dataesr/scientific-paragraphs-categorization huggingface dataset:
It is an ensemble method that aggregates K trees (forming the forest), each tree similar to ones constructed with CART (Classification and Regression Trees), and grown using a bootstrapped sample of the original data set. Each tree in the forest uses at each node only a number of variables randomly sampled as candidates from a subset of the explanatory variables (mtry), which in our case was equal to a third of the predictor variables  (Liaw and Wiener 2002). To stabilize the mean square error, we used 10,000 trees. We used the 'randomForest' package (version 4. 6-7)  (Liaw and Wiener 2002)  in R (version 3. 0. 1) (R Core Team 2014) for this analysis. We presented variable importance plots for both performance measures as the decrease in mean accuracy resulting from the removal of each variable, and presented partial dependence plots to show the effect of the main continuous predictors on the response variables  (Liaw and Wiener 2002).
4. Generation Time (GT, continuous): we used the values estimated by  Collette et al. (2011)  on a log scale as a biological predictor, because life history parameters such as growth, longevity and age of maturity are considered to be uncertain for most stocks of billfishes, if available  (Kopf et al. 2009 ). In the supplemental material of  Collette et al. (2011)  there is a detailed explanation of how GT was calculated for each stock and/or species. The range of this variable is from 1 year for skipjack to 17. 2 years for southern bluefin tuna (Table  S2 ). 5. Market price (continuous): we obtained market price for tunas and billfishes from different sources. For all tunas stocks we used the data available in the FAO economic trade and markets database. However, for billfishes, detailed information by species was not available in this database.
Therefore, US market price database for all billfish stocks was considered. In all cases, we used the average price for the last 10 years, from 2003 to 2012. Prices range from $0. 96 dollars/kg for skipjack tuna to $14. 49 dollars/kg for Southern bluefin tuna (Table  S3 ). 6. Number of countries fishing each stock (continuous): we considered the smallest number of countries that cumulatively reported more than 75% of the total catch during the past 10 years  (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012)  as a measure of how the total catch for each stock is allocated among countries (Table  2 ). 7. Total allowable catch (TAC in years, continuous): this was used to take into account the number of years under TAC enforcement. We used a continuous variable ranging from 0 for stocks with no TACs to 31 for western Atlantic bluefin tuna.
Effect of management regulations on depleted stocks The same type of statistical analysis was used to identify which management measures have the strongest effect on the recovery of previously depleted stocks. We selected those stocks that showed B<BMSY or F>FMSY ten years before the final assessment year. We used as a response variable the geometric mean of the annual rate of change of B and F during this period. We considered biomass levels increasing towards BMSY and fishing mortality rates decreasing towards FMSY as positive signs of stock rebuilding. The same input and output management measures as in the previous analysis were used as predictors. We conducted two sensitivity analyses, one removing the bluefin tuna stock from the eastern Atlantic, since it is an outlier in the rate-of-change data (Fig.
Supporting Information Acknowledgments: This project was funded by the  Pelagic Fisheries Research Program  of the  Joint Institute for Marine and Atmospheric Research , and the  Walton Family Foundation. MP was supported by a  Fulbright Fellowship. We thank  Carolina Minte-Vera  and  Alexandre Da-Silva  for making some of the IATTC stock assessment data available to update the RAM Legacy database. Also, we thank  Tom Nishida ,  Freddy Arocha ,  Adam Langley ,  Dale Kolody ,  Alec MacCall ,  Steven Teo  and  Michael Hinton  for their comments during the design of the study, and  Wataru Tanoue  and  Andrés Domingo  for their comments on the initial draft of the manuscript.
Acknowledgments This work was supported by grants from the  Spanish MINECO  ( BFU2013-41329 ) and the  European Research Council  ( ERC-2011-StG-281191-VIRMUT)  to  R. S. Open  Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http:// creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Strain Marseille-P2846 T exhibited an 86. 6% sequence identity with Christensenella minuta strain YIT 12065 T (GenBank Accession number NR_112900. 1), the phylogenetically closest species with standing nomenclature (Fig. 1 ), which putatively classifies it as a member of the new family 'Beduinellaceae' within the order Clostridiales in the phylum Firmicutes. Christensenella minuta strain YIT 12065 T is a strictly anaerobic, non-motile, non-sporeforming, Gram-negative rod that was first isolated from human faeces  [5]. Strain Marseille P2846P exhibited a 16S rRNA sequence divergence >12% with its phylogenetically closest species with standing in nomenclature  [6] ; hence, we propose the creation of the new family 'Beduinellaceae' fam. nov. , of the new genus 'Beduinella' fam. nov. , gen. nov.
