dataesr/scientific-paragraphs-categorization huggingface dataset:
2007 ), but recent evidence shows that change in foraging movements may also occur in the presence of vessels  (Torres et al. 2011; Bodey et al. 2014). This has very important implications in terms of interpreting behavior and for conservation because seabird foraging areas are used to propose or designate marine protected areas  (Lascelles et al. 2016). We used newly developed GPS loggers that record radar emissions from vessels. The loggers were fitted on wandering albatrosses (Diomedea exulans) foraging from the Crozet Islands. Our aims were to estimate the efficiency of this new technique to detect vessels at sea by comparing radar detections with VMS data of a declared long-line fishery operating around Crozet and to estimate the extent of overlap with vessels over their entire foraging range of the species.
Methods The study was carried out at Possession Island (46°S 51°E), Crozet Islands, in January-March 2015 and 2016. We fitted 53 incubating individuals with XGPS radar loggers: 6 in 2015 and 47 in 2016. Loggers were taped on back feathers. The loggers (35 g, i. e. , 0. 3-0. 4% of the bird's body mass) were well below the recommended mass to avoid potential deleterious effects on the foraging behavior of flying seabirds  (Phillips et al. 2003). Birds were caught by hand as they were relieved from their incubation shift by partners and departed to forage. Devices were recovered on their return to the nest after a foraging trip at sea. Forty-three loggers were recovered from which data were downloaded. The other 10 loggers were either lost at sea (4, detached from back feathers) or were recovered but data could not be obtained (6).
We used data from VMS (vessel GPS locations recorded hourly) of French long-liners operating within the Crozet and Kerguelen EEZ (provided by the Pecheker database hosted at the Museum National d'Histoire Naturelle, Paris  [Martin & Pruvost 2007] ). The data correspond to 7 vessels fishing under license over the Crozet and Kerguelen shelves and surrounding seamounts. These data and albatross radar-detection GPS data were imported into Google Earth (https://www. google. fr/earth), which we used to analyze spatiotemporal coincidence of radar detections by XGPS and VMS. Distances between locations of VMSequipped boats and bird GPS locations were calculated and associated with intensity of radar signals. The Préfet des TAAF and Comité de l'Environnement Polaire and CNPN (National Committee for the Protection of Nature) approved the field procedures for our study under IPEV program number 109 (permit number 2015-103, 4 September 2015).
Acknowledgments This study is a contribution to Programme IPEV 109 at Crozet and to the Program EARLYLIFE funded by a  European Research Council   Advanced Grant  under the European Community's  Seven Framework Program   FP7/ 2007-2013  (Grant Agreement ERC-2012-ADG 20120314 to H. W. ). We thank the many field workers involved in the field studies in Crozet, in particular  C. Morey-Rubio  and  G. Bernard  for help with tag deployment and recovery,  A. Prudor  for help with the data analysis. Our data are available through the Birdlife Seabird Tracking Database.
Data sources and statistical analyses To quantify the cancer burden for this age group, global, regional, and country-specific (n=184) estimates of the number of new cancer cases and cancer-associated deaths that occurred in 2012 among young adults were extracted in four 5-year bands from the International Agency for Research on Cancer's GLOBOCAN 2012. 1  To make comparisons with younger and older age groups, the estimated number of new cancer cases and deaths among individuals aged 0-19 years, 40-59 years, and 60 years and older was also extracted. Further information on the methods used to create the incidence, mortality, and population estimates used in GLOBOCAN 2012 is provided elsewhere. 1 e present the results for all cancer types included in GLOBOCAN 2012, defined by the International Classification of Disease, tenth revision (ICD-10): 10 lip and oral cavity (C00-08), nasopharynx (C11), other pharynx (C09-10, C12-14), oesophagus (C15), stomach (C16), colorectal (including anus; C18-21), liver (C22), gallbladder including biliary tract (C23-24), pancreas (C25), larynx (C32), lung including trachea (C33-34), melanoma of the skin (C43), Kaposi's sarcoma (C46), female breast (hence forth referred to as breast; C50), cervix uteri (C53), corpus uteri (C54), ovary (C56), prostate (C61), testis (C62), kidney including renal pelvis and ureter (C64-66), bladder (C67), brain and CNS (C70-72), thyroid (C73), Hodgkin's lymphoma (C81), non-Hodgkin lymphoma (C82-85, C96), multiple myeloma and immunoproliferative diseases (C88, C90),.
