Modeling the optical constants of solids using acceptance-probability-controlled simulated annealing with an adaptive move generation procedure

The acceptance-probability-controlled simulated annealing with an adaptive move generation procedure, an optimization technique derived from the simulated annealing algorithm, is presented. The adaptive move generation procedure was compared against the random move generation procedure on seven multiminima test functions, as well as on the synthetic data, resembling the optical constants of a metal. In all cases the algorithm proved to have faster convergence and superior escaping from local minima. This algorithm was then applied to fit the model dielectric function to data for platinum and aluminum.

In situ apoptosis of adaptive immune cells and the cellular escape of rabies virus in CNS from patients with human rabies transmitted by Desmodus rotundus

The aim of the current study was to investigate the apoptosis of neurons, astrocytes and immune cells from human patients that were infected with rabies virus by vampire bats bite. Apoptotic neurons were identified by their morphology and immune cells were identified using double immunostaining. There were very few apoptotic neurons present in infected tissue samples, but there was an increase of apoptotic infiltrating CD4+ and TCD8+ adaptive immune cells in the rabies infected tissue. No apoptosis was present in NK, macrophage and astrocytes. The dissemination of the human rabies virus within an infected host may be mediated by viral escape of the virus from an infected cell and may involve an anti-apoptotic mechanism, which does not kill the neuron or pro-apoptosis of TCD4+ and TCD8+ lymphocytes and which allows for increased proliferation of the virus within the CNS by attenuation of the adaptive immune response. (C) 2011 Elsevier B.V. All rights reserved.

A description of adaptive and maladaptive behaviour in children and adolescents with Cri-du-chat syndrome

Background Psychological tests can be useful to record adaptive and maladaptive behaviours of children with intellectual disability. The objective of this study was to describe the adaptive and maladaptive behaviour of children and adolescents with Cri-du-chat syndrome. Methods The sample consisted of 10 children and adolescents with Cri-du-chat syndrome (mean chronological age = 11.3 years, mean mental age = 18 months). The developmental quotient was calculated through the Psychoeducational Profile - Revised. An observational protocol was used to record adaptive and maladaptive behaviours. Results The number of maladaptive behaviours observed was different among participants. However, all of them had high rates of adaptive behaviours, such as rule-following. Conclusions These results, though preliminary, justify that we continue to think about the need for psychoeducational interventions aimed at stimulating the repertoire of adaptive behaviours, in people with Cri-du-chat syndrome.

The application of adaptive partitioned random search in feature selection problem

Feature selection is one of important and frequently used techniques in data preprocessing. It can improve the efficiency and the effectiveness of data mining by reducing the dimensions of feature space and removing the irrelevant and redundant information. Feature selection can be viewed as a global optimization problem of finding a minimum set of M relevant features that describes the dataset as well as the original N attributes. In this paper, we apply the adaptive partitioned random search strategy into our feature selection algorithm. Under this search strategy, the partition structure and evaluation function is proposed for feature selection problem. This algorithm ensures the global optimal solution in theory and avoids complete randomness in search direction. The good property of our algorithm is shown through the theoretical analysis.

Meningiomas and schwannomas: Molecular subgroup classification found by expression arrays

Microarray gene expression profiling is a high-throughput system used to identify differentially expressed genes and regulation patterns, and to discover new tumor markers. As the molecular pathogenesis of meningiomas and schwannomas, characterized by NF2 gene alterations, remains unclear and suitable molecular targets need to be identified, we used low density cDNA microarrays to establish expression patterns of 96 cancer-related genes on 23 schwannomas, 42 meningiomas and 3 normal cerebral meninges. We also performed a mutational analysis of the NF2 gene (PCR, dHPLC, Sequencing and MLPA), a search for 22q LOH and an analysis of gene silencing by promoter hypermethylation (MS-MLPA). Results showed a high frequency of NF2 gene mutations (40%), increased 22q LOH as aggressiveness increased, frequent losses and gains by MLPA in benign meningiomas, and gene expression silencing by hypermethylation. Array analysis showed decreased expression of 7 genes in meningiomas. Unsupervised analyses identified 2 molecular subgroups for both meningiomas and schwannomas showing 38 and 20 differentially expressed genes, respectively, and 19 genes differentially expressed between the two tumor types. These findings provide a molecular subgroup classification for meningiomas and schwannomas with possible implications for clinical practice.

