Reconstruction of Underlying Nonlinear Deterministic Dynamics Embedded in Noisy Spike Trains

An experimentally recorded time series formed by the exact times of occurrence of the neuronal spikes (spike train) is likely to be affected by observational noise that provokes events mistakenly confused with neuronal discharges, as well as missed detection of genuine neuronal discharges. The points of the spike train may also suffer a slight jitter in time due to stochastic processes in synaptic transmission and to delays in the detecting devices. This study presents a procedure aimed at filtering the embedded noise (denoising the spike trains) the spike trains based on the hypothesis that recurrent temporal patterns of spikes are likely to represent the robust expression of a dynamic process associated with the information carried by the spike train. The rationale of this approach is tested on simulated spike trains generated by several nonlinear deterministic dynamical systems with embedded observational noise. The application of the pattern grouping algorithm (PGA) to the noisy time series allows us to extract a set of points that form the reconstructed time series. Three new indices are defined for assessment of the performance of the denoising procedure. The results show that this procedure may indeed retrieve the most relevant temporal features of the original dynamics. Moreover, we observe that additional spurious events affect the performance to a larger extent than the missing of original points. Thus, a strict criterion for the detection of spikes under experimental conditions, thus reducing the number of spurious spikes, may raise the possibility to apply PGA to detect endogenous deterministic dynamics in the spike train otherwise masked by the observational nois

Non-invasive assessment of arterial stiffness using oscillometric blood pressure measurement

<p>Abstract</p> <p>Background</p> <p>Arterial stiffness is a major contributor to cardiovascular diseases. Because current methods of measuring arterial stiffness are technically demanding, the purpose of this study was to develop a simple method of evaluating arterial stiffness using oscillometric blood pressure measurement.</p> <p>Methods</p> <p>Blood pressure was conventionally measured in the left upper arm of 173 individuals using an inflatable cuff. Using the time series of occlusive cuff pressure and the amplitudes of pulse oscillations, we calculated local slopes of the curve between the decreasing cuff pressure and corresponding arterial volume. Whole pressure-volume curve was derived from numerical integration of the local slopes. The curve was fitted using an equation and we identified a numerical coefficient of the equation as an index of arterial stiffness (Arterial Pressure-volume Index, API). We also measured brachial-ankle (baPWV) PWV and carotid-femoral (cfPWV) PWV using a vascular testing device and compared the values with API. Furthermore, we assessed carotid arterial compliance using ultrasound images to compare with API.</p> <p>Results</p> <p>The slope of the calculated pressure-volume curve was steeper for compliant (low baPWV or cfPWV) than stiff (high baPWV or cfPWV) arteries. API was related to baPWV (<it>r </it>= -0.53, <it>P </it>< 0.05), cfPWV (<it>r </it>= -0.49, <it>P </it>< 0.05), and carotid arterial compliance (<it>r </it>= 0.32, <it>P </it>< 0.05). A stepwise multiple regression analysis demonstrated that baPWV and carotid arterial compliance were the independent determinants of API, and that API was the independent determinant of baPWV and carotid arterial compliance.</p> <p>Conclusions</p> <p>These results suggest that our method can simply and simultaneously evaluate arterial stiffness and blood pressure based on oscillometric measurements of blood pressure.</p

Quantum Approximate Optimization Algorithm Parameter Prediction Using a Convolutional Neural Network

The Quantum approximate optimization algorithm (QAOA) is a quantum-classical hybrid algorithm aiming to produce approximate solutions for combinatorial optimization problems. In the QAOA, the quantum part prepares a quantum parameterized state that encodes the solution, where the parameters are optimized by a classical optimizer. However, it is difficult to find optimal parameters when the quantum circuit becomes deeper. Hence, there is numerous active research on the performance and the optimization cost of QAOA. In this work, we build a convolutional neural network to predict parameters of depth QAOA instance by the parameters from the depth QAOA counterpart. We propose two strategies based on this model. First, we recurrently apply the model to generate a set of initial values for a certain depth QAOA. It successfully initiates depth 10 QAOA instances, whereas each model is only trained with the parameters from depths less than 6. Second, the model is applied repetitively until the maximum expected value is reached. An average approximation ratio of 0.9759 for Max-Cut over 264 Erd\H{o}s-R\'{e}nyi graphs is obtained, while the optimizer is only adopted for generating the first input of the model.Comment: 9 pages, 4 figures, 1 table

