dataesr/scientific-paragraphs-categorization huggingface dataset:
Transfected HEK293 cells were incubated with either a 15% aqueous solution of 2-hydroxypropyl-␤-cyclodextrin (Sigma) (vehicle) or NPS 2143 for one hour at the following concentrations: 0nM, 20nM, 40nM, and 80nM. Flow-assisted cell sorting was performed with a Cytomation Mo-Flo flow cytometer (Dako-Cytomation) equipped with an argon laser (Coherent Radiation), as described  (26). Baseline fluorescence ratio was measured for 2 minutes, the fluorescence ratio vs time was recorded, and data were collected for 2 minutes at each Ca 2ϩ o concentration. Cytomation summit software was used to determine the peak mean fluorescence ratio of the transient response after each individual stimulus expressed as a normalized response, as previously reported  (26, 27). The EC 50 (ie, Ca 2ϩ o concentration required for 50% of the maximal response) for each normalized concentration-response curve was determined.
Acknowledgments Address all correspondence and requests for reprints to:  Professor Rajesh V. Thakker ,  Academic Endocrine Unit, Radcliffe Department of Medicine, Oxford Centre for Diabetes, Endocrinology and Metabolism, Churchill Hospital, Oxford ,  OX3 7LJ , United Kingdom. E-mail: rajesh. thakker@ndm. ox. ac. uk. Present address for F. M. H. :  Department of Musculoskeletal Biology, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool ,  L69 3GA ,  United Kingdom. Present  address for M. A. N. :  Biomedical Sciences Research Institute, University of Ulster, Coleraine ,  BT52 1SA ,  United Kingdom. Present address for E. K. :  Department of Pathophysiology and Allergy Research, Medical University Vienna, 1090 Vienna ,  Austria. While  this manuscript was in revision, a similar report entitled " Calcilytic Ameliorates Abnormalities of Mutant Calcium-Sensing Receptor (CaSR) Knock-in Mice Mimicking Autosomal Dominant Hypocalcemia (ADH )" was published (Dong et al.
The HAMSTRAD (H 2 O Antarctica Microwave Stratospheric and Tropospheric Radiometers) instrument is a microwave radiometer installed at Dome C (Antarctica, 75°06'S, 123°21'E, 3233 m a. m. s. l. ) dedicated to the tropospheric measurements of temperature, absolute humidity and integrated water vapour (IWV). The aim of the present paper is to review the entire HAMSTRAD dataset from 2009 to 2014 with a 7-minute integration time from 0 to 10 km by comparison with coincident radiosondes launched at 12h00 UTC at Dome C. Based upon an extensive evaluation of biases and time correlation coefficients (r), we can state: i) IWV is of excellent quality (r > 0. 98) and can be used without retrieving significant bias, ii) temperature is suitable for scientific analyses over 0-10 km with a high time correlation with radiosondes (r > 0.
2013). In  Ricaud et al. (2010a Ricaud et al. ( , 2012 Ricaud et al. ( , 2013 Ricaud et al. ( , 2014a Ricaud et al. ( , 2014b Ricaud et al. ( , 2014c)) , by considering the HAMSTRAD measurements performed in 2009 and 2010 and in other datasets, e. g. in situ, radiosondes, satellite, lidar, meteorological analyses, back-trajectories, mesoscale models, we have already studied the strengths and weaknesses of this instrument by calculating biases and correlations. We have also investigated the different processes occurring in the Antarctic troposphere: i) Diurnal variability of temperature and absolute humidity in the PBL. ii) Seasonal variability of temperature and absolute humidity in the PBL and in the free troposphere linked to the origin of air masses (dry and cold periods associated with Antarctic origin of air masses whilst less dry and less cold periods associated with oceanic origin of air masses).
iii) Temperature versus absolute humidity correlation along the vertical from the lowermost to the middle troposphere. Finally, iv) episodes of clouds and diamond dusts above the station. Up to now, only the HAMSTRAD measurements performed during 2009-10 have been analysed. The aim of this paper is, firstly, to present the entire record of the HAMSTRAD measurements of tropospheric temperature and absolute humidity together with integrated water vapour (IWV) performed routinely over a six year period  since it was deployed at the Dome C station. We will highlight the major changes that have occurred during the six year period both regarding the hardware and the software, and the availability of data depending on the years considered. Secondly, we will quantify the biases of HAMSTRAD measurements against radiosondes launched on a daily basis at Dome C over the same six year period, and we will highlight the temporal correlation with the radiosonde datasets by calculating linear Pearson correlation coefficients (r).
