dataesr/scientific-paragraphs-categorization huggingface dataset:
Other agricultural activities, such as fertilization, tillage, residue management are associated mainly with biogeochemical impacts. Crop species selection, in contrast, ranks low with regard to biogeochemical and biophysical effects. But, as most land management activities are not isolated from each other, but intricately linked (e. g. crop harvest, irrigation and fertilization), robust assessment on their relative significance require the application of Earth System models and, as our review reveals, improved databases. Our review focused on documented Earth system effects of land management that have occurred over the past decades. Yet land management plays an increasing role in discussions on mitigating future climate change  (Foley et al. , 2005). This makes it particularly important to consider that management effects act on a range of timescales: While changes in land surface properties impose immediate effects on the atmosphere, changes in carbon and nitrogen fluxes invokes counter-fluxes in the coupled land-atmosphere-ocean system, causing a distinct temporal evolution and a delayed response of the Earth system  (Ciais et al.
A mixed picture emerges regarding data availability and robustness of global, long-term land management information (Table  1 ). This is a consequence of the history of research and past investments in generating the datasets. Remote sensing, while particularly well-suited to assess certain land uses at the global level (e. g. cropping, irrigation, or the outbreak of fires), encounters severe difficulties in depicting other uses such as grazing  (Erb et al. , 2007; Kuemmerle et al. , 2013). Furthermore, statistical reporting schemes focus mainly on management activities of economic interest, such as crop and forest harvest and ignore others, e. g. crop residue management. In addition, inconsistent definitions affect data robustness  (FAOSTAT, 2015; See et al. , 2015). While a comprehensive assessment of Earth system impacts induced by management requires more data and ultimately their integration in a modelling environment, as well as the inclusion of other management activites not discussed here, we conclude that management is a key factor in the Earth system, severely influencing many biogeochemical and biophysical processes and parameters.
This includes wood harvest, tree species selection, grazing, and N-fertilization, motivating calls for fostered research efforts from the global land use data community (e. g. Verburg et al. , 2016)  to develop improved datasets, e. g. by taking advantage of the increasingly available data from satellite observations  (Kuemmerle et al. , 2013; Joshi et al. , 2016) , or crowd sourcing  (See et al. , 2015) , but also alternative approaches that exploit existing databases. These management activities could be included in Earth system models but global parameterisation and validation may be difficult for now. A third group is characterized by concomitant data and knowledge gaps. The management types in this group require an intensification of efforts of both the data and the ecological communities, in order to advance the understanding of the impact of these management practices on the Earth system.
Acknowledgements The authors gratefully acknowledge the support of the International Space Science Institute (Bern) that sponsored the team on "Integrating Earth Observation data and the description of land management practices into global carbon cycle models (H. Dolman), and of the Cost Action ES0805 Terrabites (C. Reick). The work was supported by the EU, FP7 projects 26510 ("VOLANTE") and 603542 ("LUC4C") H2020 projects 640176 ("BACI"), 641538 ("ConnectinGEO"), and 640176 ("BACI"), by the ERC Grants 263522 ("LUISE") and 242564 ("DOFOCO"), Synergy Grant 610028 ("IMBALANCE-P"), the Austrian Science Fund (FWF-P20812-G11), the Einstein Foundation Berlin (EJF-2011-076), the Figure Captions.
FAOSTAT (2015) Statistical Databases. http://faostat. fao. org. Farley KA ,  Jobbágy EG ,  Jackson RB  ( 2005 ) Effects of afforestation on water yield: a global synthesis with implications for policy. Global Change Biology ,  11 ,  1565-1576. Farrell EP, Führer E,  Ryan D, Andersson F, Hüttl R, Piussi  P (2000) European forest ecosystems: building the future on the legacy of the past. Forest Ecology and Management,  132 ,  5-20. Feddema JJ ,  Oleson KW ,  Bonan GB ,  Mearns LO ,  Buja LE ,  Meehl GA ,  Washington WM  ( 2005 ) The.
