A study of the time complexity of an Elitist Genetic Algorithm used for associating optical observations of MEO and GEO Space Debris

Currently several thousands of objects are being tracked in the MEO and GEO regions through optical means. The problem faced in this framework is that of Multiple Target Tracking (MTT). In this context both the correct associations among the observations, and the orbits of the objects have to be determined. The complexity of the MTT problem is defined by its dimension S. Where S stands for the number of ’fences’ used in the problem, each fence consists of a set of observations that all originate from dierent targets. For a dimension of S ˃ the MTT problem becomes NP-hard. As of now no algorithm exists that can solve an NP-hard problem in an optimal manner within a reasonable (polynomial) computation time. However, there are algorithms that can approximate the solution with a realistic computational e ort. To this end an Elitist Genetic Algorithm is implemented to approximately solve the S ˃ MTT problem in an e cient manner. Its complexity is studied and it is found that an approximate solution can be obtained in a polynomial time. With the advent of improved sensors and a heightened interest in the problem of space debris, it is expected that the number of tracked objects will grow by an order of magnitude in the near future. This research aims to provide a method that can treat the correlation and orbit determination problems simultaneously, and is able to e ciently process large data sets with minimal manual intervention.

Expansion load: recessive mutations and the role of standing genetic variation

Expanding populations incur a mutation burden – the so-called expansion load. Previous studies of expansion load have focused on codominant mutations. An important consequence of this assumption is that expansion load stems exclusively from the accumulation of new mutations occurring in individuals living at the wave front. Using individual-based simulations, we study here the dynamics of standing genetic variation at the front of expansions, and its consequences on mean fitness if mutations are recessive. We find that deleterious genetic diversity is quickly lost at the front of the expansion, but the loss of deleterious mutations at some loci is compensated by an increase of their frequencies at other loci. The frequency of deleterious homozygotes therefore increases along the expansion axis, whereas the average number of deleterious mutations per individual remains nearly constant across the species range. This reveals two important differences to codominant models: (i) mean fitness at the front of the expansion drops much faster if mutations are recessive, and (ii) mutation load can increase during the expansion even if the total number of deleterious mutations per individual remains constant. We use our model to make predictions about the shape of the site frequency spectrum at the front of range expansion, and about correlations between heterozygosity and fitness in different parts of the species range. Importantly, these predictions provide opportunities to empirically validate our theoretical results. We discuss our findings in the light of recent results on the distribution of deleterious genetic variation across human populations and link them to empirical results on the correlation of heterozygosity and fitness found in many natural range expansions.

Exact moment calculations for genetic models with migration, mutation, and drift

Using properties of moment stationarity we develop exact expressions for the mean and covariance of allele frequencies at a single locus for a set of populations subject to drift, mutation, and migration. Some general results can be obtained even for arbitrary mutation and migration matrices, for example: (1) Under quite general conditions, the mean vector depends only on mutation rates, not on migration rates or the number of populations. (2) Allele frequencies covary among all pairs of populations connected by migration. As a result, the drift, mutation, migration process is not ergodic when any finite number of populations is exchanging genes. in addition, we provide closed form expressions for the mean and covariance of allele frequencies in Wright's finite-island model of migration under several simple models of mutation, and we show that the correlation in allele frequencies among populations can be very large for realistic rates of mutation unless an enormous number of populations are exchanging genes. As a result, the traditional diffusion approximation provides a poor approximation of the stationary distribution of allele frequencies among populations. Finally, we discuss some implications of our results for measures of population structure based on Wright's F-statistics.

