Cross-Reactivity in Skin Prick Test Results of Members within Pooideae Subfamily

Objective: Molecular similarities of grass pollen antigens have led to the view that cross-reactivity exists within members of the Pooideae subfamily of grasses. This has resulted in testing for only the most antigenically representative member of Pooideae, Timothy grass (Phleum pratense), despite little literature to support the claim that Phleum is the most representative member or that in vitro cross-reactivity correlates with in vivo cross-reactivity. The aim of the study was to determine if patients with allergic rhinitis symptoms and positive skin prick test results to meadow fescue (Festuca pratensis) also have positive results to Timothy grass. Study Design: Retrospective cross-sectional study. Setting: Tertiary care center in middle Missouri. Methods: A retrospective chart review identified patients ≥12 years old with a diagnosis of allergic rhinitis who underwent skin prick testing between March 2016 and July 2018, by using a search with CPT code 95004 (Current Procedural Terminology). Positive skin prick test results were based on wheal produced ≥3 mm than the negative control. Results: After review of 2182 charts, 1587 patients met criteria to test for Phleum and Festuca. In total, 1239 patients had a positive result for Phleum or Festuca. Of these, 479 (38.6%) tested positive for Festuca alone, while 342 (27.6%) and 418 (33.7%) tested positive for Phleum alone and Phleum+Festuca, respectively. Conclusion: Clinical cross-reactivity among Pooideae members may not be as complete as traditionally thought. P pratense may not be the most antigenically representative subfamily member, and other grasses may need to be included in skin prick testing

Numerical Solution of Differential Equations by the Parker-Sochacki Method

A tutorial is presented which demonstrates the theory and usage of the Parker-Sochacki method of numerically solving systems of differential equations. Solutions are demonstrated for the case of projectile motion in air, and for the classical Newtonian N-body problem with mutual gravitational attraction.Comment: Added in July 2010: This tutorial has been posted since 1998 on a university web site, but has now been cited and praised in one or more refereed journals. I am therefore submitting it to the Cornell arXiv so that it may be read in response to its citations. See "Spiking neural network simulation: numerical integration with the Parker-Sochacki method:" J. Comput Neurosci, Robert D. Stewart & Wyeth Bair and http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2717378

Three-dimensional MRI assessment of regional wall stress after acute myocardial infarction predicts postdischarge cardiac events

PURPOSE: To determine the prognostic significance of systolic wall stress (SWS) after reperfused acute myocardial infarction (AMI) using MRI. MATERIALS AND METHODS: A total of 105 patients underwent MRI 7.8 +/- 4.2 days after AMI reperfusion. SWS was calculated by using a three-dimensional (3D) MRI approach to left ventricular (LV) wall thickness and to the radius of curvature. Between hospital discharge and the end of follow-up, an average of 4.1 +/- 1.7 years after AMI, 19 patients experienced a major cardiac event, including cardiac death, nonfatal reinfarction or heart failure (18.3%). RESULTS: The results were mainly driven by heart failure outcome. In univariate analysis the following factors were predictive of postdischarge major adverse cardiac events: 1) at the time of AMI: higher heart rate, previous calcium antagonist treatment, in-hospital congestive heart failure, proximal left anterior descending artery (LAD) occlusion, a lower ejection fraction, higher maximal ST segment elevation before reperfusion, and ST segment reduction lower than 50% after reperfusion; 2) MRI parameters: higher LV end-systolic volume, lower ejection fraction, higher global SWS, higher SWS in the infarcted area (SWS MI) and higher SWS in the remote myocardium (SWS remote). In the final multivariate model, only SWS MI (odds ratio [OR]: 1.62; 95% confidence interval [CI]: 1.01-2.60; P = 0.046) and SWS remote (OR: 2.17; 95% CI: 1.02-4.65; P = 0.046) were independent predictors. CONCLUSION: Regional SWS assessed by means of MRI a few days after AMI appears to be strong predictor of postdischarge cardiac events, identifying a subset of at risk patients who could qualify for more aggressive management

