dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion and Outlook This study provides an estimation of potential productivity and potential livestock density over European grasslands by using the process-based ecosystem global vegetation model ORCHI-DEE-GM with a representation of grassland management. When compared to statistical data from Eurostat or FAOstat, ORCHIDEE-GM can distinguish the spatial distribution of regions with low or high productivity and livestock density. However, the model tends to overestimate absolute values of productivity and livestock density because it was built and run to reproduce a biological potential. The model-data discrepancies reflect site specific properties such as soil quality, terrain slope, nutrient availability, grass species, management intensity, land-use change and socioeconomic factors that constrain productivity to be below the biological potential.
Including these limitations in the model would probably allow future studies to estimate productivity more accurately. The effects of elevated CO 2 , climate change, nitrogen fertilization and nitrogen deposition are well reflected in the temporal evolution of productivity simulated by the model. But at regional scale the evolution of managed grassland productivity is also determined by land-use change, and by land management intensity changes, both reflecting agricultural policy and socio-economical drivers. ORCHIDEE-GM is able to reproduce the interannual variability of productivity in three of the four countries where long-term data are available (the exception is Italy), making it likely that the model will be able to give useful predictions of the dynamics of potential productivity of European grasslands under future climate change.
In addition, grass-fed livestock numbers can be realistically reproduced by ORCHIDEE-GM using simple equations and parameterization. Regional model-data misfits exist due to uncertainties caused by different farming strategies (e. g. , mowing or grazing, type of animal species), diet composition, management intensity, as well as the uncertainties in the measured data. ORCHIDEE-GM, with its capability to account for grassland management and livestock fed by grass, could contribute to our understanding of future changes in the European livestock sector and of climate impacts on grassland, as well as atmospheric feedbacks through GHG emissions and soil carbon balance.
Acknowledgments We would like to thank  Dr. Bond-Lamberty  and the anonymous reviewer for their valuable comments on the manuscript. We greatly thank  Dr. Harm Smit  and  Dr. Stefan Wirsenius , who provided data on grassland productivity and on livestock diet, respectively. We thank  Dr. Benjamin Poulter ,  Dr. Shushi Peng  and  Mr. Ulrich Eza  for their help on processing downscaled climate data, NDVI data and fertilization data, respectively. We would like to thank  Dr. John Gash  for his effort on English language editing.
GATE Monte-Carlo simulation The GaTE MCs platform  [4]  was used to model the XRs and to perform associated dosimetry calculations on phantom and patient datasets. This modeling was separated into two parts. The first part concerns the XRs, which is invariable and patient independent, while the second part included the patient/phantom description and certain variable device parts such as the applicator. The flexibility of GaTE and the versatility of its macro interface allow the modeling of physical and geometrical characteristics of the different components of the XRs, the applicators and the phantom according to manufacturer specifications. Generated particles were collected in a virtual space called phase space (PHs). The PHs is more precisely developed to store the particles coming from the patient independent part, and is stored to a volume of user defined size, including the characteristics of each particle (type, direction, energy.
Finally, although differences between real measured data and GaTE measurements were very small close to the tip of the source, they increased with depth because of the very small measured dose magnitudes and the small number of simulated events at those larger distances compared with photon fluency in realistic treatment. Concerning the angular distribution of the XRs emissions around the probe, the simulation results revealed a good conformity with the experimental measurements, confirming the specification of 15% maximal difference provided by the manufacturer. The distribution isotropy was confirmed based on the different circular and concentric isodoses obtained for the bare needle and the 40 mm diameter applicator. a CT acquisition of a patient was carried out with the applicator in place to obtain the information on source position and the distance between the applicator surface and the skin with sufficient precision for a patient-specific dosimetry validation study.
