Browsing by Author "Zouridakis, George"
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Item A Feasible Method to Evaluate RF-Induced Heating Behavior of Passive Orthopedic Implants in Human Body(2019-05) Guo, Ran; Chen, Ji; Zouridakis, George; Tsekos, Nikolaos V.; Jackson, David R.; Kainz, WolfgangMagnetic resonance imaging (MRI) radio frequency (RF) -induced heating is one of the most important concerns of MRI safety for patients with implantable medical devices. Due to the difficulties of performing direct study of the RF-induced heating in human body for an implantable medical device, the current standard method is to investigate the RF-induced heating in a standard and fully controlled phantom, which is filled with a homogeneous media to mimic the human body tissues. However, the RF-induced heating in a homogeneous regular shaped media is different from that in a heterogeneous human body, especially patients with orthopedic healthcare products. Numerical studies were already conducted to illustrate the difference of RF-induced heating on medical plates between phantom and human body. It is necessary to study the intrinsic mechanism of RF-induced heating in heterogeneous human body with passive implantable medical device and evaluate it with a feasible accurate method. Numerical modeling and simulations are conducted to study the RF-induced heating for typical orthopedic implants, such bone plates, hip prosthesis, and tibia intramedullary nails, in 1.5T and 3T magnetic resonance (MR) environment. Comparison results of RF-induced heating between phantom and human body are conducted to show the disadvantages of phantom. In order to study the mechanism of RF-induced heating for passive orthopedic implants, Huygens source and heterogeneous phantom are applied which could illustrate the effect of incident field and medium, respectively. Additionally, a homogeneous human-shape phantom is applied to study the RF-induced heating for each orthopedic implant. The effect of medium electric properties on the incident electric field distribution inside phantom structure are investigated. And local low lossy medium is added to mimic the effect of human bone tissue on RF-induced heating. Based on these results and analysis, a new phantom structure is proposed to properly evaluate the RF-induced heating behavior of passive orthopedic implants. Compared to traditional ASTM phantom method, the new phantom structure could achieve the exact RF-induced heating properties for passive orthopedic implants.Item A Genomic and Functional Analysis of Bacterial Diversity in Agricultural Soil for Chlorpyrifos Biodegradation(2018-12) Islam, Nelufa Yesmin; Iyer, Rupa; Ganapathy, Sivakumar; Cai, Chengzhi; Shireen, Wajiha; Zouridakis, GeorgeChlorpyrifos (CP) is a widely used organophosphate (OP) insecticide and a potent environemntal neurotoxin. This research project focuses on the potential of bacteria, both native to agricultural soil and part of a designed consortium composed of Iyer laboratory strains, to completely degrade CP and its toxic byproducts in different types of agricultural soil. Sequence data from isolated agricultural microorganisms was analyzed using the RAST (Rapid Annotation using Subsystem Technology) server to identify putative CP degradation biomarkers. Metabolite production and degradation kinetics analysis gas chromatography mass spectrophotometry (GCMS) analysis was conducted on each soil sample and compared to soil spiked with different combinations of bacterial consortia over a period of 7 days to determine the effectiveness of CP degradation in non-augmented and augmented soil. Genomic analysis of ranch, garden and crop soil microorganisms revealed multiple CP degradation biomarkers including a family of diverse OPHC2-like metallo-β-lactamase (MBL) enzymes, and 3-oxoadipate enol-lactonases. GCMS analysis of these soil samples inoculated with CP support putative microbial degradation activity show that 4 CP metabolites are consistently released including 3,5,6-trichloropyridinol (TCP), phosphorothioic acid, fumaric acid and ethanol. Non-augmented ranch soil and crop field soil display a greater degradation capacity than garden soil possibly due to greater CP pesticide exposure at these sites. Overall, degradation kinetics for augmented and non-augmented soil samples was 0.79d-1 and 0.19d-1 and half-life 1.03 and 5.45 days respectively. CP inoculated soil spiked with a bacterial consortium consisting of all 3 strains exhibited the highest degradation rate with 78.55% of CP degraded after 48 hours. The outcome of this study suggests that while native agricultural populations are capable of low-level CP degradation, supplementing contaminated soil with a bacterial consortium consisting of Pseudomonas putida, Ochrobactrum anthropi and Rhizobium radiobacter could be a highly effective and safe biological approach to facilitating rapid CP degradation.Item A Statistical Approach to Visual Masking and Spatial Attention(2015-12) Agaoglu, Sevda; Ogmen, Haluk; Breitmeyer, Bruno G.; Jansen, Ben