dataesr/scientific-paragraphs-categorization huggingface dataset:
There could therefore be more diversity than we could ascribe with (See figure on previous page. ) Figure  3  Phylogenetic tree (ML tree) of the hemagglutinin-neuraminidase (HN) gene of Ugandan Newcastle disease virus (NDV) isolates at the nucleotide level. The HN sequences of our Ugandan NDV isolates were compared with relevant virus sequences available in GenBank database: the reference vaccine strain Lasota, all the East African NDV strains available in the database (indicated with an open circle), Ethiopian strains, and a representative strain for each of the genotype V subgenotypes. Partial HN gene sequences are indicated with a "*" symbol after the strain name. A single representative virus was selected for strains with identical nucleotide sequences: NDV/chicken/Uganda/MU001/2011 was identical to NDV/chicken/Uganda/MU004/2011 and NDV/chicken/Uganda/MU005/2011; NDV/chicken/Uganda/MU007/2011 to NDV/chicken/Uganda/MU009/2011; NDV/chicken/Uganda/MU026/2011 to NDV/chicken/Uganda/MU028/2011; NDV/chicken/Uganda/MU059/2011 to NDV/duck/Uganda/MU062/2011 and NDV/chicken/Uganda/MU063/2011; NDV/chicken/Uganda/MU096/2011 to NDV/chicken/Uganda/MU099/2011 and NDV/chicken/Uganda/MU102/2011; and NDV/chicken/Uganda/MU098/2011 to NDV/chicken/Uganda/MU105/2011.
The number of base substitutions per site from averaging over all sequence pairs between groups are shown. Standard error estimate(s) are shown above the diagonal, in italic font. Analyses were conducted using the Maximum Composite Likelihood model. The rate variation among sites was modeled with a gamma distribution (shape parameter = 1). All positions containing gaps and missing data were eliminated. Numbers in boldface refer to genotype V viruses. the limited strains sequences in GenBank. All our strains were defined by the " 112 RRQKR*FV 118 " motif at the cleavage site. These strains with valine at position 118 have been previously classified as lineage 3  [6]  and this motif is common among most genotype V and have been reported in Madagascar  [21]  and other countries. However our isolates differ in this region where Madagascar isolates that had " 112 RRRRR*FV 118 " motif at the cleavage sites as compared to ours with RRQKR*FV motif, although all were isolated from apparently healthy nonvaccinated poultry.
Sequence analysis The sequences obtained were aligned using Clustal W and edited using Bioedit Software version 5. 0. 9  [33]. Phylogenetic analysis was performed using the MEGA version 5. 05 program  [34]. The number of bootstrap replications was set to 500 (for maximum likelihood (ML) analyses) or 1,000 (for Neighbor-Joining (NJ) analyses), and bootstrap values above 50 were labeled on major tree branches for reference. The Ugandan virus strains were clustered on the basis of nucleotides, and only dominant clusters were used to infer phylogenetic relationships. Genotype and subgenotype classification was performed as described by Diel et al. ,  [7]. The analysis included all available full sequences in GenBank or for Figures  2B  and 3  all sequences from East Africa in GenBank and the sequence of the reference vaccine strain, Lasota.
[32]ogical characteristics of NDV isolated in this study (Continued)The table presents isolates for which full HN or F genes or both were sequenced. ND = Not done; V = velogenic; M = mesogenic; the pathotype was based on MDT values as described in materials and methods; *only partial F gene sequences were obtained for these isolates with FIP1-FIP2 primers targeting the F cleavage site area(Kho, Mohd-Azmi et al. [32]). 1HNgenesequences identical to HN of NDV/chicken/Uganda/MU001/2011 (accession number: HG937535); 2 F gene sequences identical to F of NDV/chicken/Uganda/MU001/2011 (accession number: HG937567); 3 HN gene sequences identical to HN of NDV/chicken/Uganda/MU026/2011 (accession number: HG937543); 4 F gene sequences identical to F of NDV/chicken/Uganda/MU026/2011(accession number: HG937574); F gene sequence identical to F of NDV/duck/Uganda/MU062/2011 (accession number: HG937584); HN gene sequences identical to HN of NDV/chicken/Uganda/MU096/2011 (accession number: HG937563);7HN gene sequences identical to HN of NDV/chicken/Uganda/MU098/2011 (accession number: HG937564).