The corresponding beam power is 240 kW. The upgrade scenario of J-PARC accelerator  [53]  is being implemented to reach the design power of 750 kW in forthcoming years, with a typical planned parameter set as listed in Table  2. This will double the current repetition rate by (i) replacing the magnet power supplies, (ii) replacing the RF system, and (iii) upgrading injection/extraction devices. The design power of 750 kW will be achieved well before Hyper-K will start data taking. Furthermore, conceptual studies on how to realize 1∼2 MW beam powers and even beyond are now underway  [54] , such as by raising the RCS top energy, enlarging the MR aperture, or inserting an "emittance-damping" ring between the RCS and MR. Figure  2  shows an overview of the neutrino experimental facility  [55, 56].
Because there are a number of experiments planned to determine mass hierarchy in the near future, it is expected that the mass hierarchy will be determined by the time Hyper-K starts to take data. If not, Hyper-K itself has a sensitivity to the mass hierarchy by the atmospheric neutrino measurements as shown in Table  1. Furthermore, a combined analysis of the accelerator and atmospheric neutrino data in Hyper-K will enhance the sensitivity as shown in Sec. 4. 7. Thus, the mass hierarchy is assumed to be known in this analysis, unless otherwise stated. Figure  7  shows the contribution from each term of the ν µ → ν e oscillation probability formula, Eq. (  7 ), for L = 295 km, sin 2 2θ 13 = 0. 1, sin 2 2θ 23 = 1. 0, δ CP = π/2, and normal mass The effect of CP violating term can be as large as 28%, while the matter effect is much smaller.
Depending on the true value of θ 23 the sensitivity changes considerably, but for all currently allowed values of this parameter the mass hierarchy sensitivity exceeds 3 σ independent of the assumed hierarchy. If θ 23 is non-maximal, the atmospheric neutrino data can be used to discriminate the octant at 3 σ if sin 2 θ 23 < 0. 46 or sin 2 θ 23 > 0. 56. In the previous sections, the mass hierarchy is assumed to be known prior to the Hyper-K measurements. This is a reasonable assumption considering the increased opportunities, thanks to a large value of θ 13 , of ongoing and proposed projects for mass hierarchy determination. However, even if the mass hierarchy is unknown before the start of experiment, Hyper-K itself will be able to determine it with the atmospheric neutrino measurements. Because Hyper-K will observe both accelerator and atmospheric neutrinos with the same detector, the physics capability of the project can be enhanced by combining two complementary measurements.
Acknowledgments This work was supported by  MEXT   Grant-in-Aid for Scientific Research on Innovative Areas Number   25105004 , titled " Unification and Development of the Neutrino Science Frontier. "In addition, participation of individual researchers has been further supported by funds from  JSPS, Japan ; the  European Union   ERC-207282 ,  H2020 RISE-GA644294-JENNIFER  and  H2020 RISE-GA641540 -SKPLUS; RSF,  RFBR  and  MES, Russia ;  JSPS  and  RFBR  under the  Japan-Russia Research Cooperative Program.
e. the generated code behaves as prescribed by the semantics of the source program, if any. In this context, correctly implementing a time-and space-efficient dominator algorithm is not sufficient; one has to formally prove its correctness. We are not aware of any formal verification of the dominator problem outside of the field of compiler verification. Further, faced with this technical difficulty, existing verified compilers either ignore dominators, or implement simplified and under-optimized versions of dominator algorithms. For instance, the CompCert C compiler  [14, 13]  is not based on any SSA form for performing code optimization, and no global optimization uses explicit dominance information. The CompCertSSA project extends the CompCert compiler with an SSA-based middle-end. The SSA generation algorithm  [2]  is proved by a posteriori validation of an external checker.
Although we prove that the checker ensures the strictness of the SSA generated function (that is, each variable use is dominated by its definition), the checker implementation (a simple, non-iterative, CFG traversal) and soundness proof do not rely on the computation of dominators. The only phase of the CompCertSSA middle-end that depends on such a computation (that we would like to be efficient) is a common sub-expression elimination (CSE) optimization based on Global Value Numbering (GVN). It discovers equivalence classes between program variables, where variables belonging to the same class are supposed to evaluate to the same value. Its implementation, presented in  [7] , closely follows the choices made in production compilers, and performs some dominance test requests to make sure that the chosen representative of a variable class dominates the definition point of that variable.
