Investigation of physiological properties of nerves and muscles using electromyography.

The measurement and representation of the electrical activity of muscles [electromyography (EMG)] have a long history from the Victorian Era until today. Currently, EMG has uses both as a research tool, in noninvasively recording muscle activation, and clinically in the diagnosis and assessment of nerve and muscle disease and injury as well as in assessing the recovery of neuromuscular function after nerve damage. In the present report, we describe the use of a basic EMG setup in our teaching laboratories to demonstrate some of these current applications. Our practical also illustrates some fundamental physiological and structural properties of nerves and muscles. Learning activities include 1) displaying the recruitment of muscle fibers with increasing force development; 2) the measurement of conduction velocity of motor nerves; 3) the assessment of reflex delay and demonstration of Jendrassik's maneuver; and 4) a Hoffman reflex experiment that illustrates the composition of mixed nerves and the differential excitability thresholds of fibers within the same nerve, thus aiding an understanding of the reflex nature of muscle control. We can set up the classes at various levels of inquiry depending on the needs/professional requirements of the class. The results can then provide an ideal platform for a discovery learning session/tutorial on how the central nervous system controls muscles, giving insights on how supraspinal control interacts with reflexes to give smooth, precise muscular activation.

Machine learning for transient discovery in Pan-STARRS1 difference imaging

Efficient identification and follow-up of astronomical transients is hindered by the need for humans to manually select promising candidates from data streams that contain many false positives. These artefacts arise in the difference images that are produced by most major ground-based time-domain surveys with large format CCD cameras. This dependence on humans to reject bogus detections is unsustainable for next generation all-sky surveys and significant effort is now being invested to solve the problem computationally. In this paper, we explore a simple machine learning approach to real-bogus classification by constructing a training set from the image data of similar to 32 000 real astrophysical transients and bogus detections from the Pan-STARRS1 Medium Deep Survey. We derive our feature representation from the pixel intensity values of a 20 x 20 pixel stamp around the centre of the candidates. This differs from previous work in that it works directly on the pixels rather than catalogued domain knowledge for feature design or selection. Three machine learning algorithms are trained (artificial neural networks, support vector machines and random forests) and their performances are tested on a held-out subset of 25 per cent of the training data. We find the best results from the random forest classifier and demonstrate that by accepting a false positive rate of 1 per cent, the classifier initially suggests a missed detection rate of around 10 per cent. However, we also find that a combination of bright star variability, nuclear transients and uncertainty in human labelling means that our best estimate of the missed detection rate is approximately 6 per cent.

A Novel Selective Muscarinic Acetylcholine Receptor Subtype 1 Antagonist Reduces Seizures without Impairing Hippocampus-Dependent Learning

Previous studies suggest that selective antagonists of specific subtypes of muscarinic acetylcholine receptors (mAChRs) may provide a novel approach for the treatment of certain central nervous system (CNS) disorders, including epileptic disorders, Parkinson's disease, and dystonia. Unfortunately, previously reported antagonists are not highly selective for specific mAChR subtypes, making it difficult to definitively establish the functional roles and therapeutic potential for individual subtypes of this receptor subfamily. The M 1 mAChR is of particular interest as a potential target for treatment of CNS disorders. We now report the discovery of a novel selective antagonist of M-1 mAChRs, termed VU0255035 [N-(3-oxo-3-(4-(pyridine-4-yl)piperazin-1-yl)propyl)benzo[c][1,2,5]thiadiazole-4-sulfonamide]. Equilibrium radioligand binding and functional studies demonstrate a greater than 75-fold selectivity of VU0255035 for M-1 mAChRs relative to M-2-M-5. Molecular pharmacology and mutagenesis studies indicate that VU0255035 is a competitive orthosteric antagonist of M-1 mAChRs, a surprising finding given the high level of M-1 mAChR selectivity relative to other orthosteric antagonists. Whole-cell patch-clamp recordings demonstrate that VU0255035 inhibits potentiation of N-methyl-D-aspartate receptor currents by the muscarinic agonist carbachol in hippocampal pyramidal cells. VU0255035 has excellent brain penetration in vivo and is efficacious in reducing pilocarpine-induced seizures in mice. We were surprised to find that doses of VU0255035 that reduce pilo-carpine-induced seizures do not induce deficits in contextual freezing, a measure of hippocampus-dependent learning that is disrupted by nonselective mAChR antagonists. Taken together, these data suggest that selective antagonists of M-1 mAChRs do not induce the severe cognitive deficits seen with nonselective mAChR antagonists and could provide a novel approach for the treatment certain of CNS disorders.

