dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS This work is part of the '' Cartes d'Identitedes Tumeurs'' (CIT) program  funded and developed by the  ''Ligue Nationale contre le Cancer'' (LNCC)  (http://cit. ligue-cancer. net). We thank  E. Voirin ,  N. Servant ,  G. Lucotte , and  P. Hupe  for their help with bioinformatics data management and analysis. We thank members of the bladder cancer  CIT consortium (P. Mailleand D. Vordos, Henri Mondor Hospital ;  M. Sibony ,  Cochin Hospital ;  A. Laplanche , IGR, INSERM;  Y. Denoux  and  V. Molinie ,  Foch Hospital; E. Letouze, LNCC)  for their constant support. This work was supported by the  LNCC  (to '' Oncologie Moleculaire'' and ''Computational Systems Biology of Cancer '' accredited teams), the  Institut Curie  (to F. R. , E. B. , A. Z. ), the '' Centre National de la Recherche Scientifique'' (CNRS)  (to F.
R. ), the '' Institut National de la Santeet de la Recherche Medicale'' (INSERM)  (to E. B. , A. Z. , S. B. , and Y. A. ), the  INCa  ( INCa_2960  and  4382  to F. R. and Y. A. ), ITMO cancer, systems biology program (to A. Z. , E. B. , and F. R. ), the  Labex  (no. ANR-10-LBX-0038 ) part of the IDEX PSL (no. ANR-10-IDEX-0001-02 PSL ) (to F. R. ), and  York Against Cancer  (to J. S. ). A. B. was supported by a grant from the  INCa , from the  LNCC , and by  NIH  grant  5U24 CA143799-04  as part of  TCGA  project, Y. L. by a grant from the  ''Fondation Franco-Chinoise pour la Science et ses Applications'' (FFCSA) , Y. N. by a grant from the ''Fondation ARC pour la recherche sur le cancer,'' and A. K. by a grant from the  LNCC. The results published here are in part based upon data generated by the  TCGA Research Network : http://cancergenome.
The MECTUB code was developed to evaluate the risk of swelling and bursting of Steam Generator (SG) tubes. This code deals with the physic of intermediate steam-water leaks into sodium which induce a Sodium-Water Reaction (SWR). It is based on a one-dimensional calculation to describe the thermomechanical behavior of tubes under a high internal pressure and a fast external overheating. The mechanical model of MECTUB is strongly correlated with the kind of the material of the SG tubes. It has been developed and validated by using experiments performed on the alloy 800. A change to tubes made of Modified 9Cr-1Mo steel requires more knowledge of Modified 9Cr-1Mo steel behavior which influences the bursting time at high temperatures (up to 1200 ∘ C). Studies have been initiated to adapt the mechanical model and to qualify it for this material.
The Sodium-Water Reaction (SWR) was detected very late, by the measurement of the pressure increase in the intermediate loop expansion tank plenum. The secondary loop was protected by the rupture of the bursting discs with triggering of the sodium isolation, depressurization, and draining sequence. The PFR accident led to reevaluation of the leak evolution scenario due to a mechanism not taken into account prior to this accident, that is, the bursting of the tubes. Thus this led to revise the safety strategy by taking into account reference accidents involving multiple tube ruptures, instantaneous ruptures, or those occurring over a period of time. Consequently, a computational program called MECTUB code has been developed to calculate the local effects of the SWR that can induce a secondary leak hazard within the tube bundle  [2].
The bursting criteria are linked with strain; this code allows determining the temperature and the strain of target tubes during the SWR event. The mechanical model of the MECTUB code is strongly correlated with the type of the material of the SG tubes and its intrinsic behavior. It has been so developed and validated with regard to experiments performed on the alloy 800 of the SUPERPHENIX helical tubes SGs  [2]. Modified 9Cr-1Mo steel is one of the materials investigated for the SGU tubes of the future sodium-cooled fast reactors (SFRs). A change to straight tubes made of Modified 9Cr-1Mo induces different result of the heating-bursting time at high temperatures (up to 1200 ∘ C); then it requires more in-depth knowledge of Modified 9Cr-1Mo steel behavior. Studies have been initiated to adapt the mechanical model and to qualify it for this material.
