dataesr/scientific-paragraphs-categorization huggingface dataset:
A cost calculation should be connected to a revenue calculation. 2. Non-OCOG budget: If we take a very first bid concept (master plan) we tap into the politically desired cost underestimation, which was tactically relevant to position the city better in the bid race. Then we have to decide if a study like this would measure the actual cost overrun of the project or the cost overruns that arise due to bidding tactics. 3. Non-OCOG budget: If we take the official bid concept (from the candidature file), we have an acceptable starting point, as that is the budget promised to the public. However, we still have the concern (see point 2). The other problem is to find reliable bid data for all venues and the final budget that is really based on capital costs of the Olympic project. Our concern is that many non-Olympic infrastructural developments become connected to the Games and appear in some statements about final costs (often in media articles).
25 First we started with literature research and used our own private Olympic data collection as we do Olympic research for more than 25 years. Then we contacted several experts (Olympic scholars). The following table shows all experts who were contacted as well as their function and the data they could provide. Experts from former host cities were contacted to gather data that may be hidden in local archives. The next step was to visit the IOC Olympic Studies Centre (OSC) in Lausanne and the National Olympic Academy in Frankfurt in order to access a huge collection of sources about the Olympic Games. The IOC OSC and Finance Department supported this research intensively and provided us with access to public and non-public archives for OCOG and non-OCOG budgets. The ProProjekt GmbH agency, which was managing several candidate cities, including the two German bids (Munich 2018 and Hamburg 2024), supported our research.
They provided us with their database on the OCOG and non-OCOG 27 budgets of several previous Games. These data were extremely useful and were in particular used to crosscheck the data we had collected before. The final step was to contact the ministries and official departments in host cities, requesting data about the non-OCOG budgets. However, most of them could not or did not want to provide data. It proved to be difficult receiving useful data on non-OCOG budgets.
This makes at least eight years before the Olympic Games were staged and still many years before the investments were made. Until the 2012 candidature process, these figures did not take inflation into account and if they did, they must have taken the right inflation rate. Estimating the rate of inflation can also be seen as a source of error. The bid process has changed significantly over time. Since 2006 7 , the IOC has requested in candidature files that the budget must be provided in both USD and the local currency. Thus Rio 2016 were the first Games to stick to this new procedure and consequently provide transparent data about the exchange rates 7 The Candidature File for Sochi 2014 mentioned only that the existing tables with local currency and inflation adjusted figures were in the annexes of the document.
These were not accessible to us. It was only since the Candidature Process for the Olympic Games 2016 that the tables in the candidature file had to include national currency and inflation adjusted figures. used. Additionally, the IOC expects the budget to be displayed in two tables, one with current values and one with inflation-adjusted values. In the present study, for the eight Olympic Games investigated before Rio 2016, the values had to be adjusted for inflation. However, even the data provided for Rio 2016 and Pyeong-Chang 2018 needed to be re-adjusted because the budgets projected were based on inflation rates estimated at the time of bidding; at this time, the candidature committees did not know the real inflation rates and construction price index of the future. Tab. 6 shows the revenue of the Rio 2016 and PyeongChang 2018 OCOGs and compares the real inflation with the estimated inflation by the bid committee.
Thus, Rio 2016 underestimated the inflation rate, which led to 27% lower costs than were finally incurred, while PyeongChang 2018 overestimated the inflation rate and had at the end 12% less costs. This shows the power of inflation on perceived cost overruns. However, it will remain tricky if not impossible to estimate the true inflation rate, but bid committees should put more effort into using realistic rates. Reports that contain data for post-bidding years though these data pertain to earlier years than when the final costs and revenues are known and usually publish figures in local currency. Revenues and costs in the bid documents before Rio 2016 were usually in USD. To minimise conversion errors, we decided to convert the OCOG budget from the candidature file into local currency by using the current exchange rate at that time.
