Classical Theory of Optical Nonlinearity in Conducting Nanoparticles

We develop a classical theory of electron confinement in conducting nanoparticles. The theory is used to compute the nonlinear optical response of the nanoparticle to a harmonic external field.Comment: Page margins have been adjusted; otherwise, identical to the previous versio

Bioinformatics

The analysis of gene regulatory networks (GRNs) is a central goal of bioinformatics highly accelerated by the advent of new experimental techniques, such as RNA interference. A battery of reverse engineering methods has been developed in recent years to reconstruct the underlying GRNs from these and other experimental data. However, the performance of the individual methods is poorly understood and validation of algorithmic performances is still missing to a large extent. To enable such systematic validation, we have developed the web application GeNGe (GEne Network GEnerator), a controlled framework for the automatic generation of GRNs. The theoretical model for a GRN is a non-linear differential equation system. Networks can be user-defined or constructed in a modular way with the option to introduce global and local network perturbations. Resulting data can be used, e.g. as benchmark data for evaluating GRN reconstruction methods or for predicting effects of perturbations as theoretical counterparts of biological experiment

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

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

Petri Nets with Fuzzy Logic (PNFL): Reverse Engineering and Parametrization

Background: The recent DREAM4 blind assessment provided a particularly realistic and challenging setting for network reverse engineering methods. The in silico part of DREAM4 solicited the inference of cycle-rich gene regulatory networks from heterogeneous, noisy expression data including time courses as well as knockout, knockdown and multifactorial perturbations. Methodology and Principal Findings: We inferred and parametrized simulation models based on Petri Nets with Fuzzy Logic (PNFL). This completely automated approach correctly reconstructed networks with cycles as well as oscillating network motifs. PNFL was evaluated as the best performer on DREAM4 in silico networks of size 10 with an area under the precision-recall curve (AUPR) of 81%. Besides topology, we inferred a range of additional mechanistic details with good reliability, e.g. distinguishing activation from inhibition as well as dependent from independent regulation. Our models also performed well on new experimental conditions such as double knockout mutations that were not included in the provided datasets. Conclusions: The inference of biological networks substantially benefits from methods that are expressive enough to deal with diverse datasets in a unified way. At the same time, overly complex approaches could generate multiple different models that explain the data equally well. PNFL appears to strike the balance between expressive power and complexity. This also applies to the intuitive representation of PNFL models combining a straightforward graphical notation with colloquial fuzzy parameters

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