Genetic mechanisms of Na+, K+-ATPase regulation in hypertension

The spontaneously hypertensive rat (SHR) is a model of essential hypertension. During the early development of hypertension, the SHR demonstrates increased proximal tubule (PT) Na+ reabsorption. I hypothesized that the increased PT Na+ reabsorption exhibited by the young SHR was due to altered sub-cellular distribution of Na+, K +-ATPase compared to the normotensive Wistar Kyoto (WKY). The hypothesis is supported, herein, by observations of greater Na+, K +-ATPase α 1 abundance in PT plasma membrane and lower abundance in late endosomes of 4wk SHR despite no difference in total PT α 1 abundance. There is a greater amount of Ser-18 unphosphorylated α 1 in the 4wk SHR PT. Total PT Na+, K+-ATPase γ abundance is greater in SHR at 4wk and 16wk but γ abundance in plasma membrane is greater only at 4wk. The phosphatase, calcineurin, was chosen for study because it is involved in the stimulation of Na+, K +-ATPase. No difference in calcineurin coding sequence, expression, or activity was observed in SHR. Gene expression arrays were next used to find candidate genes involved in the regulation of Na+, K +-ATPase. The first candidate analyzed was soluble epoxide hydrolase (sEH). The gene encoding sEH (EPHX2) showed lower expression in SHR. There was also a reduction in sEH protein abundance but there was no correlation between protein abundance and blood pressure in F2 progeny. Two EPHX2 alleles were identified, an ancestral allele and a variant allele containing four polymorphisms. sEH activity was greater in animals carrying the variant allele but the inheritance of the variant allele did not correlate with blood pressure. Gene expression arrays also led to the examination of genes involved in redox balance/Na+, K+-ATPase regulation. A pattern of lower expression of genes involved in reactive radical detoxification in SHR was discerned. Six transcription factor binding sites were identified that occurred more often in these genes. Three transcription factors that bind to the HNF1 site were expressed at lower levels in SHR. This points to the HNF1 transcriptional complex as an important trans-acting regulator of a wide range of genes involved in altered redox balance in SHR. ^

A full pedigree based method for the statistical assessment of genetic anticipation

Genetic anticipation is defined as a decrease in age of onset or increase in severity as the disorder is transmitted through subsequent generations. Anticipation has been noted in the literature for over a century. Recently, anticipation in several diseases including Huntington's Disease, Myotonic Dystrophy and Fragile X Syndrome were shown to be caused by expansion of triplet repeats. Anticipation effects have also been observed in numerous mental disorders (e.g. Schizophrenia, Bipolar Disorder), cancers (Li-Fraumeni Syndrome, Leukemia) and other complex diseases. ^ Several statistical methods have been applied to determine whether anticipation is a true phenomenon in a particular disorder, including standard statistical tests and newly developed affected parent/affected child pair methods. These methods have been shown to be inappropriate for assessing anticipation for a variety of reasons, including familial correlation and low power. Therefore, we have developed family-based likelihood modeling approaches to model the underlying transmission of the disease gene and penetrance function and hence detect anticipation. These methods can be applied in extended families, thus improving the power to detect anticipation compared with existing methods based only upon parents and children. The first method we have proposed is based on the regressive logistic hazard model. This approach models anticipation by a generational covariate. The second method allows alleles to mutate as they are transmitted from parents to offspring and is appropriate for modeling the known triplet repeat diseases in which the disease alleles can become more deleterious as they are transmitted across generations. ^ To evaluate the new methods, we performed extensive simulation studies for data simulated under different conditions to evaluate the effectiveness of the algorithms to detect genetic anticipation. Results from analysis by the first method yielded empirical power greater than 87% based on the 5% type I error critical value identified in each simulation depending on the method of data generation and current age criteria. Analysis by the second method was not possible due to the current formulation of the software. The application of this method to Huntington's Disease and Li-Fraumeni Syndrome data sets revealed evidence for a generation effect in both cases. ^

Bayesian models for the analysis of genetic structure when populations are correlated

Motivation: Population allele frequencies are correlated when populations have a shared history or when they exchange genes. Unfortunately, most models for allele frequency and inference about population structure ignore this correlation. Recent analytical results show that among populations, correlations can be very high, which could affect estimates of population genetic structure. In this study, we propose a mixture beta model to characterize the allele frequency distribution among populations. This formulation incorporates the correlation among populations as well as extending the model to data with different clusters of populations. Results: Using simulated data, we show that in general, the mixture model provides a good approximation of the among-population allele frequency distribution and a good estimate of correlation among populations. Results from fitting the mixture model to a dataset of genotypes at 377 autosomal microsatellite loci from human populations indicate high correlation among populations, which may not be appropriate to neglect. Traditional measures of population structure tend to over-estimate the amount of genetic differentiation when correlation is neglected. Inference is performed in a Bayesian framework.

