University of Warsaw

About: University of Warsaw is a education organization based out in Warsaw, Poland. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 20832 authors who have published 56617 publications receiving 1185084 citations. The organization is also known as: Uniwersytet Warszawski & Warsaw University.

First detection of VHE gamma-rays from SN 1006 by HESS

Abstract: Recent theoretical predictions of the lowest very high energy (VHE) luminosity of SN 1006 are only a factor 5 below the previously published H.E.S.S. upper limit, thus motivating further in-depth observations of this source. Deep observations at VHE energies (above 100 GeV) were carried out with the High Energy Stereoscopic System (H.E.S.S.) of Cherenkov Telescopes from 2003 to 2008. More than 100 hours of data have been collected and subjected to an improved analysis procedure. Observations resulted in the detection of VHE gamma-rays from SN 1006. The measured gamma-ray spectrum is compatible with a power-law, the flux is of the order of 1% of that detected from the Crab Nebula, and is thus consistent with the previously established H.E.S.S. upper limit. The source exhibits a bipolar morphology, which is strongly correlated with non-thermal X-rays. Because the thickness of the VHE-shell is compatible with emission from a thin rim, particle acceleration in shock waves is likely to be the origin of the gamma-ray signal. The measured flux level can be accounted for by inverse Compton emission, but a mixed scenario that includes leptonic and hadronic components and takes into account the ambient matter density inferred from observations also leads to a satisfactory description of the multi-wavelength spectrum.

Applying dynamic Bayesian networks to perturbed gene expression data.

Abstract: A central goal of molecular biology is to understand the regulatory mechanisms of gene transcription and protein synthesis. Because of their solid basis in statistics, allowing to deal with the stochastic aspects of gene expressions and noisy measurements in a natural way, Bayesian networks appear attractive in the field of inferring gene interactions structure from microarray experiments data. However, the basic formalism has some disadvantages, e.g. it is sometimes hard to distinguish between the origin and the target of an interaction. Two kinds of microarray experiments yield data particularly rich in information regarding the direction of interactions: time series and perturbation experiments. In order to correctly handle them, the basic formalism must be modified. For example, dynamic Bayesian networks (DBN) apply to time series microarray data. To our knowledge the DBN technique has not been applied in the context of perturbation experiments. We extend the framework of dynamic Bayesian networks in order to incorporate perturbations. Moreover, an exact algorithm for inferring an optimal network is proposed and a discretization method specialized for time series data from perturbation experiments is introduced. We apply our procedure to realistic simulations data. The results are compared with those obtained by standard DBN learning techniques. Moreover, the advantages of using exact learning algorithm instead of heuristic methods are analyzed. We show that the quality of inferred networks dramatically improves when using data from perturbation experiments. We also conclude that the exact algorithm should be used when it is possible, i.e. when considered set of genes is small enough.

ANGUSTIFOLIA3 Binds to SWI/SNF Chromatin Remodeling Complexes to Regulate Transcription during Arabidopsis Leaf Development

Abstract: The transcriptional coactivator ANGUSTIFOLIA3 (AN3) stimulates cell proliferation during Arabidopsis thaliana leaf development, but the molecular mechanism is largely unknown. Here, we show that inducible nuclear localization of AN3 during initial leaf growth results in differential expression of important transcriptional regulators, including GROWTH REGULATING FACTORs (GRFs). Chromatin purification further revealed the presence of AN3 at the loci of GRF5, GRF6, CYTOKININ RESPONSE FACTOR2, CONSTANS-LIKE5 (COL5), HECATE1 (HEC1), and ARABIDOPSIS RESPONSE REGULATOR4 (ARR4). Tandem affinity purification of protein complexes using AN3 as bait identified plant SWITCH/SUCROSE NONFERMENTING (SWI/SNF) chromatin remodeling complexes formed around the ATPases BRAHMA (BRM) or SPLAYED. Moreover, SWI/SNF ASSOCIATED PROTEIN 73B (SWP73B) is recruited by AN3 to the promoters of GRF5, GRF3, COL5, and ARR4, and both SWP73B and BRM occupy the HEC1 promoter. Furthermore, we show that AN3 and BRM genetically interact. The data indicate that AN3 associates with chromatin remodelers to regulate transcription. In addition, modification of SWI3C expression levels increases leaf size, underlining the importance of chromatin dynamics for growth regulation. Our results place the SWI/SNF-AN3 module as a major player at the transition from cell proliferation to cell differentiation in a developing leaf.

The statistical security of a statistical database

Abstract: This note proposes a statistical perturbation scheme to protect a statistical database against compromise. The proposed scheme can handle the security of numerical as well as nonnumerical sensitive fields. Furthermore, knowledge of some records in a database does not help to compromise unknown records. We use Chebyshev's inequality to analyze the trade-offs among the magnitude of the perturbations, the error incurred by statistical queries, and the size of the query set to which they apply. We show that if the statistician is given absolute error guarantees, then a compromise is possible, but the cost is made exponential in the size of the database.