dataesr/scientific-paragraphs-categorization huggingface dataset:
Consequently, both trees were used to compare consistency between models using a tree containing the whole set of species but containing polytomies  [31]  and using a tree containing no polytomies but without five species from our dataset  [32]. We then used phylogenetic generalized linear models (PGLS). This statistical method estimates an index called λ that indicates whether the phylogeny correctly predicts the patterns of covariance among species on a given trait. λ varies between 0 (complete absence of phylogenetic structure, i. e. the phylogenetic structure can be represented by a star phylogeny) and 1 (phylogenetic structure in agreement with a Brownian model of the evolution of the considered traits, i. e. the phylogenetic structure can be represented by the previously constructed tree with unmodified topology and branch length)  [47].
Acknowledgments We thank  Prof. Dominique Allainé ,  Dr. Sébastien Devillard ,  Dr. Camille Ferdenzi  and  Dr. Steven Ramm  for helpful discussions and comments on the manuscript. We are grateful to  Dr. Igni Agnarsson  for kindly providing the phylogeny. MFR is supported by a  scholarship for postgraduate studies  ( Obra Social Fundació "La Caixa " and  VetAgro Sup). JFL is supported by a post-doctoral grant from the  Fyssen Foundation , Paris.
The mPlum protein (excitation and emission maxima: 590 and 649 nm, respectively) was epi-illuminated at 590/20 nm and the fluorescence emission was detected at 680/30 nm. For both BLI and FRI, mice were imaged individually and placed in the center of a constant field of view. Pseudo-color luminescent images representing the spatial distribution of emitted photons were generated using IndiGO 2 software (Berthold Technologies) and superposed to the corresponding photographs. BLI and FRI analyses were done semi-automatically by first placing a small region of interest (ROI) on the heated leg. The mean light intensity (in photons. s - 1. mm -  ) was measured within this ROI.
Acknowledgments This work was supported by the " Conseil Régional d'Aquitaine" and the "Diagnostic Molecular Imaging Project ,  DIMI ,  EC-FP6-LSHB-CT-2005-512146 ". The authors thank  Dr. Pierre Costet  ( Bordeaux ) for his efforts to maintain the double transgenic mice colony and  Dr. Vincent Pitard  ( Bordeaux ) for his technical assistance to FACS analysis,  Dr. Claire Rome  (Grenoble) and  Dr. Claire Mazzocco  ( Bordeaux ) for reading the manuscript.
Box 1. Piwi and the Argonautes Small RNAs are bound by proteins belonging to the Argonaute family, which can be broadly classified into the ubiquitously expressed AGO clade that binds approximately 21 nucleotide microRNAs (miRNAs) and small interfering RNAs (siRNAs), and the animal-specific Piwi-clade, which is mainly restricted to the gonads. This mirrors the distribution of their respective associated small RNAs: miRNAs and siRNAs are detected in almost all organisms and tissues, whereas piRNAs are specific to animals and found in the gonads. Argonaute family proteins are built to bind small RNAs; they contain characteristic PAZ, MID and PIWI modules. The 5′ and 3′ ends of the bound RNA are contacted by the MID and PAZ modules, respectively, such that the RNA bases are available for base pairing with complementary sequences.
Funding support from the  Australian Research Council  is acknowledged ( DP1096299 ) to MAD and CRP. The authors acknowledge support from  ICMG FR 2607 ,  PolyNat Carnot Institute , and  LabEx ARCANE  ( ANR-11-LABX-0003-01 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Sequence analysis, alignment, and phylogenetic analysis The sequences obtained were subjected to BLASTn identity searches in the GenBank database. Multiple sequence alignments were performed using the program Clustal W  [15]  to highlight differences between the red deer 18S rRNA sequences and to perform comparisons with related sequences found in the NCBI database  [14]. Two sets of phylogenetic trees were constructed. The first was constructed to place the new red deer isolates in the Babesia clade and define a suitable outgroup for a more refined analysis. For this purpose, 20 sequences representing the main Babesia sub-clades  [16, 17]  were selected and aligned with the sequence of the main representative of the red deer isolate sequences. The alignment was truncated manually to the size of the shortest sequence.
The GenBank accession numbers for the retrieved sequences are indicated on the tree. The scale indicates the inferred number of substitutions. The clades as described in previous phylogenetic analyses are indicated  [16]. Posterior probabilities are indicated on the nodes. Evolutionary analyses were conducted in MrBayes v3. 2. 2  [22, 23]. The clades I and III from  [17]  correspond respectively to clades V and VI from  [16]. The clade of interest in the present study is framed with a dashed line.
