Jeffrey H. Omens

Bio: Jeffrey H. Omens is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Heart failure & Ventricular remodeling. The author has an hindex of 46, co-authored 150 publications receiving 7681 citations. Previous affiliations of Jeffrey H. Omens include University of California, Berkeley & University of California.

Coupling of a 3D Finite Element Model of Cardiac Ventricular Mechanics to Lumped Systems Models of the Systemic and Pulmonic Circulation

Abstract: In this study we present a novel, robust method to couple finite element (FE) models of cardiac mechanics to systems models of the circulation (CIRC), independent of cardiac phase. For each time step through a cardiac cycle, left and right ventricular pressures were calculated using ventricular compliances from the FE and CIRC models. These pressures served as boundary conditions in the FE and CIRC models. In succeeding steps, pressures were updated to minimize cavity volume error (FE minus CIRC volume) using Newton iterations. Coupling was achieved when a predefined criterion for the volume error was satisfied. Initial conditions for the multi-scale model were obtained by replacing the FE model with a varying elastance model, which takes into account direct ventricular interactions. Applying the coupling, a novel multi-scale model of the canine cardiovascular system was developed. Global hemodynamics and regional mechanics were calculated for multiple beats in two separate simulations with a left ventricular ischemic region and pulmonary artery constriction, respectively. After the interventions, global hemodynamics changed due to direct and indirect ventricular interactions, in agreement with previously published experimental results. The coupling method allows for simulations of multiple cardiac cycles for normal and pathophysiology, encompassing levels from cell to system.

Mechanotransduction in cardiac hypertrophy and failure.

Abstract: Cardiac muscle cells have an intrinsic ability to sense and respond to mechanical load through a process known as mechanotransduction. In the heart, this process involves the conversion of mechanical stimuli into biochemical events that induce changes in myocardial structure and function. Mechanotransduction and its downstream effects function initially as adaptive responses that serve as compensatory mechanisms during adaptation to the initial load. However, under prolonged and abnormal loading conditions, the remodeling processes can become maladaptive, leading to altered physiological function and the development of pathological cardiac hypertrophy and heart failure. Although the mechanisms underlying mechanotransduction are far from being fully elucidated, human and mouse genetic studies have highlighted various cytoskeletal and sarcolemmal structures in cardiac myocytes as the likely candidates for load transducers, based on their link to signaling molecules and architectural components important in disease pathogenesis. In this review, we summarize recent developments that have uncovered specific protein complexes linked to mechanotransduction and mechanotransmission within the sarcomere, the intercalated disc, and at the sarcolemma. The protein structures acting as mechanotransducers are the first step in the process that drives physiological and pathological cardiac hypertrophy and remodeling, as well as the transition to heart failure, and may provide better insights into mechanisms driving mechanotransduction-based diseases.

An FHL1-containing complex within the cardiomyocyte sarcomere mediates hypertrophic biomechanical stress responses in mice

Abstract: The response of cardiomyocytes to biomechanical stress can determine the pathophysiology of hypertrophic cardiac disease, and targeting the pathways regulating these responses is a therapeutic goal. However, little is known about how biomechanical stress is sensed by the cardiomyocyte sarcomere to transduce intracellular hypertrophic signals or how the dysfunction of these pathways may lead to disease. Here, we found that four-and-a-half LIM domains 1 (FHL1) is part of a complex within the cardiomyocyte sarcomere that senses the biomechanical stress-induced responses important for cardiac hypertrophy. Mice lacking Fhl1 displayed a blunted hypertrophic response and a beneficial functional response to pressure overload induced by transverse aortic constriction. A link to the Galphaq (Gq) signaling pathway was also observed, as Fhl1 deficiency prevented the cardiomyopathy observed in Gq transgenic mice. Mechanistic studies demonstrated that FHL1 plays an important role in the mechanism of pathological hypertrophy by sensing biomechanical stress responses via the N2B stretch sensor domain of titin and initiating changes in the titin- and MAPK-mediated responses important for sarcomere extensibility and intracellular signaling. These studies shed light on the physiological regulation of the sarcomere in response to hypertrophic stress.

Medical image analysis: progress over two decades and the challenges ahead

Abstract: The analysis of medical images has been woven into the fabric of the pattern analysis and machine intelligence (PAMI) community since the earliest days of these Transactions. Initially, the efforts in this area were seen as applying pattern analysis and computer vision techniques to another interesting dataset. However, over the last two to three decades, the unique nature of the problems presented within this area of study have led to the development of a new discipline in its own right. Examples of these include: the types of image information that are acquired, the fully three-dimensional image data, the nonrigid nature of object motion and deformation, and the statistical variation of both the underlying normal and abnormal ground truth. In this paper, we look at progress in the field over the last 20 years and suggest some of the challenges that remain for the years to come.

Growth Factors, Matrices, and Forces Combine and Control Stem Cells

Abstract: Stem cell fate is influenced by a number of factors and interactions that require robust control for safe and effective regeneration of functional tissue. Coordinated interactions with soluble factors, other cells, and extracellular matrices define a local biochemical and mechanical niche with complex and dynamic regulation that stem cells sense. Decellularized tissue matrices and synthetic polymer niches are being used in the clinic, and they are also beginning to clarify fundamental aspects of how stem cells contribute to homeostasis and repair, for example, at sites of fibrosis. Multifaceted technologies are increasingly required to produce and interrogate cells ex vivo, to build predictive models, and, ultimately, to enhance stem cell integration in vivo for therapeutic benefit.

Microfluidic organs-on-chips

Abstract: Organ-level physiology is recapitulated in vitro by culturing cells in perfused, microfluidic devices.

Regulation of cardiac hypertrophy by intracellular signalling pathways

Abstract: The mammalian heart is a dynamic organ that can grow and change to accommodate alterations in its workload. During development and in response to physiological stimuli or pathological insults, the heart undergoes hypertrophic enlargement, which is characterized by an increase in the size of individual cardiac myocytes. Recent findings in genetically modified animal models implicate important intermediate signal-transduction pathways in the coordination of heart growth following physiological and pathological stimulation.

IEEE transactions on pattern analysis and machine intelligence

Abstract: In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to: • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information. Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.