dataesr/scientific-paragraphs-categorization huggingface dataset:
Radial velocity measurements of HD 10700 We analyzed a large data set of FEROS (117) and HARPS (153) spectra of the star HD 10700. The considered FEROS observations span a period of 5. 6 years, while the HARPS observa- RV measurements of HD 10700 using spectra reduced by FEROS-DRS. The red solid line is a sinusoidal fit with a period of 365. 2±0. 2 days and a semi-amplitude of 62. 0±1. 1 m s - 1. Only spectra with a simultaneous ThAr exposure (blue data points) were considered for the fit. The present period of 365. 2 days is caused by an inaccurate barycentric correction by the FEROS-DRS. The lower panel shows the residual of the fit. tions cover 5. 3 years. The measurement of the stellar RVs from FEROS and HARPS spectra was carried out using a self-written routine that cross-correlates the stellar spectra with a template spectrum.
We found that the long period in the FEROS data arises because the barycentric correction of the FEROS-DRS pipeline was not accurate enough. In particular, it neglects the precession of the coordinates. We applied a more precise barycentric correction using the IDL code baryvel. pro 1. The algorithm is based on  Stumpff (1980)  and is accurate to ∼1 m s -1. In Figure  3  we show the difference of the IDL-computed and pipeline-provided barycentric correction for HD 10700. The comparison between the values computed by baryvel. pro and by the HARPS pipeline (in green) demonstrates that the adopted procedure computes the barycentric correction with the required accuracy, in contrast to the FEROS-DRS pipeline (in blue). The RMS-dispersion of the difference of the barycentric correction is 1. 2 m s -1 and 34.
6 m s -1 for HARPS-baryvel. pro and FEROS-baryvel. pro, respectively. Applying an accurate barycentric correction to the FEROS spectra, the RV values of HD 10700 are basically constant (see Figure  4 ). No indication of either a one-year period or any other period is left. We also applied this routine to the HARPS data of HD 10700. The result is shown in Figure  5. In addition to the higher signal-to-noise ratio (S/N) and spectral resolution of HARPS data with respect to the FEROS data, it should be considered that HARPS is an instrument fully optimized for high radial velocity precision (e. g. improved stability, vacuum tank).
Observations The dataset of HIP 11952 includes the 77 observations presented in  Setiawan et al. (2012)  carried out from August 2009 until January 2011. We also included seven new FEROS observations not present in the analysis of  Setiawan et al. (2012). All details about the observations are reported in  We noticed that the data obtained between October 22 and 25, 2010 had poor ThAr+Ne frames.
1 for the synthetic template. We obtained similar results with the observed spectrum of HIP 11952. We obtained standard deviations of 36. 1 m s -1 using the stellar template, and 39. 3 m s -1 using the synthetic template (see Figure  6 ). RV measurements of the full data set of HIP 11952 containing 84 observations. The synthetic template was used. Light-blue data points indicate an observing mode where no simultaneous ThAr spectrum was taken. The standard deviation of the data set is 39 m s -1 , which resembles the size of the error bars. We applied the GLS periodogram to the RV data to search for periodicities (see Figure  7 ). We found no significant period in the FEROS data. In particular, we found no sign of the 290 d and 6. 95 d periods claimed in  Setiawan et al. (2012) , in agreement with the HARPS N result presented by  Desidera et al.
1  was added to each computed sine function to account for the inaccurate barycentric correction by the FEROS-DRS. For every setup we computed a GLS periodogram to determine whether the applied period could still be identified. For pure sinusoidal RV variations, i. e. for variations without eccentricity, we draw the following conclusions: -All periods can be identified for RV variations with semiamplitudes greater than ∼150 m s -1. -For semi-amplitudes of around 100 m s -1 periods between ∼100 and ∼200 days cannot be clearly identified. -If the semi-amplitude is smaller than 100 m s -1 , all periods present in the RV data set cannot be identified or are difficult to identify. -Short periods 10 days can be detected reliably if the RV amplitude is greater than about twice the RV residuals.
