dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We thank the crews of  R/V L'Europe ,  R/V Professor Logachev ,  R/V Suroit  and  R/V Tethys II  for their help and dedication during the cruises. We thank  Lars-Eric Heimbürger  for valuable comments and language correction. We acknowledge the support of the  EU  projects  SESAME  ( GOCE-2006-036949 ),  HERMIONE  ( FP7-ENV-2008-1-226354 ), the  French  projects  EXTREMA-ANR-06-VULN-005  and  LEFE-IDAO-42N5E , and  French network MOOSE (Mediterranean Oceanic Observing System in Environment). We thank the referees  Tommaso Tesi  and  Leonardo Langone  for their constructive comments which helped to improve the quality of our manuscript. Loic Houpert acknowledges the support of the  Direction Générale de l'Armement  (supervisor:  Elisabeth Gibert-Brunet ). The French HydroChanges team acknowledges  J.
VBN Model The VBN micturition model [4] is a quantitative description of the phenomena governing micturition; these phenomena are contractility, elasticity and visco-elasticity of the bladder  [5] , elasticity of the urethra [6], compression of the urethra by the sphincter, hydrodynamics of a turbulent incompressible fluid  [7]  and nervous control of both detrusor and sphincter  [8]. Each phenomenon considered separately is accurately described and can be easily studied. However, when combined as during voiding, they constitute such an intricate set that to precisely analyze micturition, elaborate software is needed. The VBN ® software allows to deduct the effects (changes observed in the uroflow curve) from the causes (changes in the voiding parameters) and vice versa. As explained in a prior publication [4], the VBN calculations need only a set of simple entries including gender, initial bladder volume, and catheter diameter for PF.
In addition, it is independent of initial bladder volume and of circumstantial factors such as non repeatable changes of nervous control during voiding (delayed sphincter opening, fading of detrusor excitation) which might affect the value of Qmax  [2]. In current pratice, evaluation of D index can be obtained from a simple device which associates a flowme-ter and a "click and read" version of the VBN ® software. Such a simple device can be used without a complex and expensive equipment and can help for quick screening and watchful waiting of BPH patients. The use of D index has a high sensitivity (83. 3 vs. 80. 9%) and a high PPV (75. 0 vs. 89. 4%) in the subgroups analyzed in this comparative study, suggesting that obstructed patients can be correctly identified. False negatives and false positives of the D index could be the consequence of its inability to distinguish between two scenarios: a severe obstruction with high detrusor contractility vs.
Acknowledgements We thank  Derek J. Griffiths PhD  ( Division of Geriatric Medicine, University of Pittsburg, PA, USA ) and  Jean-François Hermieu MD  ( Department of Urology, Hôpital Bichat, Paris, France ) who has kindly supplied us with pressure-flow records. We thank also  Smith Kline Beecham  who allowed us to use the curves obtained in some clinical trials. Curr Urol 2012;6: 124-128   Valentini/Nelson/Zimmern.
Environmental data Exposition location Itineraries were defined by the list of sites where individuals stayed at least one night. Geographical coordinates of the locations visited during the journey were obtained using GPS (Global Positioning System) and were recorded for each group by managers in the military logs. When GPS information was not directly available, the location of the visited places (site, village and town) was attributed using the National Geospatial-Intelligence Agency (NGA) and the US Board on Geographic Names (USBGN) databases. For this location, two coordinates separated by less than 30 arc seconds (approximately 1 kilometre) were considered as similar. Each site where a traveller stayed was recorded using (GIS) Arcview W 8. 3 (Environmental Systems Research Institute, Redlands, CA), a geographical information system (GIS) software.
The data was coded in 8 bits and needed to be corrected according to the following formula: [NDVI value = 0. 004 x (value of NDVI in the pixel) -0. 1]. Each picture was formatted using the Crop VGT W v1. 1 software (University IUAV of Venice, Italy). For the period between March 2004 and December 2006, data was provided by Terra satellite and his spectoradiometer MODIS IV (MODerate-resolution Imaging Spectroradiometer) launched by the NASA (National Aeronautics and Space Administration). The data was coded in 16 bits and needed to be corrected according to the following formula: [NDVI value = value of NDVI in the pixel/10 000]. Pictures were provided in HDF-EOS format (Hierarchical Data Format -Earth Observing System) in a sinusoidal projection system. Pictures needed to be rectified with the Modis Reprojection Tool W software (MRT) (Land Processes Distributed Active Archive Centre, Sioux Falls, SD).