Experimental Results Implementations. We compare experimentally the following dominance tests: I-CHK This is the implementation of the CHK algorithm available from the Vellvm project  [19]. To be able to plug it inside our middle-end, we have performed the slightest adaptation possible (essentially by-passing the abstract data-type of atoms and making them be bare positive; these are used to define program points in the CFG). We have kept the choices of data-structures used in their available development. I-ACZ This is the implementation of the AC algorithm available from the Vellvm project  [19]. We performed the same adaptations as in I-CHK. I-AC This is the implementation of the AC algorithm initially available in CompCertSSA  [2, 7]. The implementation uses a classical Kildall workset algorithm for solving the data-flow equations, and its correctness is proved directly (no a posteriori validator).
The evolution of aerosols is described with an eight-bin size distribution (from about 40 nm to 10 µm in diameter) and includes the main physical processes such as nucleation, coagulation, condensation/evaporation, adsorption/desorption, wet and dry deposition and scavenging. Anthropogenic emissions of gaseous and particulate origin come from the EMEP database. Concerning OC and BC emissions, the inventory of  Junker and Liousse (2008)  has been used. Natural soil dust is dynamically produced within the domain according to the methodology of  Vautard et al. (2005). SOA formation is represented through oxidation processes of relevant precursors of biogenic and anthropogenic origin and gas particle partitioning schemes  (Bessagnet et al. , 2008). VOC and NO emissions from vegetation are calculated using the Model of Emissions of Gases and Aerosols from Nature (MEGAN)  (Guenther et al.
This figure confirms the moderate overall impact of the aerosol solar extinction on the ozone production, with a maximum diminution reaching 7-10 % during the aerosol plume overpass (7, 8 and 9 August), which leads to a slightly reduced bias between model and observations. Depending on the day, the model simulates lower or higher hourly values compared to observations. The ozone model biases presented in Fig. 9  clearly show that some processes are missing and/or not well represented in the CHIMERE model. Among these processes, adding the aerosol direct radiative impact on the simulation of photolysis rates could improve the estimation of ozone formation for some days. However, it is obvious that this process does not entirely explain model biases. As indicated previously, uncertainties on the EMEP emission database for this specific region could be a possible reason for these biases.
Acknowledgements. The authors are grateful to the  CRI at Lille 1, François Thieuleux at LOA and Anthony Ung at INERIS  for their technical support. Olga Kislova  at  Mosecomonitoring (Moscow)  is acknowledged for providing the surface air quality measurements used in this study. The authors would also like to thank  Isabelle Chiapello  at  LOA  for fruitful scientific discussion. The  French CNES  is acknowledged for its financial support. Edited by:  J. West.
Numerical model This paper is an investigation into the micromechanics of a system which is highly constrained by external boundaries. For this reason, it is ideal to use a particle based method to model the behaviour, as the total number of grains in the system is small. Towards this end, we use a conventional soft sphere discrete particle approach, implemented in the open source code MercuryDPM (www. mercurydpm. org) [17, 21]. The geometry considered here, shown in Figure  1 , consists of a rigid piston, oriented in a space r = {x, y, z}, with normal along the x axis, which pushes particles between two rigid walls, separated by a spacing D and having normals in the ±z directions, with two periodic boundaries in the remaining perpendicular direction, y. The coordinate system moves with the piston, such that it is located at x = 0 at all times.
[7, 8] ) complementing the results obtained from the studies of lepton beam induced reactions. In particular, the PANDA experimental program includes dedicated measurements of the time-like electromagnetic form factors of the proton, mainly through the annihilation process pp → e + e -, for which feasibility studies with the PANDA detector have already been performed at several antiproton beam energies  [9]. The high intensity of the antiproton beam, together with the performance of the PANDA detector, including particle identification capabilities, will render an unprecedent accuracy in the measurements over a large range of four-momentum transfer squared, as shown by the simulations. Outside the resonance region (i. e. for sufficiently high invariant mass of the lepton pair) the nucleon electromagnetic form factors admit a factorized description within the perturbative QCD (pQCD) approach and follow a scaling law  [10, 11].