, and of the strain Marseille-P2846 T as the type strain of 'Beduinella massiliensis' fam. nov, gen. nov, sp. nov. , the first representative species of this new family. 'Beduinella massiliensis' fam. nov. , gen. nov. , sp. nov. (Be. dui. nel. 'la. N. L fem. n. Beduinella from the Latin name of nomadic Bedouin population from Saudi Arabia where the strain was collected), (mas. si. li. en'sis, L. , fem. adj. , massiliensis for Massilia, the Roman name of Marseille, where strain Marseille-P2846 T was isolated). MALDI-TOF-MS spectrum accession number. The MALDI-TOF-MS spectrum of 'B. massiliensis' is available at: http://mediterranee-infection. com/article. php?laref=256&titre= urms-database Nucleotide sequence accession number. The 16S rRNA gene sequence was deposited in GenBank under Accession number LT576387.
Prediction of female genotypes at the α s1 casein locus The probabilities of each possible genotype at the α s1 casein locus for all the females with phenotypes, that had not been genotyped (152,554 Alpine and 126,738 Saanen goats), were obtained separately for each breed based on the iterative peeling procedure implemented in the software developed by Vitezica  [16, 17]. Iterations were stopped when the summed absolute difference in genotype probabilities between two iterations were less than 10 -3 for all females and for each genotype probability. For computational reasons, predictions were made separately for three groups of animals per breed, i. e. animals born before 2000, born between 2000 and 2007, and born after 2007. The genotype probabilities were computed using data on all available α s1 casein genotypes (i.
Estimation of the effects of α s1 casein genotypes based on DYD phenotypes The significance of the effect of each α s1 casein genotype on the five milk production traits was tested on the combined dataset (Alpine + Saanen) and separately on the dataset for each breed by using analysis of variance in the GLM procedure of the SAS ® software. For this analysis, a simple model was used where DYD were explained only by a α s1 casein genotype effect and a breed effect for the combined dataset. Genotypes with less than 10 animals were excluded from this analysis. The amount of DYD phenotypic variance explained by the α s1 casein genotype was estimated by a restricted maximum likelihood (REML) algorithm implemented in the remlf90 software  [33]. Estimations were obtained for the same populations as above (Alpine + Saanen, and Alpine and Saanen, separately) for the five traits using a simplified pedigree of 37,669 individuals going back 10 generations from the genotyped males.
Breeding values estimated based on genomic information; 4. Breeding values estimated based on genomic infor- mation and considering the α s1 casein genotype as a fixed effect. These analyses were carried out on a multi-breed (Alpine + Saanen) dataset because the effects of α s1 casein genotypes on milk production traits seem to be similar in both breeds (see "Effect of α s1 casein genotypes on traits selected for in French dairy goats" section) and the official genetic evaluation is a multi-breed evaluation since variance components are similar for both breeds  [34]. Analyses were based on the following model: where, as in Model (1), X is an incidence matrix for the fixed effects β (i. e. breed, mean and with or without an effect for α s1 casein genotype) and u is a vector of the breeding values considered as random effects.
Single-step model based on female phenotypes The last part of this study consisted in including the effect of the α s1 casein genotype in one-step genomic evaluations for protein content based on recorded females from official genetic evaluations. Since the α s1 casein genotype has a clear effect on protein content, only this trait was considered here. In a first approach, we used genotype probabilities at the α s1 casein locus that were computed as explained in "Prediction of female genotypes at the α s1 casein locus" section. Two models were tested for each of the three datasets: (1) both breeds (Alpine + Saanen), (2) Saanen, and (3) Alpine goats, only. The first model was as follows: (2) DYD = Xβ + Zu + e, G = 0. 95 × MM ′ 2 p j=1 q j 1 -q j + 0. 05 × A, where y 1 corresponds to the response vector for protein content for: (1) 7,351,831 Alpine and Saanen records, (2) 3,173,516 Saanen records only, or (3) 4,178,315 Alpine records only.
e. s 1 ∼ N 0, I s 1 σ 2 s 1. T 1 is an incidence matrix relating the elements of s 1 to observations in y 1. The vector e 1 is an error term assumed to be normally distributed i. e. e 1 ∼ N 0, σ 2 e 1 I v. Z 1 is an incidence matrix relating observations to normally distributed breeding values (u 1 ) with Var(u 1 ) = Hσ 2 u 1 , where matrix H com- bines pedigree and genomic (50 k SNP chip) information as derived in  [30]. Variance components for this model were estimated by the REML algorithm using remlf90 software  [33]. For the second model tested, the 19 possible genotypes were divided into three groups according to their estimated effects on protein content obtained from REML estimations as described above (cf. "Estimation of the effects of α s1 casein genotypes based on DYD phenotypes" section): group 1 included the genotypes that increased protein content by more than 1.