A Dynamical Modeling to Study the Adaptive Immune System and the Influence of Antibodies in the Immune Memory

Immunological systems have been an abundant inspiration to contemporary computer scientists. Problem solving strategies, stemming from known immune system phenomena, have been successfully applied to chall enging problems of modem computing. Simulation systems and mathematical modeling are also beginning use to answer more complex immunological questions as immune memory process and duration of vaccines, where the regulation mechanisms are not still known sufficiently (Lundegaard, Lund, Kesmir, Brunak, Nielsen, 2007). In this article we studied in machina a approach to simulate the process of antigenic mutation and its implications for the process of memory. Our results have suggested that the durability of the immune memory is affected by the process of antigenic mutation.and by populations of soluble antibodies in the blood. The results also strongly suggest that the decrease of the production of antibodies favors the global maintenance of immune memory.

Cytokine Signatures of Innate and Adaptive Immunity in 17DD Yellow Fever Vaccinated Children and Its Association With the Level of Neutralizing Antibody

Background. The live attenuated yellow fever (YF) vaccines have been available for decades and are considered highly effective and one of the safest vaccines worldwide. Methods. The impact of YF-17DD-antigens recall on cytokine profiles of YF-17DD-vaccinated children were characterized using short-term cultures of whole blood samples and single-cell flow cytometry. This study enrolled seroconverters and nonseroconverters after primovaccination (PV-PRNT(+) and PV-PRNT(-)), seroconverters after revaccination (RV-PRNT(+)), and unvaccinated volunteers (UV-PRNT(-)). Results. The analysis demonstrated in the PV-PRNT(+) group a balanced involvement of pro-inflammatory/regulatory adaptive immunity with a prominent participation of innate immunity pro-inflammatory events (IL-12(+) and TNF-alpha(+) NEU and MON). Using the PV-PRNT(+) cytokine signature as a reference profile, PV-PRNT(+) presented a striking lack of innate immunity proinflammatory response along with an increased adaptive regulatory profile (IL-4(+) CD4(+) T cells and IL-10(+) and IL-5(+) CD8(+) T cells). Conversely, the RV-PRNT(+) shifted the overall cytokine signatures toward an innate immunity pro-inflammatory profile and restored the adaptive regulatory response. Conclusions. The data demonstrated that the overall cytokine signature was associated with the levels of PRNT antibodies with a balanced innate/adaptive immunity with proinflammatory/regulatory profile as the hallmark of PV-PRNT(MEDIUM+), whereas a polarized regulatory response was observed in PV-PRNT(-) and a prominent proinflammatory signature was the characteristic of PV-PRNT(HIGH+).

Aspects of adaptive management of coastal wetlands: Case studies of processes, conservation, restoration, impacts and assessment

Coastal wetlands are dynamic and include the freshwater-intertidal interface. In many parts of the world such wetlands are under pressure from increasing human populations and from predicted sea-level rise. Their complexity and the limited knowledge of processes operating in these systems combine to make them a management challenge.Adaptive management is advocated for complex ecosystem management (Hackney 2000; Meretsky et al. 2000; Thom 2000;National Research Council 2003).Adaptive management identifies management aims,makes an inventory/environmental assessment,plans management actions, implements these, assesses outcomes, and provides feedback to iterate the process (Holling 1978;Walters and Holling 1990). This allows for a dynamic management system that is responsive to change. In the area of wetland management recent adaptive approaches are exemplified by Natuhara et al. (2004) for wild bird management, Bunch and Dudycha (2004) for a river system, Thom (2000) for restoration, and Quinn and Hanna (2003) for seasonal wetlands in California. There are many wetland habitats for which we currently have only rudimentary knowledge (Hackney 2000), emphasizing the need for good information as a prerequisite for effective management. The management framework must also provide a way to incorporate the best available science into management decisions and to use management outcomes as opportunities to improve scientific understanding and provide feedback to the decision system. Figure 9.1 shows a model developed by Anorov (2004) based on the process-response model of Maltby et al. (1994) that forms a framework for the science that underlies an adaptive management system in the wetland context.

An adaptive neural-wavelet model for short term load forecasting

This paper proposed a novel model for short term load forecast in the competitive electricity market. The prior electricity demand data are treated as time series. The forecast model is based on wavelet multi-resolution decomposition by autocorrelation shell representation and neural networks (multilayer perceptrons, or MLPs) modeling of wavelet coefficients. To minimize the influence of noisy low level coefficients, we applied the practical Bayesian method Automatic Relevance Determination (ARD) model to choose the size of MLPs, which are then trained to provide forecasts. The individual wavelet domain forecasts are recombined to form the accurate overall forecast. The proposed method is tested using Queensland electricity demand data from the Australian National Electricity Market. (C) 2001 Elsevier Science B.V. All rights reserved.