dataesr/scientific-paragraphs-categorization huggingface dataset:
Representation of Wetland inundation extent To make spatially and temporally explicit estimates of net CO 2 and CH 4 exchanges between the land surface and the atmosphere due to changes in wetland inundation extent in northern high latitudes (north of 45°N), we apply TEM to two datasets of static wetland inundation extent and one time-series dataset where the maximum wetland inundation extent changes annually from 1993 to 2004. The first static wetland inundation extent dataset is extracted from the NASA/GISS global natural wetland dataset at a spatial resolution of 1°×1°  (Matthews and Fung 1987). This dataset is resampled to a spatial resolution of 0. 5°×0. 5°(referred to as Wet1). The second static wetland inundation extent dataset is from the Global Lakes and Wetlands Database (GLWD,  Lehner and Döll 2004)  where we resample a 30 s raster wetland distribution map to a spatial resolution of 0.
5°×0. 5°(referred to as Wet2). The time-series dataset of dynamic inundation extent  (Papa et al 2010)  contains the monthly distribution of surface water extent at a ∼25 km sampling resolution and we resampled to 0. 5°×0. 5°raster maps, which are geographically consistent with Wet1 and Wet2. To associate the inundation data with our model parameterizations for wetland ecosystems, we overlay the monthly changes of inundation extent from 1993 to 2004 to the Wet1 dataset to create 144 monthly maps. The new wetland inundation extent data set (hereafter referred to as Wet3) contains the wetland types defined in  Matthews and Fung (1987) , but with annual maximum inundation extents (figure  S2 ). Permanent inland water bodies are excluded in the original satellite data  (Prigent et al 2007).
Specifically, for any particular year within the period 1993-2004, an annual map of maximum inundation extent is developed. The annual map consists of the maximum inundation extent for every pixel, which is determined based on its monthly inundation data. These annual maximum inundation extent maps with a mean annual inundation area of 3. 15 Mkm 2 are used in Wet3 simulations (figure 1(b)). Note that inundation area for some months could be larger than the static wetland area in Wet1 for some pixels. The extra inundated-area for a particular pixel in the Wet3 data is assumed to be covered with the same wetland types as in the Wet1 data for that pixel.
Other input datasets To drive TEM, we also use spatially-explicit data of monthly climate, land cover, soil texture, soil-water pH, elevation, and leaf area index (LAI). The static data sets include potential natural vegetation  (Melillo et al 1993) , soil texture  (Zhuang et al 2003) , and soilwater pH  (Carter and Scholes 2000). The historical monthly air temperature, precipitation, vapor pressure, and cloudiness data sets are from the National Centers for Environmental Prediction (http://www. ncep. noaa. gov/). LAI is internally estimated as a function of vegetation carbon within TEM for each 0. 5°latitude×0. 5°longitude grid cell. More details about the input data can be found in supplementary material.
Field-based estimates of NEE (table  S1 ) are used to compare with model simulations to optimize TEM parameters. To examine the effect of inter-annual changes in wetland inundation extent on carbon fluxes, we conduct a 'core' regional simulation with the organized wetland (Wet3) and upland data for the period of 1993-2004. In this simulation, the annual maximum inundation area detected with satellite is treated as wetlands, i. e. , the wetland distribution and area change from year to year. To illustrate the effect of the interannual changes of wetland inundation extent, we conduct two other simulations with the 'static' inundation extent datasets Wet1 and Wet2. Each simulation was spun-up for 100 years. For each simulation, the model is run twice for each pixel, once for the upland parameterization and once for the wetland parameterization.
In the cases where a grid cell goes from wetland to upland or from upland to wetland, we multiply the fluxes for each land cover type by the new fraction of wetland and upland. With the 'core' simulation, we first examine the carbon balance in the region with an explicit consideration of both wetland and upland ecosystems. We then analyze how CO 2 and CH 4 fluxes are related to changes of climate and wetland area extent at an annual time step. In addition, we examine the effects of soil moisture, soil temperature, water table depth, and active layer depth on CO 2 and CH 4 dynamics in the region. To quantify the sensitivity of both CO 2 and CH 4 fluxes to the uncertain wetland inundation extent in the region, we conduct two additional simulations with slight modifications of the Wet3 dataset.
