Doctor of Philosophy

dissertationBiomedical data are a rich source of information and knowledge. Not only are they useful for direct patient care, but they may also offer answers to important population-based questions. Creating an environment where advanced analytics can be performed against biomedical data is nontrivial, however. Biomedical data are currently scattered across multiple systems with heterogeneous data, and integrating these data is a bigger task than humans can realistically do by hand; therefore, automatic biomedical data integration is highly desirable but has never been fully achieved. This dissertation introduces new algorithms that were devised to support automatic and semiautomatic integration of heterogeneous biomedical data. The new algorithms incorporate both data mining and biomedical informatics techniques to create "concept bags" that are used to compute similarity between data elements in the same way that "word bags" are compared in data mining. Concept bags are composed of controlled medical vocabulary concept codes that are extracted from text using named-entity recognition software. To test the new algorithm, three biomedical text similarity use cases were examined: automatically aligning data elements between heterogeneous data sets, determining degrees of similarity between medical terms using a published benchmark, and determining similarity between ICU discharge summaries. The method is highly configurable and 5 different versions were tested. The concept bag method performed particularly well aligning data elements and outperformed the compared algorithms by iv more than 5%. Another configuration that included hierarchical semantics performed particularly well at matching medical terms, meeting or exceeding 30 of 31 other published results using the same benchmark. Results for the third scenario of computing ICU discharge summary similarity were less successful. Correlations between multiple methods were low, including between terminologists. The concept bag algorithms performed consistently and comparatively well and appear to be viable options for multiple scenarios. New applications of the method and ideas for improving the algorithm are being discussed for future work, including several performance enhancements, configuration-based enhancements, and concept vector weighting using the TF-IDF formulas

Accuracy of temperature measurements with the VACM

For the past five years the Buoy Group at Woods Hole Oceanographic Institution has included temperature as one of the variables recorded in its current meters. These measurements began with the first successful deployments of Vector Averaging Current Meters (VACMs) in 1971. Circuitry designed for making highly accurate temperature measurements has been included in all the Buoy Project's VACMs. During the past year we have begun to add similar circuitry to the EG&G 850 current meters. This report is intended to describe what we have learned about making water temperature measurements with VACMs.Prepared for the Office of Naval Research under Contract N00014-76-C-0197; NR 083-400

Motivations for personal financial management: A Self-Determination Theory perspective

Financial knowledge and sound financial decision making are now broadly recognized to be important determinants of both personal and societal prosperity, but research has yet to examine how distinct qualities of motivation may be associated with the way people manage their money. In two studies we applied the framework of Self-Determination Theory (SDT) to examine people's autonomous (volitional) and controlled (pressured) motivation for understanding and managing their finances, as well as their amotivation (lack of motivation) for doing so, and the differential associations these motives have with financial knowledge and financial well-being. American participants (Study 1, N = 516; Study 2, N = 534) completed detailed demographic surveys and questionnaires assessing the financial variables of interest. As hypothesized, SDT's motivational constructs were associated with financial outcomes over and above participants' age, gender, income, household wealth, and educational attainment. Autonomous motivation was positively associated with a host of positive financial behaviors and characteristics (e.g., saving/investing and financial self-efficacy, well-being, and self-awareness). Controlled motivation was negatively associated with financial well-being. Amotivation was positively associated with overspending and negatively associated with financial self-efficacy and well-being. These findings support the relevance of SDT's framework in this domain and suggest that interventions aimed at promoting financial knowledge and wellness may benefit by adopting evidence-supported strategies for optimizing more autonomous motivations and addressing amotivations

Paths to the light and dark sides of human nature : A meta-analysis of the prosocial benefits of autonomy and the antisocial costs of control

Self-determination theory (SDT) posits that experiences of autonomy lead people to be more prosocial, whereas experiences of control lead to antisocial actions. In this meta-analysis, we tested the links between autonomy and prosociality and control and antisociality, across 139 reports (167 studies) with 1,189 effect sizes (N = 75,546 participants). We used two-stage structural equation modeling including both correlational and longitudinal study designs. We found support for the hypothesized direct links between autonomy and prosociality and between control and antisociality, with cross-paths between these constructs being weaker. In line with SDT’s claims that the salutary effects of autonomy are universal, results also showed that the hypothesized links were consistent across cultures, genders, and age categories. We also reviewed emerging experimental research on the effect of autonomy-priming interventions on prosociality. To conclude, we discuss the theoretical and practical implications of these findings and lay out an agenda for future research. (PsycInfo Database Record (c) 2022 APA, all rights reserved

Image-Domain Material Decomposition for Dual-energy CT using Unsupervised Learning with Data-fidelity Loss

Background: Dual-energy CT (DECT) and material decomposition play vital roles in quantitative medical imaging. However, the decomposition process may suffer from significant noise amplification, leading to severely degraded image signal-to-noise ratios (SNRs). While existing iterative algorithms perform noise suppression using different image priors, these heuristic image priors cannot accurately represent the features of the target image manifold. Although deep learning-based decomposition methods have been reported, these methods are in the supervised-learning framework requiring paired data for training, which is not readily available in clinical settings. Purpose: This work aims to develop an unsupervised-learning framework with data-measurement consistency for image-domain material decomposition in DECT