They share the same tick Consensus tree from the Bayesian analysis of 18S rRNA gene sequences of all complete 18S rRNA gene sequences from the monophyletic group described in Figure  1 , with Theileria sequences as the outgroup. The GenBank accession numbers for the retrieved sequences are indicated on the tree. Sequences were aligned and gaps were removed manually. The Bayesian analysis was conducted in MrBayes v3. 2. 2  [22, 23] , and the posterior probabilities are indicated on the nodes. The red deer sequence group is indicated with a bracket, and the closely related sequence of Babesia sp. Xinjiang with a star. Long branches have been shortened for presentation purposes and are indicated with //. vector, I. ricinus  [31] [32] [33] , and the absence of these three Babesia species in the study region may be explained by the absence or very low abundance of this tick species in these regions  [34].
Additional files Additional file 1: Complementary phylogenetic analyses of the complete 18S rRNA sequences used in Figure  1. The GenBank accession numbers for the retrieved sequences are indicated on the tree. The analysis involved 20 nucleotide sequences that were aligned and all positions containing gaps and missing data were eliminated manually. There were a total of 1391 positions in the final dataset. Evolutionary analyses were conducted in MEGA5  [18]. (A) Neighbor-Joining method. The bootstrap consensus tree inferred from 500 replicates is taken to represent the evolutionary history of the taxa analyzed. Branches corresponding to partitions reproduced in less than 50% bootstrap replicates are collapsed. The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (500 replicates) are shown next to the branches.
Figure1Consensus tree from the Bayesian analysis of 18 18S rRNA gene sequences of Babesia, with Theileria sequences as the outgroup. The sequence determined in the present study is highlighted with an arrow, and the monoplyletic group it belongs to by a bold vertical line. The GenBank accession numbers for the retrieved sequences are indicated on the tree. The scale indicates the inferred number of substitutions. The clades as described in previous phylogenetic analyses are indicated [16]. Posterior probabilities are indicated on the nodes. Evolutionary analyses were conducted in MrBayes v3. 2. 2 [22, 23]. The clades I and III from [17]  correspond respectively to clades V and VI from [16]. The clade of interest in the present study is framed with a dashed line.
Figure2Consensus tree from the Bayesian analysis of 18S rRNA gene sequences of all complete 18S rRNA gene sequences from the monophyletic group described in Figure1, with Theileria sequences as the outgroup. The GenBank accession numbers for the retrieved sequences are indicated on the tree. Sequences were aligned and gaps were removed manually. The Bayesian analysis was conducted in MrBayes v3. 2. 2 [22, 23] , and the posterior probabilities are indicated on the nodes. The red deer sequence group is indicated with a bracket, and the closely related sequence of Babesia sp. Xinjiang with a star. Long branches have been shortened for presentation purposes and are indicated with //.
Acknowledgments We thank  P. Roy ,  J. A. Barasona  and  J. Queiros  for technical assistance, and  C. Rispe  for phylogenetic analysis advice. We also wish to thank the  CEFS of INRA (F-31326 Castanet Tolosan ) for kindly providing blood from a roe deer. This research was supported by funds from the  Institut National de la Recherche Agronomique , the  European Union Cooperation  Project  PIROVAC  ( KBBE-3-245145 ) and from the project " Zoonosis transmitidas por vectores hematófagos en Castilla-La Mancha: Mapa de riesgos " ( JCCM POII09-0141-8176 ) and  CDTI/Medianilla. F. Ruiz-Fons  and  I. G. Fernández de Mera  are supported by a  Juan de la Cierva  contract from the  Spanish Ministry for Economy and Competitiveness.
e. the cycle at which fluorescence from amplification exceeds the background fluorescence) with the EF1a and b-actin genes. We used the NormFinder software  (Andersen et al. 2004 ) to investigate the repeatability (i. e. stability of gene expression) of their Cq values by measuring the standard deviations of Cq (SD Cq ) over the 15 individuals (EF1a: SD Cq =0. 337; b-actin: SD Cq =0. 646). Avarre et al. (2014)  set up a criterion of SD Cq <0. 5 to qualify genes as 'good' reference genes. We did not use other reference genes in this study as there is no rationale to increase the number reference genes when investigating for gene expression variation at only two target genes. Hence, results are hereby presented for EF1a, which recovered good efficiencies (97. 5%) and higher repeatability of Cq values.