Discussion and summary 4. 1. Regional wetland emissions and atmospheric concentrations of CH 4 Nine inversion studies estimated that net CH 4 emissions of boreal region wetlands are 34±13 Tg CH 4 yr -1 (Chen and Prinn 2006). Another inversion constrained by atmospheric observations of methane and its 13 C/ 12 C isotopic ratio data indicates that the boreal region releases 30-64 Tg CH 4 yr -1 , presumably mainly from wetlands  (Mikaloff Fletcher et al 2004 , McGuire et al 2012). A more recent study using satellite data estimated that mean summer emissions are 53 Tg CH 4 yr -1 from boreal-Arctic wetlands  (Watts et al 2014). The 'static' wetland inundation dataset Wet1 estimates CH 4 emissions that are comparable with the various inversions, while both Wet2 and dynamic wetland inundation extent dataset Wet3 estimates much higher emissions due to the larger wetland or inundated area in the latter two datasets (table  1 ).
Our simulations further indicate that the three wetland datasets estimate similar seasonal dynamics of methane emissions with different magnitudes over the region (figure  S4(b) ). The peak emissions occur in July. These seasonal dynamics are dominated by climatic factors, rather than wetland inundation extent. To consider the seasonal rather than annual wetland inundation extent in a future analysis will improve the estimates of seasonal dynamics. To date, most atmospheric transport chemistry and inversion models have primarily based their analyses on methane fluxes estimated without considering changes of wetland inundation extent (e. g. ,  Houweling et al 1999 , Cunnold et al 2002 , Dentener et al 2003 , Wang et al 2004 , Chen and Prinn 2005 , 2006). Coupling estimates of methane emissions considering both seasonal and interannual wetland inundation extent dynamics with these atmospheric models should improve modeling atmospheric methane concentrations.
Regional NEE Our estimates of NEE account for the CO 2 emissions from both aerobic decomposition and anaerobic decomposition of the soils (see equation (1)). Based on the static wetland inundation extent of the Wet1 and Wet2 datasets, the region is estimated to be a consistent sink (table  1 ). The annual NEE ranges from -1. 23 to -1. 50 Pg C yr -1 for the period 1993-2004. The dynamic wetland inundation extent estimates the region to be a similar sink at -1. 28±0. 03 Pg C yr -1. The larger regional sink under static wetland inundation conditions is due to the larger wetland area prescribed by the Wet2 dataset that enhances the carbon sink. The smaller regional sink is mainly due to the decrease of the regional wetland carbon sink as wetlands area decreases under the dynamic wetland inundation condition.
In comparison with an earlier estimate of a sink of 51. 1 Tg C yr -1 over the 1997-2006 time period (McGuire et al 2010), our estimated regional NEE magnitudes based on the satellite-detected dynamic wetland extent dataset are much larger. If we account for fire emissions (246. 5 Tg C yr -1 ) and the decomposition of agricultural and forestry products (13. 5 Tg C yr -1 ) estimated by  McGuire et al (2010)  for the same region in our analysis, our estimates of NEE will be a large sink of -1. 0 Pg C yr -1 under the dynamic wetland conditions. A recent inter-model comparison shows that the multi-model mean annual NEE for Alaska is largely carbon neutral  (Fisher et al 2014)  while some observations suggest that portions of the region have been a source of CO 2 to the atmosphere  (Oechel et al 2000 (Oechel et al , 2014)).
4. 3. Regional GWPs of CO 2 and CH 4 Previous evaluation of regional radiative forcing estimated as GWPs was based on regional methane emissions and NEEs that have not considered changes in wetland inundation extent (e. g. ,  Zhuang et al 2007). Consideration of wetland inundation dynamics reduced the estimated regional negative climate forcing (figure  4 , table  1 ). Overall, the estimated regional GWP sink using either static or dynamic inundated area data increases during the study period (figure  4 ). The increase estimated using dynamic inundation data is mainly due to the decrease of CH 4 emissions resulting from the declining inundated area (figure  1 ). The slight increase estimated using two static wetland datasets is due to the slight increase of the carbon sink (table 1). Our sensitivity analysis indicates that the regional GWP under the conditions of either a 10% increase or a 10% decrease in inundation area of the Wet3 dataset differs little (less than 6%) from the estimates with Wet3 (table 1).
