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

Multivariate curve resolution of time course microarray data

BACKGROUND: Modeling of gene expression data from time course experiments often involves the use of linear models such as those obtained from principal component analysis (PCA), independent component analysis (ICA), or other methods. Such methods do not generally yield factors with a clear biological interpretation. Moreover, implicit assumptions about the measurement errors often limit the application of these methods to log-transformed data, destroying linear structure in the untransformed expression data. RESULTS: In this work, a method for the linear decomposition of gene expression data by multivariate curve resolution (MCR) is introduced. The MCR method is based on an alternating least-squares (ALS) algorithm implemented with a weighted least squares approach. The new method, MCR-WALS, extracts a small number of basis functions from untransformed microarray data using only non-negativity constraints. Measurement error information can be incorporated into the modeling process and missing data can be imputed. The utility of the method is demonstrated through its application to yeast cell cycle data. CONCLUSION: Profiles extracted by MCR-WALS exhibit a strong correlation with cell cycle-associated genes, but also suggest new insights into the regulation of those genes. The unique features of the MCR-WALS algorithm are its freedom from assumptions about the underlying linear model other than the non-negativity of gene expression, its ability to analyze non-log-transformed data, and its use of measurement error information to obtain a weighted model and accommodate missing measurements

Risk assessment by the scientific committee of the Belgian Federal Agency for the Safety of the food chain

peer reviewe

Tensor-based Analysis of ECG changes prior to in-hospital cardiac arrest

© 2017 IEEE Computer Society. All rights reserved. This works presents an analysis in the changes in beat morphology prior to in-hospital cardiac arrest. We have used tensor decomposition methods to extract features from the ECG signal. After preprocessing and R peak detection, a tensor is constructed for each ECG signal by segmenting the signal in individual heartbeats and stacking them in a 3D manner. The result of the tensor decomposition are 3 factor vectors corresponding to each tensor dimension. The temporal vector, representing the standard heartbeat over all leads in the signal, is further processed to calculate 10 different features: 4 features characterizing global changes in beat morphology and 6 detailed features describing changes in timing and amplitude of the waveforms. We analyzed a dataset of 20 patients who experienced a cardiac arrest in the intensive care unit at the end of the recording. For each patient, a stable signal (in the beginning of the recording) and an unstable signal (near the cardiac arrest) were extracted and processed. Statistical analysis of the results in both time windows (e.g. stable and unstable) show significant changes in the values of 2 out of 4 global parameters and 4 out of 6 detailed parameters. The results indicate that the use of tensor-based methods can be a robust way to characterize ECG changes, and may be a useful tool in identifying patients at risk for cardiac arrest.status: publishe

Stakeholders’ perceptions, attitudes and practices towards risk prevention in the food chain

An online survey was conducted to describe stakeholders' perceptions, attitudes and practices towards risk prevention in the food chain and to explore if common features could be extracted from different fields of competency or groups of stakeholders. Out of 80 participants, 60% believed that pathogenic microorganisms were the main hazard to prevent. Twenty-four percent perceived climate change as the main risk factor. Seventy-three percent believed that hazards in the food chain are preventable and they often showed a positive attitude towards risk prevention measures. The opinion of 75% of stakeholders was that prevention measures should be compulsory and under the shared responsibility of both food business operators and competent authority. Seventy-five percent of the respondents had recent experience with particular hazards and declared to have undertaken risk reduction measures. Incentives to implement measures were policy obligation and public health consequences whereas barriers were budgetary reasons and doubts about their effectiveness. However, there was not always a complete agreement between the perceived usefulness of risk prevention measures and their effective implementation, and conversely. No significant difference could be observed in the perceptions, attitudes and practices towards risk prevention between neither groups of stakeholders nor their fields of competency. The results are important for improving the risk communication process because the same issues can be emphasized when promoting risk prevention in the food chain regardless of the type of food sectors and the groups of stakeholders