dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2003b). Emissions of CO in SeSE Asia from BB sources exhibit strong seasonality with a primary peak during Februarye April and secondary peak during AugusteOctober  (Streets et al. , 2003b). In SE Asia, including Indonesia and Malaysia, emissions of CO from BB were estimated to be w118 Tg y r À1  (Streets et al. , 2003b). The spatial and temporal variations of CO have been characterized based on the measurements using different techniques such as remote sensing from the space, ground based remote sensing, and in-situ sampling. Emmons et al. (2000)  have compiled the global tropospheric data of CO obtained from a number of aircraft campaigns during 1983e1999. However, the aircraft observations in Asia are negligibly small compared to those in North America and Europe. Most of the studies of CO in Asia have relied on data from space borne techniques such as the Measurement of Air Pollution from Space (MAPS) (web page http://www.
The absorption of IR radiation at a wavelength of 4. 67 micron was used for the detection of CO. The measurement precision of AE5 ppbv (signal noise), AE5% (calibration) and minimum detection limit of 10 ppbv were achieved for a 30 s integration time. Detailed description of measurement technique, instrument validation and quality testing were reported in  Nedelec et al. (2003). The flight observations taken during both take-off and landing of the aircraft provide the vertical profile data over the airports. In this study, we have used about 143 profiles of CO measured over Bangkok during year 2005e2006. The histogram of the number of MOZAIC profiles in different months is shown in Fig. 1. MOZART is a 3-D global chemical transport model of atmospheric composition designed to simulate both chemical and transport processes in the troposphere.
MOZART-2 deals with 63 different chemical constituents employing 133 gas phase, 2 heterophase, and 33 photolytic reactions  (Horowitz et al. , 2003; Sheel et al. , 2010). The horizontal resolution used in this run is 2. 8 Â 2. 8 (equivalent to T42) translating to 128 longitude and 64 latitude grid points. Model can provide the simulations at 28 vertical sigma levels from surface to w40 km of altitude. The emission data of most species were taken from the Precursors of Ozone and their Effect on the Troposphere (POET). The POET inventory is based on the European projects of the Global Emission Inventory Activity (GEIA) and the Emissions Database for Global Atmospheric Research (EDGAR)  (Olivier et al. , 2003; 2005). The POET inventory gridded for 1 Â 1 includes anthropogenic, fire, and natural emission data.
The fire and natural emissions are monthly data, while anthropogenic emissions are annual. Anthropogenic emissions of CO and NO x were replaced by the emission inventory for Asia (Regional Emission inventory in ASia (REAS) version 1. 1) available at 0. 5 Â 0. 5 grid. In this study, the meteorological fields taken from the National Centers for Environmental Prediction (NCEP) reanalysis is used for the MOZART simulations  (Sheel et al. , 2010). In our institute (Physical Research Laboratory), we run the model on the 3 Terraflop HPC Linux cluster with 20 nodes. A typical one year run takes about 12 h of time. The modifications of MOZART-4 over MOZART-2 include detailed anthropogenic hydrocarbon chemistry, improved scheme for determination of albedo, inclusion of tropospheric aerosols and online calculations of photolysis rates, dry deposition, H 2 O concentration and biogenic emissions  (Horowitz et al.
, 2003). MOZART-4 simulates comprehensive tropospheric chemistry with 85 gas phase species, 12 bulk aerosol species, 39 photolysis and 157 gas phase reactions  (Emmons et al. , 2010). The model simulated at a resolution of 2. 8 Â 2. 8 with 28 sigma pressure levels from the surface to w2 mb using the NCEP's Global Forecast System (GFS) meteorological fields have been used in the present work. The emission inventories for major anthropogenic sources, i. e. FF combustion and biofuel burning used in the present simulations are taken from the POET for year 2000  (Granier et al. , 2004). These anthropogenic emissions have been updated over Asia for year 2006 using REAS inventory developed by  Ohara et al. (2007). The monthly averaged BB emissions for the given year of simulation are taken from the Global Fire Emission Database, version 2 (GFED-v2) (van der  Werf et al.
