Promoting Partnership, Cultivating Colleagueship: The SUMMIT-P Project at Norfolk State University

Norfolk State University (NSU) is the only public Historically Black College and University (HBCU) member institution in SUMMIT-P. At NSU, a strong collaboration between the Department of Mathematics and its partner discipline, the Department of Engineering, has been established for the Calculus I and Differential Equations classes as part of the SUMMIT-P project. In this paper, we record a brief history of this collaboration project at NSU, the various structures within the SUMMIT-P Project, the site visit that occurred in Spring 2019, and how recent activities helped guide the direction of the project at NSU

Non-Contact Trapping and Stretching of Biological Cells Using Dual-Beam Optical Stretcher On Microfluidic Platform

Optical stretcher is a tool in which two counter-propagating, slightly diverging, and identical laser beams are used to trap and axially stretch microparticles in the path of light. In this work, we utilized the dual-beam optical stretcher setup to trap and stretch human embryonic kidney (HEK) cells and mammalian breast cancer (MBC) cells. Experiments were performed by exposing the HEK cells to counter-propagating laser beams for 30 seconds at powers ranging from 100 mW to 561 mW. It was observed that the percentage of cell deformation increased from 16.7% at 100 mW to 40.5% at 561 mW optical power. The MBC cells exhibited significantly higher cell stretching compared to HEK cells at the same power (80 mW). Moreover, the minimum trapping power in HEK cells was 80.5mW as compared to 65.2mW in MBC cells. This study provides useful insights into the characterization of cytoskeletal elasticity in different cell types based on non-contact optical cell stretching

Modelling the role of the purkinje system in cardiac arrhythmias

Bibliography: p. 157-177Some pages are in colour

Calcium Dynamics and Cardiac Arrhythmia

This Special Collection will gather all studies highlighting recent advances in theoretical and experimental studies of arrhythmia, with a specific focus on research seeking to elucidate links between calcium homeostasis in cardiac cells and organ-scale disruption of heart rhythm

Gabor Filter-Embedded U-Net with Transformer-Based Encoding for Biomedical Image Segmentation

Medical image segmentation involves a process of categorization of target regions that are typically varied in terms of shape, orientation and scales. This requires highly accurate algorithms as marginal segmentation errors in medical images may lead to inaccurate diagnosis in subsequent procedures. The U-Net framework has become one of the dominant deep neural network architectures for medical image segmentation. Due to complex and irregular shape of objects involved in medical images, robust feature representations that correspond to various spatial transformations are key to achieve successful results. Although U-Net-based deep architectures can perform feature extraction and localization, the design of specialized architectures or layer modifications is often an intricate task. In this paper, we propose an effective solution to this problem by introducing Gabor filter banks into the U-Net encoder, which has not yet been well explored in existing U-Net-based segmentation frameworks. In addition, global self-attention mechanisms and Transformer layers are also incorporated into the U-Net framework to capture global contexts. Through extensive testing on two benchmark datasets, we show that the Gabor filter-embedded U-Net with Transformer encoders can enhance the robustness of deep-learned features, and thus achieve a more competitive performance

Gabor Filter-Embedded U-Net with Transformer-Based Encoding for Biomedical Image Segmentation

Medical image segmentation involves a process of categorization of target regions that are typically varied in terms of shape, orientation and scales. This requires highly accurate algorithms as marginal segmentation errors in medical images may lead to inaccurate diagnosis in subsequent procedures. The U-Net framework has become one of the dominant deep neural network architectures for medical image segmentation. Due to complex and irregular shape of objects involved in medical images, robust feature representations that correspond to various spatial transformations are key to achieve successful results. Although U-Net-based deep architectures can perform feature extraction and localization, the design of specialized architectures or layer modifications is often an intricate task. In this paper, we propose an effective solution to this problem by introducing Gabor filter banks into the U-Net encoder, which has not yet been well explored in existing U-Net-based segmentation frameworks. In addition, global self-attention mechanisms and Transformer layers are also incorporated into the U-Net framework to capture global contexts. Through extensive testing on two benchmark datasets, we show that the Gabor filter-embedded U-Net with Transformer encoders can enhance the robustness of deep-learned features, and thus achieve a more competitive performance

Frequency Dependency of Pacing Determinants of an IK1-mediated Rotor Drift in the Posterior Left Atrial Wall toward the Pulmonary Veins

