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A Prescription of Positive Psychology: Bridging the Intention-Behavior Gap in Social Prescribing in the UK
Get PDFAlthough people have intentions to change their behavior, many do not take any action, and this discrepancy is called the intention-behavior gap. Studies estimate the gap is as high as 50%, a figure of some significance in health behavior change. This paper explores the intention-behavior gap in the context of social prescribing in the UK. It looks at the current problems of measurement and evaluation within social prescribing and the potential impact of the intention-behavior gap. The paper also considers the current research addressing the gap and proposes an alternative solution based on a positive psychology framework and positive psychology interventions. Research for these proposals is drawn from the field of motivation science, and the positive psychology models and concepts of self-determination theory, mindfulness, and positive emotions
Collation Model for LJS 267: De ludo schacchorum seu de moribus...
Get PDFCompilation, mostly in Latin, of religious, literary, historical, and natural-historical works, including classical and contemporary selections, as well as letters by humanist writers Francesco Petrarca and Donatus Albanzani. Over a quarter of the manuscript is devoted to the De ludo scachorum of Jacobus de Cessolis, a collection of sermons about the proper relationships between a king and various classes of subjects, compared to the rules of chess (f. 1r-56r). Other moderately substantial texts include descriptions of various geographic regions from Honorius of Autun\u27s De imagine mundi (f. 61r-71r); a brief history of the Roman civil wars (f. 71v-92r); and the Computus of Bono da Lucca, which deals with the calendar, lunar cycles, and calculations for determining the date of Easter (f. 109r-124v). The manuscript is a palimpsest, with the lower text from the 14th century, probably legal.https://repository.upenn.edu/sims_models/1054/thumbnail.jp
The Aspiring Entrepreneur Model for Flourishing - A Model Designed to Keep Aspiring Entrepreneurs Engaged, Committed, and Flourishing
Get PDFThe entrepreneurial experience is not for the faint of heart. Due to the stress associated with starting a new business, it can challenge the well-being of even the best intended entrepreneurs, especially those early on in their journey, whom I call aspiring entrepreneurs (AE). It is possible for an AE to flourish, where they thrive and continue to grow through the experience, as long as they invest in certain key well-being dimensions that positive psychology researchers and practitioners have identified as foundational to entrepreneurial flourishing. This paper explores three of these dimensions, (1) purpose in life, (2) autonomy, and (3) positive relationships with others; and provides a literature review on their potential impact of the AE’s ability to flourish, even during difficult trials and challenges
Collation Model for LJS 359: Liber canonis
Get PDFSections from Books 1 and 2 of Avicenna\u27s 11th-century comprehensive medical work, as translated into Latin in the 12th century by Gherardo da Cremona. Book 1 addresses medicine generally; the section in the manuscript is from the first treatise and concerns the four elements. Book 2 is devoted to materia medica. A few small stemmata are drawn in the lower margins in Book 1(f. 3r-4r), and marginal notes and headings appear throughout, with marginal chapter numbers in the section from Book 2 (f. 12r-17v). Repairs to the centers of leaves in the section from Book 1, with vellum patches and text supplied in the first half of the 14th century (f. 5-11; Quaritch).https://repository.upenn.edu/sims_models/1079/thumbnail.jp
Enhanced Co-Stimulatory Signaling Improves Car T Cell Effector Responses In Chronic Lymphocytic Leukemia
No full textAdoptive transfer of CD19-redirected chimeric antigen receptor (CAR) T cells has shown remarkable activity against B-cell cancers. While second-generation CARs induce complete remission in \u3e80% of patients with relapsed/refractory acute lymphoblastic leukemia, similar mono-therapy induces long-term remissions in only 26% of chronic lymphocytic leukemia (CLL) patients. This disparity is attributed to cell-intrinsic defects in autologous CLL-derived T cells, including poor proliferative, low levels of effector cytokine secretion, unstructured immune synapse formation, and poor cytotoxicity. However, the mechanisms by which leukemic cells impact CAR T cell potency are poorly understood. Herein we describe an in vitro assay that recapitulates endogenous CLL-mediated T cell defects in healthy donor CAR T cells. This contact with CLL cells does not irreversibly impair CAR T cell function, but instead insufficiently activates the cells. This state is rescuable by either a strong antigenic stimulus or IL-2, and is not driven by immune-suppression on the part of the CLL tumor. Rather, this activation defect is attributable to low levels of co-stimulatory molecules on CLL cells. We confirmed that exogenous co-stimulation enhanced CAR T cell activation. We also assessed the stimulatory phenotype of CLL cells derived from different immune niches within the same