A distributed bio-inspired method for multisite grid mapping

Computational grids assemble multisite and multiowner resources and represent the most promising solutions for processing distributed computationally intensive applications, each composed by a collection of communicating tasks. The execution of an application on a grid presumes three successive steps: the localization of the available resources together with their characteristics and status; the mapping which selects the resources that, during the estimated running time, better support this execution and, at last, the scheduling of the tasks. These operations are very difficult both because the availability and workload of grid resources change dynamically and because, in many cases, multisite mapping must be adopted to exploit all the possible benefits. As the mapping problem in parallel systems, already known as NP-complete, becomes even harder in distributed heterogeneous environments as in grids, evolutionary techniques can be adopted to find near-optimal solutions. In this paper an effective and efficient multisite mapping, based on a distributed Differential Evolution algorithm, is proposed. The aim is to minimize the time required to complete the execution of the application, selecting from among all the potential ones the solution which reduces the use of the grid resources. The proposed mapper is tested on different scenarios

A Distributed Bio-Inspired Method for Multisite Grid Mapping

Computational grids assemble multisite and multiowner resources and represent the most promising solutions for processing distributed computationally intensive applications, each composed by a collection of communicating tasks. The execution of an application on a grid presumes three successive steps: the localization of the available resources together with their characteristics and status; the mapping which selects the resources that, during the estimated running time, better support this execution and, at last, the scheduling of the tasks. These operations are very difficult both because the availability and workload of grid resources change dynamically and because, in many cases, multisite mapping must be adopted to exploit all the possible benefits. As the mapping problem in parallel systems, already known as NP-complete, becomes even harder in distributed heterogeneous environments as in grids, evolutionary techniques can be adopted to find near-optimal solutions. In this paper an effective and efficient multisite mapping, based on a distributed Differential Evolution algorithm, is proposed. The aim is to minimize the time required to complete the execution of the application, selecting from among all the potential ones the solution which reduces the use of the grid resources. The proposed mapper is tested on different scenarios

Predictors of carotid occlusion intolerance?during proximal protected?carotid artery?stenting.

OBJECTIVES: The aim of this study was to identify predictors of occlusion intolerance (OI) developing during proximal protected carotid artery stenting (CAS). BACKGROUND: The use of proximal embolic protection devices, such as endovascular occlusion, during CAS has been demonstrated to be particularly safe and effective. However, endovascular occlusion can expose the ipsilateral hemisphere to hypoperfusion and produce transient neurological symptoms (OI). METHODS: From March 2010 to March 2012, 605 consecutive patients underwent proximal protected CAS at our institution. To identify independent predictors of OI, a multivariate logistic regression model was developed that included all patients' clinical/angiographic and procedural characteristics. RESULTS: OI developed in a total of 184 patients (30.4%). Compared with patients in whom OI did not develop, those who experienced OI had lower occlusion pressure (OP) (42.3 ± 12.7 mm Hg vs. 61.9 ± 15.4 mm Hg, p < 0.001). Receiver-operating characteristic curve analysis demonstrated that OP was the most consistent predictor of OI with a C-statistic of 0.85 (95% confidence interval [CI]: 0.82 to 0.88) with best cutoff being ≤40 mm Hg (sensitivity, 68.5%; specificity, 93.3%). By logistic regression analysis, the most powerful independent predictor of OI developing was an OP ≤40 mm Hg (odds ratio: 33.2, 95% CI: 19.1 to 57.7) and the most powerful clinical predictor of such OP was the presence of contralateral internal carotid artery occlusion (odds ratio: 3.1, 95% CI: 1.5 to 6.2). CONCLUSIONS: OI may occur in as many as one-third of the patients undergoing proximal protected CAS. This event is more common in those patients with an OP ≤40 mm Hg. Patients presenting with concomitant occlusion of the contralateral internal carotid artery more frequently have an OP ≤40 mm Hg

Fast Evolutionary Adaptation for Monte Carlo Tree Search

This paper describes a new adaptive Monte Carlo Tree Search (MCTS) algorithm that uses evolution to rapidly optimise its performance. An evolutionary algorithm is used as a source of control parameters to modify the behaviour of each iteration (i.e. each simulation or roll-out) of the MCTS algorithm; in this paper we largely restrict this to modifying the behaviour of the random default policy, though it can also be applied to modify the tree policy

