Toward the Equitable Distribution of Enriching Educational Experiences: Faculty Engagement with Racial Minority Students

Peña, Bensimon, and Colyar (2006) noted: “Not only do African Americans, Hispanics, and Native Americans have lower graduation rates than [do] Whites and Asian Americans, they also experience inequalities in just about every indicator of academic success – from earned grade point average to placement on the dean’s list to graduation rates in competitive majors” (p. 48). While these and other racialized outcomes disparities cannot be attributed to a narrow set of explanatory factors, one thing is known for sure: College students who are actively engaged inside and outside the classroom are considerably more likely than are their disengaged peers to persist through baccalaureate degree attainment. Furthermore, engaged students typically accrue the desired outcomes that are central to liberal education. This is especially true for engagement in what Kuh (2008) refers to as “high-impact” educational experiences – study abroad programs, learning communities, undergraduate research programs, service learning opportunities, and summer internships, to name a few. Unfortunately, racial minority undergraduates are considerably less likely than are their White peers to enjoy the educational benefits associated with these experiences.

A Functional-Based Distribution Diagnostic for a Linear Model with Correlated Outcomes: Technical Report

Despite the widespread popularity of linear models for correlated outcomes (e.g. linear mixed modesl and time series models), distribution diagnostic methodology remains relatively underdeveloped in this context. In this paper we present an easy-to-implement approach that lends itself to graphical displays of model fit. Our approach involves multiplying the estimated marginal residual vector by the Cholesky decomposition of the inverse of the estimated marginal variance matrix. Linear functions or the resulting "rotated" residuals are used to construct an empirical cumulative distribution function (ECDF), whose stochastic limit is characterized. We describe a resampling technique that serves as a computationally efficient parametric bootstrap for generating representatives of the stochastic limit of the ECDF. Through functionals, such representatives are used to construct global tests for the hypothesis of normal margional errors. In addition, we demonstrate that the ECDF of the predicted random effects, as described by Lange and Ryan (1989), can be formulated as a special case of our approach. Thus, our method supports both omnibus and directed tests. Our method works well in a variety of circumstances, including models having independent units of sampling (clustered data) and models for which all observations are correlated (e.g., a single time series).

Osseointegration of hollow cylinder based spinal implants in normal and osteoporotic vertebrae: a sheep study

INTRODUCTION: Osteoporosis is not only responsible for an increased number of metaphyseal and spinal fractures but it also complicates their treatment. To prevent the initial loosening, we developed a new implant with an enlarged implant/bone interface based on the concept of perforated, hollow cylinders. We evaluated whether osseointegration of a hollow cylinder based implant takes place in normal or osteoporotic bone of sheep under functional loading conditions during anterior stabilization of the lumbar spine. MATERIALS AND METHODS: Osseointegration of the cylinders and status of the fused segments (ventral corpectomy, replacement with iliac strut, and fixation with testing implant) were investigated in six osteoporotic (age 6.9 +/- 0.8 years, mean body weight 61.1 +/- 5.2 kg) and seven control sheep (age 6.1 +/- 0.2 years, mean body weight 64.9 +/- 5.7 kg). Osteoporosis was introduced using a combination protocol of ovariectomy, high-dose prednisone, calcium and phosphor reduced diet and movement restriction. Osseointegration was quantified using fluorescence and conventional histology; fusion status was determined using biomechanical testing of the stabilized segment in a six-degree-of-freedom loading device as well as with radiological and histological staging. RESULTS: Intact bone trabeculae were found in 70% of all perforations without differences between the two groups (P = 0.26). Inside the cylinders, bone volume/total volume was significantly higher than in the control vertebra (50 +/- 16 vs. 28 +/- 13%) of the same animal (P<0.01), but significantly less (P<0.01) than in the near surrounding (60 +/- 21%). After biomechanical testing as described in Sect. "Materials and methods", seven spines (three healthy and four osteoporotic) were classified as completely fused and six (four healthy and two osteoporotic) as not fused after a 4-month observation time. All endplates were bridged with intact trabeculae in the histological slices. CONCLUSIONS: The high number of perforations, filled with intact trabeculae, indicates an adequate fixation; bridging trabeculae between adjacent endplates and tricortical iliac struts in all vertebrae indicates that the anchorage is adequate to promote fusion in this animal model, even in the osteoporotic sheep.