Acknowledgements The authors are grateful to all the military personnel who agreed to participate in the study. We also thank those who worked on the " Impact -Vector " project and participated in the collection or the gathering of the data, in particular the  Defence Historical Service. We thank  Dr C. Dane  for her irreplaceable support. Financial support from  French Ministry of Defence  (programme impactvectorgrant  02CO011 , n°010808 from the  Délégation Générale pour l'Armement)  was of great help. V. Machault received financial support from the  Direction Générale de l'Armement  ( DGA -Contrat d'Objectif n°07CO402 ).
Strikingly H1. 2 appears enriched at chromosome X, with no preference for any particular DNA feature analyzed  (Figures 1B, 1C, and S1E) , and H1. 1 on the highly transcriptionally active chromosome 19  (Figures 1B, 1C, and S1D). In order to identify distinct combinations of H1 subtypes that are recurrent throughout the genome, we performed segmentation using an unbiased hidden Markov model (HMM) approach. We found that a five-state model optimally described our DamID data (see Materials and Methods). In this model, state 3 is the most abundant one, covering 50% of all probed locations, and corresponds to an average steady-state level of all five H1 subtypes binding (Figure  1D ). State 1 corresponds to regions strongly depleted of all H1 subtypes in the DamID data sets, and shows 3. 6% genome coverage.
EXPERIMENTAL PROCEDURES The DamID experiments were performed in IMR90 cells as previously described  (Moorman et al. , 2006). Normalization and analyses of DamID data were performed using custom R scripts (http://www. r-project. org/), GenPlay software  (Lajugie and Bouhassira, 2011) , and Galaxy  (Blankenberg et al. , 2010). For more details, see Extended Experimental Procedures. Coordinates for the human RefSeq genes, introns, exons, 5 0 UTRs, 3 0 UTRs, CpG islands, and LADs were retrieved from the UCSC Table  Browser. Histone-modification maps, gene-expression data, and bisulfite sequencing data for IMR90 cells were obtained from the Gene Expression Omnibus (GEO; data set GSE16256). The CTCF map for IMR90 cells was retrieved from GEO (data set GSE5559). Table  S1  summarizes the Gene Ontology (GO) analysis of genes present in the HMM states.
ACKNOWLEDGMENTS We thank  Ulrich Braunschweig ,  Nicolas Fourel , and  Nicolas Delhomme  for valuable computational advice and members of  R. S. 's laboratory for helpful discussions. Work in R. S. 's laboratory  is supported by the  DFG  (through SFB  746 ), an  ERC starting grant , and the  FRM (E ´quipe Labilise). J. K. is supported by  EMBO LTF  and  NWO-ALW VENI. B. v. S. is supported by  NWO-ALW VICI. Received: October 15, 2012 Revised: February 19, 2013 Accepted: May 3, 2013 Published: June 6, 2013.
46 (0. 74-2. 87) Previously treated 31 23 (74) Reference Unknown 5 5 (100) - Intensity of dependence High 139 101 (73) 0. 46 (0. 31-0. 66)* Low 410 359 (87) Reference Unknown 5 4 (80) Schéma : Etude de cohorte sur base de données recueillies en routine. Résultats :.
The aim of the present study was to evaluate the effects of posts with different morphologies on stress distribution in an endodontically treated mandibular premolar by using finite element models (FEMs). MATERIALS AND METHODS. A mandibular premolar was modeled using the ANSYS software program. Two models were created to represent circular and oval fiber posts in this tooth model. An oblique force of 300 N was applied at an angle of 45° to the occlusal plane and oriented toward the buccal side. von Mises stress was measured in three regions each for oval and circular fiber posts. RESULTS. FEM analysis showed that the von Mises stress of the circular fiber post (426. 81 MPa) was greater than that of the oval fiber post (346. 34 MPa). The maximum distribution of von Mises stress was in the luting agent in both groups.
MATERIALS AND METHODS A 22 mm long mandibular premolar with a 7 mm crown height and a 15 mm root length was modeled for this study. 12 The ANSYS software program (ver 12. 1; ANSYS Inc. , Canonsburg, PA, USA) was used to model the restored tooth and surrounding tissues. Two models of a restored tooth composed of a porcelain crown, dentin, composite core, crown-core adhesive layer, dentin-post adhesive layer, cortical bone, spongy bone, root canal gutta-percha filling, and oval and circular fiber posts, respectively, were created (Fig. 1  and Fig. 2 ). Material properties are listed in Table  1. 12  Using an optimization process based on convergence analyses, threedimensional meshes (mean size, 0. 5 mm) were created through 10 nodes of tetrahedral elements with quadratic displacement shape functions and three degrees of freedom per node.
