dataesr/scientific-paragraphs-categorization huggingface dataset:
Page 1 of 9 ( 9. ß The Author(s) 2013. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/ licenses/by/3. 0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. page number not for citation purposes) Database, Vol. 2013, Article ID bat036, doi:10. 1093/database/bat036.
PCR amplification and DNA sequencing Three PE (PE3, PE4, and PE35) and 6 PE_PGRS (PE_PGRS12, PE_PGRS26, PE_PGRS29, PE_PGRS35, PE_PGRS51, and PE_PGRS62) genes scattered throughout the M. tuberculosis H37Rv genome were selected. The sequence of the primers used for PCR amplification and sequencing of each gene fragment is provided in Table  S2. The specificity of each primer was checked using the updated TubercuList database (http:// tuberculist. epfl. ch/) [56]. Although the genome sequence of some reference strains is publicly available, we PCR amplified and sequenced the PE/PE_PGRS selected members from the DNA of all these strains, since the accuracy of genome sequences in these highly GC-rich and repetitive regions is questioned. The amplification reaction mixture contained 20 ng of template genomic DNA, 10 ml of 10x buffer (Qiagen), 10 ml DMSO, 2 ml of 10 mM nucleotide mix (Amersham Biosciences), 2 ml of each primer (20 mM stock), 0, 25 ml (1.
Genetic polymorphism and diversity The DnaSP software package, version. 4. 10 [57]  was used to carry out several population genetic analyses. For each locus, we determined the number of haplotypes (h), number of polymorphic sites (S), nucleotide diversity (p), and the per-site population mutation rate, h (2N e m). To test for adaptive selection, we determined the nucleotide substitution changes and the ratio of nonsynonymous (dN) to synonymous (dS) substitutions per site (dN/dS), using the analysis developed by Nei-Gojobori  [58]  after Jukes-Cantor correction for multiple substitutions.
Phylogenetic analysis Phylogenetic relationships were reconstructed by taking into account all polymorphic sites. To assess the possible confounding effect of positive selection  [59] , we compared the obtained trees with those built with synonymous changes only. Analyses were either performed on a single gene basis or on the concatenated sequence of the six most variable PE/PE_PGRS genes (PE3, PE4, PE_PGRS26, PE_PGRS35, PE_PGRS51, and PE_PGRS62). Alignments of gene sequences were performed using the ClustalW program  [60]. Maximum likelihood (ML) methods were used to infer phylogenetic relationships for each of the six most variable PE/ PE_PGRS genes, as well as for their concatenated sequence. ML analyses were performed using RaxML version 7. 2. 8  [61]. Bootstrap confidence levels were based on 1000 resampling.
The trees were visualized using FigTree (http://tree. bio. ed. ac. uk/ software/figtree/). Prior to ML analyses, a DNA substitution model for each data set was selected. To determine the best substitution model, we compared the results of the TOPALi v2 package  [62]  with those of the Hyphy-package that is available through the web site www. datamonkey. org [63]. Two different scores, Akaike information criterion (AIC) and Bayesian information criterion (BIC), have been calculated in order to determine, for each data set, the best model. The two analyses yielded the same results. The best substitution models obtained with TOPAli are provided in the File S1. For the majority of the data sets the model F81  [64]  was determined to be optimal. To test for the topological congruence between trees, we computed the Icong index, which is based on maximum agreement subtrees (MAST)  [65].
Significance of the PHI statistic is assessed with the normal approximation of a permutation test where, under the null hypothesis of no recombination, sites along the alignment are randomly permuted to obtain the null distribution of PHI. P values,0. 05 indicate significant presence of recombination  [69]. Further we used several other recombination detection algorithms: (a) Hudson and Kaplan's R min  [70] : The  Hudson and  Kaplan's lower bound on the minimal number of recombination events in an infinite site model was computed using DnaSP v4. 0 program  [57]. (b) Maximum chi-square  [71] : A nonparametric method that detects regions of the aligned sequences delimited by putative recombination breakpoints. Maximum chi-square is a component of the software package RDP 2. 0  [72]. (c) The Web-based service GARD (genetic algorithm for recombination detection)  [73] : a model-based approach that searches for putative breakpoints delimiting sequence regions having distinct phylogenies.
