Exact reconstruction of gene regulatory networks using compressive sensing.

BackgroundWe consider the problem of reconstructing a gene regulatory network structure from limited time series gene expression data, without any a priori knowledge of connectivity. We assume that the network is sparse, meaning the connectivity among genes is much less than full connectivity. We develop a method for network reconstruction based on compressive sensing, which takes advantage of the network's sparseness.ResultsFor the case in which all genes are accessible for measurement, and there is no measurement noise, we show that our method can be used to exactly reconstruct the network. For the more general problem, in which hidden genes exist and all measurements are contaminated by noise, we show that our method leads to reliable reconstruction. In both cases, coherence of the model is used to assess the ability to reconstruct the network and to design new experiments. We demonstrate that it is possible to use the coherence distribution to guide biological experiment design effectively. By collecting a more informative dataset, the proposed method helps reduce the cost of experiments. For each problem, a set of numerical examples is presented.ConclusionsThe method provides a guarantee on how well the inferred graph structure represents the underlying system, reveals deficiencies in the data and model, and suggests experimental directions to remedy the deficiencies

Exfoliated Graphite Nanoplatelet-Carbon Nanotube Hybrid Composites for Compression Sensing

In this study, we investigated the gauge factor and compressive modulus of hybrid nanocomposites of exfoliated graphite nanoplatelets (xGnP) and multiwalled carbon nanotubes (MWCNTs) in a polydimethylsiloxane matrix under compressive strain. Mechanical and electrical tests were conducted to investigate the effects of nanofiller wt %, the xGnP size, and xGnP:MWCNT ratio on the compressive modulus and sensitivity of the sensors. It was found that nanofiller wt %, the xGnP size, and xGnP:MWCNT ratio significantly affect the electromechanical properties of the sensor. The compressive modulus increased with an increase in the nanofiller wt % and a decrease in the xGnP size and xGnP:MWCNT ratio. However, the gauge factor decreases with a decrease in the nanofiller wt % and xGnP size and an increase in the xGnP:MWCNT ratio. Therefore, by investigating the piezoresistive effects of various factors for sensing performance, such as wt %, xGnP size, and xGnP:MWCNT ratio, the concept of one- and two-dimensional hybrid fillers provides an effective way to tune both mechanical properties and sensitivity of nanocomposites by tailoring the network structure of fillers

Large Electronic Anisotropy and Enhanced Chemical Activity of Highly Rippled Phosphorene

We investigate the electronic structure and chemical activity of rippled phosphorene induced by large compressive strains via first-principles calculation. It is found that phosphorene is extraordinarily bendable, enabling the accommodation of ripples with large curvatures. Such highly rippled phosphorene shows a strong anisotropy in electronic properties. For ripples along the armchair direction, the band gap changes from 0.84 to 0.51 eV for the compressive strain up to -20% and further compression shows no significant effect, for ripples along the zigzag direction, semiconductor to metal transition occurs. Within the rippled phosphorene, the local electronic properties, such as the modulated band gap and the alignments of frontier orbitals, are found to be highly spatially dependent, which may be used for modulating the injection and confinement of carriers for optical and photovoltaic applications. The examination of the interaction of a physisorbed NO molecule with the rippled phosphorene under different compressive strains shows that the chemical activities of the phosphorene are significantly enhanced at the top and bottom peaks of the ripples, indicated by the enhanced adsorption and charge transfer between them. All these features can be ascribed to the effect of curvatures, which modifies the orbital coupling between atoms at the ripple peaks

Efficient Image Processing Via Compressive Sensing Of Integrate-And-Fire Neuronal Network Dynamics

Integrate-and-fire (I&F) neuronal networks are ubiquitous in diverse image processing applications, including image segmentation and visual perception. While conventional I&F network image processing requires the number of nodes composing the network to be equal to the number of image pixels driving the network, we determine whether I&F dynamics can accurately transmit image information when there are significantly fewer nodes than network input-signal components. Although compressive sensing (CS) theory facilitates the recovery of images using very few samples through linear signal processing, it does not address whether similar signal recovery techniques facilitate reconstructions through measurement of the nonlinear dynamics of an I&F network. In this paper, we present a new framework for recovering sparse inputs of nonlinear neuronal networks via compressive sensing. By recovering both one-dimensional inputs and two-dimensional images, resembling natural stimuli, we demonstrate that input information can be well-preserved through nonlinear I&F network dynamics even when the number of network-output measurements is significantly smaller than the number of input-signal components. This work suggests an important extension of CS theory potentially useful in improving the processing of medical or natural images through I&F network dynamics and understanding the transmission of stimulus information across the visual system

Compressive high-frequency waves riding on an Alfv\'en/ion-cyclotron wave in a multi-fluid plasma

In this paper, we study the weakly-compressive high-frequency plasma waves which are superposed on a large-amplitude Alfv\'en wave in a multi-fluid plasma consisting of protons, electrons, and alpha particles. For these waves, the plasma environment is inhomogenous due to the presence of the low-frequency Alfv\'en wave with a large amplitude, a situation that may apply to space plasmas such as the solar corona and solar wind. The dispersion relation of the plasma waves is determined from a linear stability analysis using a new eigenvalue method that is employed to solve the set of differential wave equations which describe the propagation of plasma waves along the direction of the constant component of the Alfv\'en wave magnetic field. This approach also allows one to consider weak compressive effects. In the presence of the background Alfv\'en wave, the dispersion branches obtained differ significantly from the situation of a uniform plasma. Due to compressibility, acoustic waves are excited and couplings between various modes occur, and even an instability of the compressive mode. In a kinetic treatment, these plasma waves would be natural candidates for Landau-resonant wave-particle interactions, and may thus via their damping lead to particle heating.Comment: 15 pages, 5 figure