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Description
The Hippo-YAP/TAZ signaling pathway plays a critical role in tissue homeostasis, tumorigenesis, and degeneration disorders. The regulation of YAP/TAZ levels is controlled by a complex regulatory network, where several feedback loops have been identified. However, it remains elusive how these feedback loops contain the YAP/TAZ levels and maintain the system in a healthy physiological state or trap the system into pathological conditions. Here, a mathematical model was developed to represent the YAP/TAZ regulatory network. Through theoretical analyses, three distinct states that designate the three physiological and pathological outcomes were found. The transition from the physiological state to the two pathological states is mechanistically controlled by coupled bidirectional bistable switches, which are robust to parametric variation and stochastic fluctuations at the molecular level. This work provides a mechanistic understanding of the regulation and dysregulation of YAP/TAZ levels in tissue state transitions.
ContributorsBarra Avila, Diego Rodrigo (Author) / Tian, Xiaojun (Thesis advisor) / Wang, Xiao (Committee member) / Plaisier, Christopher (Committee member) / Arizona State University (Publisher)
Created2021
Description
Image segmentation is an important and challenging area of research in computer vision with various applications in medical imaging. Image segmentation refers to the process of partitioning an image into meaningful parts having similar attributes. Traditional manual segmentation approaches rely on human expertise to outline object boundaries in images which is a tedious and expensive process. In recent years, Deep Convolutional Neural Networks have demonstrated excellent performance in tasks such as detection, localization, recognition and segmentation of objects. However, these models require a large set of labeled training data which is difficult to obtain for medical images. To solve this problem, interactive segmentation techniques can be used to serve as a trade-off between fully automated and manual approaches. This allows a human expert in the loop as a form of guidance and refinement together with deep neural networks. This thesis proposes an interactive training strategy for segmentation, where a robot-user is utilized during training to mimic an actual annotator and provide corrections to the predicted masks by drawing scribbles. These scribbles are then used as supervisory signals and fed to the network; which interactively refines the segmentation map through several iterations of training. Further, the conducted experiments using various heuristic click strategies demonstrate that user interaction in the form of curves inside the organ of interest achieve optimal editing performance. Moreover, by using the popular image segmentation architectures based on U-Net as base models, segmentation performance is further improved; signifying that the accuracy gain of the interactive correction conform to the accuracy of the initial segmentation map.
ContributorsGoyal, Diksha (Author) / Liang, Jianming Dr. (Thesis advisor) / Wang, Yalin Dr. (Committee member) / Demakethepalli Venkateswara, Hemanth Kumar Dr. (Committee member) / Arizona State University (Publisher)
Created2021
Description
This work focuses on qualifying the performance of an optoelectrical measurement system designed to analyze ribonucleic acid (RNA) within a micro sample. The system is capable of measuring light intensity converted to voltage versus time and is a fast, inexpensive, and portable method for rapid detection of biologics such as SARS-CoV-2 virus, or Covid-19 disease. The measurement system consists of a microfluidic chip and a point of care fluorescent reader.The intent of this research is to measure consistency and robustness of the fluorescent reader combined with the microfluidic chip. The consistency and the robustness of the fluorescent reader within the duty cycle of the system power and the measurement system were analyzed with Six Sigma methods. Control charts, analysis of variance (ANOVAs), and variance components calculations were implemented to characterize the reader system. Through the process of this analysis, baseline characteristics were measured and documented providing valuable data for the improved instrument design.
The existing microfluidic chip is a prototype that works in combination with the reader based on fluorescent detection. Baseline studies were required to define any issues related to microfluidic autofluorescence. Multiple designs were tested to measure reduction in autofluorescence in the microfluidics. It was found that certain designs performed better than others. One approach for improvement in the microfluidic chip may be achieved by characterizing and source controlling materials, optimizing layers, mask apertures, and mask orientations to determine reliability in the measurable output through the fluorescent reader. Since the reader and the microfluidic are designed to work together, any future studies should explore testing where the two components are considered a coupled system.
