Effect of the Heart Rate Variability Representations on the Quantification of the Cardiorespiratory Interactions during Autonomic Nervous System Blockade

The Heart Rate Variability (HRV) is a noninvasive tool to evaluate the activity of the autonomic nervous system. To study the HRV, different mathematical representations can be used. The selection of a representation might have an effect on the evaluation of the mechanisms that modulate the Heart Rate (HR). One of these mechanisms is the Respiratory Sinus Arrhythmia (RSA), i.e. an increased HR during inhalation and a decreased HR during exhalation. Different methods exist to quantify the RSA. A common approach is to calculate the power in the High Frequency (HF, 0.15 - 0.4 Hz) band of the spectrum of the HRV representation. More recently proposed methods use the respiratory signals to estimate the strength of the RSA.This paper studies the effect of the HRV representations on the quantification of the RSA. To this end, an experiment is used in which the sympathetic and parasympathetic branches of the autonomic nervous system are selectively blocked. Three different HRV representations are considered. Afterwards, the strength of the RSA is estimated using three approaches, namely the spectral content in the HF band of the HRV representations, orthogonal subspace projections and a time-frequency representation.The results suggest that the selection of an HRV representation does not have a significant impact on the RSA estimates in a healthy population

Image denoising based on nonlocal Bayesian singular value thresholding and Stein's unbiased risk estimator

© 1992-2012 IEEE. Singular value thresholding (SVT)- or nuclear norm minimization (NNM)-based nonlocal image denoising methods often rely on the precise estimation of the noise variance. However, most existing methods either assume that the noise variance is known or require an extra step to estimate it. Under the iterative regularization framework, the error in the noise variance estimate propagates and accumulates with each iteration, ultimately degrading the overall denoising performance. In addition, the essence of these methods is still least squares estimation, which can cause a very high mean-squared error (MSE) and is inadequate for handling missing data or outliers. In order to address these deficiencies, we present a hybrid denoising model based on variational Bayesian inference and Stein's unbiased risk estimator (SURE), which consists of two complementary steps. In the first step, the variational Bayesian SVT performs a low-rank approximation of the nonlocal image patch matrix to simultaneously remove the noise and estimate the noise variance. In the second step, we modify the conventional SURE full-rank SVT and its divergence formulas for rank-reduced eigen-triplets to remove the residual artifacts. The proposed hybrid BSSVT method achieves better performance in recovering the true image compared with state-of-the-art methods

Model-Based Evaluation of Methods for Respiratory Sinus Arrhythmia Estimation

OBJECTIVE: Respiratory sinus arrhythmia (RSA) refers to heart rate oscillations synchronous with respiration, and it is one of the major representations of cardiorespiratory coupling. Its strength has been suggested as a biomarker to monitor different conditions and diseases. Some approaches have been proposed to quantify the RSA, but it is unclear which one performs best in specific scenarios. The main objective of this study is to compare seven state-of-the-art methods for RSA quantification using data generated with a model proposed to simulate and control the RSA. These methods are also compared and evaluated on a real-life application, for their ability to capture changes in cardiorespiratory coupling during sleep. METHODS: A simulation model is used to create a dataset of heart rate variability and respiratory signals with controlled RSA, which is used to compare the RSA estimation approaches. To compare the methods objectively in a real-life application, regression models trained on the simulated data are used to map the estimates to the same measurement scale. RESULTS AND CONCLUSION: RSA estimates based on cross entropy, time-frequency coherence and subspace projections showed the best performance on simulated data. In addition, these estimates captured the expected trends in the changes in cardiorespiratory coupling during sleep similarly. SIGNIFICANCE: An objective comparison of methods for RSA quantification is presented to guide future analyses. Also, the proposed simulation model can be used to compare existing and newly proposed RSA estimates. It is freely accessible online

Systematic analysis of secreted proteins reveals synergism between IL6 and other proteins in soft agar growth of MCF10A cells

<p>Abstract</p> <p>Introduction</p> <p>Breast cancer, the most common malignancy in women, still holds many secrets. The causes for non-hereditary breast cancer are still unknown. To elucidate any role for circulating naturally secreted proteins, a screen of secreted proteins' influence of MCF10A cell anchorage independent growth was set up.</p> <p>Methods</p> <p>To systematically screen secreted proteins for their capacity to transform mammalian breast epithelial cells, a soft agar screen of MCF10A cells was performed using a library of ~ 470 secreted proteins. A high concentration of infecting viral particles was used to obtain multiple infections in individual cells to specifically study the combined effect of multiple secreted proteins.</p> <p>Results</p> <p>Several known breast cancer factors, such as Wnt, FGF and IL were retained, as well as factors that were previously unknown to have a role in breast cancer, such as paraoxonase 1 and fibroblast growth factor binding protein 2. Additionally, a combinatory role of Interleukin 6 with other factors in MCF10A anchorage-independent growth is demonstrated.</p> <p>Conclusion</p> <p>The transforming effect of combinations of IL6 with other secreted proteins allows studying the transformation of mammary epithelial cells <it>in vitro</it>, and may also have implications in <it>in vivo </it>studies where secreted proteins are upregulated or overexpressed.</p