H.; Sheth, Bhavin R.; Zouridakis, GeorgeA stimulus (mask) reduces the visibility of another stimulus (target) when they are presented in close spatio-temporal vicinity of each other, a phenomenon called visual masking. Visual masking has been extensively studied to understand the dynamics of information processing in the visual system. Visual spatial attention is also known to modulate information processing and transfer within the visual system. Since both processes control the transfer of information from sensory memory to visual short-term memory (VSTM), a natural question is whether these processes interact or operate independently. Here, we modeled visual masking by using a statistical framework, and used this theoretical framework along with psychophysical experiments to determine whether and how masking and attention interact. In a psychophysical experiment, observers were asked to report the orientation of a target bar under three different masking paradigms. The distribution of response errors was modeled by using statistical mixture-models. Our results show that in all three types of masking, the reduction of a target’s signal-to-noise ratio (SNR) was the primary process whereby masking occurred. We interpret these findings as the mask reducing the target’s SNR (i) by suppressing or interrupting the signal of the target in para-/meta- contrast, (ii) by increasing noise in pattern masking by noise, and (iii) a combination of the two in pattern masking by structure. Recent evidence suggests that the studies that reported interactions between masking and attention suffered from ceiling and/or floor effects. We investigated interactions between metacontrast masking and attention by using an experimental design in which saturation effects were avoided. In these experiments, attention was controlled either by set-size or by spatial pre-cues. We examined attention-masking interactions based on two types of dependent-variables: (i) the mean absolute response errors and (ii) the distribution of signed response errors. Our results show that both the voluntary (endogenous) and reflexive (exogenous) mechanisms of attention affect observers’ performance without interacting with masking. Statistical modeling of response errors suggests that attention and metacontrast masking exert their effects mainly through independent modulations of the guessing component of the mixture model. Taken together, our results suggest that visual masking and attention operate independently.Item Advanced Electromagnetic Numerical Modeling Techniques for Various Periodic and Quasi-Periodic Systems(2014-12) Guo, Xichen; Chen, Ji; Jackson, David R.; Wilton, Donald R.; Zouridakis, George; Benhaddou, Driss; Onofrei, DanielThis dissertation is mainly concerned with several advanced electromagnetic modeling techniques for practical complex systems, which involve periodic analyses. The focus is to reveal the physics of the electromagnetic wave interaction with the complex structures, and also to arrive at improved computational algorithms. This dissertation consists of three self-contained parts, each discussing one modeling technique. Examples presented in this dissertation include (a) an analysis of conductor surface-roughness effects, (b) a novel model for vertical interconnects (vias) and (c) a leaky-wave study of a Fabry-Perot resonant cavity antenna. The first part investigates conductor surface roughness effects for stripline. An equivalent rough-surface-impedance is extracted using a periodic full-wave analysis and is then used for the modification of the transmission line per-unit-length parameter. The second part proposes a semi-analytical analysis for massively-coupled vias with arbitrarily-shaped antipads, based on the reciprocity theorem. The use of reciprocity yields simple design formulas and is seen to greatly improve the computational efficiency, due to the fast-converging mode-matching calculation. The third part presents a leaky-wave study of a Fabry-Perot cavity antenna made from a patch array. The patch current densities are calculated using the array scanning method. Based on this, a "leaky-wave current" is defined and calculated using residue integration. In addition, the radiation properties of a large finite-size array (truncation effects) are evaluated. All three proposed models are verified by full-wave simulations and/or measurements. Numerical results prove the effectiveness and accuracy of these models.Item An Efficient Hair Removal Algorithm for Skin Lesion Images(2017) Pan, Rangeet; Zouridakis, GeorgeItem Assessing Recovery of Mild Traumatic Brain Injury Patients Using Diffusion Tensor Imaging(2016-12) Mvula, Ngemba Esther; Zouridakis, George; Pollonini, Luca; Lancaster, KeithIn this study we investigated whether Diffusion Tensor Imaging (DTI) could be used to assess recovery in patients with mild traumatic brain injury (mTBI). Thirteen acute mTBI patients 18-50 years of age and seven age- and sex-matched controls with no head injury were recruited from the emergency department of Huntington