Browsing by Author "Kelley, Patrick"

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    Automated Fovea Detection Based on Unsupervised Retinal Vessel Segmentation Method
    (IEEE, 2017-10) Tavakoli, Meysam; Kelley, Patrick; Nazar, Mahdieh; Kalantari, Faraz; Physics, School of Science
    The Computer Assisted Diagnosis systems could save workloads and give objective diagnostic to ophthalmologists. At first level of automated screening of systems feature extraction is the fundamental step. One of these retinal features is the fovea. The fovea is a small fossa on the fundus, which is represented by a deep-red or red-brown color in color retinal images. By observing retinal images, it appears that the main vessels diverge from the optic nerve head and follow a specific course that can be geometrically modeled as a parabola, with a common vertex inside the optic nerve head and the fovea located along the apex of this parabola curve. Therefore, based on this assumption, the main retinal blood vessels are segmented and fitted to a parabolic model. With respect to the core vascular structure, we can thus detect fovea in the fundus images. For the vessel segmentation, our algorithm addresses the image locally where homogeneity of features is more likely to occur. The algorithm is composed of 4 steps: multi-overlapping windows, local Radon transform, vessel validation, and parabolic fitting. In order to extract blood vessels, sub-vessels should be extracted in local windows. The high contrast between blood vessels and image background in the images cause the vessels to be associated with peaks in the Radon space. The largest vessels, using a high threshold of the Radon transform, determines the main course or overall configuration of the blood vessels which when fitted to a parabola, leads to the future localization of the fovea. In effect, with an accurate fit, the fovea normally lies along the slope joining the vertex and the focus. The darkest region along this line is the indicative of the fovea. To evaluate our method, we used 220 fundus images from a rural database (MUMS-DB) and one public one (DRIVE). The results show that, among 20 images of the first public database (DRIVE) we detected fovea in 85% of them. Also for the MUMS-DB database among 200 images we detect fovea correctly in 83% on them.
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    Docosahexaenoic Acid Controls Pulmonary Macrophage Lipid Raft Size and Inflammation
    (Elsevier, 2024) Pennington, Edward Ross; Virk, Rafia; Bridges, Meagan D.; Bathon, Brooke E.; Beatty, Nari; Gray, Rosemary S.; Kelley, Patrick; Wassall, Stephen R.; Manke, Jonathan; Armstrong, Michael; Reisdorph, Nichole; Vanduinen, Rachel; Fenton, Jenifer I.; Gowdy, Kymberly M.; Shaikh, Saame Raza; Physics, School of Science
    Background: Docosahexaenoic acid (DHA) controls the biophysical organization of plasma membrane sphingolipid/cholesterol-enriched lipid rafts to exert anti-inflammatory effects, particularly in lymphocytes. However, the impact of DHA on the spatial arrangement of alveolar macrophage lipid rafts and inflammation is unknown. Objectives: The primary objective was to determine how DHA controls lipid raft organization and function of alveolar macrophages. As proof-of-concept, we also investigated DHA's anti-inflammatory effects on select pulmonary inflammatory markers with a murine influenza model. Methods: MH-S cells, an alveolar macrophage line, were treated with 50 μM DHA or vehicle control and were used to study plasma membrane molecular organization with fluorescence-based methods. Biomimetic membranes and coarse grain molecular dynamic (MD) simulations were employed to investigate how DHA mechanistically controls lipid raft size. qRT-PCR, mass spectrometry, and ELISAs were used to quantify downstream inflammatory signaling transcripts, oxylipins, and cytokines, respectively. Lungs from DHA-fed influenza-infected mice were analyzed for specific inflammatory markers. Results: DHA increased the size of lipid rafts while decreasing the molecular packing of the MH-S plasma membrane. Adding a DHA-containing phospholipid to a biomimetic lipid raft-containing membrane led to condensing, which was reversed with the removal of cholesterol. MD simulations revealed DHA nucleated lipid rafts by driving cholesterol and sphingomyelin into rafts. Downstream of the plasma membrane, DHA lowered the concentration of select inflammatory transcripts, oxylipins, and IL-6 secretion. DHA lowered pulmonary Il6 and Tnf-α mRNA expression and increased anti-inflammatory oxylipins of influenza-infected mice. Conclusions: The data suggest a model in which the localization of DHA acyl chains to nonrafts is driving sphingomyelin and cholesterol molecules into larger lipid rafts, which may serve as a trigger to impede signaling and lower inflammation. These findings also identify alveolar macrophages as a target of DHA and underscore the anti-inflammatory properties of DHA for lung inflammation.
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    Text Mining Online Discussions in an Introductory Physics Course
    (2018) Kelley, Patrick; Gavrin, Andrew; Lindell, Rebecca S.; Physics, School of Science
    We implemented a social networking platform called Course Networking (CN) in IUPUI’s introductory calculus based mechanics course and recorded three semesters of online discussions. We used the Syuzhet package in R to evaluate sentiment in the recorded discussions, and to quantify the incidence of eight basic emotions: anger, anticipation, disgust, fear, joy, sadness, surprise, and trust. We applied this text mining method to over nine thousand posts and replies to identify and analyze student sentiment during three semesters. We also investigated the variation of these emotions throughout the semester, the role played by the most vocal students as compared to the least frequent posters, and gender differences. With an abundance of students’ online discussions, text mining offers an expedient and automated means of analysis, providing a new window into students thinking and emotional state during semester-long physics courses