Estimates of Population Size Population size estimates of European bird species were obtained from websites and persons responsible for the Pan-European Common Bird Monitoring Scheme in every European country. We obtained data for only 12 countries, either because in some countries this scheme has started very recently or has not started yet, or because information was unavailable upon request. Population indices from some countries or regions could not be used in this study owing to incomplete information (e. g. Wallonia), or because bird censuses were done only in one type of habitat (e. g. Latvia). Countries with available and suitable information on avian population size, the source of this information, number of bird species per country, mean number of years with data in every country (not all species were always surveyed the same number of years in a country), and last year with information on population size are shown in Table  1.
Population Latitude and Marginality In order to estimate the marginality of bird populations, i. e. , whether populations were central or marginal within the breeding distribution range, we first calculated a latitude index for each of the 12 European countries included in the study (Table  1 ). This latitude index was estimated as the latitude of the mid-point between the northernmost and the southernmost mainland points of every country. This information was obtained using maps in atlases and online with the software Google Earth (http://earth. google. com). Islands were not considered because they generally add a lot of distance in terms of latitudinal degrees, but very small area in relation to the entire country. The exception was Denmark, because a large part of that country is made up by islands, and the southernmost point of the country was located at Falster.
Specifically, for every bird population (i. e. , every species in every country) we ranked population indices decreasingly and then divided the data in two halves, one with the largest population indices and the other with the smallest population indices. When number of study years was an odd number, the mid-point population index was included in both halves. In this way, the two subsets for a given population always included the same number of population indices. Then, CV of population indices was estimated for each of the two subsets, CV high for large population indices and CV low for small population indices. Since number of study years (n) used to calculate CV varied among countries and sometimes also among species within a country, CV values were transformed to obtain CV corrected for sample size (CV*) with the following transformation: CV* = CV (1 + 1/4n)  [51].
Methods used by EEA to calculate fragmentation indices and a map of Europe at a 10x10 km grid resolution showing levels of fragmentation can be found at the website http://www. eea. europa. eu/data-andmaps/figures/fragmentation-by-urbanisation-infrastructure-andagriculture. In this map, the degree of fragmentation is shown by different colours representing six categories of fragmentation from minimal to extreme. We first calculated the percentage of the area of each country for every category of fragmentation by using the software Adobe Photoshop CS4 Extended, v. 11. 0. 2 (Adobe Systems Inc. , San Jose, CA). We then assigned a value of fragmentation from one to six to the different degrees of fragmentation and calculated the mean fragmentation index weighted by area for every country (Table  1 ).
However, these countries did not have a strong influence on the results, because exclusion of them from the analyses generally provided qualitatively similar results (Appendix S1). The most remarkable difference was that the relationship between slope and latitude was statistically significant only after removal of non-significant factors from the model (see Appendix S1). All statistical analyses were two-tailed with a significance level of 0. 05, and performed with Stastistica v. 9. 0 (http:// www. statsoft. com), except RMA regressions that were performed with RMA Software for Reduced Major Axis Regression v. 1. 17 (http://www. bio. sdsu. edu/pub/andy/ RMA. html). An example of how population parameters were calculated in a common bird species (the great tit Parus major) can be found in Appendix S2 (Supporting Information).
Acknowledgements We are grateful to all the people who kindly replied to our messages during the search for information on population indices of European bird species, particularly to  Martin Flade ,  Magne Husby ,  Jaroslaw Krogulec ,  Robert Kwak ,  Jiří Reif ,  Norbert Teufelbauer ,  Tibor Szép  and  Dansk Ornithologisk Forening. We thank  Bernt-Erik Saether  for useful discussions.
An MR acquisition protocol and a processing method using distributed computing on the European Grid Infrastructure (EGI) to allow 3D liver perfusion parametric mapping after Magnetic Resonance Dynamic Contrast Enhanced (MR-DCE) imaging are presented. Seven patients (one healthy control and six with chronic liver diseases) were prospectively enrolled after liver biopsy. MR-dynamic acquisition was continuously performed in free-breathing during two minutes after simultaneous intravascular contrast agent (MS-325 blood pool agent) injection. Hepatic capillary system was modeled by a 3-parameters one-compartment pharmacokinetic model. The processing step was parallelized and executed on the EGI. It was modeled and implemented as a grid workflow using the Gwendia language and the MOTEUR workflow engine.
