dataesr/scientific-paragraphs-categorization huggingface dataset:
The coupling of models was assessed for various events, with rather different characteristics. Rainfall radar data and discharge data from five stations were used. This article is organized as follows. Part two provides a description of the Gardon catchment, the hydrologic data used, and the events studied. Part three describes the strategy for implementing the coupling approach, the hydrologic and hydraulic models, and the parameters adjustment. Finally, the article ends with a discussion and a description of the results. Figures.
Hydrological data and events studied Discharge data from five hydrometric stations in the catchment were used. Figure  1  indicates the locations of these stations. Table  1  provides data on the surface area drained and the catchment outlet distances for each station. Rainfall radar images at 1 km resolution were also analysed. They come from two Météo-France radars, located near the catchment, in the cities of Bollène and Manduel (Fig. 1 ). The radar images were corrected beforehand according to the rain gauge network measurements, using CALAMAR ® software  (Ayral et al. , 2005; Thierion et al. , 2011). These discharge and rainfall data were supplied by the regional flood warning service SPC-GD ("Service de Prévision des Crues Grand Delta"), and have a 5 min time step. This fine time step is used for modelling, as it is well adapted to the fast kinetics of events in this catchment.
For this study, seven events were analysed, which occurred between 2005 and 2011. These events were among the most important ones during the period, for which hydrological data are the most complete. Table  2  summarises some of their characteristics. Total rainfall upstream to Russan varied between 135 mm for event no. 6 and 372 mm for event no. 7. Peak flows in this station were between 700 m 3 s -1 (event no. 5) and 1420 m 3 s -1 (event no. 4). Figure  2  provides data for the cumulated rainfall distribution in the catchment for each event. Two general trends can be seen: -For events no. 1 and 5, cumulated rainfall is more significant in the intermediarydownstream part of the catchment. Table  2  shows for these two cases an increase in the volume at the downstream stations, indicating the proportionally important contribution of lateral inflows in these zones.
are about 50 inflows, with two major upstream inflows (the Alès and Anduze subcatchments), and 48 lateral inflows (Fig. 3 ). Lateral inflows were defined on the basis of a minimum threshold area of 1 km 2. The average area of lateral sub-catchments is 20 km 2 , for a median value of 5 km 2. Sub-catchments no. 2, 20, 26, 28, and 39 have an area greater than 50 km 2 , the maximum being 203 km 2 for inflow no. 39. All in all, the selected lateral sub-catchments cover 92 % of the area between both upstream stations and the Remoulins station. The coupling uses the SCS-LR hydrologic model implemented in the ATHYS modelling platform (http://www. athys-soft. org), and the MASCARET one-dimensional hydraulic modelling code. The ATHYS platform is developed by the IRD ("Institute of Research for Development"), and the MASCARET code by EDF ("Electricité De France" -French Electric Company), and the CETMEF ("Centre d'Etudes Techniques Maritimes et Fluviales").
Performance assessment The performance of the coupled models was evaluated by analysing discharge data from five stations in the catchment area, as indicated in Sect. 2. 2. The quality of the hydrologic modelling was estimated on the basis of hydrographs recorded at Anduze and Alès, and for lateral inflows according to the differences in volume observed between two consecutive stations. The performance of the coupling was evaluated at three sta- between the observed and simulated peaks ∆T Qm (min): RE Qm = Qm MOD -Qm OBS Qm OBS × 100 (11) ∆T Qm = T MOD -T m OBS ( 12 ) with Qm MOD and Qm OBS as the modelled and observed peak flows (m 3 s -1 ), and T m MOD and T m OBS as the corresponding times. A positive RE Qm value indicates an overestimation in the peak modelled, and conversely. The ∆T Qm index is positive when the peak modelled is late, and negative if it is early.
-Option no. 2: only the hydraulic model is used, without lateral inflows. As for COUPL MOD , upstream inflows were the hydrographs modelled. This option is expressed as SV MOD. -Option no. 3: identical to the previous one, but upstream inflows were the hydrographs recorded. This approach is expressed as SV OBS. -Option no. 4: is identical to the previous one, but lateral inflows are added. In other words, the differences with COUPL MOD concern only hydrographs at upstream inflows, and the observed data were taken into consideration. This option is expressed as COUPL OBS. As mentioned above, the hydrographs recorded are not available at the Alès station for events no. 1 and 2. The hydrographs recorded at Anduze and modelled at Alès are taken into account for the SV OBS and COUPL OBS options in these two cases.