Cholesky Residuals for Assessing Normal Errors in a Linear Model with Correlated Outcomes: Technical Report

Despite the widespread popularity of linear models for correlated outcomes (e.g. linear mixed models and time series models), distribution diagnostic methodology remains relatively underdeveloped in this context. In this paper we present an easy-to-implement approach that lends itself to graphical displays of model fit. Our approach involves multiplying the estimated margional residual vector by the Cholesky decomposition of the inverse of the estimated margional variance matrix. The resulting "rotated" residuals are used to construct an empirical cumulative distribution function and pointwise standard errors. The theoretical framework, including conditions and asymptotic properties, involves technical details that are motivated by Lange and Ryan (1989), Pierce (1982), and Randles (1982). Our method appears to work well in a variety of circumstances, including models having independent units of sampling (clustered data) and models for which all observations are correlated (e.g., a single time series). Our methods can produce satisfactory results even for models that do not satisfy all of the technical conditions stated in our theory.

Model Evaluation Based on the Distribution of Estimated Absolute Prediction Error

The construction of a reliable, practically useful prediction rule for future response is heavily dependent on the "adequacy" of the fitted regression model. In this article, we consider the absolute prediction error, the expected value of the absolute difference between the future and predicted responses, as the model evaluation criterion. This prediction error is easier to interpret than the average squared error and is equivalent to the mis-classification error for the binary outcome. We show that the distributions of the apparent error and its cross-validation counterparts are approximately normal even under a misspecified fitted model. When the prediction rule is "unsmooth", the variance of the above normal distribution can be estimated well via a perturbation-resampling method. We also show how to approximate the distribution of the difference of the estimated prediction errors from two competing models. With two real examples, we demonstrate that the resulting interval estimates for prediction errors provide much more information about model adequacy than the point estimates alone.

Semiparametric Normal Transformation Models for Spatially Correlated Survival Data

There is an emerging interest in modeling spatially correlated survival data in biomedical and epidemiological studies. In this paper, we propose a new class of semiparametric normal transformation models for right censored spatially correlated survival data. This class of models assumes that survival outcomes marginally follow a Cox proportional hazard model with unspecified baseline hazard, and their joint distribution is obtained by transforming survival outcomes to normal random variables, whose joint distribution is assumed to be multivariate normal with a spatial correlation structure. A key feature of the class of semiparametric normal transformation models is that it provides a rich class of spatial survival models where regression coefficients have population average interpretation and the spatial dependence of survival times is conveniently modeled using the transformed variables by flexible normal random fields. We study the relationship of the spatial correlation structure of the transformed normal variables and the dependence measures of the original survival times. Direct nonparametric maximum likelihood estimation in such models is practically prohibited due to the high dimensional intractable integration of the likelihood function and the infinite dimensional nuisance baseline hazard parameter. We hence develop a class of spatial semiparametric estimating equations, which conveniently estimate the population-level regression coefficients and the dependence parameters simultaneously. We study the asymptotic properties of the proposed estimators, and show that they are consistent and asymptotically normal. The proposed method is illustrated with an analysis of data from the East Boston Ashma Study and its performance is evaluated using simulations.