patient. Lymph node-derived (LN) CLL cells had a strong co-stimulatory phenotype and promoted better CAR T cell degranulation and cytokine production than matched peripheral blood (PB) CLL cells. Finally, we showed that in vitro CD40L activation can model a LN-resident CLL phenotype; these activated CLL cells acquire a stimulatory phenotype similar to the LN-derived tumor and stimulate improved CAR T cell proliferation, cytokine production, and cytotoxicity. Together these data identify insufficient activation as a driver of poor CAR T cell responses in CLL. The co-stimulatory phenotype of CLL cells drives differential CAR T cell responses, and can be augmented by improving co-stimulatory signaling in these cells. Finally, the finding that LN-derived CLL cells have a better stimulatory phenotype implicates this niche as a site of active CAR T cell killing in patients
Learning Environmental Models With Multi-Robot Teams Using A Dynamical Systems Approach
Get PDFRobots monitoring complex, spatiotemporal phenomena require rich, meaningful representations of the environment. This thesis presents methods for representing the environment as a dynamical system with machine learning techniques. Specifically, we formulate machine learning methods that lend to data-driven modeling of the phenomena. The data-driven modeling explicitly leverages theoretical foundations of dynamical systems theory. Dynamical systems theory offers mathematical and physically interpretable intuitions about the environmental representation. The contributions presented include distributed algorithms, online adaptation, uncertainty quantification, and feature extraction to allow for the actualization of these techniques on-board robots. The environmental representations guide robot behavior in developing strategies such as optimal sensing and energy-efficient navigation. The methods and procedures provided in this thesis were verified across complex, spatiotemporal environments and on experimental robots
Surface Modification of Solid Oxide Cell Electrodes to Improve the Electrochemical Performance
No full textSolid Oxide Fuel Cells are high temperature, solid-state, electrochemical devices that can convert fuels into electricity or produce fuels from excess electricity. Oxygen is reduced at the cathode to oxygen ions which move through the ceramic to the anode. These oxygen ions are used to oxidize fuels at the anode compartment, producing heat and electrons that will move through an external circuit to produce power. At the cathode the sluggish oxygen reduction kinetics impede the performance of the electrode. A common approach to enhance the cathode performance is infiltration. Often the performance of a cathode is enhanced after the addition of a variety of metal-oxide materials. The common claim is that the infiltrated materials enhance catalytic activity or conductivity. With infiltration however, it is impossible to control for changes in surface area or conductivity. Atomic Layer Deposition (ALD) was employed to change the surface chemistry of the electrode, without changing the conductivity, or surface area of the electrode. Perovskite anodes are of interest due to their resistance to many of the issues that plague Ni-cermet (ceramic metal) anode. Their catalytic activity is often lacking, and as such a variety of methods are employed to enhance this. The most efficient approach is surface modification which allows for increases in activity with minimal metal loadings. ALD was employed to deposit highly disperse oxidation catalysts inorder to minimize the metal loadings while maximizing performance. At the Ni-cermet anode, undesirable reactions, such as carbon fiber formation and Ni oxidation to NiO, limit the lifetime of the electrode. Surface modification approaches are often employed to protect the Ni surface against these processes. We investigated the use of CeO2 ALD to overcome these challenges. Perovskites with exclusively 2 + cations (Ba and Sr) in the A-site and Fe in the B-site have recently exhibited great performance as SOFC anodes. The reasoning behind the high catalytic activity of these anodes has not been thoroughly studied. To elucidate the origin of the high activity of these anodes, the performance and thermodynamics of Ba0.5Sr0.5FeO3 (BSF) anodes was investigated
Deep Learning and Uncertainty Quantification: Methodologies and Applications
No full textUncertainty quantification is a recent emerging interdisciplinary area that leverages the power of statistical methods, machine learning models, numerical methods and data-driven approach to provide reliable inference for quantities of interest in natural science and engineering problems. In practice, the sources of uncertainty come from different aspects such as: aleatoric uncertainty where the uncertainty comes from the observations or is due to the stochastic nature of the problem; epistemic uncertainty where the uncertainty comes from inaccurate mathematical models, computational methods or model parametrization. Cope with the above different types of uncertainty, a successful and scalable model for uncertainty quantification requires prior knowledge in the problem, careful design of mathematical models, cautious selection of computational tools, etc. The