If wetland inundation extent increases, there will be more regional CH 4 emissions, but less regional CO 2 emissions from a relatively smaller upland area and a stronger carbon sink in a relatively larger wetland area. As a result, the regional GWP source will be weaker (figure  4 ). Future climate predictions with general circulation models therefore should consider the feedback that is induced by these CO 2 and CH 4 dynamics under changing wetland areas in addition to the physical feedbacks of wetland distribution (e. g. ,  Gutowski et al 2007 , Prigent et al 2011). It is important to note that the greatest limitation of this analysis is the use of relatively coarse resolution (i. e. , 0. 5°×0. 5°) wetland inundation datasets, which do not capture small features within wetlands that create CH 4 hotspots.
Acknowledgments This research is supported by the  NASA   Land Use and Land Cover Change program  ( NASA-NNX09AI26G ),  Department of Energy  ( DE-FG02-08ER64599 ),  National Science Foundation  ( NSF-1028291  and  NSF-0919331 ), the  NSF   Carbon and Water in the Earth Program  ( NSF-0630319 ), and the  NSF Division of Information & Intelligent Systems  ( IIS-1028291 ). The computing is supported by  Rosen Center  of high performance computing at  Purdue. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the  US Government.
Imaging findings including x-ray computed-tomography (xray CT) and/or positron emission tomography computerized tomography ( 18 FDG-PET/CT) and biological data such as serum protein electrophoresis, calcium, angiotensin-converting enzyme (ACE), or C-reactive protein were noted when available. Cancers were classified according to stage grouping, based on TNM classifications: stage I and II corresponded to limited cancer, with tumor localized to 1 part of the body but could reach regional lymph nodes, and stages III and IV corresponded to advanced cancer that has spread to distant lymph nodes or other organs. Response to treatment of sarcoidosis was classified as follows: clinical complete response was defined as the total disappearance of clinical symptoms, partial clinical response as improvement of these symptoms, without onset of new symptoms; radiological complete response was characterized by normalization of x-ray CT and/or 18 FDG-PET/CT and partial radiological response by decrease of number or size of initial lesions without any new lesions related to sarcoidosis.
5 months. Some of patients developed general symptoms, joint, and eye involvements that are not usual in sarcoid reaction. These data are consistent with systemic sarcoidosis. Nevertheless, patients who developed sarcoidosis following cancer have clinical features that may differ from classic sarcoidosis in the general population. The age of the patients in our study at sarcoidosis diagnosis is an average of 57 years, which is similar to what was described in literature. This age is older than the peak age of onset of sarcoidosis in the general population that is evaluated to 20 to 39 years. 33 This can probably be explained by the fact that cancer develops in older age. Location of granulomatous reaction is sometimes atypical with especially bone involvement observed in 41. 7% of our patients versus 0.
found, from the Mayo Clinic database between 1950 and 1996, 14 patients who had a diagnosis of a malignant testicular tumor and sarcoidosis. Twelve of them had carcinoma diagnosed before sarcoidosis. In a retrospective chart review of all patients with a diagnosis of sarcoidosis or mediastinal adenopathy who underwent mediastinoscopy at the Swedish Medical Center and Cancer Institute from 2004 to 2008, Hunt Characteristics of 31 Patients From the Literature Review.
This identity criterion is then carried over to the events denoted by the verb, providing a way to individuate events and thus to obtain the plurality of events, which is needed for distributivity. In the remainder of this section, we will present the aspects of the Karitiana and Mandarin grammar which will be needed in order to follow the argument and understand the data presented in the paper (section 1. 1. ). The structure of the paper then is the following. In section 2, we present an overview of the typological work on distributive numerals. In section 3, we show that Reduplicated Numerals in Mandarin and in Karitiana behave as typologically expected of distributive markers. In this section, we also show that RedNums are adverbials in the two languages. In section 4, we present the different analyses of distributive numerals provided in the literature, and opt for a pluractional analysis.
