Phylogenetic implications of the 38 putative ancestral chromosome segments for four canid species

Chromosome homologies between the Japanese raccoon dog (Nectereutes procyonoides viverrinus, 2n = 39 + 2-4 B chromosomes) and domestic dog (Canis familiaris, 2n = 78) have been established by hybridizing a complete set of canine paint probes onto high-res

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.

Chromosome painting in the long-tailed field mouse provides insights into the ancestral murid karyotype

We report on the hybridization of mouse chromosomal paints to Apodemus sylvaticus, the long-tailed field mouse. The mouse paints detected 38 conserved segments in the Apodemus karyotype. Together with the species reported here there are now six species of rodents mapped with Mus musculus painting probes. A parsimony analysis indicated that the syntenies of nine M. musculus chromosomes were most likely already formed in the muroid ancestor: 3, 4, 7, 9, 14, 18, 19, X and Y. The widespread occurrence of syntenic segment associations of mouse chromosomes 1/17, 2/13, 7/19, 10/17, 11/16, 12/17 and 13/15 suggests that these associations were ancestral syntenies for muroid rodents. The muroid ancestral karyotype probably had a diploid number of about 2n = 54. It would be desirable to have a richer phylogenetic array of species before any final conclusions are drawn about the Muridae ancestral karyotype. The ancestral karyotype presented here should be considered as a working hypothesis. Copyright (C) 2004 S. Karger AG, Basel.

Comparative genome maps of the pangolin, hedgehog, sloth, anteater and human revealed by cross-species chromosome painting: further insight into the ancestral karyotype and genome evolution of eutherian mammals

To better understand the evolution of genome organization of eutherian mammals, comparative maps based on chromosome painting have been constructed between human and representative species of three eutherian orders: Xenarthra, Pholidota, and Eulipotyphla,

Chromosome painting between human and lorisiform prosimians: Evidence for the HSA 7/16 Synteny in the primate ancestral karyotype

Multidirectional chromosome painting with probes derived from flow-sorted chromosomes of humans (Homo sapiens, HSA, 2n = 46) and galagos (Galago moholi, GMO, 2n = 38) allowed us to map evolutionarily conserved chromosomal segments among humans, galagos, a

Mathematical relationships between representations of structure in linear interconnected dynamical systems

A dynamical system can exhibit structure on multiple levels. Different system representations can capture different elements of a dynamical system's structure. We consider LTI input-output dynamical systems and present four representations of structure: complete computational structure, subsystem structure, signal structure, and input output sparsity structure. We then explore some of the mathematical relationships that relate these different representations of structure. In particular, we show that signal and subsystem structure are fundamentally different ways of representing system structure. A signal structure does not always specify a unique subsystem structure nor does subsystem structure always specify a unique signal structure. We illustrate these concepts with a numerical example. © 2011 AACC American Automatic Control Council.

Augmented attribute representations

We propose a new learning method to infer a mid-level feature representation that combines the advantage of semantic attribute representations with the higher expressive power of non-semantic features. The idea lies in augmenting an existing attribute-based representation with additional dimensions for which an autoencoder model is coupled with a large-margin principle. This construction allows a smooth transition between the zero-shot regime with no training example, the unsupervised regime with training examples but without class labels, and the supervised regime with training examples and with class labels. The resulting optimization problem can be solved efficiently, because several of the necessity steps have closed-form solutions. Through extensive experiments we show that the augmented representation achieves better results in terms of object categorization accuracy than the semantic representation alone. © 2012 Springer-Verlag.

Representations and algorithms for finite-state bisimulations of linear discrete-time control systems

While a large amount of research over the past two decades has focused on discrete abstractions of infinite-state dynamical systems, many structural and algorithmic details of these abstractions remain unknown. To clarify the computational resources needed to perform discrete abstractions, this paper examines the algorithmic properties of an existing method for deriving finite-state systems that are bisimilar to linear discrete-time control systems. We explicitly find the structure of the finite-state system, show that it can be enormous compared to the original linear system, and give conditions to guarantee that the finite-state system is reasonably sized and efficiently computable. Though constructing the finite-state system is generally impractical, we see that special cases could be amenable to satisfiability based verification techniques. ©2009 IEEE.

Internal representations of temporal statistics and feedback calibrate motor-sensory interval timing.