Memorial Hospital in Pasadena, CA. Images were acquired on three different visits, two weeks and four weeks, respectively, after the first recording, using a 3.0 T. Image distortions, resulting from susceptibility-induced and by eddy current-induced off-resonance fields, were corrected using routines from the software package FSL. An affine linear registration routine part of FSL was also used to align the 32 images to the reference image. For each DTI dataset, diffusion Fractional Anisotropy (FA), Mean Diffusivity (MD) or Apparent Diffusion Coefficient (ADC), and probabilistic tractography were estimated using FSL and the software package MedInria, with an FA threshold of 200, a minimum length for the detected fibers of 20 mm, and volume sampling every 5 voxels. To perform a quantitative analysis across the two groups, we first used the Johns Hopkins University tractography atlas to define 20 regions of interest (ROI), and the scans from the control subjects to create a reference database that included the mean and standard deviation values in each ROI. Then we computed z-scores for each subject’s data and compared the groups using MANOVA with p value set at 0.05, corrected for multiple comparisons, considering group and visit as the independent variables.Item Asymmetry Measures for Automated Melanoma Detection in Dermoscopic Images(2018-05) Lancaster, Keith C.; Zouridakis, George; Chen, Ji; Jansen, Ben H.; Glover, John R.; Yuan, XiaojingDermoscopic rules such as the ABCD and Menzies rules are employed by dermatologists to determine the likelihood that a suspicious lesion is cancerous. This dissertation focuses on the improvement of automated melanoma recognition systems that implement these rules, specifically by enhancing the ability of these systems to recognize lesion asymmetry, a significant indicator of melanoma. Two approaches are proposed for asymmetry classification. The first utilizes the irregularity of the outer contour of the lesion combined with measures that compare quadrants of the lesion with respect to area, color, and melanin content. The second method uses size theory as the basis for determining asymmetry. In this approach, measuring functions are employed to expose relevant characteristics of the lesion. The one-dimensional measuring functions are mapped into size functions in R 2 and compared using the bottleneck distance. The distances are used as features for classification. Annotated dermoscopic images were used to train classifiers for both methods. Classification rates were competitive with other approaches for both methods independently, with the combined method exhibiting 95% accuracy. Additionally, decision fusion strategies were investigated as a means of combining the results from individual melanoma classifiers using the asymmetry methods developed in this study. The best approach showed 100% sensitivity and 64% specificity, exceeding the performance of the individual classifiers. Finally, a software framework for the development of medical applications is presented. This framework attempts to provide biomedical researchers with a simplified approach to creating mobile applications for medical processing.Item Automatic Recognition of Buruli Ulcer Images on Smart Handheld Devices(2013-08) Hu, Rui 1984-; Zouridakis, George; Jansen, Ben H.; Chen, Ji; Shih, Wei-Chuan; Merchant, Fatima AzizBuruli ulcer (BU) disease is a devastating flesh eating bacterial infection that each year affects thousands of people in tropical and subtropical regions. Clinically, BU usually starts as a painless subcutaneous nodule, or as other pre-ulcer forms including papule, plaque, and edema; then it evolves into a painless ulcer, and finally leads to extensive scarring, contractures, and deformations with possible total loss of articulation function. However, treatment with antibiotics at an early stage has been very successful in preventing irreversible deformity and long-term functional disability. Therefore, an easily implemented and rapid diagnostic test that can detect the early stages of BU is of high research priority. In this dissertation, a multistage computerized image-analysis system is proposed that can automatically detect BU in multispectral dermoscopic images. The specific contributions of this work focus on the development of techniques for precise lesion segmentation, efficient image sampling for feature extraction, and skin image modeling for accurate lesion classification. For lesion segmentation, a new method is proposed based on fusion of the partial masks derived from separate segmentations of the color and luminance channels of BU images. This procedure outperformed other techniques when tested on a subset of 26 BU images where the ground truth of manual segmentation by domain expert physicians was known. Internally the system represents BU lesions using the Bag-of-Features methodology, where image features are extracted from image patches. A new strategy for nonuniform image sampling was developed based on occurrence of contextual pixel saliency, whereby