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Full-Text Articles in Engineering
Data-Driven And Cell-Specific Determination Of Nuclei-Associated Actin Structure, Nina Nikitina, Nurbanu Bursa, Matthew Goelzer, Madison Goldfeldt, Chase Crandall, Sean Howard, Janet Rubin, Anamaria Zavala, Aykut Satici, Gunes Uzer
Data-Driven And Cell-Specific Determination Of Nuclei-Associated Actin Structure, Nina Nikitina, Nurbanu Bursa, Matthew Goelzer, Madison Goldfeldt, Chase Crandall, Sean Howard, Janet Rubin, Anamaria Zavala, Aykut Satici, Gunes Uzer
Mechanical and Biomedical Engineering Faculty Publications and Presentations
Quantitative volumetric assessment of filamentous actin (F-actin) fibers remains challenging due to their interconnected nature, leading researchers to utilize threshold-based or qualitative measurement methods with poor reproducibility. Herein, a novel machine learning-based methodology is introduced for accurate quantification and reconstruction of nuclei-associated F-actin. Utilizing a convolutional neural network (CNN), actin filaments and nuclei from 3D confocal microscopy images are segmented and then each fiber is reconstructed by connecting intersecting contours on cross-sectional slices. This allows measurement of the total number of actin filaments and individual actin filament length and volume in a reproducible fashion. Focusing on the role of F-actin …
Instantaneous Generation Of Subject-Specific Finite Element Models Of The Hip Capsule, Ahilan Anantha-Krishnan, Casey A. Myers, Clare K. Fitzpatrick, Chadd W. Clary
Instantaneous Generation Of Subject-Specific Finite Element Models Of The Hip Capsule, Ahilan Anantha-Krishnan, Casey A. Myers, Clare K. Fitzpatrick, Chadd W. Clary
Mechanical and Biomedical Engineering Faculty Publications and Presentations
Subject-specific hip capsule models could offer insights into impingement and dislocation risk when coupled with computer-aided surgery, but model calibration is time-consuming using traditional techniques. This study developed a framework for instantaneously generating subject-specific finite element (FE) capsule representations from regression models trained with a probabilistic approach. A validated FE model of the implanted hip capsule was evaluated probabilistically to generate a training dataset relating capsule geometry and material properties to hip laxity. Multivariate regression models were trained using 90% of trials to predict capsule properties based on hip laxity and attachment site information. The regression models were validated using …