Matching Items (30)

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Octopus Transverse and Internal Longitudinal Arm Muscles in Relation to Fetching Movements

Description

Octopus arms employ a complex three dimensional array of musculature, called a
muscular hydrostat, which allows for nearly infinite degrees of freedom of movement without
the structure of a skeletal system. This study employed Magnetic Resonance Imaging with a
Gadoteridol-based

Octopus arms employ a complex three dimensional array of musculature, called a
muscular hydrostat, which allows for nearly infinite degrees of freedom of movement without
the structure of a skeletal system. This study employed Magnetic Resonance Imaging with a
Gadoteridol-based contrast agent to image the octopus arm and view the internal tissues. Muscle
layering was mapped and area was measured using AMIRA image processing and the trends in
these layers at the proximal, middle, and distal portions of the arms were analyzed. A total of 39
arms from 6 specimens were scanned to give 112 total imaged sections (38 proximal, 37 middle,
37 distal), from which to ascertain and study the possible differences in musculature. The
images revealed significant increases in the internal longitudinal muscle layer percentages
between the proximal and middle, proximal and distal, and middle and distal sections of the
arms. These structural differences are hypothesized to be used for rapid retraction of the distal
segment when encountering predators or noxious stimuli. In contrast, a significant decrease in
the transverse muscle layer was found when comparing the same sections. These structural
differences are hypothesized to be a result of bending behaviors during retraction. Additionally,
the internal longitudinal layer was separately studied orally, toward the sucker, and aborally,
away from the sucker. The significant differences in oral and aboral internal longitudinal
musculature in proximal, middle, and distal sections is hypothesized to support the pseudo-joint
functionality displayed in octopus fetching behaviors. The results indicate that individual
octopus arm morphology is more unique than previously thought and supports that internal
structural differences exist to support behavioral functionality.

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2019-05

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Internal Longitudinal and Transverse Muscles in Relation to Octopus Arm Function

Description

The entirely soft-tissue anatomy of the octopus arm provides the animal with a large amount of freedom of movement, while still allowing the specimen to support itself despite the lack of a skeletal system. This is made possible through the

The entirely soft-tissue anatomy of the octopus arm provides the animal with a large amount of freedom of movement, while still allowing the specimen to support itself despite the lack of a skeletal system. This is made possible through the use of various muscle layers within the octopus arm, which act as muscular hydrostats. Magnetic Resonance imaging of the octopus arm was employed to view the muscle layers within the octopus arm and observe trends and differences in these layers at the proximal, middle, and distal portions of the arms. A total of 39 arms from 6 specimens were imaged to give 112 total imaged sections (38 proximal, 37 middle, 37 distal). Significant increases in both the internal longitudinal muscle layer and the nervous core were found between the proximal and middle, proximal and distal, and middle and distal sections of the arms. This could reflect selection for these structures distally in the octopus arm for predator or other noxious stimuli avoidance. A significant decrease in the transverse muscle layer was found in the middle and distal sections of the arms. This could reflect selection for elongation in the proximal portion of the octopus arm or could be the result of selection for the internal longitudinal muscle layer and nervous core distally. Previous studies on Octopus vulgaris showed a preference for using the proximal arms in the pushing movement of crawling and a preference for using the anterior arms in exploring behaviors (Levy et al., 2015 and Byrne et al., 2006). Differences between the anterior and posterior arms for the transverse muscle layer, internal longitudinal muscle layer, and the nervous core were insignificant, reflecting a lack of structure-function relationships. This could also be due to a low sample size. Differences between the left and right arms for the transverse muscle layer, internal longitudinal muscle layer, and the nervous core were insignificant, supporting previous evidence that left versus right eye and arm preferences in octopus are not population-wide, but individual. Some slight trends can be found for individual arms, but the sample size was too small to make definitive statements regarding differences among specific arms.