Acknowledgements The authors thank  Chantal Snoeck ,  Institute of Immunology, Laboratoire National de Santé, Luxembourg  for fruitful discussions on data analysis. The sample collection, virus isolation and part of the sequencing of the isolates were supported by the  World Bank Millennium Science Initiative Project  (Grant #  MSI/03/32/2010 ) to DKB through the  Uganda National Council of Science and Technology. The sequencing of the isolates was supported by the  Department of Animal Health, INRA, France , by the " IDEX-nouveaux entrants" program , and by the  French Ministry of Agriculture.
It further supported the central role played by ecdysone signaling. Indeed, as observed in other tissues, the cardiac specific transcriptome dynamics revealed the sequential activation of ecdysone responsive genes, which are known downstream effectors of activated EcRs. A number of these ecdysone response genes are themselves nuclear receptors that constitute a transcriptional cascade and may drive the dynamics of cardiac remodeling. Starting from this biological system, our goal was to evaluate the feasibility of predicting transcriptional control modalities at the temporal level, without prior knowledge of the CRMs and TFs involved. To predict potential transcription factor motifs involved in the regulatory process, we used cisTargetX, a computational approach described previously  [9]. This tool uses a comprehensive library of 1981 position weight matrices, combined with phylogenetic conservation, to identify potential cis-regulatory modules common to a cluster of coexpressed genes.
Dataset The gene expression dynamics during heart remodeling from 21 h to 48 h After Puparium Formation (8 time points) was described previously  [16]. In this experiment, we identified 2394 genes that exhibited significant differential expression between time-points in using modified tstatistic significance analysis of microarrays (SAM)  [38]  with estimated q-values (false discovery rates) of ≤ 0. 05. Among them, 1660 genes showed significant levels of differential expression at least 1. 8-fold in at least one condition through our time-course analysis. Those 1660 genes were clustered in 13 expression profiles using SOM clustering. All 13 clusters were independently submitted for cisTargetX analysis. Motif over representation analysis in the 13 clusters cisTargetX was used with the following parameters: -Assembly & Scoring: "dm2 (April 2004) with Cluster-Buster";motif collection: "Used in Aerts & al.
PLoS Biology 2010 (1981 PWM)"; Z-score thresold: "Determine threshold automatically"; receiver operating characteristic (ROC) threshold for area under the curve (AUC) calculation: "0. 03"; Genomic threshold for visualization: "1000". The search space used is 5 kb upstream of all transcripts TSS (Transcription Start Site) and their introns. If a neighboring gene (or a host gene exon in case of an intronically hosted gene) is less than 5 kb away, the search space is reduced and truncated at this neighboring gene (or exon) boundary. In the case of cluster 12, in order to inspect further PWM, cisTargetX was run a second time with the previous parameters except for the Z-score threshold, which was decreased to 2. 5.
Determination of drosophila TFs predicted to bind GCNF motif As GCNF is a motif representing the binding site of a vertebrate NRs, we selected the 22 Drosophila TFs belonging to the Nuclear hormone receptor family in the UniProt database (Hr39, EcR, tll, usp, Hr46, Hr96, Hr78, Hr4, Hr38, ftz-f1, svp, kni, Eip75B, eg, Eip78C, knrl, Hnf4, usp, Hr51, ERR, dsf, Hr83) with the following request, family: "nuclear hormone receptor family" AND organism: "7227". We collected PWMs representing NRs binding sites present in the cisTargetX library (1981 PWMs) or in the Fly Factor Survey database and use STAMP with default options to build a similarity tree (Additional file 1: Figure  S4 ).
GFP expression analysis For GFP expression analysis, a batch of 10 flies per conditions and genotypes were analyzed in triplicate and representative individuals are shown. Images were acquired using a high-resolution video camera (CoolSNAP HQ Monochrome, Roper Scientific, Inc), mounted on a Zeiss Stereo Lumar V12 binocular microscope with a Neolumar 0. 83 objective. Image acquisition was performed with the Metamorph/Metafluor software (universal Imaging, West Chester, PA). Individuals shown in Figures 4, 5 and 6 were dissected out their pupal case and mounted in Voltaleff oil 3S prior imaging.
