Variance estimators in critical branching processes with non-homogeneous immigration

The asymptotic normality of conditional least squares estimators for the offspring variance in critical branching processes with non-homogeneous immigration is established, under moment assumptions on both reproduction and immigration. The proofs use martingale techniques and weak convergence results in Skorokhod spaces.Comment: Accepted for publication in Math Population Studie

Large evolution of the bilinear Higgs coupling parameter in SUSY models and reduction of phase sensitivity

The phases in a generic low-energy supersymmetric model are severely constrained by the experimental upper bounds on the electric dipole moments of the electron and the neutron. Coupled with the requirement of radiative electroweak symmetry breaking, this results in a large degree of fine tuning of the phase parameters at the unification scale. In supergravity type models, this corresponds to very highly tuned values for the phases of the bilinear Higgs coupling parameter $B$ and the universal trilinear coupling $A_0$. We identify a cancellation/enhancement mechanism associated with the renormalization group evolution of $B$, which, in turn, reduces such fine-tuning quite appreciably without taking recourse to very large masses for the supersymmetric partners. We find a significant amount of reduction of this fine-tuning in nonuniversal gaugino mass models that do not introduce any new phases.Comment: Version to appear in Phys.Rev.D. Insignificant changes like a few typos corrected. 26 pages, 7 figures, LaTe

Development of a minigenome cassette for Lettuce necrotic yellows virus: A first step in rescuing a plant cytorhabdovirus

Rhabdoviruses are enveloped negative-sense RNA viruses that have numerous biotechnological applications. However, recovering plant rhabdoviruses from cDNA remains difficult due to technical difficulties such as the need for concurrent in planta expression of the viral genome together with the viral nucleoprotein (N), phosphoprotein (P) and RNA-dependent RNA polymerase (L) and viral genome instability in E. coli. Here, we developed a negative-sense minigenome cassette for Lettuce necrotic yellows virus (LNYV). We introduced introns into the unstable viral ORF and employed Agrobacterium tumefaciens to co-infiltrate Nicotiana with the genes for the N, P, and L proteins together with the minigenome cassette. The minigenome cassette included the Discosoma sp. red fluorescent protein gene (DsRed) cloned in the negative-sense between the viral trailer and leader sequences which were placed between hammerhead and hepatitis delta ribozymes. In planta DsRed expression was demonstrated by western blotting while the appropriate splicing of introduced introns was confirmed by sequencing of RT-PCR product

Optimal projection of observations in a Bayesian setting

Optimal dimensionality reduction methods are proposed for the Bayesian inference of a Gaussian linear model with additive noise in presence of overabundant data. Three different optimal projections of the observations are proposed based on information theory: the projection that minimizes the Kullback-Leibler divergence between the posterior distributions of the original and the projected models, the one that minimizes the expected Kullback-Leibler divergence between the same distributions, and the one that maximizes the mutual information between the parameter of interest and the projected observations. The first two optimization problems are formulated as the determination of an optimal subspace and therefore the solution is computed using Riemannian optimization algorithms on the Grassmann manifold. Regarding the maximization of the mutual information, it is shown that there exists an optimal subspace that minimizes the entropy of the posterior distribution of the reduced model; a basis of the subspace can be computed as the solution to a generalized eigenvalue problem; an a priori error estimate on the mutual information is available for this particular solution; and that the dimensionality of the subspace to exactly conserve the mutual information between the input and the output of the models is less than the number of parameters to be inferred. Numerical applications to linear and nonlinear models are used to assess the efficiency of the proposed approaches, and to highlight their advantages compared to standard approaches based on the principal component analysis of the observations

On the EDM Cancellations in D-brane models

We analyze the possibility of simultaneous electron, neutron, and mercury electric dipole moment (EDM) cancellations in the mSUGRA and D--brane models. We find that the mercury EDM constraint practically rules out the cancellation scenario in D-brane models whereas in the context of mSUGRA it is still allowed with some fine-tuning.Comment: 10 pages, to appear in Phys. Rev. Let

Emerging role of nuclear factor erythroid 2-related factor 2 in the mechanism of action and resistance to anticancer therapies

Nuclear factor E2-related factor 2 (NRF2), a transcription factor, is a master regulator of an array of genes related to oxidative and electrophilic stress that promote and maintain redox homeostasis. NRF2 function is well studied in in vitro, animal and general physiology models. However, emerging data has uncovered novel functionality of this transcription factor in human diseases such as cancer, autism, anxiety disorders and diabetes. A key finding in these emerging roles has been its constitutive upregulation in multiple cancers promoting pro-survival phenotypes. The survivability pathways in these studies were mostly explained by classical NRF2 activation involving KEAP-1 relief and transcriptional induction of reactive oxygen species (ROS) neutralizing and cytoprotective drug-metabolizing enzymes (phase I, II, III and 0). Further, NRF2 status and activation is associated with lowered cancer therapeutic efficacy and the eventual emergence of therapeutic resistance. Interestingly, we and others have provided further evidence of direct NRF2 regulation of anticancer drug targets like receptor tyrosine kinases and DNA damage and repair proteins and kinases with implications for therapy outcome. This novel finding demonstrates a renewed role of NRF2 as a key modulatory factor informing anticancer therapeutic outcomes, which extends beyond its described classical role as a ROS regulator. This review will provide a knowledge base for these emerging roles of NRF2 in anticancer therapies involving feedback and feed forward models and will consolidate and present such findings in a systematic manner. This places NRF2 as a key determinant of action, effectiveness and resistance to anticancer therapy

Coordinate Transformation and Polynomial Chaos for the Bayesian Inference of a Gaussian Process with Parametrized Prior Covariance Function

This paper addresses model dimensionality reduction for Bayesian inference based on prior Gaussian fields with uncertainty in the covariance function hyper-parameters. The dimensionality reduction is traditionally achieved using the Karhunen-\Loeve expansion of a prior Gaussian process assuming covariance function with fixed hyper-parameters, despite the fact that these are uncertain in nature. The posterior distribution of the Karhunen-Lo\`{e}ve coordinates is then inferred using available observations. The resulting inferred field is therefore dependent on the assumed hyper-parameters. Here, we seek to efficiently estimate both the field and covariance hyper-parameters using Bayesian inference. To this end, a generalized Karhunen-Lo\`{e}ve expansion is derived using a coordinate transformation to account for the dependence with respect to the covariance hyper-parameters. Polynomial Chaos expansions are employed for the acceleration of the Bayesian inference using similar coordinate transformations, enabling us to avoid expanding explicitly the solution dependence on the uncertain hyper-parameters. We demonstrate the feasibility of the proposed method on a transient diffusion equation by inferring spatially-varying log-diffusivity fields from noisy data. The inferred profiles were found closer to the true profiles when including the hyper-parameters' uncertainty in the inference formulation.Comment: 34 pages, 17 figure