A finite element and data-science assisted deep learning framework to interpret the constitutive behavior of brain white matter

The finite element methods (FEM) are widely utilized in numerical modeling of Brain White Matter (BWM) to depict traumatic brain injuries (TBI). These injuries could manifest in several forms such as tensile, compressive or shear loads on axons in both quasi-static and dynamic forms. Novel data-driven multi-scale numerical models are formulated to characterize BWM response when subjected to ensemble of traumatic loading scenarios. The purpose of this study is to propose computational solutions that connects AI (data-science), optimizations, medical imaging, multi-scale biomechanics and soft tissue modeling. These models would act as foundation studies for attaining highly repeatable and accurate data driven FEM and Machine-Learning (ML) models for multi-scale depiction of brain by incorporating axon/neuroglia composite anisotropy, axonal tracts tortuosity and brain aging/softening effects due to demyelination. Firstly, a bi-phasic 3D finite element model (FEM) has been proposed to study the mechanical response of axons embedded in ECM when subjected to tensile loads. The Ogden hyper-elastic (HE) material model describes the axons and the ECM materials. The developed model investigates several tethering scenarios between axons and oligodendrocytes using two FEM sub-models (single-OL and multi-OL) configurations. These tethering are deployed as linear spring-dashpot element. To gauge dynamic response and stress accumulation & relaxation effects, a hyper-viscoelastic (HVE) model is also proposed leveraging both analytical and optimization based frameworks. This generalizable approach enables a comprehensive analysis of the role of oligodendrocytes on stress redistribution and propagation, under static and repeated loading using a combination of static, steady-state (SSD), and explicit (ED) dynamic models. Next, a series of novel 3D micromechanical FEM proof-of-concept (POC) models are developed in house using Representative volume element (RVE) with axons embedded in extra-cellular glial matrix (ECM) for simulating the BWM response under shear. These models exhibit the Poynting effect (PE) in brain matter. PE is a nonlinear phenomenon associated with soft materials whereby they tend to elongate (positive) or contract (negative Poynting effect) in a direction perpendicular to the shearing or twisting plane. In-depth investigation is carried out using single, scaled-up and biconically modeled myelinated axons-ECM tri-phasic RVEs (Ogden hyperelastic) to characterize trends in degree of Poynting effect when subjected to ensemble of simple and pure shear loads. Leveraging the developed 3D FEMs built for simulating the Poynting effect, a deep 3D convolution neural network (CNN) algorithm combined with the 3D anisotropic REV FEM was employed to predict the WM's anisotropic stress & stiffness/material properties. 3D FEM geometrical information encoded in the voxelated locations & isotropic Ogden material properties are used as input data and consequently incorporated into a 3D CNN model that cross-references the RVEs' stress and stiffness (output). These output data are calculated in parallel using in-house developed tri-phasic 3D FEM, which depicts RVE samples as axon-myelin-glia composites. This novel hybrid CNN-FEM framework dramatically reduced the computation time compared to conventional FEMs to generate stress-stiffness tensor for sheared brain. In continuing the data-science based BWM modeling efforts, a synthetic 2D-FEM viscoelastic modeled BWM simulation dataset generated from previous research was used to build an end-to-end predictive ML forward model workflow to predict brain tissue properties (i.e., storage modulus) of a visco-elastic modeled brain. Developed framework incorporates uncertainty quantification in property estimations and also achieves model interpretability & explainability by analyzing sensitivity of constituent RVE components on predicted target BWM properties. The proposed transferable ML framework will aid soft tissue-characterization, tissue sensitivity & inverse model research for non-linear composites. Lastly, a novel high-order 3D FEM Ogden hyper-elastic brain aging model has been put forward to depict physiological aging and tissue degeneration in BWM. Magnetic Resonance Imaging (MRI) data from test subjects’ of different ages are analyzed using image processing libraries in python and MATLAB scripts used to calculate white matter volume fraction (VF) shrinkage and shear moduli depreciation functions with age. An ensemble of RVE models are developed with straight and tortuous myelinated axonal tracts embedded in glial matrix to characterize and interpret an aging brain response. Proposed novel proof-of-concept aging brain micro-mechanical FEMs would enable advanced pathology identification (brain tissue decay) and facilitate preventive health measures planning for TBI/aged brain.Ph.D.Includes bibliographical reference