patches from dermoscopically interesting regions are sampled more densely. Experiments on a set of 197 images demonstrated an advantage of this method compared to classical grid or random sampling. To obtain more accurate statistics and address the problem of class imbalance due to the small number of BU images compared to non-BU controls in the dataset, a new resampling technique was developed, which increased the sample size of the minority class by splitting lesions in the data space, rather than synthesizing new samples in the feature space. Combined with the feature selection metric of Pearson correlation coefficient, this method resulted in a classifier with more discriminative power. Additionally, a new feature representation of the multispectral images based on optical skin modeling was proposed. In this method, the distribution of physiological characteristics of a lesion, such as melanin, blood volume, and blood oxygenation were estimated for all image pixels, and features were extracted from these reconstructed maps. Experimental results with a set of 197 multispectral images showed that the use of multispectral images provided a significant improvement in classification performance compared to classification based on single white light images. Finally, a modular stand-alone application that includes all system stages has been developed in Matlab that can be further implemented on smart handheld devices.Item Determining Nipple Position and Smallest Resolvable Volume for Evaluating Breast Reconstruction Surgery(2020-08) Nowroozilarki, Zhale; Merchant, Fatima Aziz; Zouridakis, George; Pollonini, LucaBreast cancer is one of the most widespread cancers among women globally. Because of recent improvements in cancer treatment and increase in survival rate, more women are living with the consequences of breast removal surgery, known as mastectomy. In order to improve the quality of life, physical and psychological well-being after the cancer treatment process, many women decide to have reconstruction surgery. Metrics of breast aesthetics such as position and volume symmetry are often used for outcome assessment following reconstructive surgery. In order to achieve breast symmetry, many measurements which are difficult for human eyes to precisely estimate need to be done. The first aim of this study is to use a data-driven approach to help surgeons annotate the nipple position on reconstructed breast mounds. A graphical user interface was developed to enable computations of nipple localization and symmetry measurements on 3D surface images of pre- and post-operative patients, and a linear regression model incorporating breast aesthetic measures was developed to provide personalized estimate of nipple localization. Secondly, the smallest measurable volume using 3D imaging was analyzed to quantify the resolution of the 3D imaging system. The computational tools and models developed in this study will assist surgeons with surgical planning and outcome assessment and provide a framework for visualization to support physician-patient communication during clinical consultations. This research aims to benefit breast cancer survivors as well as their care givers.Item Development of an Absorbing Radio Frequency Shield for Safe Magnetic Resonance Imaging(2017-12) Li, Lintong; Chen, Ji; Jackson, David R.; Chen, Jiefu; Benhaddou, Driss; Zouridakis, GeorgeIn this dissertation, we propose a design for an absorbing radio frequency shield (ARFS) that helps to reduce the RF-induced heating effects at the tip of a deep brain stimulation (DBS) lead. We first review the transfer function method used to evaluate RF-induced temperature rise at a DBS lead tip, including both theory and measurements. Next, an ARFS shell is designed for a generic head-trunk human model for basic electromagnetic full-wave and thermal validations. A multi-layered ARFS shell structure is proposed, which consists of a highly conducting layer (HCL) embedded in a thick absorbing conductive layer (ACL) and an insulating layer. Furthermore, a similar ARFS shell is applied to an anatomically-correct human model with implanted DBS leads. The temperature rise at the lead tip is calculated and the shielding effectiveness of the proposed ARFS is analyzed. A total number of 297 lead-pathways are investigated. A head-only ARFS is also discussed for higher flexibility. Finally, the lead-tip temperature rise is measured inside a phantom for seven typical lead trajectories, in order to validate the methodology and the effectiveness of the proposed ARFS. The proposed ARFS structure is demonstrated to effectively reduce the temperature rise at the DBS lead tip for the trajectories studied in this dissertation. The average percentage reduction is 49.0% from the experimental results and 55.6% from the corresponding simulation results.Item Establishing Quantitative Measures of Quality of Functional Near Infrared Spectroscopy Data(2020-05) Gopal Dhamodaran, Dhanalakshmi; Pollonini, Luca; Zouridakis, George; Merchant, Fatima AzizFunctional Near-Infrared