pH-responsive drug delivery system based on hollow silicon dioxide micropillars coated with polyelectrolyte multilayers

We report on the fabrication of polyelectrolyte multilayer-coated hollow silicon dioxide micropillars as pH-responsive drug delivery systems. Silicon dioxide micropillars are based on macroporous silicon formed by electrochemical etching. Due to their hollow core capable of being loaded with chemically active agents, silicon dioxide micropillars provide additional function such as drug delivery system. The polyelectrolyte multilayer was assembled by the layer-by-layer technique based on the alternative deposition of cationic and anionic polyelectrolytes. The polyelectrolyte pair poly(allylamine hydrochloride) and sodium poly(styrene sulfonate) exhibited pH-responsive properties for the loading and release of a positively charged drug doxorubicin. The drug release rate was observed to be higher at pH 5.2 compared to that at pH 7.4. Furthermore, we assessed the effect of the number of polyelectrolyte bilayers on the drug release loading and release rate. Thus, this hybrid composite could be potentially applicable as a pH-controlled system for localized drug release.     

Acidity and catalytic activity of the mesoporous aluminosilicate molecular sieve MCM-41

The acidity and catalytic properties of aluminosilicate mesoporous molecular sieves with the MCM-41 structure and bulk Si/Al ratios in the 10–60 range have been investigated. The incorporation of 4-coordinate aluminium into the structure of MCM-41 generates both BrØnsted and Lewis acid sites in amounts increasing with the degree of incorporation. However, the BrØnsted/Lewis acid population ratio is independent of the content of aluminium. The number and strength of acid sites generated are comparable to those of a pillared acid-activated clay and lower than in zeolite H-Y with Si/Al=3.65. Aluminosilicate MCM-41 is a moderate catalyst for the conversion of cumene which proceeds predominantly via catalytic cracking to propene and benzene. The sample of MCM-41 with the highest content of framework aluminium (Si/Al=10) has the largest number of BrØnsted acid sites and exhibits highest catalytic activity.     

Identification of Phenylpyrazolone Dimers as a New Class of Anti-Trypanosoma cruzi Agents

Chagas disease is becoming a worldwide problem; it is currently estimated that over six million people are infected. The two drugs in current use, benznidazole and nifurtimox, require long treatment regimens, show limited efficacy in the chronic phase of infection, and are known to cause adverse effects. Phenotypic screening of an in-house library led to the identification of 2,2′-methylenebis(5-(4-bromophenyl)-4,4-dimethyl-2,4-dihydro-3H-pyrazol-3-one), a phenyldihydropyrazolone dimer, which shows an in vitro pIC₅₀ value of 5.4 against Trypanosoma cruzi. Initial optimization was done by varying substituents of the phenyl ring, after which attempts were made to replace the phenyl ring. Finally, the linker between the dimer units was varied, ultimately leading to 2,2′-methylenebis(5-(3-bromo-4-methoxyphenyl)-4,4-dimethyl-2,4-dihydro-3H-pyrazol-3-one (NPD-0228) as the most potent analogue. NPD-0228 has an in vitro pIC₅₀ value of 6.4 against intracellular amastigotes of T. cruzi and no apparent toxicity against the human MRC-5 cell line and murine cardiac cells.     

Towards a dynamic modeling of the predator prey problem

This article addresses a new dynamic optimization problem (DOP) based on the Predator-Prey (PP) relationship in nature. Indeed, a PP system involves two adversary species where the predator’s objective is to hunt the prey while the prey’s objective is to escape from its predator. By analogy to dynamic optimization, a DOP can be seen as a predation between potential solution(s) in the search space, which represents the predator, and the moving optimum, as the prey. Therefore we define the dynamic predator-prey problem (DPP) whose objective is to keep track of the moving prey, so as to minimize the distance to the optimum. To solve this problem, a dynamic approach that continuously adapts to the changing environment is required. Accordingly, we propose a new evolutionary approach based on three main techniques for DOPs, namely: multi-population scheme, random immigrants, and memory of past solutions. This hybridization of methods aims at improving the evolutionary approaches ability to deal with DOPs and to restrain as much as possible their drawbacks. Our computational experiments show that the proposed approach achieves high performance for DPP and and competes with state of the art approaches.     