2 Set-up of the future PANDA experiment An extensive description of the PANDA detector can be found in Ref. [7]. Here we give a brief outline of the main components which are relevant to this analysis. A High Energy Storage Ring (HESR), in which both stochastic and electron cooling systems are foreseen, will provide a high quality antiproton beam of momentum between 1. 5 and 15 GeV. The concept of the detector, the read out and the data acquisition system are similar to that of other recently built detectors, such as ATLAS, CMS, COMPASS and BaBar. However, the high expected rate of 2•10 7 interactions per second and the multipurpose character of the detector, including the measurement of low cross sections in the charm sector, de-mand unique detection capabilities in PANDA. These include geometrical acceptance of almost 4π, energy and momentum resolutions at the level of a few percent, fast data acquisition and high radiation hardness.
PANDA detector reconstruction capabilities In the physics analysis, the generated events by the Monte Carlo programs (corresponding to signal or background channels) are, in a first step, passed through a full simulation of the PANDA detector, based on the Geant 4 package  [23] , which takes care of the propagation of particles through the detector. Hit and energy loss information is then digitized according to a model simulating electronic properties, including electronic noise, yielding a response of the different detectors. The second step is the reconstruction of the relevant physical quantities for the identification of electrons, such as momentum, ratio of energy loss to path length dE/dx in the STT, Cherenkov angle in the DIRC detectors, and energy deposit in the EMC from the simulated data.
We remark that the cross section  (10)  does not contain QED radiative corrections, so the PHOTOS package  [33]  has been consistently switched off in the Geant simulation. For the cross section of the most severe background channel, i. e. three-pion production pp → π + π -π 0 , no theoretical calculations in the kinematic region of interest are available and the few existing low-precision measurements  [34, 35, 36, 37, 38]  are not sufficient to constrain models. Inspired by the expectation for the total cross section ratio σ(pp → π + π -)/σ(pp → e + e -) ∼ 10 6 (see  [9, 39]  and references therein), we have assumed that the same relation holds for the case σ(pp → π + π -π 0 )/σ(pp → e + e -π 0 ). Even when data sets suggest that three-pion production is about an order of magnitude higher than two-pion production, the totally unknown pp → e + e -π 0 cross section supports the assumption on the signal to background ratio.
Genetic risk prediction has several potential applications in medical research and clinical practice and could be used, for example, to stratify a heterogeneous population of patients by their predicted genetic risk. However, for polygenic traits, such as psychiatric disorders, the accuracy of risk prediction is low. Here we use a multivariate linear mixed model and apply multi-trait genomic best linear unbiased prediction for genetic risk prediction. This method exploits correlations between disorders and simultaneously evaluates individual risk for each disorder. We show that the multivariate approach significantly increases the prediction accuracy for schizophrenia, bipolar disorder, and major depressive disorder in the discovery as well as in independent validation datasets. By grouping SNPs based on genome annotation and fitting multiple random effects, we show that the prediction accuracy could be further improved.
The gain in prediction accuracy of the multivariate approach is equivalent to an increase in sample size of 34% for schizophrenia, 68% for bipolar disorder, and 76% for major depressive disorders using single trait models. Because our approach can be readily applied to any number of GWAS datasets of correlated traits, it is a flexible and powerful tool to maximize prediction accuracy. With current sample size, risk predictors are not useful in a clinical setting but already are a valuable research tool, for example in experimental designs comparing cases with high and low polygenic risk.
4, 5, 9  major factor determining how well a polygenic model can predict a trait value in an independent sample is the sample size of the discovery data. 10, 11 Using more individuals will provide more information and hence increase the accuracy of the estimated effect size of a specific SNP. Sample size can also be effectively increased through datasets measured for correlated traits. Recently, we estimated the genetic relationships among five psychiatric disorders from the Psychiatric Genomics Consortium (PGC) by using a bivariate linear mixed model demonstrating that there are significant shared genetic risk factors across the disorders and that measurement of one trait provides information on other genetically correlated traits. 12 Here we extend our bivariate approach to a multivariate linear mixed model and apply multi-trait genomic best linear unbiased prediction (MTGBLUP)  13, 14  for genetic risk prediction of disease.
