Macrophage Polarization in Cardiac Tissue Repair Following Myocardial Infarction

Cardiovascular disease is the leading cause of mortality and morbidity around the globe, creating a substantial socio-economic burden as a result. Myocardial infarction is a significant contributor to the detrimental impact of cardiovascular disease. The death of cardiomyocytes following myocardial infarction causes an immune response which leads to further destruction of tissue, and subsequently, results in the formation of non-contractile scar tissue. Macrophages have been recognized as important regulators and participants of inflammation and fibrosis following myocardial infarction. Macrophages are generally classified into two distinct groups, namely, classically activated, or M1 macrophages, and alternatively activated, or M2 macrophages. The phenotypic profile of cardiac macrophages, however, is much more diverse and should not be reduced to these two subsets. In this review, we describe the phenotypes and functions of macrophages which are present in the healthy, as well as the infarcted heart, and analyze them with respect to M1 and M2 polarization states. Furthermore, we discuss therapeutic strategies which utilize macrophage polarization towards an anti-inflammatory or reparative phenotype for the treatment of myocardial infarction.     

Average-case analysis of VCG with approximate resource allocation algorithms

The Vickrey-Clarke-Groves (VCG) mechanism offers a general technique for resource allocation with payments, ensuring allocative efficiency while eliciting truthful information about preferences. However, VCG relies on exact computation of an optimal allocation of resources, a problem which is often computationally intractable, and VCG that uses an approximate allocation algorithm no longer guarantees truthful revelation of preferences. We present a series of results for computing or approximating an upper bound on agent incentives to misreport their preferences. Our first key result is an incentive bound that uses information about average (not worst-case) performance of an algorithm, which we illustrate using combinatorial auction data. Our second result offers a simple sampling technique for amplifying the difficulty of computing a utility-improving lie. An important consequence of our analysis is an argument that using state-of-the-art algorithms for solving combinatorial allocation problems essentially eliminates agent incentives to lie.     

A Novel Cartesian Plot Analysis for Fixed Monolayers That Relates Cell Phenotype to Transfer of Contents between Fibroblasts and Cancer Cells by Cell-Projection Pumping

We recently described cell-projection pumping as a mechanism transferring cytoplasm between cells. The uptake of fibroblast cytoplasm by co-cultured SAOS-2 osteosarcoma cells changes SAOS-2 morphology and increases cell migration and proliferation, as seen by single-cell tracking and in FACS separated SAOS-2 from co-cultures. Morphological changes in SAOS-2 seen by single cell tracking are consistent with previous observations in fixed monolayers of SAOS-2 co-cultures. Notably, earlier studies with fixed co-cultures were limited by the absence of a quantitative method for identifying sub-populations of co-cultured cells, or for quantitating transfer relative to control populations of SAOS-2 or fibroblasts cultured alone. We now overcome that limitation by a novel Cartesian plot analysis that identifies individual co-cultured cells as belonging to one of five distinct cell populations, and also gives numerical measure of similarity to control cell populations. We verified the utility of the method by first confirming the previously established relationship between SAOS-2 morphology and uptake of fibroblast contents, and also demonstrated similar effects in other cancer cell lines including from melanomas, and cancers of the ovary and colon. The method was extended to examine global DNA methylation, and while there was no clear effect on SAOS-2 DNA methylation, co-cultured fibroblasts had greatly reduced DNA methylation, similar to cancer associated fibroblasts.     

Adaptive fuzzy clustering with a variable fuzzifier

The problem of fuzzy clustering of multivariate observations is considered and a group of Kohonen neural network adaptive self-learning algorithms is proposed. The algorithms allow for online possibilistic fuzzy clustering with variable fuzziness levels and are computationally simple and flexible when operating under a priori uncertainty about the nature of data distribution in clusters.     

Adaptive learning of fuzzy BSB and GBSB neural models

This article deals with a special class of neural autoassociative memory, namely, with fuzzy BSB and GBSB models and their learning algorithms. These models defined on a hypercube solve the problem of fuzzy clusterization of a data array owing to the fact that the vertices of the hypercube act as point attractors. A membership function is introduced that allows one to classify data that belong to overlapping clusters. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 6, pp. 18–28, November–December 2006.     

Ray Traversal of Spatial Structures

This article presents an efficient algorithm for ray traversal of uniform and hierarchical spatial structures. Only simple integer and logical operations are used. The problem of error accumulation during ray casting is considered. The algorithm is well suited for efficient software and hardware implementation.     

Voltage-gated ion transport through semiconducting conical nanopores formed by metal nanoparticle-assisted plasma etching

Nanopores with conical geometries have been found to rectify ionic current in electrolytes. While nanopores in semiconducting membranes are known to modulate ionic transport through gated modification of pore surface charge, the fabrication of conical nanopores in silicon (Si) has proven challenging. Here, we report the discovery that gold (Au) nanoparticle (NP)-assisted plasma etching results in the formation of conical etch profiles in Si. These conical profiles result due to enhanced Si etch rates in the vicinity of the Au NPs. We show that this process provides a convenient and versatile means to fabricate conical nanopores in Si membranes and crystals with variable pore-diameters and cone-angles. We investigated ionic transport through these pores and observed that rectification ratios could be enhanced by a factor of over 100 by voltage gating alone, and that these pores could function as ionic switches with high on-off ratios of approximately 260. Further, we demonstrate voltage gated control over protein transport, which is of importance in lab-on-a-chip devices and biomolecular separations.     

On-line kernel clustering based on the general regression neural network and T. Kohonen’s self-organizing map

The clustering system based on the evolving general regression neural network and self-organizing map of T.Kohonen, is proposed in the paper. The tuning of system is based on “lazy” learning and self-learning using the principle “Winner takes more” at the same time as neighborhood function the output signal of the hybrid network is used. The system’ implementation is characterized by numerical simplicity. The evolving neural network processes data in an online mode and doesn’t suffer from the curse of dimensionality.     

Constrained automated mechanism design for infinite games of incomplete information

We present a functional framework for automated Bayesian and worst-case mechanism design, based on a two-stage game model of strategic interaction between the designer and the mechanism participants. At the core of our framework is a black-box optimization algorithm which guides the process of evaluating candidate mechanisms. We apply the approach to several classes of two-player infinite games of incomplete information, producing optimal or nearly optimal mechanisms using various objective functions. By comparing our results with known optimal mechanisms, and in some cases improving on the best known mechanisms, we provide evidence that ours is a promising approach to parametrized mechanism design for infinite Bayesian games.