Acknowledgements We thank  Roy Gereau  for translating the diagnosis into Latin, and  Daniel  and  Irène Létocart  for helping with the organization of the field work conducted on Paéoua in 2010. We are grateful to the team at the  NOU herbarium (IRD, Nouméa ), in particular  Jacqueline Fambart-Tinel  and  Michèle Magat  for assistance with specimens, and to  Roger Lala Andriamiarisoa  ( Missouri Botanical Garden -Madagascar ) for providing the fine illustration. MWC is especially grateful to  Philippe Morat  for sharing his knowledge on this elegant genus on several occasions. We thank the authorities of New Caledonia's Northern and  Southern Provinces  for providing collect permits and for other courtesies, as well as  Wendy Applequist  and  Tanguy Jaffré  for helpful comments on an earlier version of the manuscript.
GPS time series The GPS data from the CRAC-ICE (Collaborative Research project into Antarctic Calving and ICeberg Evolution) November 2007 field program were collected by two receivers (Topcon GB1000 with PGA1 antennas), deployed to each side of the main western rift of Mertz Glacier Tongue with a 30 s sampling interval. The GPS receivers recorded data over 60 days, from 15 November 2007 to 15 January 2008. The data from both GPS 4 and GPS 5 receivers have been processed using the GINS-PPP software developed by the Centre National d'Etudes Spatiales, France  (Marty and others, 2012). This software allows the use of a Precise Point Positioning kinematic methodology and solves for ambiguities as integer values  (Blewitt, 2008). Such GPS data processing is usually achieved using a double-difference processing strategy.
The new strategy provides an ideal way to process GPS data without the need for a reference or base station (i. e. relative positioning) and it still achieves a 2 cm accuracy  (Lescarmontier and others, 2012). The GPS positions were determined in Cartesian geocentric coordinates, in the ITRF2005 reference frame, taking into account the modeled solid earth and ocean tide loading displacements. Lescarmontier and others (2012)  provide further details of the processing and the possible spurious signals remaining. We projected our solutions into a local orthogonal topocentric coordinate system (x, y, z) with the x axis defined as the along-tongue direction (defined as the velocity averaged over the whole observation period), y to the left, across the tongue, and z the local up direction (the local vertical, orthogonal to the x and y axes; Fig.
CONCLUSION In studying Mertz Glacier, we have used data collected by two in situ surveys. The work of  Legresy and others (2004)  included three days of GPS measurements spread over 4 days in 2000 near the tip of Mertz Glacier Tongue. During 2007, GPS data were collected continuously over 60 days across an active rift approximately midway along Mertz Glacier Tongue. These data have allowed us to view Mertz Glacier Tongue under, primarily, a bending regime  (in 2000)  and then under rifting behavior (in 2007), giving us insight into an ice tongue operating under different mechanical conditions. In both surveys, we captured the basic flow of Mertz Glacier Tongue. The combination of the two surveys, however, has allowed us to analyze the variable forces acting on Mertz Glacier Tongue driven by tides, atmospheric pressure, sea-surface height and sea-surface slope, and bed-shape/wall friction.
ACKNOWLEDGEMENTS This study is part of the  CRAC-ICE  project, supported by the  CNES  ( Centre National d'Etudes Spatiales, France) ,  ANR  ( Agence Nationale de la Recherche, France)   DACOTA grant , the  IPEV (Institut Polaire Paul-Emile Victor, France) ,  CNRS/INSU  ( Centre National de la Recherche Scientifique/ Institut National des Sciences de l'Univers, France ) and the  University of Tasmania. Part of this research was also supported by an  Australian Research Council   Discovery grant  ( DP0666733 ) awarded to  Richard Coleman  and was undertaken in the  Antarctic Climate & Ecosystems Cooperative Research Centre , supported by the  Australian Government's Cooperative Research Centres Program. We thank the  Master and crew of MV L'Astrolabe  and helicopters for their assistance in the deployment of the GPS stations on  Mertz Glacier Tongue  and the retrieval of the GPS data.
The precision of the sensor is <0. 01N, with a range of 0-9N. Force data of each finger was sampled to a CED 1401 (with 10 kHz sampling rate/digit) connected to a computer running Spike 2v6 (Cambridge Electronic Design, www. ced. co. uk) software. Customwritten CED scripts provided real-time visual display of digit forces and target instructions or target forces.