12 ase fatality, a measure of the severity of a disease, was approximated by dividing the mortality ASR by the incidence ASR (M:I). To document key patterns in the cancer burden, we draw comparisons between low-income and middle-income countries (LMICs) and high-income countries (HICs). Furthermore, we present the results by geographical region, using 19 UN-defined geographical sub-regions, and development level, using the Human Development Index (HDI; low [least developed], medium, high, and very high [most developed]; appendix p 1); 13 the HDI is a socioeconomic development indicator comprising life expectancy, education, and gross national income. All analyses were undertaken using Stata 13. 1 statistical software.
21 Studies undertaking cost benefit analyses of improving young adult cancer care, particularly in LMICs, are needed to inform policy makers and advocates. 0 Our results provide a comprehensive estimation of young adult cancers worldwide in 2012 but have several limitations. The GLOBOCAN cancer groupings do not directly correspond with the recommended classification scheme for tumours diagnosed in young adults by Birch and colleagues. 29 Furthermore, our results are estimates from the GLOBOCAN database, which were compiled using a hierarchy of methods; thus, accuracy was dependent on the quality and availability of the source information at a given time. 1  Clearly, the inclusion of uncertainty intervals for the estimates provided in this study would be useful, and collaborative work is currently underway to create such intervals that take into account deficiencies in the quality and availability of the source information at the national level, as well as inherent statistical uncertainty.
Methods: In 2016, the ERA-EDTA Registry received data on patients who in 2014 where undergoing RRT for ESRD, from 51 national or regional renal registries. Thirty-two registries provided individual patient level data and 19 provided aggregated patient level data. The incidence, prevalence and survival probabilities of these patients were determined. Results: In 2014, 70 953 individuals commenced RRT for ESRD, equating to an overall unadjusted incidence rate of 133 per million population (pmp). The incidence ranged by 10-fold; from 23 pmp in the Ukraine to 237 pmp in Portugal. Of the patients commencing RRT, almost two-thirds were men, over half were aged 65 years and a quarter had diabetes mellitus as their primary renal diagnosis. By day 91 of commencing RRT, 81% of patients were receiving haemodialysis.
Acknowledgements The ERA-EDTA Registry would like to thank the patients and staff of all the dialysis and transplant units who have contributed data via their national and regional renal registries. In addition, we would like to thank the persons and organizations listed in the paragraph 'affiliated registries' for their contribution to the work of the ERA-EDTA Registry. The ERA-EDTA Registry is funded by the  European Renal Association -European Dialysis and Transplant Association (ERA-EDTA ). This article was written by M. P. et al. on behalf of the  ERA-EDTA Registry , which is an official body of the  ERA-EDTA.
For any given model structure, and assuming linear dynamic rates, we demonstrate how the model parameters can be efficiently and accurately estimated by divergence minimisation. We then apply our method to an experimental dataset and compare the estimates and goodness-of-fit to those obtained by maximum likelihood estimation. While both sets of parameter estimates had overlapping confidence regions, the new method produced lower values for the division and death rates of bacteria: these improved the goodness-of-fit at the second time point at the expense of that of the first time point. This flexible framework can easily be applied to a range of experimental systems. Its computational efficiency paves the way for model comparison and optimal experimental design.
