Reflexology

Reflexology is the practice of applying pressure to particular points on the ears, hands and feet to impact the health of specific parts of the body. It is a form of complementary therapy that is used for diseases and conditions that have long lasting symptoms and need pain management. In reflexology, each pressure point acts as a sensor on the feet and hands and is linked with organs, glands and muscles in specific parts of the body. It involves the idea that a force or energy is flowing along paths, called meridians, in the body to all organs and any kind of blockage in this flow will lead to an impairment of function. The purpose of reflexology is to normalize the body’s function, break down tension, alleviate stress, and improve nerve function and blood supply throughout the body. The specific physiological mechanisms of reflexology are unknown, however, this practice has shown benefits in a wide variety of medical conditions

Siblings

From "Siblings." Published by Penguin Studio Editions. Reprinted by permission of International Creative Management, INC. Text copyright 1998 Anna Quindlen, photographers copyright 1998 Nick Kelsh.captions by Anna Quindlen ; photos by Nick Kels

Mechano-to-Neural Transduction of the Pacinian Corpuscle

University of Minnesota Ph.D. dissertation. October 2017. Major: Biomedical Engineering. Advisor: Victor Barocas. 1 computer file (PDF); xv, 207 pages.Cutaneous mechanoreceptors are responsible for our ability to distinguish between different touch modalities and experience the physical world around us. Mechanoreceptors are innervated by afferent mechanosensitive neurons that transduce mechanical stimuli into action potentials and terminate in specialized end organs. The Pacinian corpuscle (PC) has been studied more than any of our other mechanoreceptors due to its large size and ease of identification during dissection. The PC, which is found primarily within the dermis of glabrous skin, responds to low-amplitude, high-frequency vibrations in the 20-1000 Hz range. The PC functions as a bandpass filter to vibrations, an effect attributed to the structural and mechanical complexity of its end organ. The PC contains a central mechanosensitive nerve fiber (neurite) that is encapsulated by alternating layers of flat, epithelial-type cells (lamellae) and fluid. The overarching goal of this thesis was to unify the anatomical and electrophysiological observations of the PC via a detailed mechanistic model of PC response to mechanical stimulation, requiring a multiphysics, multiscale approach. First, we developed a multiscale finite-element mechanical model to simulate the equilibrium response of the PC to indentation while accounting for the layered, anisotropic structure of the PC and its deep location within the skin. Next, we developed a three-stage finite-element model of the PC’s mechanical and neural responses to a vibratory input that accounted for the lamellar mechanics and neurite electrochemistry. This mechano-neural model was able to simulate the PC’s band-pass filtration of vibratory stimuli and rapid adaptation to sustained mechanical stimuli. We then used this model to evaluate the relationship between the PC’s material and geometric parameters and its response to vibration and developed dimensionless expressions for the relationship between these parameters and peak frequency or bandwidth. We then embedded multiple mechano-neural PC models within a finite-element model of human skin to simulate the mechanical and neural behavior of a PC cluster in vivo. We then performed a literature search to compile the structural parameters of PCs from various species and used our mechano-neural model to simulate the frequency response across species. Finally, we isolated PCs from human cadaveric hands and performed micropipette aspiration experiments to determine an apparent Young’s modulus of the PC. The computational and experimental work performed in this thesis contribute to the understanding of the fundamental behavior of mechanoreceptors, which is a necessary first step towards the development of haptic feedback-enabled devices

Closed-Loop Statistical Verification of Stochastic Nonlinear Systems Subject to Parametric Uncertainties

This paper proposes a statistical verification framework using Gaussian processes (GPs) for simulation-based verification of stochastic nonlinear systems with parametric uncertainties. Given a small number of stochastic simulations, the proposed framework constructs a GP regression model and predicts the system's performance over the entire set of possible uncertainties. Included in the framework is a new metric to estimate the confidence in those predictions based on the variance of the GP's cumulative distribution function. This variance-based metric forms the basis of active sampling algorithms that aim to minimize prediction error through careful selection of simulations. In three case studies, the new active sampling algorithms demonstrate up to a 35% improvement in prediction error over other approaches and are able to correctly identify regions with low prediction confidence through the variance metric.Comment: 8 pages, submitted to ACC 201