Abstract Maintenance of paroxysmal atrial fibrillation (AF) by fast rotors near or at the pulmonary veins (PV

Automated segmentation and reconstruction of patient-specific cardiac anatomy and pathology from in vivo MRI

This paper presents an automated method to segment left ventricle (LV) tissues from functional and delayed-enhancement (DE) cardiac magnetic resonance imaging (MRI) scans using a sequential multi-step approach. First, a region of interest (ROI) is computed to create a subvolume around the LV using morphological operations and image arithmetic. From the subvolume, the myocardial contours are automatically delineated using difference of Gaussians (DoG) filters and GSV snakes. These contours are used as a mask to identify pathological tissues, such as fibrosis or scar, within the DE-MRI. The presented automated technique is able to accurately delineate the myocardium and identify the pathological tissue in patient sets. The results were validated by two expert cardiologists, and in one set the automated results are quantitatively and qualitatively compared with expert manual delineation. Furthermore, the method is patient-specific, performed on an entire patient MRI series. Thus, in addition to providing a quick analysis of individual MRI scans, the fully automated segmentation method is used for effectively tagging regions in order to reconstruct computerized patient-specific 3D cardiac models. These models can then be used in electrophysiological studies and surgical strategy planning.1.435 JCR (2012) Q1, 21/90 Engineering, multidisciplinary; Q2, 21/57 Instruments & instrumentatio

Mechanisms by Which Ranolazine Terminates Paroxysmal but Not Persistent Atrial Fibrillation

BACKGROUND: Ranolazine inhibits Na+ current (INa), but whether it can convert atrial fibrillation (AF) to sinus rhythm remains unclear. We investigated antiarrhythmic mechanisms of ranolazine in sheep models of paroxysmal (PxAF) and persistent AF (PsAF). METHODS: PxAF was maintained during acute stretch (N=8), and PsAF was induced by long-term atrial tachypacing (N=9). Isolated, Langendorff-perfused sheep hearts were optically mapped. RESULTS: In PxAF ranolazine (10 μmol/L) reduced dominant frequency from 8.3±0.4 to 6.2±0.5 Hz (P<0.01) before converting to sinus rhythm, decreased singularity point density from 0.070±0.007 to 0.039±0.005 cm-2 s-1 (P<0.001) in left atrial epicardium (LAepi), and prolonged AF cycle length (AFCL); rotor duration, tip trajectory, and variance of AFCL were unaltered. In PsAF, ranolazine reduced dominant frequency (8.3±0.5 to 6.5±0.4 Hz; P<0.01), prolonged AFCL, increased the variance of AFCL, had no effect on singularity point density (0.048±0.011 to 0.042±0.016 cm-2 s-1; P=ns) and failed to convert AF to sinus rhythm. Doubling the ranolazine concentration (20 μmol/L) or supplementing with dofetilide (1 μmol/L) failed to convert PsAF to sinus rhythm. In computer simulations of rotors, reducing INa decreased dominant frequency, increased tip meandering and produced vortex shedding on wave interaction with unexcitable regions. CONCLUSIONS: PxAF and PsAF respond differently to ranolazine. Cardioversion in the former can be attributed partly to decreased dominant frequency and singularity point density, and prolongation of AFCL. In the latter, increased dispersion of AFCL and likely vortex shedding contributes to rotor formation, compensating for any rotor loss, and may underlie the inefficacy of ranolazine to terminate PsAF.This work was supported by: grants from the National Institutes of Health National Heart, Lung, and Blood Institute R01-HL118304 (Dr Berenfeld) and R01-HL122352 (Dr Jalife); the Leducq Foundation (Drs Jalife, Berenfeld, and Pandit); the University of Michigan Health System and Peking University Health Sciences Center Joint Institute for Translational and Clinical Research (Dr Jalife); Ministerio de Economia y Competitividad and Fondo Europeo de Desarrollo Regional (FEDER; Dr Jalife); the JHRS fellowship program from Medtronic Japan, Uehara Memorial Foundation (Dr Takemoto); American Heart Association postdoctoral fellowship (Dr Takemoto); research grants from Gilead Sciences Inc (Drs Jalife and Pandit); and research support and assistance with implantable devices from St Jude Medical and Medtronic Inc (Drs Jalife and Berenfeld).S