In most published IT studies, bacteria can grow at different rates in different locations within an animal, and migrate from one location to another, generating a network of subpopulations (or metapopulation). This increases the dimensions of both the state variable space (as the model must keep track of the multivariate distribution of bacterial abundance) and the parameter space. Calculating the likelihood of such a model given an experimental dataset requires solving a complex stochastic model, which will rarely be possible analytically. Even a linear birth-death process with a non-Poisson immigration process (representing the transfer of a finite inoculum dose) is sufficient to prevent a fully analytical treatment, and results in a computationally intensive estimation process  [12]. Alternatively, approximate-likelihood (e.
g. , iterative filtering  [16] ) or likelihood-free (e. g. , approximate Bayesian computation or ABC  [17] ) methods involve the generation of a large number of stochastic simulations of the model of interest, which can be equally timeconsuming, even when taking advantage of parallel computation. Although this may not be a problem when fitting a single model to a single dataset, it limits our ability to compare multiple models across complex datasets (typically involving multiple experimental treatments) and, beyond that, use these inference tools for the purpose of optimising experimental design  [18]. Hence, there is a need for alternative inference methods using suitable approximations to achieve greater gains in computational efficiency. The dynamics of multivariate Markovian processes can be approximated using momentclosure methods  [19].
We then compare the accuracy of several inference options against simulated data, and finally apply the most promising method to a likelihood-based method in order to assess the quality of inference and increased computational efficiency. The massive gain in speed compared to likelihood-based inference allows us to use parametric bootstrap to quantify parameter uncertainty and goodness-of-fit. Finally, we provide a re-analysis of a recent dataset on the dynamics of Salmonella enterica serovar Typhimurium in the blood, liver and spleen of vaccinated mice  [11]. We also demonstrate how empirically derived noise terms (e. g. , caused by imprecise data collection) can be taken into account.
Parameter inference by divergence minimisation Given a dataset consisting of one or more matrices D of bacterial counts (as per section 2. 1), and a stochastic model (as per section 2. 2), we now describe methods to estimate the parameter values of the model that minimise the divergence between the predicted and observed distributions of bacterial abundance, using only the lower moments of those distributions. Specifically, we evaluate the means, variances and covariances of the N i variables at a given time t. From the corresponding matrix D, we calculate the vector of observed means μ (D) and the matrix of observed variance-covariance V (D) ; and from the model's solution given a set of parameters θ, we compute the vector of predicted means μ (θ) and the matrix of predicted variances-covariances V (θ) which can be derived from M 2 (t).
Furthermore, we only require that the calculated divergence here is ordered in the same way as the true divergence between these distributions, so that the minimum occurs at the same parameter values. We use these properties for the purpose of parameter inference, given a dataset and a model. The set of parameter values θ that minimises the divergence measure is termed the "minimum divergence estimate" (MDE). All code was written in R  [28]. We used the UObyQA optimisation routine in the powell package  [29].
Although both error distributions were centred on the true bacterial numbers (i. e. the mean numbers of bacteria were not biased), the variance and covariance in the reported data would not have been accurate estimates of the variance and covariance within the animals: hence our MDE could be biased if we did not account for observational errors. In our present reanalysis of those data, we integrate both error terms before performing parameter inference, using the following procedure. Our goal is to propose a simple heuristic which could be applied to any experimental dataset with known (or assumed) observational error distributions. First, we generate a large number of stochastic simulations of the model under a biologically reasonable range of parameter values (i. e. using uniform prior distributions across sensible ranges) to generate "perfect" observations (1.
in  Fig 2). We then calculate the corresponding "perfect moments" from every simulated dataset (1M. in Fig  2 ), which we cannot observe from our experimental procedure. Next, we apply the observation noise to the simulated data (2. in Fig 2 ), as is the case in the experimental procedure, and calculate the corresponding "observed moments" (2M. in  Fig 2). We then use linear regression models to establish a relationship between each of the perfect and observed moments (with transformations where appropriate), with weights given by the simulations proximity to the actual observed moments from the experiment. This calibrated regression model is then applied to the moments of the experimental data of interest, in order to estimate the moments of the true, unobserved bacterial loads in the animals.
