On interference effects in concurrent perception and action

Recent studies have reported repulsion effects between the perception of visual motion and the concurrent production of hand movements. Two models, based on the notions of common coding and internal forward modeling, have been proposed to account for these phenomena. They predict that the size of the effects in perception and action should be monotonically related and vary with the amount of similarity between what is produced and perceived. These predictions were tested in four experiments in which participants were asked to make hand movements in certain directions while simultaneously encoding the direction of an independent stimulus motion. As expected, perceived directions were repelled by produced directions, and produced directions were repelled by perceived directions. However, contrary to the models, the size of the effects in perception and action did not covary, nor did they depend (as predicted) on the amount of perception–action similarity. We propose that such interactions are mediated by the activation of categorical representations

A binary level set method based on k-Means for contour tracking on skin cancer images

A great challenge of research and development activities have recently highlighted in segmenting of the skin cancer images. This paper presents a novel algorithm to improve the segmentation results of level set algorithm with skin cancer images. The major contribution of presented algorithm is to simplify skin cancer images for the computer aided object analysis without loss of significant information and to decrease the required computational cost. The presented algorithm uses k-means clustering technique and explores primitive segmentation to get initial label estimation for level set algorithm. The proposed segmentation method provides better segmentation results as compared to standard level set segmentation technique and modified fuzzy cmeans clustering technique

Self-Organized Vortices of Circling Self-Propelled Particles and Curved Active Flagella

Self-propelled point-like particles move along circular trajectories when their translocation velocity is constant and the angular velocity related to their orientation vector is also constant. We investigate the collective behavior of ensembles of such circle swimmers by Brownian dynamics simulations. If the particles interact via a "velocity-trajectory coordination" rule within neighboring particles, a self-organized vortex pattern emerges. This vortex pattern is characterized by its particle-density correlation function $G_\rho$, the density correlation function $G_c$ of trajectory centers, and an order parameter $S$ representing the degree of the aggregation of the particles. Here, we systematically vary the system parameters, such as the particle density and the interaction range, in order to reveal the transition of the system from a light-vortex-dominated to heavy-vortex-dominated state, where vortices contain mainly a single and many self-propelled particles, respectively. We also study a semi-dilute solution of curved, sinusoidal-beating flagella, as an example of circling self-propelled particles with explicit propulsion mechanism and excluded-volume interactions. Our simulation results are compared with previous experimental results for the vortices in sea-urchin sperm solutions near a wall. The properties of the vortices in simulations and experiments are found to agree quantitatively.Comment: 14 pages, 15 figure

Collective cell motility in 3-dimensions: dynamics, adhesions, and emergence of heterogeneity

Collective cell migration is ubiquitous in biology, from development to cancer; it is influenced by heterogeneous cell types, signals and matrix properties, and requires large scale regulation in space and time. Understanding how cells achieve organized collective motility is crucial to addressing cellular and tissue function and disease progression. While current two-dimensional model systems recapitulate the dynamic properties of collective cell migration, quantitative three-dimensional equivalent model systems have proved elusive. The overarching hypothesis of this work is that cell collectives are heterogeneous in nature; and that the influence of biochemical, physical, and mechanical factors combined leads to diverse physical behaviors. The central goal of this work is to establish standard tools for the understanding of 3D collective cell motility by treating individual cell-collectives as independent entities. An experimental model studies cell collectives by tracking individual cells within cell cohorts embedded in three dimensional collagen scaffolding. A computational model of 3-dimensional multi-scale self-propelled particles recreates experimental data and accounts for intercellular adhesion dynamics. A custom algorithm identifies cellular cohorts from experimental and simulated data so these may be treated as independent entities. A second custom algorithm quantifies the temporal and spatial heterogeneity of motion in cell cohorts during ‘motility events’ observed in experiments and simulations. The results show that cell-cohorts in 3D are dynamic with spatial and temporal heterogeneity; cohesive motility events can emerge without an external driving agent. Simulated cohorts are able to recreate experimental motility event signatures. Together these model systems and analytical techniques are some of the first to address collective motility of adhesive cellular cohorts in 3-dimensions

