dataesr/scientific-paragraphs-categorization huggingface dataset:
AMSR-E brightness temperatures over ocean are assimilated in the ERA-Interim reanalysis; the two datasets are therefore not independent. Section 2 presents the relationship between water vapor and wet tropospheric path delay, the instruments used, and the radiometer datasets. The results of the analyses are divided into two parts. Section 3 is dedicated to the relative errors. Analyses from global and constant biases to dynamic and regional discrepancies are presented: the observed differences are analyzed in terms of intensity, spatial distribution, temporal trends, and variability patterns. Section 4 concerns the estimation of the error standard deviation of each mission using the methodology called triple collocation. Section 5 is dedicated to interpretation and discussions of the results.
Water vapor, instruments, and datasets a. Relationship between the integrated water vapor content and the wet tropospheric path delay The wet tropospheric correction in altimetry is required to correct the altimeter range from the additional delay due to water vapor molecules in the troposphere. Therefore, the integrated water vapor content and the wet tropospheric path delay are highly correlated. Their relationship is almost linear (Fig. 1 ). However, they are not equivalent physical quantities as the wet tropospheric path delay also depends on temperature, pressure, and humidity profiles of the atmosphere  (Fu and Cazenave 2000) : dh 5 K ð r(z) T(z) dz, ( 1 ) where dh is the wet tropospheric path delay (cm), r(z) is the density of water vapor at the altitude z (g cm 23 ), T(z) is the temperature at the altitude z, and K is a constant equal to 1720.
b. Radiometer and mission characteristics Main and nominal characteristics of the radiometers and their mission are summarized in Table  1. A change of orbit occurred for Envisat in October 2010. The extension orbit had a new altitude of 782 km. In the extension orbit only the altitude is monitored, meaning that the inclination may be drifting. In April 2012, Jason-1 was put in a drifting geodetic orbit at 1324. 0 km following the anomaly at the end of February 2012 and early in March 2012, where Jason-1 was in ''safe hold'' mode. AMSR-E stopped providing data since August 2011 due to the rotating mechanism of the radiometer being damaged. Only data from 2004 to 2010 are considered in this paper. This corresponds to the period when all radiometers (including SSM/IS F-16) were in function. The study also stops in 2010 to minimize the effects of orbits changes.
Full years were used to minimize annual effects on the estimation of the water vapor trends. AMSR-E data that we used are produced by Remote Sensing Systems and are provided in daily ascending and descending orbit grids with a resolution of 0. 258. The version v. 07 of the reprocessed data is used. Envisat and Jason-1 datasets of water vapor take form of level 2, 1-Hz along-track products. Jason-1 data used are from the Geophysical Data Record (GDR). In this study, GDR version c is used as it forms a homogeneous dataset over the period 2004-10 for Jason-1. Envisat data used are from the GDR. In this study, the reprocessed version of the data (V2. 1) is used. Data from cycles 9 to 92 (from 2002 to 2011) were reprocessed into a homogeneous standard. ERA-Interim data are produced by ECMWF, which provides daily fields at 0000, 6000, 1200, and 1800 UTC at a resolution of 0.
758. The differences in the format of different data reflect the discrepancies in the spatiotemporal sampling of the radiometers. While the orbits of AMSR-E and Envisat are similar, the architecture of the radiometers differs. The radiometers of Jason-1 and Envisat are nadir-viewing radiometers whereas AMSR-E observes Earth with a conical-scanning mechanism allowing a rotation of the sensor over an azimuth angle of 1328. In one scan, the measurements, made with an incidence angle of 558, cover a swath width of 1400 km. A nearly complete coverage of the surface of Earth is achieved in 2 or 3 days. AMSR-E has more channels than JMR and MWR. AMSR-E is a dual-polarized radiometer whose measurements of brightness temperature are made at the following frequencies: 6. 9,  10. 7, 18. 7, 23. 8, 36.
