Gene Signal (Switzerland)

In contrast with linear systems, forced oscillations at particular frequencies may result in more than one stable limit cycle in the dynamic response of a nonlinear system. Also known as jump phenomenon, in these frequency regions, a nonlinear system would exhibit various dynamic behaviors critically depending on the initial conditions. The current study investigates the impact of initial conditions on nonlinear oscillations of an acoustically excited electroacoustic viscoelastic membrane. Accordingly, a mathematical model is developed to simulate the nonlinear dynamics of the membrane, and the method of multiple scales is then utilized to solve the governing equation of motion of the system. Frequency response of the system is extracted to examine the effect of system nonlinearity level on jump phenomenon. Various combinations for initial states of the membrane are taken into consideration, and corresponding phase portraits are provided to study the dynamic response of the system in the jump region. Finally, basins of attraction for stable oscillations are mapped in a plane spanned by initial conditions of the system for different excitation frequencies.

Abstract In patients with coronary artery disease the prediction of future cardiac events such as myocardial infarction (MI) remains a major challenge. In this work, we propose a novel anatomy-informed multimodal deep learning framework to predict future MI from clinical data and Invasive Coronary Angiography (ICA) images. The images are analyzed by Convolutional Neural Networks (CNNs) guided by anatomical information, and the clinical data by an Artificial Neural Network (ANN). Embeddings from both sources are then merged to provide a patient-level prediction. The performance of our framework on a clinical study of 445 patients admitted with acute coronary syndromes confirms that multimodal learning increases the predictive power and achieves good performance, which outperforms the prediction obtained by each modality independently as well as that of interventional cardiologists. To the best of our knowledge, this is the first attempt towards combining multimodal data through a deep learning framework for future MI prediction.

The effective representation, processing, analysis, and visualization of large-scale structured data, especially those related to complex domains, such as networks and graphs, are one of the key questions in modern machine learning. Graph signal processing (GSP), a vibrant branch of signal processing models and algorithms that aims at handling data supported on graphs, opens new paths of research to address this challenge. In this article, we review a few important contributions made by GSP concepts and tools, such as graph filters and transforms, to the development of novel machine learning algorithms. In particular, our discussion focuses on the following three aspects: exploiting data structure and relational priors, improving data and computational efficiency, and enhancing model interpretability. Furthermore, we provide new perspectives on the future development of GSP techniques that may serve as a bridge between applied mathematics and signal processing on one side and machine learning and network science on the other. Cross-fertilization across these different disciplines may help unlock the numerous challenges of complex data analysis in the modern age.

This study presents a method for automated detection of misplaced supplementary precordial leads, including the right-sided V3R, V4R and the posterior V8, V9 leads. Considering their uncommon use in clinical routine, a lead reversal is quite probable and could result in erroneous diagnosis and treatment. The method allows real-time implementation by scoring inter-lead cross-correlations over continuous 4s episodes, scanning the normal progression of PQRST waveforms within leads [V4R, V3R, V3, V4] and [V4, V5, V6, V8, V9]. A large 16-lead ECG database with 1333 chest pain patients is used to test the performance of the method for all possible 23 swaps between the supplementary leads V4R, V3R, V8, V9, assuming correct positions of the standard V1–V6. The sensitivity (Se) for lead reversals is Se=94.1±4.6%, ranged between 78.5% and 97.8%, with the most difficult detection of V3R/V4R swap (Se=78.5%), V4R/V9 swap (Se=83.7%), V8/V9 swap (Se=91.8%). The achieved specificity for the correct lead positions is Sp=83.4%.

A region tracking technique with particular emphasis on rotation robustness is presented. It is based on region matching divided in two consecutive steps, gradient orientation histogram matching and template matching through normalised cross correlation (NCC). Given the orientation histograms of two image patches, a novel technique is used to estimate the rotation between them together with the similarity. This estimation enhances the performance and speeds-up the process of patch recognition. Fast computation of histograms using the integral histogram approach is exploited. Experiments show a high accuracy in the estimation of location and orientation.

Inadequate islet adaptation to insulin resistance leads to glucose intolerance and type 2 diabetes. Here we investigate whether beta-cell cAMP is crucial for islet adaptation and prevention of glucose intolerance in mice. Mice with a beta-cell-specific, 2-fold overexpression of the cAMP-degrading enzyme phosphodiesterase 3B (RIP-PDE3B/2 mice) were metabolically challenged with a high-fat diet. We found that RIP-PDE3B/2 mice early and rapidly develop glucose intolerance and insulin resistance, as compared with wild-type littermates, after 2 months of high-fat feeding. This was evident from advanced fasting hyperinsulinemia and early development of hyper-glycemia, in spite of hyperinsulinemia, as well as impaired capacity of insulin to suppress plasma glucose in an insulin tolerance test. In vitro analyses of insulin-stimulated lipogenesis in adipocytes and glucose uptake in skeletal muscle did not reveal reduced insulin sensitivity in these tissues. Significant steatosis was noted in livers from high-fat-fed wild-type and RIP-PDE3B/2 mice and liver triacyl-glycerol content was 3-fold higher than in wild-type mice fed a control diet. Histochemical analysis revealed severe islet perturbations, such as centrally located alpha-cells and reduced immunostaining for insulin and GLUT2 in islets from RIP-PDE3B/2 mice. Additionally, in vitro experiments revealed that the insulin secretory response to glucagon-like peptide-1 stimulation was markedly reduced in islets from high-fat-fed RIP-PDE3B/2 mice. We conclude that accurate regulation of beta-cell cAMP is necessary for adequate islet adaptation to a perturbed metabolic environment and protective for the development of glucose intolerance and insulin resistance.

Toll-like receptors (TLRs) are activated by pathogen-associated molecular patterns to induce innate immune responses and production of pro-inflammatory cytokines, interferons and anti-inflammatory cytokines. TLRs activate downstream effectors through adaptors that contain Toll/interleukin-1 receptor (TIR) domains, but the mechanisms accounting for diversification of TLR effector functions are unclear. To dissect biochemically TLR signalling, we established a system for isolating signalling complexes assembled by dimerized adaptors. Using MyD88 as a prototypical adaptor, we identified TNF receptor-associated factor 3 (TRAF3) as a new component of TIR signalling complexes that is recruited along with TRAF6. Using myeloid cells from TRAF3- and TRAF6-deficient mice, we show that TRAF3 is essential for the induction of type I interferons (IFN) and the anti-inflammatory cytokine interleukin-10 (IL-10), but is dispensable for expression of pro-inflammatory cytokines. In fact, TRAF3-deficient cells overproduce pro-inflammatory cytokines owing to defective IL-10 production. Despite their structural similarity, the functions of TRAF3 and TRAF6 are largely distinct. TRAF3 is also recruited to the adaptor TRIF (Toll/IL-1 receptor domain-containing adaptor-inducing IFN-beta) and is required for marshalling the protein kinase TBK1 (also called NAK) into TIR signalling complexes, thereby explaining its unique role in activation of the IFN response.