dataesr/scientific-paragraphs-categorization huggingface dataset:
This is also likely due to the spatial resolution. Combining both effects (thermal and orographic, Fig. 1d ) leads to amplitudes of SLP anomalies in better agreement with NCEP, especially over North Atlantic, North Pacific and Eurasia. Over the Barents-Kara seas, the negative anomaly due to the orographic effect clearly appears, but its amplitude is too small and overtaken by the influence of the thermal effect. As expected the high pressure over North America has a larger extent than that simulated under the thermal forcing alone, but its amplitude remains too weak with respect to the NCEP data set.
NADPHd activity was revealed by incubating cells at 37uC for 45 minutes with 1 mL PBS supplemented with 0. 5 mL NADPH (1. 25 mg/mL), nitroblue tetrazolium (NBT, 100 mg/mL), 0. 2% Triton X-100 in 0. 1 mol/L phosphate buffer (pH 8. 0). The cells were washed and left in PBS for microscopy. Images were acquired on a Zeiss Axiovert 200 M microscope, using an Axiocam HRc camera controlled by Zeiss Axiovision software (all from Carl Zeiss, Heidelberg, Germany). Immunofluorescence assay. The following reagents were used to detect the human RBC NOS enzyme and/or the intraparasitic NOS: a mouse monoclonal antibody of NOS within uRBCs or PfRBCs using rabbit polyclonal antibody against uNOS (B1, green) or mouse monoclonal antibody against eNOS (B2, green). To differentiate infected from uninfected RBCs, parasite nucleus was labeled in blue with Hoechst 33342.
Statistical analysis. The Wilcoxon signed-rank test for matched pairs was used to compare continuous outcomes at different time points. The Mann Whitney U test or OneWay ANOVA with a Tukey posthoc analysis was used to compare continuous outcomes of different groups at the same time point. The correlation between different continuous measures was determined using the Spearman correlation coefficient. All p-values were two-sided, and p-values of less than 0. 05 were considered statistically significant. All statistical analyses were performed with SPSS statistical software (PASW statistic version 18).
Acknowledgments This work was supported by the  Institut Pasteur Transverse Research Programs ,  PTR 85. The research leading to these results received funding from the  European Union   Seventh Framework Program  (FP7/2007-2013, contract  242095 , Evimalar), and from the Project:  Mechanisms of erythrocytic Infection & Anemia in Malaria (PI Kasturi Haldar ), and the  National Institutes of Health  (Award Number  5P01HL078826-06 ). I. S. was funded by a joint fellowship from the  Dim Malinf of the Region Ile de France and Institut Pasteur, Paris, France. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
We used the Heritrix (1. 14) web crawler  (Mohr et al. , 2004)  and an older version of the texrex web page cleaning toolkit  (Schäfer and Bildhauer, 2012). The tools perform, among other things, boilerplate detection and text quality evaluation in the form of the so-called Badness score  (Schäfer et al. , 2013). A document receives a low Badness score if the most frequent function words of the target language have a high enough frequency in the document. The Badness score is based on previous ideas from language identification and web document filtering  (Grefenstette, 1995; Baroni et al. , 2009). Originally, this experiment was carried out in the context of an evaluation of sources of different seed URLs for crawls. In a preliminary step, we began by collecting seed URLs from various sources: 1.
the DMOZ directory 2. the Etools meta search engine 3. the FriendFeed social service aggregator 4. the identi. ca social bookmarking service 5. Wikipedia dumps We scraped the content behind the URLs and ran a state-of-the-art language identifier  (Lui and Baldwin, 2012)  on it in order to obtain languageclassified seed URLs  (Barbaresi, 2013). 2  We then looked specifically at the following languages associated as the single dominant language with at least one top-level domain (TLD): 1. Dutch (. nl) 2. French (. fr) 3. Indonesian (. id) 4. Swedish (. se) We randomly sampled 1, 000 seed URLs for each of the 20 permutations of seed sources and languages/TLDs, downloaded them and used texrex to determine the document yield ratio for the documents behind the 1, 000 seeds. The software was configured to perform boilerplate removal, removal of documents based on high Badness scores, perfect duplicate removal, and deletion of documents shorter than 1, 000 characters (after boilerplate removal).
