MAPPING BURNED AREAS IN THE FLINT HILLS OF KANSAS AND OKLAHOMA, 2000-2010

Prescribed burning is commonly used to prevent succession oftallgrass prairie to woody vegetation, which preserves the prairie\u27s value to ranching and native wildlife. However, burning has negative effects as well, including potentially harming wildlife and releasing pollutants into the atmosphere. Research concerning the effects of fire on vegetation dynamics, wildlife, and air quality would benefit greatly from maps of burned areas in the Flint Hills, as no reliable quantification of burned areas currently exists. We used Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery to map burned areas in the Flint Hills for each year from 2000 to 2010. Our maps revealed the total amount and spatial pattern of burning for each year. They also revealed the frequency with which different parts of the study area were burned during the II-year study period. Finally, our maps showed that nearly all burning took place during the month of April

Validity and Accuracy of Atmospheric Air Quality Models

Effective evaluation of air pollution control strategies requires the use of validated and reliable mathematical models that can relate pollutant emissions to atmospheric air quality. The derivation and use of such models, at least for inert and linearly decaying pollutants such as CO and SO_2, has received a great deal of attention. Much less work has been devoted to assessing how the model predictions are related to actual atmospheric concentrations. The objectives of this paper are to formulate the concepts of validity and accuracy and to suggest and describe some experiments that can be performed to assess these features

Developing Clinical Reasoning Skills in Teacher Candidates Using a Problem-Based Learning Approach

In 2013, the Educational Leadership Department at Middle Tennessee State University (MTSU) implemented a redesign of the teacher preparation program to transition from a traditional on-campus model to one delivered both on-campus and in off-campus school sites while using a problem-based learning method. This new program closely follows the medical school model of residency experiences coupled with problem-based learning events. This article describes the problembased learning process used in this program, comparing it with the early versions of medical school problem-based learning that encouraged the development of “clinical reasoning” skills. Similarities and differences are highlighted, along with key components of the learning model in use at MTSU. The article presents lessons learned and next steps to be used in implementing the problem-based learning approach in a teacher preparation setting

The Family \u3cem\u3eRhabdoviridae\u3c/em\u3e: Mono- and Bipartite Negative-Sense RNA Viruses with Diverse Genome Organization and Common Evolutionary Origins

The family Rhabdoviridae consists of mostly enveloped, bullet-shaped or bacilliform viruses with a negative-sense, single-stranded RNA genome that infect vertebrates, invertebrates or plants. This ecological diversity is reflected by the diversity and complexity of their genomes. Five canonical structural protein genes are conserved in all rhabdoviruses, but may be overprinted, overlapped or interspersed with several novel and diverse accessory genes. This review gives an overview of the characteristics and diversity of rhabdoviruses, their taxonomic classification, replication mechanism, properties of classical rhabdoviruses such as rabies virus and rhabdoviruses with complex genomes, rhabdoviruses infecting aquatic species, and plant rhabdoviruses with both mono- and bipartite genomes

Hierarchical Bayesian Spatio-Temporal Analysis of Climatic and Socio-Economic Determinants of Rocky Mountain Spotted Fever

Citation: Raghavan, R. K., Goodin, D. G., Neises, D., Anderson, G. A., & Ganta, R. R. (2016). Hierarchical Bayesian Spatio-Temporal Analysis of Climatic and Socio-Economic Determinants of Rocky Mountain Spotted Fever. Plos One, 11(3), 17. doi:10.1371/journal.pone.0150180This study aims to examine the spatio-temporal dynamics of Rocky Mountain spotted fever (RMSF) prevalence in four contiguous states of Midwestern United States, and to determine the impact of environmental and socio-economic factors associated with this disease. Bayesian hierarchical models were used to quantify space and time only trends and spatio-temporal interaction effect in the case reports submitted to the state health departments in the region. Various socio-economic, environmental and climatic covariates screened a priori in a bivariate procedure were added to a main-effects Bayesian model in progressive steps to evaluate important drivers of RMSF space-time patterns in the region. Our results show a steady increase in RMSF incidence over the study period to newer geographic areas, and the posterior probabilities of county-specific trends indicate clustering of high risk counties in the central and southern parts of the study region. At the spatial scale of a county, the prevalence levels of RMSF is influenced by poverty status, average relative humidity, and average land surface temperature (> 35 degrees C) in the region, and the relevance of these factors in the context of climate-change impacts on tick-borne diseases are discussed

