ECG beat classification using PCA,LDA, ICA and Discrete Wavelet Transform

Electrocardiogram (ECG) is the P-QRS-T wave, representing the cardiac function. The information concealed in the ECG signal is useful in detecting the disease afflicting the heart. It is very difficult to identify the subtle changes in the ECG in time and frequency domains. The Discrete Wavelet Transform (DWT) can provide good time and frequency resolutions and is able to decipher the hidden complexities in the ECG. In this study, five types of beat classes of arrhythmia as recommended by Association for Advancement of Medical Instrumentation (AAMI) were analyzed namely: non-ectopic beats, supra-ventricular ectopic beats, ventricular ectopic beats, fusion betas and unclassifiable and paced beats. Three dimensionality reduction algorithms; Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Independent Component Analysis (ICA) were independently applied on DWT sub bands for dimensionality reduction. These dimensionality reduced features were fed to the Support Vector Machine (SVM), neural network (NN) and probabilistic neural network (PNN) classifiers for automated diagnosis. ICA features in combination with PNN with spread value (σ) of 0.03 performed better than the PCA and LDA. It has yielded an average sensitivity, specificity, positive predictive value (PPV) and accuracy of 99.97%, 99.83%, 99.21% and 99.28% respectively using ten-fold cross validation scheme.     

Application of higher order statistics for atrial arrhythmia classification

Atrial fibrillation (AF) and atrial flutter (AFL) are the two common atrial arrhythmia encountered in the clinical practice. In order to diagnose these abnormalities the electrocardiogram (ECG) is widely used. The conventional linear time and frequency domain methods cannot decipher the hidden complexity present in these signals. The ECG is inherently a non-linear, non-stationary and non-Gaussian signal. The non-linear models can provide improved results and capture minute variations present in the time series. Higher order spectra (HOS) is a non-linear dynamical method which is highly rugged to noise. In the present study, the performances of two methods are compared: (i) 3rd order HOS cumulants and (ii) HOS bispectrum. The 3rd order cumulant and bispectrum coefficients are subjected to dimensionality reduction using independent component analysis (ICA) and classified using classification and regression tree (CART), random forest (RF), artificial neural network (ANN) and k-nearest neighbor (KNN) classifiers to select the best classifier. The ICA components of cumulant coefficients have provided the average accuracy, sensitivity, specificity and positive predictive value of 99.50%, 100%, 99.22% and 99.72% respectively using KNN classifier. Similarly, the ICA components of HOS bispectrum coefficients have yielded the average accuracy, sensitivity, specificity and PPV of 97.65%, 98.16%, 98.75% and 99.53% respectively using KNN. So, the ICA performed on the 3rd order HOS cumulants coupled with KNN classifier performed better than the HOS bispectrum method. The proposed methodology is robust and can be used in mass screening of cardiac patients.     

Extracts of Adipose Derived Stem Cells Slows Progression in the R6/2 Model of Huntington's Disease

Stem cell therapy is a promising treatment for incurable disorders including Huntington''s disease (HD). Adipose-derived stem cell (ASC) is an easily available source of stem cells. Since ASCs can be differentiated into nervous stem cells, it has clinically feasible potential for neurodegenerative disease. In addition, ASCs secrete various anti-apoptotic growth factors, which improve the symptoms of disease from transplanted ASCs. Thus, cell-free extracts of ASCs (ASCs-E) could be a potential candidate for treatment of HD. Here, we investigated effects of ASCs-E on R6/2 HD mouse model and neuronal cells. In R6/2 HD model, injection of ASCs-E improved the performance in Rotarod test. ASCs-E also ameliorated striatal atrophy and mutant huntingtin aggregation in the striatum. In Western blot increased expressions of p-Akt, p-CREB and PGC1α were noted by injection of ASCs-E, when comparing to the R6/2 HD model. Neuro2A neuroblastoma cells treated with ASCs-E showed increased expression of p-CREB and PGC1α. In conclusion, ASCs-E delayed disease progression in animal model of HD by restoring of CREB-PGC1α pathway and could be a potential resource for treatment of HD.     

Oral Ingestion of Transgenic RIDL Ae. aegypti Larvae Has No Negative Effect on Two Predator Toxorhynchites Species

Dengue is the most important mosquito-borne viral disease. No specific treatment or vaccine is currently available; traditional vector control methods can rarely achieve adequate control. Recently, the RIDL (Release of Insect carrying Dominant Lethality) approach has been developed, based on the sterile insect technique, in which genetically engineered ‘sterile’ homozygous RIDL male insects are released to mate wild females; the offspring inherit a copy of the RIDL construct and die. A RIDL strain of the dengue mosquito, Aedes aegypti, OX513A, expresses a fluorescent marker gene for identification (DsRed2) and a protein (tTAV) that causes the offspring to die. We examined whether these proteins could adversely affect predators that may feed on the insect. Aedes aegypti is a peri-domestic mosquito that typically breeds in small, rain-water-filled containers and has no specific predators. Toxorhynchites larvae feed on small aquatic organisms and are easily reared in the laboratory where they can be fed exclusively on mosquito larvae. To evaluate the effect of a predator feeding on a diet of RIDL insects, OX513A Ae. aegypti larvae were fed to two different species of Toxorhynchites (Tx. splendens and Tx. amboinensis) and effects on life table parameters of all life stages were compared to being fed on wild type larvae. No significant negative effect was observed on any life table parameter studied; this outcome and the benign nature of the expressed proteins (tTAV and DsRed2) indicate that Ae. aegypti OX513A RIDL strain is unlikely to have any adverse effects on predators in the environment.     

