Invasive angiology in acute arterial pathology in a critically ill newborn infant

Four patients who complained of symptoms and signs compatible with biliary tract disease and in whom the diagnosis of gallbladder (three cases) and common bile duct ascariasis (one case) was made sonographically are reported. In two patients with gallbladder ascariasis, cholecystectomy corroborated the presence of the round-worms, and was curative. The third patient was a pregnant woman (32nd gestational week) who took piperazine citrate, and a second sonogram performed 29 days later was completely normal. The fourth patient with common bile duct ascariasis underwent choledochotomy with extraction of one ascaris lumbricoides from the common duct, and a T tube was left in place for 15 days. All patients received antiparasitics and had a favorable outcome. Ultrasonography is an important noninvasive diagnostic procedure in the work-up of these patients, who usually describe a clinical picture suggesting gallstone disease: this is especially true in the presence of pregnancy.     

Proposal of a uniform color scale for virgin olive oils

A new color scale was developed from a broad data set of 1700 virgin olive oil samples over four crop seasons, which can be considered highly representative of the whole color range of virgin olive oils available in Spain. This color scale provides a new set of 60 color standards, improving the results achieved by the old 60-color standards proposed by the bromthymol blue method. Seeking the greatest possibility of including a near match between colors of virgin olive oils and proposed standards, we developed our new color scale using a recent uniform color space, with standards placed in a regular rhombohedral lattice like the one employed by the Uniform Color Scales of the Optical Society of America. The average color difference between each of the 1700 virgin olive oils and its nearest standard is reduced from 8.17 CIELAB units, using the bromthymol blue standards, to 3.99 CIELAB units using the new standards. Within a color tolerance of 7.0 CIELAB units, 93.2% of our virgin olive oils can be classified with the new standards, but only 59.1% with the bromthymol blue ones. In the interest of future adoption, the performance of the new color standards should be tested by industry and researchers.     

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.     

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.     

Prediction of parking space availability in real time

Intelligent parking reservation (IPR) systems allow customers to select a parking facility according to their preferences, rapidly park their vehicle without searching for a free stall, and pay their reservation in advance avoiding queues. Some IPR systems interact with in-vehicle navigation systems and provide users with information in real time such as capacity, parking fee, and current parking utilization. However, few of these systems provide information on the forecast utilization at specific hours - a process that requires the study of the competition between parking alternatives for the market share. This paper proposes a methodology for predicting real-time parking space availability in IPR architectures. This methodology consists of three subroutines to allocate simulated parking requests, estimate future departures, and forecast parking availability. Parking requests are allocated iteratively using an aggregated approach as a function of simulated drivers’ preferences, and parking availability. This approach is based on a calibrated discrete choice model for selecting parking alternatives. A numerical comparison between a one-by-one simulation-based forecast and the proposed aggregated approach indicates that no significant discrepancies exists, validating and suggesting the use of the less time consuming proposed aggregated methodology. Results obtained from contrasting predictions with real data yielded small average error availabilities. The forecast improves as the system registers arrivals and departures. Thus, the forecast is adequate for potential distribution in real-time using different media such as Internet, navigation systems, cell phones or GIS.     

Multi‐objective optimization using metaheuristics: non‐standard algorithms

In recent years, the application of metaheuristic techniques to solve multi‐objective optimization problems has become an active research area. Solving this kind of problems involves obtaining a set of Pareto‐optimal solutions in such a way that the corresponding Pareto front fulfils the requirements of convergence to the true Pareto front and uniform diversity. Most of the studies on metaheuristics for multi‐objective optimization are focused on Evolutionary Algorithms, and some of the state‐of‐the‐art techniques belong this class of algorithms. Our goal in this paper is to study open research lines related to metaheuristics but focusing on less explored areas to provide new perspectives to those researchers interested in multi‐objective optimization. In particular, we focus on non‐evolutionary metaheuristics, hybrid multi‐objective metaheuristics, parallel multi‐objective optimization and multi‐objective optimization under uncertainty. We analyze these issues and discuss open research lines.     

The effect of ionising radiation on photosynthetic oxygenic microorganisms for survival in space flight revealed by automatic photosystem II-based biosensors

Photosynthetic microorganisms are expected to be useful to maintain an oxygenic atmosphere and to provide biomass for astronauts in the International Space Station as well as in future long-term space flights. However, fluxes of complex ionizing radiation of various intensities and energies make space an extreme environment for the microorganisms, affecting their photosynthetic efficiency. To automatically monitor the photosynthetic Photosystem II (PSII) activity of microorganisms under space conditions an optical biosensor, which utilizes chlorophyll fluorescence as biological transduction system, was built; the PSII activity was monitored by the biosensor during balloon flights at stratospheric altitudes of about 40 km. The effect of space stress on quantum yield of PSII varied among the tested species depending on the growth light conditions at which they were exposed during the flights.     

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.     

Evaluation and Characterization of Post-Stroke Lung Damage in a Murine Model of Cerebral Ischemia

After stroke and other brain injuries, there is a high incidence of respiratory complications such as pneumonia or acute lung injury. The molecular mechanisms that drive the brain-lung interaction post-stroke have not yet been elucidated. We performed transient middle cerebral artery occlusion (MCAO) and sham surgery on C57BL/6J mice and collected bronchoalveolar lavage fluid (BALF), serum, brain, and lung homogenate samples 24 h after surgery. A 92 proteins-panel developed by Olink Proteomics^® was used to analyze the content in BALF and lung homogenates. MCAO animals had higher protein concentration levels in BALF than sham-controls, but these levels did not correlate with the infarct volume. No alteration in alveolar-capillary barrier permeability was observed. A total of 12 and 14 proteins were differentially expressed between the groups (FDR < 0.1) in BALF and lung tissue homogenates, respectively. Of those, HGF, TGF-α, and CCL2 were identified as the most relevant to this study. Their protein expression patterns were verified by ELISA. This study confirmed that post-stroke lung damage was not associated with increased lung permeability or cerebral ischemia severity. Furthermore, the dysregulation of HGF, TGF-α, and CCL2 in BALF and lung tissue after ischemia could play an important role in the molecular mechanisms underlying stroke-induced lung damage.     

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.