5, and 89. 0 GHz. However, and 36. 5-GHz brightness temperatures are used in the water vapor retrieval algorithm  (Wentz and Meissner 2007). JMR measurements are also performed at 18. 7, 23. 8, and 34 GHz  (Keihm et al. 1995). MWR has only two channels, at 23. 8 and 36. 5 GHz. Information on the sea surface roughness, usually given by a low-frequency channel, is provided by the Ku-band backscattering coefficient of the RA-2 altimeter, aboard Envisat  (Obligis et al. 2006). Each mission uses a semistatistical algorithm to retrieve the water vapor content and/or the wet tropospheric path delay from the brightness temperatures. Nevertheless, the statistical regression methods are different, as well as the learning databases. For AMSR-E, the water vapor is retrieved using a twostep log-linear model, built on a global database consisting of radiosonde measurements  (Wentz and Meissner 2007).
As AMSR-E is used as the common reference, the choice has been done to perform the comparisons between water vapor products instead of the wet tropospheric path delay. For JMR, a two-step log-linear model built on a radiosonde database is also used but to retrieve the wet tropospheric path delay  (Keihm et al. 1995). The integrated water vapor content provided in the product is then deduced using an empirical relationship between the wet tropospheric path delay and the integrated water vapor. For MWR, a neural network regression applied on a global database consisting of ECMWF analyses is used and allows a direct retrieval of the water vapor from the   Obligis et al. (2006). The differences between missions in terms of spatial and temporal sampling, radiometer architecture, or data processing methods could lead to static or time-varying biases among the water vapor products.
c. Harmonization of the data To use a common format for the datasets, each dataset is put into monthly grids between 6668 of latitude with a resolution of 38. The choice of this spatial and temporal resolution is a compromise between the highest possible resolution and a sufficient number of measurements per bin to provide a good statistical estimation of parameters, such as mean or standard deviation. For each dataset, only measurements validated by their respective processing teams are kept. Moreover, bins are rejected from the study if for a given month less than 50 measurements are present in either one of the AMSR-E, JMR, or MWR corresponding bins. Therefore, the number of valid bins remains constant at each time and for each mission. For the data from JMR and MWR, we only kept measurements whose distance to the coast is higher than 100 km to avoid the contamination of brightness temperatures by land.
b. Water vapor trends The aim of this part is to determine whether the three radiometers observe the same evolution of water vapor over time: do all radiometers qualitatively show an increase (or a decrease) of the tropospheric water vapor between 2004 and 2010? To answer that question, statistical models are used to assess whether the total amount of water vapor has significantly changed. For each dataset, a monthly time series of water vapor is built by averaging every bin of 38 from the same month. Then, the following time series decomposition is applied to model the evolution of the average water vapor content through time: wv r,t 5 U r 1 T r,t 1 S r,t 1 R r,t , (3) where wv r,t is the atmospheric water vapor at time t measured by the radiometer r, U r is the mean of the water vapor content from 2004 to 2010, T r,t is the trend, S r,t is the annual seasonality, and R r,t are the residuals.
To test the significance of the parameters of the model, it is required to characterize the law of the residuals. The classical assumption of independent and identically distributed (i. i. d. ) Gaussian variables is not made here as the residuals show signs of positive autocorrelation (Fig. 6 ). The analysis of the correlogram and the partial correlogram (not shown here), for all water vapor products, indicates that the residuals can be modeled by a stationary autoregressive process of order 1. All parameters of the model are computed at the same time by generalized least squares to take into account the correlation structure between the observations. The estimations of trends from the different datasets of water vapor as well as the test of nullity of the trend are given in Table  3. The estimated trends for JMR and MWR are negative, whereas for AMSR-E the trend is slightly positive.
Estimation of the absolute error standard deviation: Triple collocation analysis Up to now, only comparative studies have been made using as reference the AMSR-E dataset. However, we are not able to determine which water vapor product is closer to the truth. Indeed, the three radiometer products are relatively close from each other but significant discrepancies are present, especially in terms of longterm trend. In this section, we will not be able to determine which product is of highest quality but we will estimate how large the uncertainty is. Only the random part of the absolute error can be characterized with the triple collocation analysis: absolute systematic errors (absolute biases) are not considered. The triple collocation analysis was used by  Janssen et al. (2007)  to estimate the noise (or rather its standard deviation) of three independent sets of measurements made ideally at the same time and place.