Acknowledgements. The authors thank  Yann Laborda , from  SPC-GD , for supplying the hydrological data useful to this study, and its sensible advice. Thanks are also presented to  Fabrice Zaoui , from  EDF , for its advice relating to the MASCARET code. Thanks to  Guy Delrieu , from  LTHE (Laboratoire d'étude des Transferts en Hydrologie et Environnement ), to have supplied river cross sections, necessary for this study. Finally, thanks to  Gilles Rocquelain ,  Fabrice Cebron  and  Marie-Christine Germain , from  BRL Ingénierie , for their advice and remarks.
Acknowledgements. BM is supported by the  DOE  Grant No. DE-FG0295-ER40896  and wishes to thank the research Office and the  Faculty of Science of the University of the Witwatersrand. RG wishes to thank the  Department of Science and Technology, Government of India , for support under grant no. SR/S2/JCB-64/2007. KM acknowledges  CSIR  for financial support. AD thanks the  CERN TH unit  for the kind hospitality; he is supported by the  ERC Advanced Grant Higgs@LHC. The authors would like to thank  A. Kruse  and  Z. Zhang  for the careful reading of the manuscript. RG and KM would like to acknowledge collaboration with  David Miller  and  Chris White  on the the implementation of FEYNRULES for the anomalous HV V couplings.
Outbreak data From a database of oyster related outbreaks in France, outbreaks were selected if the exposed population and the attack rate were known, as well as the numbers of oysters consumed, and if the concentration of NoVs was known from analysis of a sample of oysters from the same batch. Further conditions were that the same strain of NoV should have been detected in human stools and oysters linked with the outbreak, and symptoms had to be consistent with NoV gastroenteritis. In some of the outbreaks other enteric viruses (enterovirus, rotavirus) were detected in stool and oysters, but it was concluded retrospectively that the main cause was NoV  [19]. A case was defined by the sudden onset of vomiting or diarrhoea or both with maximum incubation period of 48h  [20] , exposed subjects were included if they ate from the same contaminated meal.
The study concluded that ivacaftor would not be beneficial for those who are homozygous for the F508del-CFTR mutation  [24]. Nevertheless, a clinical study is underway to assess the efficacy and safety of VX-809 (a CFTR corrector) used alone and in combination with ivacaftor (a CFTR potentiator) to treat CF patients homozygous or heterozygous for the F508del-CFTR mutation (clinicaltrials. gov NCT01225211). If positive, the outcome of this trial could benefit the vast majority of CF patients worldwide.
Survey procedures and sampling Biopsy samples were collected in 2009 and 2010 during boat-based surveys of inshore dolphins near the Proserpine River in the MWC, and in Port Curtis and near the Fitzroy River within the FRC (Fig. 1 ). Biopsy samples were collected from adult dolphins not accompanied by calves using the PAXARMS biopsy system  (Krützen et al. , 2002). Sampled individuals were photo-identified to limit the risk of resampling the same individuals. Additional data collected included sampling location, location of the biopsy sample in relation to the dorsal fin and dolphin reaction level. Samples were preserved in liquid nitrogen immediately after collection and stored in a À80 C freezer once on land. Overall, during the study we collected a total of 35 full size (>0. 5 g of blubber and epidermal layers) biopsy samples (humpback dolphins: FRC ¼ 13, MWC ¼ 5; snubfin dolphins: FRC ¼ 8, MCW ¼ 9) (Fig.
Conclusion This study provides initial data on the concentrations of common persistent organic pollutants in biopsy samples of humpback and snubfin dolphins from the central and southern Great Barrier Reef, as well as raises concerns about the adverse health effects these and the new generation of contaminants could be having on local populations of these important top order predators. Overall the low levels of HCB and DDTs detected in this study corroborate available information which indicates that these contaminants still persist in the water, sediments and biota of the Great Barrier Reef, but at levels of no concern to marine organisms  (Haynes et al. , 2005; Mortimer, 2000). DDTs and HCB were banned from Australia by 1987; therefore the low levels detected in this study are likely to indicate residuals from historical sources of these products.
