dataesr/scientific-paragraphs-categorization huggingface dataset:
Even though the estimated emissions do not correspond to the simulated year and the interannual variability in biogenic emissions is of the order of 10 % in this period  (Lathière et al. , 2006) , we decided to use this dataset in the present work for consistency with the simulations conducted within the framework of the AR5  (Szopa et al. , 2012). The DD emissions are pre-calculated offline at a higher resolution (1. 125 • × 1. 125 • ) using the 6-hourly horizontal 10 m wind speed from the European Centre for Medium-Range Weather Forecasts (ECMWF) and are then re-gridded to the model resolution while conserving the global flux. For SS we use the formulation of  Monahan et al. (1986)  as a function of the 10 m wind speed (HCB12). In addition to the above-described dataset, we explore the sensitivity of the estimated emissions, on one hand by replacing the GFEDv3 biomass burning emissions with the GFAS v1.
We follow the authors' recommendation and apply a global enhancement factor of 3. 4 to particulate matter emissions to better match observed aerosol distributions. The AeroCom inventory corresponds to a compilation of anthropogenic emissions of BC, OC and SO 2 for the period 1980-2010 to facilitate intercomparison of hindcast simulations of aerosols (http://aerocom. met. no/). The BC and OC emissions are based on the gridded inventory for 1996 from  Bond et al. (2004)  while for SO 2 , the emissions are based on the Emissions Database for Global Atmospheric Research release version 4. 1 (EDGARv4. 1) inventory. This inventory provides SO 2 emissions from 1975 to 2000 every five years and then yearly from 2000 until 2006. For BC and OC, the emissions are updated over the period to account for regional emission trends using data from  Streets et al.
Results We assess the validity and the quality of the estimated emission by examining first the model performance to reproduce the assimilated AOD and an independent AOD dataset (Sect. 4. 1). We then analyse the estimated fluxes both in terms of global and regional values, assess their robustness to the choice of the a priori emission inventories and compare them to values from the literature (Sect. 4. 2). To finalize, we will present the uncertainties of the estimated fluxes and compare them to values found in the literature (Sect. 4. 3). The analysis will be conducted focusing on the ACFED combination of inventories. We make use of the tools developed at the Laboratoire des Sciences du Climat et de l'Environnement (LSCE) in the framework of the AeroCom project, which includes a platform for detailed evaluation of aerosol simulation in global models (http://aerocom.
Statistical analysis The first step to validate the estimated fluxes is to assess their ability to improve the simulation of the AOD, with respect to both the assimilated and independent observations. We do so by examining the difference between the first guess (FG) and the analysis (AN) to the observations via the root mean square (RMS) error, mean bias and Pearson correlation coefficient (R). As independent dataset, we use measurements from the AErosol RObotic NETwork (AERONET). This is a global network of more than 300 sun photometers that monitor AOD and aerosol properties under various different atmospheric aerosol loads  (Holben et al. , 1998 (Holben et al. , , 2001)). Although AERONET also provides instantaneous and daily-averaged AOD data, we shall focus on the monthly values in accordance with the scale of the state vector (Sect.
2. 3). We compute the model monthly mean by sampling only those days when AERONET data are available. We use available stations with measurements for the year 2010. Stations above 1000 m a. s. l. are excluded since we do not correct the model AOD for the station altitude. For the analysis with respect to the MODIS data, the model monthly mean is computed using only days and grid boxes when MODIS observations are available. The assimilation is effective in bringing the simulated AOD closer to MODIS, for both total (Table  1 ) and fine mode AOD (Table  2 ), in terms of root mean square error and correlation coefficient. Larger impacts are seen in the total AOD than in the fine mode AOD. For the former the RMS error (correlation coefficient) is reduced (increased) by 21 and 25 %, respectively, whereas for the latter, the RMS error (correlation) is decreased (increased) by 5 and 18 %, respectively.
Acknowledgements. This study was co-funded by the  European Commission  under the  EU Seventh Research Framework Programme  (grant agreement No. 283576 ,  MACC II)  and the  French Ministère de l'Ecologie, du Développement Durable, des Transports et du Logement (MEDDE)  under the  GMES-MDD programme. The authors are grateful to  Michael Schulz  for assistance and help in the use of the AeroCom model evaluation tool. The authors would also thank  Philippe Bousquet  for providing the meteorological fields necessary for the inversion. We also thank the  AERONET  project at the  NASA/GSFC  for providing the ground-based aerosol data. Edited by:  W. Lahoz The publication of this article is financed by  CNRS-INSU.