Primers used for IGF-1 were designed according to the full genomic sequence of IGF-1 provided by  Quéré et al. (2010)  (Genbank accession number: GQ924783). Note that IGF-1 primers used in this study were preferred to IGF-1 primers used in former sea bass gene expression studies (Genbank accession: AY800248; e. g. Varsamos et al. 2006 , Terova et al. 2007 , Mazurais et al. 2008 ), as they retrieved, e. g. , better repeatability of C q values and efficiencies (details not reported). GHR expression was not previously investigated in sea bass. Two transcripts (Genbank accessions: AF438177 and AY642116) were available in databases as GHR1 and GHR2, respectively. Fukamachi and Meyer (2007)  demonstrated that sequences available in databases and reported as GHR (1 or 2) could be somatolactin receptors (SLR).
Statistical analyses of gene expression data The relative expression of both GHR and IGF-1 normalized to the retained reference gene was assessed following a method presented by Pfaffl (2001). Relative expression (RE) is determined using the equation: RE = [(E target ) ΔCqtarget (control-sample) ] / [(E ref ) ΔCqref (control-sample) ] where E target is the amplification efficiency of the target (i. e. gene of interest) and E ref is the amplification efficiency of EF1a. The corresponding qPCR efficiency of one cycle in the exponential phase of amplification was calculated according to the equation E = 10 [-1/slope] (Pfaffl 2001). The qPCR assays were optimized with linear standard curve with R 2 ≥0. 98 (EF1a: R 2 =0. 99; GHR: R 2 =0. 98; IGF-1: R 2 =0. 99). A Kruskal-Wallis test was first used to compare gene expression differences between the three samples of UFW individuals.
Genotype-phenotype relationship Quéré et al. (  2010 ) developed a microsatellite marker labelled as μIGF-1 and located in the IGF-1 gene in sea bass. Polymorphism of all SSW, SFW and UFW individuals was screened at locus μIGF-1 according to PCR protocol and primer reported by these authors. Amplifications were performed on a PTC-200 (MJ Research). Genotyping of individuals was performed by allele sizing on an ABI PRISM ® 3130xl Genetic Analyser (Life Technologies, St Aubin, France), using 5'-labelled reverse primers and the GeneScan TM 600 LIZ ® Internal Line Standard (Life Technologies) as internal size standard. Allele scoring was performed using the GeneMapper software v. 4. 0 (Life Technologies). Genetic differentiation at locus μIGF-1 among SSW, SFW and UFW individuals was estimated using F ST  (Weir and Cockerham 1984) , and significance tested by permutation (1000 replicates) in Genetix v4.
Gene expression variation The Kruskal-Wallis test revealed no significant changes for gene expression of GHR and IGF-1 among sub-samples of UFW individuals taken on July 1, August 27 and October 23 (all P's>0. 05; 2 df; details not shown). This suggested that identical changes in gene expression occurred on several occasions during development. To increase statistical power and for comparison with gene expression of GHR and IGF-1 in SFW and SSW fish sampled on October 23, individual gene expression data of the three UFW sub-samples (each n=10) were gathered in a single sample (n=30) for each organ. The Kolmogorov-Smirnov (K-S) tests were previously performed to check the normality of gene expression data in UFW, SSW (n=20), and SFW (n=20) for each gene and each organ. K-S tests were not significant except for the expression of GHR in liver of SSW fish (P<0.
ACKNOWLEDGEMENTS The authors thank  G. Sposito  for kindly making available the fish used in this study and  M. Cantou  and  L. Libicz  for their help in fish husbandry at the aquaculture facilities of the  Station Méditerranéenne de l'Environnement Littoral. Comments by two anonymous reviewers improved the manuscript. Molecular data were produced with the technical facilities of the qPHD and  SeqGen platforms (LabEx CeMEB , Montpellier).
Acknowledgments We acknowledge the  Vienna Drosophila RNAi Center  and the  Bloomington Drosophila Stock Center  for providing fly stocks. We thank  Lucy O'Brien  for the kind provision of fly stocks and reagents. YTI is supported by an  NIH  grant ( DK83450 ), is a member of the UMass DERC ( DK32520 ), a member of the  UMass Center for Clinical and Translational Science  ( UL1TR000161 ) and a member of the  Guangdong Innovative Research Team Program  (No. 201001Y0104789252 ). Work in the Perrimon laboratory is supported by  HHMI  and  NIH. Work in DF's laboratory was funded by  CNRS  and  ANR Drosogut. IB was supported by the  Sao Paulo regional government (Brazil).
