A novel olfactory pathway is essential for fast and efficient blood-feeding in mosquitoes

In mosquitoes, precise and efficient finding of a host animal is crucial for survival. One of the poorly understood aspects of mosquito blood-feeding behavior is how these insects target an optimal site in order to penetrate the skin and blood vessels without alerting the host animal. Here we provide new findings that a piercing structure of the mouthpart of the mosquitoes, the stylet, is an essential apparatus for the stage in blood feeding. Indeed, the stylet possesses a number of sensory hairs located at the tip of the stylet. These hairs house olfactory receptor neurons that express two conventional olfactory receptors of Aedes aegypti (AaOrs), AaOr8 and AaOr49, together with the odorant co-receptor (AaOrco). In vivo calcium imaging using transfected cell lines demonstrated that AaOr8 and AaOr49 were activated by volatile compounds present in blood. Inhibition of gene expression of these AaOrs delayed blood feeding behaviors of the mosquito. Taken together, we identified olfactory receptor neurons in the stylet involved in mosquito blood feeding behaviors, which in turn indicates that olfactory perception in the stylet is necessary and sufficient for mosquitoes to find host blood in order to rapidly acquire blood meals from a host animal

Mutator Dynamics on a Smooth Evolutionary Landscape

We investigate a model of evolutionary dynamics on a smooth landscape which features a ``mutator'' allele whose effect is to increase the mutation rate. We show that the expected proportion of mutators far from equilibrium, when the fitness is steadily increasing in time, is governed solely by the transition rates into and out of the mutator state. This results is a much faster rate of fitness increase than would be the case without the mutator allele. Near the fitness equilibrium, however, the mutators are severely suppressed, due to the detrimental effects of a large mutation rate near the fitness maximum. We discuss the results of a recent experiment on natural selection of E. coli in the light of our model.Comment: 4 pages, 3 figure

EGIA–evolutionary optimisation of gene regulatory networks, an integrative approach

Quantitative modelling of gene regulatory networks (GRNs) is still limited by data issues such as noise and the restricted length of available time series, creating an under-determination problem. However, large amounts of other types of biological data and knowledge are available, such as knockout experiments, annotations and so on, and it has been postulated that integration of these can improve model quality. However, integration has not been fully explored, to date. Here, we present a novel integrative framework for different types of data that aims to enhance model inference. This is based on evolutionary computation and uses different types of knowledge to introduce a novel customised initialisation and mutation operator and complex evaluation criteria, used to distinguish between candidate models. Specifically, the algorithm uses information from (i) knockout experiments, (ii) annotations of transcription factors, (iii) binding site motifs (expressed as position weight matrices) and (iv) DNA sequence of gene promoters, to drive the algorithm towards more plausible network structures. Further, the evaluation basis is also extended to include structure information included in these additional data. This framework is applied to both synthetic and real gene expression data. Models obtained by data integration display both quantitative and qualitative improvement

Pedestrian, Crowd, and Evacuation Dynamics

This contribution describes efforts to model the behavior of individual pedestrians and their interactions in crowds, which generate certain kinds of self-organized patterns of motion. Moreover, this article focusses on the dynamics of crowds in panic or evacuation situations, methods to optimize building designs for egress, and factors potentially causing the breakdown of orderly motion.Comment: This is a review paper. For related work see http://www.soms.ethz.c

Technical Design Report for the PANDA Solenoid and Dipole Spectrometer Magnets

This document is the Technical Design Report covering the two large spectrometer magnets of the PANDA detector set-up. It shows the conceptual design of the magnets and their anticipated performance. It precedes the tender and procurement of the magnets and, hence, is subject to possible modifications arising during this process.Comment: 10 pages, 14MB, accepted by FAIR STI in May 2009, editors: Inti Lehmann (chair), Andrea Bersani, Yuri Lobanov, Jost Luehning, Jerzy Smyrski, Technical Coordiantor: Lars Schmitt, Bernd Lewandowski (deputy), Spokespersons: Ulrich Wiedner, Paola Gianotti (deputy