Flow cytometry Antibodies for fluorescein isothiocyanate (FITC)-conjugated anti-CD123 or BDCA-2 (Miltenyi Biotec, Auburn, CA), Phycoerythrin (PE)-conjugated CD11c (IOTest/Beckman Coulter, Marseille, FR), Allophycocyanin (APC)-conjugated anti-BDCA-4 (Miltenyi Biotec) and Allophycocyanin-Cy7 (APC-Cy7)-conjugated anti-CD14 (BD Biosciences, San Jose, CA), Vioblue-conjugated anti-CD4 (Miltenyi Biotec), V500 anti-CD3 (BD Biosciences) or with appropriate isotype-matched control antibodies (at 5 mg/mL each) in PBS containing 2% fetal bovine serum (Hyclone) and 2 mM EDTA (Gibco). Human PBMCs or isolated monocytes/ pDCs were incubated for 20 min at 4uC with antibody cocktails. Cells were washed twice in ice-cold PBS and flow cytometry acquisition was performed on FACSCanto 7 colors or FACS Aria 13 colors flow cytometers using FACSDiva software (BD Biosciences).
CD3 2 CD4 + CD14 2 CD123 + /BDCA-2 + BDCA-4 + gated cells were then tested for the expression of surface markers using PE-labeled anti-TRAIL (BD Biosciences). Mosquito C6/36 cell line monolayers were washed with PBS-1% bovine serum albumin (Sigma) and incubated for 60 min at 4uC with purified anti-DENV-complex (Millipore, Billerica, MA, USA) then 30 min with goat anti-mouse Alexafluor647 (Molecular Probes/ Life Technologies) and fixed. Intracellular antigen staining for C6/36 or cocultures was performed using 2% paraformaldehyde (Sigma) followed by antibodies staining steps with 0,1% saponin (Sigma) buffer. Cells were analyzed by C6 Cytometer (Accuri/BD Biosciences). FlowJo software (Treestar, Ashland, OR, USA) was used to analyze flow cytometry data.
Dengue patients differentially exhibit TRAIL + pDCs, soluble IFN-a and TRAIL levels We studied a cohort of DENV infected patients and classified them regarding the severity of the disease. Detailed demographic, clinical, and laboratorial data from dengue patients are summarized in Table  1. From 43 patients enrolled, 10 were classified as severe DF and the remaining as DF including those with warning signs for severity (WS), according to latest WHO classification  [42, 43]. In order to explore pDC activation by DENV infection, we first characterized the CD4 + /CD14 2 /BDCA-2/4 + /CD123 + pDC frequency/profile in 40 patients compared to 20 healthy controls (figure  1A ). As described by others  [51] , pDC frequencies in healthy individuals range from 0. 2% to 0. 8% of peripheral blood mononuclear cells (PBMCs).
DENV-2 and TRAIL location within pDCs by 3-dimension microscopy To better characterize DENV-2-activated pDCs, we analyzed them by 3-dimension (3D) microscopy. Focal plane analysis revealed the presence of intracellular TRAIL expression in unstimulated pDC (figure  3A , upper panels), confirming our cytometry data and our previous study  [48]. Images also revealed some 'peripheral' TRAIL expression that did not seem to be localized in the cytoplasm but rather on the membrane (figure  3A , middle panels). TRAIL expression profile in DENV-2-stimulated pDC did not seem to differ from unstimulated cells, even if TRAIL appeared to be decreased in the cytoplasm at the expense of ''peripheral'' TRAIL (figure  3A , middle panels). However, it remained hard to distinguish between intracellular and membrane TRAIL profile expression in both conditions without the use of a membrane marker.
