502 research outputs found
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 --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
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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
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