Is Dynamic Autocrine Insulin Signaling Possible? A Mathematical Model Predicts Picomolar Concentrations of Extracellular Monomeric Insulin within Human Pancreatic Islets

Insulin signaling is essential for -cell survival and proliferation in vivo. Insulin also has potent mitogenic and anti-apoptotic actions on cultured -cells, with maximum effect in the high picomolar range and diminishing effect at high nanomolar doses. In order to understand whether these effects of insulin are constitutive or can be subjected to physiological modulation, it is essential to estimate the extracellular concentration of monomeric insulin within an intact islet. Unfortunately, the in vivo concentration of insulin monomers within the islet cannot be measured directly with current technology. Here, we present the first mathematical model designed to estimate the levels of monomeric insulin within the islet extracellular space. Insulin is released as insoluble crystals that exhibit a delayed dissociation into hexamers, dimers, and eventually monomers, which only then can act as signaling ligands. The rates at which different forms of insulin dissolve in vivo have been estimated from studies of peripheral insulin injection sites. We used this and other information to formulate a mathematical model to estimate the local insulin concentration within a single islet as a function of glucose. Model parameters were estimated from existing literature. Components of the model were validated using experimental data, if available. Model analysis predicted that the majority of monomeric insulin in the islet is that which has been returned from the periphery, and the concentration of intra-islet monomeric insulin varies from 50–300 pM when glucose is in the physiological range. Thus, our results suggest that the local concentration of monomeric insulin within the islet is in the picomolar ‘sweet spot’ range of insulin doses that activate the insulin receptor and have the most potent effects on -cells in vitro. Together with experimental data, these estimations support the concept that autocrine/paracrine insulin signalling within the islet is dynamic, rather than constitutive and saturated.     

Solid Phase Synthesis of Mitochondrial Triphenylphosphonium-Vitamin E Metabolite Using a Lysine Linker for Reversal of Oxidative Stress

Mitochondrial targeting of antioxidants has been an area of interest due to the mitochondria''s role in producing and metabolizing reactive oxygen species. Antioxidants, especially vitamin E (α-tocopherol), have been conjugated to lipophilic cations to increase their mitochondrial targeting. Synthetic vitamin E analogues have also been produced as an alternative to α-tocopherol. In this paper, we investigated the mitochondrial targeting of a vitamin E metabolite, 2,5,7,8-tetramethyl-2-(2′-carboxyethyl)-6-hydroxychroman (α-CEHC), which is similar in structure to vitamin E analogues. We report a fast and efficient method to conjugate the water-soluble metabolite, α-CEHC, to triphenylphosphonium cation via a lysine linker using solid phase synthesis. The efficacy of the final product (MitoCEHC) to lower oxidative stress was tested in bovine aortic endothelial cells. In addition the ability of MitoCEHC to target the mitochondria was examined in type 2 diabetes db/db mice. The results showed mitochondrial accumulation in vivo and oxidative stress decrease in vitro.     

Coupling-induced population synchronization in an excitatory population of subthreshold Izhikevich neurons

We consider an excitatory population of subthreshold Izhikevich neurons which exhibit noise-induced firings. By varying the coupling strength J, we investigate population synchronization between the noise-induced firings which may be used for efficient cognitive processing such as sensory perception, multisensory binding, selective attention, and memory formation. As J is increased, rich types of population synchronization (e.g., spike, burst, and fast spike synchronization) are found to occur. Transitions between population synchronization and incoherence are well described in terms of an order parameter $\mathcal{O}$ . As a final step, the coupling induces oscillator death (quenching of noise-induced spikings) because each neuron is attracted to a noisy equilibrium state. The oscillator death leads to a transition from firing to non-firing states at the population level, which may be well described in terms of the time-averaged population spike rate $\overline{R}$ . In addition to the statistical-mechanical analysis using $\mathcal{O}$ and $\overline{R}$ , each population and individual state are also characterized by using the techniques of nonlinear dynamics such as the raster plot of neural spikes, the time series of the membrane potential, and the phase portrait. We note that population synchronization of noise-induced firings may lead to emergence of synchronous brain rhythms in a noisy environment, associated with diverse cognitive functions.     

Unintended polar metabolite profiling of carotenoid-biofortified transgenic rice reveals substantial equivalence to its non-transgenic counterpart

Substantial equivalence is a critical concept for biosafety assessment of genetically modified (GM) crops. To investigate substantial equivalence among carotenoid-biofortified GM rice and five conventional rice cultivars having common white (three) and red (two) grain colors, profiles of 52 polar metabolites were analyzed using gas chromatography time-of-flight mass spectrometry. The results were compared to evaluate the differences among GM and non-GM rice cultivars using principal components analysis. The GM rice is more comparable to its non-transgenic counterpart rice variety according to the closer co-separation than for other cultivars tested. This suggests that profiling of unintended polar metabolites could be a useful tool to reveal substantial equivalence of GM rice.