Optimization of soil mixtures in bioretention cells to reduce nutrient loading to the environment from storm water

Abstract taken from short.pdf file."December 2013.""A Thesis presented to the Faculty of the Graduate School at the University of Missouri--Columbia In Partial Fulfillment of the Requirements for the Degree Master of Science."Thesis supervisor: Enos C. Inniss, Ph.D.Bioretention cells are used as a way to remove some pollutants and particulate matter from stormwater. Typically a bioretention cell is placed at the edge of a parking lot or effluent stormwater structure to capture stormwater runoff. The bioretention cell has limited treatment of phosphorous which can lead to eutrophication in receiving waters. To help with the uptake of phosphorous, drinking water treatment plant residuals (DWTRs) from Missouri were added to bioretention cell soil mixes. The DWTR is created when coagulant is added to the influent drinking water at the treatment plant. Flocs form and fall to the bottom of the clarifier. The sludge which is created from this process (or DWTR) is then pumped to a lagoon for holding and drying. All of the DWTRs in this study were taken from the lagoons at the respective drinking water treatment plants. The DWTRs tested included lime, ferric chloride, polyaluminum chloride, and aluminum sulfate. First, an equilibrium study was conducted to test the sorption capacity of each DWTR. It was found that the DWTRs can sequester 15 to 40 milligram phosphorous per gram of DWTR when tested alone of the bioretention mix. When added to the lab-scale bioretention cell the removal efficiency was approximately 54% (lime), 87% (ferric chloride), and 99% (aluminum sulfate) at 5% DWTR addition (by volume) to the bioretention mix with 6.2 mg/L of phosphorous in the influent water for 3 hours.Includes bibliographical references (pages 80-86)

Poor compliance with an antibiotic directive—A call for intensified monitoring

Background In April 2017, the Central Denmark Region Antibiotic Stewardship Committee issued a directive to reduce the general use of piperacillin-tazobactam and prescribe narrow-spectrum antibiotics for mild and moderate pneumonia. The directive was distributed to all regional hospital clinicians. Methods Electronic medical records were used to obtain de-identified details of all antibiotics administered (together with diagnosis codes) to all in-hospital patients (pre-directive and post-directive) in the nine regional hospitals. Average moving range statistical process control charts were used to analyze pre-directive and post-directive variation in antibiotic usage patterns. Results Upon the distribution of the directive, a period of decline of the overall usage of piperacillin-tazobactam ensued. Rather than benzylpenicillin, as recommended for pneumonia, the initial decline in piperacillin/tazobactam usage was accompanied by increased use of cefuroxime. Conclusions A steward-directed reduction in piperacillin-tazobactam usage was accompanied by less desirable usage of a broad-spectrum alternative. Future antibiotic stewardship initiatives will hopefully benefit from close monitoring and timely feedback to clinicians. A dialogue with clinicians based on near real-time data is predicted to improve antibiotic stewardship actions

EFFECTS OF ANTERIOR-POSTERIOR LOAD PLACEMENTS IMPOSED BY A TRANSFORMER BAR ON SQUAT BIOMECHANICS

Examining the effect of anterior-posterior load placements imposed by a transformer bar could provide additional options for squatting exercises. The purpose of this study was to quantify trunk and pelvis angles and low back and lower extremity joint moments among the regular back and front squats and four squats with a transformer bar. Twelve males and 12 females performed six different squatting variations with a load of 70% of their one-repetition maximum of the regular front squat: back and front squats with a regular bar, back and front squats with a transformer bar, and squats with more anterior or posterior loads with a transformer bar. Joint angles and moments were extracted at the thigh angle of 70° in the ascending phases, corresponding to a posture close to a parallel squat. Trunk flexion angles were the highest for the transformer bar back squat and transformer bar posterior load squat. The greatest pelvis flexion angles were observed for the regular back squat, transformer bar back squat, and transformer bar posterior load squat. Low back joint moments were the highest for the transformer bar anterior load squat. Hip joint moments were significantly lower for the regular bar front squat compared to the other squat conditions. More posterior load placements resulted in decreased low back moments, increased trunk and pelvis flexion angles, and similar hip and knee moments compared to more anterior load placements. Changing the load placement does not affect low back and lower extremity loading as expected because the trunk and pelvis angles could be adjusted according to load placements. An anterior load placement may result in greater low back moments while a posterior load placement has greater trunk and pelvis flexion, which should be taken into consideration for people with low back impairments

Accounting for autocorrelation in multi-drug resistant tuberculosis predictors using a set of parsimonious orthogonal eigenvectors aggregated in geographic space