Briefly, GARD compares a nonrecombinant model in which the sequence data are fitted to a single phylogeny to models in which breakpoints partition the sequence data into two or more regions having varying phylogenies. Site-by-site substitution rate was assumed to be constant between sites. The identified breakpoints were further confirmed using the akaike information criterion (AIC) score and Kishino-Hasegawa topological incongruence test. (d) Recco  [74] : A sensitive method for detecting recombination events, based on a cost minimization approach. The probability of detecting recombination was adjusted such as to classify a dataset as recombinant if the P-value does not exceed 0. 12 (MaxSavings feature of Recco). The mutation cost matrix is set to Hamming, which means that for any a and b characters m(a,a) = 0 and m(a,b) = 1 for any a! = b.
Estimation of the per-locus population recombination rates The program pairwise from the LDhat package  [75]  was used to obtain an approximate-likelihood estimate of the population recombination rate (2N e r), r, by combining the coalescent likelihoods of all pairwise comparisons of segregating sites. The per-site population mutation rate (2Nem) was estimated using the Watterson's estimator of Theta implemented in the LDhat package. This program is accessible through http://www. stats. ox. ac. uk/ ˜mcvean. PE_PGRS62 was omitted from these analyses due to evidence that it might be under positive selection.
Tests of selection Evidence of positive selection in a protein's amino acid sequence is generally indicated by an excess of nonsynonymous substitutions relative to synonymous substitutions, the dN/dS ratio (or v). Evidence of positive selection for amino acid replacements is suggested when v. 1, purifying selection is inferred when v,1, whereas neutral evolution is assumed when v = 1. First we measured the v over the entire length of a gene, then we performed a codon-by-codon analysis using codeml as implemented in the software package PAML (Phylogenetic Analysis by Maximum Likelihood) v. 4. 4e  [76]. For this purpose we used ''site models'' where codon sites are allowed to fall into categories depending on their v values. First, we compared a ''nearly neutral model'', M1a, to a ''positive selection'' model, M2a.
A significantly higher likelihood of the alternative model than that of the null model indicating positive selection in the data set examined. For models comparisons, we used degree of freedom, df = 2. For each analysis, correction for multiple testing (Bonferroni correction) was applied. Only in cases where LRT was significant, we used the Bayes empirical Bayes (BEB) procedure to calculate the posterior probabilities (PPs) to identify sites under positive selection  [79]. Signals of positive selection were also searched along all branches of the constructed phylogenetic trees. For this purpose we used a parsimony approach for ancestral sequence reconstruction  [80] , coupled with a covarion-based approach for the calculation of dN/dS ratios  [81].
Nucleotide sequence accession numbers STB PE/PE_PGRS sequences obtained in this study were deposited in the EMBL database under accession numbers HE855688 to HE855735. For comparative analyses, nucleotide sequences of the house keeping genes (HKG), gyrA, gyrB, hsp65, katG, and rpoB, generated in the study of Gutierrez et al. [12] , were used.
Similar results were obtained when both nsSNPs and sSNPs were taken into account (data not shown). We also investigated split decomposition networks based upon a concatenated alignment of the six variable PE/PE_PGRS genes. Evidence for recombination was supported by highly significant PHI test values (P = 3. 17E24; Fig. 2 ). The PHI test was still significant (P = 0. 028) after eliminating the PE_PGRS51 sequence, the only gene that showed consistent evidence of recombination. Overall, the above phylogenetic analyses provided strong signals of both intragenic (within PE_PGRS51) and intergenic recombi-nation in STB PE/PE_PGRS genes. To further support these findings, we performed additional recombination detection tests. As recommended in previous studies  [66, 82] , we used a combination of various methods (Hudson and Kaplan's Rmin, Maximum chi-square, Recco, and GARD) since the detection abilities of different tests can vary markedly for a given dataset.
Evidence of positive selection The first indication of adaptive selection is suggested by the fact that, in STB, PE/PE_PGRS genes accumulated 15 times more nonsynonymous substitutions (nsSNP) than did HKG (dN mean value of 0. 003 vs 0. 0002, respectively) (Table  4 ). By contrast, the mean value of the rate of synonymous substitution (dS) in HKG proved 2. 6-fold higher compared to that of PE/PE_PGRS genes. The dN/dS ratio over all codon sites for each one of the six most variable PE/PE_PGRS genes was mostly,1 with a mean value of 0. 438 (Table  4 ). Only for a single gene, PE_PGRS62, was the dN/ dS greater than 1 (1. 422), indicating that it is subject to positive selection. Because the apparent purifying selection acting on the other PE/PE_PGRS genes may be masking positive selection of a few codons, we performed a codon by codon maximum likelihood test using the program codeml (PAML package).