Spectroscopy (fNIRS) is an optical neuroimaging technique that can be used to examine and quantify tissue hemodynamics on the brain. fNIRS signals are contaminated by measurement noises and physiology-based systemic noises, such as a periodic pulsation of optical signals associated with the cardiac activity. Several approaches exist to filter out all sorts of noises and to remove channels with a low signal-to-noise ratio (SNR) that are deemed unreliable to estimate cortical hemodynamics. However, amongst the systemic noises which are undesirable for cerebral hemodynamics, strong cardiac pulsations usually indicate a good contact between the optical probe and the scalp. This thesis aims at evaluating the performance of physiology-based measures of quality of fNIRS data, namely 1) the Scalp Contact Index (SCI) and 2) the Peak Power (PP) of the spectrum, and understand how would they vary as a function of a range of pair of wavelengths, and for experiments conducted with different experimental setups such as 1) the source-detector distance, 2) the integration time of photodetectors and 3) the anatomical location on the head where signals are collected. So, while keeping other parameters constant, we are going to vary only one parameter at a time and collect the data and compute the SCI and PP for that data to compare its quality.Item Examining In Vivo Changes in Lamina Cribrosa in Non-human Primates with Experimental Glaucoma(2015-05) Sredar, Nripun 1983-; Zouridakis, George; Porter, Jason; Vilalta, Ricardo; Tsekos, Nikolaos V.; Subhlok, JaspalGlaucoma is a disease that results in the degeneration of retinal ganglion cell axons and the death of retinal ganglion cells (RGCs). It is one of the leading causes of permanent blindness worldwide. Clinical examinations currently in practice are limited in their ability to detect glaucoma prior to loss of RGC axons. The main goal of this work is to characterize early changes in the optic nerve head of monkeys with experimental glaucoma (EG) using in vivo and non-invasive methods to better understand the mechanisms behind glaucoma. In vivo images of the lamina cribrosa were acquired using a spectral domain optical coherence tomography and an adaptive optics scanning laser ophthalmoscope (AOSLO). We transformed 2D AOSLO images onto a 3D anterior lamina cribrosa surface (ALCS) and computed the 3D morphometry of the ALCS. Using principal component analysis (PCA), we estimated the predominant local ALCS beam orientation directly from raw grayscale in vivo images without the need for binary segmentation. Subsequently, we developed an automated method to segment the lamina cribrosa pores using level sets. Our 3D transformation method provides a better representation of the ALCS from in vivo images. Following 3D transformation, mean pore area increased by 5.1 ± 2.0% in 11 normal eyes and 16.2 ± 5.9% in 4 glaucomatous eyes due to the increased curvatures. Our PCA technique yielded small errors in local orientation (0.2 ± 0.2◦) when tested on synthetic data, accurately determined local beam orientation and was repeatable in control eyes over time. In addition, automated segmentation of pore boundaries using level sets method was comparable to manual segmentation (sensitivity = 83%, specificity = 95%) and yielded repeatable values over time. In conclusion, the PCA beam orientation and level sets segmentation methods can be used to accurately and objectively detect and track in vivo changes in lamina cribrosa microarchitecture during the progression of EG.Item Exploring the Relationship between Postural Control and Brain Activity using Dual-Task Methodology(2019) Shams-ul-hooda, Akeil B.; John, Isaac; Young, D. R.Understanding how attention is allocated during a balance task, when paired with competing cognitive tasks, can be used to develop therapeutic protocols for elderly individuals as well as those with particular disease conditions requiring a higher efficacy of balance control. Using dual-task methodology, a balance task was paired with a cognitive distractor task. Attention tradeoff between the two tasks was monitored using functional near-infrared spectroscopy (fNIRS), which measured oxygen utilization in various regions of the brain to determine how oxygen use patterns varied in single and dual-tasks. It was hypothesized that participants will prioritize balance/posture and cognitive task scores will drop, and that this performance pattern will be associated with particular patterns of frontal lobe oxygen utilization that can be detected with fNIRS. There were three test conditions, all while the subjects stood. The first condition was a cognitive task that required subjects to listen and identify the number of times they heard the ‘probe’ sound. The second condition was a balance control task that required the participant to sway about the ankles in response to light vibration applied to the abdomen and lower back. If the participant accurately responded to the vibration by swaying either forward or backward, they followed the programmed pattern. A