Genotypic spectrum and genotype-phenotype correlation of trimethylaminuria

Trimethylaminuria (TMAU) or Fish odor syndrome is an autosomal recessive disease that is characterized by pungent body odor with subsequent psychosocial complications. There are limited studies of the sequence variants causing TMAU in the literature with most studies describing only one or two patients and lacking genotype-phenotype correlations. Also to date, there is no laboratory in the US or Europe that offers TMA genetic testing on a clinical basis. We have recently validated genetic testing in the University of Colorado DNA Diagnostic Laboratory. We have a database of a few dozen patients with a biochemical diagnosis of TMA at the University of Colorado at Denver Health Sciences Center (UCDHSC) which includes a few patients with the classical form of the disease. We have used the newly established clinical test in our institution to attempt to characterize the genotype (sequence variants including mutations and polymorphisms) of classical TMAU patients and to establish a genotype-phenotype (biochemical and clinical) association. The questionnaire results confirmed most of the previously reported epidemiological findings of TMAU and also indicated that TMAU patients use multiple intervention measures in attempt to control their symptoms with dietary control being most effective. Despite the complexity of intervention, most patients did not have any medical follow up and there was underutilization of specialist care. In a set of our patients, two deleterious mutations were identified in 4/12 patients including a novel T237P sequence variant, while the majority of our patients (8/12) did not reveal any mutations. Some of the latter were double heterozygous for the E158K and E308G polymorphisms which could explain a mild phenotype while others had only the E158K variant which raised the question of undetected mutations. These results indicate that further experiments are needed to further delineate the full mutational spectrum of the FMO3 gene. ^

Genetic diversity among alfalfa genotypes (Medicago sativa L.) of non-dormant cultivars using SSR markers and agronomic traits

The aim of this study was to assess genetic diversity among 40 alfalfa (Medicago sativa L.) genotypes of different non-dormant (FD=8) cultivars. Biomass yield, regrowth speed and reaction to spring black stem, lepto leaf spot, and rust were evaluated. Analyses of variances were performed using a mixed model to examine the agronomic variation among individuals. A principal component analysis on standardized agronomic data was performed. Agronomic data were also used to calculate Gower's distance and UPGMA algorithm. For the molecular analysis, six SSR markers were evaluated and 84 alleles were identified. The genetic distance was estimated using standard Nei's distance. Average standard genetic diversity was 0.843, indicating a high degree of variability among genotypes. Finally, a generalized procrustes analysis was performed to calculate the correlation between molecular and agronomic distance, indicating a 65.4% of consensus. This value is likely related to the low number of individuals included in the study, which might have underestimated the real phenotypic variability among genotypes. Despite the low number of individuals and SSR markers analyzed, this study provides a baseline for future diversity studies to identify genetically distant alfalfa individuals or cultivars.

Competition may explain the fine-scale spatial patterns and genetic structure of two co-occurring plant congeners