Based on this analysis, we predict that the correct leader sequence is MKTSLLALGLLAALPFAASA. Most of the Ax21 homologs from other strains and species are annotated with similar longer leader sequences. The program SignalP 4. 1, which predicts the presence of classical, but not GG-type signal peptides, indicates that both the annotated shorter sequence (16 amino acids) and the longer sequence (20 amino acids) are predicted signal peptides (Fig. 2A )  (Petersen et al. , 2011). The Ax21 leader sequence has the hallmarks of a typical Sec signal peptide as shown in Fig. 2A. These include a positively charged N-terminus (n-region), a hydrophobic core (h-region), and a short C-terminal region containing the motif AXA  (Natale, Brüser & Driessen, 2008). The leader peptidase cleaves the protein immediately after the AXA motif.
S2 ). There are several known 10-stranded beta barrel proteins with solved three-dimensional structure  (Vandeputte-Rutten et al. , 2001; Prince, Achtman & Derrick, 2002; Eren et al. , 2010). However, these have very low amino acid sequence similarity to Ax21. Using the automated homology-modeling program I-TASSER, Ax21 is predicted to form an 8-stranded β-barrel structure. Despite the variation between these prediction tools, all three show that Ax21 has clear features of an outer-membrane protein with a β-barrel structure  (Koebnik, Locher & Van Gelder, 2000). Because Ax21 is found in supernatants and due to its predicted β-barrel structure, we hypothesized that secreted Ax21 is embedded in outer membrane vesicles (OMVs). OMVs are spherical structures derived from the budding of sections of the outer membrane  (McBroom & Kuehn, 2007).
Disease lesions were scored 13-14 days post inoculation. Bars represent averages of at least 15 leaves ± SE. Statistical analysis was done using the Tukey-Kramer honestly significant difference test for mean comparison using the JMP software. Different letters within plant represent significant differences (P < 0. 01) within each plant genotype. mutant strains revealed that several strains were mixed up or had the incorrect genetic profile (Fig. S5 )  (Ronald, 2013). We identified a correct PXO99 ax21 mutant strain in the collection, which we validated by PCR, Southern blot and Western blot (Fig. S5 ). The correct mutant had a kanamycin resistance cassette inserted near the C-terminus of the gene by double homologous recombination. We inoculated TP309 and TP309-XA21 with the validated mutant using the scissor clipping method.
MATERIALS AND METHODS For simulating apple tree (Malus × domestica) topology and geometry over years, we used the architectural model MAppleT, i. e. Markov Apple Tree, which was parameterized for the Fuji cultivar  (Costes et al. , 2008). The simulated tree topology was tested through comparisons of GU counts per tree and years with observed data on digitized trees. The light interception of the simulated trees was estimated using MmSLIM, namely the Multi-Scale Light Interception Model, which is a model capable of combining a detailed and statistical description of foliage at different scales to estimate radiation attenuation  (Da Silva et al. , 2008). The tree total leaf area (TLA) and silhouette to total area ratio (STAR) estimated from the combination of MAppleT and MmSLIM were compared with those of digitized trees of the same cultivar (Fuji) and with independent published data on different cultivars  (Da Silva et al.
Among metamodelling approaches  (Storlie and Helton, 2008; Villa-Vialaneix et al. , 2012) , we selected the generalized additive model (GAM)  (Wood, 2006)  and modelled STAR as an additive sum of non-parametric functions of each input parameter: STAR i = f 0 + S k f k (X k,i ) + e i ( 4 ) where k is the number of parameters X for tree i and e i is its residual error term, with the constraints that all the functions f k have an integrand equal to 0. The residual error term e i corresponds to the modelling error plus the stochastic effect due to the Markovian random process included in the MAppleT model. The functions considered here correspond to the default option in the mgcv library in R software version 2. 13. 1, i. e. thin plate regression splines. This modelling permits exploration of the space of parameters without replicates (as in  Lurette et al.
The experimental design for exploring the space of input parameter values was performed with LHS (Latin hypercube sample) where the distribution chosen for each input parameter was the non-informative one, with the corresponding procedure in the package lhs of R software version 2. 13. 1. For this experiment, a sample of 300 sets of parameters was generated. Each set of parameters was then used to simulate the growth of an apple tree over 5 years. All other parameters were kept with default values  (Costes et al. , 2008). Samples of these simulated tree architectures are shown in Fig. 1. The LIE was estimated at the whole-tree level, on June 30 of each year. This date corresponds to the time when growth stops for most of the shoots, in particular the short and the medium shoots. Topology. To investigate the effect of the branching during early growth, tree simulations were initiated with sequences of lateral types along the first year of growth of the trunks previously observed  (Segura et al.