Iterative Layerwise Training for Quantum Approximate Optimization Algorithm

The capability of the quantum approximate optimization algorithm (QAOA) in solving the combinatorial optimization problems has been intensively studied in recent years due to its application in the quantum-classical hybrid regime. Despite having difficulties that are innate in the variational quantum algorithms (VQA), such as barren plateaus and the local minima problem, QAOA remains one of the applications that is suitable for the recent noisy intermediate scale quantum (NISQ) devices. Recent works have shown that the performance of QAOA largely depends on the initial parameters, which motivate parameter initialization strategies to obtain good initial points for the optimization of QAOA. On the other hand, optimization strategies focus on the optimization part of QAOA instead of the parameter initialization. Instead of having absolute advantages, these strategies usually impose trade-offs to the performance of the optimization problems. One of such examples is the layerwise optimization strategy, in which the QAOA parameters are optimized layer-by-layer instead of the full optimization. The layerwise strategy costs less in total compared to the full optimization, in exchange of lower approximation ratio. In this work, we propose the iterative layerwise optimization strategy and explore the possibility for the reduction of optimization cost in solving problems with QAOA. Using numerical simulations, we found out that by combining the iterative layerwise with proper initialization strategies, the optimization cost can be significantly reduced in exchange for a minor reduction in the approximation ratio. We also show that in some cases, the approximation ratio given by the iterative layerwise strategy is even higher than that given by the full optimization.Comment: 9 pages, 3 figure

A Feasibility-Preserved Quantum Approximate Solver for the Capacitated Vehicle Routing Problem

The Capacitated Vehicle Routing Problem (CVRP) is an NP-optimization problem (NPO) that arises in various fields including transportation and logistics. The CVRP extends from the Vehicle Routing Problem (VRP), aiming to determine the most efficient plan for a fleet of vehicles to deliver goods to a set of customers, subject to the limited carrying capacity of each vehicle. As the number of possible solutions skyrockets when the number of customers increases, finding the optimal solution remains a significant challenge. Recently, a quantum-classical hybrid algorithm known as Quantum Approximate Optimization Algorithm (QAOA) can provide better solutions in some cases of combinatorial optimization problems, compared to classical heuristics. However, the QAOA exhibits a diminished ability to produce high-quality solutions for some constrained optimization problems including the CVRP. One potential approach for improvement involves a variation of the QAOA known as the Grover-Mixer Quantum Alternating Operator Ansatz (GM-QAOA). In this work, we attempt to use GM-QAOA to solve the CVRP. We present a new binary encoding for the CVRP, with an alternative objective function of minimizing the shortest path that bypasses the vehicle capacity constraint of the CVRP. The search space is further restricted by the Grover-Mixer. We examine and discuss the effectiveness of the proposed solver through its application to several illustrative examples.Comment: 9 pages, 8 figures, 1 tabl

Attention Networks in ADHD Adults after Working Memory Training with a Dual n-Back Task

Patients affected by Attention-Deficit/Hyperactivity Disorder (ADHD) are characterized by impaired executive functioning and/or attention deficits. Our study aim is to determine whether the outcomes measured by the Attention Network Task (ANT), i.e., the reaction times (RTs) to specific target and cue conditions and alerting, orienting, and conflict (or executive control) effects are affected by cognitive training with a Dual n-back task. We considered three groups of young adult participants: ADHD patients without medication (ADHD), ADHD with medication (MADHD), and age/education-matched controls. Working memory training consisted of a daily practice of 20 blocks of Dual n-back task (approximately 30 min per day) for 20 days within one month. Participants of each group were randomly assigned into two subgroups, the first one with an adaptive mode of difficulty (adaptive training), while the second was blocked at the level 1 during the whole training phase (1-back task, baseline training). Alerting and orienting effects were not modified by working memory training. The dimensional analysis showed that after baseline training, the lesser the severity of the hyperactive-impulsive symptoms, the larger the improvement of reaction times on trials with high executive control/conflict demand (i.e., what is called Conflict Effect), irrespective of the participants' group. In the categorical analysis, we observed the improvement in such Conflict Effect after the adaptive training in adult ADHD patients irrespective of their medication, but not in controls. The ex-Gaussian analysis of RT and RT variability showed that the improvement in the Conflict Effect correlated with a decrease in the proportion of extreme slow responses. The Dual n-back task in the adaptive mode offers as a promising candidate for a cognitive remediation of adult ADHD patients without pharmaceutical medication