ContributorsShabtai, Bat-El (Author) / Blain Christen, Jennifer (Thesis advisor) / Abbas, James (Thesis advisor) / Maass, Eric (Committee member) / Beeman, Scott (Committee member) / Arizona State University (Publisher)
Created2021
Description
Behavior-driven obesity has become one of the most challenging global epidemics since the 1990s, and is presently associated with the leading causes of death in the U.S. and worldwide, including diabetes, cardiovascular disease, strokes, and some forms of cancer. The use of system identification and control engineering principles in the design of novel and perpetually adaptive behavioral health interventions for promoting physical activity and healthy eating has been the central theme in many recent contributions. However, the absence of experimental studies specifically designed with the purpose of developing control-oriented behavioral models has restricted prior efforts in this domain to the use of hypothetical simulations to demonstrate the potential viability of these interventions. In this dissertation, the use of first-of-a-kind, real-life experimental results to develop dynamic, participant-validated behavioral models essential for the design and evaluation of optimized and adaptive behavioral interventions is examined. Following an intergenerational approach, the first part of this work aims to develop a dynamical systems model of intrauterine fetal growth with the prime goal of predicting infant birth weight, which has been associated with subsequent childhood and adult-onset obesity. The use of longitudinal input-output data from the “Healthy Mom Zone” intervention study has enabled the estimation and validation of this fetoplacental model. The second part establishes a set of data-driven behavioral models founded on Social Cognitive Theory (SCT). The “Just Walk” intervention experiment, developed at Arizona State University using system identification principles, has lent a unique opportunity to estimate and validate both black-box and semiphysical SCT models for predicting physical activity behavior. Further, this dissertation addresses some of the model estimation challenges arising from the limitations of “Just Walk”, including the need for developing nontraditional modeling approaches for short datasets, as well as delivers a new theoretical and algorithmic framework for structured state-space model estimation that can be used in a broader set of application domains. Finally, adaptive closed-loop intervention simulations of participant-validated SCT models from “Just Walk” are presented using a Hybrid Model Predictive Control (HMPC) control law. A simple HMPC controller reconfiguration strategy for designing both single- and multi-phase intervention designs is proposed.
ContributorsFreigoun, Mohammad T (Author) / Raupp, Gregory B (Thesis advisor) / Tsakalis, Konstantinos S (Thesis advisor) / Spanias, Andreas S (Committee member) / Forzani, Erica S (Committee member) / Muhich, Christopher L (Committee member) / Arizona State University (Publisher)
Created2021
ContributorsBeeler, Adeline (Author) / McNally, Mikayla (Co-author) / Schaefer, Sydney (Thesis director) / Lohse, Keith (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2021-12
ContributorsBeeler, Adeline (Author) / McNally, Mikayla (Co-author) / Schaefer, Sydney (Thesis director) / Lohse, Keith (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2021-12
ContributorsMcNally, Mikayla (Author) / Beeler, Adeline (Co-author) / Schaefer, Sydney (Thesis director) / Lohse, Keith (Committee member) / Barrett, The Honors College (Contributor) / Watts College of Public Service & Community Solut (Contributor)
Created2021-12
ContributorsMcNally, Mikayla (Author) / Beeler, Adeline (Co-author) / Schaefer, Sydney (Thesis director) / Lohse, Keith (Committee member) / Barrett, The Honors College (Contributor) / Watts College of Public Service & Community Solut (Contributor)
Created2021-12
Detect and analyze the 3-D head movement patterns in marmoset monkeys using wireless tracking system
Description
Head movement is a natural orienting behavior for sensing environmental events around us. Head movement is particularly important for identifying through the sense of hearing the location of an out-of-sight, rear-approaching target to avoid danger or threat. This research aims to design a portable device for detecting the head movement patterns of common marmoset monkeys in laboratory environments. Marmoset is a new-world primate species and has become increasingly popular for neuroscience research. Understanding the unique patterns of their head movements will improve its values as a new primate model for uncovering the neurobiology of natural orienting behavior. Due to their relatively small head size (5 cm in diameter) and body weight (300-500 g), the device has to meet several unique design requirements with respect to accuracy and workability. A head-mount wireless tracking system was implemented based on inertial sensors that are capable of detecting motion in the Yaw, Pitch and Roll axes. The sensors were connected to the encoding station, which transmits wirelessly the 3-axis movement data to the decoding station at the sampling rate of ~175 Hz. The decoding station relays this information to the computer for real-time display and analysis. Different tracking systems, based on the accelerometer and Inertial Measurement Unit is implemented to track the head movement pattern of the marmoset head. Using these systems, translational and rotational information of head movement are collected, and the data analysis focuses on the rotational head movement in body-constrained marmosets. Three stimulus conditions were tested: 1) Alert, 2) Idle 3) Sound only. The head movement patterns were examined when the house light was turned on and off for each stimulus. Angular velocity, angular displacement and angular acceleration were analyzed in all three axes.
