Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Action potentials (1)
- Adolescent (1)
- Biological tissues (1)
- Care and treatment (1)
- Classification (1)
-
- Collaborative local models (1)
- Computer simulation (1)
- Contraction (1)
- Convection (1)
- DOTO-2 (1)
- Data-driven global model (1)
- Deep learning (1)
- Diagnosis (1)
- Dielectrohoreisis (1)
- Diffusion (1)
- Distinct imaging features (1)
- ERG1 potassium channel - genetics (1)
- Electric fields (1)
- Electrocardiography (1)
- Electrodes (1)
- Epithelial cells (1)
- Evaluation (1)
- Exercise (1)
- Feature extraction (1)
- Federated learning (1)
- Female (1)
- Flow (1)
- Gene mutations (1)
- Genetic aspects (1)
- Global model shifts (1)
Articles 1 - 5 of 5
Full-Text Articles in Engineering
Identification And Characterization Of Two Novel Kcnh2 Mutations Contributing To Long Qt Syndrome, Anthony Owusu-Mensah, Jacqueline Treat, Joyce Bernardi, Ryan Pfeiffer, Robert Goodrow, Bright Tsevi, Victoria Lam, Michel Audette, Jonathan M. Cordeiro, Makarand Deo
Identification And Characterization Of Two Novel Kcnh2 Mutations Contributing To Long Qt Syndrome, Anthony Owusu-Mensah, Jacqueline Treat, Joyce Bernardi, Ryan Pfeiffer, Robert Goodrow, Bright Tsevi, Victoria Lam, Michel Audette, Jonathan M. Cordeiro, Makarand Deo
Electrical & Computer Engineering Faculty Publications
We identified two different inherited mutations in KCNH2 gene, or human ether-a-go-go related gene (hERG), which are linked to Long QT Syndrome. The first mutation was in a 1-day-old infant, whereas the second was in a 14-year-old girl. The two KCNH2 mutations were transiently transfected into either human embryonic kidney (HEK) cells or human induced pluripotent stem-cell derived cardiomyocytes. We performed associated multiscale computer simulations to elucidate the arrhythmogenic potentials of the KCNH2 mutations. Genetic screening of the first and second index patients revealed a heterozygous missense mutation in KCNH2, resulting in an amino acid change (P632L) in the …
Adversarial Training Based Domain Adaptation Of Skin Cancer Images, Syed Qasim Gilani, Muhammad Umair, Maryam Naqvi, Oge Marques, Hee-Cheol Kim
Adversarial Training Based Domain Adaptation Of Skin Cancer Images, Syed Qasim Gilani, Muhammad Umair, Maryam Naqvi, Oge Marques, Hee-Cheol Kim
Electrical & Computer Engineering Faculty Publications
Skin lesion datasets used in the research are highly imbalanced; Generative Adversarial Networks can generate synthetic skin lesion images to solve the class imbalance problem, but it can result in bias and domain shift. Domain shifts in skin lesion datasets can also occur if different instruments or imaging resolutions are used to capture skin lesion images. The deep learning models may not perform well in the presence of bias and domain shift in skin lesion datasets. This work presents a domain adaptation algorithm-based methodology for mitigating the effects of domain shift and bias in skin lesion datasets. Six experiments were …
Recent Progress In Microrna Detection Using Integrated Electric Fields And Optical Detection Methods, Logeeshan Velmanickam, Dharmakeerthi Nawarathna
Recent Progress In Microrna Detection Using Integrated Electric Fields And Optical Detection Methods, Logeeshan Velmanickam, Dharmakeerthi Nawarathna
Electrical & Computer Engineering Faculty Publications
Low-cost, highly-sensitivity, and minimally invasive tests for the detection and monitoring of life-threatening diseases and disorders can reduce the worldwide disease burden. Despite a number of interdisciplinary research efforts, there are still challenges remaining to be addressed, so clinically significant amounts of relevant biomarkers in body fluids can be detected with low assay cost, high sensitivity, and speed at point-of-care settings. Although the conventional proteomic technologies have shown promise, their ability to detect all levels of disease progression from early to advanced stages is limited to a limited number of diseases. One potential avenue for early diagnosis is microRNA (miRNA). …
Estimation Of Differential Pathlength Factor From Nirs Measurement In Skeletal Muscle, B. Koirala, A. Concas, A. Cincotti, Yi Sun, A. Hernández, M. L. Goodwin, L. B. Gladden, N. Lai
Estimation Of Differential Pathlength Factor From Nirs Measurement In Skeletal Muscle, B. Koirala, A. Concas, A. Cincotti, Yi Sun, A. Hernández, M. L. Goodwin, L. B. Gladden, N. Lai
Electrical & Computer Engineering Faculty Publications
The utilization of continuous wave (CW) near-infrared spectroscopy (NIRS) device to measure non-invasively muscle oxygenation in healthy and disease states is limited by the uncertainties related to the differential path length factor (DPF). DPF value is required to quantify oxygenated and deoxygenated heme groups' concentration changes from measurement of optical densities by NIRS. An integrated approach that combines animal and computational models of oxygen transport and utilization was used to estimate the DPF value in situ. The canine model of muscle oxidative metabolism allowed measurement of both venous oxygen content and tissue oxygenation by CW NIRS under different oxygen delivery …
Wavelet-Based Harmonization Of Local And Global Model Shifts In Federated Learning For Histopathological Images, W. Farzana, A. Temtam, K. M. Iftekharuddin
Wavelet-Based Harmonization Of Local And Global Model Shifts In Federated Learning For Histopathological Images, W. Farzana, A. Temtam, K. M. Iftekharuddin
Electrical & Computer Engineering Faculty Publications
Federated Learning (FL) is a promising machine learning approach for development of data-driven global model using collaborative local models across multiple local institutions. However, the heterogeneity of medical imaging data is one of the challenges within FL. This heterogeneity is caused by the variation in imaging scanner protocols across institutions, which may result in weight shift among local models leading to deterioration in predictive accuracy of global model. The prevailing approaches involve applying different FL averaging techniques to enhance the performance of the global model, ignoring the distinct imaging features of the local domain. In this work, we address both …