Psychophysical evaluation of calibration curve for diagnostic LCD monitor

金沢大学大学院医学系研究科量子医療技術学Purpose. In 1998, Digital Imaging Communications in Medicine (DICOM) proposed a calibration tool, the grayscale standard display function (GSDF), to obtain output consistency of radiographs. To our knowledge, there have been no previous reports of investigating the relation between perceptual linearity and detectability on a calibration curve. Materials and methods. To determine a suitable calibration curve for diagnostic liquid crystal display (LCD) monitors, the GSDF and Commission Internationale de l\u27Eclairage (CIE) curves were compared using psychophysical gradient δ and receiver operating characteristic (ROC) analysis for clinical images. Results. We succeeded in expressing visually recognized contrast directly using δ instead of the just noticeable difference (JND) index of the DICOM standard. As a result, we found that the visually recognized contrast at low luminance areas on the LCD monitor calibrated by the CIE curve is higher than that calibrated by the GSDF curve. On the ROC analysis, there was no significant difference in tumor detectability between GSDF and CIE curves for clinical thoracic images. However, the area parameter Az of the CIE curve is superior to that of the GSDF curve. The detectability of tumor shadows in the thoracic region on clinical images using the CIE curve was superior to that using the GSDF curve owing to the high absolute value of δ in the low luminance range. Conclusion. We conclude that the CIE curve is the most suitable tool for calibrating diagnostic LCD monitors, rather than the GSDF curve. © Japan Radiological Society 2006

TMC1 and TMC2 Are Components of the Mechanotransduction Channel in Hair Cells of the Mammalian Inner Ear

SummarySensory transduction in auditory and vestibular hair cells requires expression of transmembrane channel-like (Tmc) 1 and 2 genes, but the function of these genes is unknown. To investigate the hypothesis that TMC1 and TMC2 proteins are components of the mechanosensitive ion channels that convert mechanical information into electrical signals, we recorded whole-cell and single-channel currents from mouse hair cells that expressed Tmc1, Tmc2, or mutant Tmc1. Cells that expressed Tmc2 had high calcium permeability and large single-channel currents, while cells with mutant Tmc1 had reduced calcium permeability and reduced single-channel currents. Cells that expressed Tmc1 and Tmc2 had a broad range of single-channel currents, suggesting multiple heteromeric assemblies of TMC subunits. The data demonstrate TMC1 and TMC2 are components of hair cell transduction channels and contribute to permeation properties. Gradients in TMC channel composition may also contribute to variation in sensory transduction along the tonotopic axis of the mammalian cochlea

Light-dependent induction of Edn2 expression and attenuation of retinal pathology by endothelin receptor antagonists in Prominin-1- deficient mice

Retinitis pigmentosa (RP) and macular dystrophy (MD) are prevalent retinal degenerative diseases associated with gradual photoreceptor death. These diseases are often caused by genetic mutations that result in degeneration of the retina postnatally after it has fully developed. The Prominin-1 gene (Prom1) is a causative gene for RP and MD, and Prom1- knockout (KO) mice recapitulate key features of these diseases including light-dependent retinal degeneration and stenosis of retinal blood vessels. The mechanisms underlying progression of such degeneration have remained unknown, however. We here analysed early events associated with retinal degeneration in Prom1-KO mice. We found that photoreceptor cell death and glial cell activation occur between 2 and 3 weeks after birth. High-throughput analysis revealed that expression of the endothelin-2 gene (Edn2) was markedly up-regulated in the Prom1-deficient retina during this period. Expression of Edn2 was also induced by light stimulation in Prom1-KO mice that had been reared in the dark. Finally, treatment with endothelin receptor antagonists attenuated photoreceptor cell death, gliosis, and retinal vessel stenosis in Prom1-KO mice. Our findings suggest that inhibitors of endothelin signalling may delay the progression of RP and MD and therefore warrant further study as potential therapeutic agents for these diseases