To prevent ambiguities for events satisfying the selection requirements of more than one box  [33] , the boxes are arranged in a predefined hierarchy, as given in Table  1. Each event is uniquely assigned to the first box whose criteria are satisfied by the event. box M R > 400 GeV, 0. 18 < R 2 < 0. 5 Six additional boxes are formed for events with at least one b-jet tagged using the Track-Counting High-Efficiency (TCHE) b-tagging algorithm with 1% misidentification rate  [40, 41]. These six boxes define the razor inclusive analysis of data samples with enhanced heavy-flavor content. The razor analysis is guided by studies of simulated events generated with the PYTHIA6  [42]  and MADGRAPH  [43]  Monte Carlo programs, implemented using the CMS GEANT4-based  [44]  detector simulation, and then processed by the same software as that used to reconstruct data.
Events with QCD multijets, top quarks, and electroweak bosons are generated with MAD-GRAPH interfaced with PYTHIA for parton showering, hadronization and the underlying event description. To generate Monte Carlo samples for SUSY, the mass spectrum is first calculated with SOFTSUSY  [45]  and the decays with SUSYHIT  [46]. The PYTHIA6 program is used with the SLHA interface  [47]  to generate the events. Next-to-leading order (NLO) plus next-to-leadinglogarithm (NLL) cross section calculations are used  [48] [49] [50] [51] [52] [53]. For each of the main SM backgrounds, a control data sample is defined from a subset of the data dominated by this particular background. It is used to obtain a description of the shapes of the background components from data. In both simulation and control data samples, the distributions of the major SM background events (QCD multijets, tt + jets, Z + jets, and W + jets) are found to have a simple exponential dependence on the razor variables R 2 and M R over a large part of the R 2 -M R plane.
The values of the shape parameters that maximize the likelihood in these fits, along with the corresponding covariance matrix, are used to define the background model and the uncertainty associated with it. Additional background shape uncertainties due to the choice of the functional form were found to be negligible  [33]. The result of the ML fit projected on M R and R 2 is shown in Fig. 1  for the HAD box. No significant discrepancy is observed between the data and the fit model in any of the six boxes  [33]. 2 ) and projected into the full analysis region. Only the statistical uncertainty band on the background predictions is drawn in these projections. In order to establish the compatibility of the background model with the observed dataset, we define six signal regions (SRi) in the tail of the background distribution.
In summary, we performed a search for squarks and gluinos using a data sample of 4. 7 fb -1 of CMS data at √ s = 7 TeV proton-proton collisions in the razor variable space using a 2D shape description of the relevant standard model processes. No significant excess over the background expectations is observed, and the results are presented as a 95% CL limit in the (m 0 , m 1/2 ) CMSSM parameter space. For m( q) ∼ m( g) we exclude squarks and gluinos up to 1. 35 TeV, and for m( q) > m( g) we exclude gluinos up to 800 GeV. For simplified models we exclude up to 1 TeV for the gluino mass and up to 800 GeV for the first and second generation squark masses. For direct production of pairs of stops or sbottoms we exclude stop and sbottom masses up to 400 GeV depending on the LSP mass.
2012), the Amiga Halo Finder (hereafter   Gill et al. 2004; Knollmann & Knebe 2009) , and the FOF halofinder from the Gadget-3 N-Body cosmological code  (Springel 2005 , an update to the publicly available Gadget-2 code). 3072 h -1 Mpc 3072 3 6144 3 50 h -1 kpc 7. 49 × 10 11 h -1 M ⊙ 1. 49 × 10 12 h -1 M ⊙ 6000 h -1 Mpc 6000 3 12000 3 50 h -1 kpc 7. 49 × 10 11 h -1 M ⊙ 1. 49 × 10 12 h -1 M ⊙ 'AHF', The CPMSO halofinder utilises a fine mesh from the CUBEP 3 M code (with spacing twice as fine as the mean interparticle separation) and an interpolation scheme to identify local peaks in the density field. The code first builds the fine-mesh density using either Cloud-In-Cell (CIC) or Nearest-Grid-Point (NGP) interpolation. It then proceeds to search for and record all local density maxima above a certain threshold (typically set to 100 above the mean density) within the physical volume.
