Generalized methods and solvers for noise removal from piecewise constant signals. II. New methods

Removing noise from signals which are piecewise constant (PWC) is a challenging signal processing problem that arises in many practical scientific and engineering contexts. In the first paper (part I) of this series of two, we presented background theory building on results from the image processing community to show that the majority of these algorithms, and more proposed in the wider literature, are each associated with a special case of a generalized functional, that, when minimized, solves the PWC denoising problem. It shows how the minimizer can be obtained by a range of computational solver algorithms. In this second paper (part II), using this understanding developed in part I, we introduce several novel PWC denoising methods, which, for example, combine the global behaviour of mean shift clustering with the local smoothing of total variation diffusion, and show example solver algorithms for these new methods. Comparisons between these methods are performed on synthetic and real signals, revealing that our new methods have a useful role to play. Finally, overlaps between the generalized methods of these two papers and others such as wavelet shrinkage, hidden Markov models, and piecewise smooth filtering are touched on

Generalized methods and solvers for noise removal from piecewise constant signals. I. Background theory

Removing noise from piecewise constant (PWC) signals is a challenging signal processing problem arising in many practical contexts. For example, in exploration geosciences, noisy drill hole records need to be separated into stratigraphic zones, and in biophysics, jumps between molecular dwell states have to be extracted from noisy fluorescence microscopy signals. Many PWC denoising methods exist, including total variation regularization, mean shift clustering, stepwise jump placement, running medians, convex clustering shrinkage and bilateral filtering; conventional linear signal processing methods are fundamentally unsuited. This paper (part I, the first of two) shows that most of these methods are associated with a special case of a generalized functional, minimized to achieve PWC denoising. The minimizer can be obtained by diverse solver algorithms, including stepwise jump placement, convex programming, finite differences, iterated running medians, least angle regression, regularization path following and coordinate descent. In the second paper, part II, we introduce novel PWC denoising methods, and comparisons between these methods performed on synthetic and real signals, showing that the new understanding of the problem gained in part I leads to new methods that have a useful role to play

Optimized parameter search for large datasets of the regularization parameter and feature selection for ridge regression

In this paper we propose mathematical optimizations to select the optimal regularization parameter for ridge regression using cross-validation. The resulting algorithm is suited for large datasets and the computational cost does not depend on the size of the training set. We extend this algorithm to forward or backward feature selection in which the optimal regularization parameter is selected for each possible feature set. These feature selection algorithms yield solutions with a sparse weight matrix using a quadratic cost on the norm of the weights. A naive approach to optimizing the ridge regression parameter has a computational complexity of the order with the number of applied regularization parameters, the number of folds in the validation set, the number of input features and the number of data samples in the training set. Our implementation has a computational complexity of the order . This computational cost is smaller than that of regression without regularization for large datasets and is independent of the number of applied regularization parameters and the size of the training set. Combined with a feature selection algorithm the algorithm is of complexity and for forward and backward feature selection respectively, with the number of selected features and the number of removed features. This is an order faster than and for the naive implementation, with for large datasets. To show the performance and reduction in computational cost, we apply this technique to train recurrent neural networks using the reservoir computing approach, windowed ridge regression, least-squares support vector machines (LS-SVMs) in primal space using the fixed-size LS-SVM approximation and extreme learning machines

Serological markers for prediction of response to anti-tumor necrosis factor treatment in Crohn's disease

peer reviewedOBJECTIVES: The use of monoclonal anti-tumor necrosis factor (TNF) antibodies (infliximab, Remicade) is a new therapeutic approach for severe refractory luminal or fistulizing, Crohn's disease (CD). However, up to 30% of patients do not respond to this treatment. So far, no parameters predictive of response to anti-TNT have been identified. Our aim was to determine whether serological markers ASCA (anti-Saccharomyces cerevisiae antibodies) or pANCA (perinuclear antineutrophil cytoplasmic antibodies) could identify Crohn's patients likely to benefit from anti-TNF therapy. METHODS: Serum samples of 279 CID patients were analyzed for ASCA and pANCA before anti-TNF therapy. A blinded physician determined clinical response at week 4 (refractory luminal CD) or week 10 (fistulizing CD) after the first infusion of infliximab (5 mg/kg). RESULTS: Overall, there was no relationship between ASCA or pANCA and response to therapy. However, lower response rates were observed for patients with refractory intestinal disease carrying the pANCA+/ASCA- combination, although this lacked significance (p = 0.067). CONCLUSIONS: In this cohort of infliximab-treated patients, neither ASCA nor pANCA could predict response to treatment. However, the combination pANCA+/ASCA- might warrant further investigation for its value in predicting nonresponse in patients with refractory luminal disease

Mangroves as nature-based mitigation for ENSO-driven compound flood risks in a large river delta