Investigating tRNA-Val-CAC-1-1: acting as a tRNA or tRF in Drosophila melanogaster

Animal development is regulated by the organismal genes. These genes are broadly divided into protein coding and non-coding DNA. The latter in comprised of different functional groups, here we focus on investigating the role of transfer RNA (tRNA) in posttranscriptional gene regulation. Some tRNAs can function as tRNA fragments (tRFs). These RNAs act similarly to miRNAs by regulating mRNAs post transcriptionally. The tRNA:Val-CAC-1-1 (ValT) (CR31572) was computationally predicted as tRF in Drosophila melanogaster. One of the predicted 51 targets is Sprouty (Sty), a known negative regulator of the epidermal growth factor receptor (EGFR) signaling pathway. We performed experiments aiming to determine whether ValT acts as tRNA and/or tRF. Using genome engineering, CRISPR/Cas9, the following modifications were done in ValT, including eliminating either its normal tRNA or tRF function by changing the predicted tRF or anticodon domains. All substituted ValTs were homozygous viable. In delta ValT, we expected to see an increase in Sty, which in turn decreases EGFR signaling. Yet, the predicted eggshell phenotypes, which are sensitive to changes in EGFR signaling, were not consistent among the mutants, and no significant change was observed. Next, to achieve a better understanding of ValT functions, all transgenic lines were analyzed by RNAseq. We found groups of genes that were transcribed at different levels that were consistent across mutations in the tRF and others that were consistent across anticodon substitutions. These genes indicate potential targets for future studies into the function of ValT as a tRNA and a tRF.M.S.Includes bibliographical reference

A machine learning framework for predicting failures in rail infrastructure assets

Infrastructure safety is crucial for the rail industry, with signal functionality and track integrity being among essential components. This thesis presents a machine learning framework to predict failures in rail infrastructure assets, focusing on two critical areas: urban rail transit signal failures and broken rails in commuter rail systems. Integrating historical failure data, maintenance data, and track condition data, and operational data, the proposed framework applies machine learning models to identify high-risk locations and predict rail asset failures. Because rail infrastructure asset failures are relatively rare, imbalanced data mining techniques such as SMOTE, ADASYN, and random resampling are also employed to improve predictive accuracy. In the first case study, our model achieves an AUC of 75% and demonstrates the ability to identify approximately one-third of rail signal failures by focusing on 10% of signal locations on the network within the one-month prediction period. Our second case study focused on commuter rail segments, in which our model gives an AUC of 74% and 71% overall accuracy. The results show the potential of this framework to identify high-risk hot spots for prioritized inspection and maintenance, given limited resources.M.S.Includes bibliographical reference

Dynamics of lateralized electrophysiological and cellular processes in the zebra finch auditory system

