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

Coherent pion production in neutrino nucleus collision in the 1 GeV region

We calculate cross sections for coherent pion production in nuclei induced by neutrinos and antineutrinos of the electron and muon type. The analogies and differences between this process and the related ones of coherent pion production induced by photons, or the (p,n) and $(^3 He, t)$ reactions are discussed. The process is one of the several ones occurring for intermediate energy neutrinos, to be considered when detecting atmospheric neutrinos. For this purpose the results shown here can be easily extrapolated to other energies and other nuclei.Comment: 13 pages, LaTex, 8 post-script figures available at [email protected]

Damping mechanisms of the Delta resonance in nuclei

The damping mechanisms of the Delta(1232) resonance in nuclei are studied by analyzing the quasi-free decay reactions 12C(pi+,pi+ p)11B and 12C(3He,t pi+ p)11B and the 2p emission reactions 12C(pi+,pp)10B and 12C(3He,t pp)10B. The coincidence cross sections are calculated within the framework of the isobar-hole model. It is found that the 2p emission process induced by the decay of the Delta resonance in the nucleus can be consistently described by a pi+rho+g' model for the Delta+N -> N+N decay interaction.Comment: 9 pages, 5 Postscript figures, uses RevTex, psfig.sty. Accepted by Physical Review

Delta excitation in K^+-nucleus collisions

We present calculations for \Delta excitation in the (K^+,K^+) reaction in nuclei. The background from quasielastic K^+ scattering in the \Delta region is also evaluated and shown to be quite small in some kinematical regions, so as to allow for a clean identification of the \Delta excitation strength. Nuclear effects tied to the \Delta renormalization in the nucleus are considered and the reaction is shown to provide new elements to enrich our knowledge of the \Delta properties in a nuclear medium.Comment: 11 pages, 6 figures, LaTe

Comparison of Different Methods of Automated Landform Classification at the Drainage Basin Scale: Examples from the Southern Italy

In this work, we tested the reliability of two different methods of automated landform classification (ACL) in three geological domains of the southern Italian chain with contrasting morphological features. ACL maps deriving from the TPI-based (topographic position index) algorithm are strictly dependent to the search input parameters and they are not able to fully capture landforms of different size. Geomorphons-based classification has shown a higher potential and can represent a powerful method of ACL, although it should be improved with the introduction of additional DEM-based parameters for the extraction of landform classe

Project CoachLearn - Report #2 Recognition of Prior Learning and Work-Based Experience in Coach Development

The recognition of prior learning and work experience (RPL&WBE) in coach development is a fundamental part of project CoachLearn. CoachLearn is co-funded by Erasmus+ under the Strategic Partnerships Action within Key Action 2 – Cooperation and Innovation for Good Practices. It seeks to enhance sport coaches' learning, mobility and employment through the development of a European Sport Coaching Framework. This report provides an introduction to the topic and subsequently presents the findings from a survey aimed at gathering the views of a cross-section of coach education stakeholders across the European Union. Stakeholders represented in the sample included national lead coaching organisations, national Olympic committees, national and international governing bodies of sport and vocational and higher education institutions. The main objectives were to identify key challenges faced by organisations in relation to RPL&WBE and existing models of good practice. Central to this goal was defining major factor for the development of successful systems

Mapping of Submerged Aquatic Vegetation in Rivers From Very High Resolution Image Data, Using Object Based Image Analysis Combined with Expert Knowledge

The use of remote sensing for monitoring of submerged aquatic vegetation (SAV) in fluvial environments has been limited by the spatial and spectral resolution of available image data. The absorption of light in water also complicates the use of common image analysis methods. This paper presents the results of a study that uses very high resolution (VHR) image data, collected with a Near Infrared sensitive DSLR camera, to map the distribution of SAV species for three sites along the Desselse Nete, a lowland river in Flanders, Belgium. Plant species, including Ranunculus aquatilis L., Callitriche obtusangula Le Gall, Potamogeton natans L., Sparganium emersum L. and Potamogeton crispus L., were classified from the data using Object-Based Image Analysis (OBIA) and expert knowledge. A classification rule set based on a combination of both spectral and structural image variation (e.g. texture and shape) was developed for images from two sites. A comparison of the classifications with manually delineated ground truth maps resulted for both sites in 61% overall accuracy. Application of the rule set to a third validation image, resulted in 53% overall accuracy. These consistent results show promise for species level mapping in such biodiverse environments, but also prompt a discussion on assessment of classification accuracy

DMF inhibits PDGF-BB induced airway smooth muscle cell proliferation through induction of heme-oxygenase-1

Airway wall remodelling is an important pathology of asthma. Growth factor induced airway smooth muscle cell (ASMC) proliferation is thought to be the major cause of airway wall thickening in asthma. Earlier we reported that Dimethylfumarate (DMF) inhibits platelet-derived growth factor (PDGF)-BB induced mitogen and stress activated kinase (MSK)-1 and CREB activity as well as IL-6 secretion by ASMC. In addition, DMF altered intracellular glutathione levels and thereby reduced proliferation of other cell types

Inflammatory Transcriptome Profiling of Human Monocytes Exposed Acutely to Cigarette Smoke

<div><h3>Background</h3><p>Cigarette smoking is responsible for 5 million deaths worldwide each year, and is a major risk factor for cardiovascular and lung diseases. Cigarette smoke contains a complex mixture of over 4000 chemicals containing 10¹⁵ free radicals. Studies show smoke is perceived by cells as an inflammatory and xenobiotic stimulus, which activates an immune response. The specific cellular mechanisms driving cigarette smoke-induced inflammation and disease are not fully understood, although the innate immune system is involved in the pathology of smoking related diseases.</p> <h3>Methodology/Principle findings</h3><p>To address the impact of smoke as an inflammagen on the innate immune system, THP-1 cells and Human PBMCs were stimulated with 3 and 10% (v/v) cigarette smoke extract (CSE) for 8 and 24 hours. Total RNA was extracted and the transcriptome analysed using Illumina BeadChip arrays. In THP-1 cells, 10% CSE resulted in 80 genes being upregulated and 37 downregulated by ≥1.5 fold after 8 hours. In PBMCs stimulated with 10% CSE for 8 hours, 199 genes were upregulated and 206 genes downregulated by ≥1.5 fold. After 24 hours, the number of genes activated and repressed by ≥1.5 fold had risen to 311 and 306 respectively. The major pathways that were altered are associated with cell survival, such as inducible antioxidants, protein chaperone and folding proteins, and the ubiquitin/proteosome pathway.</p> <h3>Conclusions</h3><p>Our results suggest that cigarette smoke causes inflammation and has detrimental effects on the metabolism and function of innate immune cells. In addition, THP-1 cells provide a genetically stable alternative to primary cells for the study of the effects of cigarette smoke on human monocytes.</p> </div