Identification and Quantification of Metabolites in a Human Plasma Standard Reference Material by Multiple Mass Spectrometry Methods

peer reviewe

Integration of Machine Learning and Mechanistic Models Accurately Predicts Variation in Cell Density of Glioblastoma Using Multiparametric MRI

Glioblastoma (GBM) is a heterogeneous and lethal brain cancer. These tumors are followed using magnetic resonance imaging (MRI), which is unable to precisely identify tumor cell invasion, impairing effective surgery and radiation planning. We present a novel hybrid model, based on multiparametric intensities, which combines machine learning (ML) with a mechanistic model of tumor growth to provide spatially resolved tumor cell density predictions. The ML component is an imaging data-driven graph-based semi-supervised learning model and we use the Proliferation-Invasion (PI) mechanistic tumor growth model. We thus refer to the hybrid model as the ML-PI model. The hybrid model was trained using 82 image-localized biopsies from 18 primary GBM patients with pre-operative MRI using a leave-one-patient-out cross validation framework. A Relief algorithm was developed to quantify relative contributions from the data sources. The ML-PI model statistically significantly outperformed (p \u3c 0.001) both individual models, ML and PI, achieving a mean absolute predicted error (MAPE) of 0.106 ± 0.125 versus 0.199 ± 0.186 (ML) and 0.227 ± 0.215 (PI), respectively. Associated Pearson correlation coefficients for ML-PI, ML, and PI were 0.838, 0.518, and 0.437, respectively. The Relief algorithm showed the PI model had the greatest contribution to the result, emphasizing the importance of the hybrid model in achieving the high accuracy

Absolute quantification of amino acids in plasma using stable isotope dilution LC-MS/MS – application to a reference material for metabolomics

peer reviewe

Identification of Metabolites in a Human Plasma Standard Reference Material by Comprehensive two Dimensional Gas Chromatography-Time-of-Flight Mass Spectrometry

peer reviewe

Derivatization of Amino Acids in Human Plasma for Quantitation by Comprehensive Two Dimensional Gas Chromatography Time of Flight Mass Spectrometry

peer reviewe

Remote computer data acquisition for satellite

The purpose of this project was to continue the design and construction of the sensor subsystem for a prototype nanosatellite (NANOSAT) called the Powder Metallurgy and Navigation Satellite (PANSAT). Every satellite has a subsystem that is responsible for the Health and Safety of the satellite. The sensor subsystem is accountable for monitoring other subsystems and their components to make sure they are operating properly. The main requirements for the sensor subsystem are listed below: -Monitor the temperatures of satellite subsystems. -Monitor the voltages of satellite subsystems. -Monitor the current of satellite subsystems. -Monitor the magnetic field of the earth in Low Earth Orbit

Toward Automated Instructor Pilots in Legacy Air Force Systems: Physiology-based Flight Difficulty Classification via Machine Learning

The United States Air Force (USAF) is struggling to train enough pilots to meet operational requirements. Technology has advanced rapidly over the last 70 years but USAF pilot training has not. Modern operational requirements demand a change and, for this reason, USAF senior leadership has advocated for innovation. The automation of instructor and evaluator pilots in select bottlenecks (e.g., simulators) is one such measure. However, to implement this vision, numerous technical issues must be mitigated. Accurate classification of flight difficulty is a foundational problem underpinning many of these technical issues, which requires either the acquisition of new systems or the development of new procedures. Therefore, given this need and the costly nature of purchasing new equipment, physiological-based classification of flight difficulty is our focus herein. Leveraging multimodal data from a designed experiment of pilots landing a simulated aircraft, we develop a high-quality machine learning pipeline for classifying flight difficulty, called the Multi-Modal Functional-based Decision Support System (MMF-DSS). MMF-DSS distills a tabular set of features from our multimodal and functional data through the use of functional principal component analysis, summary statistics, and BorutaSHAP. In this manner, information is derived from the time-series data via the generation of hundreds of features, of which a small subset having the most predictive capability is discerned. Four full factorial designs are used to perform hyperparameter tuning on a set of classifiers. In so doing, a superlative technique is identified. Impacts on executive decision making are examined as well as associated policymaking implications. Alternative classifiers are considered for use within our pipeline that trade predictive accuracy for cost efficiency, and recommendations for choosing among these alternatives is provided