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2019-05

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Stochastic parameterization of the proliferation-diffusion model of brain cancer in a Murine model

Description

Glioblastoma multiforme (GBM) is a malignant, aggressive and infiltrative cancer of the central nervous system with a median survival of 14.6 months with standard care. Diagnosis of GBM is made using medical imaging such as magnetic resonance imaging (MRI) or

Glioblastoma multiforme (GBM) is a malignant, aggressive and infiltrative cancer of the central nervous system with a median survival of 14.6 months with standard care. Diagnosis of GBM is made using medical imaging such as magnetic resonance imaging (MRI) or computed tomography (CT). Treatment is informed by medical images and includes chemotherapy, radiation therapy, and surgical removal if the tumor is surgically accessible. Treatment seldom results in a significant increase in longevity, partly due to the lack of precise information regarding tumor size and location. This lack of information arises from the physical limitations of MR and CT imaging coupled with the diffusive nature of glioblastoma tumors. GBM tumor cells can migrate far beyond the visible boundaries of the tumor and will result in a recurring tumor if not killed or removed. Since medical images are the only readily available information about the tumor, we aim to improve mathematical models of tumor growth to better estimate the missing information. Particularly, we investigate the effect of random variation in tumor cell behavior (anisotropy) using stochastic parameterizations of an established proliferation-diffusion model of tumor growth. To evaluate the performance of our mathematical model, we use MR images from an animal model consisting of Murine GL261 tumors implanted in immunocompetent mice, which provides consistency in tumor initiation and location, immune response, genetic variation, and treatment. Compared to non-stochastic simulations, stochastic simulations showed improved volume accuracy when proliferation variability was high, but diffusion variability was found to only marginally affect tumor volume estimates. Neither proliferation nor diffusion variability significantly affected the spatial distribution accuracy of the simulations. While certain cases of stochastic parameterizations improved volume accuracy, they failed to significantly improve simulation accuracy overall. Both the non-stochastic and stochastic simulations failed to achieve over 75% spatial distribution accuracy, suggesting that the underlying structure of the model fails to capture one or more biological processes that affect tumor growth. Two biological features that are candidates for further investigation are angiogenesis and anisotropy resulting from differences between white and gray matter. Time-dependent proliferation and diffusion terms could be introduced to model angiogenesis, and diffusion weighed imaging (DTI) could be used to differentiate between white and gray matter, which might allow for improved estimates brain anisotropy.

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2016-05

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Utilizing MRI Texture Analysis and APOE Genotype to Predict the Aging Brain as a Potential Method for Early Assessment of Alzheimer's Disease

Description

Background: Noninvasive MRI methods that can accurately detect subtle brain changes are highly desirable when studying disease-modifying interventions. Texture analysis is a novel imaging technique which utilizes the extraction of a large number of image features with high specificity and

Background: Noninvasive MRI methods that can accurately detect subtle brain changes are highly desirable when studying disease-modifying interventions. Texture analysis is a novel imaging technique which utilizes the extraction of a large number of image features with high specificity and predictive power. In this investigation, we use texture analysis to assess and classify age-related changes in the right and left hippocampal regions, the areas known to show some of the earliest change in Alzheimer's disease (AD). Apolipoprotein E (APOE)'s e4 allele confers an increased risk for AD, so studying differences in APOE e4 carriers may help to ascertain subtle brain changes before there has been an obvious change in behavior. We examined texture analysis measures that predict age-related changes, which reflect atrophy in a group of cognitively normal individuals. We hypothesized that the APOE e4 carriers would exhibit significant age-related differences in texture features compared to non-carriers, so that the predictive texture features hold promise for early assessment of AD. Methods: 120 normal adults between the ages of 32 and 90 were recruited for this neuroimaging study from a larger parent study at Mayo Clinic Arizona studying longitudinal cognitive functioning (Caselli et al., 2009). As part of the parent study, the participants were genotyped for APOE genetic polymorphisms and received comprehensive cognitive testing every two years, on average. Neuroimaging was done at Barrow Neurological Institute and a 3D T1-weighted magnetic resonance image was obtained during scanning that allowed for subsequent texture analysis processing. Voxel-based features of the appearance, structure, and arrangement of these regions of interest were extracted utilizing the Mayo Clinic Python Texture Analysis Pipeline (pyTAP). Algorithms applied in feature extraction included Grey-Level Co-Occurrence Matrix (GLCM), Gabor Filter Banks (GFB), Local Binary Patterns (LBP), Discrete Orthogonal Stockwell Transform (DOST), and Laplacian-of-Gaussian Histograms (LoGH). Principal component (PC) analysis was used to reduce the dimensionality of the algorithmically selected features to 13 PCs. A stepwise forward regression model was used to determine the effect of APOE status (APOE e4 carriers vs. noncarriers), and the texture feature principal components on age (as a continuous variable). After identification of 5 significant predictors of age in the model, the individual feature coefficients of those principal components were examined to determine which features contributed most significantly to the prediction of an aging brain. Results: 70 texture features were extracted for the two regions of interest in each participant's scan. The texture features were coded as 70 initial components andwere rotated to generate 13 principal components (PC) that contributed 75% of the variance in the dataset by scree plot analysis. The forward stepwise regression model used in this exploratory study significantly predicted age, accounting for approximately 40% of the variance in the data. The regression model revealed 5 significant regressors (2 right PC's, APOE status, and 2 left PC by APOE interactions). Finally, the specific texture features that contributed to each significant PCs were identified. Conclusion: Analysis of image texture features resulted in a statistical model that was able to detect subtle changes in brain integrity associated with age in a group of participants who are cognitively normal, but have an increased risk of developing AD based on the presence of the APOE e4 phenotype. This is an important finding, given that detecting subtle changes in regions vulnerable to the effects of AD in patients could allow certain texture features to serve as noninvasive, sensitive biomarkers predictive of AD. Even with only a small number of patients, the ability for us to determine sensitive imaging biomarkers could facilitate great improvement in speed of detection and effectiveness of AD interventions..