Quality of Data and Building of Categories The available data about the costs and revenues of the Olympic Games and in particular investments for Olympic venues is limited. This study has collected more than 220 sources, but still many data are missing and may not be available at all. We were able to gather at least for all Games cost estimates from the candidature files and, in most cases, final costs as presented in the official reports. The exceptions are Rio 2016 and PyeongChang 2018, whose Organising Committees are not yet closed, and thus there is no final financial data for the Games. Several times we found cost estimates during the preparation period or the final cost statements in other sources than the official report. We collected them all to display the development of cost and revenue overruns.
g. , anomalous couplings or new exotic light states  [20]. Accurate knowledge of potential backgrounds from Z decays to quarkonia will be essential for these measurements. This analysis uses proton-proton (pp) collision data recorded by the CMS experiment at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 35. 9 fb -1. We report the observation of the Z → ψ + -decay channel, where ψ represents the contributions from direct J/ψ and J/ψ mesons from ψ(2S) decays, and the J/ψ is detected via its µ + µ - decay channel. We measure the ratio of the branching fraction of this decay to that of the Z → µ + µ -µ + µ -decay, to take advantage of a partial cancellation of systematic uncertainties. The central feature of the CMS apparatus is a superconducting solenoid of 6 m internal diameter, providing a magnetic field of 3.
175  [25, 26]  with parameters set by the CUETP8M1 tune  [27]  for parton showering, hadronization, and the underlying event. The parton distribution functions are taken from the NNPDF 3. 0  [28]  set. For the Z → J/ψ + - signal we use PYTHIA 8. 175 (same tune as for Z → µ + µ -µ + µ -) to simulate the production of Z bosons, with an unpolarized phase-space model for the Z → J/ψ + -decay. Matrix-element effects are evaluated by comparison with data and treated as systematic uncertainties. The detector response is simulated with a model of the CMS detector implemented in the GEANT4 package  [29]. We measure the fiducial branching fraction restricted to a region of phase space covered by the acceptance of the measurement, as described below. The trigger and offline selection criteria closely follow the previous CMS analysis of Z → 4 decays  [2] [3] [4].
Using data from proton-proton collisions collected with the CMS detector at √ s = 13 TeV, corresponding to an integrated luminosity of 35. 9 fb -1 , 13. 0 ± 3. 9 events of the Z → ψ µ + µ -and 11. 2 ± 3. 4 events of the Z → ψ e + e -decay are obtained. This is the first observed Z boson decay to a vector meson and two oppositely charged same-flavor leptons. The ratio of the branching fraction for this decay to the one for the reference channel Z → µ + µ -µ + µ -in the fiducial phase space of the measurement, as defined in Table  1 , after subtracting the ψ(2S) feed-down, is R J/ψ + -= 0. 67 ± 0. 18 (stat) ± 0. 05 (syst). Using the known branching fraction for Z → µ + µ -µ + µ -results in a branching fraction for Z → J/ψ + -consistent with standard model predictions. acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses.
Ae. aegypti larval bioassays 79 In accordance with WHO instructions 11 , late third instar larvae of F1 generation were used for 80 determining the resistance of mosquito larvae to temephos. 81 Temephos (Sigma, Pestanal analytical grade, 250 mg) was diluted in ethanol to produce a stock solution 82 of 1000 mg/L. The main stock solution was diluted into several working concentrations better suited for 83 Insecticide-synergist assay using piperonyl butoxide (PBO) was conducted to measure the effect of pre-107 exposure to a synergist on the expression of insecticide resistance. Adult Aedes were pre-exposed to this 108 synergist for one hour before exposure to insecticide. KD and mortality were recorded the same way as 109 standard tests. 110 Data management and statistical analysis 111 Knock down and mortality were registered at 1 hour and 24 hours post-exposure respectively.
RRs for 112 larvae and adult mosquitos were calculated by dividing the average mortality found in each field 113 population by the mortality obtained with the USDA susceptible reference strain. 114 For larvae results, LC50 and LC90 were obtained by plotting the mortality using log probit analysis. 115 Statistical analysis (ANOVA and mean comparison) were completed to compare the mortality of adults 116 to permethrin and deltamethrin with or without the use of PBO. Graphs and data analysis were done 117 with R software  12. 118.