Correlation between normalized difference vegetation index and malaria in a subtropical rain forest undergoing rapid anthropogenic alteration

Time-series of coarse-resolution greenness values derived through remote sensing have been used as a surrogate environmental variable to help monitor and predict occurrences of a number of vector-borne and zoonotic diseases, including malaria. Often, relationships between a remotely-sensed index of greenness, e.g. the normalized difference vegetation index (NDVI), and disease occurrence are established using temporal correlation analysis. However, the strength of these correlations can vary depending on type and change of land cover during the period of record as well as inter-annual variations in the climate drivers (precipitation, temperature) that control the NDVI values. In this paper, the correlation between a long (260 months) time-series of monthly disease case rates and NDVI values derived from the Global Inventory Modeling and Mapping Studies (GIMMS) data set were analysed for two departments (administrative units) located in the Atlantic Forest biome of eastern Paraguay. Each of these departments has undergone extensive deforestation during the period of record and our analysis considers the effect on correlation of active versus quiescent periods of case occurrence against a background of changing land cover. Our results show that time-series data, smoothed using the Fourier Transform tool, showed the best correlation. A moving window analysis suggests that four years is the optimum time frame for correlating these values, and the strength of correlation depends on whether it is an active or a quiescent period. Finally, a spatial analysis of our data shows that areas where land cover has changed, particularly from forest to non-forest, are well correlated with malaria case rates

Bayesian spatio-temporal analysis and geospatial risk factors of Human Monocytic Ehrlichiosis

Citation: Raghavan RK, Neises D, Goodin DG, Andresen DA, Ganta RR (2014) Bayesian Spatio-Temporal Analysis and Geospatial Risk Factors of Human Monocytic Ehrlichiosis. PLoS ONE 9(7): e100850. doi:10.1371/journal.pone.0100850Variations in spatio-temporal patterns of Human Monocytic Ehrlichiosis (HME) infection in the state of Kansas, USA were examined and the relationship between HME relative risk and various environmental, climatic and socio-economic variables were evaluated. HME data used in the study was reported to the Kansas Department of Health and Environment between years 2005–2012, and geospatial variables representing the physical environment [National Land cover/Land use, NASA Moderate Resolution Imaging Spectroradiometer (MODIS)], climate [NASA MODIS, Prediction of Worldwide Renewable Energy (POWER)], and socio-economic conditions (US Census Bureau) were derived from publicly available sources. Following univariate screening of candidate variables using logistic regressions, two Bayesian hierarchical models were fit; a partial spatio-temporal model with random effects and a spatio-temporal interaction term, and a second model that included additional covariate terms. The best fitting model revealed that spatio-temporal autocorrelation in Kansas increased steadily from 2005–2012, and identified poverty status, relative humidity, and an interactive factor, ‘diurnal temperature range x mixed forest area’ as significant county-level risk factors for HME. The identification of significant spatio-temporal pattern and new risk factors are important in the context of HME prevention, for future research in the areas of ecology and evolution of HME, and as well as climate change impacts on tick-borne diseases

Looking Back, Looking Forward: A Retrospective Analysis of a Pre-service Teacher Preparation Course