Finally, the tube can burst after having swollen under internal pressure action. Figure  1  shows the boundary conditions, initial conditions, and model data. Considering overheating, thermal conditions, mechanical characteristics of materials, and mechanical laws of the tube, MECTUB thermomechanical code was developed to calculate swelling and bursting local effects which can induce a secondary leak. The code is able to predict the bursting time of the tube and the strain rise during tube swelling phase (wastage effects could also be taken into account).
Acquisitions Systems and Control System. The control system consists in a module integral PXI-1050: (i) a processor, (ii) a record (disk) of storage of the data, (iii) a card of acquisition (PXI-6259) connected with various modules of input-output: (a) SCXI-1303 for the temperatures measurement (ambient temperature, temperature of the power cable), (b) SCXI-1161 for the flow, pressure, position, temperature obtained by pyrometer measurement, (c) SCB-68 for the analog exit of heating instruction, the commands output, and the input (defects, safety, thresholds). This control system is managed by a program "LabVIEW on 2011. " After a phase of initialization, 2 independent buckles are used: (i) a buckle is used for the acquisition of the measurements and their recording (a period of 120 ms), (ii) a second buckle does the command and controlsystem part and interfaces human machine (cadence of 100 ms).
Conclusions The MECTUB code has been developed in order to model the thermomechanical behaviour of pressurized tubes under overheating load. This code has been set up and validated using experiments performed on the alloy 800 of the SUPER-PHENIX helical tubes SGs. A new test bench was set up and qualified to burst heated tubes. The time to burst was successfully detected using the internal pressure measurements. The results obtained with this test bench highlight a logarithmic trend in the decrease of the bursting time versus the increase of the overheating temperature for a given internal pressure. The experimental studies shown in this paper allowed us to adapt the mechanical model and to qualify it for Modified 9Cr-1Mo material. Mechanical modelling takes into account the elastic, plastic, and creep effects up to high temperature (1200 ∘ C).
We study the quality of the solution, as a function of the regularization parameters and noise, on synthetic datas for which the solution is a-priori known. We apply the method on experimental data in the case of Differential Phase Tomography. For this case we use an original approach which consists in using vectorial patches, each patch having two components: one per each gradient component. The resulting algorithm, implemented in the ESRF tomography reconstruction code PyHST, results to be robust, efficient, and well adapted to strongly reduce the required dose and the number of projections in medical tomography.
Acknowledgment We thank  Emmanuelle Gouillart  and  Gael Varoquaux  for the interesting discussions and for having pointed us the possibilities of dictionary learning techniques and the examples contained in the scikit-learn package [13]. We thank the  ESRF  for providing the experimental facilities, the  ESRF ID17  team for assistance in operating the facilities. We thank  Alberto Bravin ,  Sergei Gasilov ,  Alberto Mittone  for their help during experiments. This work was partially supported by the  DFG-Cluster of Excellence Munich-Centre for Advanced Photon-.
We use the publicly available shapelets software package 4 described in  Massey & Refregier (2005)  in order to make shapelet decompositions for all our objects. This code is well tested using optical data (c. f. Heymans et al. 2006 and Massey et al. 2007a ). In  Patel et al. (2010)  we indicated the applicability of this code to radio data with some modifications; with our radio image simulations, we are now in a position to demonstrate its ability to accurately recover ellipticities in §3. In all that follows we have used the shapelet equivalent to Gaussian weighted quadrupole moments to estimate ellipticities for all our objects. In the polar shapelet convention this estimator takes the form 1,2 = √ 2f 2,2 f0,0 -f4,0 , (9) as is fully discussed in  Massey et al. (2007b). 4 http://www. astro.
Simulations Pipeline Our simulation pipeline is implemented using the MeqTrees 5  software system. MeqTrees is a software package for implementing the Measurement Equation for radio interferometers and is fully described in  Noordam & Smirnov (2010)  and  Smirnov (2011). The heart of a Measurement Equation is formed by the 2 × 2 Jones matrices  (Jones 1941)  which describes the various effects associated with observations that can corrupt the measured visibilities. The formulation for a generic Radio Interferometer Measurement Equation (RIME) was developed by  Hamaker et al. (1996) , after preparatory work by  Morris et al. (1964). Hamaker (2000)  then recast the formalism into 2 × 2 matrix form which is used within MeqTrees 6. The RIME provides an elegant mathematical framework for generic radio instruments, both existing and future, to be better understood, simulated and calibrated.
