Visualizing Depressive Symptom Improvement: Implementing the 17-Item Hamilton Depression Rating Scale

Visualizing Depressive Symptom Improvement: Implementing the 17-Item Hamilton Depression Rating Scale Abstract Problem Depression, a mental health diagnosis, has affected about 18.5% of adults (Villarroel & Terlizzi, 2020). Ketamine, a medication initially used as an anesthetic, has improved depressive symptoms in individuals struggling with treatment-resistant depression. Method This quality improvement (QI) project used the 17-item Hamilton Depression Rating Scale questionnaire to assess depressive symptom changes in patients receiving intramuscular ketamine for treatment-resistant depression. The questionnaire was administered to patients pre-and post-intramuscular ketamine administration. The data was collected on injections one, three, and six on each participant’s set schedule of injections and participation period. The primary outcomes measured were the questionnaire scores before and after the administration of ketamine intramuscularly and the sum of the depressed mood questions (1-3), insomnia questions (4-6), physical symptoms questions (9-11, 16), anxiety questions (9-11, 15), and insight question (17). Paired-samples t-testing analyses were performed on the collected data. Results The data showed that week one’s participants’ (n=14) scores significantly reduced the following categories: depressed mood and thought, anxiety, and physical symptoms. Week three’s (n=13) and week six’s (n=8) data showed a significant reduction in depressed mood and thought and anxiety categories. Implications for Practice This QI project provided encouraging data examining depression severity changes after the administration of intramuscular ketamine for patients with major depressive disorder or bipolar disorder. The results provide reassuring objective information for providers concerned about possible medication tolerance or misuse during treatment while ensuring providers that ketamine can be used to effectively improve depressive symptoms

A study of the holy as an aesthetic phenomenon

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Methodological Development for Identifying Foraging Behaviors from GPS Data Among Artisanal Fishers in the Caribbean

This project addresses questions about human foraging behavior in the ethnographic context of small-scale fishing-foraging in the Commonwealth of Dominica, an island in the Eastern Caribbean. The first goal of this project is to further develop a method of inferring foraging behaviors from GPS data by testing a recent partial sum approach—the CUMSUM method. The principle underpinning this method of research is that remotely gathered movement data can be accurately translated into meaningful data on foraging activities. GPS data produces movement tracks that are used to parse out changes in behavior, but segmentation of GPS tracks into different bouts of foraging activities is not straightforward. Previous research demonstrates that the CUMSUM method has benefits for detecting behavioral shifts and identifying patches of resources behaviorally, but it has seen limited testing across different foraging contexts. Developing this method has broad application across a range of disciplines, and one relevant utility is using CUMSUM segments to test foraging models. A second goal of this project is to demonstrate by testing a prediction of the marginal value theorem. The MVT explores generalized decision-making rules on patch residence time and was primarily developed in experimental settings with non-human animals. There are few tests of the MVT among human populations in naturalistic settings. Research activities took place across three field sessions in the rural village of Desa Ikan, Dominica, among artisanal fisher-foragers. I tested the CUMSUM method with fishing data and found the method correctly identifies about 90% of patches with relatively small error rates. The strength of this approach is using both directly observed behavioral data to ground-truth simultaneously collected GPS data. I tested an aspect of the MVT using patch data from both observational data and CUMSUM segment data. Observational data supports the theoretical prediction that fishers spend more time in patches with higher travel costs, while support from CUMSUM model-generated data is equivocal

Methodological Development for Identifying Foraging Behaviors from GPS Data Among Artisanal Fishers in the Caribbean