fast growth in deep learning, probabilistic methods and the large volume of data available across different research areas enable researchers to take advantage of these recent advances to propose novel methodologies to solve scientific problems where uncertainty quantification plays important roles. The objective of this dissertation is to address the existing gaps and propose new methodologies for uncertainty quantification with deep learning methods and demonstrate their power in engineering applications. On the methodology side, we first present a generative adversarial framework to model aleatoric uncertainty in stochastic systems. Secondly, we leverage the proposed generative model with recent advances in physics-informed deep learning to learn the uncertainty propagation in solutions of partial differential equations. Thirdly, we introduce a simple and effective approach for posterior uncertainty quantification for learning nonlinear operators. Fourthly, we consider inverse problems of physical systems on identifying unknown forms and parameters in dynamical systems via observed noisy data. On the application side, we first propose an importance sampling approach for sequential decision making. Second, we propose a physics-informed neural network method to quantify the epistemic uncertainty in cardiac activation mapping modeling and conduct active learning. Third, we present an anto-encoder based framework for data augmentation and generation for data that is expensive to obtain such as single-cell RNA sequencing
GSK3 Inhibition Rescues Growth and Telomere Dysfunction in Dyskeratosis Congenita IPSC-Derived Type II Alveolar Epithelial Cells
No full textDyskeratosis congenita (DC) is a rare genetic disorder characterized by deficiencies in telomere maintenance leading to very short telomeres and the premature onset of certain age-related diseases, including pulmonary fibrosis (PF). PF is thought to derive from epithelial failure, particularly that of type II alveolar epithelial (AT2) cells, which are highly dependent on Wnt signaling during development and adult regeneration. We use human iPSC-derived AT2 (iAT2) cells to model how short telomeres affect AT2 cells. Cultured iAT2 cells with a mutation in DKC1, the most common cause of DC, accumulate shortened, uncapped telomeres and manifest defects in the growth of alveolospheres, hallmarks of senescence, and apparent defects in Wnt signaling. The GSK3 inhibitor, CHIR99021, which mimics the output of canonical Wnt signaling, enhances telomerase activity and rescues the defects. These findings support further investigation of Wnt agonists as potential therapies for DC related pathologies. Furthermore, this thesis describes the development of a transplantation of iAT2 cells into immunocompromised mice as well as the development of a novel iPS line with another DC mutation
Relationships between Structure, Dynamics, and Flow in Sheared Amorphous Materials
No full textAmorphous solids, those composed of haphazardly arranged constituents, are found everywhere from our windows as silicate glass, in the ground and foundations as mud and concrete, and our grocery stores as granular piles of oranges. Even though they can be found over a huge range of length scales, it remains a challenge to systematically design their mechanical properties using knowledge of their microstructure. In this thesis, I investigate the link between the microstructure and the mechanical properties of a-thermal solids. First, I probe the particle trajectories for chaotic signatures that relate to bulk rheology. Particles are confirmed to exhibit chaotic, Brownian like motion during cyclic shear, even though the particles are large enough that thermal motion is negligible. I also find that, the average area traced by returning particles is proportional to the amplitude of strain, which could be useful for in situ measurements in industrial, granular, mixing applications. Next, I examine the interconnection between particle dynamics and the arrangements of the constituents. I calculate the characteristic time for particles to shift past each other, called relaxation time, and the configurational entropy of the system in excess of a reference ideal gas. I show that the relaxation time at any given instant is related to the excess entropy a quarter shear cycle later, which implies that the dynamics of particles shape the eventual structure. This means it is possible to take a snapshot of particle positions and infer its mechanical past. Finally, I focus on the interplay between particle positions and bulk yield by using concepts from kinetics, thermodynamics, statistical mechanics, and shear transformation zone theory. I establish a relationship between excess entropy and energy dissipation and uncover a novel definition for the yield transition based on memory signatures within the microstructure. Using these observations, I derive a phenomenological model that links the microstructure to bulk rheology that is physically informed and whose parameters are all quantitatively measurable. This dissertation elucidates how the statistics of particle configurations and dynamics give rise to the macroscopic transition from elasticity to plasticity during yield of amorphous, a-thermal solids