For a full description of the mathematical formalism for the measurement equation of interferometers we refer the reader to  Hamaker et al. (1996) ,  Smirnov (2011)  and references therein. The MeqTrees software was originally designed to implement the measurement equations for the purposes of simulation and calibrating  (Noordam & Smirnov 2010). We use MeqTrees in this work to simulate the sky as would be observed by a specific radio interferometer. The galaxy images created above are fed into the MeqTrees simulator, which calculates observed visibilities; these visibilities are then imaged. The user can specify many options such as the level of noise on the visibilities, the specific method used to deconvolve the dirty beam, and the weighting scheme applied to the visibility data prior to imaging.
One subtlety is that the input in our work is an image rather than a set of visibilities. In order to obtain uv samples from the image, we use a module of the MeqTrees software that implements a 'de-gridding' algorithm. This algorithm is an interpolation scheme that allows one to transform between the regularly gridded image and a sparsely sampled Fourier (or uv) plane. The details of how this algorithm work is explained fully in  (Tan 1986). The well known general relationship between observed visibilities and the true sky brightness for an interferometer is given by: I( , m, n) = V (u, v, w) e 2iπ(u +vm+w(n-1)) √ 1 -2 -m 2 dudv, ( 10 ) where n = √ 1 -2 -m 2. This expression can be reduced to a two dimension Fourier Transform if the field-of view is small  (Clark 1999) , i. e. if n 1. Since our study consists of fields that are less than an arc minute on a side we have adopted this simplification here such that: I( , m) V (u, v)e 2iπ(u +vm) dudv, (11) Equation 11 should also include the sampling function term, S(u, v) on the left hand side.
The sampling function accounts for the fact that the visibilities are collected at a set of discrete locations in the uv plane. Its functional form is simply a linear combination of Dirac delta functions at all (u, v) where data is collected. The Fourier inversion of Equation 11 leads to the dirty image, which is the convolution between the sampling function (in the image plane) and true sky brightness. Since Direct Fourier Transforms become computationally expensive with large datasets, Fast Fourier Transforms (FFTs) need to be used. Although the use of FFTs speeds up computations, it requires that we grid the data. The process by which this de-gridding is done, and its implication for the recovered image is described in  Tan (1986). The MeqTrees software employs the use of prolate spheroidal functions in the de-gridding process, which have been shown to reduce the effect of aliasing  (Tan 1986 ).
Source Extraction and Shapelet Modelling Since the images we create have known source positions, we bypass the source extraction stage of a lensing analysis. We instead use SExtractor in the ASSOC mode whereby we input a catalogue of positions where objects are known to lie. Of course, even in this mode we will only detect objects which meet the internal requirements to be classified as an object. We use the default SExtractor settings of Table  1 ; with these settings we find that we are able to detect 85 -90% of the objects that are in the original images, with detected number densities ranging from n 120 -130 arcmin -2. We use the shapelets software as described in §2. 1 to estimate the ellipticities of the point sources and the galaxies. We first examine the point sources, in order to estimate the beam, which we can then use to deconvolve from the galaxies.
Point Sources For the point sources we use a straightforward approach for shapelet modelling, decomposing each source into shapelets while examining how the distribution of ellipticities changes if we vary the β parameter. We try the algorithm employed in the shapelet software  (Massey & Refregier 2005)  to optimise the β and nmax parameters, as well as adopting β values of 1, 1. 5 and 2 pixels with a fixed nmax = 10. We find the mean point source FWHM is approximately ∼ 2 pixels for the eMERLIN simulations and ∼ 6 pixels for the LOFAR ones (where the pixel size is chosen to be the same as for the input images). In Figure  5  we show the normalised ellipticity distributions of the point sources for each measurement set; note that the 'Massey' label refers to ellipticities obtained using the optimisation algorithm in the shapelets software  (Massey & Refregier 2005).