Images Preprocessing. Images were first imported on a personal computer running an in-house developed application written in Matlab r2010a (The MathWorks, Natick, MA, USA). Due to free-breathing acquisition, spatial shifts linked to motion had to be corrected. Hence, each volume was automatically registered. The registration method ignored nonrigid aspects of liver transformation during breathing. Only translations and rotations (rigid transformations) were taken into account. This method consisted in the estimation of the transformation vector needed to register each moving images in relation to a static reference image. For each image from 2D + t volumes, a pixel-based method was used (iconic approach) to control the transformation of an input image. An error measure was used to measure the registration error between the moving and static image.
The parallelization was handled at the input data level, by splitting each volume into several pieces. Each piece was processed by independent jobs running in parallel on multiple grid resources and eventually merged. The whole processing operation was modeled and implemented as a grid workflow using the Gwendia language  [17]  and the MOTEUR workflow engine  [18]. The splitting and merging algorithms were developed in C++, while the processing algorithm was developed in Matlab. All three programs were compiled on a grid compliant operating system (CentOS) and deployed on the fly on the grid nodes. For the Matlab code we used the Matlab Compiler and the Matlab Compiler Runtime (MCR). The interface with the grid resources was provided by the VIP web platform (https://vip. creatis. insa-lyon.
These restrictions (SNR and coverage) can be overcome with the latest imaging MR systems with improved acquisition capabilities using 32 receiver channels with multiple element array coils. An SNR value of 80 was measured based on preliminary test performed at our institution with a 3T GEHC MR 750 (GEHC, Milwaukee, WI, USA) with 32 ch body coil. The parallelization of the method brings significant speedup and renders it feasible despite its prohibitive computing time. Nevertheless, performance can be still significantly improved. Currently the poor scheduling of the last tasks is largely due to platform heterogeneity and multiple task resubmissions caused by high error ratios. Data transfers account for most of the errors, while the rest are mostly application failures due to improper grid node configuration.
Acknowledgments This work was conducted in the framework of the LabEX PRIMES. The authors thank  France-Grilles  and  EGI  for the grid infrastructure and technical support. They also thank  T. Glatard ,  R. Silva ,  F. Bellet ,  J-P. Roux ,  V. Romanello-Perez , and  M. Orkisz  for technical support and fruitful discussions.
A Gaussian overlap function can be used as an alternative to the blackdisk nucleon-nucleon overlap function  [23]. It makes no significant difference within systematic uncertainty in the global event properties. The number of collisions N coll and the number of participants N part are determined by counting, respectively, the binary nucleon collisions and the nucleons that experience at least one collision. Following the notation in  [2] , the geometric nuclear overlap function T AA is then calculated as T AA = N coll /σ inel NN , and represents the effective nucleon luminosity in the collision process. For nuclear collisions at √ s NN = 2. 76 TeV, we use σ inel NN = (64 ± 5) mb, estimated by interpolation  [11]  of pp data at different center-of-mass energies and from cosmic rays  [12, 14] , and subtracting the elastic scattering cross section from the total cross section.
Each chip provides a fast signal if at least one of its pixels is hit. The signals from the 1200 chips are combined in a programmable logic unit which supplies a trigger signal. The fraction of SPD channels active during 2010 data taking was 70% for the inner and 78% for the outer layers. The VZERO detector consists of two arrays of 32 scintillator cells placed at distances z = 3. 4 m and z = -0. 9 m from the nominal interaction point, along the beam line, covering the full azimuth. The VZERO detector is within 2. 8 < η < 5. 1 (VZERO-A) and -3. 7 < η < -1. 7 (VZERO-C). Both amplitude and time of signals in each scintillator are recorded. The VZERO time resolution is better than 1 ns, allowing discrimination of beam-beam collisions from background events produced upstream of the experiment.
