An epidemiological analysis of potential associations between C-reactive protein, inflammation, and prostate cancer in the male US population using the 2009–2010 National Health and Nutrition Examination Survey (NHANES) data

Prostate cancer is the second leading cause of cancer-related deaths in US males, yet much remains to be learned about the role of inflammation in its etiology. We hypothesized that preexisting exposure to chronic inflammatory conditions caused by infectious agents or inflammatory diseases increase the risk of prostate cancer. Using the 2009-2010 National Health and Nutrition Examination Survey, we examined the relationships between demographic variables, inflammation, infection, circulating plasma C-reactive protein (CRP), and the risk of occurrence of prostate cancer in US men over 18 years of age. Using IBM SPSS, we performed bivariate and logistic regression analyses using high CRP values as the dependent variable and five study covariates including prostate cancer status. From 2009 – 2010, an estimated 5,448,373 men reported having prostate cancer of which the majority were Caucasian (70.1%) and were aged 40 years and older (62.7%). Bivariate analyses demonstrated that high CRP was not associated with an increased risk of prostate cancer. Greater odds of having prostate cancer were revealed for men that had inflammation related to disease (OR = 1.029, CI 1.029-1.029) and those who were not taking drugs to control inflammation (OR = 1.330, CI 1.324-1.336). Men who did not have inflammation resulting from non-infectious diseases had greater odds of not having prostate cancer (OR = 1.031, CI 1.030-1.031). Logistic regression analysis yielded that men with the highest CRP values had greater odds of having higher household incomes and lower odds of having received higher education, being aged 40 years or older, being of a race or ethnicity different from other, and of having prostate cancer. Our results show that chronic inflammation of multiple etiologies is a risk factor for prostate cancer and that CRP is not associated with this increased risk. Further research is needed to elucidate the complex interactions between inflammation and prostate cancer

A population-based cross-sectional study of health service deficits among U.S. adults with depressive symptoms

BACKGROUND: Depression is a psychiatric condition that affects approximately one in five U.S. adults in their lifetime. No study that we know of has examined depressive symptoms and health service deficits in rural compared with non-rural populations. Four factors constitute the variable health service deficits: did not have health insurance, did not have a healthcare provider, deferred medical care because of cost and did not have a routine medical exam, all within the last 12 months. The aim of this study was to ascertain the prevalence of health service deficits in rural versus non-rural adults with depressive symptoms. Examining depressive symptoms by health service deficits is important because it allows us to approximate those with the condition who might not be receiving care for it. By analyzing national, population-based data, this study sought to fill in some important epidemiological gaps regarding depressive symptoms and health service deficits. METHODS: For this analysis the population of interest was U.S. adults identified as currently having depressive symptoms using the PHQ-8 criteria. Behavior Risk Factor Surveillance Survey 2006 data were used in this analysis. Health service deficits was the primary dependent variable. Multivariate logistic regression analysis was performed to examine health service deficits experienced by adults with depression controlling for socioeconomic status, race and ethnicity and geographic locale (rural or non-rural). RESULTS: Logistic regression analysis yielded that U.S. adults currently having depressive symptoms who were of low socioeconomic status, Hispanic ethnicity, or living in a rural locale were more likely to have at least one health service deficit. CONCLUSION: Analyzing data collected by a large surveillance system such as BRFSS, allows for an analysis incorporating an array of covariates not available from clinically-based data such as electronic health records. By identifying clinically depressed U.S. adults who also have at least one health service deficit, we were able to ascertain those most likely not receiving care for this debilitating condition. We believe community pharmacists are well suited to assist in connecting depressed, vulnerable populations with appropriate and needed care. This care would be best provided by an inter-professional team led by a primary care provider

Parameter Estimation for Data with Lower Limit of Detection Values under the Truncated Model – EM Solutions

Computing unbiased parameter estimates from a distribution using a sample with observations appearing below a lower limit of detection (LLOD) can be challenging. Frequently, LLOD observations are excluded from calculations for parameter estimates, or the LLOD observations are replaced with arbitrary values (LLOD, LLOD/2, LLOD/√2) prior to the calculations. Despite the frequent use of these simple approaches, the approaches are known to provide biased parameter estimates. Alternative approaches include implementing a left truncation or left censoring approach. In the first dissertation aim, we will explore and establish a general theoretical relationship between accurately estimating parameters under left truncated and left censored models. Estimation methods under both models require iterative algorithms. The left truncation approach is applied through an Expectation-Maximization (EM) algorithm. While the left censoring approach is implemented by the Newton-Raphson method. We conclude in the first aim that the left truncation and left censoring approaches yielded equivalent parameter estimates. Computationally, we favored the left truncation approach that is implemented through an EM algorithm. The left truncation approach for estimating parameters is utilized in the remaining aims. In the second aim of this dissertation, we propose an EM algorithm for estimating parameters from a normal distribution when there are multiple LLOD values present. The third aim includes solutions to an EM algorithm for estimating bivariate normal distribution parameters. In the third aim, the data under the left truncation approach can be categorized into 24 scenarios. The construction of the EM algorithm includes the scenarios. All dissertation aims are motivated by toxicology and serology data collected in the Systemic Lupus Erythematosus in Gullah Health study

Analysis of Bonjor Sugar Business Feasibility Study in Karyamukti Village (Case Study on Mr. Lukman’s MSMEs)