The blocking of endosomal acidification by chloroquine use revealed the same profile as mock-stimulated pDCs. Thus, to better characterize TRAIL localization in pDCs, 3D reconstruction (focal plan, XZ and YZ-stacks) analysis was performed (figure  3B-D ). 3D interactive surface plot plugin of ImageJ software combined with phase contrast acquisition allowed us to visualize with precision internal or external localization of TRAIL (membrane delimitation) (figure  3B ). This combined analysis clearly showed intracytoplasmic TRAIL repartition of mock stimulated pDC (figure  3B  and C upper panel). DENV-2stimulated pDC (figure  3C , middle panel) mainly harbored membrane TRAIL localization in contrast to the restrictive intracellular TRAIL expression of pDC from mock stimulated cells (figure  3A-B , right panels).
Acknowledgments We thank  Amanda Torrentes de Carvalho ,  Cintia Marinho ,  Debora Batista de Oliveira ,  Ana Rita Motta Castro  for their assistance with patient recruitment and sample collection. We also thank  Iris Peixoto Alvim  and  Geraldo Pereira  for the help in the  FACS Aria Facility, FIOCRUZ. We acknowledge Plinio Barcelar Laboratory, in Campos de Goytacazes, RJ  for laboratory confirmation of dengue patients.
The photodetector is scanned along the bearing surface of the girder and along the module at the radius at which the inner radius shims are glued. The data were recorded and used to determine the shim thicknesses to be used for the interface between each module pair. This is the nominal shim thickness. It was used for all modules mounted during the pre-assembly and was adjusted for subsequent modules during cavern assembly as discussed below. Thus, using simple tooling, the shim thicknesses along the length of the module were adjusted to insure that the calorimeter met its radial specification and also the planarity specification for the -17 -surface formed by the endplates of +2 mm relative to nominal. This was a particular issue for the barrel calorimeter due to its length of almost 6 m.
More detail can be found in  [23]. Due to the self-supporting feature of the design of the Tile Calorimeter, and the size of the structure, the assembly of the calorimeters began with a full surface assembly to establish the validity of the planned assembly approach prior to installation in the cavern  [24]. Geometrical surveys were carried out at each major stage in the assembly procedure to provide data for subsequent use in performing the assembly process in the cavern. These measurements included detailed surveys of individual components used in the assembly (the assembly cradle and the support saddles) and of the cradle, saddles and the mounted modules which were measured periodically until the full calorimeter had been constructed. The survey results from the pre-assembly were used to determine the best fit calorimeter axis and to anticipate the position of the liquid argon calorimeters for use in the cavern assembly.
Several measurement techniques were used to monitor the geometry of the calorimeter during pre-assembly, all of which used the fiducial marks described above as the reference points. The techniques comprised: a custom precision ruler used to measure the chord length between opposite modules; optical survey of the positions of the fiducial marks, caliper measurements between fiducial marks on adjacent modules, and photogrammetric measurements of the partially assembled or entire calorimeter. Following disassembly of the structure, the shims at the bearing surfaces were removed and measured by micrometer. Several key observations were made using the data obtained from the surface assemblies: 1. The precision required for the shim thickness is of order 50 µm. 2. Shim deformation at the inner radius is large and dependent on the exact load conditions (e.
Acknowledgments This report documents the construction of the ATLAS Tile Calorimeter from design to installation in the ATLAS cavern. This work brought together scientists engineers and technicians from 3 continents, who had the privilege and pleasure to be part of this effort; a special thanks to them, including the CERN support transport group. We gratefully acknowledge the support of  The Ministry of Economical Development and Trade, Armenia; State Committee on Science and Technologies of the Republic of Belarus ;  CNPq  and  FINEP , Brazil; CERN;  Ministry of Education, Youth and Sports of the Czech Republic, Ministry of Industry and Trade of the Czech Republic , and Committee for Collaboration of the Czech Republic with CERN; IN2P3, France; Georgian Academy of Sciences; GSRT and NKUA/SARG, Greece; INFN, Italy; GRICES and FCT, Portugal;  Ministry of Education and Research, Romania; Ministry of Education and Science of the Russian Federation, Russian Federal Agency of Science and Innovations, and Russian Federal Agency of Atomic Energy ;  JINR ;  Ministry Department of International Science and Technology Cooperation, Ministry of Education of the Slovak Republic; Ministerio de Educación y Ciencia (MEC), Spain ; The  Swedish Research Council , The  Knut and Alice Wallenberg Foundation, Sweden ;  DOE  and  NSF ,  United States of America.