The cluster data are used to reconstruct the charged particle trajectory as well as to calculate the particle's specific energy loss used to identify the species of the particle which has produced the track. The two Zero Degree Calorimeters (ZDC) in the ALICE experiment measure the energy of spectator (non-interacting) nucleons: ZP measures protons and ZN measures neutrons. They are situated about 114 m from the interaction point on each side of the experiment  [10]. Each ZDC consists of two quartz fiber sampling calorimeters: the neutron calorimeter positioned between the two beam pipes downstream of the first machine dipole that separates the two charged particle beams, and the proton calorimeter positioned externally to the outgoing beam pipe. The energy resolution at beam energy is estimated to be 20% for the neutron (20.
Data set and online event selection During the first LHC Pb-Pb run in 2010, beams of four bunches with about 10 7 Pb ions per bunch collided at √ s NN = 2. 76 TeV, with an estimated luminosity of 5 × 10 23 cm -2 s -1. AL-ICE collected about 90 million nuclear collision events using different interaction triggers with increasingly tighter conditions. These triggers used VZERO and SPD detector signals in coincidence with a bunch crossing corresponding to a beam-beam collision: The threshold in the VZERO detector for each of the VZERO tiles corresponded approximately to the energy deposition of one minimum ionizing particle. Control events were also collected with the same trigger logic, in coincidence with only one beam crossing the ALICE interaction point (from either the A or the C side) or with no beam at all ("empty").
The location of the divergence between the data and simulation can be used to define the AP. The centrality determination is performed for different trigger and detector settings. The different triggers change the fraction of accepted events from EM processes, which determines the shape of the multiplicity distribution for very peripheral collisions below the AP. The position of the AP is very stable for the entire 2010 (and 2011) run period and does not change within the quoted systematic uncertainty discussed below. Small variations in detector conditions induce small changes in the position of the edge of the multiplicity distribution for most central events. Nevertheless, the centrality determination, adjusted to account for small changes in the detector configuration, provides a stable centrality selection for the entire data-taking period (the mean fraction of events in the 0-1% bin is 0.
The sum of the simulations is normalized to the data in the region 150 < VZERO amplitude < 500, where there is no background contamination. The contribution from QED is completely removed by the V0AND trigger. The dashed lines, indicating respectively 80% and 90% of the hadronic cross section, show that there is no significant background contamination for collisions more central than 90%. The region 90-100% is reasonably understood as the agreement between data and simulation is quite good. The remaining discrepancy between the data and the sum of all contributions is included in the systematic uncertainty. To assign a systematic uncertainty, the comparison is made for the three online interaction triggers and other event selections requiring (i) V0AND + TPC: one track fully reconstructed in the TPC on top of the V0AND trigger; (ii) V0AND + ZDC: 3σ cut above single neutron peak in ZDC on top of the V0AND trigger.
Summary Heavy-ion collisions can be characterized by the number of charged particles produced in the collision. In principle, when normalized to the trigger efficiency used to collect the data sample, the charged particle multiplicity could provide a measurement of the hadronic cross section. However, at the LHC the large cross section for EM processes contaminates the very peripheral collisions. This problem was overcome in two ways. In the first method, dedicated simulations of hadronic and EM processes (Fig. 8 ) were performed and data were corrected for efficiency of the event selection (Fig. 7 ) and purity of the event sample (Fig. 9 ). In the second method, the measured multiplicity distribution was fitted with a Glauber calculation (Fig. 10 ). Both methods allow to determine a centrality value above which the background contamination is negligible and the event selection is fully efficient.