We computed MDE and MLE separately from each simulated dataset, hence producing 30 estimates for each combination of parameters (see Supplementary Materials for complete set of results). We did not consider more complex models, due to the computational burden of the likelihood-based method. Fig  11  shows the difference in computation time between the moments-based method, and the likelihood-based method, across the different observation times. In particular, solving the moment equations by matrix exponentiation results in computation times independent of the duration of the simulated experiments. In contrast, the likelihood-based method requires solving a very large system of differential equation forward through time, and so the later the observation time, the longer it takes to compute the parameter estimate.
Although this could be avoided in the case of the single-compartment migration-birth-death model by deriving the likelihood at the observation time through inversion of the probability generating functions  [12] , such semi-analytical solutions are not available for more complex models. As a result, likelihood-based methods become impractical for data collected several days after inoculation  [11]. Fig  12  shows that, across the sets of simulated data, MDE achieves similar or greater accuracy than MLE. Unexpectedly, while the mean absolute relative error (MARE) of MDE decreases as the size of the dataset (number of simulations) increases, the improvement is much less pronounced with MLE. As is visible in  Fig 12,  there are large subsets of simulations which lead to much better accuracy by MDE than MLE, especially as the number of simulations and their duration increase.
Importantly, both methods used the same optimisation routine, either to minimise the divergence or to maximise the likelihood. We should reiterate the algorithm used to compute the likelihood was designed to trade off accuracy for speed: while it should be possible to reduce the MARE discrepancy between the two methods, this could only be achieved by increasing the running time of MLE, which is already far greater than that of the MDE. It is also worth keeping in mind that, no matter how accurate each estimation method is, our aim is to apply them to experimental datasets that typically comprise of under 100 replicate observations per time point  [7, 11, 12]. As illustrated above in Fig  5 , multiple sets of 100 simulations vary sufficiently from each other to generate wide variance in the parameter estimates.
The corresponding observed moments were then calculated by applying the observation noise model-binomial sampling to represent the fractional sampling of organs within each mouse, and log-Normal noise to represent the qPCR noise-to each simulation, and calculating the moments. The variances were log-transformed for the purpose of the regression models to predict the corresponding perfect moments from those observed from the experiment. Model goodness-of-fit. In calculating the MDE parameter estimates for the bootstrap samples, we evaluated divergence measures for datasets simulated from the model. Thus, the collection of these divergences provide a suitable representation of the null distribution of The approximate p-values associated with the goodness-of-fit tests for the naive and LV experimental groups 0.
Discussion We have described a functional and flexible moments-based method to estimate parameters of stochastic metapopulation models, and applied it to experimental data on within-host bacterial infection dynamics. Compared to simulation-based methods, our technique delivers accurate estimation orders of magnitude faster. Although simulation-based inference has grown in popularity in computational biology-either within likelihood-free  [17]  or approximate-likelihood  [16]  approaches-even with the increasing availability of fast, multi-core computers, algorithms can still take days to converge in multi-parameter and multi-variable problems. While this is not a major issue when fitting a single model to a single dataset, it is a hindrance for more ambitious applications: indeed there is a growing demand in biology for model selection and model-based optimal experimental design, which are much more computationally intensive tasks (e.
Numerical implementation & coupling strategy The main two codes used in this study are Serpent for the neutronics, and OpenFOAM for the thermal hydraulics. Serpent is a Monte Carlo neutronics calculation code  (Leppänen, 2015) , and OpenFOAM is an open source CFD calculation code  (Jasak et al. , 2007). In both cases, specific procedures have been included directly in the source code to implement the TFM approach.
