Browsing by Subject "recovery"

Now showing 1 - 6 of 6
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    Comparing the impact of the moderate and heavy exercise domains on autonomic control, circulating cortisol, and next-day endurance performance in trained runners
    (2022-05) Foreman, Nicholas
    This thesis examines the impact of exercise in the moderate and heavy domainson aspects of the hypothalamic-pituitary-adrenocortical axis, cardiac autonomic control, and subsequent high-intensity and maximal endurance performance. To better understand post-exercise autonomic control, we measured heart rate and heart rate variability (HRV) during recovery from exercise in the moderate and heavy domains in ten well-trained endurance athletes. Blood was drawn during recovery for measurement of circulating cortisol. The following day, participants completed three high-intensity intervals before running a 3,000m time trial. Exercise in the heavy domain led to a delay in the recovery of HRV after exercise for the first 20 minutes after exercise with no differences at subsequent timepoints. Exercise in the heavy domain did not increase circulating cortisol or alter whole body metabolism during high intensity exercise the following day; similarly, time trial performance was not impaired following exercise in the heavy domain. These findings suggest that exercise in the heavy domain is well-tolerated by endurance athletes. Further research is needed to better understand these findings in the context of chronic training.
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    Disaster Survivors Evaluate "Recovery After Disaster: The Family Financial Toolkit"
    (University of Minnesota Extension, 2013-10) Croymans, Sara; Scharmer, Lori
    North Dakota State University Extension Service and University of Minnesota Extension jointly developed Recovery after Disaster: The Family Financial Toolkit that helps families recover financially from a natural disaster: The Toolkit was distributed in North Dakota during floods along the Missouri and Souris Rivers in summer 2011. Over 1,700 copies of the Toolkit were distributed to flood-displaced families. In-person training sessions were held for disaster survivors, case managers, and faith leaders in three counties. Some 817 survivors requested disaster case managers, who used the Toolkit while advising disaster survivors. In fall 2012, 15 months after the floods, an online survey of disaster survivors was administered to determine whether the Toolkit was useful in the financial recovery process. This poster highlights survey results including where survivors received a copy of the Toolkit, who encouraged them to use the Toolkit, and an assessment of the usefulness of each unit of the Toolkit.
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    Electromigration-Induced Interconnect Aging and its Repercussions on the Performance of Nanometer-Scale VLSI Circuits
    (2016-06) Mishra, Vivek
    Modern electronic machines are powered by the integrated-circuit (IC), a semiconductor device consisting of compact electronic circuits on a silicon substrate. ICs can contain over a billion fundamental computing elements (transistors) that are connected by a network of metal wires called interconnects. Presently, interconnects constitute a primary bottleneck in achieving required IC performance. One of the major hurdles towards achieving good interconnect performance is electromigration (EM), a physical wear-out mechanism that occurs in metal wires carrying electrical current. EM is projected to limit the performance in future generations of ICs, especially for the wires carrying unidirectional (DC) currents, and is becoming a growing concern in on-chip interconnects across applications ranging from mobile computing to automotive domains. EM results in redistribution of metal atoms in interconnects that may result in the formation of either voids (empty spaces inside the wire) or extrusions (metal accumulation into the dielectric), and for modern copper-based interconnects, experimental works have observed that failure happens typically through the formation and growth of voids. For modern interconnects carrying large current densities, EM-induced voids can cause a resistance increase in the wire, rendering a wire EM-mortal. The resistance increase in these mortal interconnects can potentially result in circuit performance failure within the lifetime of a product. The classical methods for EM circuit analysis that are used in the industry to design EM-safe ICs do not capture the reality of EM physics in the realm of modern copper-based interconnects. IC designers use simple, deterministic, empirical EM models and there is a significant gap between such empirical models and the physics-based models that accurately capture the effect of EM in interconnects. The focus of this thesis is to attempt to reduce this gap by combining the two types of models efficiently, capturing the essence of physics-based models into the IC design, thereby enabling the design of EM-robust IC in future technologies. Unlike the classical EM analysis methods that rely on determining failure by extrapolating the EM characteristics of isolated single wires to IC operating conditions, the approach described in this thesis captures the circuit context and uses system failure rather than single-wire failure as the criterion for determining the lifetime of a circuit. The first part of the thesis proposes a statistical framework to evaluate the circuit performance degradation in on-chip wires through circuit level analysis. Typical on-chip power grids are inherently robust to EM due to redundancies in the interconnect network structure. In these grids, where interconnects typically carry unidirectional currents, the traditional approach to EM analysis is based on the weakest link model, whereby a single wire failure causes the grid to fail. It is shown here that the power grid can maintain supply integrity even under multiple elemental failures, and this can result in longer and more realistic lifetime predictions as compared with classical approaches. The next part of the thesis addresses signal interconnects that carry bidirectional (AC) currents as they transport logic signals within the digital system. For these wires, it is shown that EM is not only