MTGBLUP is expected to be more powerful because it uses correlations between disorders and jointly evaluates individual risk across disorders. To date, the information from other correlated traits has been little exploited in the context of risk prediction although recently Li et al. 9  applied bivariate ridge regression to two genetically correlated diseases to improve risk prediction. An important advantage of the MTGBLUP approach is that it does not require multiple phenotypes to be measured on the same individuals and therefore can be readily applied to any number of existing datasets of genetically related traits. This is particularly beneficial for disease studies that are limited to a single phenotype but typically aim for large sample sizes. Moreover, it is not necessary for the datasets to be genotyped with the same SNP array because SNPs can be imputed to a common set of SNPs, such as those available from the HapMap or 1000 Genomes reference panels.
The SNP-BLUP model is computationally more demanding for a large number of SNPs. Therefore, it is desirable to estimate genetic values (GBLUP) for efficiency and to transform them to SNP-BLUP. The SNP-BLUP can be projected to predict genetic risk for independent validation sample without the need to have access to the training individuals. The SNP-BLUP estimates can be applied to independent datasets as the SNP weights used to create a risk profile score, for example using the PLINK-score command. The individual BLUP model is 2 6 4 g 1 « g n 3 7 5 ¼ 2 6 4 s 2 g 1 / s g 1n « 1 « s g n1 / s 2 gn 3 7 55A , 2 6 4 Z 1 / 0 « 1 « 0 / Z n 3 7 5 0 ,V À1 2 6 4 y 1 À X 1 b 1 « y n À X n b n 3 7 5: (Equation  1 ) SNP-BLUP model is 2 6 4 u 1 « u n 3 7 5 ¼ 2 6 4 s 2 u 1 / s u 1n « 1 « s u n1 / s 2 un 3 7 55I , 2 6 4 W 1 / 0 « 1 « 0 / W n 3 7 5 0 ,U À1 2 6 4 y 1 À X 1 b 1 « y n À X n b n 3 7 5 where W i is a N 3 M matrix of standardized SNP coefficients with N being the number of individuals and M the number of SNPs, 5 is the Kronecker product function, and the variance covariance matrix for SNP-BLUP mode is defined as U ¼ 2 4 WIs 2 u 1 W 0 þ Is 2 e 1.
The model is y 1 ¼ X 1 b 1 þ Z 1 g 1 CNS þ Z 1 g 1 nonÀCNS þ e 1 for trait 1 « y n ¼ X n b n þ Z n g n CNS þ Z n g n nonÀCNS þ e n for trait n where g CNS is a vector of random genetic effects due to the CNS genes and g non-CNS is a vector of random genetic effects resulting from the non-CNS region. We also tested another gene set that included candidate genes set for schizophrenia, autism, and intellectual disability (SAI). 3 We matched these candidate genes with UCSC Genome Browser human genome version 18 (on which the discovery dataset was built) and retained 4,133 autosomal genes. It is noted that we excluded 479 genes flanking GWAS SNPs identified in the Swedish sample  28  to avoid artifact inflation in prediction accuracy. We annotated SNPs within the SAI genes (28% of the SNPs) and fitted genomic similarity matrices of the annotated SNPs and the rest of SNPs in the two-component model.
We had access to the PGC Cross-Disorder data and three independent validation datasets. The details of the PGC Cross-Disorder data with additionally available ADHD samples are described elsewhere. 12 The datasets stored in the PGC central server follow strict guidelines with local ethics committee approval. Genotype data from each study cohort were processed through the stringent PGC pipeline and imputation of autosomal SNPs was carried out with the HapMap3 reference sample. 29 In each imputation cohort, we retained only SNPs with MAF >0. 01 and imputation R 2 >0. 6. The number of SNPs used in this study was 745,705. We excluded certain individuals to ensure that all samples from the five disorders were completely unrelated in the conventional sense, so that no pair of individuals had a genome-wide similarity relationship greater than 0.