Data analysis Task performance was analyzed using Matlab (v7. 5, The MathWorks, Inc. , Natick, MA, USA). The four force signals were first down-sampled to 100 Hz for the analysis. Finger force tracking: all performance measures were calculated trial-by-trial (N = 24). Tracking error was calculated as the root-mean-square error (RMSE) between the actual applied force and the target force. The error was separately extracted during the ramp and the hold phase. The time of the force onset in response to the target ramp and the time of the release onset at the end of the hold phase were calculated as threshold crossings of dF/dt. The release duration was computed as the time taken to reduce the force from 75 to 25 % of the target force  [18]. The coefficient of variation (CV) of force (i. e. SD/mean across time bins) was calculated during the hold phase and averaged across trials.
Acknowledgements We thank  Julie Paquereau  and  Leire Santisteban  at the  Service de Médecine Physique et de Réadaptation de l'Hôpital Sainte-Anne , for their help in the recruitment and in the clinical assessment. This work was in part supported by the  CNRS  ( Centre national de la recherche scientifique ) and by  Inserm  ( Institut national de la santé et de la recherche médicale ).
The directional solidification was realized by a 'power down' method applying the same cooling rate on both hot and cold zone heating elements during the entire experiment. This method ensured a constant thermal gradient and growth rate during directional solidification. As shown by the photo inserted in the sketch in Figure  1 (b), a sintered NbFeB permanent magnet mounted close to the Bridgman furnace was used and provided a uniform 0. 08 T transverse static magnetic field within the sample. The recorded images were analyzed by a free code ImageJ.
Equations [1]and [2] are solved in terms of the electric scalar potential V in both liquid and solid. More details about the equations and corresponding boundary conditions can be found in Reference 34. With a fixed and prescribed solid-liquid interface, this system is solved both in liquid and solid phases simultaneously by a commercial finite element code COMSOL Multiphysics using its packed electric current module. It should be mentioned that using a given interface is to propose a heuristic model but not standing for any kinds of the real solid-liquid interface, and qualitatively illustrate how the TEC and TEMHDF generate, behave, and their impacts on the solute distribution around the solid-liquid interface. Figure  3 (a) shows the geometry used to perform the simulation of TEC and its dimensions.
B. Computed Results and Discussions Numerical simulations were carried out in order to confirm that TEMHDF produced by a transverse static magnetic field in our experiments could have the one-direction flow pattern at the vicinity of solid-liquid interface. The finite element method-based code COM-SOL Multiphysics was used, and the pre-packed heat transfer, electric current, and fluid flow modules were coupled. The prescribed interface structure is curved near the side wall as shown in Figure  5(a). It was to be known that this model only considered solid-liquid interface curved periphery feature as indicated by the inserted picture in Figure  5(a). The electric currents density (rð u ! Â B ! Þ) induced by the melt motion was considered in these simulations because it can generate damping forces when computing the fluid flow.
ACKNOWLEDGMENTS This study is partly supported by the  Shanghai Committee of Science and Technology  (Nos. 13521101102 ,  13DZ1108200 , and  13521101102 ), the program of  Youth Eastern Scholar  ( QD2015035 ), and the  CNRS/ANR OPTIMAG Project in France. We are grateful to  T. Lafford  and  X. Guichard  in  European Synchrotron Radiation Facility (ESRF, Grenoble ) for their help on experiments, and the first author appreciates the support from program of  Marie-Curie IPACTS  (No. 268696 ). Fig. 8-(a) The red arrows show the computed flow field of TEMHDF under a 0. 5 T axial static magnetic field (B) with the geometry shown in Figure 3(a), and the colored x-z plane indicates their magnitudes (the unit in caption is m/s, G = 6000 K/m ( 6000 °C/m) ); (b) Top view of the computed flow flied of TEMHDF.
The red and blue lights were equiluminant for photopic sensitivity at 2. 3 log cd/m2, according to the manufacturer of the Ganzfield apparatus (=14. 9 log quanta for blue light and 15. 1 log quanta for red light). The pupils were recorded in darkness for 30 seconds after the red light and for 60 seconds after the blue light. Analysis of pupil tracings. The video signal from the pupillometer was relayed to a processing board which recorded the maximal pupil diameter in real time into a text file. Analysis of the pupil data was performed in a spreadsheet. A customized filter was applied to remove gross artifacts from blinking and eye movements (Microsoft Excel 2010, Visual Basic for Applications). Baseline pupil size was defined from the average size during 1 second before onset of each light stimulus.