Reciprocal chromosome painting illuminates the history of genome evolution of the domestic cat, dog and human

Domestic cats and dogs are important companion animals and model animals in biomedical research. The cat has a highly conserved karyotype, closely resembling the ancestral karyotype of mammals, while the dog has one of the most extensively rearranged mammalian karyotypes investigated so far. We have constructed the first detailed comparative chromosome map of the domestic dog and cat by reciprocal chromosome painting. Dog paints specific for the 38 autosomes and the X chromosomes delineated 68 conserved chromosomal segments in the cat, while reverse painting of cat probes onto red fox and dog chromosomes revealed 65 conserved segments. Most conserved segments on cat chromosomes also show a high degree of conservation in G-banding patterns compared with their canine counterparts. At least 47 chromosomal fissions (breaks), 25 fusions and one inversion are needed to convert the cat karyotype to that of the dog, confirming that extensive chromosome rearrangements differentiate the karyotypes of the cat and dog. Comparative analysis of the distribution patterns of conserved segments defined by dog paints on cat and human chromosomes has refined the human/cat comparative genome map and, most importantly, has revealed 15 cryptic inversions in seven large chromosomal regions of conserved synteny between humans and cats.

Reciprocal chromosome painting among human, aardvark, and elephant (superorder Afrotheria) reveals the likely eutherian ancestral karyotype

The Afrotheria, a supraordinal grouping of mammals whose radiation is rooted in Africa, is strongly supported by DNA sequence data but not by their disparate anatomical features. We have used flow-sorted human, aardvark, and African elephant chromosome painting probes and applied reciprocal painting schemes to representatives of two of the Afrotherian orders, the Tubulidentata (aardvark) and Proboscidea (elephants), in an attempt to shed additional light on the evolutionary affinities of this enigmatic group of mammals. Although we have not yet found any unique cytogenetic signatures that support the monophyly of the Afrotheria, embedded within the aardvark genome we find the strongest evidence yet of a mammalian ancestral karyotype comprising 2n = 44. This karyotype includes nine chromosomes that show complete conserved synteny to those of man, six that show conservation as single chromosome arms or blocks in the human karyotype but that occur on two different chromosomes in the ancestor, and seven neighbor-joining combinations (i.e., the synteny is maintained in the majority of species of the orders studied so far, but which corresponds to two chromosomes in humans). The comparative chromosome maps presented between human and these Afrotherian species provide further insight into mammalian genome organization and comparative genomic data for the Afrotheria, one of the four major evolutionary clades postulated for the Eutheria.

Avian comparative genomics: reciprocal chromosome painting between domestic chicken (Gallus gallus) and the stone curlew (Burhinus oedicnemus, Charadriiformes) - An atypical species with low diploid number

The chicken is the most extensively studied species in birds and thus constitutes an ideal reference for comparative genomics in birds. Comparative cytogenetic studies indicate that the chicken has retained many chromosome characters of the ancestral avia

Trade-off between reciprocal mutualists: local resource availability-oriented interaction in fig/fig wasp mutualism

The mechanisms that prevent competition (conflict) between the recipient and co-operative actor in co-operative systems remain one of the greatest problems for evolutionary biology. Previous hypotheses suggest that self-restraint, dispersal or spatial con

Sensor graphs for guaranteed cooperative localization performance

A group of mobile robots can localize cooperatively, using relative position and absolute orientation measurements, fused through an extended Kalman filter (ekf). The topology of the graph of relative measurements is known to affect the steady-state value of the position error covariance matrix. Classes of sensor graphs are identified, for which tight bounds for the trace of the covariance matrix can be obtained based on the algebraic properties of the underlying relative measurement graph. The string and the star graph topologies are considered, and the explicit form of the eigenvalues of error covariance matrix is given. More general sensor graph topologies are considered as combinations of the string and star topologies, when additional edges are added. It is demonstrated how the addition of edges increases the trace of the steady-state value of the position error covariance matrix, and the theoretical predictions are verified through simulation analysis.