Aims Study 303 (clinicaltrials. gov: NCT01405053) is an ongoing Phase III, multicenter, randomized, active-controlled, openlabel, 2-year study designed to evaluate the safety, pharmacokinetics, and cognitive/behavioral effects of adjunctive rufinamide treatment for control of seizures associated with inadequately controlled LGS in a pediatric population (!1 to <4 years of age). It is from this study that the reported 6month interim safety and PK analysis was generated.
Acknowledgments The data reported in this paper were presented as a poster at the 67th American Academy of Neurology (AAN) Annual Meeting; April 18e25, 2015; Washington, DC,  USA. We wish to sincerely thank all investigators and their teams having participated in the study (Supplementary Table 1), the patients and their families. All authors were involved in the analysis and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.
2001). The relative proportions of each peak were determined using the program Diamir (JMBS, Perkin Elmer Fr, Villebon-sur-Yvette, France); these data were then transferred into a Statgraphics matrix (Statgraphics v. 4. 0 and Uniwin Plus v. 3. 0, Francestat, France (www. statgraphics. fr)) to perform PCA. Of the 98 chromatograms analyzed, 61 were new to this study; 37 were published previously by  Kutnik et al. (2004).
All of the COI sequences analyzed were new to this study as were 39 of the 65 COII sequences (the other 26 were from  Kutnik et al. 2004 ). Of the 59 ITS2 sequences analyzed, 38 were new to this study; the other 21 were reported in  Kutnik et al. (2004). Amplified DNA was purified using the Qiagen PCR Purification Kit and cycle-sequenced in both directions using an automated AB 3100-Avant sequencer. Each sequence generated by this study was deposited in the Gen-Bank database (accession numbers: JQ431045 to JQ431094 and KP721513 to KP721527 for COI sequences; JQ430995 to JQ431043 and KP721528 to KP721542 for COII sequences; and JQ431095 to JQ431133 and KP721543 to KP721557 for ITS2 sequences [see Table  S2 ]). To characterize population genetic structure, we used ten microsatellite loci: Rf6-1, Rf15-2, Rf21-1, Rf24-2, RS1, RS10, RS15, RS62, RS76, and RS78 (Table  1 ; code S-x).
Analyses of nuclear genetic data The level of intraspecific polymorphism in ITS2 genes is low in European Reticulitermes termites and is often used to quickly identify different taxa  (Jenkins et al. 2001; Uva et al. 2004). We therefore used it here as a quick and reliable marker with which to flesh out the taxonomic identity and compared the results it yielded with those of the other markers used. Termite populations on the Iberian Peninsula and in southern France were analyzed using 10 microsatellite loci for the 15 more intensively sampled sites (A to O). We studied 16-33 colonies per site using one individual per colony. The program Genetix 4. 05  (Belkhir et al. 1996 ) was used to calculate expected heterozygosity and the mean allele number per locus for each population. To infer population structure, the multilocus genotypes obtained were analyzed using a model-based clustering method implemented in the program STRUCTURE  (Pritchard et al.
Microsatellite genotypes Extensive sampling was also conducted in parallel within smaller areas throughout the distribution ranges of R. grassei and R. banyulensis. The goal was to obtain a data set more suitable to the analysis of population genetic structure  (Ross et al. 2007; Caldera et al. 2008). A total of 341 termites from 15 sites (A to O; Table  1  and Fig. 1 ) were examined. A total of 121 alleles were detected across the 10 microsatellite loci. The main results of the STRUCTURE analysis (K = 2 and K = 6) are shown in Figure  7 , but the histograms for the relevant runs from K = 2 to K = 7 are provided in Figure  S2. The most likely number of populations was estimated at K = 6 using the Each haplotype is represented as an oval whose color corresponds to the phylogenetic clade to which it was assigned via Bayesian analysis (cf.
Acknowledgments We are grateful to  Dow AgroSciences  for providing part of the financial support for  M. Kutnik 's PhD and to the  CNRS  for the  CNRS-NSF  grant no. 14574  awarded to  A. -G. Bagn  eres and  E. L. Vargo. This work was also supported by a grant from the  National Science Foundation (NSF)  (No. INT-0233238 ) to E. L. Vargo. Many thanks go to  C. DeHeer ,  Tom Juba ,  L. Leniaud , and  Vanessa Wehbi  for their technical help and assistance in collecting termites. We thank the  Dow AgroSciences  staff for helping collect some termite's populations and  A. Lenoir  for the Moroccan samples. We also thank  Jessica Pearce  for the English revision.