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2016-05

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A Functional and Structural MRI Investigation of the Neural Signatures of Dyslexia in Adults

Description

The International Dyslexia Association defines dyslexia as a learning disorder that is characterized by poor spelling, decoding, and word recognition abilities. There is still no known cause of dyslexia, although it is a very common disability that affects 1 in

The International Dyslexia Association defines dyslexia as a learning disorder that is characterized by poor spelling, decoding, and word recognition abilities. There is still no known cause of dyslexia, although it is a very common disability that affects 1 in 10 people. Previous fMRI and MRI research in dyslexia has explored the neural correlations of hemispheric lateralization and phonemic awareness in dyslexia. The present study investigated the underlying neurobiology of five adults with dyslexia compared to age- and sex-matched control subjects using structural and functional magnetic resonance imaging. All subjects completed a large battery of behavioral tasks as part of a larger study and underwent functional and structural MRI acquisition. This data was collected and preprocessed at the University of Washington. Analyses focused on examining the neural correlates of hemispheric lateralization, letter reversal mistakes, reduced processing speed, and phonemic awareness. There were no significant findings of hemispheric differences between subjects with dyslexia and controls. The subject making the largest amount of letter reversal errors had deactivation in their cerebellum during the fMRI language task. Cerebellar white matter volume and surface area of the premotor cortex was the largest in the individual with the slowest reaction time to tapping. Phonemic decoding efficiency had a high correlation with neural activation in the primary motor cortex during the fMRI motor task (r=0.6). Findings from the present study suggest that brain regions utilized during motor control, such as the cerebellum, premotor cortex, and primary motor cortex, may have a larger role in dyslexia then previously considered. Future studies are needed to further distinguish the role of the cerebellum and other motor regions in relation to motor control and language processing deficits related to dyslexia.

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2016-12

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Estimating GL-261 cell growth: A murine model for Glioblastoma Multiforme

Description

Glioblastoma Multiforme (GBM) is an aggressive and deadly form of brain cancer with a median survival time of about a year with treatment. Due to the aggressive nature of these tumors and the tendency of gliomas to follow white matter

Glioblastoma Multiforme (GBM) is an aggressive and deadly form of brain cancer with a median survival time of about a year with treatment. Due to the aggressive nature of these tumors and the tendency of gliomas to follow white matter tracks in the brain, each tumor mass has a unique growth pattern. Consequently it is difficult for neurosurgeons to anticipate where the tumor will spread in the brain, making treatment planning difficult. Archival patient data including MRI scans depicting the progress of tumors have been helpful in developing a model to predict Glioblastoma proliferation, but limited scans per patient make the tumor growth rate difficult to determine. Furthermore, patient treatment between scan points can significantly compound the challenge of accurately predicting the tumor growth. A partnership with Barrow Neurological Institute has allowed murine studies to be conducted in order to closely observe tumor growth and potentially improve the current model to more closely resemble intermittent stages of GBM growth without treatment effects.