Safety of MF59-adjuvanted influenza vaccination in the elderly: results of a comparative study of MF59-adjuvanted vaccine versus nonadjuvanted influenza vaccine in Northern Italy

MF59-adjuvanted trivalent influenza vaccine (Novartis Vaccines and Diagnostics, Siena, Italy) has been shown to be more effective than nonadjuvanted vaccine in the elderly population. Here we present results from a large-scale, observational, noninterventional, prospective postlicensure study that evaluated the safety of MF59-adjuvanted vaccine in elderly subjects aged 65 years or more. The study was performed in 5 northern Italian health districts during the 2006-2007, 2007-2008, and 2008-2009 influenza seasons. The choice of vaccine - either adjuvanted vaccine or a nonadjuvanted influenza vaccine - was determined by individual providers on the basis of local influenza vaccination policy. Hospitalizations for potential adverse events of special interest (AESIs) were identified from hospital databases and then reviewed against recognized case definitions to identify confirmed cases of AESI. Cumulative incidences were calculated for AESIs in predefined biologically plausible time windows, as well as in a 6-month window following vaccination. During the 3-year study period, 170,988 vaccine doses were administered to a total of 107,661 persons. Despite the large study size, cases of AESI resulting in hospitalization were rare, and risks of AESI were similar in both the MF59-adjuvanted and nonadjuvanted vaccination groups. In conclusion, similar safety profiles were observed for both nonadjuvanted and MF59-adjuvanted seasonal influenza vaccines in elderly recipients

A Novel 5-Enolpyruvylshikimate-3-Phosphate Synthase Shows High Glyphosate Tolerance in Escherichia coli and Tobacco Plants

A key enzyme in the shikimate pathway, 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) is the primary target of the broad-spectrum herbicide glyphosate. Identification of new aroA genes coding for EPSPS with a high level of glyphosate tolerance is essential for the development of glyphosate-tolerant crops. In the present study, the glyphosate tolerance of five bacterial aroA genes was evaluated in the E. coli aroA-defective strain ER2799 and in transgenic tobacco plants. All five aroA genes could complement the aroA-defective strain ER2799, and AM79 aroA showed the highest glyphosate tolerance. Although glyphosate treatment inhibited the growth of both WT and transgenic tobacco plants, transgenic plants expressing AM79 aroA tolerated higher concentration of glyphosate and had a higher fresh weight and survival rate than plants expressing other aroA genes. When treated with high concentration of glyphosate, lower shikimate content was detected in the leaves of transgenic plants expressing AM79 aroA than transgenic plants expressing other aroA genes. These results suggest that AM79 aroA could be a good candidate for the development of transgenic glyphosate-tolerant crops

Investigating One-Class Classifiers to Diagnose Alzheimer’s Disease from Handwriting

The analysis of handwriting and drawing has been adopted since the early studies to help diagnose neurodegenerative diseases, such as Alzheimer’s and Parkinson’s. Departing from the current state-of-the-art methods that approach the problem of discriminating between healthy subjects and patients by using two- or multi-class classifiers, we propose to adopt one-class classifier models, as they require only data by healthy subjects to build the classifier, thus avoiding to collect patient data, as requested by competing techniques. In this framework, we evaluated the performance of three models of one-class classifiers, namely the Negative Selection Algorithm, the Isolation Forest and the One-Class Support Vector Machine, on the DARWIN dataset, which includes 174 subjects performing 25 handwriting/drawing tasks. The comparison with the state-of-the-art shows that the methods achieve state-of-the-art performance, and therefore may represent a viable alternative to the dominant approach

Mimicking the immune system to diagnose Parkinson's disease from handwriting

We introduce a method adopting the Negative Selection Algorithm, which mimics the way the human immune system learns to discriminate body cells from external antigens, for the computer-aided diagnosis of Parkinson's disease from online handwriting. The major advantage of the proposed method with respect to the current state-of-the-art machine learning methods is that it is trained only on data from healthy subjects, thus avoiding the burden of collecting patients' data. Moreover, it has only two parameters to set, and its implementation is by far simpler than those of most of, if not all, the methods proposed in the literature. The performance of the proposed method is evaluated on the PaHaW dataset, which includes handwriting samples drawn by 75 subjects. The results show that it outperforms the state-of-the-art methods and uses fewer features