Molecular Characterization of Zebrafish Interferon, MX, and MX Promoter

Type I interferons (IFNs) represent a family of biological molecules whose antiviral, antitumor, and immunomodulatory role is well known. IFNs were first identified in the 1950\u27s and have since been used extensively for the treatment of various cancers, and viral infections. In order to more fully characterize the IFN response, it is often necessary to use animal models. Although the mouse has been used extensively for IFN studies, a lower order vertebrate model is also desirable, as it would provide information about the structure and function of a more ancestral IFN. To this end, herein is described the cloning and characterization of an IFN gene from the zebrafish, Danio rerio, as well as the IFN-inducible gene Mx. Zebrafish IFN (zflFN) has a nucleotide sequence of 558 bases in length, with a deduced amino acid sequence 185 residues in length. Alignment with known IFN sequences reveals a low but significant similarity at the amino acid level, indicating the distant evolutionary relationship of zflFN to mammalian IFNs. To further characterize zflFN, zebrafish liver cells in culture were treated with the synthetic double-stranded RNA molecule polyinosinic:polycytidylic acid (Poly IC), which acts as a viral mimetic and thus an IFN-inducer. Analysis of messenger RNA (mRNA) levels at various times post-induction revealed maximal expression of zflFN mRNA at six and 12 hours postinduction, with a dramatic decrease to basal expression levels by 24 hours. This expression profile fits the pattern of early induction and rapid degradation of mRNA that is a hallmark of higher order vertebrate IFNs, and thus lends hrther support to the role of zflFN as an evolutionary precursor to mammalian IFN. To demonstrate the antiviral activity of zfIFN, zebrafish cells were transiently transfected with an expression construct containing zfIFN DNA and subsequently infected with virus. Cells transfected with zflFN showed a 36% reduction in the number of plaques formed, compared to cells that were not treated with zfIFN. Having determined the validity of zflFN as a true member of the IFN family the next step was to characterize the regulatory effect of zflFN on the zebrafish antiviral gene Mx (zfMx). Zebrafish liver cells produced high levels of zfMx mRNA in response to induction by Poly IC, with peak expression at 24 hours post-induction, indicating upregulation of zfMx by zflFN. To further characterize this regulation, the zfMx promoter region was cloned and inserted upstream of a reporter gene. Addition of zfIFN to cells transfected with the zfMx promoter resulted in high level expression of the reporter gene. Examination of the zfMx promoter revealed the presence of two DNA elements known to bind IFN-inducible transcription factors. Deletion of these elements from the zfMx promoter led to a marked reduction in reporter gene expression, demonstrating the importance of these elements in zflFN-induced upregulation of zfMx. Together, these data definitively prove the existence of IFN in a lower order vertebrate, as well as provide a mechanism for the regulation of zfMx by zflFN. Conservation of this pathway throughout evolution indicates its success in dealing with viral invasion

Fast online 3D reconstruction of dynamic scenes from individual single-photon detection events

In this paper, we present an algorithm for online 3D reconstruction of dynamic scenes using individual times of arrival (ToA) of photons recorded by single-photon detector arrays. One of the main challenges in 3D imaging using single-photon Lidar is the integration time required to build ToA histograms and reconstruct reliable 3D profiles in the presence of non-negligible ambient illumination. This long integration time also prevents the analysis of rapid dynamic scenes using existing techniques. We propose a new method which does not rely on the construction of ToA histograms but allows, for the first time, individual detection events to be processed online, in a parallel manner in different pixels, while accounting for the intrinsic spatiotemporal structure of dynamic scenes. Adopting a Bayesian approach, a Bayesian model is constructed to capture the dynamics of the 3D profile and an approximate inference scheme based on assumed density filtering is proposed, yielding a fast and robust reconstruction algorithm able to process efficiently thousands to millions of frames, as usually recorded using single-photon detectors. The performance of the proposed method, able to process hundreds of frames per second, is assessed using a series of experiments conducted with static and dynamic 3D scenes and the results obtained pave the way to a new family of real-time 3D reconstruction solutions

Bayesian nonlinear hyperspectral unmixing with spatial residual component analysis

