<p>Tactile speech communication allows individuals to understand speech by sensations transmitted through the sense of touch. Devices that enable tactile speech communication can be an effective means to transmit important messages when the visual and/or auditory systems are overloaded or impaired. This has applications in silent communication and for people with hearing and/or visual impairments. An effective artificial speech communication system must be learned in a reasonable time and be easily remembered. Moreover, it must transmit any word at suitable rates for speech communication. The pursuit of a system that fulfills these requirements is a complex task that requires work in different areas. This thesis presents advancements in four of them. First is the matter of encoding speech information. Here, a phonemic-based approach allowed participants to recognize of tactile phonemes, words, phrases and full sentences. Second is the issue of training users in the use of the system. To this end, this thesis investigated the phenomenon of incidental categorization of vibrotactile stimuli as the foundation of more natural methods to learn a tactile speech communication system. Third is the matter of the neural processing of the tactile speech information. Here, an exploration of the functional characteristics of the phonemic-based approach using EEG was conducted. Finally, there is the matter of implementing the system for consumer use. In this area, this work addresses practical considerations of delivering rich haptic effects with current wearable technologies. These are informative for the design of actuators used in tactile speech communication devices.</p>

<p>Cardiovascular disease (CVD) is the leading cause of death worldwide and is expected to increase direct medical costs in the U.S. to $749 billion by the year 2035. Diagnosis of CVD through imaging techniques can improve our understanding of CVD progression and its associated risks through visualization of anatomical features and biological constituents. Non-invasive imaging relies on optimal image quality for visualization of such tissue structures that can be difficult to identify and segment. While various imaging modalities are used to determine tissue characteristics, many lack the spatial resolution that optics-based imaging can provide, which can assess hemodynamic parameters in preclinical models of ischemic disease. Acoustic-based imaging can complement optics-based imaging by providing anatomical and location-specific information of tissues with greater penetration depth. Even with all the advancements in imaging technology, however, limitations still exist in non-invasively, efficiently, and accurately capturing biologically relevant information with adequate spatial and temporal resolution. Furthermore, reproducible feature extraction is difficult due to a lack of standardization in the field, making it difficult to implement when image quality varies. In this work, we implement spatial frequency domain imaging (SFDI), ultrasound, and photoacoustic imaging in preclinical models of 1) peripheral artery disease, 2) traumatic brain injury, and 3) myocardial ischemia to capture imaging biomarkers of vascular and cardiac health in longitudinal studies. We also implement deep learning on preclinical ultrasound and photoacoustic images of the cardiac left ventricle to automatically extract regions of interest to calculate radial strain and oxygen saturation. Eventually findings from this work may help improve clinical cardiovascular disease diagnosis, prognosis, and treatment.</p>

<p dir="ltr">The brain is a dynamic tissue that is passively driven by a combination of the cardiac cycle, respiration, and slow wave oscillations. The function of the brain relies on its ability to maintain a normal homeostatic balance between its mechanical environment and metabolic demands, which can be greatly altered in the cases of neurodegeneration or traumatic brain injury. It has been a challenge in the field to quantify the dynamics of the tissue and cerebrospinal fluid flow in human subjects on a patient-specific basis over the many spatial and temporal scales that it relies upon. Non-invasive imaging tools like structural, functional, and dynamic MRI sequences provide modern researchers with an unprecedented view into the human brain. Our work leverages these sequences by developing novel, open-source pipelines to 1) quantify the biomechanical environment of the brain tissue over 133 functional brain regions, and 2) estimate real-time cerebrospinal fluid velocity from flow artifacts on functional MRI by employing breathing regimens to enhance fluid motion. These pipelines provide a comprehensive view of the macroscale tissue and fluid motion in a given patient. Additionally, we sought to understand how the transmission of macroscale forces, in the context of traumatic brain injury, contribute to neuronal damage by 3) developing a digital twin to simulate 30-200 g-force loading of 2D neuronal cultures and observing the morphological and electrophysiological consequences of these impacts in vitro by our collaborators. Taken together, we believe these works are a steppingstone that will enable future researchers to deeply understand the mechanical contributions that underly clinical neurological outcomes and perhaps lead to the development of earlier diagnostics, which is of dire need in the case of neurodegenerative diseases.</p>

