Blade loss transient dynamics analysis. Volume 3: User's manual for TETRA program

The users manual for TETRA contains program logic, flow charts, error messages, input sheets, modeling instructions, option descriptions, input variable descriptions, and demonstration problems. The process of obtaining a NASTRAN 17.5 generated modal input file for TETRA is also described with a worked sample

Monitoring brain activity with protein voltage and calcium sensors

© The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Scientific Reports 5 (2015): 10212, doi:10.1038/srep10212.Understanding the roles of different cell types in the behaviors generated by neural circuits requires protein indicators that report neural activity with high spatio-temporal resolution. Genetically encoded fluorescent protein (FP) voltage sensors, which optically report the electrical activity in distinct cell populations, are, in principle, ideal candidates. Here we demonstrate that the FP voltage sensor ArcLight reports odor-evoked electrical activity in the in vivo mammalian olfactory bulb in single trials using both wide-field and 2-photon imaging. ArcLight resolved fast odorant-responses in individual glomeruli, and distributed odorant responses across a population of glomeruli. Comparisons between ArcLight and the protein calcium sensors GCaMP3 and GCaMP6f revealed that ArcLight had faster temporal kinetics that more clearly distinguished activity elicited by individual odorant inspirations. In contrast, the signals from both GCaMPs were a saturating integral of activity that returned relatively slowly to the baseline. ArcLight enables optical electrophysiology of mammalian neuronal population activity in vivo.Supported by US NIH DC005259, WCI 2009-003 from the National Research Foundation of Korea, a James Hudson Brown – Alexander Brown Coxe fellowship from Yale University, and a Ruth L. Kirschstein National Research Service Award DC012981

Simultaneous optical diagnostic velocity and scalar field by molecular tagging technique Stratified Flames View project

International audienc

NOSA, an Analytical Toolbox for Multicellular Optical Electrophysiology

Understanding how neural networks generate activity patterns and communicate with each other requires monitoring the electrical activity from many neurons simultaneously. Perfectly suited tools for addressing this challenge are genetically encoded voltage indicators (GEVIs) because they can be targeted to specific cell types and optically report the electrical activity of individual, or populations of neurons. However, analyzing and interpreting the data from voltage imaging experiments is challenging because high recording speeds and properties of current GEVIs yield only low signal-to-noise ratios, making it necessary to apply specific analytical tools. Here, we present NOSA (Neuro-Optical Signal Analysis), a novel open source software designed for analyzing voltage imaging data and identifying temporal interactions between electrical activity patterns of different origin. In this work, we explain the challenges that arise during voltage imaging experiments and provide hands-on analytical solutions. We demonstrate how NOSA’s baseline fitting, filtering algorithms and movement correction can compensate for shifts in baseline fluorescence and extract electrical patterns from low signal-to-noise recordings. NOSA allows to efficiently identify oscillatory frequencies in electrical patterns, quantify neuronal response parameters and moreover provides an option for analyzing simultaneously recorded optical and electrical data derived from patch-clamp or other electrode-based recordings. To identify temporal relations between electrical activity patterns we implemented different options to perform cross correlation analysis, demonstrating their utility during voltage imaging in Drosophila and mice. All features combined, NOSA will facilitate the first steps into using GEVIs and help to realize their full potential for revealing cell-type specific connectivity and functional interactions

The Butterfly Fauna Of The Italian Maritime Alps:Results Of The «Edit» Project

Bonelli, Simona, Barbero, Francesca, Casacci, Luca Pietro, Cerrato, Cristiana, Balletto, Emilio (2015): The butterfly fauna of the Italian Maritime Alps: results of the EDIT project. Zoosystema 37 (1): 139-167, DOI: 10.5252/z2015n1a6, URL: http://dx.doi.org/10.5252/z2015n1a

Accurate and Fast Simulation of Channel Noise in Conductance-Based Model Neurons by Diffusion Approximation

Stochastic channel gating is the major source of intrinsic neuronal noise whose functional consequences at the microcircuit- and network-levels have been only partly explored. A systematic study of this channel noise in large ensembles of biophysically detailed model neurons calls for the availability of fast numerical methods. In fact, exact techniques employ the microscopic simulation of the random opening and closing of individual ion channels, usually based on Markov models, whose computational loads are prohibitive for next generation massive computer models of the brain. In this work, we operatively define a procedure for translating any Markov model describing voltage- or ligand-gated membrane ion-conductances into an effective stochastic version, whose computer simulation is efficient, without compromising accuracy. Our approximation is based on an improved Langevin-like approach, which employs stochastic differential equations and no Montecarlo methods. As opposed to an earlier proposal recently debated in the literature, our approximation reproduces accurately the statistical properties of the exact microscopic simulations, under a variety of conditions, from spontaneous to evoked response features. In addition, our method is not restricted to the Hodgkin-Huxley sodium and potassium currents and is general for a variety of voltage- and ligand-gated ion currents. As a by-product, the analysis of the properties emerging in exact Markov schemes by standard probability calculus enables us for the first time to analytically identify the sources of inaccuracy of the previous proposal, while providing solid ground for its modification and improvement we present here