This paper aims at quantifying the influence of six meteorological variables as well as 39 static inputs on the dynamic thermal behaviour of a net zero energy building. To do so, a method that stochastically generates consistent meteorological data is used and is adapted to the purpose of global sensitivity analysis. The results show a high dispersion of the cooling requirements, for which the direct solar radiation, the albedo and the window solar factor can be held accountable. Thus the variability of solar resources and their interaction with the building have the greatest impact on the performance of the building. The variability of meteorological data needs to be considered to evaluate confidence intervals on energy performance. Furthermore, the impact of static parameters should not be overlooked, because their influence may remain significant.
The input variation for the UASA To perform sensitivity and uncertainty analyses, a set of samples is generated based on the input variation defined. The variability definition of the input conditioned the results of the analysis. In this study the static and dynamic input uncertainty corresponds to the partial knowledge at the design stage. A sampling-based method generates stochastic meteorological time series according to the typical meteorological EnergyPlus file. In this case study the Strasbourg IWEC (International Weather for Energy Calculations) is used and the generations are made for the month of July. The weather data set for UASA is built to be representative of the natural year-to-year variability of a summer month of July for Strasbourg. The sampling-based method is based on the procedure of Iman and Conover and maintains the statistical features of the meteorological time series (autoand cross-correlations)  (Goffart et al.
2017). The weather data sample generated allows the estimation of Sobol's indices  (Sobol, 2001)  of the first-order effect on the energy needs. The influence of each weather variable on the output variability can be quantified. The weather data set for the UASA is composed of six inputs: the dry bulb temperature, the relative humidity, the direct normal solar radiation, the diffuse horizontal solar radiation and the wind velocity and direction. In the case of static inputs, the variability on the thermophysical properties of materials and windows are taken into account in the study as specific heat, conductivity, density in the case of materials and conductance and the solar factor for triple-glazing windows. Also considered in the study are airtightness, orientation, internal load of occupants for activity (90 W) and sleep (63 W), ventilation and ground reflectance.
The UASA with natural variability assigns more realistic confidence bounds to the building performance and characterises the building's behaviour according to the weather data input from year to year. This might contribute to the adjustment process. Because of the importance of a building's solar characteristics, one should focus on the sun management components such as opening and closing the shutters. Optimal management can be carried out by the occupant or using an automatic strategy and may decrease the cooling needs significantly. However, the occupants' behaviour is complex and highly uncertain. With in-situ sociological studies and measurements, it should be possible to characterise and include the building's specific occupancy in the UASA statistical approach.
This report discusses the combination of precision irradiation systems, small animal imaging (CT, MRI, PET, SPECT, bioluminescence) systems, image registration, treatment planning, and data processing. It also provides guidelines for reporting on studies. Ó 2017 The Authors. Published by Elsevier Ireland Ltd. Radiotherapy and Oncology 126  (2018)   [471] [472] [473] [474] [475] [476] [477] [478]  This is an open access article under the CC BY-NC-ND license (http://creativecommons. org/licenses/by-ncnd/4. 0/). Translational and radiobiological research is currently undergoing a revolution due to two key developments: (1) the availability of advanced tumor models with more clinically relevant tumor environments, and (2) the availability of technology that allows precise radiation targeting, using onboard integrated imageguidance which can mimic clinically advanced radiotherapy treatments in an experimental setting (Fig.
Computed tomography (CT) CT is a pre-requisite for heterogeneity corrections in dose calculation  [27, 43]  for treatment planning. CT images in DICOM format can be visualized using common research software platforms (e. g. MatLab, Osirix, 3D Slicer). There are different technological solutions for preclinical CT  [4, 44]. Dedicated micro-CTs have a rotating X-ray tube and imaging panel configuration. Modern animal research platforms use either a fixed animal and rotating X-ray tube/imager or vice versa, resulting in a cone beam CT (CBCT) scan. Typical X-ray energies are between 35 and 90 keV. Amorphous silicon imaging panels are standard, and image reconstruction is based on the Feldkamp algorithm  [45]. The technical realization and the underlying projection geometry vary and have a direct impact on the imaging parameters.