mGluR5 Positive Allosteric Modulators Facilitate both Hippocampal LTP and LTD and Enhance Spatial Learning

Highly selective positive allosteric modulators (PAMs) of metabotropic glutamate receptor subtype 5 (mGluR5) have emerged as a potential approach to treat positive symptoms associated with schizophrenia. mGluR5 plays an important role in both long-term potentiation (LTP) and long-term depression (LTD), suggesting that mGluR5 PAMs may also have utility in improving impaired cognitive function. However, if mGluR5 PAMs shift the balance of LTP and LTD or induce a state in which afferent activity induces lasting changes in synaptic function that are not appropriate for a given pattern of activity, this could disrupt rather than enhance cognitive function. We determined the effect of selective mGluR5 PAMs on the induction of LTP and LTD at the Schaffer collateral-CA1 synapse in the hippocampus. mGluR5-selective PAMs significantly enhanced threshold theta-burst stimulation (TBS)-induced LTP. In addition, mGluR5 PAMs enhanced both DHPG-induced LTD and LTD induced by the delivery of paired-pulse low-frequency stimulation. Selective potentiation of mGluR5 had no effect on LTP induced by suprathreshold TBS or saturated LTP. The finding that potentiation of mGluR5-mediated responses to stimulation of glutamatergic afferents enhances both LTP and LTD and supports the hypothesis that the activation of mGluR5 by endogenous glutamate contributes to both forms of plasticity. Furthermore, two systemically active mGluR5 PAMs enhanced performance in the Morris water maze, a measure of hippocampus-dependent spatial learning. Discovery of small molecules that enhance both LTP and LTD in an activity-appropriate manner shows a unique action on synaptic plasticity that may provide a novel approach for the treatment of impaired cognitive function. Neuropsychopharmacology (2009) 34, 2057-2071; doi:10.1038/npp.2009.30; published online 18 March 2009

Distributed Adaptive Learning Framework for Wide Area Monitoring of Power Systems Integrated with Distributed Generations

This paper presents a preliminary study of developing a novel distributed adaptive real-time learning framework for wide area monitoring of power systems integrated with distributed generations using synchrophasor technology. The framework comprises distributed agents (synchrophasors) for autonomous local condition monitoring and fault detection, and a central unit for generating global view for situation awareness and decision making. Key technologies that can be integrated into this hierarchical distributed learning scheme are discussed to enable real-time information extraction and knowledge discovery for decision making, without explicitly accumulating and storing all raw data by the central unit. Based on this, the configuration of a wide area monitoring system of power systems using synchrophasor technology, and the functionalities for locally installed open-phasor-measurement-units (OpenPMUs) and a central unit are presented. Initial results on anti-islanding protection using the proposed approach are given to illustrate the effectiveness.

Min-BDeu and Max-BDeu Scores for Learning Bayesian Networks

This work presents two new score functions based on the Bayesian Dirichlet equivalent uniform (BDeu) score for learning Bayesian network structures. They consider the sensitivity of BDeu to varying parameters of the Dirichlet prior. The scores take on the most adversary and the most beneficial priors among those within a contamination set around the symmetric one. We build these scores in such way that they are decomposable and can be computed efficiently. Because of that, they can be integrated into any state-of-the-art structure learning method that explores the space of directed acyclic graphs and allows decomposable scores. Empirical results suggest that our scores outperform the standard BDeu score in terms of the likelihood of unseen data and in terms of edge discovery with respect to the true network, at least when the training sample size is small. We discuss the relation between these new scores and the accuracy of inferred models. Moreover, our new criteria can be used to identify the amount of data after which learning is saturated, that is, additional data are of little help to improve the resulting model.

High accuracy android malware detection using ensemble learning

With over 50 billion downloads and more than 1.3 million apps in Google’s official market, Android has continued to gain popularity amongst smartphone users worldwide. At the same time there has been a rise in malware targeting the platform, with more recent strains employing highly sophisticated detection avoidance techniques. As traditional signature based methods become less potent in detecting unknown malware, alternatives are needed for timely zero-day discovery. Thus this paper proposes an approach that utilizes ensemble learning for Android malware detection. It combines advantages of static analysis with the efficiency and performance of ensemble machine learning to improve Android malware detection accuracy. The machine learning models are built using a large repository of malware samples and benign apps from a leading antivirus vendor. Experimental results and analysis presented shows that the proposed method which uses a large feature space to leverage the power of ensemble learning is capable of 97.3 % to 99% detection accuracy with very low false positive rates.