dataesr/scientific-paragraphs-categorization huggingface dataset:
The Angular Correlation Function The angular correlation function can be measured using a number of estimators. In this paper, we use the Landy-Szalay estimator, which is given by: w(θ) = DD -2 DR + RR RR (1) where DD is the number of galaxy-galaxy pairs, DR is the number of galaxy-random pairs and RR is the number of random-random pairs. The Landy-Szalay estimator avoids the issue of linear biases seen in the direct estimator caused by spurious signal between the data and survey window. The random catalogues used to evaluate the estimator are produced using the survey geometry with the optical and NIR masks described above applied. The random galaxy catalogues each contain a total number of random data points equal to 20× the number of galaxies in the 'real-data' catalogue with which the correlation function is being calculated.
(2012)  for this same data and model combination. as a function of number density. The points show the results from the WIRDS analysis separated by redshift. The lines show the equivalent trends, again separated by redshift, from the galform model. The trend of increasing clustering strength with decreasing space density is significant in the results. < M [h -1 M ⊙ ] ≤ 9. 6 samples, squares 9. 6 < M [h -1 M ⊙ ] ≤ 10. 6, ×'s M [h -1 M ⊙ ] > 10. 6 and circles M [h -1 M ⊙ ] >.
This lack of evolution with redshift is consistent with previous results at lower redshift. In the redshift range 0. 4 z 1. 0, LRG observations (of samples of approximately uniform absolute magnitude) are consistent with no evolution of r γ/1. 8 0 as a function of redshift  (Sawangwit et al. 2011; Nikoloudakis et al. 2012 , shown in Fig. 7 ). The WIRDS data are consistent with the LRG results, but extend this conclusion to lower mass samples than the LRGs and to higher redshift. Returning to the stellar mass dependence of the clustering for passive galaxies, as stated we find that the data show no significant correlation here. In fact, the lower mass samples are found to have higher clustering sxtrengths than the higher mass samples at the lower redshifts (z 0. 8) in our study. This is similar to the results reported by  Hartley et al.
In each case the points are split by redshift, with the diamonds corresponding z = 0. 5, the triangles to z = 0. 7, the squares to z = 0. 9, the times symbols to z = 1. 1, the stars to z = 1. 35 and the crosses to z = 1. 75. Interestingly, the galform predictions suggest little dependence of clustering strength on galaxy number density for the passive galaxies. This is closely related to the small galaxy stellar mass dependence seen in the previous figure. Using the WIRDS data alone it is difficult to constrain this prediction, however we can add in results from previous work to aid the analysis. As such we include the previous points for LRGs, which represents a strongly clustered low number-density population, and the passive galaxies analysed by  Coil et al. (2008). These are consistent with the WIRDS points, with the  Coil et al.
78 × 10 11 Ω 0 m h 2 M ⊙ Mpc -3. Combining equations 9, 10, 11 and 12 allows us to estimate the dark matter halo mass from the clustering bias, which we match to the calculated bias for each of the galaxy samples. We show the results of the bias matching in Fig. 9 , where we plot the minimum dark matter halo mass, M DM , versus the mean galaxy sample stellar mass, M *. This is based on the full galaxy sample clustering as presented in Fig. 3. The WIRDS data is split by redshift with the black diamonds showing the z = 0. 5 points, the dark blue triangles showing the z = 0. 7 points, the blue squares showing the z = 0. 9 points, the cyan times symbols the z = 1. 1 points, the green stars the z = 1. 35 points and the red crosses the z = 1. 75 points. At each redshift, a clear dependence of M DM on stellar mass is seen.
In addition, there is some sign of a redshift evolution, with M DM appearing to move lower for a given stellar mass with increasing redshift. Comparing to equivalent datasets in the literature, the grey circles show the points of Foucaud et al. (  2010 ) and the long-dashed curve shows a fit from  Wake et al. (2011). The   2011 ) for galaxies at 1 < z < 2 in the NEWFIRM survey. Foucaud et al. (2010)  points are based on equivalent redshift and mass bins to our highest redshift bins. As in Fig. 3 , the WIRDS and  Foucaud et al. (2010)  results agree well except where the latter find large clustering results: in this case halo masses of M DM 10 13 h -1 Mpc. Our results at these masses and redshifts (green stars & red crosses) suggest much lower halo masses M DM 4 -8 × 10 12 h -1 Mpc. However, given the large error bars on the  Foucaud et al.
