dataesr/scientific-paragraphs-categorization huggingface dataset:
Material costs are based on costs of a total cemented or total uncemented hip arthroplasty. for calculation of costs of the cemented option with acetabular impaction bone grafting, we used data of 79 most recently operated patients younger than 50 years in the period 2007-2010 at the Radboud UMC in the Netherlands. Costs of the uncemented option were calculated as if they were performed at the Radboud UMC. We have averaged the registered prices of 3 very commonly used uncemented total hip designs in the Netherlands. the mean operation time of cemented hip arthroplasty using impaction bone grafting was 123 minutes and we assumed that an uncemented hip arthroplasty at our hospital would take 40 minutes less (20 minutes for the impaction bone grafting procedure, two times 10 minutes for cementation).
Maric and haynes (1993)  described a group of 17 hips, including four uncemented hips, and reported that 6% of the acetabular components were revised after an average followup of 9. 3 years. We found a rate of revision of 11. 6% for the acetabular component at a mean followup of 8. 4 years. We included in our data all patients that underwent revision and found reasonable functional scores except in one case. this study had a relatively large group (26%) of patients with juvenile rheumatoid arthritis. these patients are probably less active compared to the other patients with a localized osteoarthritis of the hip in our series  (Chandler et al. 1981 (Chandler et al. , severt et al. 1991)). in the literature, not all studies have the same conclusion regarding the outcome of thA in patients with rheumatoid arthritis.
2012). Another recent study on cost-effectiveness of different types of total hip replacements in older patients concludes that uncemented prostheses do not improve health outcomes sufficiently to justify their higher costs  (Pennington et al. 2013). these studies confirm our findings in the way that uncemented hip designs are less cost-effective compared to cemented total hip prostheses. A strength of our study is that in our model we acknowledge the fact that the technique of impaction bone grafting takes additional time and materials and we have included these costs in our model. We also have incorporated three revisions with accompanying costs in our model and we have used advanced statistical software (treeAge pro suite 2009) to construct our model. A point of criticism on our study could be that the survival rates of primary uncemented cups are worse than the rates of cemented cups.
Over the last decade, the evidence is mounting that several aspects of black hole accretion physics proceed in a mass-invariant way. One of the best examples of this scaling is the empirical "Fundamental Plane of Black Hole Accretion" relation linking mass, radio and X-ray luminosity over eight orders of magnitude in black hole mass. The currently favored theoretical interpretation of this relation is that the physics governing power output in weakly accreting black holes depends more on relative accretion rate than on mass. In order to test this theory, we explore whether a mass-invariant approach can simultaneously explain the broadband spectral energy distributions from two black holes at opposite ends of the mass scale but at similar Eddington accretion fractions. We find that the same model, with the same value of several fitted physical parameters expressed in mass-scaling units to enforce self-similarity, can provide a good description of two datasets from V404 Cyg and M81*, a stellar and supermassive black hole, respectively.
2003 ) and 12 M  (Shahbaz et al. 1994) , respectively. We use the compiled SED of V404 from  Hynes et al. (2009) , where the X-ray (Chandra-ACIS), UV (Hubble Space Telescope; HST) and radio data (VLA) were simultaneous, while optical/infrared constraints (e. g. , from Spitzer and ground based instruments) were archival. Similarly for M81* we include archival HST (IR/UV) and Spitzer data, as well as ground-based constraints from ISO and MIRLIN (see  Markoff et al. 2008  for details). We apply for the first time a multi-zone, multiwavelength model jointly to the datasets from two sources, separated by a huge dynamic range in mass, tying together several model parameters across this mass range. We have developed this new approach within the data analysis software package ISIS  (Houck 2002). Note that scale-free parameters correspond to different physical values, therefore features in the model SEDs corresponding to, e.
