The emerging principles of mammalian prion propagation and transmissibility barriers: Insight from studies in vitro

Self-perpetuating conformational conversion of the cellular prion protein PrP(C) into the beta-sheet-rich "scrapie" conformer (PrP(Sc)) is believed to be the central molecular event in pathogenesis of a group of diseases known as transmissible spongiform encephalopathies. Recent advances provide growing support for the notion that a misfolded protein alone might act as an infectious agent. Furthermore, findings regarding the mechanism of prion protein structural rearrangement, the role of folding intermediates in conformational conversion, and "conformational adaptability" in the propagation of prion amyloids in vitro yield molecular-level insight into such phenomena as inherited prion diseases, prion transmission barriers, and prion strains.     

A granular description of ECG signals

In this paper, we develop a general framework of a granular representation of ECG signals. The crux of the approach lies in the development and ongoing processing realized in the setting of information granules-fuzzy sets. They serve as basic conceptual and semantically meaningful entities using which we describe signals and build their models (such as various predictive schemes or classifiers). A comprehensive two-phase scheme of the design of the information granules is proposed and described. At the first phase, we discuss the temporal granulation through a series of temporal windows (granular windows) and an aggregation of the values of signal by means of fuzzy sets. To address this issue, offered is a detailed method of building a fuzzy set based on numeric data and a certain optimization criterion that strikes a balance between the highest experimental relevance of the fuzzy set supported by numeric data and its substantial specificity. At the next phase of the granular design, a collection of information granules is further summarized with the use of fuzzy clustering (Fuzzy C-Means). The resulting prototypes (centroids) formed by this grouping process serve as elements of the granular vocabulary. We discuss ways of using these vocabularies in the knowledge-based representation, modeling, and classification of ECG beats.     

Development of Granular Fuzzy Relation Equations Based on a Subset of Data

Developing and optimizing fuzzy relation equations are of great relevance in system modeling, which involves analysis of numerous fuzzy rules. As each rule varies with respect to its level of influence, it is advocated that the performance of a fuzzy relation equation is strongly related to a subset of fuzzy rules obtained by removing those without significant relevance. In this study, we establish a novel framework of developing granular fuzzy relation equations that concerns the determination of an optimal subset of fuzzy rules. The subset of rules is selected by maximizing their performance of the obtained solutions. The originality of this study is conducted in the following ways. Starting with developing granular fuzzy relation equations, an interval-valued fuzzy relation is determined based on the selected subset of fuzzy rules (the subset of rules is transformed to interval-valued fuzzy sets and subsequently the interval-valued fuzzy sets are utilized to form interval-valued fuzzy relations), which can be used to represent the fuzzy relation of the entire rule base with high performance and efficiency. Then, the particle swarm optimization (PSO) is implemented to solve a multi-objective optimization problem, in which not only an optimal subset of rules is selected but also a parameter ε for specifying a level of information granularity is determined. A series of experimental studies are performed to verify the feasibility of this framework and quantify its performance. A visible improvement of particle swarm optimization (about 78.56% of the encoding mechanism of particle swarm optimization, or 90.42% of particle swarm optimization with an exploration operator) is gained over the method conducted without using the particle swarm optimization algorithm.      

An Overview and Experimental Study of Learning-Based Optimization Algorithms for the Vehicle Routing Problem

The vehicle routing problem(VRP) is a typical discrete combinatorial optimization problem, and many models and algorithms have been proposed to solve the VRP and its variants. Although existing approaches have contributed significantly to the development of this field, these approaches either are limited in problem size or need manual intervention in choosing parameters. To solve these difficulties, many studies have considered learning-based optimization(LBO) algorithms to solve the VRP. This p...     