(2010)  points, their results are consistent with those of WIRDS as well as the relation derived in  Wake et al. (2011). Now taking the galform predictions, we find that these agree with the WIRDS results to within ≈ 1σ of the data points across the different redshift ranges, excepting the z = 0. 7 WIRDS results. As discussed previously, the z = 0. 7 data-points seem to be pushed to higher clustering due to the number of large clusters in this redshift range in the COSMOS field. Interestingly, we see that the galform results predict a redshift evolution in the halo masses for a given stellar mass cut. The range covered by the WIRDS results and the associated errors limit our ability to confirm whether this is a genuine evolution. We also note that in the model, the relationship between M DM and M ⋆ is primarily driven by the star-forming population, whilst the halo masses of passive galaxies show little dependence on the stellar mass of the galaxies.
e. from black for low redshift to red for high redshift). One point to note from the WIRDS data is that plotted in this way, the z = 0. 7 points are consistent with the z = 0. 5 and z = 0. 9 results. However, above z ∼ 1, we now see a tentative trend for the WIRDS results to shift to lower halo masses for a given number density. This effect is also present in the galform predictions, with the dashed lines moving down and to the left (note that the galform results are based on the same stellar mass bins as the data points) with increasing redshift. In summary, from the halo mass analysis, we confirm the relationship between galaxy stellar mass and halo mass across the redshift range of 0. 4 z 2. 0 in agreement with previous work, whilst finding tentative evidence for an evolution in the relationship between number density and halo mass.
This is consistent with the dependence with b j -band magnitude of galaxies brighter than M bj < -21  (Norberg et al. 2002 ) (a range within which the majority of our galaxy mass samples are within). Taking the starforming population, a stronger dependence of clustering strength on stellar mass is observed than for the passive galaxies, a trend that is seen in both the WIRDS data and the galform model predictions. Finally we have investigated the relation between galaxy stellar mass and mean dark matter halo mass for the samples described above. Based on the  Mo & White (1996)  formalism, we have estimated mean dark matter halo masses from the clustering measurements for the full galaxy sample. We see a significant trend of mean halo mass increasing with galaxy stellar mass across a range of redshifts.
Advancements in seismic and geodetic technology are now being harnessed to observe and document the rapid changes occurring in the ice sheet and to illuminate interactions between multiple Earth systems. The Greenland Ice Sheet Monitoring Network (GLISN) project-a collaboration between Canada, Denmark, France, Germany, Italy, Japan, Norway, Poland, South Korea, Switzerland, and the United States-provides real-time broadband seismological observations to help address critical, poorly understood aspects of the Arctic system (Figure  1 ). Geodetic observations are also included at selected stations. Seismic data from GLISN record changes at high temporal resolution and reflect deformation and structures in-ternal to the ice and solid Earth. These data complement existing observations from satellite and airborne remote sensing, icepenetrating radar, and GPS geodesy.
Acknowledgments Major funding for GLISN is provided by the  U. S. National Science Foundation ,  Geological Survey of Denmark and Greenland, Swiss Science Foundation ,  Helmholtz Centre Potsdam GeoForschungsZentrum ,  Centre National de la Recherche Scientifique  and the  French Ministry of Research, Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research , and the  Korean Polar Research Institute. All GLISN partners provide contributions of equipment, operating costs, and/or data management. GLISN is made possible by the dedicated efforts of many field and support staff.
, 2012; Yennek and Tajbakhsh, 2013). The vast majority of micropatterns contained a single cell following plating (data not shown; see Movies S1 and S2). After a second division during the chase period, DNA segregation and cell fate outcomes were assessed by immunostaining. In some cases, videomicroscopy was used in parallel to ensure that single cells were seeded and daughter cell pairs were obtained on the patterns (Movies S1 and S2). These videos showed the extensive motility of the myogenic cells on the micropattern motifs before and after mitosis. As a control, Pax7-nGFP + myogenic cells were seeded on fibronectin/fibrinogen-Alexa Fluor 594-coated slides to assess the frequency of asymmetric divisions on a nonpatterned surface. Strikingly, on both types of asymmetric micropattern motifs, the majority of the cells performed non-random DNA segregation with a frequency of 60%-62% (n = 2 mice; Asym1, n = 163 daughter cell pairs; Asym2, n = 189 daughter cell pairs; Figures  2B  and 2C ), which is significantly greater than previous results obtained at 5 DPI  (Rocheteau et al.