Magnetic resonance imaging (MRI) The clinical use of MRI is rapidly growing, i. e. for MR based treatment planning including dose calculation, and multiparametric MRI for tissue characterization and response assessment. This trend is based on advantages such as a superior soft-tissue contrast compared to CT and imaging without dose burden, which also impacts pre-clinical research  [56] [57] [58] [59]. Main disadvantages of MRI are the relatively long acquisition times and its implication on animal anesthesia, the high cost of an MRI scanner and high operational costs. In the standard irradiation setting, MRI scans are not directly used for dose calculations, but a registered CT/MRI dataset provides the necessary information for targeting (MRI) and for dose calculations (CT)  [60]. Geometric aspects including distortion corrections, scanner calibration and image registration need to be considered, for which dedicated phantoms exist  [42, 61, 62].
Acknowledgements This research was supported by  NIH NCI   U01 CA178960  to  Michael Dwinell  and  BK (MPIs) ,  NIH NCI   R01 CA208648  to  Ming You  and  BK (MPIs) , and the  Quadracci Endowment. The content is solely the responsibility of the authors and does not necessarily represent the official views of the  NIH. This teaching review is the third in a series of publications in this journal and is based on the lectures on cancer biology that BK presented as part of  Anna University 's  Global Initiative Academic Networks (GIAN) program ,  Chennai, India.
Funding This study was funded by  LABEX  grant  ANR-11-LABX-0050_G-EAU-THERMIE-PROFONDE  (this research, therefore, benefited from state funding managed by the  Agence National de la Recherche (ANR)  as part of the  "Investissements d'avenir" program ) and  ANR  grant  CANTARE  ( ANR-15-CE06-0014-01 ).
Data Year of diagnosis and age at diagnosis were available in the registry data. Age classes were computed in four groups for the population description, and then by fiveyear intervals for incidence standardization. Immunohistochemical data such as oestrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor (HER2) were recorded for each new breast cancer case. If HER2 expression status was uncertain by immunohistochemistry, analysis by fluorescence in situ hybridization (FISH) was performed. Immunohistochemical analyses and FISH analyses were performed in routine laboratories, no special analysis was performed for this study. Molecular subtypes were defined as follows: triple negative (TN) (HER2 negative, ER negative and PR negative), HR+/HER2-(HER2 negative, ER positive, PR positive or negative), HR +/HER2+ (HER2 positive, ER positive, PR positive or negative), HR-/HER2+ (HER2 positive, ER negative, PR negative).
Statistical analysis Descriptive analysis of available data was performed. Crude incidence rates were estimated year by year, for the overall cases, and then by molecular subtype. Standardized incidence rates were estimated, according to the World standard population from the World Health Organization (WHO)  [7]. Standardized incidence rates were also estimated by year, for the overall population and for each molecular subtype. Poisson models were used to analyse the incidence rates by year, age class and molecular subtype. Interaction was tested by the likelihood ratio test. Bsplines were used to adjust semi-quantitative variable such as age class. Statistical analyses were performed with R 3. 2. 2 software  [8].
Discussion Our study is based on population data from established and exhaustive cancer registries. For this study, molecular information about breast tumours was recorded, and these data about molecular subtype are now recognized as useful for predicting prognosis and therefore, for making treatment decisions. The results show different evolution of molecular profile incidence among age class and among the studied years of diagnosis. The strength of this study is the report of valid population data, with a good registration of incident cases. Nevertheless, the immunohistochemical data were not available for all incident cases. This missing data were more frequent in early years and for old patients. The SEER reports  [4, 5]  had missing data about immunohistochemical analyses for 12% of patients in 2010.
The report from Norway  [6]  produced imputed estimation. Here, missing data were described by age class and by year of diagnosis, to show that they were not missing at random. Missing data were more frequent in first year of immunohistochemical data registration and for elderly patients. The spread of the different molecular subtypes is similar to that described by Howlader and Kohler  [4, 5]  in the SEER study. In their article, Howlader et al. found 73% Luminal A tumours, defined as HR+/HER2-tumours (versus 77. 6% in our study), 12% triple-negative tumours (versus 10. 7% in our study), 5% HER 2 positive tumours with negative hormone receptors (versus 4. 3%) and 10% HR+/HER2 + tumours (versus 7. 3% in our study). These two studies, both conducted on population databases, thus reported coherent results, and both reported a different spread in molecular subtypes than that described in clinical trials, where HER2 positive tumours are described as being much more frequent than in population databases (15-30% vs 11.
