Landau damping in thin films irradiated by a strong laser field

The rate of linear collisionless damping (Landau damping) in a classical electron gas confined to a heated ionized thin film is calculated. The general expression for the imaginary part of the dielectric tensor in terms of the parameters of the single-particle self-consistent electron potential is obtained. For the case of a deep rectangular well, it is explicitly calculated as a function of the electron temperature in the two limiting cases of specular and diffuse reflection of the electrons from the boundary of the self-consistent potential. For realistic experimental parameters, the contribution of Landau damping to the heating of the electron subsystem is estimated. It is shown that for films with a thickness below about 100 nm and for moderate laser intensities it may be comparable with or even dominate over electron-ion collisions and inner ionization.Comment: 15 pages, 2 figure

Size-Dependent Surface Plasmon Dynamics in Metal Nanoparticles

We study the effect of Coulomb correlations on the ultrafast optical dynamics of small metal particles. We demonstrate that a surface-induced dynamical screening of the electron-electron interactions leads to quasiparticle scattering with collective surface excitations. In noble-metal nanoparticles, it results in an interband resonant scattering of d-holes with surface plasmons. We show that this size-dependent many-body effect manifests itself in the differential absorption dynamics for frequencies close to the surface plasmon resonance. In particular, our self-consistent calculations reveal a strong frequency dependence of the relaxation, in agreement with recent femtosecond pump-probe experiments.Comment: 8 pages + 4 figures, final version accepted to PR

Size-dependent Correlation Effects in Ultrafast Optical Dynamics of Metal Nanoparticles

We study the role of collective surface excitations in the electron relaxation in small metal particles. We show that the dynamically screened electron-electron interaction in a nanoparticle contains a size-dependent correction induced by the surface. This leads to new channels of quasiparticle scattering accompanied by the emission of surface collective excitations. We calculate the energy and temperature dependence of the corresponding rates, which depend strongly on the nanoparticle size. We show that the surface-plasmon-mediated scattering rate of a conduction electron increases with energy, in contrast to that mediated by a bulk plasmon. In noble-metal particles, we find that the dipole collective excitations (surface plasmons) mediate a resonant scattering of d-holes to the conduction band. We study the role of the latter effect in the ultrafast optical dynamics of small nanoparticles and show that, with decreasing nanoparticle size, it leads to a drastic change in the differential absorption lineshape and a strong frequency dependence of the relaxation near the surface plasmon resonance. The experimental implications of our results in ultrafast pump-probe spectroscopy are also discussed.Comment: 29 pages including 6 figure

An HDG Method for Dirichlet Boundary Control of Convection Dominated Diffusion PDE

We first propose a hybridizable discontinuous Galerkin (HDG) method to approximate the solution of a \emph{convection dominated} Dirichlet boundary control problem. Dirichlet boundary control problems and convection dominated problems are each very challenging numerically due to solutions with low regularity and sharp layers, respectively. Although there are some numerical analysis works in the literature on \emph{diffusion dominated} convection diffusion Dirichlet boundary control problems, we are not aware of any existing numerical analysis works for convection dominated boundary control problems. Moreover, the existing numerical analysis techniques for convection dominated PDEs are not directly applicable for the Dirichlet boundary control problem because of the low regularity solutions. In this work, we obtain an optimal a priori error estimate for the control under some conditions on the domain and the desired state. We also present some numerical experiments to illustrate the performance of the HDG method for convection dominated Dirichlet boundary control problems

Controlling the shape of a quantum wavefunction

The ability to control the shape and motion of quantum states(1,2) may lead to methods for bond-selective chemistry and novel quantum technologies, such as quantum computing. The classical coherence of laser light has been used to guide quantum systems into desired target states through interfering pathways(3-5). These experiments used the control of target properties-such as fluorescence from a dye solution(6), the current in a semiconductor(7,8) 8 Or the dissociation fraction of an excited molecule(9)-to infer control over the quantum state. Here we report a direct approach to coherent quantum control that allows us to actively manipulate the shape of an atomic electron's radial wavefunction, We use a computer-controlled laser to excite a coherent state in atomic caesium. The shape of the wavefunction is then measured(10) and the information fed back into the laser control system, which reprograms the optical field. The process is iterated until the measured shape of the wavefunction matches that of a target wavepacket, established at the start of the experiment. We find that, using a variation of quantum holography(11) to reconstruct the measured wavefunction, the quantum state can be reshaped to match the target within two iterations of the feedback loop.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62625/1/397233a0.pd