Our conclusion is that uniform (and robust, since it uses the same principles) weighting will always induce a PSF ellipticity, especially with sparser arrays. For weak lensing measurements, we can treat this as a constant bias. In principle, more advanced weighting methods such as that suggested by  Boone (2013)  should eliminate this effect, but no mainstream imaging software currently implements them. We can now use these models from the point sources to construct a PSF in each of our four measurement sets and use them to deconvolve the galaxies. The peaks in the histograms provide us with a tight measure of the beam ellipticity, as does using the mean of the uncorrected whisker plots; the measured ellipticities for the beam are summarised in Table  2  for our four MSs. To construct our PSF models, we stack all our point sources and perform a shapelet decom- LOFAR δ = +90 • 0.
Recovered Ellipticity Distributions We now turn to the main result of this work, which is to demonstrate how well we can recover the input ellipticities given the data reduction and imaging pipelines described above. We have described how we obtain measurements of input ellipticity t i and measured ellipticities i; now we compare the two. In Figure  8  we show how our four MSs perform in this comparison (these are the blue curves in Figure  7 ). For each of the four cases, we compute the Pearson's correlation coefficient to quantify the correlation between the input and measured ellipticities, and also calculate the best fitting linear model ( i -t i = mi t i + ci) as well as a χ 2 dof to assess the goodness of fit, all of which we summarise in Table  3. mi = 0 is indicative of a calibration bias, which usually results from poor correction of factors that circularise images, such as poor PSF correction.
Assessing Noise Levels To assess the noise levels in the image plane corresponding to the noise input parameter in the MeqTrees software, we   write the total noise nt in an image as n 2 t = n 2 sl + n 2 m , (14) where n sl is noise associated with side lobes (which is present even in the absence of measurement noise) and nm is the contribution arising from the corruption of the visibilities. We use the noise-free simulations described in the previous section to estimate the n sl term, and we run test simulations with varying noise in the visiblilities to estimate the nt terms. Hence we can estimate the nm terms using the equation above. We find that the relationship between the measured nm and the visibility noise is linear, and calibrate the chosen level of noise accordingly. We calculate the SNR of each  We choose the noise level such that our objects have SNR 10; this is the typical level to which weak lensing measurements are currently made.
CONCLUSIONS AND FUTURE WORK In this paper, we have made an exploratory study of a radio imaging pipeline suitable for studying weak gravitational lensing, and have tested a shear measurement pipeline which has been adapted for radio data. Using our simulations, we can obtain a better understanding of suitable shear measurement techniques to use with eMERLIN, LOFAR and ultimately the SKA. We have constructed a pipeline to simulate current and future weak lensing observations with radio interferometers. We were motivated by  Patel et al. (2010)  in which we attempted to measure a weak lensing signal using radio data from MERLIN and VLA. In that work we found systematic contamination in the data; indeed, a primary conclusion of that work was the need for a detailed study of the systematics involved in trying to measure weak lensing using radio datasets.
In the current work, we have been able to assess some of the possible systematics, and have demonstrated the reliability of our shape measurement method on realistic simulated radio images. Using the shapelets method we have created a set of images containing a collection of point sources and realistic galaxy shapes. The point sources were used to probe the behaviour of the beam, while the galaxies were used to test how well one can recover known ellipticities in the presence of a radio data reduction and imaging pipeline. The 'true' images were run through our simulator to mimic observations made with the eMERLIN and LO-FAR arrays, at two different declinations, and with different numbers of CLEAN iterations. We measured ellipticities for galaxies via a shapelets decomposition, including deconvolution of the PSF.
ACKNOWLEDGEMENTS PP is funded by a  NRF SKA Postdoctoral Fellowship. PP would like to thank  Mathew Smith ,  Russell Johnston  and  Matt Jarvis  for all the helpful discussions during the length of this project. BR acknowledges support from the  European Research Council  in the form of a Starting Grant with number  240672. FBA acknowledges the support of the  Royal society  via an RSURF. OMS's research supported by the  South African Research Chairs Initiative of the Department of Science and Technology and National Research Foundation. Any opinion, findings and conclusions or recommenda-.
