Keeping track of worm trackers

C. elegans is used extensively as a model system in the neurosciences due to its well defined nervous system. However, the seeming simplicity of this nervous system in anatomical structure and neuronal connectivity, at least compared to higher animals, underlies a rich diversity of behaviors. The usefulness of the worm in genome-wide mutagenesis or RNAi screens, where thousands of strains are assessed for phenotype, emphasizes the need for computational methods for automated parameterization of generated behaviors. In addition, behaviors can be modulated upon external cues like temperature, O2 and CO2 concentrations, mechanosensory and chemosensory inputs. Different machine vision tools have been developed to aid researchers in their efforts to inventory and characterize defined behavioral “outputs”. Here we aim at providing an overview of different worm-tracking packages or video analysis tools designed to quantify different aspects of locomotion such as the occurrence of directional changes (turns, omega bends), curvature of the sinusoidal shape (amplitude, body bend angles) and velocity (speed, backward or forward movement)

The concept of strong and weak virtual reality

We approach the virtual reality phenomenon by studying its relationship to set theory, and we investigate the case where this is done using the wellfoundedness property of sets. Our hypothesis is that non-wellfounded sets (hypersets) give rise to a different quality of virtual reality than do familiar wellfounded sets. We initially provide an alternative approach to virtual reality based on Sommerhoff's idea of first and second order self-awareness; both categories of self-awareness are considered as necessary conditions for consciousness in terms of higher cognitive functions. We then introduce a representation of first and second order self-awareness through sets, and assume that these sets, which we call events, originally form a collection of wellfounded sets. Strong virtual reality characterizes virtual reality environments which have the limited capacity to create only events associated with wellfounded sets. In contrast, the more general concept of weak virtual reality characterizes collections of virtual reality mediated events altogether forming an entirety larger than any collection of wellfounded sets. By giving reference to Aczel's hyperset theory we indicate that this definition is not empty, because hypersets encompass wellfounded sets already. Moreover, we argue that weak virtual reality could be realized in human history through continued progress in computer technology. Finally, we reformulate our characterization into a more general framework, and use Baltag's Structural Theory of Sets (STS) to show that within this general hyperset theory Sommerhoff's first and second order self-awareness as well as both concepts of virtual reality admit a consistent mathematical representation.Comment: 17 pages; several edits in v

Optimizing cyanobacterial product synthesis: Meeting the challenges.

The synthesis of renewable bioproducts using photosynthetic microorganisms holds great promise. Sustainable industrial applications, however, are still scarce and the true limits of phototrophic production remain unknown. One of the limitations of further progress is our insufficient understanding of the quantitative changes in photoautotrophic metabolism that occur during growth in dynamic environments. We argue that a proper evaluation of the intra- and extracellular factors that limit phototrophic production requires the use of highly-controlled cultivation in photobioreactors, coupled to real-time analysis of production parameters and their evaluation by predictive computational models. In this addendum, we discuss the importance and challenges of systems biology approaches for the optimization of renewable biofuels production. As a case study, we present the utilization of a state-of-the-art experimental setup together with a stoichiometric computational model of cyanobacterial metabolism for quantitative evaluation of ethylene production by a recombinant cyanobacterium Synechocystis sp. PCC 6803

Structural Kinetic Modeling of Metabolic Networks

To develop and investigate detailed mathematical models of cellular metabolic processes is one of the primary challenges in systems biology. However, despite considerable advance in the topological analysis of metabolic networks, explicit kinetic modeling based on differential equations is still often severely hampered by inadequate knowledge of the enzyme-kinetic rate laws and their associated parameter values. Here we propose a method that aims to give a detailed and quantitative account of the dynamical capabilities of metabolic systems, without requiring any explicit information about the particular functional form of the rate equations. Our approach is based on constructing a local linear model at each point in parameter space, such that each element of the model is either directly experimentally accessible, or amenable to a straightforward biochemical interpretation. This ensemble of local linear models, encompassing all possible explicit kinetic models, then allows for a systematic statistical exploration of the comprehensive parameter space. The method is applied to two paradigmatic examples: The glycolytic pathway of yeast and a realistic-scale representation of the photosynthetic Calvin cycle.Comment: 14 pages, 8 figures (color