Distribution of amyloid precursor protein and amyloid-beta immunoreactivity in DBA/2J glaucomatous mouse retinas

PURPOSE: Evidence suggests that altered metabolism of amyloid precursor protein (APP) may play a role in the pathophysiology of retinal ganglion cell (RGC) death in the etiology of glaucoma. The authors sought to determine the distribution of APP and amyloid-beta (Abeta) in DBA/2J glaucomatous mouse retinas. METHODS: The retinas of 3- and 15-month-old DBA/2J mice and C57/BL-6 mice (control group) were fixed with 4% paraformaldehyde and processed for immunohistochemistry. Antibodies used included a polyclonal antibody to the C terminus of Abeta 40 and a polyclonal antibody to the APP ectodomain. Immunohistochemically stained tissue was graded using light microscopy. Distribution and semiquantitative expression of APP and Abeta in young and old glaucomatous and normal retinas were determined and compared. RESULTS: Strong APP and Abeta immunoreactivity was found in the RGC layer, optic nerve, and pia/dura of old DBA/2J retinas, with considerably higher intensity found in the old compared with the young DBA/2J mice. In contrast to glaucomatous mice, the control group did not show any notable age-related difference. CONCLUSIONS: Disruption of the homeostatic properties of secreted APP with consecutive Abeta cytotoxicity might be a contributing factor of ganglion cell loss in glaucomatous mouse retinas.

Evaluation of flood risk in response to climate variability

Standard procedures for forecasting flood risk (Bulletin 17B) assume annual maximum flood (AMF) series are stationary, meaning the distribution of flood flows is not significantly affected by climatic trends/cycles, or anthropogenic activities within the watershed. Historical flood events are therefore considered representative of future flood occurrences, and the risk associated with a given flood magnitude is modeled as constant over time. However, in light of increasing evidence to the contrary, this assumption should be reconsidered, especially as the existence of nonstationarity in AMF series can have significant impacts on planning and management of water resources and relevant infrastructure. Research presented in this thesis quantifies the degree of nonstationarity evident in AMF series for unimpaired watersheds throughout the contiguous U.S., identifies meteorological, climatic, and anthropogenic causes of this nonstationarity, and proposes an extension of the Bulletin 17B methodology which yields forecasts of flood risk that reflect climatic influences on flood magnitude. To appropriately forecast flood risk, it is necessary to consider the driving causes of nonstationarity in AMF series. Herein, large-scale climate patterns—including El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), and Atlantic Multidecadal Oscillation (AMO)—are identified as influencing factors on flood magnitude at numerous stations across the U.S. Strong relationships between flood magnitude and associated precipitation series were also observed for the majority of sites analyzed in the Upper Midwest and Northeastern regions of the U.S. Although relationships between flood magnitude and associated temperature series are not apparent, results do indicate that temperature is highly correlated with the timing of flood peaks. Despite consideration of watersheds classified as unimpaired, analyses also suggest that identified change-points in AMF series are due to dam construction, and other types of regulation and diversion. Although not explored herein, trends in AMF series are also likely to be partially explained by changes in land use and land cover over time. Results obtained herein suggest that improved forecasts of flood risk may be obtained using a simple modification of the Bulletin 17B framework, wherein the mean and standard deviation of the log-transformed flows are modeled as functions of climate indices associated with oceanic-atmospheric patterns (e.g. AMO, ENSO, NAO, and PDO) with lead times between 3 and 9 months. Herein, one-year ahead forecasts of the mean and standard deviation, and subsequently flood risk, are obtained by applying site specific multivariate regression models, which reflect the phase and intensity of a given climate pattern, as well as possible impacts of coupling of the climate cycles. These forecasts of flood risk are compared with forecasts derived using the existing Bulletin 17B model; large differences in the one-year ahead forecasts are observed in some locations. The increased knowledge of the inherent structure of AMF series and an improved understanding of physical and/or climatic causes of nonstationarity gained from this research should serve as insight for the formulation of a physical-casual based statistical model, incorporating both climatic variations and human impacts, for flood risk over longer planning horizons (e.g., 10-, 50, 100-years) necessary for water resources design, planning, and management.