Songbirds provide a powerful model for studying adult neuroplasticity in the auditory cortex as a function of recent auditory experience due to many parallels with the human auditory system, which is similarly tasked with processing complex conspecific vocalizations. As in human speech processing, lateralized auditory responses are evident in the songbird’s higher auditory cortex, NCM (caudomedial nidopallium), which encodes stimulus-specific auditory memories through a process of adaptation that leads to reduced responses to familiar sounds. The right NCM typically shows larger auditory responses and adaptation rates than the left for conspecific song, suggesting lateral differences in auditory representations and memory; this pattern of lateralization is known to depend on normal rearing conditions, however the ontogeny or the stability of auditory lateralization in adulthood have not been explored. Furthermore, the songbird brain incorporates new neurons in adulthood, including in NCM. In a series of coordinated experiments, Zebra Finches (ZFs; Taeniopygia guttata) were used to explore the effects of auditory exposure on learning and lateralized NCM responses via electrophysiology, immunohistochemistry, and behavioral assays. 1) we show that adult-like, right-lateralized auditory responses emerge out of left-biased patterns about halfway through development and that auditory experience in development shapes NCM responses in adulthood. 2) we document the time course of changes in lateralized auditory responses in adult ZFs exposed to a foreign, heterospecific (canary) acoustic environment; these changes are characterized as shifts in lateralization, whereby lateralization transiently reverses to a left-biased state, followed by a return to the original right-biased state after prolonged exposure; finches that experienced both the reversal and return to typical right-lateralized patterns of activity exhibited enhanced ability to behaviorally discriminate between test stimuli (canary songs). 3) these dynamic changes in the pattern of lateralized activity are shown to occur successively when ZFs are sequentially exposed to two different heterospecific environments (canary and budgerigar); in addition, we characterize how exposure to these environments leads to learning at the neural level (multiunit and single-unit in NCM). 4) we show that the shifts in lateralized activity manifest at the cellular level and potentially at different loci of the auditory afferent pathway. Furthermore, the exposure paradigm elicits shifts in the lateral distribution of new neurons in NCM, suggesting a possible neural substrate of lateralized neuroplasticity. 5) Finally, we provide evidence for learning in ZFs exposed to a heterospecific environment for different durations, using a novel behavioral method that explores learning in a consequence-free head-turning assay, Together, the results suggest that lateralization represents the current state of an organism, whereby adult-like lateralization is maintained by the current stimulus statistics, and dramatic changes in the stimulus statistics reverts the brain to a learning (developmental-like) state out of which (re)emerges the adult-like state; further, these changes in lateralization states are observed in loci along the afferent auditory pathway and we propose that they are a read-out of neurobiological substrates of learning.Ph.D.Includes bibliographical reference

Towards socially aware visual navigation with hierarchical learning

Reinforcement learning (RL) has made significant strides in the last few years by proposing increasingly more complex networks that use larger and larger amounts of data to solve a vast host of problems, from playing games to autonomous navigation. Continuing along this trajectory is infeasible for those who do not have access to the large amounts of computing power, data storage, or time required to perpetuate this trend. Additionally, these networks suffer from low sample efficiency and struggle to generalize to out of distribution data. This thesis proposes that leveraging the hierarchical structure inherent in many real world problems, specifically navigation, while efficiently incorporating socially cognizant design into model training and ideation can provide an alternative to this data- and compute-hungry approach. We start with the hypothesis that using networks that mirror the hierarchical structure inherent in many tasks will allow for better overall task performance using simpler networks. We take inspiration from the temporal abstraction of human cognitive processes and compare the performance of several flat neural network architectures and hierarchical paradigms in the maze traversal task. The temporally abstracted actions, also called subroutines, of hierarchical networks happen over multiple time steps and allow agents to reason over complex skills and actions (like leaving a room or going around a corner) instead of low level motor commands. We find that learning a policy over these temporally abstracted actions leads to faster training times, more training stability, and increased accuracy over standard RL or supervised learning with LSTMs. Using this insight, we next explore whether a predefined set of subroutines used by hierarchical networks provides better performance than a learned set. We create a hierarchical framework, comprised of a manager network that passes information to a worker network via a goal vector, for autonomous vehicle steering angle prediction from egocentric videos. The manager network learns an embedding space of subroutines from historical vehicle information. This learned subroutine embedding from the manager allows the worker network to more accurately predict the next steering angle than when using predefined subroutines. Additionally, this hierarchical framework shows improvements over state of the art steering angle prediction methods. In the real world, it is uncommon for the full set of subroutines needed to accomplish a task to be known a priori. Additionally, in order to have a complete autonomous navigation agent, it is imperative that the agent has a model of pedestrian behavior. In the next set of experiments, we aim to address these concerns by building a network to learn a dictionary of pedestrian social behaviors in a self-supervised manner. We use this dictionary to analyze the relationship between pedestrian behavior and the spaces they inhabit as well as the relationships between subroutines themselves. We also use this behavior embedding network in a hierarchical framework to constrain the state space for a worker network, allowing for future pedestrian trajectories to be predicted using a very simple architecture. Finally, we combine our findings into a hierarchical, socially cognizant, visual navigation agent. Instead of formalizing navigation into a traditional reinforcement learning framework, we implicitly learn to mimic optimal human navigation policies from collected demonstrations for the image-goal task in a simulated environment. We build a hierarchical framework with three levels. The first network builds a latent space that acts as a memory module for the navigation agent. The second network predicts waypoints in the current observation space indicating which area of the environment to move towards. The third network predicts which action to execute in the environment with a simple classifier network. The key to this method's success is that each of these networks operates at a different temporal or spatial scale, thus allowing them to bootstrap off of each other to incrementally solve a much larger navigational task and achieve SOTA results without the use of RL, grpahs, odometry, metric maps, or other computationally complex and memory intensive methods.Ph.D.Includes bibliographical reference