Only for PE4 and PE_PGRS62, was the likelihood ratio test close to significance for both models comparisons M1a vs M2a and M8a vs M8 (data not shown), and a few amino acid sites were identified under positive selection with Bayes empirical Bayes (BEB) analysis (Table  5 ). The leucine residue on position 115 of PE4 was identified by both models comparisons with a BEB posterior probability. 95% (Table  5 ). In PE_PGRS62, the three amino acid sites (106Q, 253N, and 307Q) undergoing positive selection were identified only in the conservative M8-M8a comparison with BEB posterior probability values close to 95%. The three positively selected residues map to the highly repetitive PGRS part of PE_PGRS62. Finally, we searched for signals of positive selection along specific branches of STB PE/PE_PGRS phylogenetic trees.
Acknowledgments We thank  Maherzia Ben Fadhel  for her assistance with nucleotide sequencing. Figure 5. Estimation of dN/dS ratios along branches of STB PE/PE_PGRS individual trees using the covarion-based approach described by Siltberg and Liberles [80]. dN and dS rates (top and bottom numbers, respectively) are shown on branches. Branches where positive selection was detected are drawn in red. doi:10. 1371/journal. pone. 0064718. g005.
The force constant k ij is a parameter controlling the strength of the interaction and is specified differently in the different models. In this work, the equilibrium conformation used to calculate the ENMs is in all cases taken to be an X-ray structure model obtained from the Protein Data Bank (PDB). 45, 46 dding the potential energy terms for each pair of nodes and expanding the result in a Taylor series around r 0 , the potential for an ENM is expressed as =- - U rr r H r r ( ) () () 0T 0 (2) where H is the Hessian of the system and T denotes the transpose. H has the form: ∑ = - || -|| -- ≠ -= ≠ ⎧ ⎨ ⎪ ⎪ ⎩ ⎪ ⎪ k ij ij H rr rr rr H () () , , ij ij ij ij ij li il , 00 2 00 00 T , (3) As only internal interactions are considered, rotations and translations have no energetic cost, and since moreover the Hessian is evaluated at an energy minimum where the energy gradients are zero, the rank of H is at most 3N -6, with N the number of nodes in the network.
This is obtained by simply subtracting the outer product of the normalized eigenvector describing uniform displacement, u. The null-matrix for the GNM Z 0 is =- ZI u u 0 T (28) Note that every entry of uu T is equal to 1/N. These matrices have rank 3N -6 and N -1 with all nonzero eigenvalues being the same. Motion represented by these matrices is not completely uncorrelated between atoms, but as the variance is equal along all principal components, collective and local motions have the same energetic cost. The Bhattacharyya measure conveniently allows us to do meaningful comparisons with the null model, even with this degeneracy of eigenvalues. 52 D Simulations. For two of the structures we used MD trajectories previously published. We refer to these publications for the modeling details. A 10 ns trajectory of lysozyme (PDB ID: 193L) was obtained with the Amber94 force field 55 by Kneller and Hinsen  56  (the reference reports a 1.
2 ns trajectory, which was later extended to 10 ns). A 285 ns trajectory of the GroEL subunit (PDB ID: 1XCK, chain A) was obtained with the Amber03 force field 57 by Skjaerven et al. 58  A 20 ns of trajectory of phospholipase-C (PDB ID: 1PTG) solvated in TIP3P-solvent  59  was obtained with the CHARMM27 force field 60 by co-worker Cedric Grauffel, who kindly offered this trajectory for analysis in this study. Integration was done with NAMD 2. 8,  61  with an integration step of 1 fs and a temperature of 300 K. Distance constraints were enforced on all bonds between hydrogens and heavy atoms by the SHAKE algorithm. 62 or the remaining four structures we solvated proteins in TIP3P-solvent and simulated using the CHARMM27 force-field. In the ATCase complex zinc ions are coordinated by four proximal cysteine residues, a type of interaction not well represented in CHARMM27.
This was the system with the longest simulation time and the lowest internal consistency (MD 1/3 ). Averaging over all 10 comparisons this gave a mean Bhattacharyya coefficient of 0. 84, with a standard deviation of only 0. 019. We did not attempt to quantify this dependence for other proteins. Considering the comparison of parts of the trajectories to each other (MD 1/3 ), we note high coefficients indicating that the trajectories give stable estimates of the covariances. For the GroEL subunit, for which we have data from multiple trajectories, we see that the best performing ENMs are as similar to the MD covariances as covariances obtained from these trajectories are to each other. This is in agreement with earlier observations that a good ENM can be as reliable as a single MD trajectory for capturing the covariance structure of a protein.
