = V max [NTP] h /((K 0. 5 ) h +[NTP] h ). Data were found to fit best to the classic Michaelis-Menten equation where the Hill coefficient is 1: v i = V max [NTP]/(K M + [NTP]); K M = Michaelis constant, V max = maximum velocity at infinite substrate concentration. The catalytic constant k cat is equiv- alent to V max /[protein]. Catalytic efficiency corresponds to k cat /K M. The catalytic parameters of NS5 and NS5-Pol were calculated from the equations above by using the GraphPad Prism software.
Mapping and cloning of the moss/ago1-103 mutation Low-resolution mapping of the moss mutation was performed as previously described  (Ponce et al. , 2006). In brief, the DNA of 50 F 2 phenotypically mutant plants was individually extracted and used as a template to multiplex PCR co-amplify 32 simple sequence length polymorphism (SSLP) and insertion/ deletion (In/Del) molecular markers using fluorescently labelled oligonucleotides as primers. For fine mapping, 421 F 2 plants were used to assess iteratively linkage between moss and molecular markers designed according to the polymorphisms between Ler and Col-0 described at the Monsanto Arabidopsis Polymorphism Collection database (http://www. arabidopsis. org). For sequencing of the moss allele, PCR products spanning the AGO1 transcriptional units were obtained using the oligonucleotide primers described in  Jover-Gil et al.
Expression analyses For quantitative PCR (RT-qPCR), 2000ng of total RNA, extracted with the RNeasy Plant Mini Kit (Qiagen), were treated with DNase (DNA-free kit, AmbioN) and used for cDNA synthesis, performed with the SuperScriptII cDNA synthesis kit (Invitrogen). The qPCR mix was prepared in a final volume of 20 mL containing 1200 ng of the cDNA, 1× Power SYBR Green master mix (Applied Biosystems) and 300 nM primers. Primers used to amplify the GUS and TFL1 cDNAs were: GUS-Fw and GUS-Rv for GUS, and TFL1-Fw and TFL1-Rv for TFL1 (Supplementary Data Table  S1 ). Results were normalized to the expression of the UBQ10 gene  (Czechowski et al. , 2005) , with the primers UBQ-Fw and UBQ-Rv (Supplementary Data Table  S1 ). The PCRs were run and analysed using the ABI PRISM 7700 (Applied Biosystems) sequence detection system.
We analysed the expression of TFL1::GUS in the shoot apex during development (Fig. 2A ). The analysis was performed with plants derived from the moss BC 3 line that had lost the original pBTG1 reporter and where the reporter pBTG6 was introduced. The pBTG6 reporter construct is essentially identical to pBTG1 but contains the complete TFL1 3 ′ intergenic region (approx. 3. 7 kb; Supplementary Data Fig. S1 ) and, thus, it reproduces the expression of the endogenous TFL1 gene more accurately. In moss pBTG6 plants, the expression of TFL1::GUS was stronger in the SAM both in the vegetative and in the inflorescence phases, becoming ectopic in the flowers of the mutant in the adult inflorescence (Fig. 2A ). We also analysed by RT-qPCR the expression of the endogenous TFL1 gene in adult inflorescence apices.
ACKNOWLEDGEMENTS We thank  HerveVaucheret  for the ago1-26 seeds,  Antonio Serrano-Mislata  for the pBTG6 construct, and  Cristina Ferra  ´ndiz for critical reading of the manuscript. The collaboration of the IBMCP staff from the greenhouse, sequencing and microscopy facilities is also acknowledged. This work was supported by grants from the  Spanish Ministerio de Ciencia e Innovacion  ( BIO2009-10876  and  CSD2007-00057 ), the  Spanish Ministerio de Economı ´a y Competitividad  ( BFU2012-38929 ) and the  Generalitat Valenciana  ( ACOMP2012-101 ). P. F. N. was supported by a fellowship from the  I3P program  of  CSIC.
Four different gametocyte carriers were used (two for each egg-lay, Figure  1b ). Additional file 1 shows infection rate and intensity in F0 females. Binomial and negative binomial generalized linear mixed models on offspring infection rate and intensity, respectively, were fitted. The best model (Generalized Linear Mixed Model with maternal parasite exposure vs GLMM without maternal exposure) was selected based on the Akaike Information Criterion (AIC). Maternal exposure was coded as a fixed factor. Maternal identity (experiment 1) and replicate (experiment 2) were coded as random factors. For experiment 2, the data were also analysed with replicate set as a fixed effect (Additional file 3). The relationship between infection intensity in daughters and mothers was investigated using a negative binomial GLMM.
Acknowledgments This study was funded by the  ANR  ( 11-PDOC-006-01 ) and  FP7  ( n°242095 ,  n°223736 ). We would like to thank all children and their parents for participating in this study, as well as to the local authorities for their support. We are very grateful to the  IRSS  staff in  Burkina Faso  for technical assistance. We also thanks three anonymous referees for helpful and constructive comments.
The ENHanCEd Infectious Diseases (EID2) database integrates published data sources on pathogens, their hosts (including vectors) and geographic ranges  [7]. By coupling the H-index method with the EID2, the primary aim of this study was to establish priority lists of human and domestic animal pathogens (including zoonoses) present in Europe. We then consider reasons for the omission of some pathogens in our lists from those of other disease prioritisations: the 2010 Global Burden of Disease (GBD) estimates  [8] , communicable human diseases reportable in the European Community [9], the OIE list of notifiable animal diseases, infections and infestations  [10] , and the EU FP7 DISCON-TOOLS project  [11]. The GBD 2010 study was a large collaborative five year project which used all relevant published and unpublished evidence to create the strongest evidence-based epidemiological assessment of people's infectious and non-infectious health problems around the world  [8].
EID2 pathogen information The EID2 database collates data on human and domestic animal pathogens: where, when, and in which hosts there is evidence of their occurrence. The database is built largely using automated procedures to interrogate publicly available databases. An EID2 background has been described previously  [7] ; here, we used similar criteria to define pathogens, including pathogenic status (frequently pathogenic: a pathogen which frequently causes a clinically pathogenic effect -morbidity or mortality -in humans or domestic animals; non-pathogenic: an organism which causes no clinical signs within any of its hosts; unknown pathogenicity: an organism for which there is insufficient evidence to decide), evidence of pathogens affecting hosts ('host-pathogen interactions': evidence from at least one piece of meta-data uploaded with DNA or RNA sequence information to  [12] , which describes where, when and from which host the pathogen came, or specific scientific publications [13]), and evidence of pathogens occurring within countries (evidence from at least one piece of meta-data  [12] , or at least five publications in  [13]  where pathogen name and a country MeSH-term  [14]  co-occurred in the title/abstract).