Online distribution management framework by incorporating wind generation

In distribution system operations, dispatchers at control center closely monitor system operating limits to ensure system reliability and adequacy. This reliability is partly due to the provision of remote controllable tie and sectionalizing switches. While the stochastic nature of wind generation can impact the level of wind energy penetration in the network, an estimate of the output from wind on hourly basis can be extremely useful. Under any operating conditions, the switching actions require human intervention and can be an extremely stressful task. Currently, handling a set of switching combinations with the uncertainty of distributed wind generation as part of the decision variables has been nonexistent. This thesis proposes a three-fold online management framework: (1) prediction of wind speed, (2) estimation of wind generation capacity, and (3) enumeration of feasible switching combinations. The proposed methodology is evaluated on 29-node test system with 8 remote controllable switches and two wind farms of 18MW and 9MW nameplate capacities respectively for generating the sequence of system reconfiguration states during normal and emergency conditions.

Afghanistan connection : heroin production, distribution, and consumption

Heroin prices are a reflection of supply and demand, and similar to any other market, profits motivate participation. The intent of this research is to examine the change in Afghan opium production due to political conflict affecting Europe’s heroin market and government policies. If the Taliban remain in power, or a new Afghan government is formed, the changes will affect the heroin market in Europe to a certain degree. In the heroin market, the degree of change is dependent on many socioeconomic forces such as law enforcement, corruption, and proximity to Afghanistan. An econometric model that examines the degree of these socioeconomic effects has not been applied to the heroin trade in Afghanistan before. This research uses a two-stage least squares econometric model to reveal the supply and demand of heroin in 36 different countries from the Middle East to Western Europe in 2008. An application of the two-stage least squares model to the heroin market in Europe will attempt to predict the socioeconomic consequences of Afghanistan opium production.

Application of well log tomography to the Dundee and Rogers City Limestones, Michigan Basin, USA

The Michigan Basin is located in the upper Midwest region of the United States and is centered geographically over the Lower Peninsula of Michigan. It is filled primarily with Paleozoic carbonates and clastics, overlying Precambrian basement rocks and covered by Pleistocene glacial drift. In Michigan, more than 46,000 wells have been drilled in the basin, many producing significant quantities of oil and gas since the 1920s in addition to providing a wealth of data for subsurface visualization. Well log tomography, formerly log-curve amplitude slicing, is a visualization method recently developed at Michigan Technological University to correlate subsurface data by utilizing the high vertical resolution of well log curves. The well log tomography method was first successfully applied to the Middle Devonian Traverse Group within the Michigan Basin using gamma ray log curves. The purpose of this study is to prepare a digital data set for the Middle Devonian Dundee and Rogers City Limestones, apply the well log tomography method to this data and from this application, interpret paleogeographic trends in the natural radioactivity. Both the Dundee and Rogers City intervals directly underlie the Traverse Group and combined are the most prolific reservoir within the Michigan Basin. Differences between this study and the Traverse Group include increased well control and “slicing” of a more uniform lithology. Gamma ray log curves for the Dundee and Rogers City Limestones were obtained from 295 vertical wells distributed over the Lower Peninsula of Michigan, converted to Log ASCII Standard files, and input into the well log tomography program. The “slicing” contour results indicate that during the formation of the Dundee and Rogers City intervals, carbonates and evaporites with low natural radioactive signatures on gamma ray logs were deposited. This contrasts the higher gamma ray amplitudes from siliciclastic deltas that cyclically entered the basin during Traverse Group deposition. Additionally, a subtle north-south, low natural radioactive trend in the center of the basin may correlate with previously published Dundee facies tracts. Prominent trends associated with the distribution of limestone and dolomite are not observed because the regional range of gamma ray values for both carbonates are equivalent in the Michigan Basin and additional log curves are needed to separate these lithologies.