Acknowledgements This research was funded by the  Australian Marine Mammal Centre  and the  Fitzroy Basin Association. The research was conducted under permits no. G10/33405. 1  and  G09/29714. 1 issued by the  Great Barrier Reef Marine Park Authority  and permits no. WISP05836609  and  WISP05700909  issued by the  Environmental Protection Agency. Ethics approvals were provided by the  Southern Cross University Animal Care and Ethics Committee  (no. 22/11 ) and  Flinders University Animal Welfare Committee  (no. E297 ). We thank all volunteers who participated in the collection of field data for this study.
Deep Scuba-2 maps, which are much less limited by confusion noise, are the scientific complement to the large-scale mapping done with Herschel. This paper presents 450µm and 850µm observations from Scuba-2 in a central region of the Cosmic Evolution Survey (COSMOS) field  (Scoville et al. 2007 ) surrounding the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS) area. Recently,  Geach et al. (2013)  presented deep Scuba-2 450µm observations in the central ∼100 arcmin 2 of this area. Our observations have a wider, more uniform coverage than the data presented in  Geach et al. (2013) , with the motivation of selecting a homogeneous sample, while the  Geach et al. (2013)  data are deeper and patterned to find fainter sources. The pointing on the CANDELS area is motivated by the perceived high correlation of submillimeter emission with near-infrared emission (e.
The pong-900 mapping pattern was used to achieve a uniform RMS over a large area ≈15×15 arcmin in preference to the Daisy mapping which has substantial patterning and non-uniformity and covers a smaller area. Raw data were downloaded via the Canadian Astronomy Data Centre (CADC) JCMT Science Archive and processed with the Scuba-2 software packages Smurf, the Sub-Millimetre User Reduction Facility, and Picard, Pipeline for Combining and Analyzing Reduced Data. Data were split up by night, wavelength, and sub-array for processing. Smurf processing was first executed on each night's sub-array data (which is equivalent to combining each subarray per Minimum Scheduable Block (MSB), and then combining all MSBs). Partial maps were constructed using the makemap routine  (Chapin et al. 2013)  in Smurf with the Figure  3.
Signal-to-noise maps are constructed using the Picard recipe makesnr; these are not shown here but are quite similar to the final reduced maps within the central area. We found no evidence for a systematic astrometric offset necessary after inspection of the radio data described in the next section (following the astrometric calibration procedure of  Chen et al. 2013). Signal-to-noise maps are used for the identification of point sources. To avoid contamination from the less sensitive edges of the map in calculating an overall characteristic map noise, we first measured the effective area of the sensitive region of the map, where the RMS was less than or equal to a factor of two times the RMS in the centre. We determined this boundary by measuring the RMS in concentric annuli 5 wide from a jackknife map, having subtracted out the signal from point sources.
The parameters which reproduce the raw number counts most accurately are used in the Monte Carlo tests for boosting factor, positional uncertainty and estimating contamination and completeness rates, as given in the previous section. Once we arrive at the corrected number counts, we then remeasure our best-fit parameters with a χ 2 test which agrees with our initial injected counts within uncertainty. We find the power-law fit produces a better fit than a Schechter fit to both datasets Best-Fit Parameters to Equation 1 450µm Parameters 850µm Parameters N 0 = (1. 4±0. 4)×10 3 (1. 5±0. 4)×10 3 S 0 = 18±8 4. 3±1. 8 α = 1. 91±0. 40 1. 34±0. 09 β = 5. 5±1. 0 4. 50±0. 38 Best-Fit Parameters to Equation 2 450µm Parameters 850µm Parameters N 0 = (1. 1±0. 4)×10 4 (3. 3±1. 4)×10 3 S 0 = 6. 1±1. 0 3.
Infrared luminosities & dust properties We can go beyond basic FIR colour comparisons with our Scuba-2 data and use SED fits to measure infrared luminosities, temperatures and in some cases dust masses. Simple greybody SED fitting combined with a mid-infrared powerlaw gives us this information with a minimum number of free parameters or model assumptions. We fit all available infrared photometry, from 24µm MIPS, 70µm MIPS, 450µm Scuba-2, 850µm Scuba-2, 870µm LABOCA, 1. 1 mm AzTEC, 1. 2 mm MAMBO, and 1. 3 mm PdBI. The combination of a greybody and mid-infrared powerlaw accounts for both galaxy-wide cold dust emission and smaller-scale warm dust emission  (Blain et al. 2003; Kovács et al. 2006). The fitting method is described in full in  Casey (2012)  and is represented by the equation Sν = N bb (1 -e -(ν/ν 0 ) β )ν 3 e hcν/kT -1 + N pl (c/ν) α e -(νc/ν) 2 (3) where Sν is flux density (in units of mJy), T is greybody dust temperature, β is the emissivity index of the greybody, α is the mid-infrared powerlaw slope, and N bb and N pl are the normalisations of the greybody and powerlaw, respectively (N pl is a fixed function of N bb , T , and α).