1. The spatial distribution of individual plants within a population and the population’s genetic structure are determined by several factors, like dispersal, reproduction mode or biotic interactions. The role of interspecific interactions in shaping the spatial genetic structure of plant populations remains largely unknown. 2. Species with a common evolutionary history are known to interact more closely with each other than unrelated species due to the greater number of traits they share. We hypothesize that plant interactions may shape the fine genetic structure of closely related congeners. 3. We used spatial statistics (georeferenced design) and molecular techniques (ISSR markers) to understand how two closely related congeners, Thymus vulgaris (widespread species) and T. loscosii (narrow endemic) interact at the local scale. Specific cover, number of individuals of both study species and several community attributes were measured in a 10 × 10 m plot. 4. Both species showed similar levels of genetic variation, but differed in their spatial genetic structure. Thymus vulgaris showed spatial aggregation but no spatial genetic structure, while T. loscosii showed spatial genetic structure (positive genetic autocorrelation) at short distances. The spatial pattern of T. vulgaris’ cover showed significant dissociation with that of T. loscosii. The same was true between the spatial patterns of the cover of T. vulgaris and the abundance of T. loscosii and between the abundance of each species. Most importantly, we found a correlation between the genetic structure of T. loscosii and the abundance of T. vulgaris: T. loscosii plants were genetically more similar when they were surrounded by a similar number of T. vulgaris plants. 5. Synthesis. Our results reveal spatially complex genetic structures of both congeners at small spatial scales. The negative association among the spatial patterns of the two species and the genetic structure found for T. loscosii in relation to the abundance of T. vulgaris indicate that competition between the two species may account for the presence of adapted ecotypes of T. loscosii to the abundance of a competing congeneric species. This suggests that the presence and abundance of close congeners can influence the genetic spatial structure of plant species at fine scales.

Genetic diversity and relationships in accessions from different cultivar groups and origins in the tree tomato (Solanum betaceum Cav.)

Abstract Tree tomato (Solanum betaceum) is an Andean small tree cultivated for its juicy fruits. Little information is available on the characterization of genetic resources and breeding of this neglected crop. We have studied the molecular diversity with AFLP markers using 11 combinations of primers of a collection of 25 S. betaceum accessions belonging to four cultivar groups, most of which had been previously morphologically characterized, as well as one accession of the wild relative S. cajanumense.Atotal of 197 AFLP fragments were scored, of which 84 (43 %) were polymorphic. When excluding S. cajanumense from the analysis, the number of polymorphic AFLP fragments was 78 (40 %). Unique AFLP fingerprints were obtained for every accession, but no AFLP fragments specific and universal to any of the four cultivar groups were found. The total genetic diversity (HT) of cultivated accessions was HT = 0.2904, while for cultivar groups it ranged from HT = 0.1846 in the orange group to HT = 0.2498 in the orange pointed group. Genetic differentiation among cultivar groups (GST) was low (GST = 0.2248), which was matched by low values of genetic distance among cultivar groups. The diversity of collections from Ecuador, which we hypothesize is a center of diversity for tree tomato, was similar to that from other origins (HT = 0.2884 and HT = 0.2645, respectively). Cluster and PCoA analyses clearly separated wild S. cajanumense from the cultivated species. However, materials of different cultivar groups and origins were intermingled in both analyses. The Mantel test correlation coefficient of the matrices of morphological and AFLP distances was low (-0.024) and non-significant. Overall, the results show that a wide diversity is present in each of the cultivar groups, indicate that Ecuador may be regarded as a center of accumulation of diversity for this crop, and confirm that AFLP and morphological characterization data are complementary. The results obtained are of value for the conservation of genetic resources and breeding of tree tomato, as an assessment of the genetic diversity and relationships among differen

Improved damage detectability in a wind turbine blade by optimal selection of vibration signal correlation coefficients

Piotr Omenzetter and Simon Hoell’s work within the Lloyd’s Register Foundation Centre for Safety and Reliability Engineering at the University of Aberdeen is supported by Lloyd’s Register Foundation. The Foundation helps to protect life and property by supporting engineering-related education, public engagement and the application of research.

Gene targeting approaches to complex genetic diseases: atherosclerosis and essential hypertension.

Gene targeting allows precise, predetermined changes to be made in a chosen gene in the mouse genome. To date, targeting has been used most often for generation of animals completely lacking the product of a gene of interest. The resulting "knockout" mice have confirmed some hypotheses, have upset others, but have rarely been uninformative. Models of several human genetic diseases have been produced by targeting--including Gaucher disease, cystic fibrosis, and the fragile X syndrome. These diseases are primarily determined by defects in single genes, and their modes of inheritance are well understood. When the disease under study has a complex etiology with multiple genetic and environmental components, the generation of animal models becomes more difficult but no less valuable. The problems associated with dissecting out the individual genetic factors also increases substantially and the distinction between causation and correlation is often difficult. To prove causation in a complex system requires rigorous adherence to the principle that the experiments must allow detection of the effects of changing only a single variable at one time. Gene targeting experiments, when properly designed, can test the effects of a precise genetic change completely free from the effects of differences in any other genes (linked or unlinked to the test gene). They therefore allow proofs of causation.