We have used the meteorological data recorded at Don Muang Airport (DMK) in Bangkok (http://www. wunderground. com). Based on the daily data, the monthly mean temperature varies between a minimum of 27 C in the month of December and maximum of 32 C in the month of March. The temperature tends to increase from March to April (or till mid-May) therefore; this period is known as the summer season. The daily wind speed data shows large variability in Bangkok. During both the years, the monthly wind speed data shows slightly higher range (1. 0e2. 0 m s À1 ) in the wet season, while calm weather (<1. 0 m s À1 ) prevailed during the winter months. The monthly median of sea level pressure varies in the ranges of 1004e1012 hPa and 1006e1012 hPa during the year 2005 and 2006, respectively with the lowest during June and highest in the month of December.
Biomass burning emissions and long-range transport Emissions of CO from global BB sources show strong seasonal and inter-annual variations (van der  Werf et al. , 2006). In this study, we have used the fire count data detected by the Along Track Scanning Radiometer (ATSR). Details of the ATSR methodologies can be found elsewhere  (Buongiorno et al. , 1997; Arino et al. , 2001). The seasonal maps of fire counts detected over SeSE Asia during the years 2005 and 2006 are shown in Fig. 8. In the wet season, however which is dry season in the southern hemisphere regions of Asia, the fire count data shows major BB activities only in the southern regions of SeSE Asia. The fires in the forested regions make the highest contribution among the other BB sources in Indonesia. The BB activities in Indonesian islands during year 2006 were significantly high compared to those during year 2005.
Also in this season, the density of hot spots detected in Thailand during the summer of year 2006 was lesser than those during the year 2005. The monthly fire count data detected over different countries in Se SE Asia during year 2005 and 2006 are plotted in Fig. 9. In all countries of northern hemisphere of SeSE Asia the BB emissions are significant during FebruaryeMay when relatively dry weather prevails. The highest BB in Thailand was during the months of FebruaryeMarch. In India, the BB occurs from February to May but the month of April experiences the highest activities. From Mid of June to October the activities shift toward SE Asia when activities are only due to the fires in Indonesian and Malaysian islands. In summary, the analysis of fire count data reveals systematic seasonal and inter-annual variations in BB emissions over SeSE Asia.
However, the roles of CO emitted from BB and anthropogenic sources in different regions of SeSE Asia on the vertical distribution of CO over Bangkok can be ascertained by incorporating the seasonality in the long-range transport. Back trajectory models have been widely used to track the transport of air parcels originated from different source regions. In this study, the isentropic back trajectories were calculated using the Japanese 25-year Reanalysis data (JRA-25, 6 h, 1. 25 Â 1. 25;  Onogi et al. , 2007)  with the same advection algorithm as  Draxler and Hess (1997). The trajectories were calculated for a total run time of 168 h (7 days) at 1 km, 5 km and 10 km of altitudes with a time step of 5 min. For both the year of 2005 and 2006, the origins and pathways of trajectories arriving over Bangkok (at 1 km of altitude) show very clear and systematic seasonality (Fig.
Bars, 5 µm. (A) Diagram illustrating the changes in junctional length upon wounding. Junctions perpendicular to the wound are compressed and shortened due to tissue displacement that results from wound opening, and junctions parallel to the wound increase their length. The color code in matched with the previous panel and with Fig. 3  Aii. (Bi) Quantitative analysis of junctional length and relative E-cadherin levels in consecutive rows of cells around the wound in wild-type tissue. The green dataset represents the length ratio of parallel over perpendicular junctions, whereas the purple dataset represents the E-cadherin intensity ratio of parallel over perpendicular junctions. The proximity of junctions (i-iv) to the wound edge are as in A; junctions i are the closest and iv the most distant.
40; Nikon). This system, controlled by iQ software (Andor Technology), is suited for fast in vivo time-lapse microscopy allowing Z-stack acquisition every 10 s (2 s for calcium). After data acquisition, raw images were preprocessed in ImageJ (National Institutes of Health) to generate maximum intensity projections and bleaching correction. Hemocyte cells were tracked every 30 s using the "Manual Tracking" Im-ageJ plugin. For image segmentation E-cadherin-GFP, images were analyzed using a semi-automatic segmentation script made in MATLAB. Each image was smoothed with a Gaussian low-pass filter (size and sigma of 5), and its background was removed (Gaussian low-pass filtered image with filter size and sigma of 30). The operations, morphological reconstruction, and morphological opening were performed to remove small clusters of pixels and to remove gaps in the edges.