The morphological identity of insect dendrites

Dendrite morphology is the most prominent feature of nerve cells, investigated since the origins of modern neuroscience. The last century of neuroanatomical research has revealed an overwhelming diversity of different dendritic shapes and complexities. Its great variability, however, largely interferes with understanding the underlying principles of neuronal wiring and its functional implications. This work addresses this issue by studying a morphological and functional exception- ally conserved network of neurons located in the visual system of flies. Lobula Plate Tangential Cells (LPTCs) have been shown to compute motion vision and contribute to the impressive flight capabilities of flies. Cells of this system exhibit a high degree of constancy in topographic location, morphology and function over all individuals of one species. This constancy allows investigation of functionally identical cells over a large population of flies, and therefore potentially to truly understand the underlying principles of their morphologies. Supported by a large database of in vivo cell reconstructions and a computational quantification framework, it was possible to uncover some of those principles of LPTC anatomy. We show that the key to the cells’ morphological identity lies in the size and shape of the area they span into. Their detailed branching structure and topology is then merely a result of a common growth program shared by all analyzed cells. Application of a previously published branching theory confirmed this finding. When grown into the spanning fields obtained from the in vivo cell reconstruction, artificial cells could be synthesized that resembled all anatomical properties that characterize their natural counterparts. Furthermore, the morphological comparison of the same identified cells in Calliphora and Drosophila allowed to study a functionally conserved system under the influence of extensive down-scaling. The huge size reduction did not affect the underlying branching principles: Drosophila LPTCs followed the very same rules as their Calliphora coun- terparts. On the other hand, we observed significant differences in complexity and relative diameter scaling. An electrotonic analysis revealed that these differences can be explained by a common functional architecture implemented in the LPTCs of both species. Finally, we could modify the LPTC neuronal interaction behavior thanks to the genetical accessibility of Drosophila’s wiring program. The transmembrane protein family Dscam has been shown to mediate the process of adhesion and repulsion of neurites. By manipulating the molecular Dscam profile in Drosophila LPTCs it was possible to change their morphological expansion. The low variability of the LPTCs spanning field in wild type flies and their two-dimensional extension allowed to thoroughly map these morphological alterations in flies with Dscam modifications. In line with the LPTCs retinotopic input arrangement, electrophysiological experiments yielded an inherent linear relationship of their locally reduced dendritic coverage and their locally reduced stimulus sensitivity. With this work I hope to contribute to the general understanding of neuronal morphology of LPTCs and to present a valuable workflow for the analysis of neuronal structure

Experiments and Molecular Simulations into the Phenomenon of Oiling Out

Oiling out in mixture of phenol-n-hexane was studied by experiments and molecular simulations. Oiling out encompasses the crystallization of phenol from solution of n-hexane via the formation of liquid droplets of phenol dispersed inside n-hexane that at a later stage crystallize. Oiling out was found to take place at intermediate mole fractions from Xphenoi : Xn-hexane ~ 10 : 90 to Xphenoi \u27■ Xn-hexane ss 30 : 70, while direct crystallization took place at other concentrations. It was also found that application of mixing diminished the size of liquid-liquid separation zone. Direct microscopic snapshots captured the formation and coalescence of liquid phenol droplets in n-hexane as well as the formation of crystals by decreasing the temperature. The liquid liquid phase separation and solid liquid separation regions for phenol-n-hexane mixture were identified using the Focused Beam Reflectance Method (FBRM) that provides valuable information about the conditions of temperature and concentration for liquid-liquid and solid-liquid phase separation. Detailed study was performed for the selection of the chord lengths of particles, which is a critical quantity in the operation of FBRM. Gas Chromatography experiments revealed the phenol distribution in mixtures of n-hexane-phenol. Direct molecular dynamics was performed in pure n-hexane, phenol and several of their mixtures at various temperatures. The simulations revealed clustering in pure phenol systems that is enhanced by lowering the temperature. Their structure and degree of solidification was characterized by radial distribution functions and clustering profiles. Diffusion coefficients and the Arrhenius activation energy were computed, that show that pure phenol and n-hexane diffuse with different rate. The simulations test the quality of force field and validate its use for large scale simulations even in the micrometer range