Contrasting benchmarks against MD covariances with benchmarks against B-factors is complicated by the differences between the measures of similarity involved. For a more direct comparison we also compare to the benchmark against MD-predicted atomic fluctuation profiles, using the same similarity measure that was used for the B-factors. These results are in much better agreement with the MD covariances (Figure  2 ), indicating that the discrepancy between B-factors and MD covariances are due to the quality or applicability of the data source and not the measure of comparison. Moreover, we show that the collective effects in the models that are optimized for reproducing B-factors are so small that the resulting covariance matrix can be very well approximated with the null model (Figure  4 ).
This supports the notion that the strong long-range interactions obtained from optimizing against B-factors reflect the loss of collective motion in the crystalline environment. An alternative approach to validate ENMs against crystallographic data involves modeling protein crystals as elastic networks. 37 The crystal ENM would then not be expected to have larger collective motion than the experimental data it would be benchmarked against. This can possibly be used to parametrize ENMs. The implied assumptions would be that the parametrization is transferrable to a solvated system and that nonthermal contributions to positional variance are nonsystematic. Our current analysis does not address the merits of such methods. A severe limitation with benchmarking against MD simulations is the intractability of the time-scales at which collective motion can be sampled to its full extent.
ACKNOWLEDGMENTS We gratefully acknowledge  Lars Skjaerven  and  Cedric Grauffel  for providing trajectories for the GroEL subunit and the phospholipase-C, respectively. We also thank  Sandhya P. Tiwari  and  Angele Abboud  for carefully reading and commenting on manuscript drafts. BioStruct, the  Norwegian national graduate school in Structural Biology , provided financial support to E. F. for traveling in conjunction with this project. N. R. gratefully acknowledges funding from the  Bergen Research Foundation. Computational resources for the project was provided by  Synchrotron SOLEIL.
Root growth measurements The position of the tip of each root was labelled every day on the back of the Petri dish. Six days after transfer to the new medium, all Petri dishes were scanned (EPSON Perfection 1250, EPSON TWAIN5 software) and root growth was measured with ImageJ software. Dose-response curves were expressed relative to the control growing on DMSO. The time course of the effect of inhibitors was expressed as the ratio of each daily length of treated roots compared with the corresponding length of the control growing on DMSO.
Microscopy Pictures of the aerial parts of plants and the differentiated zone containing root hairs were taken directly from the Petri dish with a Zeiss stereomicroscope (Stereo Discovery V12). For meristem observations, roots were mounted in chloral hydrate solution (100 g of chloral hydrate, 30 ml of water, and 5 ml of glycerine) and observed with a Zeiss microscope Axo imager M2 with a differential interference contrast illumination system. Cell surface area, cell length, and root hair measurements were done with ImageJ software and expressed relative to the control growing on DMSO, unless otherwise specified. The size of the meristematic zone (MZ), corresponding to the distance between the quiescent centre and the first elongated cortical cell, was measured as described previously  (Perilli and Sabatini, 2010).
Acknowledgements This work was supported by the  Agence Nationale de la Recherche  grants  ENVI-DEVO  ( ANR-08-JCJC-0054-01 ) and  TRANSLATOR  ( ANR-BSV-010-01 ). We acknowledge  Stefan Jouannic  ( IRD, Montpellier, France ),  Cécile Vriet  (LGBP),  Trevor L. Wang  ( John Innes Centre, Norwich, UK ), and  Muriel Reissolet  ( LGBP ) for WT seeds, and  Raju Datla  ( NRCC, Canada ) for TOR/tor-3 seeds. We thank  Nathanael S. Gray  and  Liu Qingsong  for the generous gifts of the Torin1, Torin2, and QL-IX-55 inhibitors. We also acknowledge  Coralie Horin  for her help during the first experiments with asTORis. The microscopy was done in the IBDML imaging facility of  Aix-Marseille University. The help of  Elsa Crudeli  and  Cédric Matthews  is acknowledged, as well as  Alexandre Tremeau-Bravard ,  Ben Field , and  Samantha J.
, 2013). One reason for this is that a protein such as VAP has many partners, some on LEs, others on the plasma membrane, mitochondria and trans-Golgi network  (Lev et al. , 2008). Only a small proportion is likely to bridge one class of MCSs, and this may not be detectable against a high background of widespread ER elements. To overcome this problem, we applied the recently developed, highly sensitive technique of in situ proximity ligation assay (PLA) to the LE-ER MCS  (So ¨derberg et al. , 2006). With this assay, proximity (,40 nm) between two proteins is revealed by fluorescent dots. Using antibodies to endogenous STARD3, STARD3NL and VAP-A proteins (since VAP-A is more highly expressed than VAP-B in HeLa cells as analyzed by RT-qPCR; data not shown), we demonstrated proximity between LE and ER markers.