Acknowledgments This work was supported by  ANR , partner of the ERASysBio + initiative supported under the  EU ERA-NET Plus  scheme in FP7 (CH and LP), by an  ANR "JCJC"  grant to CH and LP and by funding from the  AFM (LP). DP and DS were supported by a doctoral fellowship from  MESR. We thank the  Bloomington Stock center ,  V. Monnier  for fly stocks,  K. Davie  for careful reading of the manuscript and  Annelies Clayes  for her help on molecular cloning.
A novel technique to identify and split clusters created by multiple charged particles in the AT-LAS pixel detector using a set of artificial neural networks is presented. Such merged clusters are a common feature of tracks originating from highly energetic objects, such as jets. Neural networks are trained using Monte Carlo samples produced with a detailed detector simulation. This technique replaces the former clustering approach based on a connected component analysis and charge interpolation. The performance of the neural network splitting technique is quantified using data from proton-proton collisions at the LHC collected by the ATLAS detector in 2011 and from Monte Carlo simulations. This technique reduces the number of clusters shared between tracks in highly energetic jets by up to a factor of three.
Shared measurements typically provide a worse position estimate and hence degrade the quality of the reconstructed track. A correct modelling of shared measurements in Monte Carlo simulation is important for the control of systematic uncertainties on the track reconstruction efficiency. In this paper, a novel technique to identify and split merged clusters in the ATLAS pixel detector by using a set of artificial neural networks (NN), or NN clustering, is introduced. This leads to a reduction of approximately a factor of three in the number of measurements shared between tracks. It also significantly improves the precision of the position estimate of the clusters and the track performance as will be discussed in section 4. The data used in this document were collected by the ATLAS experiment in 2011 with the LHC colliding protons at a centre-of-mass energy of √ s = 7 TeV.
The studies presented here did not use the full dataset but used sufficient data events for the statistical uncertainty to be negligible. In all cases, this required only a small fraction of the 2011 dataset: in some cases a single run or even fraction of a run. The NN clustering was introduced during the 2011 data-taking period and became the default approach for the rest of the Run-1 data taking. Data taken earlier in 2011, prior to the introduction of the NN clustering algorithm, were reprocessed to guarantee a consistent and improved dataset for analyses of the entire Run-1 dataset. The new technique will be an essential component of the ATLAS track reconstruction in the upcoming Run-2, when the ID will be equipped with a new additional innermost silicon layer, which will be located closer to the beam interaction region than the innermost layer of the pixel detector  [5].
These parameters are extracted from either Monte Carlo simulations or data  [9]. The charge interpolation technique was the default ATLAS clustering approach until mid-2011 and will be referred to as CCA clustering. In dense environments, such as in the cores of energetic jets, there is an increased risk that pixels traversed by different particles are merged by the CCA, or even that charge induced by -5 -several particles is collected in a single pixel. This problem occurs more and more often as the spatial separation between the particles on the sensor plane approaches the pixel size. This cluster merging is depicted in figure  3 , which illustrates an event in which the charge induced by three particles is reconstructed as a single cluster. Figure  4  shows the average separation in the transverse ( δ min x ) and longitudinal ( δ min y ) direction of the two closest stable charged particles in jets, at the radius of the innermost pixel layer in the barrel.
Neural network training Ten neural networks are needed for up to N = 3 possible particles: NN par estimates the number of particles traversing a cluster and the remaining nine estimate the positions (NN pos ) and errors (NN err x and NN err y ). The training data are a mixed sample of top-antitop pair production (t t) and highly energetic dijet events (140 < p jet T < 560 GeV). The t t events were generated using the MC@NLO  [14]  generator interfaced with HERWIG  [15]  to model the parton shower and the dijet events were generated using PYTHIA. Both samples were passed through the standard GEANT4  [16]  simulation of the ATLAS detector  [17]  and the pixel digitisation model. The detector simulation includes effects such as ionisation loss, the production of knock-out shell electrons, δrays, and hadronic spallation from the interactions of charged particles in the pixel modules.
