Flavored exotic multibaryons and hypernuclei in topological soliton models

The energies of baryon states with positive strangeness, or anti-charm (-beauty) are estimated in chiral soliton approach, in the "rigid oscillator" version of the bound state soliton model proposed by Klebanov and Westerberg. Positive strangeness states can appear as relatively narrow nuclear levels (Theta-hypernuclei), the states with heavy anti-flavors can be bound with respect to strong interactions in the original Skyrme variant of the model (SK4 variant). The binding energies of anti-flavored states are estimated also in the variant of the model with 6-th order term in chiral derivatives in the lagrangian as solitons stabilizer (SK6 variant). The latter variant is less attractive, and nuclear states with anti-charm or anti-beauty can be unstable relative to strong interactions. The chances to get bound hypernuclei with heavy antiflavors are greater within "nuclear variant" of the model with rescaled model parameter (Skyrme constant e or e' decreased by ~30%) which is expected to be valid for baryon numbers greater than B ~10. The rational map approximation is used to describe multiskyrmions with baryon number up to ~30 and to calculate the quantities necessary for their quantization (moments of inertia, sigma-term, etc.).Comment: 24 pages, 7 table

A developmental approach to predicting neuronal connectivity from small biological datasets: a gradient-based neuron growth model.

PMCID: PMC3931784 Open Access article: BB/G006652/1 and BB/G006369/1.Relating structure and function of neuronal circuits is a challenging problem. It requires demonstrating how dynamical patterns of spiking activity lead to functions like cognitive behaviour and identifying the neurons and connections that lead to appropriate activity of a circuit. We apply a "developmental approach" to define the connectome of a simple nervous system, where connections between neurons are not prescribed but appear as a result of neuron growth. A gradient based mathematical model of two-dimensional axon growth from rows of undifferentiated neurons is derived for the different types of neurons in the brainstem and spinal cord of young tadpoles of the frog Xenopus. Model parameters define a two-dimensional CNS growth environment with three gradient cues and the specific responsiveness of the axons of each neuron type to these cues. The model is described by a nonlinear system of three difference equations; it includes a random variable, and takes specific neuron characteristics into account. Anatomical measurements are first used to position cell bodies in rows and define axon origins. Then a generalization procedure allows information on the axons of individual neurons from small anatomical datasets to be used to generate larger artificial datasets. To specify parameters in the axon growth model we use a stochastic optimization procedure, derive a cost function and find the optimal parameters for each type of neuron. Our biologically realistic model of axon growth starts from axon outgrowth from the cell body and generates multiple axons for each different neuron type with statistical properties matching those of real axons. We illustrate how the axon growth model works for neurons with axons which grow to the same and the opposite side of the CNS. We then show how, by adding a simple specification for dendrite morphology, our model "developmental approach" allows us to generate biologically-realistic connectomes

Model validation for a noninvasive arterial stenosis detection problem

Copyright @ 2013 American Institute of Mathematical SciencesA current thrust in medical research is the development of a non-invasive method for detection, localization, and characterization of an arterial stenosis (a blockage or partial blockage in an artery). A method has been proposed to detect shear waves in the chest cavity which have been generated by disturbances in the blood flow resulting from a stenosis. In order to develop this methodology further, we use both one-dimensional pressure and shear wave experimental data from novel acoustic phantoms to validate corresponding viscoelastic mathematical models, which were developed in a concept paper [8] and refined herein. We estimate model parameters which give a good fit (in a sense to be precisely defined) to the experimental data, and use asymptotic error theory to provide confidence intervals for parameter estimates. Finally, since a robust error model is necessary for accurate parameter estimates and confidence analysis, we include a comparison of absolute and relative models for measurement error.The National Institute of Allergy and Infectious Diseases, the Air Force Office of Scientific Research, the Deopartment of Education and the Engineering and Physical Sciences Research Council (EPSRC)

Measurement of the B+ --> p pbar K+ Branching Fraction and Study of the Decay Dynamics

With a sample of 232x10^6 Upsilon(4S) --> BBbar events collected with the BaBar detector, we study the decay B+ --> p pbar K+ excluding charmonium decays to ppbar. We measure a branching fraction Br(B+ --> p pbar K+)=(6.7+/-0.5+/-0.4)x10^{-6}. An enhancement at low ppbar mass is observed and the Dalitz plot asymmetry suggests dominance of the penguin amplitude in this B decay. We search for a pentaquark candidate Theta*++ decaying into pK+ in the mass range 1.43 to 2.00 GeV/c2 and set limits on Br(B+ --> Theta*++pbar)xBr(Theta*++ --> pK+) at the 10^{-7} level.Comment: 8 pages, 7 postscript figures, submitted to Phys. Rev. D (Rapid Communications

Bifurcation study of a neural field competition model with an application to perceptual switching in motion integration.

Perceptual multistability is a phenomenon in which alternate interpretations of a fixed stimulus are perceived intermittently. Although correlates between activity in specific cortical areas and perception have been found, the complex patterns of activity and the underlying mechanisms that gate multistable perception are little understood. Here, we present a neural field competition model in which competing states are represented in a continuous feature space. Bifurcation analysis is used to describe the different types of complex spatio-temporal dynamics produced by the model in terms of several parameters and for different inputs. The dynamics of the model was then compared to human perception investigated psychophysically during long presentations of an ambiguous, multistable motion pattern known as the barberpole illusion. In order to do this, the model is operated in a parameter range where known physiological response properties are reproduced whilst also working close to bifurcation. The model accounts for characteristic behaviour from the psychophysical experiments in terms of the type of switching observed and changes in the rate of switching with respect to contrast. In this way, the modelling study sheds light on the underlying mechanisms that drive perceptual switching in different contrast regimes. The general approach presented is applicable to a broad range of perceptual competition problems in which spatial interactions play a role

25th Annual Computational Neuroscience Meeting: CNS-2016

Abstracts of the 25th Annual Computational Neuroscience Meeting: CNS-2016 Seogwipo City, Jeju-do, South Korea. 2–7 July 201