HFRS and climatic data Records of HFRS cases from 1991 to 2010 were obtained from the the Hunan Notifiable Disease Surveillance System (HNDSS). The data were from a passive surveillance system and all HFRS cases were first diagnosed by clinical symptoms, as defined by a national standard  [7]. Clinical diagnosis criteria include: A person who had exposure to rodents and their feces, saliva and urine urine or who had traveled to the HFRS endemic area within two months prior to the onset of illness, and who had an acute illness with at least two of the following clinical symptoms: fever, chill, hemorrhage, headache, back pain, abdominal pain, acute renal dysfunction, and hypotension. Then, antibody-based serological tests were used (e. g. MacELISA, IFA). A confirmed case of HFRS had to have had at least one of the laboratory criteria for diagnosis.
The results most likely indicate that moisture not only influences growth of food sources that determine rodent population size, thereby affecting the HFRS transmission, but also directly influences rodent activity and hantavirus infectivity. These findings offer insights in understanding possible causes of HFRS transmission, and can be used in disease surveillance and risk management to provide early warning of potential epidemics of this disease. Monthly climatic data from 1991 to 2010 in Changsha were collected from the China Meteorological Data Sharing Service System (http://cdc. cma. gov. cn/index. jsp). Climatic factors included monthly mean relative humidity and monthly accumulated precipitation. Absolute humidity was generated from a transformation of air pressure, relative humidity and temperature: r w ~e R v T ð1Þ Q~e E |100% ð2Þ e s (T)~e s (T 0 )|exp L R v 1 T 0 { 1 T ð3Þ where r w is absolute humidity, e is vapor pressure, Rv is the gas constant of water evaporation, L is the latent heat of water evaporation, e s (T) is the saturation vapor pressure at the temperature T, Q is relative humidity and E is saturation vapor pressure.
" Discussions of logistics and routines for sequencing and assembling various arthropod genomes were extensive and it was evident that many researchers were moving ahead with generating sequences for their favored species. Standardized protocols and data pipelines were identified as pressing needs. These pipelines would be updated frequently and would present methods for minimizing genetic variation within source individuals, sample collection, sequencing, assembly, annotation, and comparative genomics. Several workshop participants led by S. Richards (Baylor College of Medicine) began work to develop this set of standard operating procedures for successful arthropod genome projects, and their work continues. Additional breakout groups from the workshop developed wiki nodes that will become open forums for discussion (http://arthropodgenomes.
org/wiki/i5K_working_groups). As the workshop ended, i5K leader Dr. G. Robinson provided a bridge by delivering the Plenary introduction to the 6th Arthropod Genomics Symposium, a venue for discussing progress and tools in arthropod research. Since the workshop, Dr. K. Hackett of the USDA's Agricultural Research Service has coordinated regular teleconference and data calls to discuss the questions posted above and to look for new avenues for funding and facilitating genome projects. The topics of these calls cover each of the i5K working groups, and an effort is made to bring in outside participants from genome centers and funding bodies. One exciting direct outcome of these discussions is a commitment by the Baylor College of Medicine's Human Genome Sequencing Center to sequence, assemble, and annotate 30 key arthropod species.
Providing a Roadmap for Genome Projects and Arthropod Resources Researchers hoping to exploit genomic data to understand phenotypes, test biological hypotheses, or apply insights for pest control or the preservation of beneficial arthropods require guidance to find relevant sequence and metadata resources. Similarly, groups that are poised to start genome projects need current data on planned or in-hand genomic, transcriptomic, or metagenomic resources for their targets. Nominations at the i5K taxon wiki currently exceed 800 species, and these entries are serving a need by connecting community members and potentially competing groups focused on specific taxa. Additionally, the inclusion of available genome size estimates makes it apparent that many taxa, among them the crickets and grasshoppers, will be challenging targets due to large or repetitive genome sequences.
Moving forward, this community-driven list will continue to be populated with contributions from the i5K membership and with regular searches through large, institutional databases such as at the National Center for Biotechnology Information (NCBI; http://www. ncbi. nlm. nih. gov/genome/?term=arthropoda) and the European Bioinformatics Institute (http://www. ebi. ac. uk/services). The expectation is that the i5K site and other resources will also serve to alert community members to genome projects on their favored organisms and provide a forum for early genome planning stages.