Evaluation of Methods to Characterize the Change of the Respiratory Sinus Arrhythmia with Age in Sleep Apnea Patients

The High Frequency (HF) band of the power spectrum of the Heart Rate Variability (HRV) is widely accepted to contain information related to the respiration. However, it is known that this often results in misleading estimations of the strength of the Respiratory Sinus Arrhythmia (RSA). In this paper, different approaches to characterize the change of the RSA with age, combining HRV and respiratory signals, are studied. These approaches are the bandwidths in the power spectral density estimations, bivariate phase rectified signal averaging, information dynamics, a time-frequency representation, and a heart rate decomposition based on subspace projections. They were applied to a dataset of sleep apnea patients, specifically to periods without apneas and during NREM sleep. Each estimate reflected a different relationship between RSA and age, suggesting that they all capture the cardiorespiratory information in a different way. The comparison of the estimates indicates that the approaches based on the extraction of respiratory information from HRV provide a better characterization of the age-dependent degradation of the RSA

Model-Based Evaluation of Methods for Respiratory Sinus Arrhythmia Estimation

Objective: Respiratory sinus arrhythmia (RSA) refers to heart rate oscillations synchronous with respiration, and it is one of the major representations of cardiorespiratory coupling. Its strength has been suggested as a biomarker to monitor different conditions and diseases. Some approaches have been proposed to quantify the RSA, but it is unclear which one performs best in specific scenarios. The main objective of this study is to compare seven state-of-the-art methods for RSA quantification using data generated with a model proposed to simulate and control the RSA. These methods are also compared and evaluated on a real-life application, for their ability to capture changes in cardiorespiratory coupling during sleep. Methods: A simulation model is used to create a dataset of heart rate variability and respiratory signals with controlled RSA, which is used to compare the RSA estimation approaches. To compare the methods objectively in a real-life application, regression models trained on the simulated data are used to map the estimates to the same measurement scale. Results and conclusion: RSA estimates based on cross entropy, time-frequency coherence and subspace projections showed the best performance on simulated data. In addition, these estimates captured the expected trends in the changes in cardiorespiratory coupling during sleep similarly. Significance: An objective comparison of methods for RSA quantification is presented to guide future analyses. Also, the proposed simulation model can be used to compare existing and newly proposed RSA estimates. It is freely accessible online

Optimisation of the Schizosaccharomyces pombe urg1 expression system

The ability to study protein function in vivo often relies on systems that regulate the presence and absence of the protein of interest. Two limitations for previously described transcriptional control systems that are used to regulate protein expression in fission yeast are: the time taken for inducing conditions to initiate transcription and the ability to achieve very low basal transcription in the "OFF-state". In previous work, we described a Cre recombination-mediated system that allows the rapid and efficient regulation of any gene of interest by the urg1 promoter, which has a dynamic range of approximately 75-fold and which is induced within 30-60 minutes of uracil addition. In this report we describe easy-to-use and versatile modules that can be exploited to significantly tune down P urg1 "OFF-levels" while maintaining an equivalent dynamic range. We also provide plasmids and tools for combining P urg1 transcriptional control with the auxin degron tag to help maintain a null-like phenotype. We demonstrate the utility of this system by improved regulation of HO-dependent site-specific DSB formation, by the regulation Rtf1-dependent replication fork arrest and by controlling Rhp18(Rad18)-dependent post replication repair

Screening for data clustering in multicenter studies: the residual intraclass correlation

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On the Design of a Web-Based Decision Support System for Brain Tumour Diagnosis Using Distributed Agents

This paper introduces HealthAgents, an EC-funded research project to improve the classification of brain tumours through multi-agent decision support over a distributed network of local databases or Data Marts. HealthAgents will not only develop new pattern recognition methods for a distributed classification and analysis of in vivo MRS and ex vivo/in vitro HRMAS and DNA data, but also define a method to assess the quality and usability of a new candidate local database containing a set of new cases, based on a compatibility score