Optimization of Novel Human Acellular Dermal Dressing Sterilization for Routine Use in Clinical Practice

Gamma rays and electrons with kinetic energy up to 10 MeV are routinely used to sterilize biomaterials. To date, the effects of irradiation upon human acellular dermal matrices (hADMs) remain to be fully elucidated. The optimal irradiation dosage remains a critical parameter affecting the final product structure and, by extension, its therapeutic potential. ADM slides were prepared by various digestion methods. The influence of various doses of radiation sterilization using a high-energy electron beam on the structure of collagen, the formation of free radicals and immune responses to non-irradiated (native) and irradiated hADM was investigated. The study of the structure changes was carried out using the following methods: immunohistology, immunoblotting, and electron paramagnetic resonance (EPR) spectroscopy. It was shown that radiation sterilization did not change the architecture and three-dimensional structure of hADM; however, it significantly influenced the degradation of collagen fibers and induced the production of free radicals in a dose-dependent manner. More importantly, the observed effects did not disrupt the therapeutic potential of the new transplants. Therefore, radiation sterilization at a dose of 35kGy can ensure high sterility of the dressing while maintaining its therapeutic potential.     

Salinity tolerance of the invasive round goby: Experimental implications for seawater ballast exchange and spread to North American estuaries

The Eurasian round goby (Neogobius melanostomus) invaded the freshwater North American Great Lakes in ~ 1990 via accidental introduction from ballast water discharge. Its genotypes in the Great Lakes traced to estuaries in the northern Black Sea, where the round goby flourishes in a variety of salinities to 22 parts per thousand (ppt). To prevent further introductions, U.S. and Canadian Coast Guard regulations now require that vessels exchange ballast water at sea before entering the Great Lakes. Since salinity tolerance of the invasive round goby population is poorly understood, we tested 230 laboratory-acclimated fish in three experimental scenarios: (1) rapid salinity increases (0–40 ppt), simulating ballast water exchange, (2) step-wise salinity increases, as during estuarine tidal fluxes or migration from fresh to saltwater, and (3) long-term survivorship and growth (to 4 months) at acclimated salinities. Almost all gobies survived experiments at 0–20 ppt, whereas none survived ≥ 30 ppt, and at 25 ppt only 15% withstood rapid changes and 30% survived step-wise increases. Ventilation frequencies were lowest at 10–15 ppt in step-wise experiments, in conditions that were near isotonic with fish internal plasma concentrations, reflecting lower energy expenditure for osmoregulation. Growth rates appeared greatest at 5–10 ppt, congruent with the larger sizes reached by gobies in Eurasian brackish waters. Thus, we predict that the Great Lakes round goby would thrive in brackish water estuaries along North American coasts, if introduced. However, oceanic salinities appear fatal to the invasive round goby, which likely cannot withstand complete seawater ballast exchanges or oceanic habitats.     

Multilevel microwave design optimization with automated model fidelity adjustment

A computationally efficient algorithm for electromagnetic (EM)‐simulation‐driven design optimization of microwave structures is proposed. Our technique exploits variable‐fidelity EM simulations and the multilevel design approach where an approximate optimum of the lower accuracy but faster EM model of the structure under design is used as a starting point for optimizing a more accurate model. Several enhancements of the basic multifidelity method are introduced, including an efficient algorithm of optimizing EM models that is based on local response surface approximations, as well as automated adjustment of model fidelity. Convergence of the procedure to the optimum design is ensured by defaulting to the higher fidelity model whenever the prediction given by the lower fidelity fails to improve the design. Distribution of the computational effort between the models of different fidelity allows for making larger steps in the design space at a low cost, as well as substantial reduction of the number of high‐fidelity model evaluations, because the high‐fidelity model is only referred to in the last design stage. The article provides comprehensive numerical verification of our technique. Substantial computational savings are demonstrated in comparison to the benchmark methods: over 40% on average as compared to a basic version of the multifidelity optimization approach and over 95% as compared to direct optimization of the high‐fidelity model. © 2013 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:281–288, 2014.     

Structure generation and performance comparison of elliptic Gm‐C filters

Starting from a set of matrices describing a general G_m‐C filter topology, a procedure is developed for generating structures of lowpass filters. As the matrices and the filter topologies have a one‐to‐one correspondence, an algebraic method is used to identify filter topologies with desired properties, here, transfer functions with finite jω‐axis transmission zeros, specifically elliptic filters. Sensitivity expressions for these structures are derived and a performance comparison based on a set of chosen criteria is made. For a specified elliptic transfer function, filters with only grounded capacitors and those containing also floating capacitors emerge as alternative realizations, as are filters with a single input and those with distributed inputs. For third‐order functions, a detailed comparison is performed of leapfrog (LF) and inverse follow‐the‐leader‐feedback (IFLF), the most popular special cases, and of topologies that have also floating capacitors (LF_f, IFLF_f), as well as of a novel configuration that uses also distributed inputs (DI_f) and leads to a reduced element count. Design guidelines and restrictions are given, which follow from the derived results with focus on the circuits' sensitivity performance and other properties important for IC implementation. Copyright © 2004 John Wiley & Sons, Ltd.     