A Markovian event-based framework for stochastic spiking neural networks

In spiking neural networks, the information is conveyed by the spike times, that depend on the intrinsic dynamics of each neuron, the input they receive and on the connections between neurons. In this article we study the Markovian nature of the sequence of spike times in stochastic neural networks, and in particular the ability to deduce from a spike train the next spike time, and therefore produce a description of the network activity only based on the spike times regardless of the membrane potential process. To study this question in a rigorous manner, we introduce and study an event-based description of networks of noisy integrate-and-fire neurons, i.e. that is based on the computation of the spike times. We show that the firing times of the neurons in the networks constitute a Markov chain, whose transition probability is related to the probability distribution of the interspike interval of the neurons in the network. In the cases where the Markovian model can be developed, the transition probability is explicitly derived in such classical cases of neural networks as the linear integrate-and-fire neuron models with excitatory and inhibitory interactions, for different types of synapses, possibly featuring noisy synaptic integration, transmission delays and absolute and relative refractory period. This covers most of the cases that have been investigated in the event-based description of spiking deterministic neural networks

GeNN: a code generation framework for accelerated brain simulations

Large-scale numerical simulations of detailed brain circuit models are important for identifying hypotheses on brain functions and testing their consistency and plausibility. An ongoing challenge for simulating realistic models is, however, computational speed. In this paper, we present the GeNN (GPU-enhanced Neuronal Networks) framework, which aims to facilitate the use of graphics accelerators for computational models of large-scale neuronal networks to address this challenge. GeNN is an open source library that generates code to accelerate the execution of network simulations on NVIDIA GPUs, through a flexible and extensible interface, which does not require in-depth technical knowledge from the users. We present performance benchmarks showing that 200-fold speedup compared to a single core of a CPU can be achieved for a network of one million conductance based Hodgkin-Huxley neurons but that for other models the speedup can differ. GeNN is available for Linux, Mac OS X and Windows platforms. The source code, user manual, tutorials, Wiki, in-depth example projects and all other related information can be found on the project website http://genn-team.github.io/genn/

Simulation of networks of spiking neurons: A review of tools and strategies

We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on the exact timing of the spikes. We overview different simulators and simulation environments presently available (restricted to those freely available, open source and documented). For each simulation tool, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which simulator is appropriate for a given task. Finally, we provide a series of benchmark simulations of different types of networks of spiking neurons, including Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based or conductance-based synapses, using clock-driven or event-driven integration strategies. The same set of models are implemented on the different simulators, and the codes are made available. The ultimate goal of this review is to provide a resource to facilitate identifying the appropriate integration strategy and simulation tool to use for a given modeling problem related to spiking neural networks.Comment: 49 pages, 24 figures, 1 table; review article, Journal of Computational Neuroscience, in press (2007

Polyaromatic hydrocarbons in pollution: a heart-breaking matter

Air pollution is associated with detrimental effects on human health, including decreased cardiovascular function. However, the causative mechanisms behind these effects have yet to be fully elucidated. Here we review the current epidemiological, clinical and experimental evidence linking pollution with cardiovascular dysfunction. Our focus is on particulate matter (PM) and the associated low molecular weight polycyclic aromatic hydrocarbons (PAHs) as key mediators of cardiotoxicity. We begin by reviewing the growing epidemiological evidence linking air pollution to cardiovascular dysfunction in humans. We next address the pollution‐based cardiotoxic mechanisms first identified in fish following the release of large quantities of PAHs into the marine environment from point oil spills (e.g. Deepwater Horizon). We finish by discussing the current state of mechanistic knowledge linking PM and PAH exposure to mammalian cardiovascular patho‐physiologies such as atherosclerosis, cardiac hypertrophy, arrhythmias, contractile dysfunction and the underlying alterations in gene regulation. Our aim is to show conservation of toxicant pathways and cellular targets across vertebrate hearts to allow a broad framework of the global problem of cardiotoxic pollution to be established. AhR; Aryl hydrocarbon receptor. Dark lines indicate topics discussed in this review. Grey lines indicate topics reviewed elsewhere.publishedVersio

Dynamic clamp with StdpC software

Dynamic clamp is a powerful method that allows the introduction of artificial electrical components into target cells to simulate ionic conductances and synaptic inputs. This method is based on a fast cycle of measuring the membrane potential of a cell, calculating the current of a desired simulated component using an appropriate model and injecting this current into the cell. Here we present a dynamic clamp protocol using free, fully integrated, open-source software (StdpC, for spike timing-dependent plasticity clamp). Use of this protocol does not require specialist hardware, costly commercial software, experience in real-time operating systems or a strong programming background. The software enables the configuration and operation of a wide range of complex and fully automated dynamic clamp experiments through an intuitive and powerful interface with a minimal initial lead time of a few hours. After initial configuration, experimental results can be generated within minutes of establishing cell recording