A Multi-commodity network flow model for cloud service environments

Next-generation systems, such as the big data cloud, have to cope with several challenges, e.g., move of excessive amount of data at a dictated speed, and thus, require the investigation of concepts additional to security in order to ensure their orderly function. Resilience is such a concept, which when ensured by systems or networks they are able to provide and maintain an acceptable level of service in the face of various faults and challenges. In this paper, we investigate the multi-commodity flows problem, as a task within our D 2 R 2 +DR resilience strategy, and in the context of big data cloud systems. Specifically, proximal gradient optimization is proposed for determining optimal computation flows since such algorithms are highly attractive for solving big data problems. Many such problems can be formulated as the global consensus optimization ones, and can be solved in a distributed manner by the alternating direction method of multipliers (ADMM) algorithm. Numerical evaluation of the proposed model is carried out in the context of specific deployments of a situation-aware information infrastructure

Tracking Target Signal Strengths on a Grid using Sparsity

Multi-target tracking is mainly challenged by the nonlinearity present in the measurement equation, and the difficulty in fast and accurate data association. To overcome these challenges, the present paper introduces a grid-based model in which the state captures target signal strengths on a known spatial grid (TSSG). This model leads to \emph{linear} state and measurement equations, which bypass data association and can afford state estimation via sparsity-aware Kalman filtering (KF). Leveraging the grid-induced sparsity of the novel model, two types of sparsity-cognizant TSSG-KF trackers are developed: one effects sparsity through $\ell_1$-norm regularization, and the other invokes sparsity as an extra measurement. Iterative extended KF and Gauss-Newton algorithms are developed for reduced-complexity tracking, along with accurate error covariance updates for assessing performance of the resultant sparsity-aware state estimators. Based on TSSG state estimates, more informative target position and track estimates can be obtained in a follow-up step, ensuring that track association and position estimation errors do not propagate back into TSSG state estimates. The novel TSSG trackers do not require knowing the number of targets or their signal strengths, and exhibit considerably lower complexity than the benchmark hidden Markov model filter, especially for a large number of targets. Numerical simulations demonstrate that sparsity-cognizant trackers enjoy improved root mean-square error performance at reduced complexity when compared to their sparsity-agnostic counterparts.Comment: Submitted to IEEE Trans. on Signal Processin

Architecture of Pol II(G) and molecular mechanism of transcription regulation by Gdown1.

Tight binding of Gdown1 represses RNA polymerase II (Pol II) function in a manner that is reversed by Mediator, but the structural basis of these processes is unclear. Although Gdown1 is intrinsically disordered, its Pol II interacting domains were localized and shown to occlude transcription factor IIF (TFIIF) and transcription factor IIB (TFIIB) binding by perfect positioning on their Pol II interaction sites. Robust binding of Gdown1 to Pol II is established by cooperative interactions of a strong Pol II binding region and two weaker binding modulatory regions, thus providing a mechanism both for tight Pol II binding and transcription inhibition and for its reversal. In support of a physiological function for Gdown1 in transcription repression, Gdown1 co-localizes with Pol II in transcriptionally silent nuclei of early Drosophila embryos but re-localizes to the cytoplasm during zygotic genome activation. Our study reveals a self-inactivation through Gdown1 binding as a unique mode of repression in Pol II function

Enhancement of immune response of HBsAg loaded poly(L-lactic acid) microspheres against Hepatitis B through incorporation of alum and chitosan

Purpose: Poly (L-lactic acid) (PLA) microparticles encapsulating Hepatitis B surface antigen (HBsAg) with alum and chitosan were investigated for their potential as a vaccine delivery system. Methods: The microparticles, prepared using a water-in-oil-in-water (w/o/w) double emulsion solvent evaporation method with polyvinyl alcohol (PVA) or chitosan as the external phase stabilising agent showed a significant increase in the encapsulation efficiency of the antigen. Results: PLA-Alum and PLA-chitosan microparticles induced HBsAg serum specific IgG antibody responses significantly higher than PLA only microparticles and free antigen following subcutaneous administration. Chitosan not only imparted a positive charge to the surface of the microparticles but was also able to increase the serum specific IgG antibody responses significantly. Conclusions: The cytokine assays showed that the serum IgG antibody response induced is different according to the formulation, indicated by the differential levels of interleukin 4 (IL-4), interleukin 6 (IL-6) and interferon gamma (IFN-γ). The microparticles eliciting the highest IgG antibody response did not necessarily elicit the highest levels of the cytokines IL-4, IL-6 and IFN-γ

