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Articles 1 - 8 of 8
Full-Text Articles in Engineering
Evaluation Of A Novel Non-Penetrating Electrode For Use In Dna Vaccination, Amy Donate, Domenico Coppola, Yolmari Cruz, Richard Heller
Evaluation Of A Novel Non-Penetrating Electrode For Use In Dna Vaccination, Amy Donate, Domenico Coppola, Yolmari Cruz, Richard Heller
Bioelectrics Publications
Current progress in the development of vaccines has decreased the incidence of fatal and non-fatal infections and increased longevity. However, new technologies need to be developed to combat an emerging generation of infectious diseases. DNA vaccination has been demonstrated to have great potential for use with a wide variety of diseases. Alone, this technology does not generate a significant immune response for vaccination, but combined with delivery by electroporation (EP), can enhance plasmid expression and immunity. Most EP systems, while effective, can be invasive and painful making them less desirable for use in vaccination. Our lab recently developed a non-invasive …
Eeg Artifact Removal Using A Wavelet Neural Network, Hoang-Anh T. Nguyen, John Musson, Jiang Li, Frederick Mckenzie, Guangfan Zhang, Roger Xu, Carl Richey, Tom Schnell, Thomas E. Pinelli (Ed.)
Eeg Artifact Removal Using A Wavelet Neural Network, Hoang-Anh T. Nguyen, John Musson, Jiang Li, Frederick Mckenzie, Guangfan Zhang, Roger Xu, Carl Richey, Tom Schnell, Thomas E. Pinelli (Ed.)
Electrical & Computer Engineering Faculty Publications
In this paper we developed a wavelet neural network. (WNN) algorithm for Electroencephalogram (EEG) artifact removal without electrooculographic (EOG) recordings. The algorithm combines the universal approximation characteristics of neural network and the time/frequency property of wavelet. We compared the WNN algorithm with the ICA technique and a wavelet thresholding method, which was realized by using the Stein's unbiased risk estimate (SURE) with an adaptive gradient-based optimal threshold. Experimental results on a driving test data set show that WNN can remove EEG artifacts effectively without diminishing useful EEG information even for very noisy data.
Nanosecond Pulsed Electric Field Induced Cytoskeleton, Nuclear Membrane And Telomere Damage Adversely Impact Cell Survival, Michael W. Stacey, P. Fox, S. Buescher, Juergen F. Kolb
Nanosecond Pulsed Electric Field Induced Cytoskeleton, Nuclear Membrane And Telomere Damage Adversely Impact Cell Survival, Michael W. Stacey, P. Fox, S. Buescher, Juergen F. Kolb
Bioelectrics Publications
We investigated the effects of nanosecond pulsed electric fields (nsPEF) on three human cell lines and demonstrated cell shrinkage, breakdown of the cytoskeleton, nuclear membrane and chromosomal telomere damage. There was a differential response between cell types coinciding with cell survival. Jurkat cells showed cytoskeleton, nuclear membrane and telomere damage that severely impacted cell survival compared to two adherent cell lines. Interestingly, disruption of the actin cytoskeleton in adherent cells prior to nsPEF exposure significantly reduced cell survival. We conclude that nsPEF applications are able to induce damage to the cytoskeleton and nuclear membrane. Telomere sequences, regions that tether and …
Nanosecond Pulse Electrical Fields Used In Conjunction With Multi-Wall Carbon Nanotubes As A Potential Tumor Treatment, Michael W. Stacey, Christopher Osgood, Bhargava Subhash Kalluri, Wei Cao, Hani Elsayed-Ali, Tarek Abdel-Fattah
Nanosecond Pulse Electrical Fields Used In Conjunction With Multi-Wall Carbon Nanotubes As A Potential Tumor Treatment, Michael W. Stacey, Christopher Osgood, Bhargava Subhash Kalluri, Wei Cao, Hani Elsayed-Ali, Tarek Abdel-Fattah
Bioelectrics Publications
The objectives of this communication were to fabricate pure samples of multi-walled carbon nanotubes (MWCNTs) and to determine their toxicity in tumor cell lines. MWCNTs were dispersed in a concentration of the surfactant T80 that was minimally toxic. Cell-type variation in toxicity to MWCNTs was observed but was not significantly different to unexposed controls. Additionally, we investigated the increased cell killing of the pancreatic cancer cell line PANC1 when exposed to ultrashort (nanosecond) pulsed electrical fields (nsPEF) in the presence of MWCNTs as a potential form of cancer therapy. We hypothesized that the unique electronic properties of MWCNTs disrupt cell …