) For M81*, we replace the non-X-ray statistical error bars with 15% fractional error bars (i. e. , comparable to the intrinsic radio variability), except for the non-simultaneous IR/UV spectra where we adopt 40% fractional error bars. For the UV data, there is some debate whether these are detections of the emission from M81*, or are merely upper limits to the central object emission (e. g. ,  Maoz et al. 2005). Adopting these large error bars thus allows the HST and other non-simultaneous data to influence, but not dominate, the model fits, and act as upper limits. These choices admittedly contain a degree of subjective judgement. "Best practices" for combining datasets from multiple, independent instruments remains an area of active research, with some promising Bayesian methods allowing a more formal approach to including priors for instrument systematics (see, e.
DISCUSSION During stage 9 of oogenesis, the follicular epithelium exhibits morphogenetic rearrangements, including a cuboidal-tosquamous transition at the anterior pole of the follicle. Here, we investigate the role of TGFb signalling in this transition by first collecting detailed cellular metrics in WT and mutant conditions. We then use these metrics to generate databases in order to enable the quantitative phenotypic characterisation of various mutants, of interactions between these mutants, and of the extent of rescue by the forced expression of a gene in different mutant contexts. From these analyses on TGFb signalling, we conclude that the TGFb pathway: (i) is essential in controlling the timing, the dynamics and the degree of flattening; (ii) plays a role in determining the elongated, final shape of the StC, and (iii) does not control the direction of cell elongation or the A/P-oriented wave of flattening.
Acknowledgements We thank  H. Bellen ,  R. Cagan ,  R. Karess ,  R. Leborgne ,  T. Lee ,  T. Tabata ,  P. ten Dijke ,  P. Therond ,  K. Wharton , the  Developmental Studies Hybridoma Bank , the  Bloomington Stock Center , for strains and antibodies; the  Universite Lyon I Imaging Facility (CTm) , the  ENS Lyon Imaging Facility (PLATIM)  and the  UMR5534   Drosophila Facility  for technical help and  P. Das  for critical comments on the manuscript.
Results: We developed 'VING' , a stand-alone R script that takes as input NGS mapping files and genome annotations to produce accurate snapshots of the NGS coverage signal for any specified genomic region. VING offers multiple viewing options, including strand-specific views and a special heatmap mode for representing multiple experiments in a single figure. Conclusions: VING produces high-quality figures for NGS data representation in a genome region of interest. It is available at http://vm-gb. curie. fr/ving/. We also developed a Galaxy wrapper, available in the Galaxy tool shed with installation and usage instructions.
Background NGS is now widely used to study all aspects of gene expression from chromatin conformation (Hi-C) to protein-DNA binding (chromatin immunoprecipitation sequencing, ChIP-seq), transcription (native elongating transcript sequencing, NET-seq), RNA abundance (RNA-seq) and translation (ribosome profiling). A common step in most NGS approaches is the mapping of sequenced reads to a reference genome and analysis of the resulting signal. Multiple tools have been developed for quantitative analysis of NGS data. However, data visualization remains difficult because of the large quantity of information to display. Genome browsers such as Artemis  [1] , IGV  [2]  or Gbrowse  [3]  enable rapid navigation along the genome and coverage visualization, but are not fit for accurate, publication-quality image, neither for displaying multiple libraries.
Alternatively, combinations of software such as BEDtools  [4]  and R or Matlab functions can produce customized plots, but require programming skills. Likewise, the Gviz R package  [5] , which enables customized display of a variety of genome annotation tracks, including NGS data, requires mastering the R environment and R objects. Here, we describe 'VING' , an R package dedicated to the custom visualization of NGS data that can be easily launched using a single Unix command line, or within the Galaxy environment. VING introduces functionalities to handle data produced by the most recent NGS protocols, in a strand-specific manner. The code is optimized to enable a fast figure generation, even for the largest mapping files and genomes.
Inputs VING uses as input bam alignment files  [6]  and gff annotation files (description of the gff format can be found at http://www. sanger. ac. uk/resources/software/gff/). VING loads bam files using the Bioconductor package "Rsamtools". Single-end or paired-end data are allowed and the library type can be specified as a parameter to assign reads to the proper strands. For paired-end data, each properly paired read is loaded as one single fragment. Users can also provide weights for normalization of each bam file. Annotation files are read by a custom function that only loads genomic features within coordinates defined by the users, enabling a faster operation. Users can also select the features to display.