Cells were sorted using a FACS Aria III (BD Biosciences) and collected in 1 ml of 2% FCS/1% PS/ DMEM. Cells were displayed as phycoerythrin (PE, red) on the FACS profile. All analyses and quantitations were performed using FlowJo software. Satellite cells were cultured in 1:1 DMEM (Invitrogen)/MCDB (Sigma) containing 20% FCS, 2% Ultroser (Pall), and 1% PS. The medium was filtered through a 0. 2 mm filter. Cells were cytospun on the chip at 40 g for 4 min, and 1 hr after plating, nonattached cells were washed with media. Cells were kept in an incubator (37 C, 5% CO 2 , 3% O 2 ) for 12 hr.
ACKNOWLEDGMENTS We thank  Theo Cambier  for the optical mask design and members of the lab for helpful discussions. S. T. acknowledges support from the  Institut Pasteur ,  Agence Nationale de la Recherche  ( Laboratoire d'Excellence Revive, Investissement d'Avenir ;  ANR-10-LABX-73  and  ANR-06-BLAN-0039 ), the  Association pour la Recherche sur le Cancer , and the  European Research Council  ( Advanced Research Grant   332893 ). S. Y. is funded by a PhD fellowship from  La Ligue  contre le Cancer. M. T. acknowledges support from the  ERC  (Starting Grant  310472 ) and the  ISI program of BPIFrance.
Statistical analysis We calculated a sample size of 200 HAT cases would be needed to estimate the sensitivity of the HAT Sero-K-SeT within 5% and assuming a sensitivity of at least 85%. Conversely, we calculated a sample size of 350 controls would be needed to show a higher specifi city of HAT Sero-K-SeT than of CATT while expecting a specifi city of 95% for HAT Sero-K-SeT and 90% for CATT. However, enrolment had to be stopped prematurely because the accrual rate of cases was much slower than was expected. All results were entered in an Excel database. We calculated sensitivities and specifi cities with 95% binomial Wilson confi dence intervals with Stata 10. We used the McNemar χ² test in Stata 10 to test diff erences in sensitivity and specifi city of the HAT Sero-K-SeT compared with CATT/T b gambiense and immune trypanolysis.
Results We included participants between June 6, 2012, and Feb 25, 2013. We initially included 135 patients with HAT, although one participant was excluded from analysis because of missing parasitological results in the database. Thus, we included 134 confi rmed cases in the analysis (58 [43%] male and 76 [57%] female), with a median age of 33•0 years (IQR 24•5-48•0). 64 patients with HAT were included at the treatment centres while waiting for the start of their treatment. 56 (42%) patients with HAT presented swollen cervical lymph nodes, with confi rmed parasite presence in 42 (84%) of 50 participants who had their lymph node fl uid tested for presence of parasites. Trypanosomes were detected on blood microscopy in 88 (83%) of 106 patients with HAT. 131 participants had lumbar punctures (three patients had no lumbar puncture for medical reasons such as pregnancy).
We noted trypanosomes in the cerebrospinal fl uid of 51 (42%) of 122 patients (nine patients had missing parasitological cerebrospinal fl uid results in database). 14 patients with positive cerebrospinal fl uid samples were negative for trypanosomes in blood and lymph analyses. 39 (32%) of 122 cases (nine had insuffi cient results for staging in database) were in the fi rst stage of the disease. We noted microfi laria in two cases on mAECT. We included 363 participants as non-HAT controls, of whom seven were excluded from the analysis because of inconsistent or missing results from the database (four participants) or no plasma available for immune trypanolysis test (three participants). Thus, we included 356 controls in the analysis (144 [40%] male and 212 [60%] female, with a median age of 29•0 years (IQR 21•0-42•0).
Acknowledgments This study received fi nancial support from the  NIDIAG network  ( Collaborative Project ) supported by the  European Commission  under the  Health Cooperation Work Programme of the 7th Framework Programme  (Grant Agreement 260260). Further fi nancial support was received from the  WHO Control of Neglected Tropical Diseases, Innovative  and  Intensifi ed Disease Management. We are grateful to the members of the mobile teams of  Masi Manimba  and  Popokabaka. Joris Menten  ( Institute of Tropical Medicine, Antwerp, Belgium ) is acknowledged for his advice on statistical analysis.