Examining the Heterogeneous Genome Content of Multipartite Viruses BMV and CCMV by Native Mass Spectrometry

Since the concept was first introduced by Brian Chait and co-workers in 1991, mass spectrometry of proteins and protein complexes under non-denaturing conditions (native MS) has strongly developed, through parallel advances in instrumentation, sample preparation, and data analysis tools. However, the success rate of native MS analysis, particularly in heterogeneous mega-Dalton (MDa) protein complexes, still strongly depends on careful instrument modification. Here, we further explore these boundaries in native mass spectrometry, analyzing two related endogenous multipartite viruses: the Brome Mosaic Virus (BMV) and the Cowpea Chlorotic Mottle Virus (CCMV). Both CCMV and BMV are approximately 4.6 megadalton (MDa) in mass, of which approximately 1 MDA originates from the genomic content of the virion. Both viruses are produced as mixtures of three particles carrying different segments of the genome, varying by approximately 0.1 MDA in mass (~2%). This mixture of particles poses a challenging analytical problem for high-resolution native MS analysis, given the large mass scales involved. We attempt to unravel the particle heterogeneity using both Q-TOF and Orbitrap mass spectrometers extensively modified for analysis of very large assemblies. We show that manipulation of the charging behavior can provide assistance in assigning the correct charge states. Despite their challenging size and heterogeneity, we obtained native mass spectra with resolved series of charge states for both BMV and CCMV, demonstrating that native MS of endogenous multipartite virions is feasible. [Figure: see text] ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s13361-016-1348-6) contains supplementary material, which is available to authorized users

Analyzing the Subspace Structure of Related Images: Concurrent Segmentation of Image Sets

Abstract. We develop new algorithms to analyze and exploit the joint subspace structure of a set of related images to facilitate the process of concurrent segmentation of a large set of images. Most existing ap-proaches for this problem are either limited to extracting a single similar object across the given image set or do not scale well to a large number of images containing multiple objects varying at different scales. One of the goals of this paper is to show that various desirable properties of such an algorithm (ability to handle multiple images with multiple ob-jects showing arbitary scale variations) can be cast elegantly using simple constructs from linear algebra: this significantly extends the operating range of such methods. While intuitive, this formulation leads to a hard optimization problem where one must perform the image segmentation task together with appropriate constraints which enforce desired alge-braic regularity (e.g., common subspace structure). We propose efficient iterative algorithms (with small computational requirements) whose key steps reduce to objective functions solvable by max-flow and/or nearly closed form identities. We study the qualitative, theoretical, and empiri-cal properties of the method, and present results on benchmark datasets.

A multi-functional fluorescent scaffold as a multi-colour probe: design and application in targeted cell imaging

WOS: 000362438300082A novel scaffold material based on a novel targeting strategy has been developed, benefiting from recent progress in the development of fluorescent bioprobes. This concept suggests that several specifications which are desired for cancer cell targeting and imaging studies can be satisfied at the same time in one multifunctional scaffold. Besides, such scaffolds exhibit multi-colour properties when combined with a targeting moiety. For this purpose, a fluorescent and functional monomer, 3-(1H-phenanthro[9,10-d] imidazol-2-yl) phenol (PIP) and an antibody labelling kit (CF555) were merged on the same scaffold to generate the proposed bioprobe. This design offers multicolour cell images by emitting at dual wavelengths with no quenching in its fluorescent property. Also, pendant alcohol groups in the structure of PIP enable covalent attachment to labelled protein, CF555/anti-CD44 in order to enhance the biological activity and specificity towards the target. After combining with the targeting moiety, the bioconjugate was characterized, tested for in vitro studies, and the cellular internalization was monitored in live cells via the fluorescence microscope technique. The present work with such a strategy explores the potential use of the proposed fluorescent probe for the first time. The aim is to achieve targeted imaging of CD44 positive U87-MG cancer cells and determine specific cellular labelling via fluorescence imaging and flow cytometry experiments