The Rohingya Crisis in Myanmar: Rethinking International Engagement Toward Better Humanitarian Protection

The Rohingyas have been described as the ‘world’s most persecuted population.’ As a Muslim minority population living in the Rakhine region of Burma, the Rohingya population continues to face political persecution and widespread discrimination at the hands of the military. In addition, the humanitarian crisis has evoked little sympathy from the Burmese population, who refers to the Rohingyas as illegal immigrants. The ethnic tensions have culminated over decades of hostility, leading to violent outbreaks in 2012, 2015, and most recently 2017. While the eruption of the Rohingya crisis has left more than 670,000 people displaced, the broader human rights community has been largely ineffective in taking actions. Their engagement strategy has been fragmented at best, leaving outside actors unable to create policies that can prevent these mass atrocities from occurring. The aim of this thesis, therefore, is to address the gap in international engagement with Burma, with the intersection of human rights and domestic politics at the foundation.International Relations and Global Studie

Biomarker-Based Characterization of Chemical Exposures and Physiological Responses

The chemisome is the chemical components of the exposome, defined as the totality of all exposures and their impact on health. Most current approaches, however, are limited in addressing this “totality” by only studying one chemical or one chemical family at a time in one exposed population. In addition, studying the links between chemical exposures and health is challenging due to an incomplete understanding of how physiological responses are associated with adverse health outcomes. This challenge is further complicated due to how chemical exposures change with demographics such as age, sex, race, and occupation. Thus, this dissertation aims to address these challenges by applying an unbiased approach to datasets of chemical biomarker levels and physiological measurements to systematically identify susceptible populations using the National Health and Nutrition Examination Survey. In the first project, I use quadratic regression models to characterize non-linear, age-based trends of chemical exposure in a sample comprised of 74,942 participants. I screen across 141 chemicals to identify those of higher concentrations in children relative to the older population. Children exhibit higher exposures to chemicals in consumer products such as phthalates, brominated flame retardants, lead, and tungsten. In contrast, restricted and highly persistent chemicals such as polychlorinated biphenyls and dioxins are higher in the older population. In the second project, I apply generalized linear models to evaluate exposure disparities by race/ethnicity for 143 chemicals in a representative sample of 38,080 US women. Compared to non-Hispanic White women, significant disparities are observed for non-Hispanic Black, Mexican American, Other Hispanic, and Other Race/Multi-Racial women. These women have higher levels of pesticides, including 2,5-dichlorophenol and 2,4-dichlorophenol, compounds in personal care products, including parabens and mono-ethyl phthalate, and heavy metals, such as mercury and arsenic. These findings are being coupled with toxicological data to prioritize chemicals to evaluate their role in health disparities. In the third project, I develop a framework using hierarchical clustering to characterize occupational exposures and physiological responses among 26,186 blue- and white-collar workers across 20 employment sectors for 108 chemicals and 27 physiological indicators. Blue-collar workers have higher levels of toxicants such as lead, cadmium, volatile organic chemicals, and polycyclic aromatic hydrocarbons compared to white-collar workers. Moreover, blue-collar workers exhibit higher levels of alkaline phosphatase (indicative of liver disease) and C-reactive proteins (indicative of inflammation). Together, these results suggest that blue-collar workers are exposed to higher levels of toxicants, which may induce physiological dysfunction. In the final project, I implement 10-fold cross-validated regression models to characterize the linear and non-linear associations between all-cause mortality and 27 physiological indicators to identify directionalities indicative of increased mortality risk in a sample of 45,032 participants. Twenty-four out of 27 indicators show non-linear associations, while height, triglycerides, and 60-second pulse show linear associations. Cholesterol-related indicators and glomerular filtration rate unexpectedly show parabolic associations, implying that higher mortality risk is associated with measurements in either extreme of the distribution instead of in one extreme. These findings highlight a need to study associations between these indicators and other health endpoints to gain insights into the physiological profiles associated with adverse health outcomes. Together, this thesis contributes to a better understanding of how chemical exposures can impact human health across multiple subpopulations. It also enables further exploration of how chemical exposures can perturb physiologic function conducive to increasing the risk for adverse health outcomes.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162947/1/nguyenvy_1.pd

Addictions, Behavioral Addictions, and Pathological Internet Use as Internet Addiction - A Literature Review