The evolution of hyperthermic intraperitoneal chemotherapy in the setting of advanced ovarian cancer

Ovarian cancer is the second most common, and first most lethal gynecological cancer. It will affect one in seventy-eight women, and is commonly diagnosed in the later stages of the disease. The majority of the cancer’s lifespan is spent within the peritoneal cavity. Hyperthermic intraperitoneal chemotherapy (HIPEC) is an innovative new treatment that has been proven as an effective treatment in other peritoneal cancers. There is strong scientific evidence to support HIPEC as an ideal treatment for advanced ovarian cancer. Over the past two decades, there has been an increase in the number of studies focused on the efficacy of HIPEC with regards to advanced ovarian cancer. These studies have shown great promise, with two very recent phase III studies showing resounding results. It is also clear that there is a need for standardization throughout these scientific studies in order to reasonably introduce HIPEC as a standard of treatment

Data-driven methods for statistical verification of uncertain nonlinear systems

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2018.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 277-290).Due to the increasing complexity of autonomous, adaptive, and nonlinear systems, engineers commonly rely upon statistical techniques to verify that the closed-loop system satisfies specified performance requirements at all possible operating conditions. However, these techniques require a large number of simulations or experiments to exhaustively search the set of possible parametric uncertainties for conditions that lead to failure. This work focuses on resource-constrained applications, such as preliminary control system design or experimental testing, which cannot rely upon exhaustive search to analyze the robustness of the closed-loop system to those requirements. This thesis develops novel statistical verification frameworks that combine data-driven statistical learning techniques and control system verification. First, two frameworks are introduced for verification of deterministic systems with binary and non-binary evaluations of each trajectory's robustness. These frameworks implement machine learning models to learn and predict the satisfaction of the requirements over the entire set of possible parameters from a small set of simulations or experiments. In order to maximize prediction accuracy, closed-loop verification techniques are developed to iteratively select parameter settings for subsequent tests according to their expected improvement of the predictions. Second, extensions of the deterministic verification frameworks redevelop these procedures for stochastic systems and these new stochastic frameworks achieve similar improvements. Lastly, the thesis details a method for transferring information between simulators or from simulators to experiments. Moreover, this method is introduced as part of a new failure-adverse closed-loop verification framework, which is shown to successfully minimize the number of failures during experimental verification without undue conservativeness. Ultimately, these data-driven verification frameworks provide principled approaches for efficient verification of nonlinear systems at all stages in the control system development cycle.by John Francis Quindlen.Ph. D

Active Sampling-based Binary Verification of Dynamical Systems

Nonlinear, adaptive, or otherwise complex control techniques are increasingly relied upon to ensure the safety of systems operating in uncertain environments. However, the nonlinearity of the resulting closed-loop system complicates verification that the system does in fact satisfy those requirements at all possible operating conditions. While analytical proof-based techniques and finite abstractions can be used to provably verify the closed-loop system's response at different operating conditions, they often produce conservative approximations due to restrictive assumptions and are difficult to construct in many applications. In contrast, popular statistical verification techniques relax the restrictions and instead rely upon simulations to construct statistical or probabilistic guarantees. This work presents a data-driven statistical verification procedure that instead constructs statistical learning models from simulated training data to separate the set of possible perturbations into "safe" and "unsafe" subsets. Binary evaluations of closed-loop system requirement satisfaction at various realizations of the uncertainties are obtained through temporal logic robustness metrics, which are then used to construct predictive models of requirement satisfaction over the full set of possible uncertainties. As the accuracy of these predictive statistical models is inherently coupled to the quality of the training data, an active learning algorithm selects additional sample points in order to maximize the expected change in the data-driven model and thus, indirectly, minimize the prediction error. Various case studies demonstrate the closed-loop verification procedure and highlight improvements in prediction error over both existing analytical and statistical verification techniques.Comment: 23 page