Cell boundaries were obtained with watershed lines using Meyer's watershed algorithm  (Meyer, 1994). This algorithm scans the grayscale image pixels to find a minima set and each minimum gets a unique label. Each label corresponds to a cell and it gets expanded (flooded) until it reaches another label or a boundary cell membrane. Pixels that remain unlabeled correspond to cell contours. Cell boundaries not detected automatically were added manually. A MATLAB function was used to obtain surface area, mean fluorescence intensity (mCherry-Moesin and Sqh-GFP), and position of the center of mass for each cell over time. In each experiment, segmented cells were divided in cell rows according to their distance to the wound edge. Area and fluorescence intensity plots were obtained by averaging the values of each cell row.
Kymographs were made in ImageJ, using a line with width of 3 pixels. Actin and myosin flows were tracked using a MATLAB script that tracks the maximum fluorescence intensity of the moving flow over time. For each time point the script scans the intensities along a radius from the center of the image to its limits, within 30 pixel intervals (average cell size). This scan is performed for the whole field by rotating the radial scan in steps of 1 degree and for 360 degrees. The intensity value displayed for each time point is the average of the maximum intensities of all radial scans. All values were normalized to average values before wounding. Average cable fluorescence intensity (Sqh-GFP or mCherry-Moesin) was obtained using measurement tools in ImageJ with a line width of 4 pixels on top of the cable.
Standard hematoxylin-eosin staining was used for morphological studies. Sections were viewed in a lightfield using a Zeiss microscope (Axioskope 2 plus, Zeiss, Oberkochen, Germany) and captured with an AxioCam HRC (Zeiss) using Axiovision software. Transgenic mice carrying a LacZ reporter were processed through a LacZ staining protocol with eosin counterstaining prior to histological analysis. For skeletal preparations, embryonic heads were deskinned and fixed in 95% ethanol. Samples were stained for cartilage with 15 mg/ml Alcian blue 8 GX in 76% ethanol and 20% glacial acetic acid for 2 weeks at room temperature, differentiated in 95% ethanol and cleared in 1% potassium hydroxide (KOH) until bones were visible. Tissues were then stained in 0. 1% aqueous Alizarin Red solution in 1% KOH and decolorized in 20% glycerol in 1% KOH.
1 s each over 360 degrees. The detector was based on a 750-μm-thick LuAG scintillator, coupled to a fast readout low noise charge coupled device camera through a lens-based optic system. Volumes were reconstructed using ESRF software PyHST, which includes the single distance phase retrieval algorithm  [92, 93]. Subvolumes were corrected for ring artifacts, and then concatenated to generate a single stack of 16 bits TIF slices. E11. 5 CD1 mouse embryos were fixed in 4% paraformaldehyde in water, dehydrated and stained using an alcoholic iodine solution  [94]. The embryos were scanned using a 0. 7 μm optic in binning mode (voxel size 1. 356 μm). The propagation distance was 50 mm. Half-acquisition was applied in local tomography using 3000 projections of 0. 2 s each. The beam was produced by the U17.
6 undulator closed at the gap of 20 mm. No filter was used. The effective energy was 19. 2 keV. A single distance phase retrieval process was used  [76, 77] , coupled to an unsharp mask on the volume  [77]. Imaging data were imported into the software MIMICS v. 13. 1 (Materialise, Leuven, Belgium) for segmentation, 3D modeling and visualization.
Acknowledgements RHK was partly funded by a grant from the  Fondation 'Les Gueules Cassées ' and by  King's College London. We thank  Cyril Charles ,  Laurent Viriot ,  Emmanuel Pasco-Viel  and the ID-19 beamline staff at the  European Synchrotron Radiation Facility  for assistance (projects  EC203  and  EC519 ). We are grateful to  Michael Depew  for his help in analysis the Gas1 -/-phenotype.
the use of RF antenna enables communication between the reader and the tag without line of sight from a distance of several decimeters  [2]. A reader can communicate sequentially with up to hundred tags per second. To provide further functionalities than bar codes, the tag may require data storage. For example, the price of a product can be stored into the tag  [3]. To know the price of a product, the customer can ask directly the tag instead of asking the database server connected with the cash register. With these new features, the adoption of RFID technology is growing: inventory without unpacking  [4] , prevention of counterfeiting  [5] , quality chain with environmental sensing  [6]  are deployed applications. The tag systems can be easily adapted for universal deployment by various industries with low prices.