sensor determined the subject’s error of movement. The third condition combined the two tasks. It was found that the dual-task condition resulted in extreme decline of cognitive task accuracy, suggesting that the balance task was prioritized in the non-threatening environment of the study.Item High Performance fNIRS System Utilizing Microzed Zynq-7000 System on Module Board(2018-12) Szymczyk, Tomasz; Pollonini, Luca; Merchant, Fatima Aziz; Zouridakis, GeorgeCommercial functional near-infrared spectroscopy (fNIRS) instruments used in cognitive neuroscience research tend to be cost prohibitive while also usually suffering from a low signal-to-noise ratio. This thesis describes the design and implementation of an application developed on the Microzed Zynq 7000 series System on Module (SoM) board, which is the core of a custom high performance modular fNIRS device. Utilizing the FPGA capabilities of the Microzed SoM allows improved signal quality by employing digital lock-in demodulation techniques to maximize signal to noise ratio, as well as communication with other electrical components required for an fNIRS system. Software was developed both on the Microzed’s ARM9 processor and external workstation PC to verify the lock-in technique and facilitate data transfer and device configuration. The results verify that the core of a cost effective and portable fNIRS instrument can be developed using an FPGA and processor system on module board.Item In Vivo Gloriosa Superba and Colchicum Autumnale Multi-Tissues Transcriptome Analysis for Colchicine Pathway and Rhizome Development Candidate Genes Identification(2018-12) Bass, John Samuel; Ganapathy, Sivakumar; Iyer, Rupa; Cirino, Patrick C.; Shireen, Wajiha; Zouridakis, GeorgeBackground: The continued emergence of side-effects caused by synthetic drugs underscores the need for plant-based drugs in human medicine. Medicinal rhizomatous crops are “the goldmine for modern drugs”, and include such species as Gloriosa superba L., and Colchicum autumnale L., the producer of colchicine, a plant-based medicine. The natural isomer of bioactive colchicine is used to effectively treat major diseases such as cancer, cardiovascular disease, and gout. The medicinal properties of colchicine are well characterized, however, almost nothing is known about its biosynthetic mechanism and colchicine pathway has not been elucidated that are significant barriers in biomanufacturing of biomedicine. The comparative transcriptomes study of G. superba and C. autumnale can serve as sequence resource and synthetic biology toolbox components for identifying biomedicine pathway and rhizome development genes, which could aid colchicine pathway metabolic engineering or synthetic biotechnology to improve colchicine biomanufacturing. Result: Predominantly colchicine synthesizing two monocots such as G. superba and C. autumnale transcriptomes were used to identify putative protein involved in the colchicine biosynthetic pathway and rhizome development along with transcription factors. Mining of the transcriptomes using Blast2GO, 20 and 29 candidate genes [3 and 1 candidate N-methyltransferase (NMT); 10 and 16 candidate 3-O-methyltransferase (3-OMT); cytochrome P450s, a class that could catalyze several steps in the pathway namely, 2 and 5 candidate CYP96T1, 1 and 4 candidate CYP82E10; 4 and 3 candidate N-acetyltransferase (NAT)] were identified in colchicine pathway for G. superba and C. autumnale, respectively. Similarly, 19 and 15 candidate rhizome developmental genes [2 and 1 candidate GIGANTEA (GI), 5 and 4 candidate CONSTANS (CO), 2 and 1 candidate Phytochrome B (PHYB), 2 and 5 candidate Sucrose Synthase (SuSy), 5 and 2 candidate Flowering Locus T (FT), and 3 and 2 candidate REVOLUTA (REV)] were identified in G. superba and C. autumnale, respectively. While 16 and 12 transcription factors in rhizome development and regulating secondary metabolic pathways in rhizomes [3 and 1 candidate MADS-box, 6 and 2 candidate AP2-EREBP, 2 and 2 candidate bHLH, 1 and 2 candidate MYB, 2 and 2 candidate NAC, and 2 and 3 WRKY] were screened in G. superba and C. autumnale, respectively. These genes could represent potential leads for metabolic engineering of G. superba or synthetic biotechnology of colchicine metabolism for enhanced colchicine and biorhizome biomass in biomanufacturing. Conclusion: The study of G. superba and C. autumnale genes predicated to encode colchicine pathway enzymes are highly significant for fundamental information on plant-based biomedicine biosynthesis, which could facilitate engineered production in biorhizomes, a potentially important area of synthetic biotechnology. Additionally, increasing our understanding of rhizome genomics could improve colchicine production in G. superba, and generate important knowledge that could be applied to many other medicinal plant species, and could allow engineered production of additional biomedicines in biorhizomes, a potentially important area of expansion for synthetic biotechnology to solve overarching biomanufacturing challenges.Item