Firstly, STARD3 seems not to be involved in cholesterol exit from late endosomes because its overexpression does not increase ACAT-mediated cholesterol esterification in the ER  (Liapis et al. , 2012). Secondly, STARD3 and STARD3NL can equally mediate the formation of LE-ER MCSs. Thirdly, overexpression of STARD3 is known to induce perinuclear clustering of enlarged LEs whereas its silencing results in the peripheral dispersion of endosomes  (Alpy et al. , 2005; Ho ¨ltta ¨-Vuori et al. , 2005; Liapis et al. , 2012; Zhang et al. , 2002) , and these effects cannot be attributed to the formation of LE-ER MCSs. STARD3 was recently proposed to be involved in cholesterol transport between LE and the plasma membrane (van der  Kant et al. , 2013). Although we cannot rule out this possibility, our data support a model in which STARD3 is primarily involved in cholesterol exchange and/or sensing between LEs and the ER.
Immunogold labeling was performed on an EM-IGL automate (Leica Microsystems). Rabbit polyclonal anti-GFP (AbCam 6556) was revealed with 10 nm gold-coupled protein A (Utrecht, Netherlands). Sections were stained with uranyl acetate and lead citrate and examined with a transmission electron microscope (Philips CM12). Images were acquired with an Orius 1000 CCD camera (Gatan). For electron tomography, tilt series (1˚steps, from 265 to 65˚) from thick sections (200 nm) were collected on a field emission gun electron microscope (Tecnai F20, FEI Company, Eindhoven, The Netherlands) with a Gatan 2K CCD camera. Tomograms were computed (back-projection method) and modeled with the IMOD package (eTomo and 3Dmod modules)  (Kremer et al. , 1996).
Immunofluorescence Immunofluorescence was performed as previously described  (Alpy et al. , 2002). Primary antibodies were anti-GFP (GFP-2A3, Euromedex), anti-FLAG (F-7425 Sigma) and for endogenous staining anti-VAP-A (K-15 -Santa Cruz Biotechnology, sc-48698) and pAbMENTHO-Ct  (Alpy et al. , 2002). Slides were mounted in ProLong Gold (Invitrogen). Observations were made with a confocal microscope (Leica SP2 UV, 636, NA 1. 4). Linescans were drawn using ImageJ software (plot profile function; http://rsbweb. nih. gov/ij/). Pearson's correlation coefficients were calculated with Excel. Colocalization was visualized using the colocalization highlighter plug-in for ImageJ.
Acknowledgements We thank  Dr Kentaro Hanada  for providing HA-VAPA and HA-VAPB expression plasmids as well as valuable advice;  Tamas Balla  for providing the Sac1 expression vector;  Luc Dupuis  for providing the anti-VAP-B antibody. We thank  Francoise Hullin-Matsuda ,  Toshihide Kobayashi ,  Guillaume Drin ,  Bruno Mesmin ,  Karim Hnia ,  Shankar Pattabhiraman  and the members of the  Molecular and Cellular Biology of Breast Cancer team (IGBMC)  for helpful advice and discussions. We thank  Marc Koch  and  Claire Pieraerts  for their excellent technical assistance;  Patrick Schultz ,  Daniele Spehner  and  Corinne Crucifix  for access to the HPF apparatus and TEM for EM tomography.
All images were analyzed using the software Advanced Image Data Analyzer AIDA (Raytest, Straubenhardt, Germany) to calculate the protein of interest/␤-actin ratio. Immunostaining and Immunogold-Immunohistochemistry and immunogold staining were performed on frozen sections, and specificity was demonstrated on Rhcg Ϫ/Ϫ tissues. Mouse kidneys were fixed by perfusion retrograde through the aorta with 3% paraformaldehyde in 0. 1 M sodium cacodylate buffer, pH 7. 2. The tissue was either trimmed into small blocks, further fixed by immersion in 1% paraformaldehyde, infiltrated with 2. 3 M sucrose for 30 min, and frozen in liquid nitrogen or prepared for routine paraffin embedding. For electron microscopy, 70 -90-nm cryosections were obtained at Ϫ100 °C with an FCS Reichert Ultracut S cryoultramicrotome as described previously  (24) , and 2-m paraffin sections were obtained with a Leica RM2165 microtome.