A quantitative meta-analysis and qualitative meta-synthesis of aged care residents’ experiences of autonomy, being controlled, and optimal functioning

Background and Objectives The poor mental health of adults living in aged care needs addressing. Improvements to nutrition and exercise are important, but mental health requires a psychological approach. Self-determination theory finds that autonomy is essential to wellbeing while experiences of being controlled undermine it. A review of existing quantitative data could underscore the importance of autonomy in aged care, and a review of the qualitative literature could inform ways to promote autonomy and avoid control. Testing these possibilities was the objective of this research. Research Design and Methods We conducted a mixed-methods systematic review of studies investigating autonomy, control, and indices of optimal functioning in aged care settings. The search identified 30 eligible reports (19 quantitative, 11 qualitative), including 141 quantitative effect sizes, 84 qualitative data items, and N = 2,668. Quantitative effects were pooled using three-level meta-analytic structural equation models, and the qualitative data were meta-synthesized using a grounded theory approach. Results As predicted, the meta-analysis showed a positive effect of aged care residents’ autonomy and their wellness, r = 0.33 [95% CI: 0.27, 0.39], and a negative effect of control, r = −0.16 [95% CI: −0.27, −0.06]. The meta-synthesis revealed seven primary and three sub-themes describing the nuanced ways autonomy, control, and help seeking are manifest in residential aged care settings. Discussion and Implications The results suggest that autonomy should be supported, and unnecessary external control should be minimized in residential aged care, and we discuss ways the sector could strive for both aims

Issues and Challenges in Applications of Artificial Intelligence to Nuclear Medicine -- The Bethesda Report (AI Summit 2022)

The SNMMI Artificial Intelligence (SNMMI-AI) Summit, organized by the SNMMI AI Task Force, took place in Bethesda, MD on March 21-22, 2022. It brought together various community members and stakeholders from academia, healthcare, industry, patient representatives, and government (NIH, FDA), and considered various key themes to envision and facilitate a bright future for routine, trustworthy use of AI in nuclear medicine. In what follows, essential issues, challenges, controversies and findings emphasized in the meeting are summarized

Modelling the exposure of wildlife to radiation: key findings and activities of IAEA working groups

The International Atomic Energy Agency (IAEA) established the Biota Working Group (BWG) as part of its Environmental Modelling for Radiation Safety (EMRAS) programme in 2004 (http://www-ns.iaea.org/projects/emras/emras-biota-wg.htm). At that time both the IAEA and the International Commission on Radiological Protection (ICRP) were addressing environmental protection (i.e. protection of non-human biota or wildlife) within the on-going revisions to the Basic Safety Standards and Recommendations respectively. Furthermore, some countries (e.g. the USA, UK) were already conducting assessments in accordance with national guidelines. Consequently, a number of assessment frameworks/models had been or were being developed. The BWG was established recognising these developments and the need to improve Member State’s capabilities with respect to protection of the environment from ionizing radiation. The work of the BWG was continued within the IAEA’s EMRAS II programme by the Biota Modelling Group (http://wwwns. iaea.org/projects/emras/emras2/working-groups/working-group-four.asp)

LSST: from Science Drivers to Reference Design and Anticipated Data Products

(Abridged) We describe here the most ambitious survey currently planned in the optical, the Large Synoptic Survey Telescope (LSST). A vast array of science will be enabled by a single wide-deep-fast sky survey, and LSST will have unique survey capability in the faint time domain. The LSST design is driven by four main science themes: probing dark energy and dark matter, taking an inventory of the Solar System, exploring the transient optical sky, and mapping the Milky Way. LSST will be a wide-field ground-based system sited at Cerro Pach\'{o}n in northern Chile. The telescope will have an 8.4 m (6.5 m effective) primary mirror, a 9.6 deg$^2$ field of view, and a 3.2 Gigapixel camera. The standard observing sequence will consist of pairs of 15-second exposures in a given field, with two such visits in each pointing in a given night. With these repeats, the LSST system is capable of imaging about 10,000 square degrees of sky in a single filter in three nights. The typical 5$\sigma$ point-source depth in a single visit in $r$ will be $\sim 24.5$ (AB). The project is in the construction phase and will begin regular survey operations by 2022. The survey area will be contained within 30,000 deg$^2$ with $\delta<+34.5^\circ$, and will be imaged multiple times in six bands, $ugrizy$, covering the wavelength range 320--1050 nm. About 90\% of the observing time will be devoted to a deep-wide-fast survey mode which will uniformly observe a 18,000 deg$^2$ region about 800 times (summed over all six bands) during the anticipated 10 years of operations, and yield a coadded map to $r\sim27.5$. The remaining 10\% of the observing time will be allocated to projects such as a Very Deep and Fast time domain survey. The goal is to make LSST data products, including a relational database of about 32 trillion observations of 40 billion objects, available to the public and scientists around the world.Comment: 57 pages, 32 color figures, version with high-resolution figures available from https://www.lsst.org/overvie