Acknowledgements The ALICE collaboration would like to thank all its engineers and technicians for their invaluable contributions to the construction of the experiment and the CERN accelerator teams for the outstanding performance of the LHC complex. The ALICE collaboration acknowledges the following funding agencies for their support in building and running the ALICE detector:  State Committee of Science, Calouste Gulbenkian Foundation from Lisbon and Swiss Fonds Kidagan ,  Armenia ;  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) ,  Financiadora de Estudos e Projetos (FINEP) ,  Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) ;  National Natural Science Foundation of China (NSFC) , the  Chinese Ministry of Education (CMOE)  and the  Ministry of Science and Technology of China (MSTC) ;  Ministry of Education and Youth of the Czech Republic ;  Danish Natural Science Research Council , the  Carlsberg Foundation  and the  Danish National Research Foundation ; The  European Research Council  under the European Community's  Seventh Framework Programme ;  Helsinki Institute of Physics  and the  Academy of Finland ;  French CNRS-IN2P3 , the  'Region Pays de Loire', 'Region Alsace', 'Region Auvergne'  and  CEA, France ;  ALICE Collaboration German BMBF  and the  Helmholtz Association ;  General Secretariat for Research and Technology, Ministry of Development, Greece ;  Hungarian OTKA  and  National Office for Research and Technology (NKTH) ;  Department of Atomic Energy and Department of Science and Technology of the Government of India ;  Istituto Nazionale di Fisica Nucleare (INFN)  and  Centro Fermi -Museo Storico della Fisica e Centro Studi e Ricerche "Enrico Fermi", Italy ;  MEXT   Grant-in-Aid for Specially Promoted Research, Japan ;  Joint Institute for Nuclear Research, Dubna ;  National Research Foundation of Korea (NRF) ;  CONACYT, DGAPA, México, ALFA-EC  and the  HELEN Program (High-Energy physics Latin-American-European Network );  Stichting voor Fundamenteel Onderzoek der Materie (FOM)  and the  Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) ,  Netherlands ;  Research Council of Norway (NFR) ;  Polish Ministry of Science and Higher Education; National Authority for Scientific Research -NASR  ( Autoritatea Naţionalȃ pentru Cercetare Ştiinţificȃ -ANCS );  Ministry of Education and Science of Russian Federation ,  International Science and Technology Center, Russian Academy of Sciences, Russian Federal Agency of Atomic Energy, Russian Federal Agency for Science and Innovations and CERN-INTAS ;  Ministry of Education of Slovakia; Department of Science and Technology, South Africa; CIEMAT, EELA, Ministerio de Educación y Ciencia of Spain, Xunta de Galicia (Consellería de Educación ),  CEADEN, Cubaenergía, Cuba , and  IAEA (International Atomic Energy Agency );  Swedish Research Council (VR)  and  Knut & Alice Wallenberg Foundation (KAW) ;  Ukraine Ministry of Education and Science; United Kingdom Science and Technology Facilities Council (STFC) ; The  United States Department of Energy , the  United States National Science Foundation , the  State of Texas , and the  State of Ohio.
Acknowledgments The authors are grateful to the patients and their families for their participation in this study. They would also like to address special thanks to  Nathalie Ruiz-Pallares ,  Department of Genetics, CHRU, Montpellier, France. This research was supported by the  Hubert Curien Partnership (PHC Tassili   10MDU794 ) and grants from the  Ministry of Foreign and European Affairs (Ministère des Affaires Étrangères et Européennes, MAEE)  and the  Ministry of Higher Education and Research of Algeria (Ministère Algérien de l'Enseignement Supérieur et de la Recherche Scientifique, MESRS).
The 39/59 signal intensity ratios of housekeeping genes detected by Affymetrix microarrays were not greater than 2. 0, supporting the integrity of the RNA samples. The. CEL files were generated by Affymetrix GCOS and subjected to the Robust Multiarray Averaging (RMA) method of normalization using Partek Genomics Suite (Partek Inc. , St. Louis, MO). The normalized microarray signal intensities were log transformed, and differentially expressed genes were identified by one-way ANOVA (p,0. 05) using Partek or by Welch's t-test (twosided, p,0. 05; further filtered for genes whose expression changed by at least 2-fold) using GeneSifter Analysis Edition v3. 7 (Geospiza, Inc. , Seattle WA). Because applications of multiple testing corrections (Bonferroni and Benjamini-Hochberg) to these microarray data sets resulted in excessive suppression of discovery power for identification of differentially expressed genes, the subsequent analyses were performed with genes selected without such corrections.