We have plans for a URL-based language guesser  (Baykan et al. , 2008)  in the form of the LanguagePredictor, and a prioritizer based on the yield from specific hosts as described in  Suchomel and Pomikálek (2012)  in the form of the HostYieldPrioritizer, which reads statistics directly from the texrex module. The texrex module extracts all hyperlinks from processed documents and tags them with the quality scores described in this paper, such that the QualityPrioritizer module can adjust the expected weight of the document behind each URL. The HeidiX architecture also features an alternative queueing strategy in the form of the RandomWalker, which allows users to obtain uniform random samples from the web based on existing algorithms  (Henzinger et al. , 2000; Rusmevichientong et al. , 2001).
Since obtaining such samples is a goal which is mostly orthogonal to the one assumed in this paper, we do not discuss this further here. Finally, a SnapshotKeeper module allows users to halt and continue crawls by writing/reading the current state of the relevant components to/from disk. We hope that HeidiX will become a valuable tool in both the efficient construction of very large web corpora (FocusedWalker) and the construction of smaller unbiased reference samples as well as web analysis (RandomWalker).
Statistical test In order to compare the results between experiments, RC (relative changes) were compared using a one-way ANOVA (analysis of variance) and a Fisher least significant difference (LSD) means comparison test (α = 0. 05). When assumptions for ANOVA were not respected, means were compared using a Kruskal-Wallis test and a post hoc Dunn's test. The statistical tests and the box plots were performed using the Addinsoft XLSTAT software (http://www. xlstat. com).
As no significant difference between CONTROL-MESO and DUST-MESO at any of the seeding periods was reported by Pulido-Villena et al. (2014a), we conclude that the HNFA did not contribute to any significant change in the particulate organic carbon pool after the seeding. This conclusion was extrapolated to DUNE-P and DUNE-Q as no data were available for HNFA of those experiments. The organic carbon pool of macrozooplankton was deduced from the biovolume of mesozooplankton with size > 200 µm that has been analyzed using a zooscan system (L. Stemmann, personal communication, 2012, and the RADEZOO service, OOV Villefranche-sur-Mer) from net sampling performed at the beginning of ex-periments outside the mesocosms and both inside and outside after the last sampling of the DUNE-P, -Q and -R experiments.
Induced fluxes changes The relative changes (RC in percentage) between DUST-MESO and CONTROL-MESO after the seeding are represented in Fig. 2. The average increases relative to CONTROL-MESO over 7 days as high as ∼ 500 % were observed only for POC export. PP did not increase after seeding during the DUNE-Q experiment (dry deposition), while it increased in the range 75-144 % for the other experiments. BR increased in the range 137-156 % in DUNE-P, DUNE-Q and DUNE-R2 ; a factor 2 higher increase in BR was observed after the DUNE-R1 seeding (291 %). PP / BR ratios were below 1 with the average value for the whole data set being 0. 30 ± 0. 22. As seen in Fig. 1 , PP / BR tends to be lower in DUST-MESO compared to CONTROL-MESO for DUNE-P and DUNE-Q, indicating that the dust deposition induced an increase in the net heterotrophic character of the tested waters in these experiments.
Tested waters were low productive with low export, and heterotrophic During all experiments, the tested waters were typical of oligotrophic conditions with low Chl a concentrations in the range 0. 07-0. 11 µg L -1 (Table  1 ). Primary production measured over 7 years at the DYFAMED (DYnamique des Flux Atmosphériques en MEDiterranée) site in the Ligurian Sea in the surface mixed layer (SML) after the spring period ranged from 2 to 10 µg C L -1 d -1  (Marty and Chiaverini, 2002) , which is in good agreement with the PP rates measured in the tested waters during the DUNE ex- ) are more similar to coastal data such as that reported by  Navarro et al. (2004). As pointed out by these authors, the production of DOC by seagrass meadows, highly abundant in the DUNE study site, may be responsible for such high BR rates.
The higher BR rates measured during DUNE-P compared to DUNE-Q and DUNE-R may be explained by the in situ conditions encountered during DUNE-P, typical of a late-spring post-bloom situation with lower seawater temperature, with smaller daily amplitude than during the other experiments. Initial POC export (2. 4-9. 8 mg C m -2 d -1 ) cannot be directly compared to any in situ data at the same depth (∼ 15 m), as sediment traps usually placed below 100 m depth (see synthesis data for the Mediterranean Sea in  Ternon et al. , 2010). Comparing POC export in surface waters (15 m in the DUNE experiments) and at 150-200 m is difficult as a high remineralization occurs reducing rapidly the amount of POC as a function of water depth. Our numbers are representative of the POC export from the SML during summer in the western Mediterranean Sea (mixed layer depth from Table  2a.