For instance, t g1 is a vector computed as the sum of the prob- abilities that an individual carries genotypes (AA, AB, AC, BB, BC) of group 1. X 2 is an incidence matrix relat- ing observations to the probabilities of the fixed effects of each α s1 casein genotype group. Here, e 1 is assumed to be normally distributed as for Model  (3). For Models (3) and (4), genomic breeding values were obtained by GBLUP using the blup90iod2 program  [33]. Finally, we used a gene content approach  [18, 35]  to include the effect of α s1 casein genotype in genomic (based on pedigree information and SNP genotypes) evaluations based on all female phenotypes. In this study, dairy goats carried six possible alleles at the α s1 casein locus. The model used here i. e. Model (5a) was a seventrait model including a gene content model for each allele (six gene contents) and the trait considered (here protein content): where y 1 is the vector of the 7,351,831 Alpine and Saanen records for protein content, X 1 is an incidence matrix relating observations to the same fixed effects β 1 as in Model (4), W 1 is an incidence matrix relating observa- tions to permanent environmental effects (p 1 ) that are assumed to be normally distributed, Z 1 is an incidence matrix of the breeding genetic values (u 1 ) for the trait considered (i.
Variance (σ 2 u 1 , σ 2 u A , …, σ 2 u O ) and covari- ance (σ 2 u 1,A , …, σ 2 u O,F ) parameters from this model were estimated by the restricted maximum likelihood (REML) algorithm using remlf90 software  [33]. (5b) y 1 = X 1 β 1 + z A α A + z B α B + z C α C + z E α E + z F α F + z O α O + Z 1 ε + WZ 1 p 1 + e 1 , Var(u 1 ) = Hσ 2 u 1 = H   σ 2 ε + 2 � i p i q i α 2 i -2 � i � j� =i p i q j α i α j   , cov    u 1 u A • • • u O    =     Hσ 2 u 1 Hσ u 1,A. Hσ u 1,O Hσ u 1,A Hσ 2 u A. Hσ u A,O • • • • • • • • • • • • Hσ u 1,O Hσ u O,A. Hσ 2 u O     cov    u A • • • u O    = 2H    p A q A -p A p B. -p A p O -p A p B p B q B • • • -p B p O. -p A p O -p B p O. p O q O   . The estimated genotypic effects obtained for Models (3), (  4 ) and (5a) were used to include the effect of the αs1 casein genotype in genomic evaluations and were compared with estimates from a model that did not include any α s1 casein effect, i.
Results Mosquito cells produce arbovirus-derived DNA in vitro. We previously identified a link between vDNA molecules and viral persistence in the insect model Drosophila melanogaster  26. However, the presence of similar mechanisms in other arthropods, including vectors of human disease such as mosquitoes, is unknown. To explore this possibility, we used the arbovirus CHIKV from the Indian Ocean lineage, and its mosquito vectors Aedes albopictus and Aedes aegypti. To assess the presence of CHIKV-specific vDNA, we first optimized PCR and primer conditions to minimize and remove nonspecific amplification (data not shown). Using multiple primer pairs spanning the entire CHIKV genome, we identified two primer pairs that generated amplicons of roughly 600 and 200 bp in the nonstructural proteins (nSP) 2 and 4, respectively, that had no cross-reactivity with uninfected cells (Fig.
bioinformatics. babraham. ac. uk/projects/fastqc/). Quality and adaptors were trimmed from each read using 'cutadapt' (https://code. google. com/p/cutadapt/). Only reads with acceptable quality (phred score Z20) were retained. A second set of graphs was generated by 'FastQC' (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/) on the fastq files create by 'cutadapt'  39. Mapping was produced by 'bowtie1'  40  with the '-v 1' (one mismatch between the read and its target). CHIKV reference genome was from the infectious clone described above  36. 'bowtie1' generates results in 'sam' format. All 'sam' files were analysed by different tools of the package 'samtools'  41  to produce 'bam' indexed files. To analyse these 'bam' files, different kind of graphs were generated using home-made R scripts with several Bioconductor libraries as 'Rsamtools' or 'Shortreads' (http://bioconductor.