The very first measurements of HAMSTRAD were collected on 22 January 2009, using the acquisition software V7. 51  (Ricaud et al. 2013)  following deployment outdoors using a blue foam to cover the instrument that was not designed for the 183-GHz H 2 O line but rather for the 22-GHz H 2 O line. (For a description of the foams used by the radiometers developed by the RPG company at different frequencies and their associated optical performances see http://www. radiometer-physics. de/rpg/html/Products_ Radiometers_Profilers. html. ) On 27 January 2009, the white foam adapted for the 183-GHz H 2 O line was installed. The 2009 summer campaign ended on 2 February 2009. In 2010, the summer campaign started on 8 January with an outdoor installation. Few days later, the shelter dedicated to HAMSTRAD was completed allowing the first indoor measurements from HAMSTRAD on 16 January 2010.
From that date, the radiometer has continuously operated in that shelter with a daily data transfer either by email and/or by ftp to France. On 26 May 2011, a major failure of the 183-GHz noise diode impacted on the internal calibration of the H 2 O spectra and prevented measurements of absolute humidity vertical profiles and IWV, and also affected the 60-GHz receiver (O 2 line to retrieve temperature). The 183-GHz noise diode was changed on 9 February 2012 and HAMSTRAD was operational again, providing meaningful measurements of temperature and absolute humidity vertical profiles together with IWV. Consequently, temperature vertical profiles from 26 May 2011 to 9 February 2012 should not be used for any scientific analysis and are not presented in this paper. On 13 January 2013, an updated version of the acquisition software (V8.
However, it has the main drawback of being unable to generate mathematical functions (such as scanning functions generated in optimal estimation) to characterize the vertical resolution of the measurement and the contamination of the a priori information on the estimated vertical profile. Ricaud et al. (2013)  showed that the linear/quadratic regression and the optimal estimation methods give consistent results with respect to the accuracy and the vertical resolution of the measurements of absolute humidity and temperature. Table  II  shows the number of days and the period of measurements of the HAMSTRAD radiometer from 2009 to 2014. If data from 2009, when the instrument was deployed outdoors for 2 weeks, and from June 2011 to January 2012, when a major failure occurred, are excluded the efficiency of the HAMSTRAD radiometer to sound the atmosphere is rather elevated, greater than 80%.
Indeed, the number of days of measurements per year is 347 (95%) in 2010, 125 (but 83% January to May) in 2011, 299 (82%) in 2012, 289 (80%) in 2013, and 306 (84%) in 2014. A number of conclusions can be drawn from the extensive validation and scientific studies that have been performed, mainly in 2009 and 2010, to assess the quality of the measurements and to explain some physical processes from these measurements. The instrument shows a very high sensitivity in the PBL, a high sensitivity in the free troposphere and a very weak sensitivity in the upper troposphere-lower stratosphere. Firstly, considering temperature  (Ricaud et al. 2014c) , in the PBL, a positive bias of 2-10 K is observed between HAMSTRAD and some of the other datasets (radiosondes, in situ analyses, the atmospheric infrared sounder (AIRS) spaceborne sensor)  (Tobin et al.
2006) , while a negative bias from -1 to -5 K is     calculated between HAMSTRAD and the infrared atmospheric sounding interferometer (IASI) spaceborne sensor  (Herbin et al. 2009 ). In the free troposphere, below 4 km, a 1-5 K negative bias is detected between HAMSTRAD and all the other measurements, and a positive bias of ~5-10 K above. Secondly, IWV is of a high quality, namely ~1-2% wetter than radiosondes and spaceborne IWV measurements. Thirdly, absolute humidity profiles are wetter by 0. 1-0. 3 g m -3 than all the other datasets below ~2 km and drier by ~0. 1 g m -3 above. Below ~6 km, the correlation coefficients (r) of the time series of HAMSTRAD measurements versus the time series of radiosonde, in situ, spaceborne data and meteorological analyses are very high (r > 0. 8) for temperature and IWV, but are much less for absolute humidity (r ~0.
Now consider the vertical distribution of the correlation coefficient (r) between HAMSTRAD and the radiosonde temperature measurements (Fig. 6 ). Note that, on average, the temporal evolution of temperature as measured by HAMSTRAD is very consistent with the one measured by the radiosonde (r > 0. 8) both in the PBL and in the free troposphere (below 6 km) for all years. The correlation degrades in the upper troposphere-lower stratosphere (above 6 km) in 2009 (r ranging from -0. 2 to +0. 5) and 2014 (r ranging from +0. 4 to +0. 8). Firstly, in the PBL (Fig. 6 , right), the correlation is extremely high (r > 0. 97) for most years, it is slightly less in 2009 (r ~0. 95) and 2012 (r ~0. 85). In 2009, the data sampling was limited to only 12 days. However, in 2012, there is a good coverage of HAMSTRAD and radiosonde measurements for the whole year.