Spatial autocorrelation is problematic for classical hierarchical cluster detection tests commonly used in multidrug resistant tuberculosis (MDR-TB) analyses as considerable random error can occur. Therefore, when MDR-TB clusters are spatially autocorrelated the assumption that the clusters are independently random is invalid. In this research, a product moment correlation coefficient (i.e. the Moran’s coefficient) was used to quantify local spatial variation in multiple clinical and environmental predictor variables sampled in San Juan de Lurigancho, Lima, Peru. Initially, QuickBird (spatial resolution = 0.61 m) data, encompassing visible bands and the near infra-red bands, were selected to synthesize images of land cover attributes of the study site. Data of residential addresses of individual patients with smear-positive MDR-TB were geocoded, prevalence rates calculated and then digitally overlaid onto the satellite data within a 2 km buffer of 31 georeferenced health centres, using a 10 m2 grid-based algorithm. Geographical information system (GIS)- gridded measurements of each health centre were generated based on preliminary base maps of the georeferenced data aggregated to block groups and census tracts within each buffered area. A three-dimensional model of the study site was constructed based on a digital elevation model (DEM) to determine terrain covariates associated with the sampled MDRTB covariates. Pearson’s correlation was used to evaluate the linear relationship between the DEM and the sampled MDR-TB data. A SAS/GIS® module was then used to calculate univariate statistics and to perform linear and non-linear regression analyses using the sampled predictor variables. The estimates generated from a global autocorrelation analyses were then spatially decomposed into empirical orthogonal bases, using a negative binomial regression with a non-homogeneous mean. Results of the DEM analyses indicated a statistically non-significant, linear relationship between georeferenced health centres and the sampled covariate elevation. The data exhibited positive spatial autocorrelation and the decomposition of Moran’s coefficient into uncorrelated, orthogonal map pattern components which revealed global spatial heterogeneities necessary to capture latent autocorrelation in the MDR-TB model. It was thus shown that Poisson regression analyses and spatial eigenvector mapping can elucidate the mechanics of MDR-TB transmission by prioritizing clinical and environmental-sampled predictor variables for identifying high risk populations

Designer Gene Networks: Towards Fundamental Cellular Control

The engineered control of cellular function through the design of synthetic genetic networks is becoming plausible. Here we show how a naturally occurring network can be used as a parts list for artificial network design, and how model formulation leads to computational and analytical approaches relevant to nonlinear dynamics and statistical physics.Comment: 35 pages, 8 figure

Remote and field level quantification of vegetation covariates for malaria mapping in three rice agro-village complexes in Central Kenya

<p>Abstract</p> <p>Background</p> <p>We examined algorithms for malaria mapping using the impact of reflectance calibration uncertainties on the accuracies of three vegetation indices (VI)'s derived from QuickBird data in three rice agro-village complexes Mwea, Kenya. We also generated inferential statistics from field sampled vegetation covariates for identifying riceland <it>Anopheles arabiensis </it>during the crop season. All aquatic habitats in the study sites were stratified based on levels of rice stages; flooded, land preparation, post-transplanting, tillering, flowering/maturation and post-harvest/fallow. A set of uncertainty propagation equations were designed to model the propagation of calibration uncertainties using the red channel (band 3: 0.63 to 0.69 μm) and the near infra-red (NIR) channel (band 4: 0.76 to 0.90 μm) to generate the Normalized Difference Vegetation Index (NDVI) and the Soil Adjusted Vegetation Index (SAVI). The Atmospheric Resistant Vegetation Index (ARVI) was also evaluated incorporating the QuickBird blue band (Band 1: 0.45 to 0.52 μm) to normalize atmospheric effects. In order to determine local clustering of riceland habitats <it>Gi*(d) </it>statistics were generated from the ground-based and remotely-sensed ecological databases. Additionally, all riceland habitats were visually examined using the spectral reflectance of vegetation land cover for identification of highly productive riceland <it>Anopheles </it>oviposition sites.</p> <p>Results</p> <p>The resultant VI uncertainties did not vary from surface reflectance or atmospheric conditions. Logistic regression analyses of all field sampled covariates revealed emergent vegetation was negatively associated with mosquito larvae at the three study sites. In addition, floating vegetation (-ve) was significantly associated with immature mosquitoes in Rurumi and Kiuria (-ve); while, turbidity was also important in Kiuria. All spatial models exhibit positive autocorrelation; similar numbers of log-counts tend to cluster in geographic space. The spectral reflectance from riceland habitats, examined using the remote and field stratification, revealed post-transplanting and tillering rice stages were most frequently associated with high larval abundance and distribution.</p> <p>Conclusion</p> <p>NDVI, SAVI and ARVI generated from QuickBird data and field sampled vegetation covariates modeled cannot identify highly productive riceland <it>An. arabiensis </it>aquatic habitats. However, combining spectral reflectance of riceland habitats from QuickBird and field sampled data can develop and implement an Integrated Vector Management (IVM) program based on larval productivity.</p