the Plasmodium species  (2, 3). Clinical syndromes were classified as severe on the basis of the World Health Organization's 2010 severe falciparum malaria criteria  (4). Statistical analysis was performed by using SPSS version 20 (http://www-01. ibm. com/software/analytics/ spss/). Averages, χ 2 test of independence, odds ratios with 95% CIs, and analysis of variance were computed when applicable. Case-patients with prior co-morbid conditions were excluded from relevant subanalyses, for example, diabetes mellitus patients were excluded from hypoglycemia analysis. All analysis was also repeated after excluding all casepatients with associated infections and comorbid illnesses. The classification "comorbidity" included all conditions in the Charlson comorbidity index for mortality  (5). The study was approved by the Aga Khan University's Ethics Review Committee.
Acknowledgments We acknowledge collaboration with  Raymond A. Smego  from the  University of the Free State, Bloemfontein ,  South Africa , whose intellectual contribution to this study continued until his untimely death. PCR studies for this project were funded by an  Aga Khan University Research Council  grant. Dr Zubairi  is an Associate Professor and Section Head in Pulmonary and Critical Care Medicine in the  Department of Medicine, Aga Khan University Hospital, Karachi. His research interests are asthma, interstitial lung disease, and respiratory tract infections.
Monte Carlo models In this section, the main features of the Monte Carlo models used in the analysis are presented, with emphasis on the implementation and tuning of the underlying event. Several tunes of the PYTHIA 6 (version 6. 424)  [23]  and PYTHIA 8 (version 8. 145)  [26]  event generators are used, each providing a different description of the underlying event in nondiffractive interactions: D6T  [24, 25] , Z2  [18]  and Z2* for PYTHIA 6 and 4C  [27]  for PYTHIA 8. The parameter settings in D6T were determined from the Tevatron data, while the other tunes were determined from the LHC data on inclusive and underlying event properties at central pseudorapidity. The more recent PYTHIA 6 Z2 and Z2* tunes, as well as PYTHIA 8, use a new model  [28]  where multiple-parton interactions are interleaved with parton showering.
Acknowledgements We would like to thank  Leif L önnblad  for making the DIPSY predictions available to the CMS collaboration. We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centres and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies:  BMWF  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ , and  FAPESP (Brazil) ;  MEYS (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  COLCIENCIAS (Colombia) ;  MSES (Croatia) ;  RPF (Cyprus) ;  MoER ,  SF0690030s09  and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRT (Greece) ;  OTKA  and  NKTH (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  NRF  and  WCU (Republic of Korea) ;  LAS (Lithuania) ;  CINVESTAV ,  CONACYT ,  SEP , and  UASLP-FAI (Mexico) ;  MSI (New Zealand );  PAEC (Pakistan) ;  MSHE  and  NSC (Poland) ;  FCT (Portugal) ;  JINR (Armenia, Belarus, Georgia, Ukraine, Uzbekistan) ;  MON, RosAtom, RAS  and  RFBR (Russia) ;  MSTD (Serbia) ;  SEIDI  and  CPAN (Spain) ;  Swiss Funding Agencies (Switzerland) ;  NSC (Taipei) ;  ThEP-Center, IPST  and  NSTDA (Thailand) ;  TUBITAK  and  TAEK (Turkey) ;  NASU (Ukraine) ;  STFC  ( United Kingdom );  DOE  and  NSF (USA).
Sections were counterstained with hematoxylin staining or Dapi. An Olympus light microscope was used to evaluate each tissue section at ×200 magnification. For each graft, at least three sections were selected with Masson's trichrome and examined with antiinsulin immunostaining to select the section that had the greatest number of positive stained cells. In each selected section, insulin positive area was calculated using Leica's QWIN imaging program (Leica Microsystems Imaging Solutions R , Cambridge, UK).
Acknowledgments This study was supported by grants awarded by the 7th  Framework Programme  of the  European Commission  ( Beta-Cell Therapy ),  European Genomic Institute for Diabetes (EGID ,  ANR-10-LABX-46 ),  INSERM  ( Programme National de Recherche sur le Diabete), Conseil Regional Nord-Pas de Calais, University of Lille2, Fonds Europ éen de Developpement Economique et Regional, Agence de Biom édecine  and  OCS-01778-08-2005  from  Oncosuisse. The authors are indebted to  Drs. C. Bonner ,  L. Arnalsteen ,  B. Lefebvre ,  B. Vandewalle ,  E Moerman ,  B Lukowiak R. Ezzouaoui ,  S. Belaich , B. and  I. Alunga  for their support.