Bayesian change-point analysis in linear regression model with scale mixtures of normal distributions

In this thesis, we consider Bayesian inference on the detection of variance change-point models with scale mixtures of normal (for short SMN) distributions. This class of distributions is symmetric and thick-tailed and includes as special cases: Gaussian, Student-t, contaminated normal, and slash distributions. The proposed models provide greater flexibility to analyze a lot of practical data, which often show heavy-tail and may not satisfy the normal assumption. As to the Bayesian analysis, we specify some prior distributions for the unknown parameters in the variance change-point models with the SMN distributions. Due to the complexity of the joint posterior distribution, we propose an efficient Gibbs-type with Metropolis- Hastings sampling algorithm for posterior Bayesian inference. Thereafter, following the idea of [1], we consider the problems of the single and multiple change-point detections. The performance of the proposed procedures is illustrated and analyzed by simulation studies. A real application to the closing price data of U.S. stock market has been analyzed for illustrative purposes.

Subcellular distribution of the insulin-like growth factor (IGF) binding proteins (IGFBPs) 2 and 3 in articular chondrocytes

The insulin-like growth factor (IGF) is a major anabolic regulator in articular cartilage. The IGF-binding proteins (IGFBPs) are increased during osteoarthritis (OA), but the function of the later proteins remains unknown. In general, the IGFBPs are pluripotential effectors capable of IGF regulation and of acting on their own to control key cell functions, including survival and proliferation. The independent functions are often associated with their cell location, and therefore this study explores the distribution of IGFBP-2 and IGFBP-3 in articular chondrocytes. Immunohistochemistry was used to localize IGFBP-2 in normal human articular cartilage. Bovine chondrocytes were used for subcellular fractionation (hypotonic cell lysis) under nonreducing conditions and nuclear purification (centrifugation on sucrose cushions). Cell fraction markers and IGFBPs were assayed in the subcellular fractions by Western immunoblot. The IHC results showed association of IGFBP-2 with chondrocytes, but not with the nuclei. Subcellular fractionation of isolated chondrocytes yielded intact nuclei as assessed at the light microscopic level; the nuclear marker histone H1 was exclusively associated with this fraction. More than 90% of the cytoplasmic marker GAPDH and all the detectable IGFBP-2 were in the cytoplasmic fraction. Immunoreactive IGFBP-3 was found in the cytoplasmic and peri-nuclear/nuclear fractions. Chondrocytes contain intracellular IGFBP-2 and IGFBP-3 but only IGFBP-3 is associated with nuclei. This suggests the hypothesis that the actions of these IGFBPs in articular cartilage extend beyond the classic modulation of IGF receptor action.

OCT4 expression in human non-small cell lung cancer: implications for therapeutic intervention

Here we investigate the expression of OCT4 human lung adenocarcinoma and bronchioloalveolar carcinoma (BAC) tumor biopsies and tumor-derived primary cell cultures. OCT4 has been detected in several human tumors suggesting a potentially critical role in tumorigenesis. We assessed the presence of OCT4 in clinical tumor samples of both adenocarcinoma and BAC at the cellular and transcriptional levels, respectively. Furthermore, we evaluated tumor-derived cell cultures for potential differences in OCT4 expression. Immunohistochemical analysis depicted OCT4 in 2 of 8 adenocarcinoma tumor samples and 3 of 5 BAC tumor samples, with no apparent difference in the degree of expression among the sections examined. These results were validated by transcript analysis. Flow cytometric assessment of 11 adenocarcinoma-derived cell cultures and 3 BAC-derived cell cultures revealed significantly higher OCT4 expression in adenocarcinoma tumors compared to their normal counterparts. This, however, was not observed in the BAC cultures. Comparative studies of OCT4 in adenocarcinoma and BAC tumor cell cultures demonstrated a dramatically higher expression in the former. The expression of OCT4 may represent a specific and effective target for therapeutic intervention in adenocarcinoma and BAC. In addition, the aberrant expression and distribution of OCT4 may indicate important parameters concerning the differences between adenocarcinoma and BAC.