Densely populated coastal river deltas are very vulnerable to compound flood risks coming from both oceanic and riverine sources. Climate change may increase these compound flood risks due to sea level rise and intensifying precipitation events. Here, we investigate to what extent nature-based flood defence strategies, through the conservation of mangroves in a tropical river delta, can contribute to mitigate the oceanic and riverine components of compound flood risks. While current knowledge of estuarine compound flood risks is mostly focussed on short-term events such as storm surges (taking 1 or a few days), longer-term events, such as El Niño events (continuing for several weeks to months) along the Pacific coast of Latin America, are less studied. Here, we present a hydrodynamic modelling study of a large river delta in Ecuador aiming to elucidate the compound effects of El Niño-driven oceanic and riverine forcing on extreme high water level propagation through the delta and, in particular, the role of mangroves in reducing the compound high water levels. Our results show that the deltaic high water level anomalies are predominantly driven by the oceanic forcing but that the riverine forcing causes the anomalies to amplify upstream. Furthermore, mangroves in the delta attenuate part of the oceanic contribution to the high water level anomalies, with the attenuating effect increasing in the landward direction, while mangroves have a negligible effect on the riverine component. These findings show that mangrove conservation and restoration programmes can contribute to nature-based mitigation, especially the oceanic component of compound flood risks in a tropical river delta.</p

P2X(3) receptors mediate visceral hypersensitivity during acute chemically-induced colitis and in the post-inflammatory phase via different mechanisms of sensitization

OBJECTIVES: Experiments using P2X3 knock-out mice or more general P2X receptor antagonists suggest that P2X3 receptors contribute to visceral hypersensitivity. We aimed to investigate the effect of the selective P2X3 antagonist A-317491 on visceral sensitivity under physiological conditions, during acute colitis and in the post-inflammatory phase of colitis. METHODS: Trinitrobenzene sulphonic-acid colitis was monitored by colonoscopy: on day 3 to confirm the presence of colitis and then every 4 days, starting from day 10, to monitor convalescence and determine the exact timepoint of endoscopic healing in each rat. Visceral sensitivity was assessed by quantifying visceromotor responses to colorectal distension in controls, rats with acute colitis and post-colitis rats. A-317491 was administered 30 min prior to visceral sensitivity testing. Expression of P2X3 receptors (RT-PCR and immunohistochemistry) and the intracellular signalling molecules cdk5, csk and CASK (RT-PCR) were quantified in colonic tissue and dorsal root ganglia. ATP release in response to colorectal distension was measured by luminiscence. RESULTS: Rats with acute TNBS-colitis displayed significant visceral hypersensitivity that was dose-dependently, but not fully, reversed by A-317491. Hypersenstivity was accompanied by an increased colonic release of ATP. Post-colitis rats also displayed visceral hypersensitivity that was dose-dependently reduced and fully normalized by A-317491 without increased release of ATP. A-317491 did not modify visceral sensitivity in controls. P2X3 mRNA and protein expression in the colon and dorsal root ganglia were similar in control, acute colitis and post-colitis groups, while colonic mRNA expression of cdk5, csk and CASK was increased in the post-colitis group only. CONCLUSIONS: These findings indicate that P2X3 receptors are not involved in sensory signaling under physiological conditions whereas they modulate visceral hypersensitivity during acute TNBS-colitis and even more so in the post-inflammatory phase, albeit via different mechanisms of sensitization, validating P2X3 receptors as potential new targets in the treatment of abdominal pain syndromes.Annemie Deiteren, Laura van der Linden, Anouk de Wit, Hannah Ceuleers, Roeland Buckinx, Jean-Pierre Timmermans, Tom G. Moreels, Paul A. Pelckmans, Joris G. De Man, Benedicte Y. De Winte

A Mathematical Model for Interpretable Clinical Decision Support with Applications in Gynecology

Over time, methods for the development of clinical decision support (CDS) systems have evolved from interpretable and easy-to-use scoring systems to very complex and non-interpretable mathematical models. In order to accomplish effective decision support, CDS systems should provide information on how the model arrives at a certain decision. To address the issue of incompatibility between performance, interpretability and applicability of CDS systems, this paper proposes an innovative model structure, automatically leading to interpretable and easily applicable models. The resulting models can be used to guide clinicians when deciding upon the appropriate treatment, estimating patient-specific risks and to improve communication with patients.We propose the interval coded scoring (ICS) system, which imposes that the effect of each variable on the estimated risk is constant within consecutive intervals. The number and position of the intervals are automatically obtained by solving an optimization problem, which additionally performs variable selection. The resulting model can be visualised by means of appealing scoring tables and color bars. ICS models can be used within software packages, in smartphone applications, or on paper, which is particularly useful for bedside medicine and home-monitoring. The ICS approach is illustrated on two gynecological problems: diagnosis of malignancy of ovarian tumors using a dataset containing 3,511 patients, and prediction of first trimester viability of pregnancies using a dataset of 1,435 women. Comparison of the performance of the ICS approach with a range of prediction models proposed in the literature illustrates the ability of ICS to combine optimal performance with the interpretability of simple scoring systems.The ICS approach can improve patient-clinician communication and will provide additional insights in the importance and influence of available variables. Future challenges include extensions of the proposed methodology towards automated detection of interaction effects, multi-class decision support systems, prognosis and high-dimensional data