ACKNOWLEDGEMENTS We would like to thank the referee for a very thoughtful and helpful report in reviewing this manuscript. CMC is generously supported by a  Hubble Fellowship  from  Space Telescope Science Institute , grant  HST-HF-51268. 01-A. CCC  and LC are generously supported by  NSF  grant  AST 0709356. AB thanks the  University of Wisconsin Research Committee  with funds granted by the  Wisconsin Alumni Research Foundation , and the  David and Lucile Packard Foundation. CMC and DBS would like to thank the  Aspen Center for Physics  and the  NSF  grant  1066293  for many fruitful conversations with the community regarding this work during "The Obscured Universe" summer workshop. The James Clerk Maxwell Telescope is operated by the  Joint Astronomy Centre on behalf of the Science and Technology Facilities Council of the United Kingdom , the  National Research Council of Canada , and (until 31 March 2013) the  Netherlands Organisation for Scientific Research.
Finite element modelling (FEM) To better understand the buckling origin and in particular the specific mechanical conditions that can lead to this blister formation, a FEM analysis of the impact conditions was performed. The objectives of this modelling work were to observe what happens in the sample (film þsubstrate) during the repeated impacts that could induce the formation and/or growth of de-laminated areas leading to buckling. For this, a numerical compromise was to be found between an accurate stress and strain field description and a reasonable computation time to take into account several tens of  repeated impacts. The Abaqus explicit software version 6. 8  [26]  was chosen. Indeed, explicit finite element analysis is well adapted to fast nonlinear dynamic problems. Nevertheless, the time step in such analysis has to be small enough to maintain stability of the solution procedure  [27].
Acknowledgements The authors wish to thank the  PMA (Pays de Montbeliard Agglomeration)  and  UTBM  for their financial support. The acquisition software of the impact tester has been financially supported by an  OSEO  project (No. 082009I ) and was realized by P. N öel (UTBM). This study is part of a Ph. D. work granted by the  French Education Ministry. The UTBM-ENISE cooperation benefits from the financial support of the  CNRS (French National Research Council)  through an  ITC program  of the  Plasma Froids network.
Acknowledgments We thank  S Romero ,  G Tran Van Nhieu ,  CL Vestergaard  and  J-F Joanny  for the productive collaboration and stimulating discussions, and  W Ahmed  for proofreading. Bassereau P thanks the  Agence Nationale de la Recherche  (Grant  08-MIEN-011-02 ) for funding. Bornschlögl T thanks the  Human Frontier Science Program  for funding.
GST/AMPK interaction is direct and rapid To obtain quantitative data on the GST/AMPK interaction in respect to kinetics and affinity, we performed a series of in vitro experiments with surface plasmon resonance spectroscopy (SPR). The GST interaction partner was chosen for covalent immobilization since it appeared more stable in this setup. We first used a sensor chip where the gold surface had been functionalized with a self assembled monolayer that reduces non-specific adsorption to the surface and allows immobilization of low ligand densities for analyzing GST-Sj. Sensorgrams with AMPK 221WT, 221TD or BSA (negative control) injected onto this surface confirmed a direct GST/AMPK interaction (Fig. 4A ) that was not affected by glutathione (not shown). The equilibrium response was 203641. 5 RU for AMPK 221WT, which was reduced to 121627 RU for AMPK 221TD, as compared to 863.
The interaction leading to GST/AMPK complexes seems to be rather specific for the GST-Mu and -Pi families, since it was not observed with GST-Alpha and -Omega isoforms (not shown). It does not involve the AMPK a-subunit, as we have recently reported for fumarate hydratase  [59] , but the b-subunit, as also seen with several other putative mammalian AMPK interactors (IntAct database,  [60] ) and the yeast and plants orthologs  [61, 62]. Our data suggest interaction with the very N-terminal part of the bsubunit, a domain that is fairly well conserved across the AMPK protein family but lacks in the solved core structures of mammalian AMPK and its yeast ortholog (  [10] ; reviewed in  [63] ), possibly due to its high flexibility. The physical interaction of AMPK with GST-Sj calls for a note of caution for using GST fusion proteins in pulldown assays to identify AMPK interaction partners.