Neutronics module The neutronics module is split in two parts, the calculation of the discretized operators G v x mx , and the numerical integration of the kinetics equations. The transient fission matrices are computed prior to the transient calculation using Serpent. Serpent estimates the fission matrices in a single calculation using a tetrahedral mesh (3600 cells, Fig. 4 -right) imported from OpenFOAM. The variations of the operator with the density and the Doppler are determined via two other distinct calculations. For each calculation, 2. 5 billions of neutron are simulated in order to obtain a statistical error on the reactivity smaller than to 1 pcm. The integration of Eqs. (  2 ) and (  3 ) is directly implemented in the thermal hydraulics source code since no more Monte Carlo calculations are required once the matrices have been generated.
Thermal hydraulics module The thermal hydraulics solution is obtained using the Open-FOAM CFD calculation code. The utilisation of this code for the MSFR study and the optimisation of the numerical parameters have been validated in the frame of a dedicated benchmark  (Merle-Lucotte et al. , 2014)  A finer mesh is required to perform the CFD calculation (87,000 cells, Fig. 4 -left) than for TFM. A specific optimization of the CFD mesh is required for the thermal hydraulics to capture the flow pattern in the inlet of the core with the detachment of the boundary layer  (Rouch et al. , 2014)  The cross-mapping of the different fields between the meshes is performed by OpenFOAM using internal libraries based on standard finite volume mapping techniques. In order to limit the computation complexity using a symmetry boundary condition, the simulations are performed on 1/16 of the reactor, corresponding to one recirculation loop.
Coupling approach The algorithm usually employed in OpenFOAM to obtain the implicit convergence of all the resolved parameters consists in iterative calculations of all the physics during one time step. These iterations are called ''outer" iterations. In this way, during transient calculations, the solution obtained satisfies all the solved equations. The same approach is used for this coupling including the neutronics module in these outer iterations, this module being directly implemented in OpenFOAM. However, the time steps used for the neutronics ($ ls) are much smaller than those of thermal hydraulics ($ms). During one thermal hydraulics time step, many neutronics time steps are performed (Fig. 5 ). The temperature field calculated by the thermal hydraulics is interpolated during the neutronics calculation, and the energy source term or the precursor source term is inferred from the average production of the neutronics steps.
Conclusions The neutronic TFM approach coupled to the thermal hydraulics code OpenFOAM proves to be an efficient tool to investigate coupled phenomena in nuclear systems It can deal with precursor motion effects on the chain reaction thanks to the TFM approach and with a complex flow pattern via the CFD calculation. The interpolation model presented here can evaluate perturbed fission matrices in order to take into account the flux redistribution and the reactivity variation due, for example, to an evolution of the temperature distribution in the reactor. Comparisons with reference calculations demonstrate how closely the interpolated solution approaches the reference calculation. Moreover, thanks to its short computation time, the interpolation can be done for each time step of the transient.
Acknowledgments The authors wish to thank the  NEEDS (Nucléaire: Energie, Environnement, Déchets, Société) French programs , the  IN2P3 department of the National Centre for Scientific Research (CNRS) ,  Grenoble Institute of Technology , and the  European program EVOL (Evaluation and Viability of Liquid Fuel Fast Reactor System)  of  FP7  for their support. We are also thankful to  Elizabeth Huffer  for her help during the proofreading of this paper.
Acromegaly is a rare disorder caused by chronic growth hormone (GH) hypersecretion. While diagnostic and therapeutic methods have advanced, little information exists on trends in acromegaly characteristics over time. The Liège Acromegaly Survey (LAS) Database, a relational database, is designed to assess the profile of acromegaly patients at diagnosis and during long-term follow-up at multiple treatment centers. The following results were obtained at diagnosis. The study population consisted of 3173 acromegaly patients from ten countries; 54. 5% were female. Males were significantly younger at diagnosis than females (43. 5 vs 46. 4 years; P < 0. 001). The median delay from first symptoms to diagnosis was 2 years longer in females (P = 0. 015). Ages at diagnosis and first symptoms increased significantly over time (P < 0.