The principle of triple collocation is hereafter explained. Suppose that the three datasets of measurements X, Y, Z are unbiased and with uncorrelated errors: X 5 T 1 « x , Y 5 T 1 « y , Z 5 T 1 « z , ( 6 ) where T is the dataset of the true values of the variable of interest; « x (similarly for « y and « z ) is the measurement error and is represented by a random variable of null mean and variance s x 2 (or s y 2 or s z 2 ) and cov(« i , « i 0 ) 5 0, for i 6 ¼ i 0 and (i, i 0 ) 2 fx, y, zg 2. Here cov and var, respectively, denote the empirical covariance and variance operator. It assumes that, for one radiometer, the noise is spatially homogeneous (which is probably not the case in reality). Then s x 2 , s y 2 , and s z 2 can be estimated by s 2 x 5 1/2[var(X 2 Y) 1 var(X 2 Z) 2 var(Y 2 Z)], s 2 y 5 1/2[var(Y 2 X) 1 var(Y 2 Z) 2 var(X 2 Z)], s 2 z 5 1/2[var(Z 2 X) 1 var(Z 2 Y) 2 var(X 2 Y)].
Assessing the product uncertainties regarding sampling or instrumental noises is especially important when several products are merged to construct a common reference dataset. The triple collocation is first applied to AMSR-E, MWR, and JMR. However, we can assume, looking at the scatterplots in Fig. 4 , that at least one water vapor product is biased. Thus, (7) cannot be used directly. We assume (even if it is obviously not true) that for each product the relative bias between instruments is linear in function of the water vapor content. Here, the analysis is made with the three radiometer datasets and following the former assumption we have wv AMSRE 5 b AMSRE 3 T 1 a AMSRE 1 « AMSRE , wv MWR 5 b MWR 3 T 1 a MWR 1 « MWR , wv JMR 5 b JMR 3 T 1 a JMR 1 « JMR. ( 8 ) where T is the true value of the water vapor to be measured; a i and b i , with i 2 fAMSRE, MWR, JMRg, are called adjustment coefficients.
12 , left panel). The estimation of the MWR scatter index is however not affected by the substitution of JMR by ERA-Interim. For ERA-Interim, the scatter index estimation is slightly higher than for AMSR-E, but is more stable through time. As for the adjustment coefficient, the AMSR-E and MWR datasets need similar adjustments to fit the ERA-Interim dataset (Fig. 12 , middle and right panels). Their slope coefficients are decreasing in a similar way and the intercept coefficients are slightly increasing. Thus, there is a gradual change in the statistical distribution of water vapor provided by the reanalysis which is not observed by the radiometers. For both radiometers, an annual cycle is visible for the adjustment coefficients although the ones from MWR show higher variations. The triple collocation has also been used with other combinations of radiometers and ERA-Interim and similar results were obtained.
Impacts of sea surface temperature, wind speed, cloud water, rain rate, and unusual profiles of pressure and temperature are also to be explored. For climatic studies, such analyses need to be extended beyond the 2004-10 period. Understanding the advantages and disadvantages of each water vapor product is a necessity when radiometer datasets are to be merged. For now, building a reference dataset of water vapor seems hardly realizable as the uncertainties related to discrepancies in the mission characteristics or in the data processing are not well understood. However, the objective is to at least provide homogeneous and consistent datasets. Data from different radiometers need to be retreated within a common framework, from the calibration of the brightness temperatures to the estimation of water vapor, to minimize the effect of the methodology used.
Acknowledgments. This study has been founded by the  CNES. AMSR-E data are produced by  Remote Sensing Systems (RSS)  and sponsored by the  NASA Earth Science Measurements DISCOVER Project  and the  AMSR-E Science Team. Data are available at www. remss. com. Jason-1 and Envisat data are available at the AVISO data center (http://www. aviso. oceanobs. com). The ERA-Interim data are provided by  ECMWF.
Estimated 18-month rates of freedom from progression and overall survival after entering the extension study were lower for patients switched from imatinib (85% and 87%, respectively) versus nilotinib 300 mg twice daily (95% and 94%, respectively). Nilotinib dose escalation was generally well tolerated and improved responses in about one-third of patients with suboptimal response/treatment failure. Switch to nilotinib improved responses in some patients with suboptimal response/treatment failure on imatinib, but many did not achieve complete cytogenetic response (clinicaltrials. gov.