Modern-Era Retrospective Analysis for Research and Applications-Land (MERRA-L) GEOS-5 Catchment LSM forced by MERRA with corrected precipitation. Base unit of LSM is watershed (as differing from the common grid point approach). Soil depth 0. 75-1 m, two soil layers, vegetation data from GEOS-5. 0. 67 (lon) 3 0. 5 monthly  Reichle et al. (2011). For Catchment LSM, see  Koster et al. (2000) , for GEOS-5, see  Rienecker et al. (2011). LandFLux-LSM (LF-LSM) Ensemble of five realizations of four off-line LSMs forced by reanalysis corrected by observational data. The ensemble includes MERRA-L, used in this study. 1 3 1 monthly  Mueller et al. (2013).
For ERAI, this is because of its overestimation of rainfall in the Andes and CArg regions  (Dee et al. , 2011). The monthly GPCP based correction of ERAI precipitation used to force ERAI-L  (Balsamo et al. , 2015)  is probably one of the reasons for a lower ET in ERAI-L than in ERAI in these regions. However, ERAI-L overestimates ET in PAT probably because GPCP overestimates P with respect to the other databases in this region (not shown). In the case of RS products, both P-LSH and PT-JPL overestimate ET in Andes and NeB (PT-JPL also overestimates ET slightly in PAT). For P-LSH, this is consistent with the global evaluation of this product based on water balance  (Zhang et al. , 2010)  that showed a tendency to overestimate ET in arid environments and underestimate in wet environments. The PT-JPL model was evaluated against flux tower data globally by  Zhang et al.
Acknowledgments We acknowledge the projects  LEFE/ INSU AO2015-876370  ( CNRS, France ),  Belmont Forum / ANR-15-JCL/-0002-01 ''CLIMAX,'' PIP No 11220110100932  ( CONICET, Argentina ) and PICT  2014-0887  ( ANPCyT, Argentina ). We thank  Jer onimo Escribano  for discussions about clustering techniques and  Pablo Zaninelli  for comments on the manuscript. GPCP is publicly available from NOAA: https://www. esrl. noaa. gov/psd/data/gridded/data. gpcp. html;UDEL from NOAA: https://www. esrl. noaa. gov/psd/data/gridded/data. Udel_ AirT_Precip. html; CPC-UNI  from NOAA: https://www. esrl. noaa. gov/psd/data/gridded/data. cpc. globalprecip. html;CRU; from  British Atmospheric Data Centre RAL : http://badc. nerc. ac. uk/data/cru/; SRBfrom NASA LaRC/ GEWEX SRB: https://eosweb. larc. nasa. gov/project/srb/srb_table; CLARA from  EUMETSAT  through  NCAR/UCAR : https://climatedataguide.
Model SH2, which starts the enrichment of r-process elements at [Fe/H] = -3. 53, well reproduces the evolutions of all elements presented here. The small dots show the observed abundances in the Galactic halo stars taken from the SAGA database (Suda et al. 2008, 2011; excluding upper limits and those of carbon-enhanced stars ([C/Fe] 0. 5 dex)). The dashed lines indicate the solar values.
Clinical evaluation of the Paradise System The preliminary safety and BP-lowering efficacy of the Paradise System have been evaluated in a total of 131 patients with RHTN in single-arm observational studies (first in human feasibility REDUCE study, n = 15 patients  35  ; REALISE, n = 20 patients, ClinicalTrials. gov identifier NCT01529372; ACHIEVE, n = 96 patients, ClinicalTrials. gov identifier NCT01789918). A clinically significant reduction in both office and 24-hour ASBP was observed 6 months after RDN in all 3 clinical studies (data on file). The number of serious adverse events was low and decreased over time (data on file). In the REDUCE study, the first version of the Paradise catheter induced thermal renal-artery injury due to insufficient cooling of the flow rate at the tip of the catheter and led to renal artery stenosis that required renal artery stenting in 3 cases.