Identification of canonical signaling pathways mediated by KLF11 and mutants Subsequently, we wished to further assess the relatedness between the gene targets significantly modulated by KLF11 and its mutants utilizing a semantic-based approach and the Ingenuity Global Canonical Pathways (GPC) algorithm. The input dataset of significantly regulated gene targets was compared against the IPA canonical pathways, which are curated from published literature. The significance of the associations between the data set and a given canonical pathway is determined by the ratio of the number of genes mapping to the pathway divided by the total number of pathway genes. A p-value is calculated using Fischer's Exact Test determining the probability that the association between the data set and the pathway occurs by chance alone.
Discussion Until recently, the relationship between chromatin and transcription factors appeared largely passive. However, the evidence that overexpression of four transcription factors-Oct4, Sox2, m-Myc, and KLF4-is sufficient to reprogram fully differentiated fibroblasts into pluripotent ES-like cells suggests an active role for transcription factors in the epigenetic regulation of chromatin state  [25]. KLF proteins, in particular, have demonstrated a multitude of feed-forward effects on epigenetic regulation of gene expression. Here, we propose KLF11 as a model for understanding the critical role of sequence-specific factors in epigenetic regulation of a multitude of pathobiological processes. KLF proteins deliver epigenetic information to gene promoters through three primary mechanisms: (i) sensing Upstream regulators of KLF11 and its mutants.
Genome-wide expression profiles of KLF11 and mutants Panc1 epithelial cells were plated at a density of 1×10 6 cells/100 mm dish and transduced with empty vector, KLF11, KLF11-A347S, KLF11-Δ486, or KLF11-EAPP at an MOI of 150. Relative expression levels were confirmed by Western Blot (Additional file 1: Figure  S1 ). RNA was prepared as previously described from pooled biological triplicates  [37]. Global gene expression profiling was carried out in technical duplicate at the Microarrays Facility of the Research Center of Laval University CRCHUL utilizing the Affymetrix Human Gene 1. 0 ST arrays (28,869 well-annotated genes and 764,885 distinct probes). Intensity files were generated by Affymetrix GCS 3000 7G and the Gene-Chip Operating Software (Affymetrix, Santa Clara, CA). A subset of genes was validated by qPCR as previously described (Additional file 7: Figure  S2 )  [20, 38].
Data analysis Data analysis, background subtraction and intensity normalization was performed using Robust Multiarray Analysis (RMA)  [42]. Genes that were differentially expressed along with false discovery rate (FDR) were estimated from t test (>0. 005) and corrected using Bayes approach  [43, 44]. A threshold of +/-1. 5 log2 fold change with a p-value with FDR of less than 0. 05 (without FDR for EAPP mutant) was used to determine significantly regulated targets. Data analysis, hierarchical clustering, and ontology were performed with the OneChanelGUI to extend affylmGUI graphical interface capabilities and Partek Genomics Suite, version 6. 6 (Partek Inc. , St. Louis, MO) with ANOVA and GO ontological analysis  [45]. Selected probes and their fold changes were loaded into Ingenuity Pathways Analysis Software (Ingenuity® Systems, www.
Acknowledgements This work was supported, in whole or in part, by  National Institutes of Health  Grants  DK52913  (to R. U. ),  T32-CA148073  (to A. G. ),  Mayo Clinic Center for Cell Signaling in Gastroenterology  ( P30DK084567 ) and a  Career Development Award  from the  Mayo Clinic SPORE in Pancreatic Cancer  ( P50 CA102701 ) (to G. L. ).
Extending over three Hubble Space Telescope (HST) cycles, the Hubble Frontier Fields (HFF) initiative constitutes the largest commitment ever of HST time to the exploration of the distant Universe via gravitational lensing by massive galaxy clusters. Here, we present models of the mass distribution in the six HFF cluster lenses, derived from a joint strong-and weak-lensing analysis anchored by a total of 88 multiple-image systems identified in existing HST data. The resulting maps of the projected mass distribution and of the gravitational magnification effectively calibrate the HFF clusters as gravitational telescopes. Allowing the computation of search areas in the source plane, these maps are provided to the community to facilitate the exploitation of forthcoming HFF data for quantitative studies of the gravitationally lensed population of background galaxies.