<p dir="ltr">Blood hemoglobin (Hgb) testing is a widely used clinical laboratory test for a variety of patient care needs. However, conventional blood Hgb measurements involve invasive blood sampling, exposing patients to potential risks and complications from needle pricks and iatrogenic blood loss. Although noninvasive blood Hgb quantification methods are under development, they still pose challenges in achieving performance comparable to clinical laboratory blood Hgb test results (i.e., gold standard). In particular, optical spectroscopy can provide reliable blood Hgb tests, but its practical utilizations in diagnostics are limited by bulky optical components, high costs, and extended data acquisition time. Mobile health (mHealth) or diagnostic colorimetric applications have a potential for point-of-care blood Hgb testing. However, achieving color accuracy for diagnostic applications is a complex matter, affected by device models, light conditions, and image file formats.</p><p dir="ltr">To address these limitations, we propose biophysics-based machine learning algorithms that combine hyperspectral learning and spectroscopic gamut-informed learning for accurate and precise mHealth blood Hgb assessments in a noninvasive manner. This method utilizes single-shot photographs of peripheral tissue acquired by onboard smartphone cameras. The palpebral conjunctiva (i.e., inner eyelid) serves as an ideal peripheral tissue site, owing to its easy accessibility, relatively uniform microvasculature, and absence of skin pigmentation (i.e., melanocytes). First, hyperspectral learning enables a mapping from red-green-blue (RGB) values of a digital camera into detailed hyperspectral information: an inverse mapping from a sparse space (tristimulus color values) to a dense space (multiple wavelengths). Hyperspectral learning employs a statistical learning framework to reconstruct a high-resolution spectrum from a digital photo of the palpebral conjunctiva, eliminating the need for complex and costly optical instrumentation. Second, comprehensive spectroscopic analyses of peripheral tissue are used to establish a unique blood Hgb gamut and design a diagnostic color reference chart highly sensitive to blood Hgb and peripheral perfusion. Informed by the domain knowledge of tissue optics and machine vision, the Hgb gamut-based learning algorithm offers device/light/format-agnostic color recovery of the palpebral conjunctiva, outperforming the existing color correction methods.</p><p dir="ltr">This mHealth blood Hgb prediction method exhibits comparable accuracy and precision to capillary blood sampling tests (e.g., finger prick) over a wide range of blood Hgb values, ensuring its reliability, consistency, and reproducibility. Importantly, by employing only a digital photograph with the Hgb gamut-learned color recovery, hyperspectral learning-based blood Hgb assessments allow noninvasive, continuous, and real-time reading of blood Hgb levels in resource-limited and at-home settings. Furthermore, our biophysics-based machine learning approaches for digital health applications can lay the foundation for the future of personalized medicine and facilitate the tempo of clinical translation, empowering individuals and frontline healthcare workers.</p>

No description available.