General design As a Monte Carlo simulation toolkit, GEANT4 profits from improved throughput via parallelism derived from the independence of modeled events and their computation. Until GEANT4 version 10. 0, parallelization was obtained with a simple distribution of inputs: each computation unit (e. g. a core of a node in a cluster) ran a separate instance of GEANT4 that was given a separate set of input events and associated random number seeds. Given a computer with k cores, the design goal of multithreaded GEANT4 was to replace k independent instances of a GEANT4 process with a single, equivalent process with k threads using the many-core machine in a memory-efficient, scalable manner. The corresponding methodology involved transforming the code for thread safety and memory footprint reduction  [3].
Results The physics and CPU performance of GEANT4 were measured by comparing the sequential version of the code to a multithreaded equivalent. The results which follow were obtained with a patched version of GEANT4 Version 10. 0, and focus on high energy physics applications. This domain was chosen because its complex physics requirements cover the most important use-cases for GEANT4 users: high and low energy electromagnetic physics, high and low energy hadronic physics, tracking in many-volume geometries and tracking in electromagnetic fields. In the near future regular testing will be extended to other user domains such as medicine and space science. Simplified description of a GEANT4 multithreaded application: the master thread prepares geometry and physics setups for the simulation, and the worker threads compete for the next (group of) events to be simulated; otherwise they are independent.
The GEANT4 binding for GDML implements all features supported by the GEANT4 geometry modeler and most of the geometrical entities defined as part of the latest version of the GDML schema. These include all shapes, either CSG or specific solids, and their Boolean combinations. Also included are any combinations of materials, from isotopes to mixtures, and the ability to import definitions compliant with the GEANT4 NIST database. All types of physical volumes are supported, from placed volumes to replicas and parameterized volumes, including assemblies, divisions and reflections. GDML supports the modularization of geometry descriptions to multiple GDML files, allowing for rational organization of the modules for complex setups. Verification of the GDML file against the latest version of the schema comes for free thanks to Xerces-Cþ þ, with the possibility to turn it on or off in the GEANT4 GDML parser.
With this format the user may describe any of the geometrical objects of GEANT4. It is possible to create materials combining elements, materials and detailed isotopic composition. Mixtures of materials can be built by providing the percentage of each material by weight, by volume or by giving the number of atoms. The user may change the default pressure, temperature and state, or set the minimum ionizing energy instead of letting GEANT4 calculate it automatically. Instead of explicitly building each material, predefined elements or materials from the GEANT4 NIST database may be specified. Most types of GEANT4 solids can be described, whether CSG or specific, by including a combination of solids through Boolean operations. Logical volumes can be defined by attaching solids to materials, and color and visualization attributes can be assigned to each one.
Advances in drivers and viewers The workhorses of the GEANT4 visualization system continues to be its OpenGL drivers. Multiple OpenGL drivers are provided because different implementations are required on different operating systems or for different user memory configurations. For example, one flavor of OpenGL driver offers higher refresh speed at the cost of using more memory, while another conserves memory at the cost of speed. The user experience has been simplified so that it is no longer necessary to specify which driver to use (such as /vis/open OGLI or /vis/open OGLSWin32). Instead a single command (/vis/openOGL) may be issued from which GEANT4 will select the most appropriate and capable viewer for the user's current system. Other improvements include speed increases through the streamlining of the set of OpenGL instructions, and the ability to print any OpenGL view to high quality output by exploiting the GL2PS  [27]  OpenGL to PostScript printing library.