Thus, we conclude that there is a significant bias in IWV between HAMSTRAD and the radiosondes. Remember that the 1-σ RMS error on the 7-min integration time HAMSTRAD IWV is 0. 05 kg m -2 or ~5%  (Ricaud et al. 2013). In general, the summer bias (~0. 1 kg m -2 ) is on average twice that of the winter bias (~0. 05 kg m -2 ) mainly because IWV is twice as great in summer (~0. 6-0. 7 kg m -2 ) as in winter (0. 3 kg m -2 ). As with temperature, when considering the yearly-averaged biases of HAMSTRAD IWV data in time coincidence within a ± 20 min window with radiosonde data, no significant changes were found.
5 km of -0. 08 g m -3 (-50%) and a maximum of +0. 05 g m -3 (+60%) at 1. 5 km. The atypical bias in 2012 suggests that the manual liquid nitrogen calibration performed once or twice a year during the summer and winter has an impact on the HAMSTRAD measurements. Finally, note that the new acquisition software has not reduced the biases in absolute humidity between HAMSTRAD and the radiosondes in either the PBL or the free troposphere. The 1-σ RMS error on the 7-min integration time HAMSTRAD absolute humidity is 0. 05 g m -3 (10%) in the PBL, 0. 03 g m -3 (10-15%) in the free troposphere and 0. 005 g m -3 (15%) in the upper troposphere-lower stratosphere  (Ricaud et al. 2013). Even considering short periods, such as in the summer of 2009 or the winter of 2011, the error on the yearly-averaged mean is less than ~2%, namely much less than the calculated biases.
Conclusions The HAMSTRAD (H 2 O Antarctica Microwave Stratospheric and Tropospheric Radiometers) radiometer is a state-of-the-art microwave radiometer dedicated to the measurement of tropospheric absolute humidity at 169-197 GHz (G-band, strong H 2 O line at 183. 3 GHz), together with tropospheric temperature from the oxygen (O 2 ) line (51-59 GHz, V-band, lower frequency wing of the O 2 line), in very cold and dry environments, such as that encountered at Dome C all year round. The HAMSTRAD radiometer samples the whole 24-h diurnal cycle with a 7-min integration time from 2009 to 2014, and from 0 to 10 km above surface, with a vertical resolution from ~30 m in the PBL to ~500 m in the upper troposphere-lower stratosphere. Considering the HAMSTRAD measurements in conjunction with other datasets (in situ, radiosondes, satellite, lidar, meteorological analyses, back-trajectories, mesoscale models), we have been able to study different processes in the Antarctic troposphere including: i) diurnal variability of temperature and absolute humidity in the PBL, ii) seasonal variability of temperature and absolute humidity in the PBL and in the free troposphere, iii) temperature versus absolute humidity correlation along the vertical from the lowermost to the middle troposphere and iv) episodes of clouds and diamond dusts above the station.
The measurements of tropospheric temperature and absolute humidity vertical profiles together with IWV provided by the HAMSTRAD radiometer have been reviewed from 2009 to 2014 in comparison with radiosondes launched at 12h00 UTC. This exercise enlarges the previous study that only concentrated on the 2010 data but encompassed, in addition to radiosondes, in situ and spaceborne measurements, together with meteorological analyses. It is well known that radiosondes have biases at polar latitudes, in particular at Dome C, both in terms of temperature and humidity. Nevertheless, our previous analyses based upon data from 2010 did show some consistency between radiosondes and all of the other datasets versus HAMSTRAD measurements. We can state the following points regarding the quality of the HAMSTRAD measurements with a 7-min integration time over the period 2009-14, based upon an extensive evaluation of biases and time correlation coefficients (r) along the vertical.
i) HAMSTRAD IWV measurements are of excellent quality showing high time correlation with radiosonde data (r > 0. 98), and can be used without retrieving significant bias. ii) HAMSTRAD temperature measurements are suitable for scientific analyses over the range of 0-10 km with high time correlation with radiosondes (r > 0. 80). Nevertheless, the vertical distribution of temperature from 0 to 10 km is subject to biases that need to be removed if the scientific analyses require the use of vertical profiling. iii) HAMSTRAD absolute humidity measurements are suitable for scientific analyses but all within the range of 0-4 km with a moderate time correlation with radiosondes (r > 0. 70). Nevertheless, the vertical distribution of absolute humidity from 0 to 4 km is subject to biases that need to be removed if the scientific analyses require the use of vertical profiling.