Humans have been shown to adapt to the temporal statistics of timing tasks so as to optimize the accuracy of their responses, in agreement with the predictions of Bayesian integration. This suggests that they build an internal representation of both the experimentally imposed distribution of time intervals (the prior) and of the error (the loss function). The responses of a Bayesian ideal observer depend crucially on these internal representations, which have only been previously studied for simple distributions. To study the nature of these representations we asked subjects to reproduce time intervals drawn from underlying temporal distributions of varying complexity, from uniform to highly skewed or bimodal while also varying the error mapping that determined the performance feedback. Interval reproduction times were affected by both the distribution and feedback, in good agreement with a performance-optimizing Bayesian observer and actor model. Bayesian model comparison highlighted that subjects were integrating the provided feedback and represented the experimental distribution with a smoothed approximation. A nonparametric reconstruction of the subjective priors from the data shows that they are generally in agreement with the true distributions up to third-order moments, but with systematically heavier tails. In particular, higher-order statistical features (kurtosis, multimodality) seem much harder to acquire. Our findings suggest that humans have only minor constraints on learning lower-order statistical properties of unimodal (including peaked and skewed) distributions of time intervals under the guidance of corrective feedback, and that their behavior is well explained by Bayesian decision theory.

Neural representations of courtship song in the Drosophila brain.

Acoustic communication in drosophilid flies is based on the production and perception of courtship songs, which facilitate mating. Despite decades of research on courtship songs and behavior in Drosophila, central auditory responses have remained uncharacterized. In this study, we report on intracellular recordings from central neurons that innervate the Drosophila antennal mechanosensory and motor center (AMMC), the first relay for auditory information in the fly brain. These neurons produce graded-potential (nonspiking) responses to sound; we compare recordings from AMMC neurons to extracellular recordings of the receptor neuron population [Johnston's organ neurons (JONs)]. We discover that, while steady-state response profiles for tonal and broadband stimuli are significantly transformed between the JON population in the antenna and AMMC neurons in the brain, transient responses to pulses present in natural stimuli (courtship song) are not. For pulse stimuli in particular, AMMC neurons simply low-pass filter the receptor population response, thus preserving low-frequency temporal features (such as the spacing of song pulses) for analysis by postsynaptic neurons. We also compare responses in two closely related Drosophila species, Drosophila melanogaster and Drosophila simulans, and find that pulse song responses are largely similar, despite differences in the spectral content of their songs. Our recordings inform how downstream circuits may read out behaviorally relevant information from central neurons in the AMMC.

Cognitive Tomography Reveals Complex, Task-Independent Mental Representations

Humans develop rich mental representations that guide their behavior in a variety of everyday tasks. However, it is unknown whether these representations, often formalized as priors in Bayesian inference, are specific for each task or subserve multiple tasks. Current approaches cannot distinguish between these two possibilities because they cannot extract comparable representations across different tasks [1-10]. Here, we develop a novel method, termed cognitive tomography, that can extract complex, multidimensional priors across tasks. We apply this method to human judgments in two qualitatively different tasks, "familiarity" and "odd one out," involving an ecologically relevant set of stimuli, human faces. We show that priors over faces are structurally complex and vary dramatically across subjects, but are invariant across the tasks within each subject. The priors we extract from each task allow us to predict with high precision the behavior of subjects for novel stimuli both in the same task as well as in the other task. Our results provide the first evidence for a single high-dimensional structured representation of a naturalistic stimulus set that guides behavior in multiple tasks. Moreover, the representations estimated by cognitive tomography can provide independent, behavior-based regressors for elucidating the neural correlates of complex naturalistic priors. © 2013 The Authors.

Motor learning of novel dynamics is not represented in a single global coordinate system: evaluation of mixed coordinate representations and local learning.

Successful motor performance requires the ability to adapt motor commands to task dynamics. A central question in movement neuroscience is how these dynamics are represented. Although it is widely assumed that dynamics (e.g., force fields) are represented in intrinsic, joint-based coordinates (Shadmehr R, Mussa-Ivaldi FA. J Neurosci 14: 3208-3224, 1994), recent evidence has questioned this proposal. Here we reexamine the representation of dynamics in two experiments. By testing generalization following changes in shoulder, elbow, or wrist configurations, the first experiment tested for extrinsic, intrinsic, or object-centered representations. No single coordinate frame accounted for the pattern of generalization. Rather, generalization patterns were better accounted for by a mixture of representations or by models that assumed local learning and graded, decaying generalization. A second experiment, in which we replicated the design of an influential study that had suggested encoding in intrinsic coordinates (Shadmehr and Mussa-Ivaldi 1994), yielded similar results. That is, we could not find evidence that dynamics are represented in a single coordinate system. Taken together, our experiments suggest that internal models do not employ a single coordinate system when generalizing and may well be represented as a mixture of coordinate systems, as a single system with local learning, or both.

Influence of two different representations of the G-matrix to the in-medium nucleon-nucleon cross sections

We calculate the in-medium nucleon-nucleon scattering cross sections from the G-matrix using the Dirac-Brueckner-Hartree-Fock (DBHF) approach. And we investigate the influence of the different representations of the G-matrix to the cross sections, the difference of which is mainly from the different effective masses.