<p>    </p> <p>Cerebrovascular diseases, such as stroke, constitute the most common life-threatening neurological disease in the United States. To support normal brain function, maintaining adequate brain perfusion (i.e., cerebral blood flow (CBF)) is important. Therefore, it is crucial to assess the brain perfusion so that early intervention in cerebrovascular diseases can be applied if abnormal perfusion is observed. The goal of my study is to develop metrics to measure the brain perfusion through modeling brain physiology using resting-state and task-based blood-oxygenation-level- dependent (BOLD) functional MRI (fMRI). My first and second chapters focused on deriving the blood arrival time using the resting-state BOLD signal. In the first chapters, we extracted the systemic low-frequency oscillations (sLFOs) in the fMRI signal from the internal carotid arteries (ICA) and the superior sagittal sinus (SSS). Consistent and robust results were obtained across 400 scans showing the ICA signals leading the SSS signals by about 5 seconds. This delay time could be considered as an effective perfusion biomarker that is associate with the cerebral circulation time (CCT). To further explore sLFOs in assessing dynamic blood flow changes during the scan, in my second chapter, a “carpet plot” (a 2-dimensional plot time vs. voxel) of scaled fMRI signal intensity was reconstructed and paired with a developed slope-detection algorithm. Tilted vertical edges across which a sudden signal intensity change took place were successfully detected by the algorithm and the averaged propagation time derived from the carpet plot matches the cerebral circulation time. Given that CO₂ is a vasodilator, controlling of inhaled CO₂ is able to modulate the BOLD signal, therefore, as a follow-up study, we focused on investigating the feasibility of using a CO₂ modulated sLFO signal as a “natural” bolus to track CBF with the tool developed from the second chapter. Meaningful transit times were derived from the CO₂-MRI carpet plots. Not only the timing, the BOLD signal deformation (the waveform change) under CO₂ challenge also reveals very useful perfusion information, representing how the brain react to stimulus. Therefore, my fourth chapter focused on characterizing the brain reaction to the CO₂ stimulus to better measure the brain health using BOLD fMRI. Overall, these studies deepen our understanding of fMRI signal and the derived perfusion parameters can potentially be used to assess some cerebrovascular diseases, such as stroke, ischemic brain damage, and steno-occlusive arterial disease in addition to functional activations. </p>

<p dir="ltr">Fibromuscular Dysplasia (FMD) is a non-inflammatory, non-atherosclerotic blood vessel disorder characterized by a series of narrowed and dilated regions of vasculature. These patients are prescribed blood thinners or anti-platelet therapeutics as treatment to this systemic disease. Current image-based diagnostic methods cannot reliably predict a patient’s risk of stroke in order to properly manage medication. There are also challenges in distinguishing FMD from other diseases that can cause arterial obstructions, like atherosclerosis or vasculitis.</p><p dir="ltr">The ultimate goal of this research is to develop a methodology for evaluating the risk of mechanical platelet activation based on medical imaging. Our hypothesis is that subject-specific assessment of platelet activation due to hemodynamic stress can improve risk stratification of FMD patients. The aims of the projects were therefore to 1) Develop a CFD-based methodology for estimating platelet activation state, and 2) Test this methodology on a small cohort of subjects with FMD, carotid artery stenosis, and healthy controls. A modeling workflow was developed, combining Eulerian and Lagrangian approaches to compute flow fields and evaluate shear stress history of particles advected through the vascular geometries. From this stress history, predictive estimates of mechanical platelet activation can be calculated utilizing a platelet activation state (PAS) metric. We applied this modeling workflow to assess platelet activation in segments of carotid arteries of patients with Fibromuscular Dysplasia, Carotid Artery Stenosis, and healthy controls for comparison against experiments performed at the Cleveland Clinic assessing mechanical platelet activation in patients with each of these conditions. This work supports the development of a patient-specific determination of these same metrics, in order to more precisely assess patient risk of stroke.</p>

<p dir="ltr">DNA is organized into chromatin – a complex polymeric structure which stores information and controls gene expressions. Advancements in microscopy have enabled us to see chromatin in motion – which was previously thought to be static, and these motions contribute to various cellular functions. In my thesis I will demonstrate the molecular tools and biophysical approaches our lab has developed to uncover the mysteries of chromatin dynamics and structures at the single nucleosome resolution; I will also discuss how these new discoveries in chromatin enable us to explore its role in cell functions. This dissertation will first describe the technology advancement of live-cell image analysis; particularly, I will discuss the utilization of AI to improve the spatial and temporal resolution of chromatin imaging. Then I will show complex nature of chromatin where depending on the temporal scale of observation we see a different behavior and how computer simulations can see these differences. Following that, I will introduce our investigation on the role of chromatin motion in DNA damage and repair. Afterwards, I will discuss how the cell regulates its chromatin dynamics in response to the metabolism indicators AMPK (AMP-activated protein kinase). I will also show how chromatin motion and structure behave without the presence of key proteins such as RAD51 that aid in DNA damage. Finally I will go over future directions and improvements we can do to our current techniques to improve our understanding of chromatin’s role is various biological functions. We expect that the exploration of the spatiotemporal dynamics in live cells will facilitate the diagnosis, treatment, and prevention of cancers.</p>

