Biomedical Commons^™

Localizing Seizure Onset With Diffusion Models, Andrew Y. Revell

Publicly Accessible Penn Dissertations

Diffusion models are models that describe the spread of anything -- atoms, ideas, people, seizures. They have developed independently across fields, from economics, computer science, and physics, to biology and medicine. They have a wide variety of applications including modeling the spread of pathogens, information, and ideas. In this dissertation, diffusion models are applied to modeling the spread of seizures. Our ability to predict how seizures spread -- its timing, speed, extent of activity, where seizures start and where seizures go -- can help us solve a critical problem in the effective treatment of refractory epilepsy: localization of seizure onset …

Speaker Encoding For Zero-Shot Speech Synthesis, Tristin W. Cory

MSU Graduate Theses

Spoken communication, for many, is an essential part of everyday life. Some individuals can lose or not be born with the ability to speak. To function on a day-to-day basis, these individuals find other ways of communication. Adaptive speech synthesis is one of those ways. It recreates a user’s previous voice or creates a voice that blends with their regional dialect. Current adaptive speech synthesis techniques that achieve human-like speech require thirty minutes, to a few hours of high-quality audio recordings of a target speaker. This amount of recorded audio is not commonly possessed by people in need of a …

Radiomic Texture Feature Descriptor To Distinguish Recurrent Brain Tumor From Radiation Necrosis Using Multimodal Mri, M. S. Sadique, A. Temtam, E. Lappinen, K. M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Despite multimodal aggressive treatment with chemo-radiation-therapy, and surgical resection, Glioblastoma Multiforme (GBM) may recur which is known as recurrent brain tumor (rBT), There are several instances where benign and malignant pathologies might appear very similar on radiographic imaging. One such illustration is radiation necrosis (RN) (a moderately benign impact of radiation treatment) which are visually almost indistinguishable from rBT on structural magnetic resonance imaging (MRI). There is hence a need for identification of reliable non-invasive quantitative measurements on routinely acquired brain MRI scans: pre-contrast T1-weighted (T1), post-contrast T1-weighted (T1Gd), T2-weighted (T2), and T2 Fluid Attenuated Inversion Recovery (FLAIR) that can …

Uncertainty Estimation In Classification Of Mgnt Using Radiogenomics For Glioblastoma Patients, W. Farzana, Z. A. Shboul, A. Temtam, K. M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Glioblastoma Multiforme (GBM) is one of the most malignant brain tumors among all high-grade brain cancers. Temozolomide (TMZ) is the first-line chemotherapeutic regimen for glioblastoma patients. The methylation status of the O6-methylguanine-DNA-methyltransferase (MGMT) gene is a prognostic biomarker for tumor sensitivity to TMZ chemotherapy. However, the standardized procedure for assessing the methylation status of MGMT is an invasive surgical biopsy, and accuracy is susceptible to resection sample and heterogeneity of the tumor. Recently, radio-genomics which associates radiological image phenotype with genetic or molecular mutations has shown promise in the non-invasive assessment of radiotherapeutic treatment. This study proposes a machine-learning framework …

Flexible Microfluidic Device With Nonplanar Interdigitated Microelectrodes, Saud Alssaidy

Theses

The lab-on-a-chip concept has improved significantly in recent years to meet global demand for various applications with the advent of new technologies. Much progress has been achieved, but many microfluidic devices still suffer from design limitations in terms of sensitivity and selectivity because they use rigid, fragile substrate materials and conventional electrodes, which do not provide high sensitivity or selectivity and suffer from signal-to-noise ratio issues. This work proposes a novel device architecture that uses flexible, transparent top and bottom layers integrated with (nonplanar interdigitated microelectrodes) to create a sandwich-like flexible substrate base. The top and bottom layers consist of …

Effect Of Levodopa On Eeg Connectivity In Parkinson's Patients, Sepehr Torab Parhiz

Electronic Thesis and Dissertation Repository

Levodopa is a dopamine replacement medication administered to patients with Parkinson’s disease (PD) to alleviate their motor symptoms. However, its long-term use can cause adverse side effects, including involuntary motor movements. We studied 16 PD patients before and after taking Levodopa based on resting-state electroencephalography (EEG) recordings to determine how Levodopa affects the functional connectivity of their brain networks. We used several metrics from graph theory, in particular the minimum spanning tree (MST) metric, and analyzed how they change after subjects take Levodopa. We observed significant changes in the lower alpha band toward a more path-like and less globally efficient …

Methods For Inference And Analysis Of Gene Networks From Rna Sequencing Data, Himangi Srivastava

Theses and Dissertations

RNA (Ribonuceic Acid) sequencing technology is a powerful technology used to give re- searchers essential information about the functionality of genes. The transcriptomic study and downstream analysis highlight the functioning of the genes associated with a specific biological process/treatment. In practice, differentially expressed genes associated with a particular treatment or genotype are subjected to downstream analysis to find some critical set of genes. This critical set of genes/ genes pathways infers the effect of the treatment in a cell or tissue. This disserta- tion describes the multiple stages framework of finding these critical sets of genes using different analysis methodologies …

Anglo-American Nursing: A Historical Timeline Of The Field From The Renaissance To World War Ii, Carlyn Homann

Honors Projects

This timeline stands as an extensive- although not comprehensive- review of the evolution of Anglo-American nursing as a profession, but it will also explore the contributions of other nations where it is appropriate. It seeks to summarize the field and its limitations from the early 15th century to World War II, as well as the capability for upward movement within an existing hierarchy while also considering the education required of a nurse during the selected time period in order to provide context for current nursing students.