Above 4 km, the instrument loses sensitivity and the amount of absolute humidity falls down. Using HAMSTRAD data from 2009 to 2014 we confirm the findings of previous studies from 2009 and 2010. The whole HAMSTRAD dataset including temperature, absolute humidity and IWV covering 2009-14 with a time resolution of 7 minutes is provided in open access to the scientific community at http://www. cnrm. meteo. fr/spip. php?article961&lang=en. In addition, the yearly-averaged biases along the vertical for 2009-14 are also provided for scientific studies that consider vertical profiling. By these means, the HAMSTRAD measurements will contribute to the investigation of physical processes above the Dome C station by combining HAMSTRAD data with other measurements (e. g. aerosol lidar, radiation budget, meteorological parameters, etc.
Acknowledgements The HAMSTRAD programme is funded by the  Institut National des Sciences de l'Univers (INSU) / Centre National de la Recherche Scientifique (CNRS) , the  Institut polaire français Paul-Emile Victor (IPEV) ,  Météo-France  and the  Centre National d'Etudes Spatiales (CNES). The permanently manned Concordia Station is jointly operated by  IPEV  and the  Italian Programma Nazionale Ricerche in Antartide (PNRA). We would like to thank all the overwinter personnel who have worked at Dome C since 2009 on the two projects: HAMSTRAD and radiosondes. Finally, the two anonymous reviewers are acknowledged for their fruitful comments.
Salinity and biogeochemical analysis Salinity was determined using a Kal Mark IV salinometer (Yeo-Kal Electronics Pty Ltd, Sydney, Australia). Si and NO 3 were measured using a Bran þ Luebbe Autoanalyser II and standard analysis methods. Chl-a was analysed by the fluorescence method following a methanol extraction. [40] DOC was measured on two replicates, previously spiked with ultrapure orthophosphoric acid (Sigma-Aldrich) and bubbled for 10 min with CO 2 -free air to purge inorganic carbon, by high-temperature catalytic oxidation using a Shimadzu TOC 5000 Total Carbon Analyser (Kyoto, Japan). [41] atistics Mann-Whitney non-parametric tests (U-tests) were used for the comparison of two independent data groups because of the nonnormal distribution of some variables (normality assessed with the Kolmogorov-Smirnov test) and the low number of samples for some groups.
Hydrocarbon concentration levels and PAH indicators of freshwater inputs Data on hydrocarbon (AHs and PAHs) concentrations in waters from tropical coral reef ecosystems and from the Indian Ocean are very scarce in the literature. [44, 45] Total dissolved þ particulate AH concentrations measured in marine waters (back reef, harbour, fore reef and oceanic waters; 0. 33 AE 0. 14 mgL À1 ) were comparable with those found in the Great Barrier Reef area (Australia) (0. 19 AE 0. 08 mgL À1 and 0. 29 AE 0. 16 mgL À1  [44]  ) as well as with those recorded from Antarctic regions (0. 25 AE 0. 16 mgL À1 and 0. 45 AE 0. 10 mgL À1 ). [46, 47] They were lower, up to one order of magnitude, than those found in the Marseilles coastal area. [15] Concerning PAHs, when taking into account only the compounds in common (i.
Acknowledgements We are grateful to  P. Durville  and the crew from the  Aquarium de La R eunion  for their help and assistance in the sampling aboard the  R/V Explorer. We thank the core parameter platform of the  Mediterranean Institute of Oceanography  for Chl-a analyses and  B. Charrie  `re for DOC analyses. We acknowledge  J. L. Join  and  J. Leze  for their help in sampling groundwater as well as R. Semp er e and  A. Delmont  for their assistance in complementary analyses. Two anonymous reviewers are thanked for their constructive and helpful comments. This work was funded by the  Ministere des Outre-Mer (MOM) -La  R eunion and received the support of the  R eserve Naturelle Marine de La R eunion (RNMR). It was also supported by the ' IBISCUS ' research project, which was funded by the  Agence Nationale de la Recherche  ( ANR) -ECOTECH program  (project  ANR-09-ECOT-009-01 ),  Aix-Marseille University (AMU)  and  CNRS.
