Genetics of Healthy Aging in Europe.The EU-Integrated Project GEHA (GEnetics of Healthy Aging)

The aim of the 5-year European Union (EU)-Integrated Project GEnetics of Healthy Aging (GEHA), constituted by 25 partners (24 from Europe plus the Beijing Genomics Institute from China), is to identify genes involved in healthy aging and longevity, which allow individuals to survive to advanced old age in good cognitive and physical function and in the absence of major age-related diseases. To achieve this aim a coherent, tightly integrated program of research that unites demographers, geriatricians, geneticists, genetic epidemiologists, molecular biologists, bioinfomaticians, and statisticians has been set up. The working plan is to: (a) collect DNA and information on the health status from an unprecedented number of long-lived 90+ sibpairs (n = 2650) and of younger ethnically matched controls (n = 2650) from 11 European countries; (b) perform a genome-wide linkage scannning in all the sibpairs (a total of 5300 individuals); this investigation will be followed by linkage disequilibrium mapping (LD mapping) of the candidate chromosomal regions; (c) study in cases (i.e., the 2650 probands of the sibpairs) and controls (2650 younger people), genomic regions (chromosome 4, D4S1564, chromosome 11, 11.p15.5) which were identified in previous studies as possible candidates to harbor longevity genes; (d) genotype all recruited subjects for apoE polymorphisms; and (e) genotype all recruited subjects for inherited as well as epigenetic variability of the mitochondrial DNA (mtDNA). The genetic analysis will be performed by 9 high-throughput platforms, within the framework of centralized databases for phenotypic, genetic, and mtDNA data. Additional advanced approaches (bioinformatics, advanced statistics, mathematical modeling, functional genomics and proteomics, molecular biology, molecular genetics) are envisaged to identify the gene variant(s) of interest. The experimental design will also allow (a) to identify gender-specific genes involved in healthy aging and longevity in women and men stratified for ethnic and geographic origin and apoE genotype; (b) to perform a longitudinal survival study to assess the impact of the identified genetic loci on 90+ people mortality; and (c) to develop mathematical and statistical models capable of combining genetic data with demographic characteristics, health status, socioeconomic factors, lifestyle habits.

The Genetics of Postglacial Colonization and Peripherality in Northwestern Populations of the Spring Peeper, Pseudacris crucifer

Glaciation over the Pleistocene induced dramatic range fluctuations for species across North America such that postglacial recolonization by southern refugial lineages has characterized the genetic structure of northern North American species. Based on the leading edge model of postglacial range expansion, dispersal and rapid population growth in these northern taxa is expected to produce vast areas of genetic homogeneity. Previous work on the widely distributed spring peeper (Pseudacris crucifer) revealed six distinct mitochondrial lineages that diverged between 3-11 mya, expanding and contracting with glacial cycles. Beginning 16,000 yBP, receding glaciers permitted Eastern lineage refugia residing in the southern Appalachians to migrate northward into the St. Lawrence Valley then westward through most of central Canada. Peripheral populations at the northwestern range limit of P. crucifer in central Manitoba are likely descended from this westward expanding Eastern lineage. According to the central-marginal hypothesis, founder effects from colonization as well as limited gene flow is expected to reveal genetic differentiation and lower genetic diversity in peripheral populations. The goal of my study is to further our understanding of peripheral range dynamics in peripheral Manitoba populations of P. crucifer by determining their genetic affinity and diversity relative to more central populations in Ontario and Minnesota. In this study I amplified and aligned cytochrome b sequences from sample sites across central Manitoba to reconstruct a Bayesian phylogeny for P. crucifer; additionally, microsatellite loci were genotyped to estimate genetic diversity. Results from this study affirmed Eastern lineage descent for peripheral Manitoba sites by aligning with Ontario. Initial colonization by the Interior lineage between glacial retreat and the appearance of arid vicariance events may explain the apparent introgression of non-Eastern lineages in Manitoba. However, genetic diversity measured in expected heterozygosity (H¬e) was not found to be significantly different in Manitoba genotypes. Greater isolation by distance and inbreeding relative to Ontario and Minnesota is likely the primary driver of genetic variation in these sites. Further sampling is necessary to generate a more complete genetic population structure for P. crucifer.

Difference equation sensor characterization algorithms for two-thermocouple probes

The characterization of thermocouple sensors for temperature measurement in variable flow environments is a challenging problem. In this paper, novel difference equation-based algorithms are presented that allow in situ characterization of temperature measurement probes consisting of two-thermocouple sensors with differing time constants. Linear and non-linear least squares formulations of the characterization problem are introduced and compared in terms of their computational complexity, robustness to noise and statistical properties. With the aid of this analysis, least squares optimization procedures that yield unbiased estimates are identified. The main contribution of the paper is the development of a linear two-parameter generalized total least squares formulation of the sensor characterization problem. Monte-Carlo simulation results are used to support the analysis.

Computationally efficient sequential learning algorithms for direct link resource-allocating networks

Computionally efficient sequential learning algorithms are developed for direct-link resource-allocating networks (DRANs). These are achieved by decomposing existing recursive training algorithms on a layer by layer and neuron by neuron basis. This allows network weights to be updated in an efficient parallel manner and facilitates the implementation of minimal update extensions that yield a significant reduction in computation load per iteration compared to existing sequential learning methods employed in resource-allocation network (RAN) and minimal RAN (MRAN) approaches. The new algorithms, which also incorporate a pruning strategy to control network growth, are evaluated on three different system identification benchmark problems and shown to outperform existing methods both in terms of training error convergence and computational efficiency. (c) 2005 Elsevier B.V. All rights reserved.

Genetic algorithms for local controller network construction

Local Controller Networks (LCNs) provide nonlinear control by interpolating between a set of locally valid, subcontrollers covering the operating range of the plant. Constructing such networks typically requires knowledge of valid local models. This paper describes a new genetic learning approach to the construction of LCNs directly from the dynamic equations of the plant, or from modelling data. The advantage is that a priori knowledge about valid local models is not needed. In addition to allowing simultaneous optimisation of both the controller and validation function parameters, the approach aids transparency by ensuring that each local controller acts independently of the rest at its operating point. It thus is valuable for simultaneous design of the LCNs and identification of the operating regimes of an unknown plant. Application results from a highly nonlinear pH neutralisation process and its associated neural network representation are utilised to illustrate these issues.

Comparison of two construction algorithms for local model networks

The divide-and-conquer approach of local model (LM) networks is a common engineering approach to the identification of a complex nonlinear dynamical system. The global representation is obtained from the weighted sum of locally valid, simpler sub-models defined over small regions of the operating space. Constructing such networks requires the determination of appropriate partitioning and the parameters of the LMs. This paper focuses on the structural aspect of LM networks. It compares the computational requirements and performances of the Johansen and Foss (J&F) and LOLIMOT tree-construction algorithms. Several useful and important modifications to each algorithm are proposed. The modelling performances are evaluated using real data from a pilot plant of a pH neutralization process. Results show that while J&F achieves a more accurate nonlinear representation of the pH process, LOLIMOT requires significantly less computational effort.