710 research outputs found
2023-2024 Catalog
The 2023-2024 Governors State University Undergraduate and Graduate Catalog is a comprehensive listing of current information regarding:Degree RequirementsCourse OfferingsUndergraduate and Graduate Rules and Regulation
Unveiling the frontiers of deep learning: innovations shaping diverse domains
Deep learning (DL) enables the development of computer models that are
capable of learning, visualizing, optimizing, refining, and predicting data. In
recent years, DL has been applied in a range of fields, including audio-visual
data processing, agriculture, transportation prediction, natural language,
biomedicine, disaster management, bioinformatics, drug design, genomics, face
recognition, and ecology. To explore the current state of deep learning, it is
necessary to investigate the latest developments and applications of deep
learning in these disciplines. However, the literature is lacking in exploring
the applications of deep learning in all potential sectors. This paper thus
extensively investigates the potential applications of deep learning across all
major fields of study as well as the associated benefits and challenges. As
evidenced in the literature, DL exhibits accuracy in prediction and analysis,
makes it a powerful computational tool, and has the ability to articulate
itself and optimize, making it effective in processing data with no prior
training. Given its independence from training data, deep learning necessitates
massive amounts of data for effective analysis and processing, much like data
volume. To handle the challenge of compiling huge amounts of medical,
scientific, healthcare, and environmental data for use in deep learning, gated
architectures like LSTMs and GRUs can be utilized. For multimodal learning,
shared neurons in the neural network for all activities and specialized neurons
for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table
AN AUTOMATED, DEEP LEARNING APPROACH TO SYSTEMATICALLY & SEQUENTIALLY DERIVE THREE-DIMENSIONAL KNEE KINEMATICS DIRECTLY FROM TWO-DIMENSIONAL FLUOROSCOPIC VIDEO
Total knee arthroplasty (TKA), also known as total knee replacement, is a surgical procedure to replace damaged parts of the knee joint with artificial components. It aims to relieve pain and improve knee function. TKA can improve knee kinematics and reduce pain, but it may also cause altered joint mechanics and complications. Proper patient selection, implant design, and surgical technique are important for successful outcomes. Kinematics analysis plays a vital role in TKA by evaluating knee joint movement and mechanics. It helps assess surgery success, guides implant and technique selection, informs implant design improvements, detects problems early, and improves patient outcomes. However, evaluating the kinematics of patients using conventional approaches presents significant challenges. The reliance on 3D CAD models limits applicability, as not all patients have access to such models. Moreover, the manual and time-consuming nature of the process makes it impractical for timely evaluations. Furthermore, the evaluation is confined to laboratory settings, limiting its feasibility in various locations.
This study aims to address these limitations by introducing a new methodology for analyzing in vivo 3D kinematics using an automated deep learning approach. The proposed methodology involves several steps, starting with image segmentation of the femur and tibia using a robust deep learning approach. Subsequently, 3D reconstruction of the implants is performed, followed by automated registration. Finally, efficient knee kinematics modeling is conducted. The final kinematics results showed potential for reducing workload and increasing efficiency. The algorithms demonstrated high speed and accuracy, which could enable real-time TKA kinematics analysis in the operating room or clinical settings. Unlike previous studies that relied on sponsorships and limited patient samples, this algorithm allows the analysis of any patient, anywhere, and at any time, accommodating larger subject populations and complete fluoroscopic sequences. Although further improvements can be made, the study showcases the potential of machine learning to expand access to TKA analysis tools and advance biomedical engineering applications
Computational Approaches to Drug Profiling and Drug-Protein Interactions
Despite substantial increases in R&D spending within the pharmaceutical industry, denovo drug design has become a time-consuming endeavour. High attrition rates led to a
long period of stagnation in drug approvals. Due to the extreme costs associated with
introducing a drug to the market, locating and understanding the reasons for clinical failure
is key to future productivity. As part of this PhD, three main contributions were made in
this respect. First, the web platform, LigNFam enables users to interactively explore
similarity relationships between âdrug likeâ molecules and the proteins they bind. Secondly,
two deep-learning-based binding site comparison tools were developed, competing with
the state-of-the-art over benchmark datasets. The models have the ability to predict offtarget interactions and potential candidates for target-based drug repurposing. Finally, the
open-source ScaffoldGraph software was presented for the analysis of hierarchical scaffold
relationships and has already been used in multiple projects, including integration into a
virtual screening pipeline to increase the tractability of ultra-large screening experiments.
Together, and with existing tools, the contributions made will aid in the understanding of
drug-protein relationships, particularly in the fields of off-target prediction and drug
repurposing, helping to design better drugs faster
A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery
Semantic segmentation (classification) of Earth Observation imagery is a
crucial task in remote sensing. This paper presents a comprehensive review of
technical factors to consider when designing neural networks for this purpose.
The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural
Networks (RNNs), Generative Adversarial Networks (GANs), and transformer
models, discussing prominent design patterns for these ANN families and their
implications for semantic segmentation. Common pre-processing techniques for
ensuring optimal data preparation are also covered. These include methods for
image normalization and chipping, as well as strategies for addressing data
imbalance in training samples, and techniques for overcoming limited data,
including augmentation techniques, transfer learning, and domain adaptation. By
encompassing both the technical aspects of neural network design and the
data-related considerations, this review provides researchers and practitioners
with a comprehensive and up-to-date understanding of the factors involved in
designing effective neural networks for semantic segmentation of Earth
Observation imagery.Comment: 145 pages with 32 figure
Geometric Data Analysis: Advancements of the Statistical Methodology and Applications
Data analysis has become fundamental to our society and comes in multiple facets and approaches. Nevertheless, in research and applications, the focus was primarily on data from Euclidean vector spaces. Consequently, the majority of methods that are applied today are not suited for more general data types. Driven by needs from fields like image processing, (medical) shape analysis, and network analysis, more and more attention has recently been given to data from non-Euclidean spacesâparticularly (curved) manifolds. It has led to the field of geometric data analysis whose methods explicitly take the structure (for example, the topology and geometry) of the underlying space into account.