Insights gained from the reverse engineering of gene networks in keloid fibroblasts

<p>Abstract</p> <p>Background</p> <p>Keloids are protrusive claw-like scars that have a propensity to recur even after surgery, and its molecular etiology remains elusive. The goal of reverse engineering is to infer gene networks from observational data, thus providing insight into the inner workings of a cell. However, most attempts at modeling biological networks have been done using simulated data. This study aims to highlight some of the issues involved in working with experimental data, and at the same time gain some insights into the transcriptional regulatory mechanism present in keloid fibroblasts.</p> <p>Methods</p> <p>Microarray data from our previous study was combined with microarray data obtained from the literature as well as new microarray data generated by our group. For the physical approach, we used the fREDUCE algorithm for correlating expression values to binding motifs. For the influence approach, we compared the Bayesian algorithm BANJO with the information theoretic method ARACNE in terms of performance in recovering known influence networks obtained from the KEGG database. In addition, we also compared the performance of different normalization methods as well as different types of gene networks.</p> <p>Results</p> <p>Using the physical approach, we found consensus sequences that were active in the keloid condition, as well as some sequences that were responsive to steroids, a commonly used treatment for keloids. From the influence approach, we found that BANJO was better at recovering the gene networks compared to ARACNE and that transcriptional networks were better suited for network recovery compared to cytokine-receptor interaction networks and intracellular signaling networks. We also found that the NFKB transcriptional network that was inferred from normal fibroblast data was more accurate compared to that inferred from keloid data, suggesting a more robust network in the keloid condition.</p> <p>Conclusions</p> <p>Consensus sequences that were found from this study are possible transcription factor binding sites and could be explored for developing future keloid treatments or for improving the efficacy of current steroid treatments. We also found that the combination of the Bayesian algorithm, RMA normalization and transcriptional networks gave the best reconstruction results and this could serve as a guide for future influence approaches dealing with experimental data.</p

Discretization Provides a Conceptually Simple Tool to Build Expression Networks

Biomarker identification, using network methods, depends on finding regular co-expression patterns; the overall connectivity is of greater importance than any single relationship. A second requirement is a simple algorithm for ranking patients on how relevant a gene-set is. For both of these requirements discretized data helps to first identify gene cliques, and then to stratify patients

Learning Transcriptional Regulatory Relationships Using Sparse Graphical Models

Understanding the organization and function of transcriptional regulatory networks by analyzing high-throughput gene expression profiles is a key problem in computational biology. The challenges in this work are 1) the lack of complete knowledge of the regulatory relationship between the regulators and the associated genes, 2) the potential for spurious associations due to confounding factors, and 3) the number of parameters to learn is usually larger than the number of available microarray experiments. We present a sparse (L1 regularized) graphical model to address these challenges. Our model incorporates known transcription factors and introduces hidden variables to represent possible unknown transcription and confounding factors. The expression level of a gene is modeled as a linear combination of the expression levels of known transcription factors and hidden factors. Using gene expression data covering 39,296 oligonucleotide probes from 1109 human liver samples, we demonstrate that our model better predicts out-of-sample data than a model with no hidden variables. We also show that some of the gene sets associated with hidden variables are strongly correlated with Gene Ontology categories. The software including source code is available at http://grnl1.codeplex.com

Bagging Statistical Network Inference from Large-Scale Gene Expression Data

Modern biology and medicine aim at hunting molecular and cellular causes of biological functions and diseases. Gene regulatory networks (GRN) inferred from gene expression data are considered an important aid for this research by providing a map of molecular interactions. Hence, GRNs have the potential enabling and enhancing basic as well as applied research in the life sciences. In this paper, we introduce a new method called BC3NET for inferring causal gene regulatory networks from large-scale gene expression data. BC3NET is an ensemble method that is based on bagging the C3NET algorithm, which means it corresponds to a Bayesian approach with noninformative priors. In this study we demonstrate for a variety of simulated and biological gene expression data from S. cerevisiae that BC3NET is an important enhancement over other inference methods that is capable of capturing biochemical interactions from transcription regulation and protein-protein interaction sensibly. An implementation of BC3NET is freely available as an R package from the CRAN repository