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Articles 1 - 8 of 8
Full-Text Articles in Medical Biomathematics and Biometrics
Assessing The Adequacy Of A Prediction Model, Abhaya Indrayan, Sakshi Mishra Ms
Assessing The Adequacy Of A Prediction Model, Abhaya Indrayan, Sakshi Mishra Ms
COBRA Preprint Series
No abstract provided.
Genetic Analysis Of Hereditary Gingival Fibromatosis Associated Sos1 Missense Variants Of Uncertain Significance In Caenorhabditis Elegans, Himani Patel
Theses
Hereditary gingival fibromatosis (HGF) is a disease that can present as benign overgrowth of gingival tissue in the mouth. The overgrowth can enclose the entire mouth and teeth in severe cases or present itself in a concentrated area. Researchers have identified that mutations in the SOS1 gene can be responsible for HGF. This disease can impair basic functions related to the mouth. Eating, smiling, speaking can all be affected. Additionally, excess inflammation can cause periodontal disease because of the difficulty in maintaining proper oral health. Periodontal disease can lead to severe bone loss which can lead to complete loss of …
Fbpp: Software To Design Pcr Primers And Probes For Nucleic Acid Base Detection Of Foodborne Pathogens, Mohamed A Soliman, Mohamed S Azab, Hala A Hussein, Mohamed M Roushdy, Mohamed N Abu El-Naga
Fbpp: Software To Design Pcr Primers And Probes For Nucleic Acid Base Detection Of Foodborne Pathogens, Mohamed A Soliman, Mohamed S Azab, Hala A Hussein, Mohamed M Roushdy, Mohamed N Abu El-Naga
Faculty, Staff and Student Publications
Foodborne pathogens can be found in various foods, and it is important to detect foodborne pathogens to provide a safe food supply and to prevent foodborne diseases. The nucleic acid base detection method is one of the most rapid and widely used methods in the detection of foodborne pathogens; it depends on hybridizing the target nucleic acid sequence to a synthetic oligonucleotide (probes or primers) that is complementary to the target sequence. Designing primers and probes for this method is a preliminary and critical step. However, new bioinformatics tools are needed to automate, specific and improve the design sets to …
Energy Harvesting Face Mask Using A Thermoelectric Generator For Powering Wearable Health Monitoring Sensors, Ugur Erturun, Cansu Yalim, James E. West
Energy Harvesting Face Mask Using A Thermoelectric Generator For Powering Wearable Health Monitoring Sensors, Ugur Erturun, Cansu Yalim, James E. West
Engineering Management & Systems Engineering Faculty Publications
A wearable energy harvester (EH) incorporating a face mask with a thermoelectric generator is demonstrated. The function of this device is to generate electrical power from the heat produced by the human body, particularly breath, with the specific aim of powering wearable sensor applications. A prototype was built using a commercially available N95 face mask, a thermoelectric generator, and a heatsink. The performance of this EH device was assessed using experimental and numerical methodologies. The experimentally tested power output of the prototype was found to be ≈100 µW, with a corresponding power density of ≈30 µW/cm3, for a temperature difference …
Effect Of Specific Data Variations On Automated Speaker Recognition, Ethan David Meighen
Effect Of Specific Data Variations On Automated Speaker Recognition, Ethan David Meighen
Graduate Theses, Dissertations, and Problem Reports (ETD)
Speaker recognition is not a new biometric modality but there are still many obstacles in the way in order for it to become as used as fingerprint recognition, facial recognition, and iris recognition. Many real-world environmental conditions, hardware device variations, and human behavior present serious challenges to the use of opportunistic voice or speaker samples for identification purposes. Non-idealities, identified as nuisance factors, include environmental noise, input device quality, length of utterance, sample rate variation, and unscripted data are common nuisance factors that can impact speaker recognition match score performance. The impact of the nuisance factors listed above were evaluated …
A Class Of Game-Theoretic And Fokker-Planck Optimal Control Frameworks In Colon And Esophageal Cancer, Mesfer Alajmi Phd
A Class Of Game-Theoretic And Fokker-Planck Optimal Control Frameworks In Colon And Esophageal Cancer, Mesfer Alajmi Phd
Mathematics Dissertations - Archive
In this dissertation, we first present a new stochastic framework for parameter estimation and uncertainty quantification in colon cancer-induced immune responses. A stochastic process that captures the system's inherent randomness determines the dynamics of colon cancer. The stochastic framework is based on the Fokker-Planck equation, which represents the evolution of the probability density function corresponding to the stochastic process. We formulate an optimization problem that takes individual patient data with randomness present and solves it to obtain the unknown parameters corresponding to the individual tumor characteristics. Furthermore, we perform a sensitivity analysis of the optimal parameter set to identify the …
Utilizing Ai Integrated Neuroimaging Technology To Expand Upon Machine Learning In Positron Emission Tomography Technology With The Aim Of Detecting Amyloid Beta Biomarkers Early In The Onset Of Alzheimer's., Ethan S. Terman
Undergraduate Research Posters
Early intervention in Alzheimer's is vital for treatment. The earlier a professional can detect symptoms and make a diagnosis the earlier a prognosis can be implemented. With the prevalence of data in our day-to-day world combined with Artificial intelligence (AI), utilizing both for machine learning can pave the way for more accurate and efficient detection of Alzheimer's and other neurodegenerative diseases. AI combined with Machine learning (ML) increases diagnostic efficiency and reduces human errors, making it a valuable resource for physicians and clinicians alike. With the increasing amount of data processing and image interpretation required, the ability to use AI …
Scalar-On-Function Regression: Estimation And Inference Under Complex Survey Designs, Ekaterina Smirnova, Erjia Cui, Lucia Tabacu, Andrew Leroux
Scalar-On-Function Regression: Estimation And Inference Under Complex Survey Designs, Ekaterina Smirnova, Erjia Cui, Lucia Tabacu, Andrew Leroux
Mathematics & Statistics Faculty Publications
Increasingly, large, nationally representative health and behavioral surveys conducted under a multistage stratified sampling scheme collect high dimensional data with correlation structured along some domain (eg, wearable sensor data measured continuously and correlated over time, imaging data with spatiotemporal correlation) with the goal of associating these data with health outcomes. Analysis of this sort requires novel methodologic work at the intersection of survey statistics and functional data analysis. Here, we address this crucial gap in the literature by proposing an estimation and inferential framework for generalizable scalar-on-function regression models for data collected under a complex survey design. We propose to: …