(1985). The main difference in the algorithm that exists in the Gadget-3 version is that the code is parallelised for distributed-memory machines. Specifically, haloes are found in local subvolumes of the simulation assigned to individual MPI tasks (created using the Gadget-3 domain decomposition which utilises a space-filling Peano-Hilbert curve -for details see the Gadget-2 paper, Springel 2005) and then haloes that extend spatially beyond the edges of the subvolumes are linked together in a final MPI communication step. We have altered the Gadget-3 code to read CUBEP 3 M's particle output format and significantly reduced its memory footprint by stripping away extraneous data structures. Due to limitations in the scaling of the codes with processor numbers and the large memory footprint of the Jubilee simu-1 AHF is freely available from http://www.
popia. ft. uam. es/AMIGA lation it was necessary to split the simulation time-slices into 27 subvolumes and run the halofinding algorithms on each subvolume independently. Each subvolume included a buffer zone which overlapped with the neighbouring ones, for correct handling of haloes straddling two or more sub-regions. We then stitched the subvolumes back together to create the final AHF and FOF halo catalogues, removing any duplicated structures in the overlapping buffers. This approach allows the handling of much larger amounts of data than otherwise possible and provides additional flexibility in terms of computational resources needed for post-processing.
Online databases It is our intention to make the data from the Jubilee simulation publicly available. This data will consist of three complementary halo catalogues of CPMSO, AHF and FOF haloes, in addition to LRG catalogues derived from the halo data and a catalogue of voids. The CPMSO will be available across a wide number of redshifts (∼ 30) from z = 0 -6, whereas the AHF and FOF data will be initially available only for z < 1. Further datasets will include smoothed density fields and maps of weak lensing and ISW signals. An SQL database has been set up so that the data can be queried to suit the requirements of individual users. Further information can be found at the Jubilee project website: http://jubilee-project. org.
The CPMSO data follows a similar trend to the AHF data. We overlay on these plots two of the fits from  Watson et al. (2013a)  -mass function results calibrated to data that included the Jubilee haloes presented here. The fit used for the left-hand panel is the redshift-dependent fit based on the CPMSO halofinder, the fit for the central panel a fit based on AHF results for z = 0 (see  Watson et al. 2013a , for further details). We compare the FOF results to those of the Millennium, Millennium-2 and Millennium-XXL simulations  (Angulo et al. 2012) , the latter containing 6720 3 particles in a box with length 3 h -1 Gpc. The FOF halo data shows agreement to within ±5% 0. 01 0. 1 1 with the Angulo et al. (  2012 ) fit for haloes with 300 or more particles. The FOF haloes are being compared with a linking length of 0.
We show in blue haloes that are colliding pairs and in red haloes that are a colliding subhalo and halo pair. We have added a normally distributed random scatter to our velocities with a width given by the error in the observed value for vs. This is to mimic the effect of Eddington bias in our simulated data. This observational bias arises when observing extreme measurements in a distribution of measurements all with some associated scatter. As there are many more data points that exist with less extreme velocities than the one in question it is likely that an extreme data point is an upscattered less extreme one. As we know very precisely the pairwise velocities of halo pairs in our simulation adding random scatter to this distribution serves to create the effect in our measurement. As can be seen from the distribution the Bullet cluster is an extreme object, but only when the radial separation of the halo pairs is considered.
We find many candidate mergers in our volume with a collision speed that equals or exceeds the more conserva-tive speed estimate for the cluster, and a few objects that are not far from the higher velocity estimate of  Markevitch et al. (2004). However, we find no objects that, at a closer separation, give rise to a large enough merging velocity. This is likely to be due to the effect of only considering one simulation output in our analysis. At any given output redshift only a handful of haloes will be undergoing a major merger event of the sort we are interested in and this is reflected by the paucity of data points that lie at a separation of less than 0. 6 h -1 Mpc. It is likely, therefore, that over the course of the Jubilee simulation run, high velocity mergers of the type observed in the Bullet Cluster do occur.