Precise control of reflection response in bandwidth‐enhanced planar antennas

In this work, the issues of bandwidth enhancement of planar antennas and the relevance of precise and automated response control through numerical optimization have been investigated. Using an example of a planar antenna with parasitic radiator we illustrate possible effects of even minor modifications of the antenna geometry (here, applied to the ground plane) on its reflection performance. In particular, a proper handling of geometry parameters may lead to considerable broadening of the antenna bandwidth. For the sake of computational efficiency, the adjustment of geometry parameters is carried out using surrogate‐based optimization methods exploiting coarse‐discretization EM simulations as the underlying low‐fidelity antenna model. Additionally, suitably defined penalty function allows us to precisely control the maximum in‐band reflection so that sufficient margin to accommodate possible manufacturing tolerances can be achieved. The optimized designs of the two antenna structures considered in this work exhibit over 1.75 GHz (>31%) and 2.15 GHz (>38%) bandwidth, respectively, for the center frequency of 5.6 GHz. Simulation results are validated using measurements of the fabricated prototypes. Comparison with state‐of‐the‐art designs is also provided. © 2016 Wiley Periodicals, Inc. Int J RF and Microwave CAE 26:653–659, 2016.     

An adaptive algorithm for anomaly and novelty detection in evolving data streams

In the era of big data, considerable research focus is being put on designing efficient algorithms capable of learning and extracting high-level knowledge from ubiquitous data streams in an online fashion. While, most existing algorithms assume that data samples are drawn from a stationary distribution, several complex environments deal with data streams that are subject to change over time. Taking this aspect into consideration is an important step towards building truly aware and intelligent systems. In this paper, we propose GNG-A, an adaptive method for incremental unsupervised learning from evolving data streams experiencing various types of change. The proposed method maintains a continuously updated network (graph) of neurons by extending the Growing Neural Gas algorithm with three complementary mechanisms, allowing it to closely track both gradual and sudden changes in the data distribution. First, an adaptation mechanism handles local changes where the distribution is only non-stationary in some regions of the feature space. Second, an adaptive forgetting mechanism identifies and removes neurons that become irrelevant due to the evolving nature of the stream. Finally, a probabilistic evolution mechanism creates new neurons when there is a need to represent data in new regions of the feature space. The proposed method is demonstrated for anomaly and novelty detection in non-stationary environments. Results show that the method handles different data distributions and efficiently reacts to various types of change.     

Multi‐objective design optimization of antennas for reflection,size, and gain variability using kriging surrogates and generalized domain segmentation

Cost‐efficient multi‐objective design optimization of antennas is presented. The framework exploits auxiliary data‐driven surrogates, a multi‐objective evolutionary algorithm for initial Pareto front identification, response correction techniques for design refinement, as well as generalized domain segmentation. The purpose of this last mechanism is to reduce the volume of the design space region that needs to be sampled in order to construct the surrogate model, and, consequently, limit the number of training data points required. The recently introduced segmentation concept is generalized here to allow for handling an arbitrary number of design objectives. Its operation is illustrated using an ultra‐wideband monopole optimized for best in‐band reflection, minimum gain variability, and minimum size. When compared with conventional surrogate‐based approach, segmentation leads to reduction of the initial Pareto identification cost by over 20%. Numerical results are supported by experimental validation of the selected Pareto‐optimal antenna designs.     

Response correction techniques for surrogate‐based design optimization of microwave structures

Simulation‐based optimization has become an important design tool in microwave engineering. However, using electromagnetic (EM) solvers in the design process is a challenging task, primarily due to a high‐computational cost of an accurate EM simulation. In this article, we present a review of EM‐based design optimization techniques exploiting response‐corrected physically based low‐fidelity models. The surrogate models created through such a correction can be used to yield a reasonable approximation of the optimal design of the computationally expensive structure under consideration (high‐fidelity model). Several approaches using this idea are reviewed including output space mapping, manifold mapping, adaptive response correction, and shape‐preserving response prediction. A common feature of these methods is that they are easy to implement and computationally efficient. Application examples are provided. © 2011 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2012.