Reaction-diffusion systems in and out of equilibrium - methods for simulation and inference

Reaction-diffusion methods allow treatment of mesoscopic dynamic phenomena of soft condensed matter especially in the context of cellular biology. Macromolecules such as proteins consist of thousands of atoms, in reaction-diffusion models their interaction is described by effective dynamics with much fewer degrees of freedom. Reaction-diffusion methods can be categorized by the spatial and temporal length-scales involved and the amount of molecules, e.g. classical reaction kinetics are macroscopic equations for fast diffusion and many molecules described by average concentrations. The focus of this work however is interacting-particle reaction-dynamics (iPRD), which operates on length scales of few nanometers and time scales of nanoseconds, where proteins can be represented by coarse-grained beads, that interact via effective potentials and undergo reactions upon encounter. In practice these systems are often studied using time-stepping computer simulations. Reactions in such iPRD simulations are discrete events which rapidly interchange beads, e.g. in the scheme A + B C the two interacting particles A and B will be replaced by a C complex and vice-versa. Such reactions in combination with the interaction potentials pose two practical problems: 1. To achieve a well defined state of equilibrium, it is of vital importance that the reaction transitions obey microscopic reversiblity (detailed balance). 2. The mean rate of a bimolecular association reaction changes when the particles interact via a pair-potential. In this work the first question is answered both theoretically and algorithmically. Theoretically by formulating the state of equilibrium for a closed iPRD system and the requirements for detailed balance. Algorithmically by implementing the detailed balance reaction scheme in a publicly available simulator ReaDDy~2 for iPRD systems. The second question is answered by deriving concrete formulae for the macroscopic reaction rate as a function of the intrinsic parameters for the Doi reaction model subject to pair interactions. Especially this work addresses two important scenarios: Reversible reactions in a closed container and irreversible bimolecular reactions in the diffusion-influenced regime. A characteristic of reactions occurring in cellular environments is that the number of species involved in a physiological response is very large. Unveiling the network of necessary reactions is a task that can be addressed by a data-driven approach. In particular, analyzing observation data of such processes can be used to learn the important governing dynamics. This work gives an overview of the inference of dynamical reactive systems for the different reaction-diffusion models. For the case of reaction kinetics a method called Reactive Sparse Identification of Nonlinear Dynamics (Reactive SINDy) is developed that allows to obtain a sparse reaction network out of candidate reactions from time-series observations of molecule concentrations

Supramolecular Semiconductivity through Emerging Ionic Gates in Ion–Nanoparticle Superlattices

The self-assembly of nanoparticles driven by small molecules or ions may produce colloidal superlattices with features and properties reminiscent of those of metals or semiconductors. However, to what extent the properties of such supramolecular crystals actually resemble those of atomic materials often remains unclear. Here, we present coarse-grained molecular simulations explicitly demonstrating how a behavior evocative of that of semiconductors may emerge in a colloidal superlattice. As a case study, we focus on gold nanoparticles bearing positively charged groups that self-assemble into FCC crystals via mediation by citrate counterions. In silico ohmic experiments show how the dynamically diverse behavior of the ions in different superlattice domains allows the opening of conductive ionic gates above certain levels of applied electric fields. The observed binary conductive/nonconductive behavior is reminiscent of that of conventional semiconductors, while, at a supramolecular level, crossing the "band gap " requires a sufficient electrostatic stimulus to break the intermolecular interactions and make ions diffuse throughout the superlattice's cavities