Hiv/Aids After The Crisis: The Evolution Of An Epidemic, Emma Paras

Honors Projects

HIV and AIDS has affected the world for over four decades. In the past, a positive HIV diagnosis could very well be a death sentence, as well as signal that an individual was a social outcast. In the past ten years, however, treatments for HIV/AIDS allow a person to live a long and relatively healthy life. In addition to examining current and future trends in HIV treatment, this paper will also discuss how the virus affects different regions of the world. Particularly in certain parts of Africa, HIV/AIDS remains deadly for many who don’t have proper access to health care …

Evaluation Of Varying Graphene Oxides Through Their Physicochemical Characterizations On Erythrocytes, Hs27 Cells, Escherichia Coli And Staphylococcus Aureus Bacteria Cell Lines, To Assess Graphene Oxides Cytotoxicity., Miriam Montana

Open Access Theses & Dissertations

Research on the toxicity of Graphene Oxides (GO) has captivated interest in the field of material science, environmental sciences, and medicine for their avail significance in biomedical engineer applications either as integrates, enhancements, or in the making of medical devices. Moreover, there is still a lack of understanding as to what characteristics on GOs causes them to be cytotoxic at a cellular level. In our study we synthesized four different GOs by varying in both the method used for oxidation (Modified Hummer’s method & Improved Marcano-Tour’s method), and the precursor parent graphite, for a standardization approach to aid in the …

Cell Bioprinting: A Novel Approach For Alpha Cell To Beta Cell Transdifferentiation, Atzimba Casas

Open Access Theses & Dissertations

Diabetes is a chronic disease that occurs in the body when the pancreas fails to either produce insulin (TID) or does not effectively use the insulin produced (TIID) and poses further health complications as well as an insurmountable economic impact.[1] Type I diabetes is an autoimmune disease characterized by a deficient amount of insulin production on account of the body’s immune system destroying its own β-cells.[2] Current diabetes treatment methods include the administration of insulin via injections or islet transplantation therapy. However, although both are viable options, they come with limitations that make the managing of this disease difficult. It …

Classifying Electrocardiogram With Machine Learning Techniques, Hillal Jarrar

Master's Theses

Classifying the electrocardiogram is of clinical importance because classification can be used to diagnose patients with cardiac arrhythmias. Many industries utilize machine learning techniques that consist of feature extraction methods followed by Naive- Bayesian classification in order to detect faults within machinery. Machine learning techniques that analyze vibrational machine data in a mechanical application may be used to analyze electrical data in a physiological application. Three of the most common feature extraction methods used to prepare machine vibration data for Naive-Bayesian classification are the Fourier transform, the Hilbert transform, and the Wavelet Packet transform. Each machine learning technique consists of …

The Factors Influencing The Acceptance Of Web-Based E-Learning System Among Academic Staffs Of Saudi Arabia, Ikhlas Zamzami

Future Computing and Informatics Journal

It is possible to learn more quickly and effectively with e-learning software development because it provides learners with convenient and flexible learning environments. This allows them to progress further in their careers. Reports on web-based e-learning systems for in-service education have frequently neglected to include the viewpoint of the instructor. In order to conduct quantitative research, a sample of 50 academic staff members was selected. The purpose of this study was to investigate various factors that influence the intention to use web-based e-learning, with the theoretical foundation being provided by university lecturers. According to the findings of the study, the …

A Statistical-Mining Techniques’ Collaboration For Minimizing Dimensionality In Ovarian Cancer Data, Mohamed Attia, Maha Farghaly, Mohamed Hamada, Amira M. Idrees Ami

Future Computing and Informatics Journal

A feature is a single measurable criterion to an observation of a process. While knowledge discovery techniques successfully contribute in many fields, however, the extensive required data processing could hinder the performance of these techniques. One of the main issues in processing data is the dimensionality of the data. Therefore, focusing on reducing the data dimensionality through eliminating the insignificant attributes could be considered one of the successful steps for raising the applied techniques’ performance. On the other hand, focusing on the applied field, ovarian cancer patients continuously suffer from the extensive analysis requirements for detecting the disease as well …

Development Of Sensor, Sensory System And Signal Processing Algorithm For Intelligent Sensing Applications, Xingzhe Zhang

Dissertations

Sensors have been receiving significant attention in the last decade and the demand for sensory systems has increased in recent years due to the rapid growth in the field of artificial intelligence (AI). Sensors can improve people’s awareness by providing them with real-time information on the environment and their immediate health conditions. This dissertation presents the fulfilment of three main projects and focuses on the development of a sensor, a sensory system, and a sensor signal recognition system for AI applications by employing printed electronics, analog circuit design, and digital signal processing techniques.