This is a fair way to assess the data as the actual collision in a Bullet Cluster-like system is expected to take a few hundred Megayears so is a relatively short event. Canvasing all our haloes in this manner assesses whether there is likely to be or whether there has been a Bullet Cluster-like collision in the simulation around z ∼ 0. 3. Further studies should include how random observers would observe these events. Lastly, halofinding algorithms are notoriously sub-optimal when trying to find and separate haloes that are merging (this is discussed in detail in  Knebe et al. 2011 , we draw the reader's attention to Figure  10  from that paper in particular). However, as the separation in question of the two haloes is relatively large, this is unlikely to be affecting our results.
ACKNOWLEDGMENTS The simulation was performed on the Juropa supercomputer of the  Jülich Supercomputing Centre (JSC). We would like to give special thanks to  Alexander Schnurpfeil  as well the whole the Jülich support team for their help during the phase of code implementation as well as during the production runs. We thank  Peter Coles ,  Aurel Schneider  and  Leonidas Christodoulou  for their helpful comments and feedback on this work. We also thank  Steffen Knollman  for his help in applying the AHF halofinder to our data. We thank  Volker Springel  for allowing us to use the Gadget-3 code with its FOF halofinder. WW thanks The  Southeast Physics Network (SEP-Net)  for providing funding for his research. ITI was supported by  The SEPNet  and the  Science and Technology Facilities Council  grants  ST/F002858/1  and  ST/I000976/1.
AK is supported by the  Spanish Ministerio de Ciencia e Innovación (MICINN) in Spain  through the  Ramón y Cajal  programme as well as the grants  AYA 2009-13875-C03-02 ,  AYA2009-12792-C03-03 ,  CSD2009-00064 ,  CAM S2009/ESP-1496  and the  Ministerio de Economa y Competitividad (MINECO)  through grant  AYA2012-31101. GY acknowledges support from  MINECO (Spain)  under research grants  AYA2009-13875-C03-02 ,  FPA2009-08958 ,  AYA2012-31101  and  Consolider Ingenio SyeC   CSD2007-0050. He also thanks  Comunidad de Madrid  for support under  PRICIT  project  ASTRO-MADRID  ( S2009/ESP-146 ). JMD and EMG acknowledge support of the consolider project  CSD2010-00064  funded by the  Ministerio de Economia y Competitividad  and  AYA2012-39475-C02-01.
To facilitate the discovery of new PPI small-molecule inhibitors, the characterization of PPI interfaces  [12, 13]  and the prediction of putative ligand binding sites are essential. Physicochemical properties of both ligand and protein are key to mediate the binding  [14] , such as cavity sizes, shape complementarity, electrostatic potential and hydrophobicity  [12, 15]. The role of alpha-helical peptides in mediating many PPIs is well demonstrated and development of small organic molecules mimicking such peptides becomes important  [16]. Recent studies have been carried out on the whole Protein Data Bank (PDB) in order to establish a druggability profile of alpha-helix mediated PPIs and to predict which of them could bind a small molecule  [17]. More specifically, terphenyl and its derivates are small organic molecules  [18] [19] [20] [21] [22] [23] [24] [25] [26]  mimicking one face of an alpha-helical peptide, i.
Methods In this study we compared the sequence and surface properties of the investigated proteins. In order to analyze the sequence similarities we performed sequence alignment using the CLUSTALW software  [28]. Interacting residues at the protein-protein interface in terms of contact distances were found using the ContPro online freely available tool  [29]. We identified the protein residues interacting with the three key residues of the alpha-helical peptide (occupying positions i, i+3 and i+7 or i, i+4 and i+7) those relative positions are mimicked by terphenyl and its derivatives. The distance threshold was set to 5 Å for the side chain atoms. In order to evaluate the protein surface properties, the bound peptide was removed for each complex. The surface characteristics of the entire protein and those of the peptide-binding cavity were analyzed.
Using the approach of the fractal geometry we quantitatively described the surface roughness for the entire protein and for the binding cavity, expressed by global surface fractal dimension (D S ) and local surface fractal dimension (D L ), respectively. In order to calculate the surface fractal dimension we used the method proposed by Lewis and Rees  [30]  based on the scaling law between the surface area (SA) and the radius of the rolling probe molecule (R) on the surface, i. e. SA is proportional to the radius to the power 2-Ds: SAeR 2-D S ð1Þ The surface fractal dimension was determined from the slope of the double logarithmical plot of SA versus R. The surface area of the protein was computed using the on-line available software GETAREA  [31]. Probe radii of 1, 1. 2, 1. 4, 1. 6, 1. 8 and 2 Å were used.