Acknowledgments We thank  Steve Calberson  ( UniversiteCatholique de Louvain, Brussels, Belgium ) for in vitro phosphorylation experiments,  Laurence Kay  ( UJF, LBFA, Grenoble, France ) for animal experimentation,  Wilfrid Boireau  ( FEMTO-ST Institute, Besancon, France ) for providing the functionalized gold chip, and  Grahame Hardie  ( University of Dundee, UK ) for providing the GST-ACC vector.
Design and Methods AZA-001 (ClinicalTrials. gov Identifier:00071799) study design is reported in detail elsewhere. 7 ll patients provided written, informed consent before participating in the study. A multivariate Cox regression analysis with response as a time-varying covariate evaluated the relationship between hematologic response and OS. CR, PR, HI, stable disease (SD), and progression were defined by IWG MDS 2000 criteria. 5 Stable Disease was The phase III AZA-001 study established that azacitidine significantly improves overall survival compared with conventional care regimens (hazard ratio 0. 58 [95% confidence interval 0. 43-0. 77], P<0. 001). This analysis was conducted to investigate the relationship between treatment response and overall survival. AZA-001 data were analyzed in a multivariate Cox regression analysis with response as a time-varying covariate.
09, [95%CI: 0. 06-0. 15]; P<0. 001). These results demonstrate azacitidine benefit on overall survival compared with conventional care regimens in patients with higher-risk myelodysplastic syndromes who achieve hematologic response but never attain complete or partial remission, in addition to the survival advantage conferred by achievement of complete or partial remission. This study was registered with clinicaltrials. gov (NCT00071799). defined as no evidence of progression without HI. Patient responses (CR and PR) were reviewed by an independent review committee (IRC) of international MDS experts blinded to treatment assignment, and an HI response was determined programmatically. Response was classified in 3 categories: Overall Response (HI, PR, or CR), Stable Disease (without HI), and Other (e.
To aid the reconstruction of dendritic structure, neurons are labeled with a volume-filling fluorescent marker. Neurons are then fixed, immunostained to permit ion channel detection, and imaged using laser scanning confocal microscopy (LSCM). Confocal stacks corresponding to the volume-filling morphological marker are then subjected to image enhancement, segmentation, and skeletonization to permit the graphical reconstruction of the salient structural features of the neuron's dendrites. These processing steps take place in a semi-automatic manner with the operator required merely to supply information about the resolution of the data and provide coordinates for a seed-point for dendritic reconstruction. The resulting midline-based skeleton is then used as a reference point to map fluorescent intensity data corresponding to the signal derived from immunofluorescent detection of ion channel subunit puncta.
Conversion, filtering, and segmentation. Confocal image stacks were saved as 8-bit format multilayer tif-files. The native Leica image stacks were imported into ImageJ (v1. 44o; http://imagej. nih. gov/ij), converted to 8-bit format and saved as separate multilayer tif-files for each channel. Subsequent image processing steps were performed on either non-deconvolved or deconvolved 8-bit multilayer image data using a Mac Pro 2. 8 GHz Quad-core Intel Xeon computer equipped with 18 GB RAM, and running MacOS 10. 6. Multilayer tif-files corresponding to the morphological signal (eGFP or mCherry) were subjected to two rounds of filtering and segmentation using the custom-written software Anisofilter and Segment as described previously  (Broser et al. , 2004; Oberlaender et al. , 2007). Skeletonization.
Dendritic skeletons (approximate midlines) were reconstructed from the aforementioned-segmented images using the custom-written program DendriteTracker, described previously in  Oberlaender et al. (2007 ), Broser et al. (2008). Briefly, the user starts DendriteTracker utilizing the segmented image as input and image size (in μm) and cell body coordinates (x, y, and z, pixel units) as parameters. The first step of the program is a raster-to-vector image conversion. The resulting vectors, hereafter referred to as compartments, contain the 3D coordinates of the foreground voxels corresponding to the neuron structure. These coordinates are subsequently used as a reference point for the generation of data sets corresponding to dendrite radius and fluorescent intensity (see below). The next step is a vector image-based midline extraction.