Quantifying Iron Overload using MRI, Active Contours, and Convolutional Neural Networks

Iron overload, a complication of repeated blood transfusions, can cause tissue damage and organ failure. The body has no regulatory mechanism to excrete excess iron, so iron overload must be closely monitored to guide therapy and measure treatment response. The concentration of iron in the liver is a reliable marker for total body iron content and is now measured noninvasively with magnetic resonance imaging (MRI). MRI produces a diagnostic image by measuring the signals emitted from the body in the presence of a constant magnetic field and radiofrequency pulses. At each pixel, the signal decay constant, T2*, can be calculated, providing insight about the structure of each tissue. Liver iron content can be quantified based on this T2* value because signal decay accelerates with increasing iron concentration. We developed a method to automatically segment the liver from the MRI image to accurately calculate iron content. Our current algorithm utilizes the active contour model for image segmentation, which iteratively evolves a curve until it reaches an edge or a boundary. We applied this algorithm to each MRI image in addition to a map of pixelwise T2* values, combining basic image processing with imaging physics. One of the limitations of this segmentation model is how it handles noise in the MRI data. Recent advancements in deep learning have enabled researchers to utilize convolutional neural networks to denoise and reconstruct images. We used the Trainable Nonlinear Reaction Diffusion network architecture to denoise the MRI images, allowing for smoother segmentation while preserving fine details

2009 Annual Meeting of the American Society of Primatologists

No abstract.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/64333/1/20230_ftp.pd

Controllable Non-Markovianity for a Spin Qubit in Diamond

We present a flexible scheme to realize non-artificial non-Markovian dynamics of an electronic spin qubit, using a nitrogen-vacancy center in diamond where the inherent nitrogen spin serves as a regulator of the dynamics. By changing the population of the nitrogen spin, we show that we can smoothly tune the non-Markovianity of the electron spin's dynamic. Furthermore, we examine the decoherence dynamics induced by the spin bath to exclude other sources of non-Markovianity. The amount of collected measurement data is kept at a minimum by employing Bayesian data analysis. This allows for a precise quantification of the parameters involved in the description of the dynamics and a prediction of so far unobserved data points.Comment: 12 pages, 9 figure, including supplemental materia

Evaluation of cholesterol and vitamin E concentrations in adult alpacas and nursing crias

The objective of this study was to determine if serum cholesterol and vitamin E concentrations change with production and physiologic state in alpacas. Blood was collected from 3 groups of alpacas. An adult female group was sampled in the periparturient period and once monthly until their offspring were weaned. Crias born to the females were sampled after birth, then once monthly until weaning. A group consisting of males was sampled once monthly throughout the study period. Serum vitamin E and cholesterol concentrations were measured and vitamin E to cholesterol ratios was calculated. Vitamin E concentrations were similar throughout the different physiologic states. Cria vitamin E concentrations closely correlated to that of their dam. Significant cholesterol concentration fluctuations in crias occurred after 4 weeks of life possibly due to milk fat content. After weaning, the cholesterol concentrations became similar to the adult animals within study. Vitamin E concentrations varied with age in crias as they transitioned from a milk to forage based diet. Cholesterol fluctuated with altered physiologic and metabolic demands, most noticeable in the crias. Further studies are needed to determine if vitamin E to cholesterol ratios would be more appropriate to fully assess the vitamin E status in nursing crias

Verifier-on-a-Leash: New schemes for verifiable delegated quantum computation, with quasilinear resources

The problem of reliably certifying the outcome of a computation performed by a quantum device is rapidly gaining relevance. We present two protocols for a classical verifier to verifiably delegate a quantum computation to two non-communicating but entangled quantum provers. Our protocols have near-optimal complexity in terms of the total resources employed by the verifier and the honest provers, with th

Developing a scale for measuring perceptions of ethical misconduct

The purpose of the study is to develop a scale to measure individual’s ethical misconduct perceptions in the workplace. The Ethics Resource Center (2014) identified the most frequent types of ethical misconduct within the United States. These behaviors served as the 28 initial items for the implicit perceptions of ethical misconduct scale. A previous study identified four dimensions of unethical misconduct: Deceit, Use of Drugs and Alcohol, Sexual Misconduct, and Theft. The perceptions of ethical misconduct survey items were reduced to reflect the four dimensions. Therefore, we propose a confirmatory factor analysis on a separate data set will confirm these dimensions. We also believe that perceptions of ethical misconduct will be positively correlated with counterproductive work behaviors (CWBs). Additionally, individuals with dark personality traits, such as psychopathy, narcissism, and Machiavellianism, were more likely to perceive unethical misconduct as ethical

LemurFaceID: a face recognition system to facilitate individual identification of lemurs

Background: Long-term research of known individuals is critical for understanding the demographic and evolutionary processes that influence natural populations. Current methods for individual identification of many animals include capture and tagging techniques and/or researcher knowledge of natural variation in individual phenotypes. These methods can be costly, time-consuming, and may be impractical for larger-scale, populationlevel studies. Accordingly, for many animal lineages, long-term research projects are often limited to only a few taxa. Lemurs, a mammalian lineage endemic to Madagascar, are no exception. Long-term data needed to address evolutionary questions are lacking for many species. This is, at least in part, due to difficulties collecting consistent data on known individuals over long periods of time. Here, we present a new method for individual identification of lemurs (LemurFaceID). LemurFaceID is a computer-assisted facial recognition system that can be used to identify individual lemurs based on photographs. Results: LemurFaceID was developed using patch-wise Multiscale Local Binary Pattern features and modified facial image normalization techniques to reduce the effects of facial hair and variation in ambient lighting on identification. We trained and tested our system using images from wild red-bellied lemurs (Eulemur rubriventer) collected in Ranomafana National Park, Madagascar. Across 100 trials, with different partitions of training and test sets, we demonstrate that the LemurFaceID can achieve 98.7% ± 1.81% accuracy (using 2-query image fusion) in correctly identifying individual lemurs. Conclusions: Our results suggest that human facial recognition techniques can be modified for identification of individual lemurs based on variation in facial patterns. LemurFaceID was able to identify individual lemurs based on photographs of wild individuals with a relatively high degree of accuracy. This technology would remove many limitations of traditional methods for individual identification. Once optimized, our system can facilitate long-term research of known individuals by providing a rapid, cost-effective, and accurate method for individual identification