Fast and large head turns were observed in the Yaw axis in response to the alert stimuli and not much in the idle and sound-only stimulus conditions. Contrasting changes in speed and range of head movement were found between light-on and light-off situations. The mean peak angular displacement was 95 degrees (light on) and 55 (light off) and the mean peak angular velocity was 650 degrees/ second (light on) and 400 degrees/second (light off), respectively, in response to the alert stimuli. These results suggest that the marmoset monkeys may engage in different modes of orienting behaviors with respect to the availability of visual cues and thus the necessity of head movement. This study provides a useful tool for future studies in understanding the interplay among visual, auditory and vestibular systems during nature behavior.
Fast and large head turns were observed in the Yaw axis in response to the alert stimuli and not much in the idle and sound-only stimulus conditions. Contrasting changes in speed and range of head movement were found between light-on and light-off situations. The mean peak angular displacement was 95 degrees (light on) and 55 (light off) and the mean peak angular velocity was 650 degrees/ second (light on) and 400 degrees/second (light off), respectively, in response to the alert stimuli. These results suggest that the marmoset monkeys may engage in different modes of orienting behaviors with respect to the availability of visual cues and thus the necessity of head movement. This study provides a useful tool for future studies in understanding the interplay among visual, auditory and vestibular systems during nature behavior.
ContributorsPandey, Swarnima (Author) / Zhou, Yi (Thesis advisor) / Tillery, Stephen H (Thesis advisor) / Buneo, Christpher A (Committee member) / Arizona State University (Publisher)
Created2015
Description
Stroke accounts for high rates of mortality and disability in the United States. It levies great economic burden on the affected subjects, their family and the society at large. Motor impairments after stroke mainly manifest themselves as hemiplegia or hemiparesis in the upper and lower limbs. Motor recovery is highly variable but can be enhanced through motor rehabilitation with sufficient movement repetition and intensity. Cost effective assistive devices that can augment therapy by increasing movement repetition both at home and in the clinic may facilitate recovery. This thesis aims to develop a Smart Glove that can enhance motor recovery by providing feedback to both the therapist and the patient on the number of hand movements (wrist and finger extensions) performed during therapy. The design implements resistive flex sensors for detecting the extensions and processes the information using the Lightblue bean microcontroller mounted on the wrist. Communication between the processing unit and display module is wireless and executes Bluetooth 4.0 communication protocol. The capacity for the glove to measure and record hand movements was tested on three stroke and one traumatic brain injured patient while performing a box and blocks test. During testing many design flaws were noted and several were adapted during testing to improve the function of the glove. Results of the testing showed that the glove could detect wrist and finger extensions but that the sensitivity had to be calibrated for each patient. It also allowed both the therapist and patient to know whether the patient was actually performing the task in the manner requested by the therapist. Further work will reveal whether this feedback can enhance recovery of hand function in neurologically impaired patients.
ContributorsSasidharan, Smrithi (Author) / Kleim, Jeffrey A. (Thesis advisor) / Santello, Marco (Committee member) / Buneo, Christopher A. (Committee member) / Arizona State University (Publisher)
Created2015