Excessive and pathological uses of the Internet are observed and discussed often in our modern conversations. Access to the Internet has become so convenient that these behaviors can lead to consequences in many areas of our lives from social relationships to academic and professional work performance. A common term that people use to address this pattern of behaviors is “Internet Addiction” or more specific ones, such as “Social Media Addiction” or “Online Gaming Addiction.” However, in clinical psychology, addiction has its own specific definitions. It refers to a category and set of criteria that are distinguished from other mental disorders in terms of symptoms, neurobiological processes, and treatment. Excessive use of the Internet per se is not enough to be addressed as an addiction. This literature review aims to present the established features of addiction from a behavioral level to a neurobiological level and compare them to the phenomenon of excessive and pathological Internet use. The goal is to explain why or why we should address not the problems of excessive and pathological Internet use as an addiction. For our purpose, the term “pathological Internet use” will be used in this literature review to indicate all the excessive and pathological Internet-related activities that are considered for the addiction model. After examining research on this subject, it is clear that excessive and pathological use of the Internet does share similar pattern of behavioral symptoms with typical drug addictions and gambling addiction. On the neuro-biological level, existing findings show relevant changes in a reward circuit that are responsible for addiction processes in the brain. However, more studies on the neuro-biological process are still needed to establish the enough evidence or addiction model to be applicable to pathological Internet use so that proper treatment can follow

Re-Assessment of Sargassum Beds at Hon Chong Area, Nha Trang Bay, Vietnam

Sargassum beds play an important role in terms of ecology and economic likelihood ("ecosystem services") for coastal communities along the Hon Chong area, in the bay of Nha Trang, Vietnam. It is a matter of concern that the Sargassum beds at Hon Chong, in particular, and Vietnam, in general, have strongly decreased due to anthropogenic perturbations including land reclamation. Our research focused on a reassessment of Sargassum beds including coverage, occupied area, species composition, branch length frequencies and output (production) over the last 30 years. Remotely-obtained (satellite) information and field data processed through GIS software were used during this study. Results show that the area covered by the Sargassum beds was reduced by 49%. In 1980 the coverage of Sargassum was 75% for all the area (30 ha), but now we observed that 75% of the coverage occurred in area of only 2.2 ha. The average length of Sargassum mcclurei and S. serratum branches recorded in 2009 were reduced by 58% and 65%, respectively, compared with data recorded in 1980. Moreover, in 1980 Sargassum crassifolium was very common in this area, however, during this study it was not found

Elliptic Curves And Power Residues

Let E1 x E2 over Q be a fixed product of two elliptic curves over Q with complex multiplication. I compute the probability that the pth Fourier coefficient of E1 x E2, denoted as ap(E1) + ap(E2), is a square modulo p. The results are 1/4, 7/16, and 1/2 for different imaginary quadratic fields, given a technical independence of the twists. The similar prime densities for cubes and 4th power are 19/54, and 1/4, respectively. I also compute the probabilities without the technical assumption on the twists in various cases. Next, I consider the sum of quadratic residue of ap as primes p and elliptic curves vary. The purpose is to test the conjecture that ap of an elliptic curve is a square modulo p about half of the time across prime numbers so that the sum is expected to be 0. Although the sum turns out to be positively biased, I show, assuming a natural independence result, that the ap are evenly distributed between squares and non-squares modulo p asymptotically

Vessel recognition in ultrasound images using machine learning techniques

Purpose: Ultrasound is an imaging modality that is commonly used during cardiovascular surgeries globally. The purpose of this thesis is to investigate how machine learning techniques can be used to identify vessel properties and probe orientation in cardiac ultrasound images. The ultimate goal is developing a machine learning algorithm that can automatically recognize vessels in the region of interest with high mean average precision, identify vessel orientation, and run in near real-time. Method: This thesis present a thoroughly data exploration of ultrasound images acquired from a multicenter study. A pilot study of three different object detection models; Yolo, RetinaNet and EfficientDet, was done to find the best model fit for the dataset in the thesis. The three object detection models were trained, tuned and evaluated on the ultrasound data. The object detection model that performed the best after the pilot study was explored further. Yolo outperformed the other models and was therefore chosen as the object detection model for the final study. To overcome the dataset's class imbalance and size problem, data augmentation, resizing and upscaling of the ultrasound images were employed. The resulting data was used to train multiple yolo models with varying hyperparameter tunings. Model selection was then performed on these trained models, and the final model was evaluated on test data. Results: The final model achieved an overall mean average precision at 50\% at 71.77\%. The vessel orientation achieved a mean average precision at 64.6\% for the longitudinal orientation and 75.8\% for the transversal orientation. The model found it easier to locate the aorta compared to the anastomosis, which proved to be more challenging. The speed of the inference of all of these task was 5.6 milliseconds. Although the overall mean average precision was lower than the objective in this thesis, the model excelled in terms of speed. Conclusion: In conclusion, this thesis explored the application of machine learning techniques on ultrasound data for vessel recognition and orientation. Although the final model did not improve the state of the art, the research from this master thesis can serve as a starting point for future reasearch in the field. It represents pioneering work in utilizing a multicenter dataset for machine learning on ultrasound images, providing valuable groundwork and shedding light on the feasibility and potential of machine learning in intraoperative ultrasound.Masteroppgave i medisinsk teknologiMTEK39