Based on long-term longitudinal datasets and capture-mark-recapture methods in three populations of the same species, we could estimate age-specific survival patterns and agespecific inter-annual variation in survival rates in females in three contrasted populations of an alpine ungulate, the chamois (Rupicapra rupicapra). We assessed the among-populations differences in (1) average survival rates per age-classes, and more specifically senescence patterns; (2) inter-annual variation in survival rates. This allowed us to examine at the intraspecific level, whether temporal variation in survival rates may be used as a proxy of the agespecific sensitivity to environmental stochasticity.
Statistical analyses We kept the observations between May and December, in order to have similar periods between the sites and to estimate winter survival (the period of highest mortality for chamois ;  Jonas et al. 2008). We first analyzed the Bauges population and the SNP populations separately (pooling Il Fuorn and Val Trupchun). Survival estimates were obtained independently of re-sighting probabilities using capture-mark-recapture (CMR) modeling based on the open population model of Cormack-Jolly-Seber  (Clobert et al. 1987). This model produces apparent survival estimates resulting from mortality and emigration. We used the program E-surge (version 1. 9. 0) to fit models  (Choquet et al. 2009). For all populations we found no transience in the data but a strong trap-happiness (details in ESM).
g. Grøtan et al. 2008). 1 ). Dotted lines are the estimates from the best linear models (models M12 and M20 in Table  1  in panels a and b respectively), and solid lines are estimates from the best age-class models (models M4 and M9 in Table  1  in panels a and b respectively). Panel c shows the estimates of a direct comparison between the two sites based on a model with 4 age-classes (Table  2 ). Juveniles (0 year old) are only in the SNP dataset.
ACKNOWLEDGEMENTS We thank the  Office National de la Chasse et de la Faune Sauvage (ONCFS) , and in particular  Jean-Michel Jullien  and  Thibaut Amblard , and the  Swiss National Park  for collecting and managing data with the help of many volunteers. This research was conducted on the  Long-Term Ecosystem Research (LTER) site Zone Atelier Alpes , a member of the  ILTER-Europe network. The study received support from the  European Research Council  (STOCHPOP research grant to B. -E. S. ). SUMMARY STATEMENT Funding:  European Research Council  (STOCHPOP research grant to B. -E. S. ).
This paper presents a methodology for recognizing, identifying and classifying built objects in dense urban areas, using a morphospectral approach applied to VNIR/SWIR hyperspectral image (HySpex). This methodology contains several image processing steps: Principal Components Analysis and Laplacian enhancement, Feature Extraction of segmented build-up objects, and supervised classification from a morpho-spectral database (i. e. spectral and morphometric attributes). The Feature Extraction toolbox automatically generates a vector map of segmented buildings and an urban object-oriented morphometric database which is merged with an independent spectral database of urban objects. Each build-up object is spectrally identified and morphologically characterized thanks to the built-in morpho-spectral database.
e. Feature Extraction, ENVI exelisvis 2008). In this study the Feature Extraction toolbox is used to generate the morphological attributes, the method as applied to the recognition and identification of build-up land makes it possible to generate two object-oriented databases: (1) a vector database of each recognized object, and (2) a geometric attributes database (i. e. morphometric database) in which, to each recognized built object are associated a number of geometric measurements such as texture, compactness, connectedness, voids, surface area, number of edges, boundaries, etc. The morphometric database  (Gadal, 2012)  will be enriched with a spectral one containing a variety of urban objects spectral attributes (e. g. spectral resolution/rate, temporal resolution, ground sample distance).
Method The goal of this novel image processing approach is to propose an accurate identification and characterization of urban objects in dense urban context using a morpho-spectral database (Figure  1 ). The first sequence of image processing consists in enhancing the visual recognition of the build-up areas by a pre-processing step (optional), the second step consists in generating a morpho-spectral database, the third sequence consists in identifying urban objects by morphological and spectral classification. The fourth step consists in merging the 2 objects identification methods to have an efficient characterization of the socio-economic urban classes. Figure  1. Image processing sequence.
Morphological attributes The second sequence of image processing consists in: (1) generating a morphometric measure of each building, and (2) producing an urban building object-oriented database. The measure of their geometries and morphologies, and the generation of the urban object-oriented database are processed by the Feature Extraction toolbox proposed by ENVI (i. e. generation of a morphological attributes of each segmented polygon. These attributes are classified into 3 classes (radiometric, spatial and textural attributes). The segmented image could be classified using the extracted attributes. The radiometric attributes are calculated for each spectral band (i. e. radiometric attributes of each polygon), concerning the spatial and textural cases, the attributes are calculated directly from the segmented polygons and the spectral dimension is not required.