No Arabic abstract
Over the past few decades, in silico modeling of organ systems has significantly furthered our understanding of their physiology and biomechanical function. In this work, we present a detailed numerical model of the upper gastrointestinal (GI) tract that not only accounts for the fiber architecture of the muscle walls, but also the multiphasic components they help transport during normal digestive function. Construction details for 3D models of representative stomach geometry are presented along with a simple strategy for assigning circular and longitudinal muscle fiber orientations for each layer. Based on our previous work that created a fully resolved model of esophageal peristalsis, we extend the same principles to simulate gastric peristalsis by systematically activating muscle fibers embedded in the stomach. Following this, for the first time, we simulate gravity driven bolus emptying into the stomach due to density differences between ingested contents and fluid contents of the stomach. This detailed computational model of the upper gastrointestinal tract provides a foundation on which future models can be based that seek to investigate the biomechanics of acid reflux and probe various strategies for gastric bypass surgeries to address the growing problem of adult obesity.
Endoscopy is a routine imaging technique used for both diagnosis and minimally invasive surgical treatment. While the endoscopy video contains a wealth of information, tools to capture this information for the purpose of clinical reporting are rather poor. In date, endoscopists do not have any access to tools that enable them to browse the video data in an efficient and user friendly manner. Fast and reliable video retrieval methods could for example, allow them to review data from previous exams and therefore improve their ability to monitor disease progression. Deep learning provides new avenues of compressing and indexing video in an extremely efficient manner. In this study, we propose to use an autoencoder for efficient video compression and fast retrieval of video images. To boost the accuracy of video image retrieval and to address data variability like multi-modality and view-point changes, we propose the integration of a Siamese network. We demonstrate that our approach is competitive in retrieving images from 3 large scale videos of 3 different patients obtained against the query samples of their previous diagnosis. Quantitative validation shows that the combined approach yield an overall improvement of 5% and 8% over classical and variational autoencoders, respectively.
Balloon dilation catheters are often used to quantify the physiological state of peristaltic activity in tubular organs and comment on their ability to propel fluid which is important for healthy human function. To fully understand this systems behavior, we analyzed the effect of a solitary peristaltic wave on a fluid-filled elastic tube with closed ends. A reduced order model that predicts the resulting tube wall deformations, flow velocities and pressure variations is presented. This simplified model is compared with detailed fluid-structure 3D immersed boundary simulations of peristaltic pumping in tube walls made of hyperelastic material. The major dynamics observed in the 3D simulations were also displayed by our 1D model under laminar flow conditions. Using the 1D model, several pumping regimes were investigated and presented in the form of a regime map that summarizes the systems response for a range of physiological conditions. Finally, the amount of work done during a peristaltic event in this configuration was defined and quantified. The variation of elastic energy and work done during pumping was found to have a unique signature for each regime. An extension of the 1D model is applied to enhance patient data collected by the device and find the work done for a typical esophageal peristaltic wave. This detailed characterization of the systems behavior aids in better interpreting the clinical data obtained from dilation catheters. Additionally, the pumping capacity of the esophagus can be quantified for comparative studies between disease groups.
A hybrid parallel approach for fully resolved simulations of particle-laden flows in sediment transport is proposed. To overcome the challenges of load imbalance in the traditional domain decomposition method when encountering highly uneven distributions of particles in space, we develop a hybrid parallel approach adopting the domain decomposition method for the carrier phase and a mirror domain technique for the disperse phase. We modify the mirror domain technique originally developed for point particles to fully resolved particle simulations, which are more challenging since a finite-sized particle may be split into different subdomains; thus, more complex treatments of particle-fluid interactions are needed. By utilizing the mirror domain technique, in which each processor handles nearly the same number of particles regardless of the particle spatial distribution, excellent load balance is achieved. The present hybrid parallel approach also shows strong scalability and high parallel efficiency in a test of a fully resolved simulation case of sediment transport. Furthermore, a novel memory optimization method is proposed for spherical particles of equal size, which can substantially reduce the memory cost and enable the simulation of millions of fully resolved particles on a common highly parallel computing platform. Our code is validated by several benchmark cases, and the results show good agreement with experimental and computational data in the literature.
Microfluidic techniques have been extensively developed to realize micro-total analysis systems in a small chip. For microanalysis, electro-magnetic forces have generally been utilized for the trapping of objects, but hydrodynamics has been little explored despite its relevance to pattern formation. Here, we report that water-in-oil (W/O) droplets can be transported in the grid of an array of other large W/O droplets. As each droplet approaches an interspace of the large droplet array, while exhibiting persistent back-and-forth motion, it is conveyed at a velocity equal to the droplet array. We confirm the appearance of closed streamlines in a numerical simulation, suggesting that a vortex-like stream is involved in trapping the droplet. Furthermore, more than one droplet is also conveyed as an ordered cluster with dynamic reposition.
We employ the horizontal visibility algorithm to map the velocity and acceleration time series in turbulent flows with different Reynolds numbers, onto complex networks. The universal nature of velocity fluctuations in high Reynolds turbulent Helium flow is found to be inherited in the corresponding network topology. The degree distributions of the acceleration series are shown to have stretched exponential forms with the Reynolds number dependent fitting parameter. Furthermore, for acceleration time series, we find a transitional behavior in terms of the Reynolds number in all network features which is in agreement with recent empirical studies.