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Influence of Transcranial Direct Current Stimulation Dosage and Associated Therapy on Motor Recovery Post-stroke: A Systematic Review and Meta-Analysis
Get PDFPurpose: (1) To determine the impact of transcranial direct current stimulation (tDCS) applied alone or combined with other therapies on the recovery of motor function after stroke and (2) To determine tDCS dosage effect.Methods: Randomized controlled trials comparing the effects of tDCS with sham, using the Barthel Index (BI), the upper and lower extremity Fugl–Meyer Assessment (FMA), and the Modified Ashworth Scale (MAS), were retrieved from PubMed, Medline (EBSCO), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) from their inception to June 2021. Calculations for each assessment were done for the overall effect and associated therapy accounting for the influence of stroke severity or stimulation parameters.Results: A total of 31 studies involving metrics of the BI, the upper extremity FMA, the lower extremity FMA, and the MAS were included. tDCS combined with other therapies was beneficial when assessed by the BI (mean difference: 6.8; P < 0.01) and these studies typically had participants in the acute stage. tDCS effects on the upper and lower extremity FMA are unclear and differences between the sham and tDCS groups as well as differences in the associated therapy type combined with tDCS potentially influenced the FMA results. tDCS was not effective compared to sham for the MAS. Stimulation types (e.g., anodal vs. cathodal) did not influence these results and dosage parameters were not associated with the obtained effect sizes. Conventional therapy associated with tDCS typically produced greater effect size than assisted therapy. The influence of stroke severity is unclear.Conclusion: Potential benefits of tDCS can vary depending on assessment tool used, duration of stroke, and associated therapy. Mechanistic studies are needed to understand the potential role of stimulation type and dosage effect after stroke. Future studies should carefully conduct group randomization, control for duration of stroke, and report different motor recovery assessments types.Systematic Review Registration: [https://www.crd.york.ac.uk/PROSPERO/], identifier [CRD42021290670].Financial support was provided by the University of Oklahoma Libraries’ Open Access Fund.Ye
Investigating technologies and techniques for flood monitoring and detecting
Get PDFThe need for a flood alerting and forecasting system is becoming ever more critical, especially given the effects of increasing severe weather over the last ten years. Flash floods, particularly, are deadly because of the short time period in which they develop, as well as the difficulty in predicting their occurrence. Consequently, floods have become recognized as the most fatal natural disaster in the United States. Recent developments in artificial intelligence (AI) and machine Learning (ML) algorithms are promising for overcoming related issues by providing data driven solutions for more reliable and efficient forecasting. The study reported in this thesis first explored market-available, industrial-based flood monitoring and alerting systems developed and deployed by several private companies and federal agencies. Weather conditions that accompany flash flood events were then analyzed as first steps in building an ML flood forecasting model. Weather attributes were analyzed both statistically and visually to uncover their importance for modeling. Subsequently, common ML classification techniques were compared with a variety of neural network architectures to determine the best model for forecasting flooding. Modeling results showed that a one-hour forecast XGBoost ensemble model outperformed others, with an 85% accuracy for forecasting a flood event and a 92% AUC score. This thesis introduces a novel approach for evaluating non-contact flood detection technologies. An open-channel testbed was designed and constructed, and several non-contact water level and velocity technologies were evaluated under various conditions. Results showed that market available radar technology delivered the most accurate readings with an MAE score of approximately 2 cm and MAE of 0.1 m/s
Characterization and incorporation of Mycobacteriophage Fulbright into a polycaprolactone nanofiber wound dressing
Get PDFNontuberculous mycobacteria infections such as Mycobacterium abscessus have become a growing concern due to the emergence of multidrug-resistant bacteria, making treatment of infections difficult. Bacteriophages, or phages, are viruses that can infect bacterial cells without harming eukaryotic cells. Bacteriophage therapy is a potential alternative for treating mycobacterial skin infections as phages only kill their bacterial host, leaving the normal flora unharmed. Mycobacteriophage Fulbright was characterized and determined to possess qualities suitable for phage therapy. Fulbright remained stable at temperatures 20-60°C and pH 4-9. The replication cycle took approximately 3 hours to complete, with a 90-minute latent phase. At high titer concentrations, Fulbright was able to lyse M. abscessus, a human pathogen. Fulbright was incorporated into polycaprolactone (PCL) fibers to serve as a model antibacterial wound dressing. PCL_Fulbright effectively reduced the concentration of M. smegmatis and was observed to be non-toxic to fibroblast cells. Incorporated into the PCL fiber, the phage was stable for up to 11 months when stored at -20°C. This project is notable because it was the first mycobacteriophage incorporated into a nanofiber for phage therapy applications and serves as a foundation for future projects
Question-Answering for Segment Retrieval on Podcast Transcripts
Get PDFPodcasting has rapidly ascended as one of the primary forms of spoken-word media in
the 21st century. The Spotify Podcast Dataset has compiled transcripts of over 100,000
podcast episodes, making it one of the largest repositories of spoken word data. The
segment retrieval task aims to find the most relevant segments to a given query from
the set of episode transcripts. This thesis presents a two-stage approach to segment
retrieval using an end-to-end question-answering (QA) deep learning architecture with
an additional step to expand answers to segments. Standard BM25 retrieval on an index
of predetermined segments from each episode serves as a baseline retrieval system.