Populating Databases with Genome-Relevant Information for Each Species Taxa with already completed genome sequences have received more extensive postsequencing analysis, curation, and database development than is likely to occur for upcoming genome candidates. At one extreme, sequence and phenotype data for numerous Drosophila species are housed in FlyBase, a longstanding and constantly updated database (www. flybase. org). In addition, genomic resources for several crop pests are presented at Agripestbase (www. agripestbase. org), while arthropods that transmit disease to humans and other vertebrates are featured at VectorBase (www. vectorbase. org). Honey bees and other bees, wasps, and ants have found a stable home at the Hymenoptera Genome Database (www. hymenopteragenome. org), while flour beetles (BeetleBase; www.
beetlebase. org), moths (i. e. , the silkworm genome databases SilkDB, www. silkworm. genomics. org. cn/, and KAIKObase, sgp. dna. affrc. go. jp/ KAIKObase/) and pea aphids (AphidBase, www. aphidbase. org) have current sites devoted to incoming genomic and/or biological data. In contrast, limited financial and informatics resources will preclude extensive project sites for many of the targeted i5K species. Nevertheless, the i5K Consortium agrees that single-page entries should be maintained for each of these species, to ensure permanence and availability of their resources. These pages are expected to provide a roadmap to finding pertinent sequence resources at NCBI and elsewhere, available literature, and phenotype data relevant to the species of interest. As one example, the USDA National Agricultural Library has developed postsequencing resources for genome projects in need of such resources (http://i5K.
Conclusions Although the i5K initiative is in its early stages, immediate needs have been identified and thus future directions have been drawn. Discussions spurred by the i5K Consortium have led community members to keep abreast of new technologies in sequencing, assembly, and annotation. Training possibilities are improving, both during i5K-sponsored workshops and via advertisements made by community members. Criteria for setting minimum requirements for pre-sequencing data and vouchering, as well as for needed landmarks for useful sequencing projects, are being actively developed. Methods to streamline the submission of genome projects into the NCBI Bioprojects schema, and to initiate public annotation tools, are being formalized. Finally, there is ongoing work toward supporting and expanding efforts to develop core ortholog sets for arthropods and to develop a controlled vocabulary for arthropodrelevant genetic traits.
44; P = 0. 036) and of 40-44 years (r 2 = 0. 51; P = 0. 019), while it decreased for athletes in the age group of 30-34 years (r 2 = 0. 66, P = 0. 0039). For women, running speed remained stable during the study period for athletes in all age groups. Conclusion: These data suggest that the number of finishers of masters runners older than 40 years increased for both sexes at the Marathon des Sables, as has been previously observed for single-stage ultramarathons. In contrast to women, men aged 35 to 44 years improved running speed during the study period. Future studies are needed to investigate the reasons for the growing numbers of masters athletes in endurance sports and their improvement in performance.
2  Men achieved their fastest running times at the age of 30-49 years. 2  In the Swiss Alpine Marathon, a 78 km mountain ultramarathon held in Davos, Switzerland, an increased age for the fastest running times for both sexes was found. 17 From 1998 to 2011, the age for the fastest running times increased for men from 34 to 38 years and for women from 32 to 39 years. 17 he reported data about finisher rates and performance trends of masters athletes refer predominantly to single-stage endurance and ultraendurance events with a nonstop racing time. 11, 12, 16, 17 The requirements of the athletes in multistage races are different, particularly regarding the total length of the race and that the time available for recovery is only overnight. A few studies investigated the age-related finisher rates and performance trends in multistage races, such as the Marathon des Sables, a multistage ultramarathon over a distance of about 240 km through the Moroccan desert.
Materials and methods All finishes in the Marathon des Sables between 2003 and 2012 were included in an analysis of age, sex, and running speed. The data set for this study was obtained from the website of the race. 18 Data before 2003 were not available. All procedures used in the study met the ethical standards of the Swiss Academy of Medical Sciences 21 and were approved by the Institutional Review Board of Kanton St Gallen, Switzerland, with a waiver of the requirement for informed consent of the participants, given the fact that the study involved the analysis of publicly available data.