Identification de ligands de la protéase adénovirale de type 2 à l'aide d'une peptothèque de phages ("phage library")

Une peptothèque de phages a été utilisée afin d'identifier des ligands à la protéase de l’Ad2. La peptothèque de phages contient une région aléatoire de 15 a.a. bordée par deux résidus cystéine qui forment un pont disulfure. Le criblage de la peptothèque contre la protéase de l’Ad2, a dégagé 29 séquences de peptides de 15 a.a. de longueur à partir de 63 phages vérifiés. Parmi ces séquences, il a été possible de distinguer certaines séquences connues, telles les sites de clivage de la protéase (M,I,L)XGG^X et (M,I,L)XGX^G. Une séquence homologue au peptide pVIct, un stimulateur de la protéase, a aussi été mise en évidence. Une région de grande homologie entre plusieurs des séquences obtenues met en évidence une séquence qui ressemble au site de clivage, cette séquence étant VEGGS. Le peptide VEGGS a été synthétisé afin de vérifier son effet sur la protéase in vitro et in vivo. A partir d'une recherche avec le logiciel Blast dans les banques de données Swissprot et Genbank, l'homologie entre les protéines avec les séquences provenant des phages isolés ont été relevées. Parmi les séquences protéiques obtenues de ces banques de données, on observe une homologie avec la protéine 100kD et la protéine Tp de l’Ad2, ainsi qu'une homologie avec la protéine Tp de l’Ad5

Kepler Presearch Data Conditioning II - A Bayesian Approach to Systematic Error Correction

With the unprecedented photometric precision of the Kepler Spacecraft, significant systematic and stochastic errors on transit signal levels are observable in the Kepler photometric data. These errors, which include discontinuities, outliers, systematic trends and other instrumental signatures, obscure astrophysical signals. The Presearch Data Conditioning (PDC) module of the Kepler data analysis pipeline tries to remove these errors while preserving planet transits and other astrophysically interesting signals. The completely new noise and stellar variability regime observed in Kepler data poses a significant problem to standard cotrending methods such as SYSREM and TFA. Variable stars are often of particular astrophysical interest so the preservation of their signals is of significant importance to the astrophysical community. We present a Bayesian Maximum A Posteriori (MAP) approach where a subset of highly correlated and quiet stars is used to generate a cotrending basis vector set which is in turn used to establish a range of "reasonable" robust fit parameters. These robust fit parameters are then used to generate a Bayesian Prior and a Bayesian Posterior Probability Distribution Function (PDF) which when maximized finds the best fit that simultaneously removes systematic effects while reducing the signal distortion and noise injection which commonly afflicts simple least-squares (LS) fitting. A numerical and empirical approach is taken where the Bayesian Prior PDFs are generated from fits to the light curve distributions themselves.Comment: 43 pages, 21 figures, Submitted for publication in PASP. Also see companion paper "Kepler Presearch Data Conditioning I - Architecture and Algorithms for Error Correction in Kepler Light Curves" by Martin C. Stumpe, et a

Pointwise Bounds for Steklov Eigenfunctions

Let (Ω,g) be a compact, real-analytic Riemannian manifold with real-analytic boundary ∂Ω. The harmonic extensions of the boundary Dirichlet-to-Neumann eigenfunctions are called Steklov eigenfunctions. We show that the Steklov eigenfunctions decay exponentially into the interior in terms of the Dirichlet-to-Neumann eigenvalues and give a sharp rate of decay to first order at the boundary. The proof uses the Poisson representation for the Steklov eigenfunctions combined with sharp h-microlocal concentration estimates for the boundary Dirichlet-to-Neumann eigenfunctions near the cosphere bundle S∗∂Ω. These estimates follow from sharp estimates on the concentration of the FBI transforms of solutions to analytic pseudodifferential equations Pu=0 near the characteristic set {σ(P)=0}

Verification of the Kepler Input Catalog from Asteroseismology of Solar-type Stars

We calculate precise stellar radii and surface gravities from the asteroseismic analysis of over 500 solar-type pulsating stars observed by the Kepler space telescope. These physical stellar properties are compared with those given in the Kepler Input Catalog (KIC), determined from ground-based multi-color photometry. For the stars in our sample, we find general agreement but we detect an average overestimation bias of 0.23 dex in the KIC determination of log (g) for stars with log (g)_KIC > 4.0 dex, and a resultant underestimation bias of up to 50% in the KIC radii estimates for stars with R_KIC < 2 R sun. Part of the difference may arise from selection bias in the asteroseismic sample; nevertheless, this result implies there may be fewer stars characterized in the KIC with R ~ 1 R sun than is suggested by the physical properties in the KIC. Furthermore, if the radius estimates are taken from the KIC for these affected stars and then used to calculate the size of transiting planets, a similar underestimation bias may be applied to the planetary radii.Comment: Published in The Astrophysical Journal Letter

Kepler Presearch Data Conditioning I - Architecture and Algorithms for Error Correction in Kepler Light Curves

Kepler provides light curves of 156,000 stars with unprecedented precision. However, the raw data as they come from the spacecraft contain significant systematic and stochastic errors. These errors, which include discontinuities, systematic trends, and outliers, obscure the astrophysical signals in the light curves. To correct these errors is the task of the Presearch Data Conditioning (PDC) module of the Kepler data analysis pipeline. The original version of PDC in Kepler did not meet the extremely high performance requirements for the detection of miniscule planet transits or highly accurate analysis of stellar activity and rotation. One particular deficiency was that astrophysical features were often removed as a side-effect to removal of errors. In this paper we introduce the completely new and significantly improved version of PDC which was implemented in Kepler SOC 8.0. This new PDC version, which utilizes a Bayesian approach for removal of systematics, reliably corrects errors in the light curves while at the same time preserving planet transits and other astrophysically interesting signals. We describe the architecture and the algorithms of this new PDC module, show typical errors encountered in Kepler data, and illustrate the corrections using real light curve examples.Comment: Submitted to PASP. Also see companion paper "Kepler Presearch Data Conditioning II - A Bayesian Approach to Systematic Error Correction" by Jeff C. Smith et a

Overview of the Kepler Science Processing Pipeline

The Kepler Mission Science Operations Center (SOC) performs several critical functions including managing the ~156,000 target stars, associated target tables, science data compression tables and parameters, as well as processing the raw photometric data downlinked from the spacecraft each month. The raw data are first calibrated at the pixel level to correct for bias, smear induced by a shutterless readout, and other detector and electronic effects. A background sky flux is estimated from ~4500 pixels on each of the 84 CCD readout channels, and simple aperture photometry is performed on an optimal aperture for each star. Ancillary engineering data and diagnostic information extracted from the science data are used to remove systematic errors in the flux time series that are correlated with these data prior to searching for signatures of transiting planets with a wavelet-based, adaptive matched filter. Stars with signatures exceeding 7.1 sigma are subjected to a suite of statistical tests including an examination of each star's centroid motion to reject false positives caused by background eclipsing binaries. Physical parameters for each planetary candidate are fitted to the transit signature, and signatures of additional transiting planets are sought in the residual light curve. The pipeline is operational, finding planetary signatures and providing robust eliminations of false positives.Comment: 8 pages, 3 figure