Experiments for both approaches involved producing and evaluating a ranked list of 20
relevant segments for 50 test topics. Comparison between the two retrieval methods
shows that the QA retriever trails the baseline in nDCG@10 by 0.128, precision@10 by
0.184, and average segment relevance score by 0.461. QA retrieval slightly outperforms
the baseline by 0.024 in recall@10 while slightly underperforming it by 0.102 in average
segment relevance score when discounting irrelevant segments. The results suggest
that the QA retrieval approach in this thesis can adequately identify and rank relevant
segments within a relevant input text. However, for some queries, it may struggle
to find enough relevant candidate documents during the first stage of retrieval. QA
retrieval shows promise in handling informational queries for the user goal of answering
a question. Future work includes improving processes such as candidate document
retrieval, answer span expansion, and data annotation
The Impact of Surface Heterogeneity on Surface Flux Estimates of the Stable Boundary Layer Using Single Column Modeling
Get PDFThe surface fluxes of momentum and heat play an important role in the evolution of the atmospheric boundary layer (ABL). Accurate representation of the fluxes in weather and climate forecasting models, especially when dealing with heterogeneous land surfaces. This is complicated under stable conditions where the fluxes are often smaller in magnitude and turbulence cannot be relied on to mix out the heterogeneity effects. Here the role of surface heterogeneity in determining the average surface fluxes is investigated through the development of a single column model (SCM) in Python. The SCM features some of the most popular PBL scheme currently implemented in the Weather Research and Forecasting model (WRF) as well as various surface layer (SL) parameterizations that can account for surface heterogeneity.
The SCM is validated against with three different cases with varying complexities. The first two cases, GABLS1 and GABLS2, are idealized atmospheric boundary layer studies that have been well cited in the literature. The third case used with the SCM is from a recent field project during the summer of 2021 by the Boundary Layer Integrated Sensing and Simulation (BLISS) group at the University of Oklahoma, where multiple different boundary layer observational instruments were deployed.
The role of heterogeneity on flux properties was investigated by altering the type and strength of the surface heterogeneity. Both surface temperature heterogeneity and surface roughness heterogeneity are investigated with the current surface models implemented in the SCM. It was found that both types of surface heterogeneity impacted the magnitude of the surface fluxes of heat and momentum, especially under stable conditions. The surface temperature heterogeneity impacted the surface heat fluxes more significantly than the surface momentum fluxes. With surface roughness heterogeneity, the surface fluxes of heat and momentum were impacted equally. Future expansion to this work is discussed including further additions to the SCM
Mapping brain activity and resting-state functional connectivity with functional near-infrared spectroscopy
No full textFunctional near-infrared spectroscopy (fNIRS) as a non-invasive optical imaging technique to measure cerebral hemodynamics has seen rapid development and increasing use in studying the human brain. fNIRS measures relative changes of oxygenated and deoxygenated hemoglobin in the local tissues as a surrogate measurement of neuronal activity, similarly to the blood-oxygenation-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) which is the current golden standard of functional neuroimaging. Compared with fMRI, fNIRS offers competitive advantages, including low cost, portability, and compatibility with medical electronic devices implanted in patients. However, fNIRS has its limitations and faces several challenges. My dissertation aims to tackle several existing challenges in fNIRS and facilitate the achievement of fNIRS’s tremendous potential in research and clinical applications.
Firstly, fNIRS suffers from its susceptibility to superficial blood flow, cardiac pulsation, respiration, and head and jaw motions, which can lead to false positives or negatives in imaging brain activation and connectivity. Most existing fNIRS studies were based on partial montages and have not yet been fully investigated in a whole-head montage in a complex task when concurrent activations from multiple distant regions arise or in a resting-state task when coherent spontaneous activity across functionally connected areas. We have established the capability to record fNIRS signals in a cap-based, whole-head and high-density montage with short-separation channels for superficial signals and auxiliary measurements for physiological noises. I have developed a novel automatic denoising method namely PCA-GLM and demonstrated the efficacy of PCA-GLM in correcting fNIRS signals recorded during a visually guided motor task.
