ECG Denoising using Angular Velocity as a State and an Observation in an Extended Kalman Filter Framework

International audienceIn this paper an efficient filtering procedure based on Extended Kalman Filter (EKF) has been proposed. The method is based on a modified nonlinear dynamic model, previously introduced for the generation of synthetic ECG signals. The proposed method considers the angular velocity of ECG signal, as one of the states of an EKF. We have considered two cases for observation equations, in one case we have assumed a corresponding observation to angular velocity state and in the other case, we have not assumed any observations for it. Quantitative evaluation of the proposed algorithm on the MIT-BIH Normal Sinus Rhythm Database (NSRDB) shows that an average SNR improvement of 8 dB is achieved for an input signal of -4 dB

On-site residence time in a driven diffusive system: violation and recovery of mean-field

We investigate simple one-dimensional driven diffusive systems with open boundaries. We are interested in the average on-site residence time defined as the time a particle spends on a given site before moving on to the next site. Using mean-field theory, we obtain an analytical expression for the on-site residence times. By comparing the analytic predictions with numerics, we demonstrate that the mean-field significantly underestimates the residence time due to the neglect of time correlations in the local density of particles. The temporal correlations are particularly long-lived near the average shock position, where the density changes abruptly from low to high. By using Domain wall theory (DWT), we obtain highly accurate estimates of the residence time for different boundary conditions. We apply our analytical approach to residence times in a totally asymmetric exclusion process (TASEP), TASEP coupled to Langmuir kinetics (TASEP + LK), and TASEP coupled to mutually interactive LK (TASEP + MILK). The high accuracy of our predictions is verified by comparing these with detailed Monte Carlo simulations

Eletron-Helium Laser-Assisted Free-Free Scattering for Incident Energies from 30 - 200 eV: Effects of Polarization Direction

We report on experiments that examine electron-helium scattering in the presence of an Nd:YAG laser field of 1.17 eV photons. At each incidentelectron energy (30, 60, and 200 eV), the laser polarization direction is varied within a plane perpendicular to the Watson approximation calculations

Multi-dimensional filtering: Reducing the dimension through rotation

Over the past few decades there has been a strong effort towards the development of Smoothness-Increasing Accuracy-Conserving (SIAC) filters for Discontinuous Galerkin (DG) methods, designed to increase the smoothness and improve the convergence rate of the DG solution through this post-processor. These advantages can be exploited during flow visualization, for example by applying the SIAC filter to the DG data before streamline computations [Steffan et al., IEEE-TVCG 14(3): 680-692]. However, introducing these filters in engineering applications can be challenging since a tensor product filter grows in support size as the field dimension increases, becoming computationally expensive. As an alternative, [Walfisch et al., JOMP 38(2);164-184] proposed a univariate filter implemented along the streamline curves. Until now, this technique remained a numerical experiment. In this paper we introduce the line SIAC filter and explore how the orientation, structure and filter size affect the order of accuracy and global errors. We present theoretical error estimates showing how line filtering preserves the properties of traditional tensor product filtering, including smoothness and improvement in the convergence rate. Furthermore, numerical experiments are included, exhibiting how these filters achieve the same accuracy at significantly lower computational costs, becoming an attractive tool for the scientific visualization community

Arylmethylamino steroids as antiparasitic agents

In search of antiparasitic agents, we here identify arylmethylamino steroids as potent compounds and characterize more than 60 derivatives. The lead compound 1o is fast acting and highly active against intraerythrocytic stages of chloroquine-sensitive and resistant Plasmodium falciparum parasites (IC50 1–5?nM) as well as against gametocytes. In P. berghei-infected mice, oral administration of 1o drastically reduces parasitaemia and cures the animals. Furthermore, 1o efficiently blocks parasite transmission from mice to mosquitoes. The steroid compounds show low cytotoxicity in mammalian cells and do not induce acute toxicity symptoms in mice. Moreover, 1o has a remarkable activity against the blood-feeding trematode parasite Schistosoma mansoni. The steroid and the hydroxyarylmethylamino moieties are essential for antimalarial activity supporting a chelate-based quinone methide mechanism involving metal or haem bioactivation. This study identifies chemical scaffolds that are rapidly internalized into blood-feeding parasites

Photobiomodulation preserves mitochondrial redox state and is retinoprotective in a rodent model of retinitis pigmentosa