Statistical analysis Incidence of suboptimal response and treatment failure in the ENESTnd core study was analyzed descriptively by treatment arm. Safety and efficacy data from the ENESTnd extension study were analyzed descriptively according to original core study treatment arm. Rates of complete cytogenetic response (CCyR) and major molecular response (MMR) at any time during the extension study (from time of entry to time of data cut off) and rates of MMR by 12 months were determined according to response at time of extension study entry. Time-to-event outcomes were analyzed using the Kaplan-Meier method. The cut-off date for all analyses was July 27, 2011 (the same cutoff date as for the 3-year analysis of the ENESTnd core study  4  ). The ENESTnd core and extension studies are being conducted according to the ethical principles of the Declaration of Helsinki, and written informed consent was obtained from each patient.
The majority of patients entering the extension study from the nilotinib 300 mg twice daily arm had suboptimal response in the core study (79%; 15 of 19), 16% (3 of 19) had treatment failure, and 5% (1 patient) entered the extension study based on the investigator's assessment without meeting the criteria for suboptimal response/treatment failure. With a median follow up of 19 months in the extension study, 63% (22 of 35) of patients previously treated with imatinib and 74% (14 of 19) of patients previously treated with nilotinib 300 mg twice daily remained on extension treatment with nilotinib 400 mg twice daily at the time of data cut off. In patients previously treated with imatinib, the median time on extension treatment was 15. 5 months (range 0. 2-35. 2 months), and the median dose intensity was 799 mg/day (range 421-800 mg/day); 17 patients (49%) experienced a dose reduction or interruption because of an AE or laboratory abnormality.
Discussion In the ENESTnd core study, rates of suboptimal response and treatment failure by 6, 12, and 18 months were lower in patients treated with front-line nilotinib than in patients treated with front-line imatinib. Strikingly, the rate of treatment failure by 18 months was nearly 4 times higher in the imatinib arm than in the nilotinib arms. This difference was most pronounced among high Sokal risk patients, who had a high rate of treatment failure on imatinib but who fared similarly to patients with low or intermediate Sokal risk when treated with nilotinib. This may be because the increased potency and selectivity of nilotinib can more effectively compensate for unfavorable host factors than imatinib. These results are consistent with previously reported efficacy data demonstrating numerically higher rates of CCyR, MMR, and deep molecular response (i.
Over larger scales, probably the most frequently used data set is the Global Fire Emissions Database (GFED), which combines burned area from  Giglio et al. (2010)  with the satellite-driven Carnegie-Ames-Stanford approach biogeochemical model that estimates fuel loads based on carbon input from net primary production (NPP), and losses through heterotrophic respiration, herbivory, fuelwood collection, and fires with a monthly timestep on a 0. 5 × 0. 5 grid  (van der Werf et al. , 2010).
At the same time, combining measurements of fire-emitted gases from several sources with inverse modeling techniques yielded better agreement with modeled fire emissions (AMMABB) in West Africa when the L3JRC burned area product  (Tansey et al. , 2008)  used in this database was boosted by a factor 2 and 1. 67 for the land cover classes open broadleaf deciduous forest and deciduous shrubs, respectively  (Liousse et al. , 2010). This emissions' estimate is about twice as high as GFED although part of the discrepancy originated from using different emission factors, so estimate carbon emissions are more similar. Using the L3JRC product,  Lehsten et al. (2009)  estimated emissions for Africa to be 0. 7 Pg C yr -1 with similar fuel consumption as GFED. These divergent results indicate that fire emissions estimates are still uncertain.
The UVP5 detects and measures particles larger than 52 µm on images acquired at high frequency  (Picheral et al. , 2010). Images were taken and data recorded at a frequency of 6 Hz, corresponding to a distance of 20 cm between images at the 1 m s -1 lowering speed of the conductivity-temperaturedepth (CTD) system. The observed volume per image is 0. 48 dm 3 ; the total volume sampled for the 500 m depth profiles at Station A3 was 1. 2 m 3. The instrument takes a digital picture of a calibrated volume lit from the side. The image is scanned for particles and particle dimensions are measured. The pixel area (S p ) for each object is converted to cross-sectional area (S m ) using the calibration equation S m = 0. 00018S 1. 452   p. An equivalent spherical diameter d is calculated for that cross-sectional area.