Magnetoelectrically driven catalytic degradation of organics

Here, we report the catalytic degradation of organic compounds by exploiting the magnetoelectric (ME) nature of cobalt ferrite-bismuth ferrite (CFO-BFO) core-shell nanoparticles. The combination of magnetostrictive CFO with the multiferroic BFO gives rise to a magnetoelectric engine that purifies water under wireless magnetic fields via advanced oxidation processes, without involvement of any sacrificial molecules or co-catalysts. Magnetostrictive CoFe2O4 nanoparticles are fabricated using hydrothermal synthesis, followed by sol-gel synthesis to create the multiferroic BiFeO3 shell. We perform theoretical modeling to study the magnetic field induced polarization on the surface of magnetoelectric nanoparticles. The results obtained from these simulations are consistent with the experimental findings of the piezo-force microscopy analysis, where we observe changes in the piezoresponse of the nanoparticles under magnetic fields. Next, we investigate the magnetoelectric effect induced catalytic degradation of organic pollutants under AC magnetic fields and obtained 97% removal efficiency for synthetic dyes and over 85% removal efficiency for routinely used pharmaceuticals. Additionally, we perform trapping experiments to elucidate the mechanism behind the magnetic field induced catalytic degradation of organic pollutants by using scavengers for each of the reactive species. Our results indicate that hydroxyl and superoxide radicals are the main reactive species in the magnetoelectrically induced catalytic degradation of organic compounds

Quasiperiodic time dependent current in driven superlattices: distorted Poincare maps and strange attractors

Intriguing routes to chaos have been experimentally observed in semiconductor superlattices driven by an ac field. In this work, a theoretical model of time dependent transport in ac driven superlattices is numerically solved. In agreement with experiments, distorted Poincare maps in the quasiperiodic regime are found. They indicate the appearance of very complex attractors and routes to chaos as the amplitude of the AC signal increases. Distorted maps are caused by the discrete well-to-well jump motion of a domain wall during spiky high-frequency self-sustained oscillations of the current.Comment: 10 pages, 4 figure

Mean-risk models using two risk measures: A multi-objective approach

This paper proposes a model for portfolio optimisation, in which distributions are characterised and compared on the basis of three statistics: the expected value, the variance and the CVaR at a specified confidence level. The problem is multi-objective and transformed into a single objective problem in which variance is minimised while constraints are imposed on the expected value and CVaR. In the case of discrete random variables, the problem is a quadratic program. The mean-variance (mean-CVaR) efficient solutions that are not dominated with respect to CVaR (variance) are particular efficient solutions of the proposed model. In addition, the model has efficient solutions that are discarded by both mean-variance and mean-CVaR models, although they may improve the return distribution. The model is tested on real data drawn from the FTSE 100 index. An analysis of the return distribution of the chosen portfolios is presented

Statistics of Partial Minima

Motivated by multi-objective optimization, we study extrema of a set of N points independently distributed inside the d-dimensional hypercube. A point in this set is k-dominated by another point when at least k of its coordinates are larger, and is a k-minimum if it is not k-dominated by any other point. We obtain statistical properties of these partial minima using exact probabilistic methods and heuristic scaling techniques. The average number of partial minima, A, decays algebraically with the total number of points, A ~ N^{-(d-k)/k}, when 1<=k<d. Interestingly, there are k-1 distinct scaling laws characterizing the largest coordinates as the distribution P(y_j) of the jth largest coordinate, y_j, decays algebraically, P(y_j) ~ (y_j)^{-alpha_j-1}, with alpha_j=j(d-k)/(k-j) for 1<=j<=k-1. The average number of partial minima grows logarithmically, A ~ [1/(d-1)!](ln N)^{d-1}, when k=d. The full distribution of the number of minima is obtained in closed form in two-dimensions.Comment: 6 pages, 1 figur

Estimating Mutual Information

We present two classes of improved estimators for mutual information $M(X,Y)$, from samples of random points distributed according to some joint probability density $\mu(x,y)$. In contrast to conventional estimators based on binnings, they are based on entropy estimates from $k$-nearest neighbour distances. This means that they are data efficient (with $k=1$ we resolve structures down to the smallest possible scales), adaptive (the resolution is higher where data are more numerous), and have minimal bias. Indeed, the bias of the underlying entropy estimates is mainly due to non-uniformity of the density at the smallest resolved scale, giving typically systematic errors which scale as functions of $k/N$ for $N$ points. Numerically, we find that both families become {\it exact} for independent distributions, i.e. the estimator $\hat M(X,Y)$ vanishes (up to statistical fluctuations) if $\mu(x,y) = \mu(x) \mu(y)$. This holds for all tested marginal distributions and for all dimensions of $x$ and $y$. In addition, we give estimators for redundancies between more than 2 random variables. We compare our algorithms in detail with existing algorithms. Finally, we demonstrate the usefulness of our estimators for assessing the actual independence of components obtained from independent component analysis (ICA), for improving ICA, and for estimating the reliability of blind source separation.Comment: 16 pages, including 18 figure