This thesis contributes to the methodology of geometric data analysis by generalizing several fundamental notions from multivariate statistics to manifolds. We thereby focus on two different viewpoints.
First, we use Riemannian structures to derive a novel regression scheme for general manifolds that relies on splines of generalized BĂ©zier curves. It can accurately model non-geodesic relationships, for example, time-dependent trends with saturation effects or cyclic trends. Since BĂ©zier curves can be evaluated with the constructive de Casteljau algorithm, working with data from manifolds of high dimensions (for example, a hundred thousand or more) is feasible. Relying on the regression, we further develop
a hierarchical statistical model for an adequate analysis of longitudinal data in manifolds, and a method to control for confounding variables.
We secondly focus on data that is not only manifold- but even Lie group-valued, which is frequently the case in applications. We can only achieve this by endowing the group with an affine connection structure that is generally not Riemannian. Utilizing it, we derive generalizations of several well-known dissimilarity measures between data distributions that can be used for various tasks, including hypothesis testing. Invariance under data translations is proven, and a connection to continuous distributions is given for one measure.
A further central contribution of this thesis is that it shows use cases for all notions in real-world applications, particularly in problems from shape analysis in medical imaging and archaeology. We can replicate or further quantify several known findings for shape changes of the femur and the right hippocampus under osteoarthritis and Alzheimer's, respectively. Furthermore, in an archaeological application, we obtain new insights into the construction principles of ancient sundials. Last but not least, we use the geometric structure underlying human brain connectomes to predict cognitive scores. Utilizing a sample selection procedure, we obtain state-of-the-art results
Southern Adventist University Undergraduate Catalog 2022-2023
Southern Adventist University\u27s undergraduate catalog for the academic year 2022-2023.https://knowledge.e.southern.edu/undergrad_catalog/1121/thumbnail.jp
Aerobic Exercise for the Promotion of Healthy Aging: Changes in Brain Structure Assessed with New Methods
As the proportion of older individuals in the population increases, so does the scientific concern surrounding age-related deterioration of brain tissue and related cognitive decline. One modifiable lifestyle factor of interest in the pursuit to slow or even reverse age-related brain atrophy is aerobic exercise. A number of studies have already demonstrated that aerobic exercise in older age can induce maintenance (i.e., reduction of loss) of both gray and white matter volume, particularly in the frontal regions of the brain, which are vulnerable to shrinkage in older age. Other magnetic resonance imaging (MRI)-based techniques, such as quantitative MRI and diffusion-weighted MRI, have been used to measure age-related deterioration of gray and white matter integrity in both voxel-wise analyses as well as on the latent level, but whether these negative changes can be ameliorated through exercise has yet to be shown. The current dissertation includes three papers which used a number of both established and novel MRI-based metrics to quantify changes in brain tissue integrity resulting from aging, as well as to investigate whether these changes can be ameliorated through aerobic exercise.
In Paper I (Wenger et al., 2022), we tested the reliability of quantitative MRI measures, namely longitudinal relaxation rate, effective transverse relaxation rate, proton density, and magnetization transfer saturation, by measuring them in a two-day, four-session design with repositioning in the scanner. Using the intra-class effect decomposition model, we found that magnetization transfer saturation could reliably detect individual differences, validating its use to investigate changes in brain structure longitudinally, as well as correlations with other variables of interest, such as change in cardiovascular fitness.
In Paper II (Polk et al., 2022), we tested the effects of aerobic exercise on a latent factor of gray-matter structural integrity, comprising observed measures of gray-matter volume, magnetization transfer saturation, and mean diffusivity, in regions of interest that have previously shown volumetric effects of aerobic exercise. We found that gray-matter structural integrity was maintained in frontal and midline regions, and that change in gray-matter structural integrity in the right anterior cingulate cortex was positively correlated with change in cardiovascular fitness within exercising participants. These results suggest a causal relationship between aerobic exercise, cardiovascular fitness, and gray-matter structural integrity in this region.
In Paper III (Polk et al., 2022), we tested the effects of aerobic exercise on white matter integrity, measured with both established and recently developed metrics. We were able to replicate findings from a previous study on the effects of aerobic exercise on white matter volume, and we also found change-change correlations between white matter volume and cardiovascular fitness as well as between white matter volume and performance on a test of perceptual speed. We also found unexpected exercise-induced changes in the diffusion weighted imaging-derived metrics of fractional anisotropy, mean diffusivity, fiber density, and fiber density and cross-section. Specifically, we found increases (or decreases in the case of mean diffusivity) within control participants and decreases (or increases in mean diffusivity) in exercisers. Furthermore, we found that percent change in fiber density and fiber density and cross-section correlated negatively with percent change in both cardiovascular fitness and cognitive performance. This casts doubt on the functional interpretation of these measures and suggests that the âmore is betterâ principle may not be universally applicable when investigating age-related and exercise-induced changes in white matter integrity.
In sum, this dissertation showed that regular at-home aerobic exercise, which may be more accessible for older individuals than supervised exercise, can be an effective tool to ameliorate age-related decreases in a latent measure of gray-matter structural integrity as well as white matter volume. It also illuminated potential limitations of other measures of white matter integrity in the context of aging and aerobic exercise, and calls for further research into these novel measures, especially when considering functional outcomes such as cognitive performance
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