However, the system was still able to export POC. The DUNE data illustrate, thus, that a system can be dominated by heterotrophy and still maintain an export of particulate organic carbon even in the absence of allochthonous carbon inputs, on the short timescale concerned by the dust deposition event. This is different for open systems that can be heterotrophic while exporting organic carbon vertically only if allochthonous carbon inputs support both fluxes  (Cole et al. , 2007).
Acknowledgements. The  DUNE  project, a DUst experiment in a low Nutrient, low chlorophyll Ecosystem, is a fundamental research project funded by the  ANR  under the contract  ANR-07-BLAN-0126-01. It was conducted in partnership with the laboratory of LISA (Créteil),  LEMAR (Brest) ,  LOCEAN (Paris) , and in collaboration with the laboratories LSCE ( Gif-sur-Yvette ),  LOMIC (Banyuls) ,  IRD/LOBP/COM (Marseille) ,  CEFREM (Perpignan) ,  CERES/ERTI ,  ENS (Paris)  and the  Regional Park of Corsica, Nature Reserve of Scandola. DUNE was also conducted in strong collaboration with the foreign research institute including the  Institut des Régions Arides (IRA), Medenine (Tunisia) ,  IFM-GEOMAR, Kiel (Germany) ,  Stazione Zoologica Anton Dohrn ,  Naples (Italy)  and the  Alfred Wegener Institute, Bremerhaven (Germany).
Thus, inhibition of NCC can evoke no further effect on ENaC activity. Conversely, stimulation of Fig. 8. Kaliuretic effect of HCTZ. A: time course of the effect of HCTZ on the transtubular K ϩ gradient (TTKG) determined using clearance protocol A (see Fig. 1 ). B: time course of net urinary K ϩ excretion (UKV). C: time course of the effect on fractional K ϩ excretion (FEK). D: relationship between V and UKV; data were obtained from the timecourse series with each point representing a different urine collection period. E: effect of BZM on TTKG determined using clearance protocol D. Data are means Ϯ 95% CI. NCC (as in Gordon's syndrome) might. In keeping with this, a mouse model of Gordon's syndrome exhibits hyperkalemia despite increased abundance and proteolytic cleavage of ENaC subunits, increased abundance of maxi-K subunits, and an increase in the lumen-negative amiloride-sensitive potential in microperfused cortical CDs  (58).
All data were derived from the final urine collection period. HCTZ-induced ⌬FENa was no different in the presence of BZM on a control diet (no significant effect of BZM by two-way ANOVA) but was significantly lower in the presence of BZM after dietary Na restriction (P Ͻ 0. 05 by t-test). E: UKV after a bolus dose of HCTZ (clearance protocol C). There was no increment in UKV after vehicle or after HCTZ in mice maintained on a control diet, but there was a significant increase after HCTZ in mice adapted to a low-Na ϩ diet (P Ͻ 0. 0001 for time; P Ͻ 0. 01 for experimental group; P Ͻ 0. 01 for interaction by two-way ANOVA; P Ͻ 0. 05 for the comparison between baseline and drug periods in the 0. 01% Na ϩ group only by post hoc Bonferroni test). F: effect of HCTZ and BZM on the relationship between V and UKV.
ACKNOWLEDGMENTS The authors are grateful to  Rita Upreti  of the  Wellcome Trust Clinical Research Facility Mass Spectrometry Core  for the assistance with the LC/ MS-MS assay. GRANTS  R. W. Hunter  received an  Edinburgh Clinical Academic Track Lectureship  from the  Wellcome Trust , which funded this work. E. Craigie  was also funded by the  Wellcome Trust. The authors acknowledge the assistance of the  British Heart Foundation Centre of Research Excellence Award and of Kidney Research UK.
The central feature of the CMS apparatus  [23]  is a superconducting solenoid with an internal diameter of 6 m, providing a magnetic field of 3. 8 T. Within the bore of the solenoid are a silicon pixel and strip tracker, a lead tungstate crystal electromagnetic calorimeter, and a brass/scintillator hadron calorimeter. Muons are measured in gas-ionization detectors embedded in the steel flux return yoke outside the magnet. In addition, CMS has extensive forward calorimetry. The detector covers a region of |η| < 5. 0, where the pseudorapidity η is defined as η =ln[tan(θ/2)], where θ is the polar angle. Data samples are selected based on triggers requiring two leptons (either an electron or muon), one with transverse momentum, p T , of at least 17 GeV and a second with p T of at least 8 GeV. All events are required to have a well-reconstructed primary vertex  [24].
