Manifold Modeling of the Beating Heart Motion

Modeling the heart motion has important applications for diagnosis and intervention. We present a new method for modeling the deformation of the myocardium in the cardiac cycle. Our approach is based on manifold learning to build a representation of shape variation across time. We experiment with various manifold types to identify the best manifold method, and with real patient data extracted from cine MRIs. We obtain a representation, common to all subjects, that can discriminate cardiac cycle phases and heart function types

Echocardiography noise reduction using sparse representation

The clarity and accuracy of echocardiography images are greatly reduced by speckle noise. Noise suppression, however, is difficult to achieve without also obscuring both rapidly moving structures and object edges. This research seeks to address these challenges by introducing a novel filtering framework based on temporal information and sparse representation. The proposed method involves smoothing intensity variation time curves (IVTCs) assessed in each pixel. Using an over-complete dictionary that contains prototype signal-atoms, IVTCs can be reconstructed as linear combinations of a few of these atoms. After a comprehensive comparison of sparse recovery algorithms, three were selected for our method: Bayesian Compressive Sensing (BCS), the Bregman iterative algorithm, and Orthogonal Matching Pursuit (OMP). The performance of the proposed method was then evaluated and compared with other speckle reduction filters. The experimental results demonstrate that the proposed algorithm can be used to achieve better-preserved edges and reduce blurring. © 201

Biexcitability and bursting mechanisms in neural and genetic circuits

This paper compares mechanisms for generating repetitive spikes (bursts) in neural and transcriptional circuits. Neurons generate bursts followed by refractory periods controlled by ion channels in the membrane. In contrast, in gene transcription the bursts occur during a short time period followed by silent periods regulated by sis-regulatory elements. The role of excitability in producing different patterns of bursts is discussed by comparing the topology of a neural model with natural and synthetic transcriptional genetic circuits. In particular, a special bi-excitable architecture which embeds two excitable states are compared in these systems

Complex recurrent interactions in systems biology: from Henri Poincaré to Robert Rosen

In the systems biology era of the life sciences turning the vast amount of biological interaction data into meaningful knowledge requires indisputably methodological advances in data mining and system modelling. Modular hierarchy of complex molecular networks implies that modules have their own dynamics and interaction manifolds. However, when they hierarchically hook together, depending on different stable and unstable attractors of each module, a new organization of interaction with intertwined interacting region will be emerged which represent a complicated higher level complex manifold. To investigate how such higher level complex manifold emerges from integration of lower level modules, this paper presents an Event-Related Recurrent Modular Modelling Approach based on actual systems biology roots. The emphasis of this approach is on recurrence theorem, which is embedded in Henri Poincaré and Robert Rosen points of views as systems and biology roots, and it could be a conceptual strategy for systems identification and design methodology, in systems biology and synthetic biology, respectively. By the use of Iterated Maps, we explain how simple signaling pathways can be embedded in networks to generate more complex behaviours such as toggle switches and oscillators as the basic building blocks of cell cycle engine