Assessment of Olfactory Processing in Parkinson’s Disease Patients

Background: Hyposmia is an early symptom of Parkinson’s Disease (PD) that often predates motor symptoms by years. Hyposmia has been shown to have a more consistent link to idiopathic PD than to other movement disorders. Olfaction has the potential to be used as a biomarker for PD, either through clinical evaluation or imaging. Objectives: This study uses functional magnetic resonance imaging (fMRI) to assess differences in olfaction pathways between anosmic early PD patients and age and gender-matched controls. Methods: 12 PD patients and 12 age- and gender-matched control subjects were recruited from the subject panel of a previous UMMS study on olfaction and PD. All PD patients were determined to be anosmic, and all controls were determined to have normal olfaction for their age and gender. All subjects underwent fMRI including periods with and without odorant exposure. Statistical analysis was performed using SPM8, using a general linear model to calculate BOLD signal changes for each scent relative to room air. A random effect model was used to infer general population effects. Results: Control subjects showed significant activation in the piriform cortex, anterior olfactory nucleus, insula, hippocampus and temporal lobe, all regions associated with olfactory processing. Relative to control subjects, PD patients showed no significant BOLD activation in the olfactory pathways of the brain. In response to a citrus scent, PD patients showed activation in the superior and middle frontal lobe, as well as the cingulate gyrus. In response to a cinnamon scent, PD patients showed significant activation in the precuneus and paracentral lobule as well as lower levels of activation in the frontal lobe. PD patients showed no significant areas of activation in response to a mint scent. Conclusion: Our results suggest that anosmic PD patients do not show activation of the olfactory pathways in the brain on exposure to these odorants. Taken together with previous studies, this suggests that BOLD activation in these regions of the brain can reflect clinical olfactory capability. In addition, PD patients show areas of increased activation, particularly in the frontal lobe. These distinct patterns of BOLD activation allow us to consider the feasibility of fMRI as a biomarker for diagnosis and evaluation of PD

Clinical, serological and DNA testing in Bengo Province, Angola further reveals low filarial endemicity and opportunities for disease elimination

The prevalence of Loa loa, Onchocerca volvulus and Wuchereria bancrofti infections in an under-surveyed area of Bengo Province, Angola, was determined by surveying 22 communities with a combination of clinical, serological and DNA diagnostics. Additional information was collected on participants' duration of residency, access to mass drug administration, knowledge of insect vectors and use of bednets. A total of 1616 individuals (38.1% male: 61.9% female), with an average age of 43 years, were examined. For L. loa, 6.2% (n = 100/16616) individuals were found to have eyeworm, based on the rapid assessment procedure for loiasis (RAPLOA) surveys, and 11.5% (n =178/1543) based on nested PCR analyses of venous blood. L. loa prevalences in long-term residents (>10 years) and older individuals (>60 years) were significantly higher, and older men with eyeworm were better informed about Chrysops vectors. For O. volvulus, 4.7% (n = 74/1567) individuals were found to be positive by enzyme-linked immunosorbent assay (Ov 16 ELISA), with only three individuals reporting to have ever taken ivermectin. For W. bancrofti, no infections were found using the antigen-based immunochromatographic test (ICT) and real-time PCR analysis; however, 27 individuals presented with lymphatic filariasis (LF) related clinical conditions (lymphoedema = 11, hydrocoele = 14, both = 2). Just under half (45.5%) of the participants owned a bednet, with the majority (71.1%) sleeping under it the night before. Our approach of using combination diagnostics reveals the age-prevalence of loiasis alongside low endemicity of onchocerciasis and LF. Future research foci should be on identifying opportunities for more cost-effective ways to eliminate onchocerciasis and to develop innovative surveillance modalities for clinical LF for individual disease management and disability prevention

Spatially targeting Culex quinquefasciatus aquatic habitats on modified land cover for implementing an Integrated Vector Management (IVM) program in three villages within the Mwea Rice Scheme, Kenya

