Scaling in a continuous time model for biological aging

In this paper we consider a generalization to the asexual version of the Penna model for biological aging, where we take a continuous time limit. The genotype associated to each individual is an interval of real numbers over which Dirac $\delta$--functions are defined, representing genetically programmed diseases to be switched on at defined ages of the individual life. We discuss two different continuous limits for the evolution equation and two different mutation protocols, to be implemented during reproduction. Exact stationary solutions are obtained and scaling properties are discussed.Comment: 10 pages, 6 figure

On the importance of hydrodynamic interactions in polyelectrolyte electrophoresis

The effect of hydrodynamic interactions on the free-solution electrophoresis of polyelectrolytes is investigated with coarse-grained molecular dynamics simulations. By comparing the results to simulations with switched-off hydrodynamic interactions, we demonstrate their importance in modelling the experimentally observed behaviour. In order to quantify the hydrodynamic interactions between the polyelectrolyte and the solution, we present a novel way to estimate its effective charge. We obtain an effective friction that is different from the hydrodynamic friction obtained from diffusion measurements. This effective friction is used to explain the constant electrophoretic mobility for longer chains. To further emphasize the importance of hydrodynamic interactions, we apply the model to end-labeled free-solution electrophoresis.Comment: 15 pages, 7 figures; accepted for publication in J. Phys.: Condens. Matte

ZnS Ultrathin interfacial layers for optimizing carrier management in Sb2S3-based photovoltaics

Antimony chalcogenides represent a family of materials of low toxicity and relative abundance, with a high potential for future sustainable solar energy conversion technology. However, solar cells based on antimony chalcogenides present open-circuit voltage losses that limit their efficiencies. These losses are attributed to several recombination mechanisms, with interfacial recombination being considered as one of the dominant processes. In this work, we exploit atomic layer deposition (ALD) to grow a series of ultrathin ZnS interfacial layers at the TiO2/Sb2S3 interface to mitigate interfacial recombination and to increase the carrier lifetime. ALD allows for very accurate control over the ZnS interlayer thickness on the ångström scale (0-1.5 nm) and to deposit highly pure Sb2S3. Our systematic study of the photovoltaic and optoelectronic properties of these devices by impedance spectroscopy and transient absorption concludes that the optimum ZnS interlayer thickness of 1.0 nm achieves the best balance between the beneficial effect of an increased recombination resistance at the interface and the deleterious barrier behavior of the wide-bandgap semiconductor ZnS. This optimization allows us to reach an overall power conversion efficiency of 5.09% in planar configuration

Embedded Agency in Institutional Theory: Problem or Paradox

In “Beyond Constraining and Enabling: Toward New Microfoundations in Institutional Theory” Professor Cardinale (2018) seeks to expose and correct “shortcomings” (p.133) in institutional theory’s conceptualization of structure, agency and their relationship. To this end, he theorizes the “different mechanism[s] through which actors are embedded in structure” (p.134). We agree that institutional theory’s microfoundations merit theoretical attention and development. However, we question the premise that the issue of agency in institutional theory is adequately, or even plausibly, formulated as one of “embeddedness”. We also challenge the relevance of Professor Cardinale’s engagement of Husserl to help solve what we argue to be a phantom problem central to his theory

Splitting of Folded Strings in AdS_4*CP^3

We study classically splitting of two kinds of folded string solutions in AdS_4*CP^3. Conserved charges of the produced fragments are computed for each case. We find interesting patterns among these conserved charges.Comment: minor changes, 14 pages, no figure

Transfer Functions for Protein Signal Transduction: Application to a Model of Striatal Neural Plasticity

We present a novel formulation for biochemical reaction networks in the context of signal transduction. The model consists of input-output transfer functions, which are derived from differential equations, using stable equilibria. We select a set of 'source' species, which receive input signals. Signals are transmitted to all other species in the system (the 'target' species) with a specific delay and transmission strength. The delay is computed as the maximal reaction time until a stable equilibrium for the target species is reached, in the context of all other reactions in the system. The transmission strength is the concentration change of the target species. The computed input-output transfer functions can be stored in a matrix, fitted with parameters, and recalled to build discrete dynamical models. By separating reaction time and concentration we can greatly simplify the model, circumventing typical problems of complex dynamical systems. The transfer function transformation can be applied to mass-action kinetic models of signal transduction. The paper shows that this approach yields significant insight, while remaining an executable dynamical model for signal transduction. In particular we can deconstruct the complex system into local transfer functions between individual species. As an example, we examine modularity and signal integration using a published model of striatal neural plasticity. The modules that emerge correspond to a known biological distinction between calcium-dependent and cAMP-dependent pathways. We also found that overall interconnectedness depends on the magnitude of input, with high connectivity at low input and less connectivity at moderate to high input. This general result, which directly follows from the properties of individual transfer functions, contradicts notions of ubiquitous complexity by showing input-dependent signal transmission inactivation.Comment: 13 pages, 5 tables, 15 figure

Learning intrinsic excitability in medium spiny neurons

We present an unsupervised, local activation-dependent learning rule for intrinsic plasticity (IP) which affects the composition of ion channel conductances for single neurons in a use-dependent way. We use a single-compartment conductance-based model for medium spiny striatal neurons in order to show the effects of parametrization of individual ion channels on the neuronal activation function. We show that parameter changes within the physiological ranges are sufficient to create an ensemble of neurons with significantly different activation functions. We emphasize that the effects of intrinsic neuronal variability on spiking behavior require a distributed mode of synaptic input and can be eliminated by strongly correlated input. We show how variability and adaptivity in ion channel conductances can be utilized to store patterns without an additional contribution by synaptic plasticity (SP). The adaptation of the spike response may result in either "positive" or "negative" pattern learning. However, read-out of stored information depends on a distributed pattern of synaptic activity to let intrinsic variability determine spike response. We briefly discuss the implications of this conditional memory on learning and addiction.Comment: 20 pages, 8 figure