Viewpoint aggregation via relational modeling and analysis: a new approach to systems physiology

The key to understanding any system, including physiologic and pathologic systems, is to obtain a truly comprehensive view of the system. The purpose of this dissertation was to develop foundational analytical and modeling tools, which would enable such a comprehensive view to be obtained of any physiological or pathological system by combining experimental, clinical, and theoretical viewpoints. Specifically, we focus on the development of analytical and modeling techniques capable of predicting and prioritizing the mechanisms, emergent dynamics, and underlying principles necessary in order to obtain a comprehensive system understanding. Since physiologic systems are inherently complex systems, our approach was to translate the philosophy of complex systems into a set of applied and quantitative methods, which focused on the relationships within the system that result in the system's emergent properties and behavior. The result was a set of developed techniques, referred to as relational modeling and analysis that utilize relationships as either a placeholder or bridging structure from which unknown aspects of the system can be effectively explored. These techniques were subsequently tested via the construction and analysis of models of five very different systems: synaptic neurotransmitter spillover, secondary spinal cord injury, physiological and pathological axonal transport, and amyotrophic lateral sclerosis and to analyze neurophysiological data of in vivo cat spinal motoneurons. Our relationship-based methodologies provide an equivalent means by which the different perspectives can be compared, contrasted, and aggregated into a truly comprehensive viewpoint that can drive research forward.Ph.D.Committee Chair: Lee, Robert; Committee Member: Kemp, Melissa; Committee Member: Prinz, Astrid; Committee Member: Ting, Lena; Committee Member: Wiesenfeld, Kur

Associative Increases in Amyotrophic Lateral Sclerosis Survival Duration With Non-invasive Ventilation Initiation and Usage Protocols

Objective: It is hypothesized earlier non-invasive (NIV) ventilation benefits Amyotrophic Lateral Sclerosis (ALS) patients. NIV typically consists of the removable bi-level positive airway pressure (Bi-PAP) for adjunctive respiratory support and/or the cough assist intervention for secretion clearance. Historical international standards and current USA insurance standards often delay NIV until percent predicted forced vital capacity (FVC %predict) is <50. We identify the optimal point for Bi-PAP initiation and the synergistic benefit of daily Bi-PAP and cough assist on associative increases in survival duration.Methods: Study population consisted of a retrospective ALS cohort (Emory University, Atlanta, GA, USA). Primary analysis included 474 patients (403 Bi-PAP users, 71 non-users). Survival duration (time elapsed from baseline onset until death) is compared on the basis of Bi-PAP initiation threshold (FVC %predict); daily Bi-PAP usage protocol (hours/day); daily cough assist usage (users or non-users); ALS onset type; ALSFRS-R score; and time elapsed from baseline onset until Bi-PAP initiation, using Kruskal-Wallis one-way analysis of variance and Kaplan Meier.Results: Bi-PAP users' median survival (21.03 months, IQR = 23.97, N = 403) is significantly longer (p < 0.001) than non-users (13.84 months, IQR = 11.97, N = 71). Survival consistently increases (p < 0.01) with FVC %predict Bi-PAP initiation threshold: <50% (20.3 months); ≥50% (23.60 months); ≥80% (25.36 months). Bi-PAP usage >8 hours/day (23.20 months) or any daily Bi-PAP usage with cough assist (25.73 months) significantly (p < 0.001) extends survival compared to Bi-PAP alone (15.0 months). Cough assist without Bi-PAP has insignificant impact (14.17 months) over no intervention (13.68 months). Except for bulbar onset Bi-PAP users, higher ALSFRS-R total scores at Bi-PAP initiation significantly correlate with higher initiation FVC %predict and longer survival duration. Time elapsed since ALS onset is not a good predictor of when NIV should be initiated.Conclusions: The “optimized” NIV protocol (Bi-PAP initiation while FVC %predict ≥80, Bi-PAP usage >8 h/day, daily cough assist usage) has a 30. 8 month survival median, which is double that of a “standard” NIV protocol (initiation FVC %predict <50, usage >4 h/day, no cough assist). Earlier access to Bi-PAP and cough assist, prior to precipitous respiratory decline, is needed to maximize NIV synergy and associative survival benefit

Astrocyte-Mediated Neuromodulatory Regulation in Preclinical ALS: A Metadata Analysis

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease characterized by progressive degradation of motoneurons in the central nervous system (CNS). Astrocytes are key regulators for inflammation and neuromodulatory signaling, both of which contribute to ALS. The study goal was to ascertain potential temporal changes in astrocyte-mediated neuromodulatory regulation with transgenic ALS model progression: glutamate, GTL-1, GluR1, GluR2, GABA, ChAT activity, VGF, TNFα, aspartate, and IGF-1. We examine neuromodulatory changes in data aggregates from 42 peer-reviewed studies derived from transgenic ALS mixed cell cultures (neurons + astrocytes). For each corresponding experimental time point, the ratio of transgenic to wild type (WT) was found for each compound. ANOVA and a student's t-test were performed to compare disease stages (early, post-onset, and end stage). Glutamate in transgenic SOD1-G93A mixed cell cultures does not change over time (p > 0.05). GLT-1 levels were found to be decreased 23% over WT but only at end-stage (p < 0.05). Glutamate receptors (GluR1, GluR2) in SOD1-G93A were not substantially different from WT, although SOD1-G93A GluR1 decreased by 21% from post-onset to end-stage (p < 0.05). ChAT activity was insignificantly decreased. VGF is decreased throughout ALS (p < 0.05). Aspartate is elevated by 25% in SOD1-G93A but only during end-stage (p < 0.05). TNFα is increased by a dramatic 362% (p < 0.05). Furthermore, principal component analysis identified TNFα as contributing to 55% of the data variance in the first component. Thus, TNFα, which modulates astrocyte regulation via multiple pathways, could be a strategic treatment target. Overall results suggest changes in neuromodulator levels are subtle in SOD1-G93A ALS mixed cell cultures. If excitotoxicity is present as is often presumed, it could be due to ALS cells being more sensitive to small changes in neuromodulation. Hence, seemingly unsubstantial or oscillatory changes in neuromodulators could wreak havoc in ALS cells, resulting in failed microenvironment homeostasis whereby both hyperexcitability and hypoexcitability can coexist. Future work is needed to examine local, spatiotemporal neuromodulatory homeostasis and assess its functional impact in ALS

