dataesr/scientific-paragraphs-categorization huggingface dataset:
294-298). 2. 1. Estimation of spatial synchrony in fish and cat populations. Using our state-space models, we found a strong pattern of synchrony among fish populations (r r ICC : mean = 0. 86, sd = 0. 11, Table  1 ) as well as among cat populations (r r GICC : mean = 0. 75, sd = 0. 22, Table  2 ). Estimates of the densitydependence parameter b 1 are mean(sd) 20. 26(0. 10) and 20. 44(0. 16) for the fish and the cat populations, respectively. The estimates of detection probabilities (p p ij ) for the cat dataset range from 0. 19 to 0. 76. On each site the average detection probability are: 0. 54 for Port-aux-Francais, 0. 61 for Port-Jeanned'Arc, 0. 54 for Port-Couvreux and 0. 52 for Ratmanoff. Estimates of p ij and their 95% credible intervals are available as supplementary file (File S6). 2.
These results show that there is a clear need to account for sampling error to accurately quantify the strength of synchrony patterns. Assessing the bias of an estimator of the strength of the synchrony pattern in the presence of sampling error requires knowing the value of r r proc , i. e. , the superpopulation parameter describing the correlation among population processes. This was not possible for the two datasets analysed here because the true states of the populations were unknown. But for the purpose of this study, we considered synchrony estimates obtained with state-space models as true values (or gold standards) because such a modelling approach has negligible bias (File S5). In this study we focus on the consequences of ignoring sampling variance on the estimation of spatial synchrony but other factors can modulate population synchrony estimates.
For instance, in small populations, demographic stochasticity can tend to dominate environmental stochasticity and consequently population synchrony is expected to increase with population size  [19, 43]. Ideally, the model fitted on the cat dataset should have included a demographic component in the process error. However, at present time there is not enough data for estimating demographic variance in the cat populations studied. We acknowledge that a fully realistic model should also have allowed for non-linear density dependence (e. g. , theta-logistic model,  [20] ) and/or spatial variations in the strength of the density dependence. Moreover, given the life expectancy of both species studied here, age structure should have been taken into account  [44]. In both illustrative examples, we assumed no age structure effect and a density-dependence linear and identical among populations.
Thus, accurately quantifying the strength of spatial synchrony requires combining both a sampling protocol that enables the estimation of sampling variance and a statistical procedure allowing proper accounting for it. In their study of the fish dataset, Tedesco et al. [24]  used eqn 2 to correct the synchrony estimate a posteriori. But this approach has some limitations. For instance, they had to assume that sampling error is independent of population size and thus to select a fixed number of replicates within each time period; they worked with a balanced subset of the data (representing 73% of the data available) to cope with the homoscedasticity assumption. By accounting for sampling variance at the observation level, statespace models overcome these limitations. By doing so, all the available data are included in the analysis and all the model.
S1 List of the main mathematical notations. (DOC) File S2 Superpopulation model definition and notation. (DOC) File S3 Definition of r GICC and r ICC. (DOC) File S4 R-program to fit the state space model and the fish dataset. (R) File S5 Monte Carlo assessment of the bias of the statespace model approach presented in this study to quantify the strength of the spatial synchrony among.
The authors thank the  World Health Organization (WHO) Onchocerciasis Control Programme (OCP) , the  'Centre National de Recherche Scientifique' (CNRS) , the  Institut Paul Emile Victor (IPEV, Programme 'Popchat ' no. 279 ), the  Zone Atelier (ZA) Program 'Environnement, Vie et Societe'  and the  French National Research Agency  ( ANR-09-PEXT-008 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
File S1 List of the main mathematical notations. (DOC) File S2 Superpopulation model definition and notation. (DOC) File S3 Definition of r GICC and r ICC. (DOC) File S4 R-program to fit the state space model and the fish dataset. (R) File S5 Monte Carlo assessment of the bias of the statespace model approach presented in this study to quantify the strength of the spatial synchrony among.
AFM imaging and particle characterization The purified samples were then deposited on poly-L-lysine-coated mica surfaces and dried for air-imaging with Atomic Force Microscope (see Material and Methods). Simple visual analysis of VLPs and cores by AFM images confirms the presence of objects with appropriate sizes and morphological features. This simple consistency check was performed for each purification. Examples of typical mature VLP and core images are shown respectively in figure  1c  and 1d. One can recognize spherical shape VLP of about 100nm in average diameter (Fig  1c ) and typical HIV conical shape cores (Fig  1d ) with an average length of 75nm and width of 50nm. In order to be able to sort in an objective way different populations within one single purification condition based on morphological features, we developed home-made automated image analysis scripts (see Material and Methods).