A complete list of the variables used can be found in the Supplemental Material (Appendix A). The distributions of the number of loose jets and the p T of the system in the 1j1t signal region are shown in Fig. 1  for all three final states (ee, eµ, and µµ) combined. The BDT analyzer provides a single discriminant value for each event. The distributions of the BDT discriminant in data and simulation are shown in Fig. 2  for the 1j1t, 2j1t, and 2j2t regions, combining all three final states together. The uncertainty from all systematic sources is determined by estimating their effect on the normalization and shape of the BDT discriminant for all regions and final states. The dominant systematic uncertainties come from the choice of thresholds for the matrix element and parton showering (ME/PS) matching in simulation of tt production and the renormalization/factorization scale.
The main characteristics of these three different substrates are compared in  [29]  with those of the textile used here. These data  The radiation patterns in E-and H-planes were measured in IETR's millimeter-wave anechoic chamber. The gain was measured by the comparison method with a 20-dBi standard horn, and the directivity is found from a 3D radiation pattern measurement. The co-polarization components measured in E-and H-planes at 60 GHz are in a good agreement with the computed ones (Fig. 25 ). In E-plane, the non-symmetry of the co-polarization component is attributed to the spurious radiation of feeding lines whose width is larger compared to standard substrates at millimeter waves like RT Duroid 5880 (see Section 3. 3), or other commonly used substrate such as fused quartz or Alumina.
Acknowledgements This work was supported by  French National Research Agency (ANR)  under Grant  ANR-09-RPDOC-003-01  ( Bio-CEM  project), by  Labex CominLabs  ( ANR program  " Investing for the Future "  ANR-10-LABX-07-01 ) and  Brittany Region  under ResCor/ BoWi  project and by  National Center for Scientific Research (CNRS) , France. Part of this work was performed using HPC resources from  GENCI-IDRIS  (grant  2013-050779 ).
Moreover, the SSC could be differentiated by the presence or lack of some shrinkage phases. Peng and Horn (2013)  identified six types of SSCs based on the number of the existing shrinkage phases in the shrinkage curves by using a large set of experimental data. Finally, few scientists tried to integrate the shrinkage curve. Compared with the existing studies, this study presents three new issues regarding to: the type of the studied soil; the apparatus used for measuring the WRC and SSC, and the models used for both WRC and SSC. Two native Aridsols, according to US soil taxonomy, in the State of Qatar were investigated in this study. This class of soil has rarely been considered in the shrinkage behavior studies. Then, a new apparatus  (Bellier and Braudeau, 2013)  was used to continuously and simultaneously measure the data pairs for WRC (gravimetric water content vs.
Hence, both curves are related to the gravimetric water content. This point was taken into account while making the measurements in this device. The whole set of measurements (soil weight, dimensions, and suction) are recorded instantaneously when the soil core weight is taken. TypoSoil™ is a modified device of the retractometer  (Braudeau et al. , 1999). The later measures only the SSC while the new device measures both SSC and WRC. The new device consists of the following components (Figure  3 ): (i) A biological stove working on a fixed temperature identified by the user (usually 30-40 • C). (ii) An electronic analytical balance with MonoBloc weighting cell with a connection point's plate fixed on it. This plate closes the electrical circuit to measure and record the data once contacted with the support platform as explained below (see also Figure  3 ).