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

After the Ice Bucket: Thawing Amyotrophic Lateral Sclerosis with Predictive Medicine

Presented on October 16, 2017 at 11:15 a.m. in the Krone Engineered Biosystems Building, Room 1005.Cassie S. Mitchell is an Assistant Professor in the Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University. Her research goal centers around expediting clinical translation from bench to bedside using data-enabled prediction. Akin to data-based models used to forecast weather, Cassie’s research integrates disparate, multi-scalar experimental and clinical data sets to dynamically forecast disease. Cassie is the principal investigator of the Laboratory for Pathology Dynamics, which uses a combination of computational, analytical, and informatics-based techniques to identify complex disease etiology, predict new therapeutics, and optimize current interventions. Cassie’s research has predominantly targeted neuropathology, but her research applications in predictive medicine expand across all clinical specialties.Runtime: 55:49 minutesThe prolific 2014 ALS Association’s Ice Bucket Challenge commenced the world-wide dumping of ice water on the heads of courageous supporters to bring awareness and research funding to a lesser-known yet fatal neurodegenerative disease, Amyotrophic Lateral Sclerosis (ALS). The Laboratory for Pathology Dynamics at Georgia Tech, which proudly dunked GT President Peterson during the GT ALS Ice Bucket Challenge, has been actively developing data analytics, informatics, and complex systems-based techniques to expedite preclinical and clinical ALS research. In short, we are vigorously stitching together a comprehensive quilt of ALS using thousands of data sets collected from cells, transgenic animal models, and patients. We will discuss how predictive medicine is revealing ALS etiological underpinnings and diagnostic markers; identifying epidemiological ALS patient commonalities; precisely forecasting survival in highly heterogeneous ALS populations; identifying future disease dynamics-based combination therapies in preclinical ALS animal models; and optimizing current ALS life-prolonging interventions. Finally, we will also discuss the application of the lab’s techniques to other research topics

<tt>SeizFt</tt>: Interpretable Machine Learning for Seizure Detection Using Wearables

This work presents SeizFt—a novel seizure detection framework that utilizes machine learning to automatically detect seizures using wearable SensorDot EEG data. Inspired by interpretable sleep staging, our novel approach employs a unique combination of data augmentation, meaningful feature extraction, and an ensemble of decision trees to improve resilience to variations in EEG and to increase the capacity to generalize to unseen data. Fourier Transform (FT) Surrogates were utilized to increase sample size and improve the class balance between labeled non-seizure and seizure epochs. To enhance model stability and accuracy, SeizFt utilizes an ensemble of decision trees through the CatBoost classifier to classify each second of EEG recording as seizure or non-seizure. The SeizIt1 dataset was used for training, and the SeizIt2 dataset for validation and testing. Model performance for seizure detection was evaluated using two primary metrics: sensitivity using the any-overlap method (OVLP) and False Alarm (FA) rate using epoch-based scoring (EPOCH). Notably, SeizFt placed first among an array of state-of-the-art seizure detection algorithms as part of the Seizure Detection Grand Challenge at the 2023 International Conference on Acoustics, Speech, and Signal Processing (ICASSP). SeizFt outperformed state-of-the-art black-box models in accurate seizure detection and minimized false alarms, obtaining a total score of 40.15, combining OVLP and EPOCH across two tasks and representing an improvement of ~30% from the next best approach. The interpretability of SeizFt is a key advantage, as it fosters trust and accountability among healthcare professionals. The most predictive seizure detection features extracted from SeizFt were: delta wave, interquartile range, standard deviation, total absolute power, theta wave, the ratio of delta to theta, binned entropy, Hjorth complexity, delta + theta, and Higuchi fractal dimension. In conclusion, the successful application of SeizFt to wearable SensorDot data suggests its potential for real-time, continuous monitoring to improve personalized medicine for epilepsy

CompositeView: A Network-Based Visualization Tool

Large networks are quintessential to bioinformatics, knowledge graphs, social network analysis, and graph-based learning. CompositeView is a Python-based open-source application that improves interactive complex network visualization and extraction of actionable insight. CompositeView utilizes specifically formatted input data to calculate composite scores and display them using the Cytoscape component of Dash. Composite scores are defined representations of smaller sets of conceptually similar data that, when combined, generate a single score to reduce information overload. Visualized interactive results are user-refined via filtering elements such as node value and edge weight sliders and graph manipulation options (e.g., node color and layout spread). The primary difference between CompositeView and other network visualization tools is its ability to auto-calculate and auto-update composite scores as the user interactively filters or aggregates data. CompositeView was developed to visualize network relevance rankings, but it performs well with non-network data. Three disparate CompositeView use cases are shown: relevance rankings from SemNet 2.0, an open-source knowledge graph relationship ranking software for biomedical literature-based discovery; Human Development Index (HDI) data; and the Framingham cardiovascular study. CompositeView was stress tested to construct reference benchmarks that define breadth and size of data effectively visualized. Finally, CompositeView is compared to Excel, Tableau, Cytoscape, neo4j, NodeXL, and Gephi