Automated image analysis Images acquired by AFM were analyzed in an automated fashion using home-made MATLAB (from Mathworks) scripts. The principle of this analysis is summarized in the figure  2. The aim of developing such automated image analysis is to be able to select particles based on objective criteria. While using this approach, caution should be exercised in order to optimize the parameters of the filters, allowing to reject aberrant particles (broken particles, aggregates. ) and select normal particles, while keeping size dispersion and good statistics. The first step of the image analysis is to use standard flattening procedure of the raw AFM image in order to get rid of typical piezo-electric drift during image acquisition. The next step is to use a low height selection (12nm in our case) in order to focus on image parts above the roughness of the naked substrate (mica+Poly-L-Lysine coating) and to make a binary image out of it.
Small sealing zones and defective podosome organization in RhoE-deficient osteoclasts RhoE is involved in the control of actin cytoskeleton in other cell types  (Guasch et al. , 1998; Klein and Aplin, 2009). We therefore investigated its role in SZ formation, given the critical importance of this actin structure in the bone resorption process. Day-4 RhoE +/+ and RhoE gt/gt FL-OCs were lifted from culture-treated plates and seeded for 24 h on bone slices. These OCs were then fixed, stained for actin, and imaged using confocal microscopy. Then, using the software ImageJ, we manually determined peripheral outlines of SZs from acquired micrographs, allowing the quantification of SZ surface areas. There was a slight yet significant 15% decrease in mean SZ size in RhoE gt/gt OCs compared with RhoE +/+ OCs (Figure  5 ).
The mean relative fluorescence intensity (RFI) of a given region of interest was measured and normalized as mentioned in  Negi and Olson (2006). The obtained RFIs were curve fitted and plotted using the formula in  Negi and Olson (2006)  with consideration of the guidelines in  Hardy (2012). Statistical analysis of recovery half-times was calculated from data fitting using the "Do the best fit values of selected parameters differ between datasets differ" function in Prism 6 (GraphPad, La Jolla, CA;  Weisswange et al. , 2009).
ACKNOWLEDGMENTS We acknowledge the contributions of the following from the  SFR Biosciences Gerland-Lyon Sud  ( UMS344/US8 ) facilities:  C. Lionnet ,  Plateau Technique Imagerie/Microscopie , for valuable input on microscopy; the  AniRA-Plateau de Biologie Expérimentale de la Souris  for animal care; the  AniRA-Vectorologie  for lentiviral particle production; and the  PAP (Production et Analyse des Proteines)  platform for murine RANKL production. D. G. is an  Early Stage Researcher  at the  Tissue Transmigration Training Network  ( T3-Net ) funded by the  European Commission  under Framework Program 7. D. G. is also funded by the  Fondation-ARC  (Aides individuelles jeunes chercheurs, ref. DOC20120605264 ). This project received research grants from the  Fondation ARC  (Subvention fixes, ref.
5 Positron Excess and Fit to the PAMELA and AMS-02 data Here we show the impact of decaying U U particles on the cosmic positron flux and diffuse gamma radiation. The so-called "PAMELA anomaly" in the cosmic positron spectrum  [88]  has been recently confirmed also by AMS-02  [89]. This anomaly cannot be explained by positrons of only secondary origin, and therefore primary positron sources are needed to explain the data. There are attempts to realize it based on decaying or annihilating dark matter models. Any scenario that provides positron excess is constrained by other observational data, mainly from the data on cosmic antiprotons, and gamma-radiation from our halo (diffuse gamma-background) and other galaxies and clusters  [90] [91] [92] [93] [94] [95] [96] [97]. If dark matter does not produce antiprotons, then the diffuse gamma-ray background gives the most stringent and model-independent constraints.
Simplified Model Spectra, or SMS for short, are effective-Lagrangian descriptions involving just a small number of new particles. They were designed as a useful tool for the characterization of new physics, see e. g. [16, 17]. A large variety of results on searches in many different channels are available from both ATLAS and CMS, providing general cross section limits for SMS topologies. However, using these results to constrain complex SUSY (or general BSM) scenarios is not straightforward. In this paper, we present a method to decompose the signal of an arbitrary SUSY spectrum into simplified model topologies and test it against all the existing LHC bounds in the SMS context. The computer package doing all this is dubbed SModelS  [18]. (A similar approach was in fact proposed some time ago in  [19].