Study participants In the present study, we analyzed 1,685 children (aged 6 to 12 years) of Mexican origin, from five different states of Mexico (San Luis Potosí, Queretaro, Tijuana, Guanajuato and Mexico city), who were randomly selected and invited to participate in a cross-sectional study between 2007 and 2011 from public and private schools. Child assent was obtained and parents provided written informed consent. We collected data from the children and parent or legal guardian per child by direct questioning. Participants were scheduled for clinical laboratory evaluation following a 12 h overnight fasting. Blood samples were drawn to assess levels of fasting glucose, fasting insulin, total cholesterol and triglycerides levels. Biochemical variables were measured using an ILab 350 Clinical Chemistry System (Instrumentation Laboratory IL).
SNPs Selection and genotyping To achieve a power of at least 50%, with an odds ratio of 1. 25, we only selected SNPs with minor allele frequencies ≥ 15% in the Mexican population according to the HapMap database. These SNPs were identified by GWAS or meta-analyses of GWAS in European populations  [6, 7, 9, 10]. Furthermore, we selected a SNP that was significantly associated with childhood obesity in a French population, according to a linkage association study  [8]. The expected statistical power per SNP (estimated by Quanto software) is reported in Additional file 1: Table  S1. Genomic DNA was isolated from peripheral blood white cells using a QIAamp DNA kit (Qiagen), purity and integrity was verified by 260/280 nm measurements and by electrophoresis in 0. 8% agarose gels, stained with ethidium bromide.
Acknowledgements We are sincerely indebted to all participants of the present study. The study was supported by the  EU  funded  EUROCHIP FP7  consortium (European Obesity Consortium studying the Hypothalamus and its Interaction with the Periphery). We also thank the  IMSS Foundation A. C. , the  Gonzalo Rio Arronte Foundation I. A. P. and Cinvestav. The project was supported by  CONACYT   SALUD-2005-C02-14412 , and  Proyectos Prioritarios   FIS/IMSS/PRIO/10/011. M. C. is a recipient of an  IMSS Foundation Scholarship, Mexico. A. M. -B. was supported in part by Fellowship 233834 by CONACyT and by  Cinvestav.
The entailment relation is the one we expect from equality, for instance, f (x, y) = f (a, g(z)) must entail x = a and y = g(z). The reader may find in  [187]  more examples of constraint systems such as the Kahn Constraint System underlying data-flow languages and the Rational Interval Constraint System. We also point to  [65]  and  [149]  that describe, respectively, an implementation of a real interval constraint system and a constraint system to deal with finite sets. Modern constraint solvers as Gecode (http://www. gecode. org/) can indeed be used as basis for the implementation of interpreters for CCP languages (see Section 4. 7).
Non-Determinism and Asynchrony in tcc The notion of non-determinism and asynchrony is ubiquitous in concurrent systems. As for non-determinism, a system may exhibit different behaviors when reacting to the same input. As for asynchrony, we may have processes that occur at some point in the future time but we do not know exactly when as in a failure model for a component that is doomed to fail. The ntcc calculus  [153]  extends tcc by adding guarded-choices for modeling non-deterministic behavior and an unbounded finite-delay operator for asynchronous behavior. Computation in this language progresses as in tcc, except for the non-determinism induced by the new constructs: Definition 7 (ntcc Processes  [153] ) The ntcc processes result from adding to the syntax in Definition 5 two constructs, i∈I when c i do P i and P.
The tccp Language In the tcc and ntcc models of computation, stores are not automatically transferred to the next time-unit. Therefore, the store is monotonically refined in each time-unit but outputs of two different time-units are not supposed to be related to each other. The tccp process calculus  [34]  is an orthogonal timed non-deterministic extension of CCP. In this language, time is identified with the time needed to ask and tell information to the store. Furthermore, unlike tcc, the information in the store is carried through the time-units. Then, streams are used in tccp to model the evolution of variable values along the time. Definition 8 (tccp Processes  [34] ) The syntax of tccp is obtained by adding the construct when c then P else Q to the language in Definition 4. The tccp calculus introduces a discrete global clock and assumes that ask and tell actions take one time-unit.