Therefore, in the following we propose an original and efficient Adecomp-SHOT based approach to extract the fiber orientations from diffusion weighted-MRI data and we prove through many validations tests the effectiveness of the proposed method. The Adecom-SHOT is based on the SHOT; therefore we propose to use the CT-FOD for reconstructing the SHOT from the DW-MRI signal, since the CT-FOD constitutes the state-of-the-art. We begin this paper by presenting first the CT-FOD algorithm, before explaining the CP-decomposition and providing a detailed version of the Adecomp-SHOT algorithm; in this section we will also present the results of the intrinsic study done on both of Adecomp-SHOT algorithm and CP-decomposition. Then we describe our Adecomp-SHOT based approach to the fiber orientations search.
Fiber Directions from Diffusion Data Using the Analytical Decomposition of Fourth Order Tensor. In this section we propose to extract the fibers orientations from the dMRI signal by decomposing analytically the three dimensional fourth order CT-FOD using the Adecomp-SHOT. The coefficients of the fourth order FOD are estimated from the dMRI data as described in Section 2. 1. Thus, we are interested to decompose the Cartesian FOD tensor in sum of powers of rank-1 linear forms: 𝑓 (𝑔) = 𝑟 ∑ 𝑖=1 𝜆 𝑖 𝐾 𝑖 (𝑔) 4 (11) with  minimal representing the tensor rank;   are rank-1 linear forms in 3 variables with the real normalized coefficients [ ,0 ,  ,1 ,  ,2 ], and   are the real positive weights. The normalized coefficients of   represent the Cartesian coordinates of the fibers orientations, weighted by the scalar   , and  represents the number of crossing fibers bundles in each voxel.
Results and Discussion To validate our proposed crossing fibers detection method, we conduct many tests, first on synthetic diffusion dataset simulated with a multitensor model with 60 gradient directions and a -value of 3000 s/mm 2 ; these data represent crossing fibers with variable separating angles from 90 ∘ to 0 ∘. On these synthetic diffusion data we have compared our method to other methods in literature such as CPdecomposition based method and the Z-eigenvalues based approach; the results of this comparison are illustrated in Figure  4. Then, the synthetic dataset is corrupted with a rician noise of different signal to noise ratio (SNR = 40, 30, 20, and 10); 100 trials of noise are performed for each SNR level and for each separating angle; the results of the effect of a rician noise are presented in Figures  5  and 6  and Table  1.
Validations on Synthetic Diffusion Dataset Comparison with CP-Decomposition and Z-Eigenvalues Based Approaches. In order to compare the angular resolution of our fiber extraction approach to the one of the CP-decomposition and the Z-eigenvalues based methods, we have conducted experiments on same noise-free synthetic diffusion dataset. From these data we reconstruct the fourth order CT-FODs and then we extract the maxima of the CT-FODs using the CP-decomposition, the Z-eigenvalues approach, and our proposed Adecomp-SHOT based approach. As it is required by the CP-decomposition, to decompose the fourth order CT-FOD we set the low-rank of the decomposition approximation to 2; that is, we assume that we know the number of fibers in the voxel. We recall that rather than the CPdecomposition our Adecomp-SHOT based approach does not require predefining the rank of the decomposition, that is, not require knowing a priori the number of fibers in the voxel.
Phantom Diffusion Dataset. We conduct other experiments on the FiberCup phantom data acquired at  = 2000 s/mm 2 with 64 acquisitions  [21, 22]  downloaded from the computer-assisted neuroimaging laboratory (LNAO) link: http://www. lnao. fr/spip. php?article112. The fourth order tensor is estimated from the phantom data using the CT-FOD algorithm and represented by spherical function in Figure  7 (a). The recovered fiber directions are plotted on Figure  7 (b). The results show that our Adecomp-SHOT based approach is able to render the fiber bundle directions.
In Vivo Human Cerebral Dataset. We conduct further tests, on real dataset obtained from the Stanford University  [23]  link: http://purl. stanford. edu/yx282xq2090. These data were acquired with 160 gradient directions with a -value of 2000 s/mm 2 ; the thickness of slice is 2 mm. The CT-FODs of fourth order are estimated from these data and decomposed with the Adecomp-SHOT; Figures  8(a ) and 8(b) show the fourth order CT-FODs and the fiber directions, respectively. On a coronal slice, Figure  8  shows that the method reliably extracts the Corpus Collum (CC), the Corticospinal Tractu (CST), the Cingulum (CG), and the Superior Longitudinal Fasciculus (SLF). Two regions representing the CC, CST, and SLF intersections are highlighted on Figures  8(c ) and 8(d) and show the ability of our approach to extract crossing fibers with a high angular resolution.