(Note that it is possible to use only a subset of experimental results in the database. ) Let us now outline the general SModelS procedure. Under the SMS assumption, to a first approximation, it is possible to reduce all the properties of a BSM model to its mass spectrum, production cross sections (σ) and decay branching ratios (B). With this knowledge we can decompose the full BSM signal in a series of independent signal topologies with their specific weights given by the corresponding production cross section times branching ratio (σ × B). Such a decomposition is extremely helpful to cast the theoretical predictions of a specific BSM model in a model-independent framework, which can then be compared against the experimental limit on this σ × B. A schematic view of the working principle is given in Fig.
3 fb ∫ 0 1 χ ∼ - l 0 1 χ ∼ + l → - L,R l ~ + L,R l ) theory SUSY σ 1 ± Observed limit ( ) exp σ ± Expected limit ( excluded R µ ∼ LEP All limits at 95% CL The cross section upper limits reported in this way are however subject to a number of assumptions made in the analysis, which also have to be described in the database. In the SModelS language described in Section 3. 1, the topologies appearing in Fig. 7 (left) are [[[L], [L]], [[ν], [τ ]]], where L = e, µ, τ and the relevant mass vectors are (m χ± 1 , m L, m χ0 1 ) and (m χ0 2 , m L, m χ0 1 ). The experimental analysis in our example assumes degenerate sleptons and m χ0 2 = m χ± 1 , so there are only three independent masses. Furthermore this analysis constraints the sum over lepton flavors and charges for the topologies listed above under the assumption that each term contributes equally (flavor democratic decays).
Moreover, the SMS result illustrated in Fig. 7  (left) is for the particular mass relation m l = (m χ± 1 + m χ0 1 )/2 and can thus only be applied for (approximately) these m l and m ν values. However, in Refs. [24, 25] , results for distinct slepton mass values are also provided, which allows us to interpolate between them and apply these results to more generic models. All these constraints and conditions are described in our analysis database in the form of "metadata". As an example we show the metadata for the CMS result in Fig. 7 -> [[['L'],['L']],[['nu'],['ta']]] fuzzycondition: TChiChipmSlepStau -> Cgtr([[['mu'],['mu']],[['nu'],['ta']]] , [[['e'],['e']],[['nu'],['ta']]]) Cgtr([[['e'],['e']],[['nu'],['ta']]] , [[['ta'],['ta']],[['nu'],['ta']]] ) category: TChiChipmSlepStau -> eweakino axes: TChiChipmSlepStau: M1 M0 005 -M1 M0 050 -M1 M0 095 by the analysis.
Gluino and squark searches • ATLAS: SUSY-2013-04  [26] , ATLAS-CONF-2012-105  [27] , ATLAS-CONF-2013-007  [28] , ATLAS-CONF-2013-047  [29] , ATLAS-CONF-2013-061  [30] , ATLAS-CONF-2013-062  [31] , ATLAS-CONF-2013-089  [32]  • CMS: SUS-11-022  [33] , SUS-11-024  [34] , SUS-12-005  [35] , SUS-12-011  [36] , SUS-12-024  [37] , SUS-12-026  [38] , SUS-12-028  [39] , SUS-13-002  [40] , SUS-13-004  [41] , SUS-13-007  [42] , SUS-13-008  [43] , SUS-13-012  [44] , SUS-13-013  [45]  Electroweakino searches • ATLAS: ATLAS-CONF-2013-028  [46] , ATLAS-CONF-2013-035  [47] , ATLAS-CONF-2013-036  [48] , ATLAS-CONF-2013-093  [49]  • CMS: SUS-11-013  [50] , SUS-12-006  [51] , SUS-12-022  [52] , SUS-13-006  [24] , SUS-13-017  [53]  Direct slepton searches • ATLAS: ATLAS-CONF-2013-049  [25]  • CMS: SUS-12-022  [52] , SUS-13-006  [24]  3rd generation: stop and bottom searches • ATLAS: ATLAS-CONF-2012-166  [54] , ATLAS-CONF-2013-001  [55] , ATLAS-CONF-2013-007  [28] , ATLAS-CONF-2013-024  [56] , ATLAS-CONF-2013-025  [57] , ATLAS-CONF-2013-037  [58] , ATLAS-CONF-2013-047  [29] , ATLAS-CONF-2013-048  [59] , ATLAS-CONF-2013-053  [60] , ATLAS-CONF-2013-062  [31] , ATLAS-CONF-2013-065  [61] , SUSY-2013-05  [62]  • CMS: SUS-11-022  [33] , SUS-12-028  [39] , SUS-13-002  [40] , SUS-13-004  [41] , SUS-13-008  [43] , SUS-13-011  [63] , SUS-13-013  [45]  Of course the database is continuously being extended as new results become available.