The most persistent soft-clique in a set of sampled graphs

When searching for characteristic subpatterns in potentially noisy graph data, it appears self-evident that having multiple observations would be better than having just one. However, it turns out that the inconsistencies introduced when different graph instances have different edge sets pose a serious challenge. In this work we address this challenge for the problem of finding maximum weighted cliques. We introduce the concept of most persistent soft-clique. This is subset of vertices, that 1) is almost fully or at least densely connected, 2) occurs in all or almost all graph instances, and 3) has the maximum weight. We present a measure of clique-ness, that essentially counts the number of edge missing to make a subset of vertices into a clique. With this measure, we show that the problem of finding the most persistent soft-clique problem can be cast either as: a) a max-min two person game optimization problem, or b) a min-min soft margin optimization problem. Both formulations lead to the same solution when using a partial Lagrangian method to solve the optimization problems. By experiments on synthetic data and on real social network data we show that the proposed method is able to reliably find soft cliques in graph data, even if that is distorted by random noise or unreliable observations. Copyright 2012 by the author(s)/owner(s).

Random function priors for exchangeable arrays with applications to graphs and relational data

A fundamental problem in the analysis of structured relational data like graphs, networks, databases, and matrices is to extract a summary of the common structure underlying relations between individual entities. Relational data are typically encoded in the form of arrays; invariance to the ordering of rows and columns corresponds to exchangeable arrays. Results in probability theory due to Aldous, Hoover and Kallenberg show that exchangeable arrays can be represented in terms of a random measurable function which constitutes the natural model parameter in a Bayesian model. We obtain a flexible yet simple Bayesian nonparametric model by placing a Gaussian process prior on the parameter function. Efficient inference utilises elliptical slice sampling combined with a random sparse approximation to the Gaussian process. We demonstrate applications of the model to network data and clarify its relation to models in the literature, several of which emerge as special cases.

Model reductions for inference: generality of pairwise, binary, and planar factor graphs.

We offer a solution to the problem of efficiently translating algorithms between different types of discrete statistical model. We investigate the expressive power of three classes of model-those with binary variables, with pairwise factors, and with planar topology-as well as their four intersections. We formalize a notion of "simple reduction" for the problem of inferring marginal probabilities and consider whether it is possible to "simply reduce" marginal inference from general discrete factor graphs to factor graphs in each of these seven subclasses. We characterize the reducibility of each class, showing in particular that the class of binary pairwise factor graphs is able to simply reduce only positive models. We also exhibit a continuous "spectral reduction" based on polynomial interpolation, which overcomes this limitation. Experiments assess the performance of standard approximate inference algorithms on the outputs of our reductions.

Measuring the composition of AlGaN layers in GaN based structures grown on 150 mm Si substrates using (2 0 5) reciprocal space maps

A critical element for the successful growth of GaN device layers on Si is accurate control of the AlGaN buffer layers used to manage strain. Here we present a method for measuring the composition of the AlGaN buffer layers in device structures which makes use of a one-dimensional x-ray detector to provide efficient measurement of a reciprocal space map which covers the full compositional range from AlN to GaN. Combining this with a suitable x-ray reflection with low strain sensitivity it is possible to accurately determine the Al fraction of the buffer layers independent of their relaxation state. © 2013 IOP Publishing Ltd.

Measurement of a reciprocal four-port transmission line structure using the 16-term error model

A new method to measure reciprocal four-port structures, using a 16-term error model, is presented. The measurement is based on 5 two-port calibration standards connected to two of the ports, while the network analyzer is connected to the two remaining ports. Least-squares-fit data reduction techniques are used to lower error sensitivity. The effect of connectors is deembedded using closed-form equations. (C) 2007 Wiley Periodicals, Inc.

Research on multi-degree-of-freedom neurons with weighted graphs

In this paper, we redefine the sample points set in the feature space from the point of view of weighted graph and propose a new covering model - Multi-Degree-of-Freedorn Neurons (MDFN). Base on this model, we describe a geometric learning algorithm with 3-degree-of-freedom neurons. It identifies the sample points secs topological character in the feature space, which is different from the traditional "separation" method. Experiment results demonstrates the general superiority of this algorithm over the traditional PCA+NN algorithm in terms of efficiency and accuracy.

Research on multi-degree-of-freedom neurons with weighted graphs

In this paper, we redefine the sample points set in the feature space from the point of view of weighted graph and propose a new covering model - Multi-Degree-of-Freedorn Neurons (MDFN). Base on this model, we describe a geometric learning algorithm with 3-degree-of-freedom neurons. It identifies the sample points secs topological character in the feature space, which is different from the traditional "separation" method. Experiment results demonstrates the general superiority of this algorithm over the traditional PCA+NN algorithm in terms of efficiency and accuracy.