Bonjor Sugar is one of the typical preparations from the Gunung Padang Cultural Heritage Site in Karyamukti Village, one of the managers of the Bonjor Sugar Business is Mr. Lukman. As a business that has been running for a long time, Mr. Lukman's business did not test the feasibility of the business he was running. There are several problems related to accessibility, technology, and limited knowledge of human resources. The purpose of this analysis is to test the feasibility of Mr. Lukman's Bonjor Sugar Business. This research uses a mixed or combination research method where data is obtained based on the results of interviews and observations. The results obtained are that there are several aspects that have not been fulfilled. Of the 7 aspects analyzed, only financial, economic and social aspects, as well as environmental aspects are considered feasible

Disparities in healthy food zoning, farmers’ market availability, and fruit and vegetable consumption among North Carolina residents

Background Context and purpose of the study. To examine (1) associations between county-level zoning to support farmers’ market placement and county-level farmers’ market availability, rural/urban designation, percent African American residents, and percent of residents living below poverty and (2) individual-level associations between zoning to support farmers’ markets; fruit and vegetable consumption and body mass index (BMI) among a random sample of residents of six North Carolina (NC) counties. Methods Zoning ordinances were scored to indicate supportiveness for healthy food outlets. Number of farmers’ markets (per capita) was obtained from the NC-Community Transformation Grant Project Fruit and Vegetable Outlet Inventory (2013). County-level census data on rural/urban status, percent African American, and percent poverty were obtained. For data on farmers’ market shopping, fruit and vegetable consumption, and BMI, trained interviewers conducted a random digit dial telephone survey of residents of six NC counties (3 urban and 3 rural). Pearson correlation coefficients and multilevel linear regression models were used to examine county-level and individual-level associations between zoning supportiveness, farmers’ market availability, and fruit and vegetable consumption and BMI. Results At the county-level, healthier food zoning was greater in more urban areas and areas with less poverty. At the individual-level, self-reported fruit and vegetable consumption was associated with healthier food zoning. Conclusions Disparities in zoning to promote healthy eating should be further examined, and future studies should assess whether amending zoning ordinances will lead to greater availability of healthy foods and changes in dietary behavior and health outcomes.ECU Open Access Publishing Support Fun

Exploring Student Algebraic Thinking to Solve TIMSS Problems in Terms of Accommodator Learning Styles

This study aims to explore students' algebraic thinking skills in solving Trends International Mathematics and Science Study (TIMSS) problems in terms of accommodator learning styles. The research design used is a case study with a qualitative analysis approach. The results showed that accommodating subjects were able to meet the indicators of algebraic thinking, namely generalization, abstraction, analytical thinking, dynamic thinking, and modeling. However, the lack of accuracy in performing calculation operations causes the answers obtained by the subject to be less precise on abstraction, analytical thinking, and dynamic thinking problems. The characteristics of subjects with accommodator learning styles who are more likely to use intuition in solving problems influence students' ability to solve problems related to algebraic thinking

Virtual Sensor Middleware: Managing IoT Data for the Fog-Cloud Platform

This paper introduces the Virtual Sensor Middleware (VSM), which facilitates distributed sensor data processing on multiple fog nodes. VSM uses a Virtual Sensor as the core component of the middleware. The virtual sensor concept is redesigned to support functionality beyond sensor/device virtualization, such as deploying a set of virtual sensors to represent an IoT application and distributed sensor data processing across multiple fog nodes. Furthermore, the virtual sensor deals with the heterogeneous nature of IoT devices and the various communication protocols using different adapters to communicate with the IoT devices and the underlying protocol. VSM uses the publish-subscribe design pattern to allow virtual sensors to receive data from other virtual sensors for seamless sensor data consumption without tight integration among virtual sensors, which reduces application development efforts. Furthermore, VSM enhances the design of virtual sensors with additional components that support sharing of data in dynamic environments where data receivers may change over time, data aggregation is required, and dealing with missing data is essential for the applications

Fault-tolerant Concurrent Branch And Bound Algorithms Derived From Program Verification

An important aspect which is often overlooked in software design of distributed environments is that of fault tolerance. Many methodologies in the past have attempted to provide fault tolerance efficiently but have never been successful at eliminating explicit time and space redundancy. One approach for providing fault tolerance is through examining the behavior and properties of the application and deriving executable assertions that detect faults. Our work focuses on transforming the assertions of a verification proof of a program to executable assertions. These executable assertions may be embedded in the program to create a fault-tolerant program. It is also shown how the natural redundancy of the program variables can be used to reduce the number of executable assertions needed. While this approach has been applied to the sequential programming environment, the distributed programming environment presents special challenges, litis paper focuses on applying concurrent programming axiomatic proof systems to generate executable assertions in a distributed environment using distributed branch and bound as a model problem

Formal Generation of Executable Assertions for Application-Oriented Fault Tolerance

Executable assertions embedded into a distributed computing system can provide run-time assurance by ensuring that the program state, in the actual run-time environment, is consistent with the logical stage specified in the assertions; if not, then an error has occurred and a reliable communication of this diagnostic information is provided to the system such that reconfiguration and recovery can take place. Application- oriented fault tolerance is a method that provides fault detection using executable assertions based on the natural constraints of the application. This paper focuses on giving application-oriented fault tolerance a theoretical foundation by providing a mathematical model for the generation of executable assertions which detect faults in the presence of arbitrary failures. The mathematical model of choice was axiomatic program verification. A method was developed that translates a concurrent verification proof outline into an error-detecting concurrent program. This paper shows the application of the developed method to several applications