Measurements of MRI Induced Heating(2013-08) Pena, Miguel A. 1987-; Chen, Ji; Jackson, David R.; Zouridakis, GeorgeThe appeal of magnetic resonance imaging (MRI) stems from the fact that it can generate internal images of the human body noninvasively, with high temporal and contrast resolution, and without requiring ionizing radiation. Instead, MRI requires the use of a relatively safe radio frequency (RF) signal, which can however be problematic for patients with implantable medical devices. The RF induced heating on twenty-four titanium rods with different diameters, coatings, and lengths, were placed, within a phantom of gelled saline, inside a 1.5 T, 64 MHz test system for 15 minutes, one-by-one. Thermal simulations were carried out in SEMCAD X. The partially insulated rods experienced the highest increase in temperature out of all the coating configurations. Also, the titanium rods that were closer to the length of a half-wavelength dipole antenna in general experienced a higher increase in temperature. Finally, the thinner rods experienced a higher increase in temperature than the thicker rods.Item MEG-Based Functional Connectivity Biomarkers of Dyslexia(2014-12) Iraola Goiburu, Inigo; Zouridakis, George; Malki, Heidar A.; Lent, RicardoDyslexia a learning disability related to reading, often characterized by difficulty with accurate word recognition, decoding, and spelling. The disorder affects approximately 10% of the population and it is typically diagnosed using neuropsychological evaluation. The main objective of this thesis has been the development of unique measures based on fast neurophysiological recordings that may used to improve detection and allow intervention at an earlier age, with improved outcomes. We used functional connectivity analysis to identify brain connectivity networks in task-free, resting-state Magnetoencephalographic recordings of brain activity obtained in two groups of participants, namely 21 dyslexia patients and 20 age-matched normal controls. In an attempt to quantify interaction among brain regions and understand how brain networks are affected by dyslexia, we used Granger causality, which can estimate cause-and-effect relationships both in terms of strength and direction. A Granger connectivity matrix was computed for each subject individually, and then group templates were estimated by averaging all matrices in each group. Furthermore, we performed classification of the subjects using support vector machines and Fisher's criterion to rank the features and identify the best subset for maximum separation of the two groups. Our results show that a combined model based on connectivity matrices and graph theory measures can provide 100% classification accuracy in separating the two groups, with 100% sensitivity and specificity. These findings suggest that analysis of functional connectivity patterns may provide a valuable tool for the early detection of dyslexia.Item Methods and Strategies for Evaluating MRI Rf-Induced Heating for Implanted Metallic Stents(2019-05) Ji, Xiaohe; Chen, Ji; Jackson, David R.; Zouridakis, George; Tsekos, Nikolaos V.; Kainz, WolfgangMagnetic resonant imaging (MRI) is a widely-used medical imaging technology, which however incurs some safety hazards with the existence of conductive implanted medical devices, and radio frequency (RF)-induced heating is one of the major concerns. As one of the general metallic implanted devices, however stents are not fully studied on its RF-induced heating behavior. In this work, evaluations of MRI RF-induced heating are carried out on implanted medical stents. Due to various designs of stents, several typical generic stent models are developed and the effects of different design parameters, e.g. stent pattern, length, diameter, structural connectivity, etc., on the RF-induced heating are investigated in the American Society for Testing and Materials (ASTM) phantom. Experimental measurements are conducted for validation. Then four search strategies are introduced for assessing the worst-case RF-induced heating based on the simulated results of the multi-configuration devices, and a stent is studied to investigate the feasibility of the search strategies. It is found that the strategy called single sequential search with the second worst case validations and the multi-iteration sequential search strategy can decrease the probability of failed worst case from 45% to 34.8% and 0% for the stent respectively. Afterwards, a neural network is applied to fast predict the RF-induced heating of stents. Furthermore, the stents are implanted into the anatomically correct adult male model Duke for in-vivo evaluation. Stents are placed into the thoracic aorta, esophagus and colon respectively. The comparison between the in-vivo results and in-vitro results indicates the failure of utilizing the ASTM phantom to conservatively estimate the RF-induced SAR in the colon. It is found that the in-vivo incident electric field is different from that in the ASTM phantom. Moreover, we propose the inhomogeneous