Acknowledgements We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia; ARC, Australia; BMWF and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN; CONICYT, Chile; CAS,  MOST  and  NSFC , China; COLCIEN-CIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF,  DNSRC  and  Lundbeck Foundation , Denmark; EPLANET,  ERC  and  NSRF ,  European Union ;  IN2P3-CNRS ,  CEA-DSM/IRFU , France; GNSF, Georgia; BMBF,  DFG ,  HGF ,  MPG  and  AvH Foundation, Germany ;  GSRT  and  NSRF, Greece ;  ISF, MINERVA, GIF, I-CORE and Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ; CNRST, Morocco; FOM and  NWO, Netherlands ;  BRF  and  RCN, Norway ;  MNiSW  and  NCN , Poland; GRICES and FCT, Portugal; MNE/IFA, Romania; MES of Russia and ROSATOM, Russian Federation; JINR; MSTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, Spain;  SRC and Wallenberg Foundation, Sweden ;  SER ,  SNSF  and  Cantons of Bern and Geneva, Switzerland ;  NSC, Taiwan ;  TAEK, Turkey ;  STFC , the  Royal Society and Leverhulme Trust ,  United Kingdom ;  DOE  and  NSF ,  United States of America.
Cell viability/cytotoxicity assay kit The LIVE/DEAD ® Cell Viability/Cytotoxicity Assay Kit (Invitrogen, Saint-Aubin, France) was used on cells after 24 h of incubation with CFTR inhibitors, cultured with 1% serum. Using this kit, living cells showed green fluorescence (detecting calcein-acetoxymethyl ester) and dead cells showed red fluorescence (detecting homodimeric ethidium bromide). Cells were visualized with a Carl Zeiss Axiovert D1 inverted microscope using a 40× LD Plan-Neofluar objective (Carl Zeiss SAS, LE Peck, France). Images were recorded using an Axiocam MRm (Carl Zeiss SAS), and quantification of green and red fluorescence was performed using individual pixel quantification with ImageJ software (NIH, Bethesda, MD, USA) on three independent micrographs per well.
Data analysis Statistical analysis was performed using R software (R Development Core Team (2011). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http:// www. R-project. org/). Dose-response relationships for each experimental condition were fitted with the Hill equation using SigmaPlot software (Systat Software, San Jose, CA, USA) to calculate IC50 values. P < 0. 05 was accepted to show significant differences between means.
Osteogenic differentiation was evaluated by amount of Alizarin red S positive pixels in osteogenic differentiation images with imageJ by setting the image/adjust/colour threshold option. The analyse option (analyse particles) calculated the percentage of pixels above threshold. The best and poorest Alizarin red S stained wells were used as reference points. 26, 27 age capture Image capture and analyses from the polymer microarrays and the coated cover slips (on 48-well plates), tissue culture plastic and gelatin wells, was carried out using a Nikon Eclipse 50i microscope with the Pathfinder software (IMSTAR, France). The differentiated cells and the stem cells in the long term cultures on coated cover slips were imaged with a Zeiss Observer microscope.
Acknowledgements This work was supported by  EU   FP7 funding  ( project  223410 ,  BEST Stem Cells ). We thank  Kay Samuel  for help with the flow cytometry experiments,  Drs Anne Hansen  and  Mei Wu  for assistance with the microarray fabrication,  Dr Steve Pells  for input on figure preparation, and  Drs Chris Armit  and  Marieke Hoeve  for helpful discussions.
Displacement components and cross-section properties are expanded in terms of power series of a known degree. Then, the shape functions are obtained by deriving the deformation shape of tapered thin-walled member as power series form. Finally, stiffness and mass matrices are carried out by means of the principle of virtual work along the member's axis. In order to measure the accuracy and check the validity of this method, the natural frequencies and buckling loads of non-prismatic thin-walled beams with web and flange tapering and various boundary conditions are obtained and compared to the results of finite element analysis using Ansys software and those of other available numerical and analytical ones. It can be seen that the results of present study are in a good agreement with other available theoretical and analytical methods.
(  19 )-(  21 ) can be determined explicitly in terms of the twelve constants (a 0 ; a 1 ; a 2 ; a 3 ), (b 0 ; b 1 ; b 2 ; b 3 ) and (c 0 ; c 1 ; c 2 ; c 3 ). Then, the solutions of equilibrium equations can be expressed in the following matrix form: fCðεÞg ¼ ½BðεÞfAg ð 33Þ In which fCðεÞg ¼ vðεÞ wðεÞ θðεÞ 8 > < > : 9 > = > ; fAg ¼ a 0 a 1 a 2 a 3 b 0 b 1 b 2 b 3 c 0 c 1 c 2 c 3 È É T In Eq. (  33 ), fAg is unknown vector. All terms (v i ðεÞ; w i ðεÞand θ i ðεÞ, i¼0-11) are derived using MATLAB software  [42].