Acknowledgements We thank members of the  Saleh, Vignuzzi and Lambrechts labs  for fruitful discussions; ChloeBaron for assistance in thumbnail design;  Isabelle Conclois  and  Catherine Lallemand  for help with mosquito rearing;  Vincent Raquin ,  Albin Fontaine  and the  Centre for Production and Infection of Anopheles (CEPIA)  for help in rabbit blood collection. We are grateful to  Karima Zouache  for providing the Ae. albopictus colony and to  Alongkot Ponlawat  for the original field sampling of the Ae. aegypti colony. This work was supported by the  European Research Council  ( FP7/2007-2013 ERC StG 242703  and  FP7/2013-2019 ERC CoG 615220 ) and the  French Government 's  Investissement d'Avenir programme, Laboratoire d'Excellence 'Integrative Biology of Emerging Infectious Diseases ' (grant no.
ANR-10-LABX-62-IBEID)  to  M. C. S. Salary  for K. A. S. was supported by a 'Equipe FRM DEQ20150331759' grant from the  French Fondation pour la Recherche Medicale  to M. V. M. C. S. and M. V. received support from  DIM Ile-de-France programme on Infectious Diseases. L. L. received support form the  City of Paris Emergence(s) Program in Biomedical Research. Work in the laboratory of G. C. was supported by the  French Government (National Research Agency, ANR)  through the  'Investments for the Future' (LABEX SIGNALIFE , no. ANR-11-LABX-0028-01 ) and by  CNRS  ( GDR 3546 ).
Himatanthus revolutus Distribution:-From Venezuela, Guyana, Suriname, French Guiana, to Amazonian Colombia, Peru and Brazil. Taxonomic notes:-This species has not been previously reported from Peru in any of the literature cited above. The identification of this species was recently made, from a new record collected in the Loreto District. Huber (1915)  described Plumeria revoluta  Huber (1915: 200)  and cited the gathering "Hab. in regione fl. Arirambae superioris in fruticetis "  Campina rana" dictis, 21 décembre 1906, leg. A. Ducke (8039). " However, he did not indicate this collection as type and did not cite the herbarium of deposit. In addition, "8039" is not a collection number, but is the accession number of this specimen of the Museu Goeldi (MG), because at that time Ducke did not use collection numbers; therefore, this gathering should be cited as "A.
Acknowledgments This study is part of a doctoral dissertation of the first author, developed at the  Escuela Doctoral Franco Peruana en Ciencias de la Vida, Universidad Peruana Cayetano Heredia and École Doctorale GAIA Biodiversité, Agriculture, Alimentation, Environnement, Terre, Eau, Université de Montpellier, France. We thank the directors and curators of the herbaria listed in the Material & Methods for loan of material, providing digital images, and for providing working space during our visits. This project was supported by a  Franco-Peruvian doctoral fellowship  from the  National Council for Science, Technology and Technological Innovation  ( Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica, CONCYTEC ) of the  Peruvian Government , and the  French Embassy in Peru. The  Institut de Recherche pour le Developpement (IRD)  also supported this project by sponsoring field work in Peru through the  Laboratorio Mixto Internacional Andino Amazónico en Química de la Vida  led by  Michel Sauvain , travel expenses to the  Herbier de Guyane (CAY) ,  Cayenne  and field work in French Guiana by the first author.
In silico studies Phylogenetic analysis was performed in silico using amino acid sequences from Vitis vinifera (F6I152), Gossypium hirsutum (D9DBX6), Populus trichocarpa (EEE86940), Prunus persica (M5VMI6), Nicotiana tabacum (ADO66667), Lotus japonicus (CCI69207), Ricinus communis (B9T717), Physcomitrella patens (XP_001758094), Aspergillus terreus (Q0CWK8), Fusarium oxysporum (N4U8H1), Penicillium marneffei (B6QIR3), Hypocrea virens (G9N6L5), Hypocrea jecorina (G9N6L5) and Selaginella moellendorffii (XP_002971714) obtained from the National Center of Biotechnology (NCBI), Uniprot and PlantGDB using the BLAST tool. The alignment of sequences was performed with PRANKSTER and Genedoc  [10]. The phylogenetic tree was created using these alignments with PROTDIST, NEIGHBOR and RETREE from the PHYLIP software package  [11]  and Mega 4  [12].