This SoTL study aimed to determine thirty-eight teacher candidates’ self-efficacy after experiencing semester one of a yearlong residency. Researchers used a pre/post survey, the Teachers’ Sense of Efficacy Scale (TSES), and a retrospective pre TSES to determine perceived levels of self-efficacy in three areas: classroom management, instructional strategies, and student engagement. Findings revealed that pre TSES (candidates scored themselves at the beginning of the semester) and retrospective pre TSES scores (candidates scored themselves at the end of the semester reflecting on where they were at the beginning of the semester) were significantly different, with the retrospective pre TSES showing lower self-efficacy than the pre TSES. Post TSES comparisons to the retro-spective pre TSES showed a significant increase, whereas, the comparison of the traditional pre/post TSES showed no significant changes. This suggests teacher candidates had an overinflated sense of self-efficacy and that teacher educators should look beyond the pre TSES to provide transformational experiences for teacher candidates. The retro-pre TSES helps teacher educators monitor teacher candidates’ change in self-efficacy and gauge program impact

Identification of windbreaks in Kansas using object-based image analysis, GIS techniques and field survey

Windbreaks are valuable resources in conserving soils and providing crop protection in Great Plains states in the US. Currently, Kansas has no up-to date inventory of windbreaks. The goal of this project was to assist foresters with future windbreak renovation planning and reporting, by outlining a series of semi-automated digital image processing methods that rapidly identify windbreak locations. There were two specific objectives of this research. First, to develop semi-automated methods to identify the location of windbreaks in Kansas, this can be applied to other regions in Kansas and the Great Plains. We used a remote sensing technique known as object-based image analysis (OBIA) to classify windbreaks visible in the color aerial imagery of National Agriculture Imagery Program. We also combined GIS techniques and field survey to complement OBIA in generating windbreak inventory. The techniques successfully located more than 4500, windbreaks covering an approximate area of 2500, hectares in 14 Kansas counties. The second purpose of this research is to determine how well the results of the automated classification schemes match with other available windbreak data and the selected sample collected in the field. The overall accuracy of OBIA method was 58.97 %. OBIA combined with ‘heads up’ digitizing and field survey method yielded better result in identifying and locating windbreaks in the studied counties with overall accuracy of 96 %

Spatially heterogeneous land cover/land use and climatic risk factors of tick-borne feline cytauxzoonosis

Background: Feline cytauxzoonosis is a highly fatal tick-borne disease caused by a hemoparasitic protozoan, Cytauxzoon felis. This disease is a leading cause of mortality for cats in the Midwestern United States, and no vaccine or effective treatment options exist. Prevention based on knowledge of risk factors is therefore vital. Associations of different environmental factors, including recent climate were evaluated as potential risk factors for cytauxzoonosis using Geographic Information Systems (GIS). Methods: There were 69 cases determined to be positive for cytauxzoonosis based upon positive identification of C. felis within blood film examinations, tissue impression smears, or histopathologic examination of tissues. Negative controls totaling 123 were selected from feline cases that had a history of fever, malaise, icterus, and anorexia but lack of C. felis within blood films, impression smears, or histopathologic examination of tissues. Additional criteria to rule out C. felis among controls were the presence of regenerative anemia, cytologic examination of blood marrow or lymph node aspirate, other causative agent diagnosed, or survival of 25 days or greater after testing. Potential environmental determinants were derived from publicly available sources, viz., US Department of Agriculture (soil attributes), US Geological Survey (land-cover/landscape, landscape metrics), and NASA (climate). Candidate variables were screened using univariate logistic models with a liberal p value (0.2), and associations with cytauxzoonosis were modeled using a global multivariate logistic model (p<0.05). Spatial heterogeneity among significant variables in the study region was modeled using a geographically weighted regression (GWR) approach. Results: Total Edge Contrast Index (TECI), grassland-coverage, humidity conditions recorded during the 9th week prior to case arrival, and an interaction variable, “diurnal temperature range×percent mixed forest area” were significant risk factors for cytauxzoonosis in the study region. TECI and grassland areas exhibited significant regional differences in their effects on cytauxzoonosis outcome, whereas others were uniform. Conclusions: Land-cover areas favorable for tick habitats and climatic conditions that favor the tick life cycle are strong risk factors for feline cytauxzoonosis. Spatial heterogeneity and interaction effects between land-cover and climatic variables may reveal new information when evaluating risk factors for vector-borne diseases