a catastrophic failure problem, but is also capable of causing parametric shifts in circuit performance over time. We perform HSPICE-based Monte Carlo simulations on a standard on-chip structure to quantify the impact of EM on circuit performance degradation. Although the damage due to EM degradation under bidirectional currents is reduced relative to the unidirectional current case due to partial EM recovery, it is demonstrated that, depending on the level of recovery, the circuit performance may degrade beyond acceptable limits and can be comparable to other transistor degradation mechanisms. The third part of the thesis addresses the issue of EM mortality in interconnects. A wire may be prevented from being mortal under EM if the maximum stress build-up, corresponding to the equilibrium between the current-induced forward stress and the back stress due to the gradient in atomic concentration along the wire, does not exceed the critical stress due to void nucleation. Alternatively, it may also not be mortal if the stress build-up does not exceed the critical stress over the lifetime of the circuit. A new efficient approach, based on multiple filters, is developed for determining the mortality of wires in a circuit. These filters greatly reduce circuit analysis time by predicting which wires can never be mortal over the circuit lifetime under its operating conditions so that detailed analysis must only be performed over a small subset of all interconnects. The final part of the thesis studies the effect of EM on via arrays, which redundantly connect the wires in multiple levels of metal in an IC. A stress analysis technique for via arrays is proposed, accounting for differential coefficients of thermal expansion in the materials that make up these structures. The combined impact of thermomechanical stress and redundancy on the via array is determined, and a new model for the impact on the failure of a larger interconnect network is developed. Using the new model, we analyze the EM-induced performance degradation in via arrays of an industrial power grid benchmark circuit.
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    Metabolomics study on Arabidopsis thaliana abiotic stress responses for priming, recovery, and stress combinations
    (2018-04) Xu, Yuan
    Temperature, water, and light are three stress factors that have major influences on plant growth, development, and reproduction. Plants can be primed to an acclimated state by a prior mild stress to enhance their resistance to future stress. ‘Priming’ is related to plant stress ‘memory’ during recovery. Plants may need to balance between keeping the memory for enhanced stress defense and resetting for maximum growth and development during recovery. In the field, plants are more often to encounter a combination of different abiotic stresses rather than a specific single stress condition. Plant responses to a combination of stresses may exhibit quite unique defense and acclimation responses as compared to the response elicited by each individual stress, which should not be simply considered as the sum of the two different stresses. However, the simultaneous occurrence of multiple stress events is rarely studied experimentally, especially by metabolomics methods. Metabolomics, the comprehensive, quantitative and qualitative analysis of small molecules, is an emerging 'omics' platform that is an important next-generation systems biology approach. By providing an instantaneous “snapshot” of metabolic processes that occur in an organism, metabolomics can potentially provide insightful molecular mechanism information to questions about physiological function in complex biological systems. The objective of this thesis research was to use both untargeted and targeted metabolomics approaches to investigate plant shared and unique metabolic features in responses to single as well as multiple abiotic stresses, the priming effect of temperature stresses, plant memory during recovery phase, and the relationships between combined stress with each of individual stresses. An ultra-high pressure liquid chromatography-high resolution mass spectrometry (UHPLC-HR-MS)-based metabolomics approach was utilized. In chapter two, a metabolomics study on Arabidopsis thaliana 11-day-old seedling’s abiotic stress responses including heat (basal and acquired), cold (basal and acquired), drought and high light with 2-day-recovery was performed using a standardized reference system. In chapter three, Arabidopsis thaliana 11-day-old seedlings that were induced by the combination of different abiotic stresses including heat, cold, drought, salinity, and high light, that mimics field environment was studied. From this thesis research, a number of potential stress signatures determined from the untargeted analysis were identified, quantified and clustered by stress response patterns. Central metabolism were found to undergo a complex regulatory process in response to stress. Shared and unique metabolic signatures were identified across different abiotic single and combined stresses. The majority of stress signatures clustered together and exhibited shared response patterns. However, cysteine, glutathione, and maltose showed unique and dramatic patterns, demonstrating large changes in glutathione biosynthesis, glutathione oxidation, and starch degradation. The results showed that only two combined stresses, including high light x cold and cold x salinity, had metabolic effects that reflected both of their constituent single stresses. Most combined stresses had one dominant stress that had a defining impact on the plant metabolic profile. Drought stress is the dominant stress for all of its stress combinations. Two combined stresses, including high light x heat and high light x salinity, showed unique metabolic stress response patterns that are not similar to any of their individual stresses. Most of these metabolic features were specifically changed only in the combined stress, which should thus be considered as novel stress conditions. In summary, this work utilized metabolomics to study plant priming effects, recovery processes, and combined stress responses. It led to an improved understanding of how plants respond to abiotic stresses and may support subsequent studies on plant abiotic stress metabolic flux analyses.