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Date Created
2014-05

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Development of a Novel Smart Contrast Agent for Magnetic Resonance Imaging

Description

Smart contrast agents allow for noninvasive study of specific events or tissue conditions inside of a patient's body using Magnetic Resonance Imaging (MRI). This research aims to develop and characterize novel smart contrast agents for MRI that respond to temperature

Smart contrast agents allow for noninvasive study of specific events or tissue conditions inside of a patient's body using Magnetic Resonance Imaging (MRI). This research aims to develop and characterize novel smart contrast agents for MRI that respond to temperature changes in tissue microenvironments. Transmission Electron Microscopy, Nuclear Magnetic Resonance, and cell culture growth assays were used to characterize the physical, magnetic, and cytotoxic properties of candidate nanoprobes. The nanoprobes displayed thermosensitve MR properties with decreasing relaxivity with temperature. Future work will be focused on generating and characterizing photo-active analogues of the nanoprobes that could be used for both treatment of tissues and assessment of therapy.

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Date Created
2014-05

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Incorporating the Sparsity of Edges into the Fourier Reconstruction of Piecewise Smooth Functions

Description

In applications such as Magnetic Resonance Imaging (MRI), data are acquired as Fourier samples. Since the underlying images are only piecewise smooth, standard recon- struction techniques will yield the Gibbs phenomenon, which can lead to misdiagnosis. Although filtering will reduce

In applications such as Magnetic Resonance Imaging (MRI), data are acquired as Fourier samples. Since the underlying images are only piecewise smooth, standard recon- struction techniques will yield the Gibbs phenomenon, which can lead to misdiagnosis. Although filtering will reduce the oscillations at jump locations, it can often have the adverse effect of blurring at these critical junctures, which can also lead to misdiagno- sis. Incorporating prior information into reconstruction methods can help reconstruct a sharper solution. For example, compressed sensing (CS) algorithms exploit the expected sparsity of some features of the image. In this thesis, we develop a method to exploit the sparsity in the edges of the underlying image. We design a convex optimization problem that exploits this sparsity to provide an approximation of the underlying image. Our method successfully reduces the Gibbs phenomenon with only minimal "blurring" at the discontinuities. In addition, we see a high rate of convergence in smooth regions.

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Date Created
2014-05

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Improving the Realism and Magnetic Resonance Imaging of Multicellular Tumor Spheroids

Description

Magnetic resonance imaging (MRI) of changes in metabolic activity in tumors and metabolic abnormalities can provide a window to understanding the complex behavior of malignant tumors. Both diagnostics and treatment options can be improved through the further comprehension of the

Magnetic resonance imaging (MRI) of changes in metabolic activity in tumors and metabolic abnormalities can provide a window to understanding the complex behavior of malignant tumors. Both diagnostics and treatment options can be improved through the further comprehension of the processes that contribute to tumor malignancy and growth. By detecting and disturbing this activity through personalized treatments, it is the hope to provide better diagnostics and care to patients. Experimenting with multicellular tumor spheroids (MCTS) allows for a rapid, inexpensive and convenient solution to studying multiple in vitro tumors. High quality magnetic resonance images of small samples, such as spheroid, however, are difficult to achieve with current radio frequency coils. In addition, in order for the information provided by these scans to accurately represent the interactions and metabolic activity in vivo, there is a need for a perfused vascular network. A perfused vascular network has the potential to improve metabolic realism and particle transport within a tumor spheroid. By creating a more life-like cancer model and allowing the progressive imaging of metabolic functions of such small samples, a better, more efficient mode of studying metabolic activity in cancer can be created and research efforts can expand. The progress described in this paper attempts to address both of these current shortcomings of metabolic cancer research and offers potential solutions, while acknowledging the potential of future work to improve cancer research with MCTS.

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2016-12

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Modeling and Characterization of Mass Transfer Kinetics in Tumor Tissue Using Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI)

Description

The objective of the research presented here was to validate the use of kinetic models for the analysis of the dynamic behavior of a contrast agent in tumor tissue and evaluate the utility of such models in determining kinetic properties

The objective of the research presented here was to validate the use of kinetic models for the analysis of the dynamic behavior of a contrast agent in tumor tissue and evaluate the utility of such models in determining kinetic properties - in particular perfusion and molecular binding uptake associated with tissue hypoxia - of the imaged tissue, from concentration data acquired with dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) procedure. Data from two separate DCE-MRI experiments, performed in the past, using a standard contrast agent and a hypoxia-binding agent respectively, were analyzed. The results of the analysis demonstrated that the models used may provide novel characterization of the tumor tissue properties. Future research will work to further characterize the physical significance of the estimated parameters, particularly to provide quantitative oxygenation data for the imaged tissue.

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Date Created
2013-12