Secondly, the capability of using whole-head fNIRS to map the resting-state functional connectivity in large-scale brain networks, especially the default mode network which is critically implicated in the aging process and Alzheimer’s disease, has not been fully established and benchmarked to fMRI. We demonstrated the feasibility of the cap-based, whole-head and high-density fNIRS system in mapping the default mode network using conventional seed-based functional connectivity analysis. Moreover, via cross-modal comparison in the same subjects, we have for the first time demonstrated the similarity between the default mode network obtainable by a portable fNIRS system and that from fMRI, especially the medial prefrontal, midline posterior and parietal structures that are important biomarkers for normal aging.
Thirdly, the aliasing effects due to an insufficient sampling rate on functional connectivity which commonly occur in functional neuroimaging modalities, especially fMRI, are still poorly understood. Meanwhile, with the advancements in hardware and growing interest in developing high-density fNIRS with a whole-head coverage, the increases in the numbers of optical sources and detectors might come at a price of a decrease in the sampling rate. We have shown that the aliased activity due to a low sampling frequency has resulted in aliased connectivity. Furthermore, for the first time, we have discovered a network organization of such aliased functional connectivity that exists within the distribution of the default mode network, which might have confounded our understanding of the resting-state functional connectivity in the human brain.
Lastly, in addition to head motion, fNIRS is also vulnerable to jaw movements, such as clenching teeth, which can affect fNIRS measurements in the auditory, parietal and prefrontal cortices in the studies of hearing, speech and cognitive functions. While many previous studies have investigated the impact and correction of head movements, the effect and handling of jaw movements in fNIRS signals remain unclear. We have designed a novel individually customized bite bar. Our experimental work has demonstrated that the bite bar and the previously developed PCA-GLM method are effective in suppressing jaw movements, removing motion artifacts, and therefore improving auditory response and resting-state functional connectivity.
The outcomes of my dissertation have made significant advancements towards tackling the technical challenges in fNIRS. My work has paved the pathway for using fNIRS as a broadly accessible and noninvasive neuroimaging technique in many clinical applications, such as measuring auditory response in cochlear implant users, detecting abnormality of neurovascular coupling in diseased or impaired conditions, and monitoring the deterioration of the brain function in normal aging and Alzheimer’s disease
A Missing Piece of a "Fine" Puzzle: Filling a Gap in American Music History Through the Oboe Music of Vivian Fine
Get PDFVivian Fine (1913-2000) was an American composer of avant-garde music. With a nearly 70-year-long career and 140 compositions, Vivian Fine deserves to be a celebrated figure in American music history. Fine’s name is hardly known despite her massive contribution to the American music repertoire and the impressive social and professional circles in which she navigated, which included famous names like Ruth Crawford Seeger, Aaron Copland, and Henry Cowell. Of her 140 compositions, Vivian Fine composed four works for solo oboe that fill major gaps in the oboe repertoire. These works have received little attention from the oboe community. This study will explore the life and music of Vivian Fine by examining the social and professional circles that Fine navigated in America during the Great Depression, the Red Scare, and the Second World War. The study will unfold the three compositional styles seen throughout Fine’s career. These stylistic periods are characterized by serially informed atonality, followed by a shift to tonality, and concluding with a transition back to serially informed atonality. This unfolding will be examined through her oboe music, followed by each piece’s pedagogical applications. The outcome of this study will advocate for the oboe music of Vivian Fine and further demonstrate her place as an important figure in American music history
Aerial photo index, Grant County, OK. Section 3 of 4
No full textAerial photograph index (photomosaic), Grant County Oklahoma. MAP 3 of
Is there a relationship between sexual orientation and perceived school safety among teachers?
Get PDFA teacher’s perception of safety is a critical component of school safety. A substantial body of scholarship explores factors related to a teacher’s perception of school safety, underscoring individual, school, neighborhood, and state-level factors that may contribute to how safe teachers feel in school. However, empirical research examining how a teacher’s sexual orientation may relate to school safety perceptions is underdeveloped in the school safety literature. Filling this gap is important because the lesbian, gay, and bisexual (LGB) community comprise 10% of the nation’s teachers, but there is only anecdotal evidence that LGB educators may not feel as safe in schools as their peers. This study examined the relationship between LGB status among teachers and how safe they feel in school, controlling for a range of individual, school, and neighborhood characteristics. For data collection, a survey was administered to public-school teachers in Oklahoma that yielded a sample of 1,605 teachers, including 113 LGB teachers. Results indicated that with controls for individual, school, and neighborhood level factors, LGB status does not have a statistically significant relationship with a teacher’s perceptions of physical and emotional safety or self-reported incidents of victimization. The results also indicated that LGB teachers who had disclosed their sexual orientation status reported statistically significant lower rates of teacher victimization. This study contributes to the literature on teachers and school safety by offering an analysis of LGB educators’ perceptions of school safety on three key measures. Findings also offer suggestive evidence that complex selection mechanisms may be underlying the patterns observed in this study
Aerial photo index, Hughes County, OK. Section 7 of 9
No full textAerial photograph index (photomosaic), Hughes Co. OKLA. -AWH- MAP 7 of