Adventitial Tertiary Lymphoid Organs as Potential Source of MicroRNA Biomarkers for Abdominal Aortic Aneurysm

Abdominal aortic aneurysm (AAA) is an inflammatory disease associated with marked changes in the cellular composition of the aortic wall. This study aims to identify microRNA (miRNA) expression in aneurysmal inflammatory cells isolated by laser microdissection from human tissue samples. The distribution of inflammatory cells (neutrophils, B and T lymphocytes, mast cells) was evaluated in human AAA biopsies. We observed in half of the samples that adventitial tertiary lymphoid organs (ATLOs) with a thickness from 0.5 to 2 mm were located exclusively in the adventitia. Out of the 850 miRNA that were screened by microarray in isolated ATLOs (n = 2), 164 miRNAs were detected in ATLOs. The three miRNAs (miR-15a-3p, miR-30a-5p and miR-489-3p) with the highest expression levels were chosen and their expression quantified by RT-PCR in isolated ATLOs (n = 4), M1 (n = 2) and M2 macrophages (n = 2) and entire aneurysmal biopsies (n = 3). Except for the miR-30a-5p, a similar modulation was found in ATLOs and the two subtypes of macrophages. The modulated miRNAs were then evaluated in the plasma of AAA patients for their potential as AAA biomarkers. Our data emphasize the potential of miR-15a-3p and miR-30a-5p as biomarkers of AAA but also as triggers of ATLO evolution. Further investigations will be required to evaluate their targets in order to better understand AAA pathophysiology.     

Transport Map sampling with PGD model reduction for fast dynamical Bayesian data assimilation

The motivation of this work is to address real-time sequential inference of parameters with a full Bayesian formulation. First, the proper generalized decomposition (PGD) is used to reduce the computational evaluation of the posterior density in the online phase. Second, Transport Map sampling is used to build a deterministic coupling between a reference measure and the posterior measure. The determination of the transport maps involves the solution of a minimization problem. As the PGD model is quasi-analytical and under a variable separation form, the use of gradient and Hessian information speeds up the minimization algorithm. Eventually, uncertainty quantification on outputs of interest of the model can be easily performed due to the global feature of the PGD solution over all coordinate domains. Numerical examples highlight the performance of the method.     

OCEAN,a flexible adaptive Network-On-Chip for dynamic applications

The dynamic and partial reconfiguration of FPGAs enables the dynamic placement of applicatives tasks in reconfigurable zones. However, the dynamic management of the tasks impacts the communications since they are not present in the FPGA during all computation time. So, the task manager should ensure the allocation of each new task and their interconnection which is performed by a flexible interconnection network. In this article, various interconnection networks are studied. Each architecture is evaluated with respect to its suitability for the paradigm of the dynamic and partial reconfiguration in FPGA implementations. This study leads us to propose the OCEAN network that supports the communication constraints into the context of dynamic reconfigurations. Thanks to a generic platform allowing in situ characterizations of network performances, fair comparisons of various Networks-On-Chip can be realized. The FPGA and ASICs implementations of the OCEAN network are also discussed.     

Adsorption of block copolymers at latex surface and their utilization in emulsion polymerization

Amphiphilic block copolymers have been investigated for their utilization in emulsion polymerization of butyl methacrylate. Special attention has been paid to the adsorption mechanism of the block copolymers from systematic measurements of equilibrium adsorption isotherms. A series of well-defined water-soluble amphiphilic block copolymers, composed of poly(butyl methacrylate) and poly(sodium methacrylate) blocks, were synthesized by anionic polymerization of butyl methacrylate and tert-butyl methacrylate followed by the thermal deprotection of the tert-butyl ester groups and final hydrolysis. The number density of emulsion polymer particles N_P varied as [copolymer]^α, α lying between 0.44 and 0.73 according to the hydrophilic content of the copolymers. In contrast with SDS taken as a reference emulsifier, the adsorption of the copolymers was very strong and this provided quite an efficient stabilization of the polymer particles during emulsion polymerization, even at low concentrations (<10⁻⁴ mol L⁻¹) and low coverages (<10% of the interfacial area).     