Effects of γ radiation on fiber‐reinforced polymer concrete

Composites were made from 30% unsaturated polyester resin + 70% calcium bentonite and marble as aggregates, as well as 0.3 and 0.4 vol% of nylon fibers. The fiber‐containing polymer concretes (PCs) were subjected to 5, 10, 50, and 100 kGy applied radiation doses. The compressive strength values depend on both the fiber concentration and the irradiation dose applied. Moreover, the polyester‐based PCs containing two mineral aggregates, calcium carbonate and marble, have lower compressive strength values than those reported earlier for PCs containing only either silica sand or CaCO₃. However, significant improvement of the compressive strain and the compression modulus of elasticity are achieved when nylon fibers are added. Both these properties go symbatically with the radiation dose. Mechanical characteristics can be related to the morphological features observed by scanning electron microscopy. POLYM. COMPOS., 2008. © 2008 Society of Plastics Engineers     

Clustering revealed in high‐resolution simulations and visualization of multi‐resolution features in fluid–particle models

Simulating natural phenomena at greater accuracy results in an explosive growth of data. Large‐scale simulations with particles currently involve ensembles consisting of between 10⁶ and 10⁹ particles, which cover 10⁵–10⁶ time steps. Thus, the data files produced in a single run can reach from tens of gigabytes to hundreds of terabytes. This data bank allows one to reconstruct the spatio‐temporal evolution of both the particle system as a whole and each particle separately. Realistically, for one to look at a large data set at full resolution at all times is not possible and, in fact, not necessary. We have developed an agglomerative clustering technique, based on the concept of a mutual nearest neighbor (MNN). This procedure can be easily adapted for efficient visualization of extremely large data sets from simulations with particles at various resolution levels. We present the parallel algorithm for MNN clustering and its timings on the IBM SP and SGI/Origin 3800 multiprocessor systems for up to 16 million fluid particles. The high efficiency obtained is mainly due to the similarity in the algorithmic structure of MNN clustering and particle methods. We show various examples drawn from MNN applications in visualization and analysis of the order of a few hundred gigabytes of data from discrete particle simulations, using dissipative particle dynamics and fluid particle models. Because data clustering is the first step in this concept extraction procedure, we may employ this clustering procedure to many other fields such as data mining, earthquake events and stellar populations in nebula clusters. Copyright © 2003 John Wiley & Sons, Ltd.     

New Trends in the Modeling of Diseases Through Computational Techniques

The computational techniques are a set of novel problem-solving methodologies that have attracted wider attention for their excellent performance. The handling strategies of real-world problems are artificial neural networks (ANN), evolutionary computing (EC), and many more. An estimated fifty thousand to ninety thousand new leishmaniasis cases occur annually, with only 25% to 45% reported to the World Health Organization (WHO). It remains one of the top parasitic diseases with outbreak and mortality potential. In 2020, more than ninety percent of new cases reported to World Health Organization (WHO) occurred in ten countries: Brazil, China, Ethiopia, Eritrea, India, Kenya, Somalia, South Sudan, Sudan, and Yemen. The transmission of visceral leishmaniasis is studied dynamically and numerically. The study included positivity, boundedness, equilibria, reproduction number, and local stability of the model in the dynamical analysis. Some detailed methods like Runge Kutta and Euler depend on time steps and violate the physical relevance of the disease. They produce negative and unbounded results, so in disease dynamics, such developments have no biological significance; in other words, these results are meaningless. But the implicit nonstandard finite difference method does not depend on time step, positive, bounded, dynamic and consistent. All the computational techniques and their results were compared using computer simulations.     

Complex validation of novel mullite-based ceramic filters for microfiltration purposes

The goal of this paper is to investigate a complex validation, developed by Rauschert in Poland, of casted ceramic filters for microfiltration. For disc manufacturing, a self-developed material RaFo-MF-401e with a filtration membrane was used. The presented experiment was conducted on component, subsystem, and system levels. Component level analysis consisted of the investigation of mechanical strength, hardness, rough lifetime, and resistance against acids and alkalis. Annealing at high temperature and humidity was used to test the subsystem. The final system test was executed on the real filtration system. The final aim of the tests was to verify the filtration efficiency of a complete module (core and membrane) in a real filtration device. The filtration quality of the whole setup was very good and the particles in the permeate (filtrated solution) were smaller than 10 μm (defined as D99). Thus, the developed discs provide the expected filtration quality in the range of microfiltration.