MODELING THE WATER RETENTION AND SOIL SHRINKAGE CURVES In this section, two processes were evaluated: (1) the extraction and estimation of the hydrostructural parameters from the continuous and simultaneous data pairs of the two characteristic curves (WRC and SSC); and (2) the efficiency of the thermodynamic and pedostructure based equations to model the two soil moisture characteristic curves (WRC and SSC) using such kind of hydrostructural parameters. In total, there are 12 hydrostructural parameters, while in this study only nine of them were used V 0 , W N , k N , K bs , K st , E mi , E ma , W miSat , W maSat due to the obtained shapes of the measured SSC. Nine parameters are still a large number and can be considered as a disadvantage compared to the other existing models. However, the following points should be kept in mind once doing such a comparison: (i) each parameter has a physical meaning and quantifies a specific hydrostructural property of the soil medium; (ii) six parameters V 0 , W N , K bs , K st , W miSat , W maSat out of nine can be extracted from well identified locations (points and slopes) on the measured SSC and WRC (see Figures  4, 5 ) and the accuracy of such a process depends on having continuous and simultaneous measurements, such measurements can be provided by TypoSoil™, and it also depends on the shape of curve as shown in section Extraction and Estimation of Pedostructure Characteristic Parameters [Hydro-structural Parameters]; thus (iii) it ends up having only three parameters k N , E mi , E ma to be optimized and used for modeling both soil moisture characteristic curves (SSC and WRC).
Data reorganization compilation. In order to let the compiler deal with data reorganization, some stream and dataflow-based languages propose a set of system agents. These builtin agents can be used thanks to reserved keywords and an appropriate syntax. This is particularly true for Streamit  [14]  (using Pipeline, Splitter, Joiner and FeedbackLoop composing filters), Sigma-C  [12]  (using Split, Join and Dup system agents), Tau-C  [11]  (using Split, Join and Dup system tasks), SPL  [13]  (using Sort, Split, Join, Aggregate stream operators) and GreenStreams  [4]  (using SplitJoin and FeedbackLoop stream filters). In a slighly different approach, CAL  [8]  proposes to bind data sequences between ports thanks to the definition of index sequences (refered as multiport input patterns). While most of the publications available for these languages deal with expressiveness, application case studies and general compilation strategies, a very few information are given on the particular problem of the implementation of data reorganization.
In the Streamit project  [2, 10] , the authors present several contributions in which the compiler is able to modify the application graph, based on the recognition of system agent patterns. Fission and reordering transformations simplify the application graph to get better performance. In  [7, 6] , transformations are also applied at compile time onto system agents in order to adapt the degree of parallelism to the execution platform. However, these optimizations remain at a high level of abstraction in the application description and do not give elements on generating an efficient runtime for data reorganization. The Sigma-C dataflow language. In this paper, we show how data reorganizations are processed at compile time to be handled in a runtime for a dataflow programming language. We focus on the Sigma-C  [12]  language that has been proposed to enhance manycore programmability, and has been successfully implemented and validated with the Kalray 256-core MPPA processor  [1].
Data Reorganization Strategies at Compile Time The Sigma-C toolchain is made of four main compilation steps. The first step applies lexical parsing and semantic analysis. The second step instantiates the application graph and adapts the parallelism degree. The third step proceeds to the buffer sizing, task scheduling and the place and route of tasks onto the hardware. Finally, the fourth step generates the runtime and processes a hierchical linking with the user code and the system software. We describe how these steps are involved in the compilation of system agents, using three different strategies. Shared Buffers and Indexes Generation. Shared buffers are one efficient strategy to reorganize data. For each system agent surface a shared buffer is created and for each com- munication port belonging to the edge of the surface, a data access pattern is calculated.
Dependencies can be implicit (between two consecutive occurrences of the same task), explicit (two different tasks) or directed by the producer-consumer pattern (for example, occurrence p 4 requires c 12 to write in the first part of the buffer to avoid overwriting unread data). A direct mapping is done between this scheduling graph and the access patterns. This direct mapping is illustrated in Figure  4 : a single split agent is connected to one producer p and two consumers a and b. On the right side is the complete Sigma-C source code of consumer b. The interface section declares one input port and the spec section indicates that the input port receives two data each time the start function is fired up (which happens at each occurrence). Within this function, a read access is made to the input port, aliased to i.
Usually, a good guess is to use the least common multiple of the sum of the productions and the sum of the consumptions. DMA Micro-Code Generation. Shared buffer compilation is tightly coupled with the targeted hardware architecture: to be efficient, agents connected to the same system surface have to be mapped  [9]  onto cores that share a physical memory (let's call it a cluster). With both complexity of applications and many-core memory hierarchies, some surfaces may not find a cluster and have to be split among them. Several many-core systems offer DMA engines to move data from one cluster to another. In the Sigma-C compiler, a set of basic surfaces have been identified (such as split-join), and it is possible to generate the corresponding DMA micro-code. This micro-code can either be made of nested loops (up to 3 if 3D DMA engine), or a sequence of indexes, which is better regarding performance.