Signal visualization The coverage signal (number of reads covering each nucleotide) is only computed for the requested genome area. Users may provide optional normalization factors for weighting each signal. These factors should be computed independently, either based on library sizes (RPM normalization) or using a dedicated package such as DESeq  [7]  or EdgeR  [8]. The signal is plotted in a strandspecific manner using any of the three visualization modes: "classic" coverage plots using solid areas (each library in a distinct panel, Fig. 1a ); "line" plots using lines of different colors and/or styles (one panel for all libraries, limited to 16 libraries, Fig. 1b, c ); "heatmap" views based on a color-code to reveal high/low-density coverage regions (one panel for each strand, libraries shown as lanes in each of the two panels, no limitation of samples, Fig.
Performance VING was tested on a variety of NGS data from different species, including yeast small RNA-seq (Fig. 1a ), ChIPseq (Fig. 1b ), NET-seq (Fig. 1c ), total RNA-seq (Fig. 1d ), and human total RNA-seq data (Fig. 1e ). Execution time depends on input files size. On an Intel Xeon 2,4 GHz processor with 32 Gb RAM, runtime ranged from 5 s and 2 min for the smaller (such as for Fig. 1a ) and larger datasets (such as for Fig. 1d, e ), respectively. Memory usage was under 500 Megabytes in all cases.
Conclusion The VING program produces high-quality figures for NGS data representation in a genome region of interest. VING input and outputs have been rendered Galaxy-compatible so that automated coverage plots can be easily incorporated in Galaxy pipelines. The resulting, (See figure on next page. ) Fig. 1  Examples of NGS signal visualization using VING. a Strand-specific "classic" visualization of 21-25 nucleotides small RNA densities along the SPAC167. 03c locus in rdp1Δ Schizosaccharomyces pombe control cells (vector) or cells overexpressing Dcr1. Signal from each library is shown in a separate panel. Reads mapped on the + andstrands are shown on the top and bottom sides of the 0 horizontal line, respectively (additional representation in different colors optional). Annotated genomic features are represented as "box" (ORF) and "line" (mRNA).
Acknowledgements We thank  N. Servant  and  A. Lermine  from the  bioinformatics platform of Institut Curie , and the  eBio bioinformatics platform (Orsay)  for their encouragement and advices. We are grateful to all the members of our labs for helpful discussions. D. Gautheret 's lab is supported by  ANR   12-ADAP-0019 RNAdapt grant. A. Morillon 's lab is supported by the  ANR "REGULncRNA", ERC "EpincRNA" starting and ERC "DARK " consolidator grants.
Availability and requirements Project name: VING. Project home page: http://vm-gb. curie. fr/ving/. Operating system(s): Linux. VING has also been successfully tested on MacOSX and Windows 7. Programming language: R. Other requirements: Bioconductor packages Genomi-cRanges and Rsamtools. License: GNU GPL (version 3, 29 June 2007). Any restrictions to use by non-academics: none.
Availability of supporting data Original raw data used in Fig. 1a, c-e  were retrieved from the NCBI Gene Expression Omnibus, accession numbers GSE52535, GSE25107, GSE63444 and GSE26284, respectively. Original raw data used in Fig. 1b  were retrieved from the NCBI Sequence Read Archive, accession number SRA030505. Truncated bam and gff files used for figure generation are provided on the VING website.
Transposable elements (TEs) are genomic repeated sequences that display complex evolutionary patterns. They are usually inherited vertically, but can occasionally be transmitted between sexually independent species, through so-called horizontal transposon transfers (HTTs). Recurrent HTTs are supposed to be essential in life cycle of TEs, which are otherwise destined for eventual decay. HTTs also impact the host genome evolution. However, the extent of HTTs in eukaryotes is largely unknown, due to the lack of efficient, statistically supported methods that can be applied to multiple species sequence data sets. Here, we developed a new automated method available as a R package "vhica" that discriminates whether a given TE family was vertically or horizontally transferred, and potentially infers donor and receptor species.