Photobiomodulation (PBM) by far-red (FR) to near-infrared (NIR) light has been demonstrated to restore the function of damaged mitochondria, increase the production of cytoprotective factors and prevent cell death. Our laboratory has shown that FR PBM improves functional and structural outcomes in animal models of retinal injury and retinal degenerative disease. The current study tested the hypothesis that a brief course of NIR (830 nm) PBM would preserve mitochondrial metabolic state and attenuate photoreceptor loss in a model of retinitis pigmentosa, the P23H transgenic rat. P23H rat pups were treated with 830 nm light (180 s; 25 mW/cm2; 4.5 J/cm2) using a light-emitting diode array (Quantum Devices, Barneveld, WI) from postnatal day (p) 10 to p25. Sham-treated rats were restrained, but not treated with 830 nm light. Retinal metabolic state, function and morphology were assessed at p30 by measurement of mitochondrial redox (NADH/FAD) state by 3D optical cryo-imaging, electroretinography (ERG), spectral-domain optical coherence tomography (SD-OCT), and histomorphometry. PBM preserved retinal metabolic state, retinal function, and retinal morphology in PBM-treated animals compared to the sham-treated group. PBM protected against the disruption of the oxidation state of the mitochondrial respiratory chain observed in sham-treated animals. Scotopic ERG responses over a range of flash intensities were significantly greater in PBM-treated rats compared to sham controls. SD-OCT studies and histological assessment showed that PBM preserved the structural integrity of the retina. These findings demonstrate for the first time a direct effect of NIR PBM on retinal mitochondrial redox status in a well-established model of retinal disease. They show that chronic proteotoxic stress disrupts retinal bioenergetics resulting in mitochondrial dysfunction, and retinal degeneration and that therapies normalizing mitochondrial metabolism have considerable potential for the treatment of retinal degenerative disease

Real-time risk analysis for hybrid earthquake early warning systems

Earthquake Early Warning Systems (EEWS), based on real-time prediction of ground motion or structural response measures, may play a role in reducing vulnerability and/or exposition of buildings and lifelines. In fact, recently seismologists developed efficient methods for rapid estimation of event features by means of limited information of the P-waves. Then, when an event is occurring, probabilistic distributions of magnitude and source-to-site distance are available and the prediction of the ground motion at the site, conditioned to the seismic network measures, may be performed in analogy with the Probabilistic Seismic Hazard Analysis (PSHA). Consequently the structural performance may be obtained by the Probabilistic Seismic Demand Analysis (PSDA), and used for real-time risk management purposes. However, such prediction is performed in very uncertain conditions which have to be taken into proper account to limit false and missed alarms. In the present study, real-time risk analysis for early warning purposes is discussed. The magnitude estimation is performed via the Bayesian approach, while the earthquake localization is based on the Voronoi cells. To test the procedure it was applied, by simulation, to the EEWS under development in the Campanian region (southern Italy). The results lead to the conclusion that the PSHA, conditioned to the EEWS, correctly predicts the hazard at the site and that the false/missed alarm probabilities may be controlled by set up of an appropriate decisional rule and alarm threshold

Increased serum tumor necrosis factor α levels in patients with lenalidomide-induced hypothyroidism

As the use of lenalidomide expands, the poorly understood phenomenon of lenalidomide-induced thyroid abnormalities will increase. In this study we compared rates of therapy-induced hypothyroidism in 329 patients with DLBCL treated with conventional chemotherapy (DLBCL-c) or conventional chemotherapy plus lenalidomide (DLBCL-len). We measured serum levels of tumor necrosis factor alpha (TNF-α), interferon gamma (IFN-γ), interleukin-6 (IL-6), interleukin-12 (IL-12), and interleukin-15 (IL-15) before and after treatment. We found a significantly higher rate of therapy-induced hypothyroidism in the DLBCL-len group (25.8% vs 1.3%), and we found a statistically significant increase in serum TNF-α in patients with lenalidomide-induced hypothyroidism

Radio-Pathomic Approaches in Pediatric Neurooncology: Opportunities and Challenges

With medical software platforms moving to cloud environments with scalable storage and computing, the translation of predictive artificial intelligence (AI) models to aid in clinical decision-making and facilitate personalized medicine for cancer patients is becoming a reality. Medical imaging, namely radiologic and histologic images, has immense analytical potential in neuro-oncology, and models utilizing integrated radiomic and pathomic data may yield a synergistic effect and provide a new modality for precision medicine. At the same time, the ability to harness multi-modal data is met with challenges in aggregating data across medical departments and institutions, as well as significant complexity in modeling the phenotypic and genotypic heterogeneity of pediatric brain tumors. In this paper, we review recent pathomic and integrated pathomic, radiomic, and genomic studies with clinical applications. We discuss current challenges limiting translational research on pediatric brain tumors and outline technical and analytical solutions. Overall, we propose that to empower the potential residing in radio-pathomics, systemic changes in cross-discipline data management and end-to-end software platforms to handle multi-modal data sets are needed, in addition to embracing modern AI-powered approaches. These changes can improve the performance of predictive models, and ultimately the ability to advance brain cancer treatments and patient outcomes through the development of such models