This paper presents a new Bayesian model and algorithm for nonlinear unmixing of hyperspectral images. The model proposed represents the pixel reflectances as linear combinations of the endmembers, corrupted by nonlinear (with respect to the endmembers) terms and additive Gaussian noise. Prior knowledge about the problem is embedded in a hierarchical model that describes the dependence structure between the model parameters and their constraints. In particular, a gamma Markov random field is used to model the joint distribution of the nonlinear terms, which are expected to exhibit significant spatial correlations. An adaptive Markov chain Monte Carlo algorithm is then proposed to compute the Bayesian estimates of interest and perform Bayesian inference. This algorithm is equipped with a stochastic optimisation adaptation mechanism that automatically adjusts the parameters of the gamma Markov random field by maximum marginal likelihood estimation. Finally, the proposed methodology is demonstrated through a series of experiments with comparisons using synthetic and real data and with competing state-of-the-art approaches

Quantum-inspired computational imaging

Computational imaging combines measurement and computational methods with the aim of forming images even when the measurement conditions are weak, few in number, or highly indirect. The recent surge in quantum-inspired imaging sensors, together with a new wave of algorithms allowing on-chip, scalable and robust data processing, has induced an increase of activity with notable results in the domain of low-light flux imaging and sensing. We provide an overview of the major challenges encountered in low-illumination (e.g., ultrafast) imaging and how these problems have recently been addressed for imaging applications in extreme conditions. These methods provide examples of the future imaging solutions to be developed, for which the best results are expected to arise from an efficient codesign of the sensors and data analysis tools.Y.A. acknowledges support from the UK Royal Academy of Engineering under the Research Fellowship Scheme (RF201617/16/31). S.McL. acknowledges financial support from the UK Engineering and Physical Sciences Research Council (grant EP/J015180/1). V.G. acknowledges support from the U.S. Defense Advanced Research Projects Agency (DARPA) InPho program through U.S. Army Research Office award W911NF-10-1-0404, the U.S. DARPA REVEAL program through contract HR0011-16-C-0030, and U.S. National Science Foundation through grants 1161413 and 1422034. A.H. acknowledges support from U.S. Army Research Office award W911NF-15-1-0479, U.S. Department of the Air Force grant FA8650-15-D-1845, and U.S. Department of Energy National Nuclear Security Administration grant DE-NA0002534. D.F. acknowledges financial support from the UK Engineering and Physical Sciences Research Council (grants EP/M006514/1 and EP/M01326X/1). (RF201617/16/31 - UK Royal Academy of Engineering; EP/J015180/1 - UK Engineering and Physical Sciences Research Council; EP/M006514/1 - UK Engineering and Physical Sciences Research Council; EP/M01326X/1 - UK Engineering and Physical Sciences Research Council; W911NF-10-1-0404 - U.S. Defense Advanced Research Projects Agency (DARPA) InPho program through U.S. Army Research Office; HR0011-16-C-0030 - U.S. DARPA REVEAL program; 1161413 - U.S. National Science Foundation; 1422034 - U.S. National Science Foundation; W911NF-15-1-0479 - U.S. Army Research Office; FA8650-15-D-1845 - U.S. Department of the Air Force; DE-NA0002534 - U.S. Department of Energy National Nuclear Security Administration)Accepted manuscrip

A Novel Algorithm for the Identification of Dirac Impulses from Filtered Noisy Measurements

International audienceIn this paper we address the recovery of a finite stream of Dirac pulses from noisy lowpass-filtered samples in the discrete-time setting. While this problem has been successfully addressed for the noise-free case using the concept of signals with finite rate of innovation, such techniques are not efficient in the presence of noise. In the FRI framework, the determination of the location of Dirac pulses is based on the singular value decomposition of a matrix whose rank in the noise-free case equals the number of Dirac pulses and the signal can be related to the non zero singular values. However, in noisy situations this matrix becomes full rank and the singular value decomposition is subject to subspace swap, meaning some singular values associated with noise become larger than some values related to the signal. This phenomenon has been recognized as the reason for performance breakdown in the method. The goal of this paper is to propose a novel algorithm that limits the alteration of these singular values in the presence of noise, thus significantly improving the estimation of Dirac pulses

Fast surface detection in single-photon Lidar waveforms

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3D reconstruction using single-photon Lidar data exploiting the widths of the returns

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Nonlinear hyperspectral unmixing using Gaussian processes

International audienceThis paper presents an unsupervised algorithm for nonlinear unmixing of hyperspectral images. The proposed model assumes that the pixel reflectances result from a nonlinear function of the abundancevectors associated with the pure spectral components. We assume that the spectral signatures of the pure components and the nonlinear function are unknown. The first step of the proposed method estimates the abundance vectors for all the image pixels using a Gaussian process latent variable model. The endmembers are subsequently estimated using Gaussian process regression. The performance of the unmixing strategy is compared with state-of-the-art unmixing strategies on synthetic data. One of the interesting propertiesof the proposed strategy is its robustness to the absence of pure pixels in the image