Additional new features Time slicing was added to allow one to produce movies that show the time development of an event. With time slicing enabled, the OpenGL line segments that represent a particle trajectory are annotated with time information. Users can then select an OpenGL view that corresponds to a given time, and a sequence of such views produces the frames of a time development movie. Users can produce these movies in any OpenGL viewer by the appropriate use of GEANT4 command macros. The Qt driver provides a simplified way for users to make such movies. GEANT4 visualization now has the ability to retain the pointers to previously viewed events, so that after visualizing a set of events, one can go back to the beginning of the set and review the events. When coupled with customized user code that specifies which events should be kept, one can potentially run a very large set of events and then afterwards choose to visualize only those events that satisfied some personal set of trigger conditions.
These three base classes are responsible for interfacing to the GEANT4 kernel, initializing the electromagnetic physics, managing the energy loss, range and cross sections tables, managing the electromagnetic models, and the built-in biasing options. Each process inherits from one of these base classes, and has one or more physics models. EM physics models were implemented via the G4VEmModel interface. A model is applied for a defined energy range and G4Region, allowing, for example, one model from the low energy and one from the high energy sub-package to be assigned to a process for a given particle type. Migration to this common design resulted in an improvement of overall CPU performance, and made it possible to provide several helper classes which are useful for a variety of user applications: G4EmCalculator: accesses or computes cross section, energy loss, and range; G4EmConfigurator: adds extra physics models per particle type, energy, and geometry region; These common interfaces enabled the full migration of EM classes to multithreading  [34]  with only minor modifications of the existing physics model codes.
Details of the calculations are presented in  [158]. Resulting calculations agree rather well with experimental data on anti-proton interactions with light and heavy target nuclei  which corresponds to an accuracy of ∼8%  [158]. Nearly all available experimental data were analyzed to get this result. The predicted antideuteron-nucleus cross sections are in agreement with the corresponding experimental data  [159]. Direct application of the Glauber approach in software packages like GEANT4 is ineffective due to the large number of numerical integrations required. To overcome this limitation, a parameterization of calculations  [146, 147]  was used, with expressions for the total and inelastic cross sections as proposed above in the discussion of the Glauber-Gribov extension. Fitting the calculated Glauber cross sections yields the effective nuclear radii presented in the expressions for pA, dA, tA and α ¯A interactions: = + R a A c A /.
Lines are FTF model calculations: solid for protons, dashed and short-dashed for π þ and π À , respectively. energy is shared by the entire nuclear system. Such an equilibrated compound nucleus is characterized by its mass, charge and excitation energy with no further memory of the steps which led to its formation. Nuclear de-excitation models. The final de-excitation of a nucleus to a thermalized state is simulated by several semi-classical models which are responsible for sampling the multiplicity of neutrons, protons, light ions, isotopes, and photons. They are: Fermi break-up (FBU)  [190] , statistical multifragmentation  [190] , fission, based on the Bohr-Wheeler semi-classical model  [191, 192] , evaporation of nucleons and light fragments, which is handled by models based on either ○ the Weisskopf-Ewing model  [193]  for fragments up to and including α particles, or ○ the generalized evaporation model (GEM)  [194]  for the emission of fragments with masses up to 28 Mg, and G4PhotonEvaporation, which simulates the emission of ○ discrete gammas according to the E1, M1 and E2 transition probabilities taken from the PhotonEvaporation database, which in turn is based on the Evaluated Nuclear Structure Data File (ENSDF)  [195] , and ○ continuous gammas according to the E1 giant dipole resonance strength distribution.
In its original formulation, G4NDL data were drawn from nine different databases, ENDF/B-VI  [205] , Brond-2. 1  [206] , CENDL2. 2 [207] , EFF-3  [208] , FENDL/E2. 0 [209] , JEF2. 2  [210] , JENDL-FF  [211] , JENDL-3  [212]  and MENDL-2  [213] , with the majority coming from the Fusion Evaluated Nuclear Data Library (FENDL). This changed in GEANT4 version 9. 5 when G4NDL became solely dependent on US ENDF/B-VI and VII (Evaluated Nuclear Data Files)  [205]. Although the other databases are no longer used by G4NDL, they are still available for use with GEANT4. Many evaluated data libraries, such as ENDF/B-VII. 0, JEFF-3. 1 and JENDL-4. 0, have been converted to the GEANT4 format  [214]  and can be obtained at the IAEA Nuclear Data Services website  [215]. NeutronHP was recently extended to include a new, detailed fission fragment generator (FFG) which was designed to model the complete detectable portion of a fission event.