In the first project, a multi-channel stethograph …

Creating Reel Designs: Reflecting On Arthrogryposis Multiplex Congenita In The Community, Iris Layadi

Purdue Journal of Service-Learning and International Engagement

Because of its extreme rarity, the genetic disease arthrogryposis multiplex congenita (AMC) and the needs of individuals with the diagnosis are often overlooked. AMC refers to the development of nonprogressive contractures in disparate areas of the body and is characterized by decreased flexibility in joints, muscle atrophy, and developmental delays. Colton Darst, a seven-year-old boy from Indianapolis, Indiana, was born with the disorder, and since then, he has undergone numerous surgical interventions and continues to receive orthopedic therapy to reduce his physical limitations. His parents, Michael and Amber Darst, have hopes for him to regain his limbic motion and are …

Benchmarking Small-Dataset Structure-Activity-Relationship Models For Prediction Of Wnt Signaling Inhibition, Mahtab Kokabi

Masters Theses

Quantitative structure-activity relationship (QSAR) models based on machine learning algorithms are powerful tools to expedite drug discovery processes and therapeutics development. Given the cost in acquiring large-sized training datasets, it is useful to examine if QSAR analysis can reasonably predict drug activity with only a small-sized dataset (size < 100) and benchmark these small-dataset QSAR models in application-specific studies. To this end, here we present a systematic benchmarking study on small-dataset QSAR models built for prediction of effective Wnt signaling inhibitors, which are essential to therapeutics development in prevalent human diseases (e.g., cancer). Specifically, we examined a total of 72 two-dimensional (2D) QSAR models based on 4 best-performing algorithms, 6 commonly used molecular fingerprints, and 3 typical fingerprint lengths. We trained these models using a training dataset (56 compounds), benchmarked their performance on 4 figures-of-merit (FOMs), and examined their prediction accuracy using an external validation dataset (14 compounds). Our data show that the model performance is maximized when: 1) molecular fingerprints are selected to provide sufficient, unique, and not overly detailed representations of the chemical structures of drug compounds; 2) algorithms are selected to reduce the number of false predictions due to class imbalance in the dataset; and 3) models are selected to reach balanced performance on all 4 FOMs. These results may provide general guidelines in developing high-performance small-dataset QSAR models for drug activity prediction.

Using Bibliometrics To Evaluate Outcomes And Influence Of Translational Biomedical Research Centers, Kristine M. Bragg, Gwen C. Marchand, Jonathan C. Hilpert, Jeffrey L. Cummings

Educational Psychology, Leadership, and Higher Education Faculty Publications

Introduction. Federal grant funding to support infrastructure development of translational biomedical research centers is a form of public health intervention. Establishing rigorous methods for measuring center success and outcomes is essential to justify continued funding. Methods. Bibliometric data compiled from a 5-year funding cycle of a neurodegeneration and translational neuroscience research center was analyzed using the package bibliometrix for open source software R and the NIH-developed research tool iCite. Results. The research team and their collaborators (n=485) produced 157 grant-citing publications from 2015-2020. The science was produced by small research teams clustered around three main communities of topics: Alzheimer's Disease, …

A Novel Model To Study Adipose-Derived Stem Cell Differentiation, Austin N. Worden

Theses and Dissertations

The use of three-dimensional (3D) culture systems (hydrogels) and adipose-derived stem cells (ADSCs) in regenerative medicine to advance early-stage investigation and modeling of the mechanisms of diseases, treatments, targets, etc. has recently increased. ADSCs, specifically, are utilized due to their innate programming during embryogenesis and in adult tissues in addition to their ability to differentiate into mesodermal, endodermal, and ectodermal cell-specific lineages. Of importance is that these advancements do not involve a model specimen (i.e. mice or rats) and simulate the numerous conflicting signals a migrating cell is exposed to in vivo such as chemokines, extracellular matrix (ECM), growth factors, …

Computer-Aided Diagnosis Of Low Grade Endometrial Stromal Sarcoma (Lgess), Xinxin Yang, Mark Stamp

Faculty Research, Scholarly, and Creative Activity

Low grade endometrial stromal sarcoma (LGESS) accounts for about 0.2% of all uterine cancer cases. Approximately 75% of LGESS patients are initially misdiagnosed with leiomyoma, which is a type of benign tumor, also known as fibroids. In this research, uterine tissue biopsy images of potential LGESS patients are preprocessed using segmentation and stain normalization algorithms. We then apply a variety of classic machine learning and advanced deep learning models to classify tissue images as either benign or cancerous. For the classic techniques considered, the highest classification accuracy we attain is about 0.85, while our best deep learning model achieves an …