Chaotic root-finding for a small class of polynomials

In this paper we present a new closed-form solution to a chaotic difference equation, $y_{n+1} = a_2 y_{n}^2 + a_1 y_{n} + a_0$ with coefficient $a_0 = (a_1 - 4)(a_1 + 2) / (4 a_2)$, and using this solution, show how corresponding exact roots to a special set of related polynomials of order $2^p, p \in \mathbb{N}$ with two independent parameters can be generated, for any $p$

UPDG: Utilities package for data analysis of Pooled DNA GWAS

<p>Abstract</p> <p>Background</p> <p>Despite being a well-established strategy for cost reduction in disease gene mapping, pooled DNA association study is much less popular than the individual DNA approach. This situation is especially true for pooled DNA genomewide association study (GWAS), for which very few computer resources have been developed for its data analysis. This motivates the development of UPDG (Utilities package for data analysis of Pooled DNA GWAS).</p> <p>Results</p> <p>UPDG represents a generalized framework for data analysis of pooled DNA GWAS with the integration of Unix/Linux shell operations, Perl programs and R scripts. With the input of raw intensity data from GWAS, UPDG performs the following tasks in a stepwise manner: raw data manipulation, correction for allelic preferential amplification, normalization, nested analysis of variance for genetic association testing, and summarization of analysis results. Detailed instructions, procedures and commands are provided in the comprehensive user manual describing the whole process from preliminary preparation of software installation to final outcome acquisition. An example dataset (input files and sample output files) is also included in the package so that users can easily familiarize themselves with the data file formats, working procedures and expected output. Therefore, UPDG is especially useful for users with some computer knowledge, but without a sophisticated programming background.</p> <p>Conclusions</p> <p>UPDG provides a free, simple and platform-independent one-stop service to scientists working on pooled DNA GWAS data analysis, but with less advanced programming knowledge. It is our vision and mission to reduce the hindrance for performing data analysis of pooled DNA GWAS through our contribution of UPDG. More importantly, we hope to promote the popularity of pooled DNA GWAS, which is a very useful research strategy.</p

The optical afterglow of the short gamma-ray burst GRB 050709

It has long been known that there are two classes of gamma-ray bursts (GRBs), mainly distinguished by their durations. The breakthrough in our understanding of long-duration GRBs (those lasting more than ~2 s), which ultimately linked them with energetic Type Ic supernovae, came from the discovery of their long-lived X-ray and optical afterglows, when precise and rapid localizations of the sources could finally be obtained. X-ray localizations have recently become available for short (duration <2 s) GRBs, which have evaded optical detection for more than 30 years. Here we report the first discovery of transient optical emission (R-band magnitude ~23) associated with a short burst; GRB 050709. The optical afterglow was localized with subarcsecond accuracy, and lies in the outskirts of a blue dwarf galaxy. The optical and X-ray afterglow properties 34 h after the GRB are reminiscent of the afterglows of long GRBs, which are attributable to synchrotron emission from ultrarelativistic ejecta. We did not, however, detect a supernova, as found in most nearby long GRB afterglows, which suggests a different origin for the short GRBs.Comment: 11 pages, 3 figures, press material at http://www.astro.ku.dk/dark

Accuracy of five algorithms to diagnose gambiense human African trypanosomiasis.

Algorithms to diagnose gambiense human African trypanosomiasis (HAT, sleeping sickness) are often complex due to the unsatisfactory sensitivity and/or specificity of available tests, and typically include a screening (serological), confirmation (parasitological) and staging component. There is insufficient evidence on the relative accuracy of these algorithms. This paper presents estimates of the accuracy of five algorithms used by past Médecins Sans Frontières programmes in the Republic of Congo, Southern Sudan and Uganda

Do contaminants originating from state-of-the-art treated wastewater impact the ecological quality of surface waters?

Since the 1980s, advances in wastewater treatment technology have led to considerably improved surface water quality in the urban areas of many high income countries. However, trace concentrations of organic wastewater-associated contaminants may still pose a key environmental hazard impairing the ecological quality of surface waters. To identify key impact factors, we analyzed the effects of a wide range of anthropogenic and environmental variables on the aquatic macroinvertebrate community. We assessed ecological water quality at 26 sampling sites in four urban German lowland river systems with a 0–100% load of state-of-the-art biological activated sludge treated wastewater. The chemical analysis suite comprised 12 organic contaminants (five phosphor organic flame retardants, two musk fragrances, bisphenol A, nonylphenol, octylphenol, diethyltoluamide, terbutryn), 16 polycyclic aromatic hydrocarbons, and 12 heavy metals. Non-metric multidimensional scaling identified organic contaminants that are mainly wastewater-associated (i.e., phosphor organic flame retardants, musk fragrances, and diethyltoluamide) as a major impact variable on macroinvertebrate species composition. The structural degradation of streams was also identified as a significant factor. Multiple linear regression models revealed a significant impact of organic contaminants on invertebrate populations, in particular on Ephemeroptera, Plecoptera, and Trichoptera species. Spearman rank correlation analyses confirmed wastewater-associated organic contaminants as the most significant variable negatively impacting the biodiversity of sensitive macroinvertebrate species. In addition to increased aquatic pollution with organic contaminants, a greater wastewater fraction was accompanied by a slight decrease in oxygen concentration and an increase in salinity. This study highlights the importance of reducing the wastewater-associated impact on surface waters. For aquatic ecosystems in urban areas this would lead to: (i) improvement of the ecological integrity, (ii) reduction of biodiversity loss, and (iii) faster achievement of objectives of legislative requirements, e.g., the European Water Framework Directive

