Biomarker and Translational Prostate Cancer Research

The existing clinical biomarkers for prostate cancer (PCa) are not ideal, since they cannot specifically differentiate between those patients who should be treated immediately and those who should avoid overtreatment. Current screening techniques lack specificity, and a decisive diagnosis of PCa is based on prostate biopsy. Although PCa screening is widely utilized nowadays, two-thirds of the biopsies performed are still unnecessary. Thus, the discovery of noninvasive PCa biomarkers remains an urgent unmet medical need. Once metastasized, there is still no curative therapy. A better understanding of sustained androgen receptor signalling in castration resistant prostate cancer (CRPC) has now led to the development of more effective therapies. We need a better understanding of the molecular and cellular aspects of prostate carcinogenesis and progression. Identification of cancer initiating cells and therapies against these populations is a promising way forward to fight this disease

Workplace Wellness Programs: Healthy Lifestyles and Economic Success

Abstract The following is a review of articles and literature on health-care costs, return on investment, employee health benefits, business success, barriers associated with workplace wellness programs and support systems, and an infrastructure that supports implementation. Research and literature on costs, benefits, barriers, and program implementation support is examined in this paper that are associated with successful workplace wellness programs. Findings from this review include a positive return on investment, lower healthcare costs for both the employee and employer, and additional benefits for the employee, employer, and the community. Also, barriers to participate in workplace wellness programs, use of incentives to increase participation in programs, and key characteristics of successful workplace wellness programs were discovered. Keywords: workplace wellness programs, employee well-being, economic success, infrastructure

Markov regime switching and unit root tests

We investigate the power and size performance of unit root tests when the data undergo Markov regime switching. All tests, including those robust to a single break in trend growth rate, have low power against a process with a Markov-switching trend. Under the null hypothesis, we find previously documented size distortions in Dickey-Fuller type tests caused by a single break in trend growth rate or variance do not generalize to most parameterizations of Markov switching in trend or variance. However, Markov switching in variance can lead to overrejection in tests allowing for a single break in the level of trend.Time-series analysis ; Business cycles

The less volatile U.S. economy: a Bayesian investigation of timing, breadth, and potential explanations

Using a Bayesian model comparison strategy, we search for a volatility reduction within the post-war sample for the growth rates of U.S. aggregate and disaggregate real GDP. We find that the growth rate of aggregate real GDP has been less volatile since the early 1980s, and that this volatility reduction is concentrated in the cyclical component of real GDP. The growth rates of many of the broad production sectors of real GDP display similar reductions in volatility, suggesting the aggregate volatility reduction does not have a narrow source. We also find a large volatility reduction in measures of final sales in the goods sector. We contrast this evidence to an existing literature documenting an aggregate volatility reduction that is shared by only one narrow sub-component, the production of durable goods, and is not present in final sales. We also document structural breaks in the persistence and conditional volatility of inflation that occurred over a similar time frame as the volatility reduction in real GDP.Econometric models ; Business cycles

Rapid Sequence Identification of Potential Pathogens Using Techniques from Sparse Linear Algebra

The decreasing costs and increasing speed and accuracy of DNA sample collection, preparation, and sequencing has rapidly produced an enormous volume of genetic data. However, fast and accurate analysis of the samples remains a bottleneck. Here we present D$^{4}$RAGenS, a genetic sequence identification algorithm that exhibits the Big Data handling and computational power of the Dynamic Distributed Dimensional Data Model (D4M). The method leverages linear algebra and statistical properties to increase computational performance while retaining accuracy by subsampling the data. Two run modes, Fast and Wise, yield speed and precision tradeoffs, with applications in biodefense and medical diagnostics. The D$^{4}$RAGenS analysis algorithm is tested over several datasets, including three utilized for the Defense Threat Reduction Agency (DTRA) metagenomic algorithm contest