Diabetes is a rapidly increasing health problem. In a global perspective,approximately 415 million people suffered from diabetes in 2015 and this number ispredicted to increase to 640 million by 2040. To tackle this pandemic there is a needfor better analytical tools by which we can increase our understanding of the disease.One discipline that has already provided much needed insight to diabetes etiology isoptical molecular imaging. Using various forms of light it is possible to create animage of the analysed sample that can provide information about molecularmechanistic aspects of the disease and to follow spatial and temporal dynamics. The overall aim of this thesis is to improve and adapt existing andnovel optical imaging approaches for their specific use in diabetes research. Hereby,we have focused on three techniques: (I) Optical projection tomography (OPT),which can be described as the optical equivalent of x-ray computed tomography(CT), and two vibrational microspectroscopic (VMS) techniques, which records theunique vibrational signatures of molecules building up the sample: (II) Fouriertransforminfrared vibrational microspectroscopy (FT-IR) and (III) Ramanvibrational microspectroscopy (Raman). The computational tools and hardware applications presented here generallyimprove OPT data quality, processing speed, sample size and channel capacity.Jointly, these developments enable OPT as a routine tool in diabetes research,facilitating aspects of e.g. pancreatic β-cell generation, proliferation,reprogramming, destruction and preservation to be studied throughout the pancreaticvolume and in large cohorts of experimental animals. Further, a novel application ofmultivariate analysis of VMS data derived from pancreatic tissues is introduced.This approach enables detection of novel biochemical alterations in the pancreasduring diabetes disease progression and can be used to confirm previously reportedbiochemical alterations, but at an earlier stage. Finally, our studies indicate thatRaman imaging is applicable to in vivo studies of grafted islets of Langerhans,allowing for longitudinal studies of pancreatic islet biochemistry.viIn summary, presented here are new and improved methods by which opticalimaging techniques can be utilised to study 3D-spatial, quantitative andmolecular/biochemical alterations of the normal and diseased pancreas.

Chronic obstructive pulmonary disease (COPD) describes a range of lung conditions including emphysema, chronic bronchitis and small airways disease. While COPD is a major cause of death and debilitating illness, current clinical assessment methods are inadequate: they are a poor predictor of patient outcome and insensitive to mild disease. A new imaging technology, hyperpolarised xenon MRI, offers the hope of improved diagnostic techniques, based on regional measurements using functional imaging. There is a need for quantitative analysis techniques to assist in the interpretation of these images. The aim of this work is to develop these techniques as part of a clinical trial into hyperpolarised xenon MRI. In this thesis we develop a fully automated pipeline for deriving regional measurements of lung function, making use of the multiple imaging modalities available from the trial. The core of our pipeline is a novel method for automatically segmenting the pulmonary lobes from CT data. This method combines a Hessian-based filter for detecting pulmonary fissures with anatomical cues from segmented lungs, airways and pulmonary vessels. The pipeline also includes methods for segmenting the lungs from CT and MRI data, and the airways from CT data. We apply this lobar map to the xenon MRI data using a multi-modal image registration technique based on automatically segmented lung boundaries, using proton MRI as an intermediate stage. We demonstrate our pipeline by deriving lobar measurements of ventilated volumes and diffusion from hyperpolarised xenon MRI data. In future work, we will use the trial data to further validate the pipeline and investigate the potential of xenon MRI in the clinical assessment of COPD. We also demonstrate how our work can be extended to build personalised computational models of the lung, which can be used to gain insights into the mechanisms of lung disease.