Numerical examples The aim of this section is to demonstrate the accuracy and the validity of the proposed finite element method in the lateral buckling stability and the flexural-torsional buckling analyses of tapered thin-walled beams with arbitrary cross-sections and boundary conditions under an eccentrically axial or lateral load and in the free vibration analysis. In order to achieve this goal, some numerical examples are performed. The LTB and FTB loads and natural frequencies provided by the numerical results obtained by aforementioned method are compared to other available analytical or numerical solutions and to finite element method analysis results using ANSYS software  [45]. In this code, each studied thin-walled beam is modeled using shell elements (SHELL63). Shell63 has both bending and membrane capabilities.
0 to 12. 0 m. The geometrical and the material data are shown in Fig. 4. Table  2  depicts the elastic buckling loads for the considered simply supported tapered beam illustrated in Fig. 4  which is subjected to constant compressive axial load at the top flange of the larger cross-section. Also, the lowest values of natural frequency computed for all mentioned cases are shown in Table  3. In these tables, the obtained results of the described cases in the stability and vibration by the present finite element method based on power series expansions have been compared with those obtained by finite element method using SHELL63 element of Ansys software  [45]. Moreover, in the case of stability analysis, a comparison with PSM, investigated by Soltani  [36]  is made. For more information, the relative errors (Δ) between the present Table  1  Effect of number of elements (n) and power series number (N) on linear critical buckling load and natural frequencies of web tapered and prismatic thin-walled beam (Fig.
6. In Table  4 , the values of Q cr obtained by the proposed FEM based on the expansions of the power series method and finite element results by means of Ansys software [45] are presented. Some other benchmark solutions are also reported: 1D model introduced by Andrade  [33]  and power series solution investigated by Asgarian  [35]  for the two load positions and finite element results available in Yang  [23]  for load position on centroid. The relative errors (Δ) is computed according to Δ ¼ ðQ cr À Q FEM cr Þ= Q FEM cr Â 100. As shown in Table  4 , the values of lateral-torsional buckling loads derived from this method are in good agreement with the numerical results obtained using Ansys code  [45]  and some other available studies proposed by Yang  [23] , Asgarian  [35]  and Andrade  [33].
The analyzed beams exhibit a tapering coefficients α and β ranging from 0. 2 to 0. 8. The beam lengths (L) have been varied from 6 to 12 m. The material and geometrical properties of the considered beam are shown in Fig. 10. Table  7  provides the values of the lowest three buckling loads (P cr ) computed by the present solution using power series expansions, those obtained by Ansys software [45], the Rayleigh-Ritz method used in Lei  [31]  and also the results of the proposed finite element method using 3D beam elements by Ronagh  [25, 26]. The illustrated beam was modeled using SHELL63 of Ansys  [45]. The relative errors (Δ) between the present method and Ansys results are also presented. An excellent agreement is observed with the values of LTB loads derived from this method and all numerical and analytical aforementioned methods.
But in this case, the mentioned beam has a singly symmetric section. The geometrical and sectional properties of all cases are shown in (Table  8 ). For all the study cases, a steel material is assumed. The elastic constants and the mass density are the same as previously. After observing the results displayed in the Table  8 , it can be concluded that there is an excellent agreement between the natural frequencies calculated by present study and those computed by finite element method, using shell element of Ansys software [45] and numerical method based on power series expansions by Soltani  [36].
5 or 1 mM) (Sigma-Aldrich). MSCs viability was not affected by 1,10 phenantroline (not shown). All systems were left for 4 hours at 37uC in a 5% CO 2 humidified atmosphere. Cells attached to the lower side of the membrane were fixed in 2% formaldehyde, stained with 49,6diamidino-2-phenylindole dihydrochloride (DAPI, Sigma-Aldrich) and counted using fluorescent-field microscopy at 100X. Captured images from three representative visual fields were analyzed using CellProfiler software (www. cellprofiler. com), and the mean number of cells/field 6SEM was calculated. For wound-healing assay, Fast-DiO-stained MSCs were seeded at 2. 5610 4 cell/cm 2 in DMEM with 10% FBS for 24 hours. Then, cells were overnight preincubated with 1 mg/ml rAMF or DMEM without FBS. The monolayers were then scratched by a 200 ml-tip, washed with PBS and incubated for 24 hours more in DMEM without FBS.