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    Understanding Emotion Regulation in Eating Disorder Recovery
    (2017-09) Durkin, Nora
    Deficits in emotion regulation and heightened negative affect have been observed across eating disorder diagnoses and are hypothesized to contribute to the maintenance of eating psychopathology. However, the extent to which emotion regulation deficits and elevated negative affect continue to persist after the cessation of eating psychopathology remains unclear despite the emergence of several novel treatments that have been designed to target emotion regulation deficits and negative affect in eating disorder populations. The purpose of this study was to determine whether individuals in recovery from eating disorders experience emotion regulation deficits and heightened negative affect compared to those with active eating disorders and those without current or past eating disorders. Participants included 269 individuals with active eating disorders (AED), 58 participants in recovery from eating disorders (RED), and 143 participants without past or present eating disorders (COMP) who completed several online questionnaires. Results indicated that the AED group reported significantly more emotion regulation difficulties and greater negative affect compared to the RED and COMP groups, who did not differ form one another with regard to emotion regulation difficulties and negative affect. These findings support emotion regulation models of eating psychopathology and suggest that emotion regulation deficits and negative affect may improve with recovery from eating disorder psychopathology. Future research should examine facets of emotion regulation and negative affect using longitudinal designs to determine the temporal relationship between improvements in eating disorder psychopathology, emotion regulation, and negative affect in order to inform treatment interventions.
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    Understanding Social Functioning Deficits in Health and First Episode Schizophrenia: A Data-driven Approach Towards Improved Identification and Treatment
    (2022-02) Miley, Kathleen
    Background: Schizophrenia and other psychotic disorders are characterized by severe disability in social functioning, reducing quality of life, increasing risk for poor health outcomes, and causing significant personal and societal burden. Remediating social functioning impairments is an urgent clinical need, however progress has been hindered by a poor understanding of bio-behavioral underpinnings of functional decline, and the resulting lack of both prognostic tools to identify individuals at risk for poor outcomes and robustly effective interventions to promote functional recovery. This dissertation has an overarching purpose to improve the understanding, identification, and remediation of social functioning deficits in schizophrenia spectrum disorders by leveraging data-driven approaches. Three manuscripts are presented. Manuscript 1 critically reviews twelve studies to characterize the state of the science of individual prognostic models for functional outcomes in schizophrenia spectrum disorders. Findings indicate that development of prognostic tools is in an early stage, with a wide range of accuracies, and no clear advantage of utilizing one data modality (i.e., neurobiological data, clinical data, or functional data) over another. Results highlight a need to evaluate and directly compare predictive models which utilize different predictor modalities to understand how to optimally balance accuracy and clinical usability. Manuscript 2 presents a study aimed to develop individual prediction models for social functioning from integrated bio-behavioral data and identify which predictors are most important for social functioning using machine learning. With data from the Human Connectome Project Healthy Young Adult sample (age 22-35, N=1,101) and machine learning methods, four prognostic models were built from variable sets of brain morphology to behavior with increasing complexity: 1) brain-only model, 2) brain-cognition model, 3) cognition-behavioral model, and 4) combined brain-cognition-behavioral model. Results show that the combined brain-cognition-behavioral and cognition-behavioral models significantly predicted social functioning with nearly identical accuracy (R2 =0.53, 95% CI [0.38, 0.62] for each model), whereas the brain-only and brain-cognition models performed significantly worse (R2 = 0.06, 95% CI [-0.07, 0.16] and R2 = 0.11 95% CI [-0.05, 0.23], respectively). Negative affect, psychological wellbeing, extraversion, withdrawal, and cortical thickness of the rostral middle-frontal and superior-temporal brain regions were the most important predictors. These results suggest that prognostic models relying on behavioral data may promote clinical usability while maintaining predictive accuracy, and identify potentially important risk markers to be explored in future research. Manuscript 3 shifts the focus to identifying potential causes of functional outcomes that could be high impact treatment targets in first episode schizophrenia. We used demographic, clinical, and psychosocial measures for 276 participants from the Recovery After an Initial Schizophrenia Episode Early Treatment Program (RAISE-ETP) trial and a causal discovery algorithm, Greedy Fast Causal Inference, to model causal relationships across baseline variables and six-month social and occupational functioning. Results were validated in an independent dataset. Our primary finding was a modeled causal pathway from baseline socio-affective capacity to motivation, and from motivation to both social and occupational functioning at six months. These findings indicate that socio-affective abilities and motivation are specific high-impact treatment needs that must be addressed to promote optimal social and occupational recovery and highlight the need to integrate evidenced based treatments for these areas into gold-standard care models to promote social recovery. Conclusions: This dissertation leverages data-driven approaches to provide foundational knowledge for developing individual prognostic models for social functioning and to guide clinical research seeking to fill critical unmet treatment needs for the remediation of functional impairments. Research and clinical agendas must continue to advance the science toward ensuring that social recovery is the expectation of mental health treatment through early identification of individuals at risk for functional decline and innovative treatments which enhance their functioning. Further synthesis and implications of this work are explored in the concluding chapter.