Craving for drugs and food in daily life: an economic decision-making and computational modeling investigation

Craving—the intense, specific desire for something—is a common part of our everyday experience. Most people report experiencing craving for chocolate and other palatable snacks. Craving (for drugs) is also a defining symptom of substance use disorders and predicts future drug use and relapse. In aiming to model the shared, defining features of food and drug craving in the laboratory, prior work has shown craving transforms subjective value for the object of craving (and similar choice options) in a multiplicative and time-bound fashion. Here, we investigated the generalizability of this laboratory signature of craving by simultaneously surveying different types of cravings and craving moments in participants’ daily lives. Treatment-engaged patients with opioid use disorder (OUD; N=67) and community control participants (N=49) took part in a 28- day experience sampling study. Each day, we asked participants to report on their momentary urge for opioids and food (sweet/savory), their immediate willingness-to-pay (subjective value) for opioids, a sweet, and a savory snack across different quantities, their current context, and past- hour exposure to drug use-associated cues. Consistent with our study goal, we captured participants’ data across a wide diversity of real-world contexts, and some of our OUD participants’ data in contexts known to provoke drug craving. Moments of drug urge were separable from sweet or savory urge moments in OUD participants, even though participants who desired opioids also tended to desire sweets. These urge moments predicted shifts in subjective value specific to the desired object: drug value by drug urge in OUD participants, and sweet value by sweet urge in both samples. Lastly, we used hidden Markov models to test whether craving moments can be reliably and formally captured under distinct craving “states” for each craving type, finding that our drug data could be well-characterized by a latent process where individuals transition between a “baseline” and a “drug craving” state marked by elevated drug urge and higher drug value. Our findings show that individuals experience craving-specific subjective value shifts in the real world where they can freely act on and be influenced by their environments. Combining this sampling design with computational modeling may provide a novel mechanism for treatment validation and allow us to infer a behavioral read-out of vulnerable moments that could be targeted with just-in-time interventions for behavior change.M.S.Includes bibliographical reference

“With heart and head”: solving the “Resettlers Problem” in the Soviet Occupation Zone and German Democratic Republic

This dissertation examines the expulsion of “ethnic Germans” from parts of Eastern Europe after World War II and their integration in the Soviet Occupation Zone (SBZ) and German Democratic Republic (GDR). The victorious Allied leaders sanctioned the expulsion of approximately 12 million “ethnic Germans” from parts of what are now Poland, Romania, Hungary, the Czech Republic, Russia, and Yugoslavia. The expulsions occurred in various stages from 1945 until the early 1950s. Around 4.3 million of these people landed in the SBZ, constituting a quarter of the population. Despite their German heritage, members of this population, called resettlers in the “East,” were a heterogeneous group, with members speaking different dialectics and observing various cultural celebrations. Their arrival in the SBZ put a strain on a struggling postwar economy but also offered a new labor force and potential political allies. However, the occupying Soviet forces and German socialists realized there was a tension between resettlers and the “natives,” the receiving population, who viewed the newcomers as competitors for resources. This study examines the triangulated relationship between the resettlers, “natives,” and members of the state to illuminate how all three negotiated Germanness in the postwar world. Ultimately, this work challenges the assumption that resettlers were simply pawns of the state rather than active and vocal members of the population. Central to this study are questions of belonging, identity, and homeland. Focusing on resettlers illuminates the connection between material and emotional connections to a place. Moreover, resettlers highlight the ever-shifting nature of national and regional identities and how they interact with legal citizenship. The first three chapters establish the complexity of Germanness before WWII and how the division of Germany into occupied zones brought questions of who and where was German to the fore. Focusing on the Soviet Occupation Zone is an important step to looking at the large degree of improvisation around policies on resettlers. Chapter Three provides a pivot point, as the Soviets and socialists declared the “resettler problem” solved. The final two chapters prove that despite public silencing and the declaration that resettlers were fully integrated, members of the state, resettlers, and natives continued to contend with what it meant to become East German in light of expulsion.Ph.D.Includes bibliographical referencesIncludes vit