This results in a factor five increase in speed. These models use the Livermore online database for their reaction data. It is based in part on ENDF/B-VII and can be obtained from the Lawrence Livermore National Laboratory ftp site  [216]. Nucleus-nucleus models. As of release 10. 0 there were six GEANT4 models capable of handling nucleus-nucleus collisions: binary light ion, abrasion/ablation, electromagnetic dissociation, QMD, INCLþ þ and FTF models. The Binary Light Ion model handles collisions in which either the projectile or the target has mass < A 13. Based on the GEANT4 Binary Cascade model  [182] , it is valid above 80 MeV and below 10 GeV/nucleon. Operating over a similar energy range, but without limits on the projectile or target masses, the G4WilsonAbrasion model, based on NUCFRG2  [217]  is faster, but less detailed, than the Binary Light Ion model.
The FTF model, covered above, is capable of modeling reactions with all combinations of projectile and target mass, with projectile energies in the range 2 GeV/nucleon to about 1 TeV/nucleon. However, validation of this application is still in progress, and collisions of two heavy nuclei are expected to be computationally expensive. The radioactive decay process. The G4RadioactiveDecay process and model handles α, β À , β þ , isomeric transition (IT) and electron capture (EC) decays, and can be applied to generic ions both in flight and at rest. Details for each decay or level transition, such as nuclear level energies, branching ratios and reaction Q values, come from the GEANT4 RadioactiveDecay database, which currently contains entries for 2798 nuclides. Details of specific gamma levels used for IT decays are taken from the GEANT4 PhotonEvaporation database.
Both the PhotonEvaporation and RadioactiveDecay databases take their data from the Evaluated Nuclear Structure Data File (ENSDF)  [195]  and have recently been rationalized so that their common nuclear levels have identical values. Beginning with GEANT4 release 9. 6 and continuing through releases currently in preparation, a number of improvements have been made to the radioactive decay package. These include: a complete review of the PhotonEvaporation and Radio- activeDecay databases, and updating to the 2013 version of ENSDF, the ability to model decays with lifetimes as short as 1 ps, decays of observationally stable ground states, that is, those having very long predicted life times, but which have not yet been observed to decay, the addition of unique first, second and third forbidden β À and β þ decays, the default invocation of the atomic relaxation model after IT and EC decays, and improved energy conservation for all decay modes.
Constructors All prepared physics lists in GEANT4 derive from the class G4VModularPhysicsList which in turn derives from the base class G4VUserPhysicsList. G4VModularPhysicsList maintains a vector of physics modules, each of which is an implementation of the G4VPhysicsConstructor class. A module, referred to here as a physics constructor, enables the logical grouping of related physics models, cross sections and particles. This allows the most accurate and CPU-appropriate physics models to be applied to given energy ranges and particle types. The ConstructParticle() and ConstructProcess() methods of G4VPhysicsConstructor can be used to instantiate the particle types needed for a given application, and to assign models and cross sections to processes. For example, all pions and kaons could be instantiated in a meson physics constructor, which would also assign two hadronic models, a cascade model at low energies and parton string model at high energies, to the processes pertaining to pions and kaons.
The weight, direction, position, kinetic energy, and type of the last adjoint particle that reaches the external source, and that would represent the primary of a forward simulation, can be obtained by public methods of the G4AdjointSimManager class. More details on how to adapt user code to use the reverse Monte Carlo scheme are provided in the Application Developer Guide  [15]. Performance. The performance of the reverse Monte Carlo can be evaluated using the execution time and relative precision of the results. A useful figure of merit (FOM) is = ( ) FOM R T 1 , 1 2 where R is the relative precision reached for a given execution time T, in minutes. For a typical application  [234] , FOM is 4. 9 for a forward method, compared to 7600 for a reverse method. This corresponds to a time speed-up factor of 1250.