phantom to study the effects of the medium on RF-induced heating. Significant differences in SAR are observed for mediums having different physiological tubular structures. However, the inhomogeneous phantom can only better estimate the case of the stent in the esophagus than the ASTM phantom regarding the in-vivo situation representation. For the stent in the colon and the thoracic aorta, the inhomogeneous phantom shows similar results to the ASTM phantom, which makes no improvement in representing the in-vivo situations, and more studies are needed.Item Micro-Patterned Substrates for Differentiating Mesenchymal Stem Cells into Insulin Producing Cells(2017-12) Friguglietti, Jefferson; Merchant, Fatima Aziz; Zagozdzon-Wosik, Wanda; Balan, Venkatesh; Ganapathy, Sivakumar; Flavier, Albert B.; Zouridakis, GeorgeConventional insulin therapy for Type 1 diabetes mellitus is often accompanied by long-term complications such as heart disease and kidney damage, if patients do not follow a very strict and controlled regime of taking insulin shots. Transplantation of pancreatic islets is a therapeutic option available for Type 1 Diabetes, where in donor islets are transplanted into patients for controlling glucose levels without the need of insulin shots. Although the current islet transplantation Edmonton protocol has made progress in successfully treating diabetic patients, a lack of viable donor cells and side effects associated with immunosuppressant drugs make alternative therapeutic options critical. Cell replacement therapy via differentiation of adult stem cells into glucose-responsive insulin producing cells (IPCs) has recently provided hope for Type I diabetes. However, inadequate functional performance of the differentiated cells with poor long-term insulin production has slowed further progress. Thus, there is a critical need for improving the total yield of differentiated cells and their functional performance. In this study we investigated the potential of a novel substrate of micro-patterned Titanium diboride (TiB2) on Silicon (Si) wafers for culturing adult human bone marrow mesenchymal stem cells (hBM-MSCs) and differentiating them into insulin producing cells (IPCs). The hypothesis is that these substrates enable formation of aggregates, thereby enabling a 3D micro-environment for differentiation. Stereomicroscopy showed MSCs preference for TiB2 patterns over Si and the formation of uniform aggregates only on the TiB2 after the differentiation protocol. Moreover, MSCs not only remained at 80% or more viable when aggregated, but phenotyping analysis for the presence of biomarker CD105 demonstrated conserved multi-lineage potential throughout the 9 day pre-differentiation incubation period. More importantly, our results suggest a 2-3 fold increase of insulin secretion from MSCs differentiated on the micro-patterned substrates when compared against differentiation in conventional tissue culture flasks.Item Mild Traumatic Brain Injury Assessment Based on EEG and MEG Analysis(2018-05) Li, Lianyang L.; Zouridakis, George; Bao, Jiming; Sheth, Bhavin R.; Merchant, Fatima Aziz; Pollonini, LucaMild traumatic brain injury (mTBI) is difficult to diagnose because patients typically lack external injuries and pathological findings in conventional imaging. The main objective of this study has been the development of reliable biomarkers for mTBI. We compared brain activation profiles in 13 mTBI patients and 9 orthopedic controls using resting-state and evoked activity obtained with Electro- (EEG) and Magneto-encephalography (MEG). First, statistical analysis, spectral characteristics, and independent component topography were used to eliminate artifacts and then, the average power spectral density of each subject’s cortical sources was used to compare the two groups. We further investigated whether functional brain connectivity could assess recovery in mTBI patients using recordings obtained at three different visits using the Stroop and n-back working memory tasks. We analyzed evoked response amplitude and latency, sensor level connectivity, signal entropy, connectivity strength and directionality, as well as the local and global topological properties of the resulting brain networks. The ANOVA/MANOVA models included group, visit, experimental condition, and their interactions as fixed effects. Overall, mTBI patients exhibited statistically significant overactivation. In particular, controls showed significantly stronger connections between the two hemispheres, whereas mTBI patients had significantly stronger connectivity within the right hemisphere. Activity across repeat sessions did not show any significant differences within each group. These findings suggest that resting-state EEG/MEG activation networks can be used as biomarkers that can help detect mTBI and assess the efficacy of intervention. Furthermore, the lack of significant differences across the three recording sessions indicates that mTBI patients improve slowly, confirming independent reports that mTBI deficits may persist for years.