Social media and social movements: connections between Occupy Wall Street and Black Lives Matter in New York City

Worsening inequality, lack of representation, and the increasing role of communications all shaped the context of the 2010s. Occupy Wall Street (OWS) and Black Lives Matter (BLM) shared this historical moment. The primary objective of my dissertation here is to examine the how OWS and BLM in NYC were organized. To this end, I describe social media use byparticipants. I show how OWS embraced prefigurative politics and a tactical media approach. I challenge the dominant scholarship on social movements and propose instead adopting societies in movement as a framework for social action. OWS moved through social media, creating what I term a participatory movement. I show how similar cultural practices continued in BLM in NYC.Ph.D.Includes bibliographical reference

A persistent yet dangerous game of solitaire: how the neoliberal university invisibilizes single student-mothers

Unmarried student-mothers are a specific college student population whose enrollment numbers continue to increase on American campuses, yet only approximately 28% of student-mothers graduate within six years (United States Government Accountability Office, 2019). With fierce determination, student-mothers enter universities out of necessity with promises of upward socioeconomic mobility; however, they soon encounter institutional campus barriers influenced by neoliberalism which expect student-mothers to conform to certain traditional college student standards. Extant literature reveals how campus cultures invisibilize student-mothers through flickering or absent services that hinder their degree-completion successes, yet there is a paucity of research dedicated to student-mothers’ experiences within college classrooms. Grounded in a three-pronged theoretical approach, this study examines three areas of inequitable college campus practices: 1) how the neoliberal university’s campus services neglect student-mothers; 2) how matricentric feminism’s call for agency and flexibility illuminates institutional barriers student-mothers face which silence their voices; and 3) how sociocultural learning theory can redirect professors’ classroom policies and practices to provide student-mothers with inclusive epistemic credibility. Six student-mothers who graduated from four New Jersey universities guide this study, which utilizes a Participatory Action Research methodology to actively elevate the participants as co-researchers and generate decolonizing new knowledge (Fine & Torre, 2021; Lennette, 2022). Their narratives foreground their lived experiences to offer recommendations that can inform universities’ future policies and practices at the administrative level, on campus at large, and within the classroom. Keywords: student-mothers, college, university, higher education, neoliberalism, matricentric feminism, communities of practice, invisibilization, sense of belonging, qualitative, participatory action research, non-traditional studentsEd.D.Includes bibliographical reference

Analysis of genomic differences in SARS-CoV-2 variants, and its Impacts on transmissibility and virulence: Washington and Florida states

This purpose of this study was to analyze genetic differences between SARS-CoV-2 and its variants, examine how these genetic differences relate to SARS-CoV-2 transmissibility and virulence, and determine the impact of SARS-CoV-2 transmission and virulence for different regions – Washington and Florida – in the United States. Methods: Genetic sequences of SARS-CoV-2 variants were obtained from the National Institutes of Health/National Center for Biotechnology Information and analyzed for genetic variations by the NIH/NCBI Sequence Alignment Tool and the Coronavirus Typing Tool. Data collected by the Center for Disease Control and Prevention, US Department of Health and Human Services, Global Initiative on Sharing All Influenza Data, CoVariants, and Washington State Department of Health was compiled to analyze SARS-CoV-2 health statistics and circulating variants between June 2021 and June 2022. Pearson correlations, unpaired t-tests, and linear regressions were conducted to assess the relationships between health statistics, variants, and region. Results: Alpha, Delta, and Omicron variants had acquired genetic mutations from the original SARS-CoV-2 strain, with Delta obtaining the most. Washington and Florida saw significant relationships between Delta and Omicron and transmissibility; however, no variant had a significant relationship with virulence in either state. similarly, experienced transmission similarly but had significant differences for SARS-CoV-2 virulence. Conclusion: This study shows that Sars CoV-2 Delta and Omicron had significant genetic mutations than Alpha. Omicron being the most significant in both states for transmissibility – cases and hospitalization. The virulence (death) was significantly different between both states.Ph.D.Includes bibliographical reference