Results and Discussion The "Vhica" Package The VHICA analysis is fully automated through the R package "vhica," available in CRAN (https://cran. r-project. org/). The package uses the seqinr package for the dS calculation  (Charif and Lobry 2007). The analysis is performed in two steps. In the first step, the average CUB (measured by the ENC) and the dS are computed for each species pair and for each reference gene, and the linear regression between ENC and dS is calculated. In the second step, the average ENC and the dS for each TE and for each species pair are used to compute the P value of the departure from the null hypothesis (vertical transmission: TEs follow the gene pattern). Different outputs can be asked for 1) a graphical matrix (similar to fig. 2 ) can be drawn for one particular element, P values being displayed according to a color bar.
Host Genes Data Set and Host Phylogeny Reconstruction Based on the data set of orthologous genes obtained from the first 12 Drosophila genomes (Drosophila 12 Genomes Consortium 2007), we arbitrarily picked 150 orthologous genes present in all genomes and used them as query in a BLASTN search against the eight new Drosophila genomes  (Chen et al. 2014). We kept only single copy genes that were present in all 20 Drosophila genomes, ending with two data set of 50 single orthologous genes, the list of used genes is available in supplementary table S1, Supplementary Material online. These gene sequences have been concatenated and used to build a phylogenetic tree of the host species using two independent methods: A maximum-likelihood analysis using the PhyML software  (Guindon et al. 2010) , and a Bayesian analysis with MrBayes software  (Ronquist et al.
Synonymous Substitutions Distance and CUB The coding regions of these representative sequences were aligned using MACSE  (Ranwez et al. 2011). All alignments were further adjusted for in frame codon alignment, using a homemade python script. Codon alignments were finally manually checked using AliView  (Larsson 2014). The dS was calculated using the method of  Li (1993)  implemented in the seqinR package  (Charif and Lobry 2007) , with a pairwise deletion option. The CUB was estimated for host genes and TEs as the ENC according to Wright's method  (Wright 1990). The ENC varies from 20 up to 61, ENC = 20 meaning that only one synonymous codon is used for each amino acid high CUB and ENC = 61 means that all synonymous codons are equally used no CUB.
Acknowledgments This work was supported by  Conselho Nacional de Desenvolvimento Cient ıfico e Tecnol ogico (CNPq)  and  Coordenacão de Aperfeicoamento de Pessoal de N ıvel Superior (CAPES). The first author of this manuscript was a " Bolsista da CAPES-Proc. No. 6145116. "The authors thank the anonymous reviewers for their fruitful comments on the first version of the manuscript.
Recently, energy-aware routing has gained increasing popularity in the networking research community. The idea is that traffic demands are aggregated over a subset of the network links, allowing other links to be turned off to save energy. In this paper, we propose GreenREa new energy-aware routing model with the support of the new technique of data redundancy elimination (RE). This technique, enabled within the routers, can identify and remove repeated content from network transfers. Hence, capacity of network links are virtually increased and more traffic demands can be aggregated. Based on our real experiments on Orange Labs platform, we show that performing RE consumes some energy. Thus, while preserving connectivity and QoS, it is important to identify at which routers to enable RE and which links to turn off so that the power consumption of the network is minimized.
Reduction of Traffic Load Internet traffic exhibits large amount of redundancy when different users access the same or similar contents. Therefore, several works the patterns of previously sent data are stored in database of the WOCs at both sending and receiving sides. The technique used to synchronize the databases at peering WOCs can be found in  [10]. Whenever the WOC at the sending side notices the same data pattern coming from the sending hosts, it sends a small signature instead of the original data (called encoding process). The receiving WOC then recovers the original data by looking up the signature in its database (called decoding process). Because signatures are only a few bytes in size, sending signatures instead of actual data gives significant bandwidth savings. Recently, the success of WOC deployment has motivated researchers to explore the benefits of deploying RE in routers across the entire Internet  [1] [2][3]  [16].
xMOD Software xMOD is a software platform that was developed at "IFP Energies Nouvelles" combining heterogeneous models and a virtual experimentation laboratory. These heterogeneous models are generated by different simulation tools, such as Matlab/Simulink, AMESim, Dymola, SimulationX and GT Power. A combination of these tools means benefiting from the advantages of each modeling and simulation tool, and the user can freely select these tools. In this work, xMOD is used to simulate the diesel engine functioning, especially the air path behavior. The simulation model produced by IFP "Energies Nouvelles" is used, to which a leak model has been added. The diameter of the leak can be freely adjusted. The simulation results can be recovered and stored in text files.