This project addresses questions about human foraging behavior in the ethnographic context of small-scale fishing-foraging in the Commonwealth of Dominica, an island in the Eastern Caribbean. The first goal of this project is to further develop a method of inferring foraging behaviors from GPS data by testing a recent partial sum approach—the CUMSUM method. The principle underpinning this method of research is that remotely gathered movement data can be accurately translated into meaningful data on foraging activities. GPS data produces movement tracks that are used to parse out changes in behavior, but segmentation of GPS tracks into different bouts of foraging activities is not straightforward. Previous research demonstrates that the CUMSUM method has benefits for detecting behavioral shifts and identifying patches of resources behaviorally, but it has seen limited testing across different foraging contexts. Developing this method has broad application across a range of disciplines, and one relevant utility is using CUMSUM segments to test foraging models. A second goal of this project is to demonstrate by testing a prediction of the marginal value theorem. The MVT explores generalized decision-making rules on patch residence time and was primarily developed in experimental settings with non-human animals. There are few tests of the MVT among human populations in naturalistic settings. Research activities took place across three field sessions in the rural village of Desa Ikan, Dominica, among artisanal fisher-foragers. I tested the CUMSUM method with fishing data and found the method correctly identifies about 90% of patches with relatively small error rates. The strength of this approach is using both directly observed behavioral data to ground-truth simultaneously collected GPS data. I tested an aspect of the MVT using patch data from both observational data and CUMSUM segment data. Observational data supports the theoretical prediction that fishers spend more time in patches with higher travel costs, while support from CUMSUM model-generated data is equivocal

Preferences, well-being, and value: a critique of the normative foundations of the economic approach to valuing non-market goods

This dissertation assesses the theory of value underpinning cost-benefit analysis. This theory of value consists of two main claims. The first is that the satisfaction of individual preferences is what counts as a benefit and their frustration is what counts as a cost. This is standardly purported to be the case because satisfying individual preferences promotes individual well-being and, in turn, social welfare. I argue that there is a close connection between well-being and preference satisfaction: an individual's well-being consists in the satisfaction of her fully-informed and rational preferences. However, I maintain that there may be occasions where a person's actual preferences would, if satisfied, diminish her wellbeing and where the state is a better judge than the person herself of what would enhance her well-being. On such occasions, the state should strive to make people informed and rational (in the relevant respects), and then after doing so, attend to and promote the satisfaction of the preferences they then have. This has the effect enhancing individual well-being, and does so in a way that respects individual autonomy. This is fundamentally compatible with CBA. The second claim is that all costs and benefits - including human lives lost or preserved, as well as changes affecting human health and comfort, the cleanliness of air and water, the welfare of non-human species, and ecological services rendered - can be expressed monetarily. I maintain that an individual's willingness to pay or to accept a certain amount of money for a good does not suffice to show that this good's value can be monetized. With respect to certain goods, an individual can have preferences that differ from one another along multiple irreducible dimensions. Different preferences will sometimes incorporate fundamentally distinct attitudes such as appreciation, fascination, respect, agitation, disgust, and shame. Thus, there will be pairs of goods of which it can be said neither that one member is simply more valuable than the other nor that the members are equal in value. This means that rational preferences cannot generate a single ordering of value, monetary or otherwise

An Empirical Analysis of Backpropagation Error Surface Initiation for Injection Molding Process Control

Backpropagation neural networks are trained by adjusting initially random interconnecting weights according to the steepest local error surface gradient. The authors examine the practical implications of the arbitrary starting point on the error landscape of the ensuing trained network. The effects on network convergence and performance are tested empirically, varying parameters such as network size, training rate, transfer function and data representation. The data used are live process control data from an injection molding plan

Endothelial Dysfunction, Inflammation, and Apoptosis in Diabetes Mellitus

Endothelial dysfunction is regarded as an important factor in the pathogenesis of vascular disease in obesity-related type 2 diabetes. The imbalance in repair and injury (hyperglycemia, hypertension, dyslipidemia) results in microvascular changes, including apoptosis of microvascular cells, ultimately leading to diabetes related complications. This review summarizes the mechanisms by which the interplay between endothelial dysfunction, inflammation, and apoptosis may cause (micro)vascular damage in patients with diabetes mellitus