Unlike CP-decomposition, our proposed approach is able to recover the entire crossing fiber directions whatever its number and that without any assumption. To exploit the Adecomp-SHOT on diffusion dataset and to take into account the ground truth properties of the diffusion, we imposed a real and nonnegative constraint by using the NNLS algorithm and by introducing a change of variables. The change of variables was done by a rotation transformation to preserve the fiber directions. This coordinate transformation permitted to overcom a significant constraint imposed by the original Adecomp-SHOT. Indeed, the Adecomp-SHOT, as initially described, enables to extract directions in the affine space whose first Cartesian coordinates are zero, but by using optimally the transformation coordinates, the entire fibers directions are recovered whatever its positions in the affine space.
Different validations tests were conducted on synthetic noisy diffusion data, phantom, and real data. The tests on synthetic dataset have shown three principal advantages. (1) The Adecomp-SHOT based approach overcomes the limits related to CP-decomp osition without loss in the angular resolution and angular accuracy. (2) Our approach is efficient, accurate, and robust to noise: for SNR equal to 30 our Adecomp-SHOT based approach has an angular resolution limit < 36 ∘ and up to 42 ∘ the mean of the mean error does not exceed 8 ∘. (3) The rician noise does not really affect the ability of the method to detect the number of fibers in a voxel; indeed, up to a crossing angle of 48 ∘ for a low SNR equal to 20 the correct number of fibers is found at 99%. Finally, the tests conducted on phantom and real data confirm the ability of the method to reliably extract the directions of fiber bundles especially in regions where many fiber bundles intersect.
Acknowledgments This work is supported by USTHB in Algeria and by  INRIA  ( International Department ) in France. The authors thank both institutions for their financial support. They would like to express also their thanks to  Bernard Mourrain  ( Inria, Galaad ) and  Aurobrata Ghosh  ( Inria, Athena ) for the useful Computational and Mathematical Methods in Medicine discussions and comments they had during the progress of this work.
Introduction The ocean mean sea level (MSL) is a key indicator of the global warming. Its long-term survey is an issue not only for climate evolution study, but also for economic and social consequences of its elevation. The mean sea level evolution is monitored and studied using tide gauge records, satellite altimetry measurements, and numerical models. Models are often combined with observations through assimilation techniques to improve the model outputs. Space observations play nowadays a major role in data assimilation. Indeed, satellites allow a nearly complete coverage of the ocean surface and data are provided in near-real time. Exploitation of altimetry measurements over ocean depends on the feasibility of correcting the altimeter range for different perturbations. One of them, the wet tropospheric correction, is nearly proportional to the integrated water vapor and is provided by a dedicated instrument, a microwave radiometer, operating around the water vapor absorption line  (22 GHz).
Understanding where and when anomalies occur will help to pinpoint their causes. Such evaluations are necessary to identify areas where potential errors remain and to propose adequate corrections. To do so, we chose to compare three radiometer products of water vapor between 2004 and 2010 from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), the Jason-1 microwave radiometer (JMR), and the Envisat microwave radiometer (MWR). These three radiometers allow the comparison of three independently built datasets: the radiometers have different architecture and each product was built using a different retrieval algorithm and different editing criteria. JMR and MWR are radiometers on board altimetry missions whereas AMSR-E is specifically built to study Earth's water cycle.
The latter has been intercalibrated by Remote Sensing Systems (RSS) with the Special Sensor Microwave Imager and the Special Sensor Microwave Imager/Sounder (SSM/IS) radiometer aboard Defense Meteorological Satellite Program (DMSP) satellites  (Wentz et al. 2003). Measurements from SSM/I and SSM/IS form the longest record of water vapor of the satellite era: reprocessed and homogeneous datasets are available since 1987. Hence, it represents one of the most elaborate records of water vapor  (Trenberth et al. 2005). For these reasons, the AMSR-E water vapor product is used as a reference in our study. At first, the analyses were also performed with SSM/IS DMSP F-16 (F-16), which was launched in October 2003. It was, however, decided not to include SSM/I radiometers in this paper. Indeed, it was chosen to only include radiometer products that have been processed independently.