ACKNOWLEDGEMENTS D. D. S. has received the support of the  European Union , in the framework of the  Marie-Curie FP7 COFUND People Programme , through the award of an  AgreenSkills' fellowship  (under grant agreement n8  267196 ), and the post-doctoral project of L. H has been supported by the  Genetic and Breeding Department of INRA (France). We are grateful to  V. Picheny  ( INRA, UR BIA ) for fruitful scientific discussions on sensitivity analyses, and to  F. Boudon  ( CIRAD, UMR AGAP ) and  C. Pradal  ( CIRAD, UMR AGAP ) for their help and support with the OpenAlea platform. Finally, we also thank  B. Pitollat  ( CIRAD, UMR AGAP ) for his helpful support in parallel computing of simulations on cluster from the  ID department.
Statistical Analysis All data were analyzed using PASW Statistics 18 (version 18. 0. 2, SPSS Inc. , Chicago, IL). For all data, the pen during the rearing period (n = 4/group) was the statistical unit according to  Gill (1989). Data for bird performance (BW, FCR, feed intake, and carcass composition) were analyzed including the experimental group in the model (3 levels: CON, CFC, or CFT). Means between treatments were compared using the Duncan test, except the means of intake of whole cereal and rich-protein pellets intake, which were compared using the Student test. Mortality and commercial grading were analyzed using the chi-squared test. Differences were treated as significant when P ≤ 0. 05.
Novel RNA transcripts are enriched in the CB In addition to the long piRNA cluster transcripts and some annotated long noncoding transcripts (Fig. 3B ), the CB transcriptome also included 4992 nonannotated transcripts that originated from regions of the genome with no reported activities (Supplemental Table  S4 ). Reads deriving from the novel nonannotated transcripts were almost twofold enriched in the CB compared with the round spermatid sample (data not shown). The novel CB-enriched transcripts mapped evenly to all chromosomes, except for the Y-chromosome, which was virtually devoid of any hits (Fig. 4A ). We analyzed the expression of some randomly selected transcripts using RT-qPCR. The majority of the analyzed transcripts were predominantly expressed in the testis. Cuff1617 was most highly expressed in the brain but was found also in the testis; lower levels of Cuff1617 were also found in the liver, kidneys, ovaries, and adrenal glands (Fig.
By the direct comparison of both the spermatid and CB transcriptome data sets, we found that (1) approximately half (45. 2%, 6686/14,788) of the mRNAs expressed in the round spermatids (rSpt+) were detected in our CB+ mRNA list; and (2) 90. 7% (6686/7367) of the CB+ genes were expressed in the round spermatids. To analyze the cellular origins of the transcripts detected in the mouse CBs, we overlapped the list of CB-High+ loci with genes detected in round spermatids and those associated with somatic (SO), mitotic (MI), meiotic (ME), or post-meiotic (PM) expression patterns  (Chalmel et al. 2007 ). The analysis revealed that the CB-High+ genes were significantly over-represented in the clusters showing a meiotic (21. 4%, 769/3592, P = 2. 72 × 10 -79 ) or a post-meiotic (18. 9%, 679/3592, P = 2.
Mass spectrometric analysis Reversed CB samples were separated in mini-protean TGX pre-cast gradient gels (BioRad) and stained with SimplyBlue SafeStain (Life Technologies). Gel pieces were excised from the sample lanes, followed by in-gel digestion with trypsin (Promega) and extraction of the peptides. The peptides were analyzed using LC-MS/MS with an Agilent 1200 series nanoflow system (Agilent Technologies) connected to a LTQ Orbitrap mass-spectrometer (Thermo Electron) equipped with a nanoelectrospray ion source (Proxeon, Odense). The data were analyzed using the Mascot search engine and the NCBI MOUSE database. For the approximate quantitation, the exponentially modified Protein Abundance Index (emPAI) was used.
AlexaFluor 488/594 conjugated secondary antibodies (Life Technologies) were used in dilution 1:500. Sections were mounted in a medium with DAPI (Santa Cruz). Photomicrographs were taken with Olympus DP72 digital color camera mounted onto a Leica DMRB microscope (Leica Microsystems) using PL FLUOTAR 40×/0. 70 and N PLAN 40×/0. 65 PH 2 objectives and cellSens Entry 1. 5 (Olympus) digital imaging software. All images were processed using Photoshop (Adobe).