BACKGROUND: Continuous land cover modification is an important part of spatial epidemiology because it can help identify environmental factors and Culex mosquitoes associated with arbovirus transmission and thus guide control intervention. The aim of this study was to determine whether remotely sensed data could be used to identify rice-related Culex quinquefasciatus breeding habitats in three rice-villages within the Mwea Rice Scheme, Kenya. We examined whether a land use land cover (LULC) classification based on two scenes, IKONOS at 4 m and Landsat Thematic Mapper at 30 m could be used to map different land uses and rice planted at different times (cohorts), and to infer which LULC change were correlated to high density Cx. quinquefasciatus aquatic habitats. We performed a maximum likelihood unsupervised classification in Erdas Imagine V8.7(® )and generated three land cover classifications, rice field, fallow and built environment. Differentially corrected global positioning systems (DGPS) ground coordinates of Cx. quinquefasciatus aquatic habitats were overlaid onto the LULC maps generated in ArcInfo 9.1(®). Grid cells were stratified by levels of irrigation (well-irrigated and poorly-irrigated) and varied according to size of the paddy. RESULTS: Total LULC change between 1988–2005 was 42.1 % in Kangichiri, 52.8 % in Kiuria and and 50.6 % Rurumi. The most frequent LULC changes was rice field to fallow and fallow to rice field. The proportion of aquatic habitats positive for Culex larvae in LULC change sites was 77.5% in Kangichiri, 72.9% in Kiuria and 73.7% in Rurumi. Poorly – irrigated grid cells displayed 63.3% of aquatic habitats among all LULC change sites. CONCLUSION: We demonstrate that optical remote sensing can identify rice cultivation LULC sites associated with high Culex oviposition. We argue that the regions of higher Culex abundance based on oviposition surveillance sites reflect underlying differences in abundance of larval habitats which is where limited control resources could be concentrated to reduce vector larval abundance

A heteroskedastic error covariance matrix estimator using a first-order conditional autoregressive Markov simulation for deriving asympotical efficient estimates from ecological sampled Anopheles arabiensis aquatic habitat covariates

<p>Abstract</p> <p>Background</p> <p>Autoregressive regression coefficients for <it>Anopheles arabiensis </it>aquatic habitat models are usually assessed using global error techniques and are reported as error covariance matrices. A global statistic, however, will summarize error estimates from multiple habitat locations. This makes it difficult to identify where there are clusters of <it>An. arabiensis </it>aquatic habitats of acceptable prediction. It is therefore useful to conduct some form of spatial error analysis to detect clusters of <it>An. arabiensis </it>aquatic habitats based on uncertainty residuals from individual sampled habitats. In this research, a method of error estimation for spatial simulation models was demonstrated using autocorrelation indices and eigenfunction spatial filters to distinguish among the effects of parameter uncertainty on a stochastic simulation of ecological sampled <it>Anopheles </it>aquatic habitat covariates. A test for diagnostic checking error residuals in an <it>An. arabiensis </it>aquatic habitat model may enable intervention efforts targeting productive habitats clusters, based on larval/pupal productivity, by using the asymptotic distribution of parameter estimates from a residual autocovariance matrix. The models considered in this research extends a normal regression analysis previously considered in the literature.</p> <p>Methods</p> <p>Field and remote-sampled data were collected during July 2006 to December 2007 in Karima rice-village complex in Mwea, Kenya. SAS 9.1.4^®was used to explore univariate statistics, correlations, distributions, and to generate global autocorrelation statistics from the ecological sampled datasets. A local autocorrelation index was also generated using spatial covariance parameters (i.e., Moran's Indices) in a SAS/GIS^®database. The Moran's statistic was decomposed into orthogonal and uncorrelated synthetic map pattern components using a Poisson model with a gamma-distributed mean (i.e. negative binomial regression). The eigenfunction values from the spatial configuration matrices were then used to define expectations for prior distributions using a Markov chain Monte Carlo (MCMC) algorithm. A set of posterior means were defined in WinBUGS 1.4.3^®. After the model had converged, samples from the conditional distributions were used to summarize the posterior distribution of the parameters. Thereafter, a spatial residual trend analyses was used to evaluate variance uncertainty propagation in the model using an autocovariance error matrix.</p> <p>Results</p> <p>By specifying coefficient estimates in a Bayesian framework, the covariate number of tillers was found to be a significant predictor, positively associated with <it>An. arabiensis </it>aquatic habitats. The spatial filter models accounted for approximately 19% redundant locational information in the ecological sampled <it>An. arabiensis </it>aquatic habitat data. In the residual error estimation model there was significant positive autocorrelation (i.e., clustering of habitats in geographic space) based on log-transformed larval/pupal data and the sampled covariate depth of habitat.</p> <p>Conclusion</p> <p>An autocorrelation error covariance matrix and a spatial filter analyses can prioritize mosquito control strategies by providing a computationally attractive and feasible description of variance uncertainty estimates for correctly identifying clusters of prolific <it>An. arabiensis </it>aquatic habitats based on larval/pupal productivity.</p