Therefore we must also verify that these conditions are satisfied, otherwise the experimental upper limit can not be applied. Finally, if the experimental assumptions are satisfied, the resulting theoretical predictions (σ × B) obtained after combining the topologies can be directly compared to the corresponding experimental upper limit. In Fig. 1  we summarize the main steps required to confront the BSM model predictions with the experimental constraints: the SMS decomposition, the combination of SMS topologies with identical or similar masses into the topology sums assumed by the analyses and finally the comparison with the experimental upper limits obtained from the database of LHC results described in Section 3. 2.
7 e performed such "regression tests" for several SMS results implemented in the SModelS database. Some illustrative examples are shown in Fig. 8. In particular, we show regression tests for ATLAS and CMS electroweak-ino searches, searches for gluinos with three-body decays into light quarks (ATLAS) or top quarks (CMS), squark pair-production with direct decay into jet+LSP (ATLAS and CMS), slepton-pair production (ATLAS), as well as stop-pair production giving t t+MET (CMS). The upper limit obtained by interpolating the experimental results is shown by the color coded background and we assumed a constant binning of 25 GeV for all plots. The official exclusion curve obtained by the experimental collaboration is shown in black (solid), while the one obtained through SModelS is shown in red (dashed).
In general we find very good agreement with the official exclusion curves once we take into account differences  in binning size and the smoothing procedure adopted by the experimental collaboration. (In Fig. 8(c ), the red line and the background do not extend above the diagonal because the results for off-shell chargino decays are not yet included in the database. ) The exception is the ATLAS q q, q → q χ0 χ ∼ q → q , q q (g) (GeV) 1 analysis  [29] : here there are regions below the official exclusion line which are actually not excluded by the 95% CL UL on the cross section. This is due to large fluctuations in the upper limit values published by the experimental collaboration, clearly visible in the colored bins in Fig. 8(e).
We also apply SUSY mass limits from LEP. The constraints imposed are listed in Table  4. In each scan, we collected more than 30K points which obey all these constraints; these points are then passed through SModelS to test their compatibility with the SMS constraints. SModelS decomposes the spectrum of each point into its SMS topologies and compares it to the experimental constraints according to the procedure described in the previous sections. It returns detailed information on the occurring topologies, which topologies are actually tested (i. e. SMS topologies which have been constrained by one or more experimental analyses in the database), and how their σ × B compares to the experimental UL. For the results presented below, we only considered the 8 TeV analyses in the database.
Conclusions We presented a new tool, SModelS, to decompose the LHC signatures expected from BSM spectra presenting a Z 2 symmetry into SMS topologies and test the predicted cross sections (σ × B) for each topology against the existing 95% CL upper limits from ATLAS and CMS. The program consists of three parts, the decomposition procedure, which can be Monte-Carlo based or SLHA based, the database of ATLAS and CMS SMS results, and the interface to match model predictions onto the experimental results. Our concrete implementation currently focusses on SUSY searches with missing energy, for which a large variety of SMS results from ATLAS and CMS are available. SModelS can be used "out of the box" for the MSSM and its extensions, like the next-to-MSSM. The approach is however perfectly general and easily extendible to any BSM model to which the experimental SMS results apply.
g. from g → tb χ± 1 or from t1 pair production with one stop decaying into t χ0 1 and the other one into b χ± 1. Third, one may use the decomposition procedure of SModelS independent of the results database in order to identify the most important signal topologies in regions of parameter space. Finally, in the case of a positive BSM signal, the SModelS framework may be helpful for characterizing which new physics scenarios may explain the observations. While SModelS is already a powerful and useful tool for phenomenological studies, there is of course still much room for improvement. For instance, as we have shown, scenarios with complicated decay patterns-as typical for e. g. light gluinos and heavy squarks-are not well constrained with the current framework, which is based on testing the σ × B upper limits topology-by-topology without the possibility of combining results.