1 with infiltration buffer (50 mM MES pH 5. 6, 2 mM Na 3 PO 4 , 0. 5% glucose, and 100 μM acetosyringone). Diluted cells were cultivated again until the culture reached an OD 600nm = 0. 2. Four-week-old N. benthamiana plants were infiltrated with the bacterial cultures and leaf discs were examined under the confocal microscope 2 days later in a Leica TCS SP5IIE scanning confocal microscope (Leica Microsystems). Data stacks were analysed and projected using ImageJ 1. 42m software (http://rsb. info. nih. gov/ij/). The yellow fluorescence signal from YFP was represented in green in all the acquisitions. The yeast CEN. PK113-5D was used to study the subcellular localization of VvXIP1-GFP with the epifluorescence microscope.
Real-time PCR studies Quantitative Real-time PCRs were performed with a QuantiTect SYBR Green PCR Kit (Qiagen) and in a CFX96 Real-Time detection System (Bio-Rad), at an annealing temperature of 55°C. RNA and cDNA samples were obtained as described above. Experiments were carried out in biological triplicates using VvGAPDH as an internal control. To verify the absence of unspecific and primer-dimer amplification, melting curves were performed after each run. Data were analyzed using gene expression tool in the CFX Manager Software 2. 0 (Bio-Rad). The primers used to study the expression of VvXIP1 and VvGAPDH are described in S1 Table. Isolation of yeast subcellular membranes YSH1172 S. cerevisiae cells transformed with pVV214-VvXIP1 and pVV214-empty vector (control) were cultivated overnight in YNB medium with leucine and tryptophan.
Acknowledgments We would like to thank  Dr. Peter Kötter  from the  Institute of Molecular Biosciences, Goethe University, Germany , for kindly providing the CEN. PK113-5D yeast strain, and  Dr. P. Van Dijck Department  of  Molecular Microbiology, VIB, Leuven, Belgium , for kindly providing the YSH1172 yeast strain. This work also benefited from the networking activities within the European project  INNOVINE  (ref. 311775) and the  European COST ACTION  ( FA1106  ' QualityFruit ').
The relation is considered to be isometric when b = 3, hypoallometric or negative allometric when b < 3, and hyperallometric or positive allometric when b > 3  (Froese et al. 2011). LWR for combined sexes was estimated for 70 species, whereas for 32, the relation was also estimated by sex: 20 speciesboth sexes, 3 species-males, and 9 species-females. To investigate whether the slope b was signifi cantly different from the isometric value, a t-test were performed (H 0 = 3), with a confi dence level of 95% (Zar 2010). The differences between males and females were also compared by the Student's t-test. The fi t of the model to the data was measured by the coeffi cient of Pearson r-squared (R²). A total of 28 099 specimens from 70 species belonging to 23 families and 7 orders were analysed (Table  1 ).
Allometric tendencies may be the result of adaptations, such as ontogenetic, reproductive or environmental variations, mainly between sexes (Froese 2006). The estimates of a and b for the majority of species also felt within the obtained by the Bayesian approach for estimating LWR in fi shes proposed by  Froese et al. (2014)  and the values are available in the database of FishBase (Froese and Pauly 2016). The LWR of 3 species: Lutjanus alexandrei Moura et Lindeman, 2007; Rhinosardinia bahiensis  (Steindachner, 1879) ; and Thalassophryne nattereri Steindachner, 1876 were determined for fi rst time and will be included in the online database of FishBase (Froese and Pauly 2016).
ACKNOWLEDGEMENTS This research has been supported by the Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco (FACEPE) and by the National Institute on Science and Technology in Tropical Marine Environments (INCT-AmbTropic). We thank the Conselho Nacional de Desenvolvimento Científi co e Tecnológico (CNPq) for providing a research grant (to FLF and TF) and a visitor research grant (to FM). The Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) provided doctoral scholarship (to VF) and master scholarship (to ASL). We would also like to acknowledge that the fi rst author (APV) received a postdoctoral fellowship (CNPq) from "Ciências sem Fronteiras".
sexes combined, F = female, M = male, SD = standard deviation, a = intercept of the regression curve, b = regression slope, R² = coeffi cient of Pearson r-squared; values in bold represent the signifi cant difference for Student's t-test , where H 0 = 3, + = positive allometry, -= negative allometry; 1 signifi cant difference for Student's t-test comparing the slopes of the regressions for males and females, where slope b as a fi rst reference in the online FishBase database.