An Integrated Computer-Aided Robotic System For Dental Implantation, Xiaoyan Sun, Yongki Yoon, Jiang Li, Frederic D. Mckenzie
An Integrated Computer-Aided Robotic System For Dental Implantation, Xiaoyan Sun, Yongki Yoon, Jiang Li, Frederic D. Mckenzie
Electrical & Computer Engineering Faculty Publications
This paper describes an integrated system for dental implantation including both preoperative planning utilizing computer-aided technology and automatic robot operation during the intra-operative stage. A novel two-step registration procedure was applied for transforming the preoperative plan to the operation of the robot, with the help of a Coordinate Measurement Machine (CMM). Experiments with a patient-specific phantom were carried out to evaluate the registration error for both position and orientation. After adopting several improvements, registration accuracy of the system was significantly improved. Sub-millimeter accuracy with the Target Registration Errors (TREs) of 0.38±0.16 mm (N=5) was achieved. The target orientation errors after …
Histogram Analysis Of Adc In Brain Tumor Patients, Debrup Banerjee, Jihong Wang, Jiang Li, Norbert J. Pelc (Ed.), Ehsan Samei (Ed.), Robert M. Nishikawa (Ed.)
Histogram Analysis Of Adc In Brain Tumor Patients, Debrup Banerjee, Jihong Wang, Jiang Li, Norbert J. Pelc (Ed.), Ehsan Samei (Ed.), Robert M. Nishikawa (Ed.)
Electrical & Computer Engineering Faculty Publications
At various stage of progression, most brain tumors are not homogenous. In this presentation, we retrospectively studied the distribution of ADC values inside tumor volume during the course of tumor treatment and progression for a selective group of patients who underwent an anti-VEGF trial. Complete MRI studies were obtained for this selected group of patients including pre- and multiple follow-up, post-treatment imaging studies. In each MRI imaging study, multiple scan series were obtained as a standard protocol which includes T1, T2, T1-post contrast, FLAIR and DTI derived images (ADC, FA etc.) for each visit. All scan series (T1, T2, FLAIR, …
Bcc Skin Cancer Diagnosis Based On Texture Analysis Techniques, Shao-Hui Chuang, Xiaoyan Sun, Wen-Yu Chang, Gwo-Shing Chen, Adam Huang, Jiang Li, Frederic D. Mckenzie
Bcc Skin Cancer Diagnosis Based On Texture Analysis Techniques, Shao-Hui Chuang, Xiaoyan Sun, Wen-Yu Chang, Gwo-Shing Chen, Adam Huang, Jiang Li, Frederic D. Mckenzie
Electrical & Computer Engineering Faculty Publications
In this paper, we present a texture analysis based method for diagnosing the Basal Cell Carcinoma (BCC) skin cancer using optical images taken from the suspicious skin regions. We first extracted the Run Length Matrix and Haralick texture features from the images and used a feature selection algorithm to identify the most effective feature set for the diagnosis. We then utilized a Multi-Layer Perceptron (MLP) classifier to classify the images to BCC or normal cases. Experiments showed that detecting BCC cancer based on optical images is feasible. The best sensitivity and specificity we achieved on our data set were 94% …
Prediction Of Brain Tumor Progression Using Multiple Histogram Matched Mri Scans, Debrup Banerjee, Loc Tran, Jiang Li, Yuzhong Shen, Frederic Mckenzie, Jihong Wang, Ronald M. Summers (Ed.), Bram Van Ginneken (Ed.)
Prediction Of Brain Tumor Progression Using Multiple Histogram Matched Mri Scans, Debrup Banerjee, Loc Tran, Jiang Li, Yuzhong Shen, Frederic Mckenzie, Jihong Wang, Ronald M. Summers (Ed.), Bram Van Ginneken (Ed.)
Electrical & Computer Engineering Faculty Publications
In a recent study [1], we investigated the feasibility of predicting brain tumor progression based on multiple MRI series and we tested our methods on seven patients' MRI images scanned at three consecutive visits A, B and C. Experimental results showed that it is feasible to predict tumor progression from visit A to visit C using a model trained by the information from visit A to visit B. However, the trained model failed when we tried to predict tumor progression from visit B to visit C, though it is clinically more important. Upon a closer look at the MRI scans …