We propose to adapt the convenient PWM model by adding dependency information at specific positions of the matrix. As documented for several TFs  [10, [15] [16] [17] , this will improve the prediction power of some PWM models. Although several tools such as RSAT  [18] , PROMO  [19] , MatInspector  [20]  or LASAGNA  [21]  are available to identify overrepresented motifs in a set of sequence and build binding models, none of them allows using PWM with dependencies nor to calculate occupancy of DNA regions using biophysical models  [22]. We have developed a new algorithm that uses PWM with any combination of dependent and independent positions. We incorporated it in a user friendly set of tools called MORPHEUS, which offers several specific advantages over existing tools: 1) it is a web tool that does not require any programming skill and can thus be widely used by the biologist community, 2) users can import their own matrices, not only those found in databases, 3) position interdependencies can be included between any positions of the matrix and in combination with independent positions, a possibility currently offered by none of the existing web tools, 4) a global "predicted occupancy" value can be computed for whole DNA regions using a biophysical model  [22]  that integrates the presence of individual binding sites.
Conclusions Morpheus web allows a user-friendly suite of tools for the calculation of TFBS relative affinity on DNA sequences. It incorporates unique features such as dependency between specific positions, occupancy calculation and ROC-AUC estimation that do not exist in any currently available webtool. We have illustrated how it can be used to infer hypothesis about TFBS functional significance or about evolution of regulatory links. Experienced users can download Morpheus scripts code for specific purpose, however no programming skills are needed to use Morpheus web-tools. With all its unique characteristics and with the possibility of using any own-modified mPWM, we believe Morpheus should have strong acceptance among biologists. Morpheus web-tools, complete user guide and downloading versions are available at Morpheus website: http://biodev.
Methods Morpheus All scripts for Morpheus tools are written in Python programming language (ver 2. 6. 7). The graphic output requires two modules: Numpy (http://numpy. scipy. org/) and Matplotlib (http://matplotlib. sourceforge. net/). Morpheus tools are available in the Morpheus web (http:// biodev. cea. fr/morpheus/), as well as downloading versions with or without graphic output, user guide and complete descriptions. The web is hosted and maintained by the GIPSI team (CEA Saclay). When the score matrix is not directly provided, Morpheus computes it based on 'Count' or 'Frequency' matrices using W n,i = Ln(f n,i /f max,i ) where W n,i is the weight at position i for nucleotide n, f n,i is the frequency of nucleotide n at position i and f max,i is the maximal frequency observed at position i  [43].
Each 4 (d) dependency matrix is preceded by a line indicating the positions involved (S2 Text). For score calculation the mPWM algorithm first get the value for each independent position from the independent matrix and then for all the dependency combinations from the 4 (d) matrices. If in vitro affinity data is available to correlate score with relative dissociation constant, the correlation values can be indicated in mPWM file or, if they are not indicated, the program will use default parameters (corresponding to a line curve with scope equal to one). From matrix file, mPWM algorithm first identifies the list of independent positions (i) and the list of dependent positions groups (j; each one associated with a 4 (d) matrix), then the score of each DNA sequence is calculated as: Sequence score ¼ X p2i score p nt þ X q2j score q dep where score p nt is the score in the position p for the nucleotide nt (A,C,G or T) in the independent matrix and score q dep is the score in the 4 (d) matrix of group q for the sequence combination dep (dinucleotide or triplet).
FUL clade genomic sequences Genomic sequences were obtained from Phytozome database  [44]  by Blast search using the protein sequence of AtAP1 (At1g69120), AtCAL (At1g26310) and AtFUL (At5g60910). Hits without transcripts information or with incomplete gene prediction were discarded. A region of 2 kb upstream the ATG were used for relative binding score calculation. All sequences used in this study can be found in S5 Text.