Acknowledgements We thank  D. Liko  for help in using the Vienna Tier-2 Grid, and the reactor physics group of  LPSC Grenoble  for the use of part of their computer resources. This work was supported in part by the  PEPS-PTI  project " LHC-itools ", by the  ANR  project  DMAstroLHC  and by  FAPESP. U. L. and D. P. S. are grateful for financial support by the  FEMtech initiative of the BMVIT of Austria. A. L. gratefully acknowledges the hospitality of LPSC Grenoble.
Context. Spectra of astronomical targets acquired from ground-based instruments are affected by the atmospheric transmission. Aims. The authors and their institutes are developing a web-based service, TAPAS (Transmissions Atmosphériques Personnalisées pour l'AStronomie, or, Transmissions of the AtmosPhere for AStromomical data). This service, freely available, is developed and maintained within the atmospheric ETHER data center. Methods. TAPAS computes the atmospheric transmission in the line-of-sight to the target indicated by the user. The user files a request indicating the time, ground location, and either the equatorial coordinates of the target or the Zenith Angle of the line-of sight (LOS). The actual atmospheric profile (temperature, pressure, humidity, ozone content) at that time and place is retrieved from the ETHER atmospheric data base (from a combination of ECMWF meteorological field and other informations), and the atmospheric transmission is computed from LBLRTM software and HITRAN data base for a number of gases: O 2 , H 2 O, O 3 , CO 2 , and Rayleigh extinction.
Today progresses in the molecular databases and radiative transfer models allow improved computations of the atmospheric transmission. Such a synthetic transmission has already been used in the past to cor-rect for water vapor absorption  (Lallement et al. , 2011) , however a unique transmission spectrum computed for a standard atmosphere was used, adapting to the air mass and humidity by simply amplifying the lines of this unique spectrum. The transmission however is a function of the altitude and the atmospheric profile at the observatory. What we propose here is an online tool providing a state-of-the-art computation of this transmission, optimized for the location and time of the observation by the use of the actual meteorological field from ECMWF (European Center for Medium-range Weather Forecast).
In the case of the 628. 4 nm DIB, strong molecular oxygen lines surround its absorption, which renders the measurement of its equivalent width (EW) difficult, especially for nearby objects (weak DIB), low resolution and large air mass. The O 2 transmission is computed with TAPAS for the altitude and location of the observatory (here the ESO Paranal and La Silla observatories in Chile). Fig. 1  shows a global adjustment to the data of a combination of stellar, telluric (TAPAS) and diffuse band models  (Chen et al. , 2013). The choice between the different methods depends on the characteristics of the spectrum and the feature to be observed. In spectral regions highly populated with strong but narrow lines, one can also take advantage of the variable Earth's velocity on its orbit. Looking at a star (not too far from ecliptic) at various periods, the comb of telluric lines is displaced, and most of the star spectrum may be reconstituted.
TAPAS computation: atmospheric profile and atmospheric transmission In the visible or near Infra-red part of the spectrum, the lines of O 2 , H 2 O and CO 2 are the most conspicuous lines affecting the atmospheric transmission (see figure  2  for O 2 and H 2 O). Their local absorption depend mainly on the absolute concentration, but also on pressure and temperature. Oxygen is a well-mixed gas and the O 2 lines depend on the ground pressure, and on the vertical profiles of pressure and temperature. For H 2 O, the integrated absorption depends also on the vertical distribution of H 2 O concentration. The TAPAS web-service is able to compute accurately the atmospheric transmission from a number of astronomical observatories. TAPAS is one of the tools developed in the frame of ETHER, a French Atmospheric Chemistry Data Center maintained from CNES and INSU/CNRS fundings (www.
pole-ether. fr). The database includes measurements acquired by satellite, balloons and aircraft or from other sources and the aim is to make them available to the entire scientific community for exploitation by modeling or assimilation. ETHER is also a vehicle for discussion and information on related scientific themes that may lead to greater comprehension and better use of existing data. A TAPAS user must be registered once, by filling a form online. This is done by logging to ether. ipsl. jussieu. fr/tapas/, then clicking on "request form". Once registered and getting a password, clicking on request form will make appear the users interface window If the simulated transmission is needed for the future (i. e, for observations planning purposes), TAPAS will use standard atmospheric profiles adequate for the season and latitude of the observatory, stored in the ETHER data base.