Efficiently finding the optimum number of clusters in a dataset with a new hybrid differential evolution algorithm: DELA

Clustering algorithms, a fundamental base for data mining procedures and learning techniques, suffer from the lack of efficient methods for determining the optimal number of clusters to be found in an arbitrary dataset. The few methods existing in the literature always use some sort of evolutionary algorithm having a cluster validation index as its objective function. In this article, a new evolutionary algorithm, based on a hybrid model of global and local heuristic search, is proposed for the same task, and some experimentation is done with different datasets and indexes. Due to its design, independent of any clustering procedure, it is applicable to virtually any clustering method like the widely used \(k\)-means algorithm. Moreover, the use of non-parametric statistical tests over the experimental results, clearly show the proposed algorithm to be more efficient than other evolutionary algorithms currently used for the same task.     

DNA fragment assembly using a grid-based genetic algorithm

In this paper we propose a genetic algorithm (GA) for solving the DNA fragment assembly problem in a computational grid. The algorithm, which is named GrEA, is a steady-state GA which uses a panmitic population, and it is based on computing parallel function evaluations in an asynchronous way. We have implemented GrEA on top of the Condor system, and we have used it to solve the DNA assembly problem. This is an NP-hard combinatorial optimization problem which is growing in importance and complexity as more research centers become involved on sequencing new genomes. While previous works on this problem have usually faced 30 K base pairs (bps) long instances, we have tackled here a 77 K bps long one to show how a grid system can move research forward. After analyzing the basic grid algorithm, we have studied the use of an improvement method to still enhance its scalability. Then, by using a grid composed of up to 150 computers, we have achieved time reductions from tens of days down to a few hours, and we have obtained near optimal solutions when solving the 77 K bps long instance (773 fragments). We conclude that our proposal is a promising approach to take advantage of a grid system to solve large DNA fragment assembly problem instances and also to learn more about grid metaheuristics as a new class of algorithms for really challenging problems.     

Selection intensity in cellular evolutionary algorithms for regular lattices

In this paper, we present quantitative models for the selection pressure of cellular evolutionary algorithms on regular one- and two-dimensional (2-D) lattices. We derive models based on probabilistic difference equations for synchronous and several asynchronous cell update policies. The models are validated using two customary selection methods: binary tournament and linear ranking. Theoretical results are in agreement with experimental values, showing that the selection intensity can be controlled by using different update methods. It is also seen that the usual logistic approximation breaks down for low-dimensional lattices and should be replaced by a polynomial approximation. The dependence of the models on the neighborhood radius is studied for both topologies. We also derive results for 2-D lattices with variable grid axes ratio.     

A parallel micro evolutionary algorithm for heterogeneous computing and grid scheduling

This work presents a novel parallel micro evolutionary algorithm for scheduling tasks in distributed heterogeneous computing and grid environments. The scheduling problem in heterogeneous environments is NP-hard, so a significant effort has been made in order to develop an efficient method to provide good schedules in reduced execution times. The parallel micro evolutionary algorithm is implemented using MALLBA, a general-purpose library for combinatorial optimization. Efficient numerical results are reported in the experimental analysis performed on both well-known problem instances and large instances that model medium-sized grid environments. The comparative study of traditional methods and evolutionary algorithms shows that the parallel micro evolutionary algorithm achieves a high problem solving efficacy, outperforming previous results already reported in the related literature, and also showing a good scalability behavior when facing high dimension problem instances.     

Parallelism and evolutionary algorithms

This paper contains a modern vision of the parallelization techniques used for evolutionary algorithms (EAs). The work is motivated by two fundamental facts: 1) the different families of EAs have naturally converged in the last decade while parallel EAs (PEAs) are still lack of unified studies; and 2) there is a large number of improvements in these algorithms and in their parallelization that raise the need for a comprehensive survey. We stress the differences between the EA model and its parallel implementation throughout the paper. We discuss the advantages and drawbacks of PEAs. Also, successful applications are mentioned and open problems are identified. We propose potential solutions to these problems and classify the different ways in which recent results in theory and practice are helping to solve them. Finally, we provide a highly structured background relating to PEAs in order to make researchers aware of the benefits of decentralizing and parallelizing an EA