Intra-population variation and geographic correlation in Canthon humectus hidalgoensis using FTIR-ATR spectroscopy

In some cases external morphology is not sufficient to discern between populations of a species, as occurs in the dung beetle Canthon humectus hidalgoensis Bates; and much less to determine phenotypic distances between them. FTIR-ATR spectroscopy show several advantages over other identification techniques (e.g. morphological, genetic, and cuticular hydrocarbons analysis) due to the non-invasive manner of the sample preparation, the relative speed of sample analysis and the low-cost of this technology. The infrared spectrum obtained is recognized to give a unique ‘fingerprint’ because vibrational spectra are specific and unique to the molecular nature of the sample. In our study, results showed that proteins, amino acids and aromatic ethers of insect exocuticle have promising discriminative power to discern between different populations of C. h. hidalgoensis. Furthermore, the correlation between geographic distances between populations and the chemical distances obtained by proteins + amino acids + aromatic ethers was statistically significant, showing that the spectral and spatial information available of the taxa together with appropriated chemometric methods may help to a better understanding of the identity, structure, dynamics and diversity of insect populations.

Genetic algorithm optimization of adaptive multi-scale GLCM features

We introduce a new second-order method of texture analysis called Adaptive Multi-Scale Grey Level Co-occurrence Matrix (AMSGLCM), based on the well-known Grey Level Co-occurrence Matrix (GLCM) method. The method deviates significantly from GLCM in that features are extracted, not via a fixed 2D weighting function of co-occurrence matrix elements, but by a variable summation of matrix elements in 3D localized neighborhoods. We subsequently present a new methodology for extracting optimized, highly discriminant features from these localized areas using adaptive Gaussian weighting functions. Genetic Algorithm (GA) optimization is used to produce a set of features whose classification worth is evaluated by discriminatory power and feature correlation considerations. We critically appraised the performance of our method and GLCM in pairwise classification of images from visually similar texture classes, captured from Markov Random Field (MRF) synthesized, natural, and biological origins. In these cross-validated classification trials, our method demonstrated significant benefits over GLCM, including increased feature discriminatory power, automatic feature adaptability, and significantly improved classification performance.

Optimal sampling strategy for estimation of spatial genetic structure in tree populations

Fine-scale spatial genetic structure (SGS) in natural tree populations is largely a result of restricted pollen and seed dispersal. Understanding the link between limitations to dispersal in gene vectors and SGS is of key interest to biologists and the availability of highly variable molecular markers has facilitated fine-scale analysis of populations. However, estimation of SGS may depend strongly on the type of genetic marker and sampling strategy (of both loci and individuals). To explore sampling limits, we created a model population with simulated distributions of dominant and codominant alleles, resulting from natural regeneration with restricted gene flow. SGS estimates from subsamples (simulating collection and analysis with amplified fragment length polymorphism (AFLP) and microsatellite markers) were correlated with the 'real' estimate (from the full model population). For both marker types, sampling ranges were evident, with lower limits below which estimation was poorly correlated and upper limits above which sampling became inefficient. Lower limits (correlation of 0.9) were 100 individuals, 10 loci for microsatellites and 150 individuals, 100 loci for AFLPs. Upper limits were 200 individuals, five loci for microsatellites and 200 individuals, 100 loci for AFLPs. The limits indicated by simulation were compared with data sets from real species. Instances where sampling effort had been either insufficient or inefficient were identified. The model results should form practical boundaries for studies aiming to detect SGS. However, greater sample sizes will be required in cases where SGS is weaker than for our simulated population, for example, in species with effective pollen/seed dispersal mechanisms.