W18O49 Nanowires as Ultraviolet Photodetector

Photodetectors in a configuration of field effect transistor were fabricated based on individual W18O49 nanowires. Evaluation of electrical transport behavior indicates that the W18O49 nanowires are n-type semiconductors. The photodetectors show high sensitivity, stability and reversibility to ultraviolet (UV) light. A high photoconductive gain of 104 was obtained, and the photoconductivity is up to 60 nS upon exposure to 312 nm UV light with an intensity of 1.6 mW/cm2. Absorption of oxygen on the surface of W18O49 nanowires has a significant influence on the dark conductivity, and the ambient gas can remarkably change the conductivity of W18O49 nanowire. The results imply that W18O49 nanowires will be promising candidates for fabricating UV photodetectors

[Introduction] Toward an anthropology of the just price: history, ethnography, critique

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Comparison of evolutionary algorithms in gene regulatory network model inference

Background: The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineering of GRNs). However, the nature of these data has made this process very di±cult. At the moment, several methods of discovering qualitative causal relationships between genes with high accuracy from microarray data exist, but large scale quantitative analysis on real biological datasets cannot be performed, to date, as existing approaches are not suitable for real microarray data which are noisy and insu±cient. Results: This paper performs an analysis of several existing evolutionary algorithms for quantitative gene regulatory network modelling. The aim is to present the techniques used and o®er a comprehensive comparison of approaches, under a common framework. Algorithms are applied to both synthetic and real gene expression data from DNA microarrays, and ability to reproduce biological behaviour, scalability and robustness to noise are assessed and compared. Conclusions: Presented is a comparison framework for assessment of evolutionary algorithms, used to infer gene regulatory networks. Promising methods are identi¯ed and a platform for development of appropriate model formalisms is established

PeptX: Using Genetic Algorithms to optimize peptides for MHC binding

<p>Abstract</p> <p>Background</p> <p>The binding between the major histocompatibility complex and the presented peptide is an indispensable prerequisite for the adaptive immune response. There is a plethora of different <it>in silico </it>techniques for the prediction of the peptide binding affinity to major histocompatibility complexes. Most studies screen a set of peptides for promising candidates to predict possible T cell epitopes. In this study we ask the question vice versa: Which peptides do have highest binding affinities to a given major histocompatibility complex according to certain <it>in silico </it>scoring functions?</p> <p>Results</p> <p>Since a full screening of all possible peptides is not feasible in reasonable runtime, we introduce a heuristic approach. We developed a framework for Genetic Algorithms to optimize peptides for the binding to major histocompatibility complexes. In an extensive benchmark we tested various operator combinations. We found that (1) selection operators have a strong influence on the convergence of the population while recombination operators have minor influence and (2) that five different binding prediction methods lead to five different sets of "optimal" peptides for the same major histocompatibility complex. The consensus peptides were experimentally verified as high affinity binders.</p> <p>Conclusion</p> <p>We provide a generalized framework to calculate sets of high affinity binders based on different previously published scoring functions in reasonable runtime. Furthermore we give insight into the different behaviours of operators and scoring functions of the Genetic Algorithm.</p

MRN complex function in the repair of chromosomal Rag-mediated DNA double-strand breaks

The Mre11–Rad50–Nbs1 (MRN) complex functions in the repair of DNA double-strand breaks (DSBs) by homologous recombination (HR) at postreplicative stages of the cell cycle. During HR, the MRN complex functions directly in the repair of DNA DSBs and in the initiation of DSB responses through activation of the ataxia telangiectasia-mutated (ATM) serine-threonine kinase. Whether MRN functions in DNA damage responses before DNA replication in G0/G1 phase cells has been less clear. In developing G1-phase lymphocytes, DNA DSBs are generated by the Rag endonuclease and repaired during the assembly of antigen receptor genes by the process of V(D)J recombination. Mice and humans deficient in MRN function exhibit lymphoid phenotypes that are suggestive of defects in V(D)J recombination. We show that during V(D)J recombination, MRN deficiency leads to the aberrant joining of Rag DSBs and to the accumulation of unrepaired coding ends, thus establishing a functional role for MRN in the repair of Rag-mediated DNA DSBs. Moreover, these defects in V(D)J recombination are remarkably similar to those observed in ATM-deficient lymphocytes, suggesting that ATM and MRN function in the same DNA DSB response pathways during lymphocyte antigen receptor gene assembly