Data processing The particles in each 5 m depth interval, with depth determined from the associated CTD measurements, were sorted into 27 diameter intervals (from 0. 052 to 27 mm, spaced geometrically), and concentrations were calculated for each diameter and depth interval. We further analyzed size spectra having a minimum of five particles per size bin and depth interval; this criterion eliminated bins with d > 1. 6 mm. The depth distributions of particles are summarized in terms of their total number N T (# L -1 ) and volume V T (mm 3 L -1 = ppm) concentrations. Particle number distributions (n) were calculated by dividing the number of particles ( N) within a given bin by the width of the ESD (Equivalent Spherical Diameter) bin ( d) and the sample volume. The resulting units are # cm -4.
, 2002). The importance of advection could be inferred from time series measurements of LADCP (Lowered Acoustic Doppler Current Profiler). The results indicated a current below 0. 1 m s -1 , with negligible changes over the survey in the 0-200 m depth layer (Y. H. P. Park, personal communication, 2014). The abundance and volume of zooplankton larger than 0. 7 mm, as well as fecal sticks/pellets and aggregates, were estimated from the identification of organisms in the vignettes recorded by the UVP using the Zooprocess imaging software (see  Picheral et al. , 2010). The volume of copepods did not increase through the early bloom survey, suggesting that they were not responsible for the observed rapid increase in particles. Ingestion rates were also estimated from zooplankton biomass using the relationship detailed in  Carlotti et al.
Acknowledgements. Thanks to the  Kerguelen Ocean and Plateau Compared Study  ( KEOPS2 ) shipboard science team and the officers and crew of  R/V Marion Dufresne  for their efforts. Christine Klaas provided helpful input on biological conditions. We acknowledge the constructive comments by  E. Laurenceau. This research was supported by the  French Agency of National Research  grant (#  ANR-10-BLAN-0614 ). G. A. Jackson was supported by  US National Science Foundation (NSF)  grant  OCE09-27863. L. Stemmann was supported by the chair VISION from  CNRS/UPMC. Edited by:  G. Herndl.
While it is generally possible to bin, differentiate and Fourier transform the empirical ESF, this binning corresponds to an implicit low-pass filtering, which introduces a systematic error into estimating the MTF. Therefore, we chose to fit an analytical model to the ESF based on assuming a shape for the LSF that can be described as a weighted sum of a normal and a Laplace distribution. From this, analytical expressions can be obtained for both the ESF (for which the fit is performed) and the MTF  (Boone 1994). The data we used to derive the MTFs were recorded at the TopoTomo beamline of the ANKA synchrotron at an x-ray energy of 35 keV. This energy is above both of the CdTe K-edges and therefore stimulates the emission of characteristic x-rays. It also somewhat corresponds to a worst-case scenario, as in this case the majority of the interaction energy is carried away by the fluorescence, leaving only a minor amount for the photoelectron that more or less stays in place.
Visually, they demonstrate a clear advantage of charge summing in terms of image noise, no matter the amount of thresholds used in SPM. Figure  10  gives the average material decomposition error (MSE) on the test set obtained for the three modes and the differences are both significant as well as substantial. The lack of spectroscopic resolution in SPM leads to a behaviour that hardly profits from adding more energy thresholds. In contrast to this, switching on charge summing produces a decomposition error which is four times lower than without it and three times lower when eight thresholds are allowed in SPM. 4 ), while the reconstructed images correspond to the ŷi in table 2. The dataset pertaining to single pixel mode and four thresholds was generated from the corresponding eight threshold measurement by only using every second threshold (see table 1).
Acknowledgments Parts of this research were funded by the  BMBF  project ' 05K10CKA: Highly Efficient Pixel Detectors for Synchrotron Applications '. We would like to acknowledge  ANKA  for the allocation of beamtime and  Thorsten Mueller  (beamline scientist) for his support. Also, we thank  Andreas Zwerger  ( FMF ) for wire-bonding the detector and  Armin Runz  ( DKFZ Heidelberg ) for handling the contrast agents.