Generalized Integrated Brownian Fields for Simulation Metamodeling

We introduce a novel class of Gaussian random fields (GRFs), called generalized integrated Brownian fields (GIBFs), focusing on the use of GIBFs for Gaussian process regression in deterministic and stochastic simulation metamodeling. We build GIBFs from the well-known Brownian motion and discuss several of their properties, including differentiability that cart differ in each coordinate, no mean reversion, and the Markov property. We explain why we desire to use GRFs with these properties and provide formal definitions of mean reversion and the Markov property for real-valued, differentiable random fields. We show how to use GIBFs with stochastic kriging, covering trend modeling and parameter fitting, discuss their approximation capability, and show that the resulting metamodel also has differentiability that can differ in each coordinate. Last, we use several examples to demonstrate superior prediction capability as compared with the GRFs corresponding to the Gaussian and Matern covariance functions

A murine model of variant late infantile ceroid lipofuscinosis recapitulates behavioral and pathological phenotypes of human disease.

Neuronal ceroid lipofuscinoses (NCLs; also known collectively as Batten Disease) are a family of autosomal recessive lysosomal storage disorders. Mutations in as many as 13 genes give rise to ∼10 variants of NCL, all with overlapping clinical symptomatology including visual impairment, motor and cognitive dysfunction, seizures, and premature death. Mutations in CLN6 result in both a variant late infantile onset neuronal ceroid lipofuscinosis (vLINCL) as well as an adult-onset form of the disease called Type A Kufs. CLN6 is a non-glycosylated membrane protein of unknown function localized to the endoplasmic reticulum (ER). In this study, we perform a detailed characterization of a naturally occurring Cln6 mutant (Cln6(nclf)) mouse line to validate its utility for translational research. We demonstrate that this Cln6(nclf) mutation leads to deficits in motor coordination, vision, memory, and learning. Pathologically, we demonstrate loss of neurons within specific subregions and lamina of the cortex that correlate to behavioral phenotypes. As in other NCL models, this model displays selective loss of GABAergic interneuron sub-populations in the cortex and the hippocampus with profound, early-onset glial activation. Finally, we demonstrate a novel deficit in memory and learning, including a dramatic reduction in dendritic spine density in the cerebral cortex, which suggests a reduction in synaptic strength following disruption in CLN6. Together, these findings highlight the behavioral and pathological similarities between the Cln6(nclf) mouse model and human NCL patients, validating this model as a reliable format for screening potential therapeutics

Methods to Improve Process Safety Performance through Flange Connection Leak Prediction and Control

Process safety is a task of preventing leaks. Leak prevention is critical because pressure vessels and piping assets in chemical plants are fabricated from materials which have limited corrosion resistance. When corrosive compounds are processed in these assets, they may suffer degradation over time due to thinning, cracking, or loss of their material properties. This problem is partially controlled by applying a safety margin known called a corrosion allowance. The corrosion allowance is determined by predicting the asset’s expected corrosion rate and its service life. However, this fixed safety margin does not consider the inherent uncertainty in an individual asset’s degradation rate due to variability in the material’s corrosion resistance, the operating parameters of the process, and the inspection techniques used to measure the progression of corrosion damage over time. Consequently, deterministic analysis is not capable of precisely estimating an asset’s safe operating life during its design stage. One of the most likely areas for leakage to occur in process equipment is at the flange connections that join assets together. Risk analyses for planning inspections of fixed equipment and piping usually treat flanges as components of their parent asset. This thesis focuses on methods to improve prediction and control of corrosion and leakage at flange connections in particular. Flange connection seal tightness can be monitored through vibration-based Non-Destruction Testing (NDT). The data gathered from this monitoring can be used to update risk models for flange connection leakage. Hierarchical Bayesian Network methods of modeling risk are demonstrated in this thesis to be capable of predicting probability of seal failure based on the mean and variance of failure rates in a population of flange connections. This allows for prediction of the probabilities based on corrosion and leak events in the plant. The results of inspection techniques are used as inputs to this risk model, enabling probabilistic decision-making