Handling causality and schedulability when designing and prototyping cyber-physical systems

Software and Systems Modeling - Cyber physical systems are built upon digital and analog circuits, making it necessary to handle different models of computation during their design and verification...     

A minimum Hellinger distance estimator for stochastic differential equations: An application to statistical inference for continuous time interest rate models

A minimum disparity estimator minimizes a φ-divergence between the marginal density of a parametric model and its non-parametric estimate. This principle is applied to the estimation of stochastic differential equation models, choosing the Hellinger distance as particular φ-divergence. Under an hypothesis of stationarity, the parametric marginal density is provided by solving the Kolmogorov forward equation. A particular emphasis is put on the non-parametric estimation of the sample marginal density which has to take into account sample dependence and kurtosis. A new window size determination is provided. The classical estimator is presented alternatively as a distance minimizer and as a pseudo-likelihood maximizer. The latter presentation opens the way to Bayesian inference. The method is applied to continuous time models of the interest rate. In particular, various models are tested using alternatively tests and their results are discussed.     

Processing intrusion detection alert aggregates with time series modeling

The main use of intrusion detection systems (IDS) is to detect attacks against information systems and networks. Normal use of the network and its functioning can also be monitored with an IDS. It can be used to control, for example, the use of management and signaling protocols, or the network traffic related to some less critical aspects of system policies. These complementary usages can generate large numbers of alerts, but still, in operational environment, the collection of such data may be mandated by the security policy. Processing this type of alerts presents a different problem than correlating alerts directly related to attacks or filtering incorrectly issued alerts.We aggregate individual alerts to alert flows, and then process the flows instead of individual alerts for two reasons. First, this is necessary to cope with the large quantity of alerts – a common problem among all alert correlation approaches. Second, individual alert’s relevancy is often indeterminable, but irrelevant alerts and interesting phenomena can be identified at the flow level. This is the particularity of the alerts created by the complementary uses of IDSes.Flows consisting of alerts related to normal system behavior can contain strong regularities. We propose to model these regularities using non-stationary autoregressive models. Once modeled, the regularities can be filtered out to relieve the security operator from manual analysis of true, but low impact alerts. We present experimental results using these models to process voluminous alert flows from an operational network.     

Real‐time updating of structural mechanics models using Kalman filtering,modified constitutive relation error,and proper generalized decomposition

The paper aims at proposing a new strategy for real‐time identification or updating of structural mechanics models defined as dynamical systems. The main idea is to introduce the modified constitutive relation error concept, which is a practical tool that enables to efficiently solve identification problems with highly corrupted data, into the Kalman filtering, which is a classical framework for data assimilation. Furthermore, a PGD‐based model reduction method is performed in order to optimize capabilities of the online updating strategy. Performances of the proposed approach, in terms of robustness gain and computational cost reduction, are illustrated on several unsteady thermal applications. Copyright © 2015 John Wiley & Sons, Ltd.     

High Efficiency Poly(acrylonitrile) Electrospun Nanofiber Membranes for Airborne Nanomaterials Filtration

The potential of poly(acrylonitrile) electrospun membranes with tuneable pore size and fiber distributions were investigated for airborne fine‐particle filtration for the first time. The impact of solution concentration on final membrane properties are evaluated for the purpose of designing separation materials with higher separation efficiency. The properties of fibers and membranes are investigated systematically: the average pore distribution, as characterized by capillary flow porometry, and thermo‐mechanical properties of the mats are found to be dependent on fiber diameter and on specific electrospinning conditions. Filtration efficiency and pressure drop are calculated from measurement of penetration through the membranes using potassium chloride (KCl) aerosol particles ranging from 300 nm to 12 μm diameter. The PAN membranes exhibited separation efficiencies in the range of 73.8–99.78% and a typical quality factor 0.0224 (1 Pa^?1) for 12 wt% PAN with nanofibers having a diameter of 858 nm. Concerning air flow rate, the quality factor and filtration efficiency of the electrospun membranes at higher face velocity are much more stable than for commercial membranes. The results suggest that the structure of electrospun membranes is the best for air filtration in terms of filtration stability at high air flow rate.