Simple, Fast and Accurate Implementation of the Diffusion Approximation Algorithm for Stochastic Ion Channels with Multiple States

The phenomena that emerge from the interaction of the stochastic opening and closing of ion channels (channel noise) with the non-linear neural dynamics are essential to our understanding of the operation of the nervous system. The effects that channel noise can have on neural dynamics are generally studied using numerical simulations of stochastic models. Algorithms based on discrete Markov Chains (MC) seem to be the most reliable and trustworthy, but even optimized algorithms come with a non-negligible computational cost. Diffusion Approximation (DA) methods use Stochastic Differential Equations (SDE) to approximate the behavior of a number of MCs, considerably speeding up simulation times. However, model comparisons have suggested that DA methods did not lead to the same results as in MC modeling in terms of channel noise statistics and effects on excitability. Recently, it was shown that the difference arose because MCs were modeled with coupled activation subunits, while the DA was modeled using uncoupled activation subunits. Implementations of DA with coupled subunits, in the context of a specific kinetic scheme, yielded similar results to MC. However, it remained unclear how to generalize these implementations to different kinetic schemes, or whether they were faster than MC algorithms. Additionally, a steady state approximation was used for the stochastic terms, which, as we show here, can introduce significant inaccuracies. We derived the SDE explicitly for any given ion channel kinetic scheme. The resulting generic equations were surprisingly simple and interpretable - allowing an easy and efficient DA implementation. The algorithm was tested in a voltage clamp simulation and in two different current clamp simulations, yielding the same results as MC modeling. Also, the simulation efficiency of this DA method demonstrated considerable superiority over MC methods.Comment: 32 text pages, 10 figures, 1 supplementary text + figur

A Prescription for Improving Drug Formulary Decision Making

Gordon Schiff and colleagues present a new tool and checklist to help formularies make decisions about drug inclusion and to guide rational drug use

Dynamic Range Compression in the Honey Bee Auditory System toward Waggle Dance Sounds

Honey bee foragers use a “waggle dance” to inform nestmates about direction and distance to locations of attractive food. The sound and air flows generated by dancer's wing and abdominal vibrations have been implicated as important cues, but the decoding mechanisms for these dance messages are poorly understood. To understand the neural mechanisms of honey bee dance communication, we analyzed the anatomy of antenna and Johnston's organ (JO) in the pedicel of the antenna, as well as the mechanical and neural response characteristics of antenna and JO to acoustic stimuli, respectively. The honey bee JO consists of about 300–320 scolopidia connected with about 48 cuticular “knobs” around the circumference of the pedicel. Each scolopidium contains bipolar sensory neurons with both type I and II cilia. The mechanical sensitivities of the antennal flagellum are specifically high in response to low but not high intensity stimuli of 265–350 Hz frequencies. The structural characteristics of antenna but not JO neurons seem to be responsible for the non-linear responses of the flagellum in contrast to mosquito and fruit fly. The honey bee flagellum is a sensitive movement detector responding to 20 nm tip displacement, which is comparable to female mosquito. Furthermore, the JO neurons have the ability to preserve both frequency and temporal information of acoustic stimuli including the “waggle dance” sound. Intriguingly, the response of JO neurons was found to be age-dependent, demonstrating that the dance communication is only possible between aged foragers. These results suggest that the matured honey bee antennae and JO neurons are best tuned to detect 250–300 Hz sound generated during “waggle dance” from the distance in a dark hive, and that sufficient responses of the JO neurons are obtained by reducing the mechanical sensitivity of the flagellum in a near-field of dancer. This nonlinear effect brings about dynamic range compression in the honey bee auditory system

Proximity curves for potential-based clustering

YesThe concept of proximity curve and a new algorithm are proposed for obtaining clusters in a finite set of data points in the finite dimensional Euclidean space. Each point is endowed with a potential constructed by means of a multi-dimensional Cauchy density, contributing to an overall anisotropic potential function. Guided by the steepest descent algorithm, the data points are successively visited and removed one by one, and at each stage the overall potential is updated and the magnitude of its local gradient is calculated. The result is a finite sequence of tuples, the proximity curve, whose pattern is analysed to give rise to a deterministic clustering. The finite set of all such proximity curves in conjunction with a simulation study of their distribution results in a probabilistic clustering represented by a distribution on the set of dendrograms. A two-dimensional synthetic data set is used to illustrate the proposed potential-based clustering idea. It is shown that the results achieved are plausible since both the ‘geographic distribution’ of data points as well as the ‘topographic features’ imposed by the potential function are well reflected in the suggested clustering. Experiments using the Iris data set are conducted for validation purposes on classification and clustering benchmark data. The results are consistent with the proposed theoretical framework and data properties, and open new approaches and applications to consider data processing from different perspectives and interpret data attributes contribution to patterns