Department of Statistics

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Browsing Department of Statistics by Department "Biological Systems Engineering"

Now showing 1 - 7 of 7
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    Die-off of E. coli and enterococci in dairy cowpats
    Soupir, M. L.; Mostaghimi, Saied; Lou, J. (American Society of Agricultural and Biological Engineers, 2008)
    E. coli and enterococci re-growth and decay patterns in cowpats applied to pasturelands were monitored during the spring, summer fall, and winter First-order approximations were used to determine die-off rate coefficients and decimal reduction times (D-values). Higher-order approximations and weather parameters were evaluated by multiple regression analysis to identify environmental parameters impacting in-field E. coli and enterococci decay. First-order kinetics approximated E. coli and enterococci decay rates with regression coefficients ranging from 0.70 to 0.90. Die-off rate constants were greatest in cowpats applied to pasture during late winter and monitored into summer months for E. coli (k = 0.0995 d(-1)) and applied to the field during the summer and monitored until December for enterococci (k = 0.0978 d(-1)). Decay rates were lowest in cowpats applied to the pasture during the fall and monitored over the winter (k = 0.0581 d(-1) for E. coli, and k = 0.0557 d(-1) for enterococci). Higher-order approximations and the addition of weather variables improved regression coefficients to values ranging from 0.82 to 0.96. Statistically significant variables used in the models for predicting bacterial decay included temperature, solar radiation, rainfall, and relative humidity. Die-off rate coefficients previously reported in the literature are usually the result of laboratory-based studies and are generally higher than the field-based seasonal die-off rate coefficients presented here. To improve predictions of in-field E. coli and enterococci concentrations, this study recommends that higher-order approximations and additional parameters such as weather variables are necessary to better capture re-growth and die-off trends over extended periods of time.
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    Generalized Likelihood Uncertainty Estimation and Markov Chain Monte Carlo Simulation to Prioritize TMDL Pollutant Allocations
    Mishra, Anurag; Ahmadisharaf, Ebrahim; Benham, Brian L.; Wolfe, Mary Leigh; Leman, Scotland C.; Gallagher, Daniel L.; Reckhow, Kenneth H.; Smith, Eric P. (2018-12)
    This study presents a probabilistic framework that considers both the water quality improvement capability and reliability of alternative total maximum daily load (TMDL) pollutant allocations. Generalized likelihood uncertainty estimation and Markov chain Monte Carlo techniques were used to assess the relative uncertainty and reliability of two alternative TMDL pollutant allocations that were developed to address a fecal coliform (FC) bacteria impairment in a rural watershed in western Virginia. The allocation alternatives, developed using the Hydrological Simulation Program-FORTRAN, specified differing levels of FC bacteria reduction from different sources. While both allocations met the applicable water-quality criteria, the approved TMDL allocation called for less reduction in the FC source that produced the greatest uncertainty (cattle directly depositing feces in the stream), suggesting that it would be less reliable than the alternative, which called for a greater reduction from that same source. The approach presented in this paper illustrates a method to incorporate uncertainty assessment into TMDL development, thereby enabling stakeholders to engage in more informed decision making.
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    Improving the success of stream restoration practices
    Thompson, Theresa M.; Smith, Eric P. (2021-06-16)
    This research focused on 3 questions:
    1. Linking stream restoration success with watershed and design characteristics
    2. Design, project, and watershed factors that affect structure success
    3. Comparison of 1-D and 2-D HEC-RAS modeling for stream restoration design
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    Improving the Success of Stream Restoration Practices – Revised and Expanded
    Thompson, Theresa M.; Smith, Eric P. (2021-06-28)
    Final Project Report submitted to the Chesapeake Bay Trust, Annapolis, MD.
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    Raman chemometric urinalysis (Rametrix) as a screen for bladder cancer
    Huttanus, Herbert M.; Vu, Tommy; Guruli, Georgi; Tracey, Andrew; Carswell, William; Said, Neveen; Du, Pang; Parkinson, Bing G.; Orlando, Giuseppe; Robertson, John L.; Senger, Ryan S. (2020-08-21)
    Bladder cancer (BCA) is relatively common and potentially recurrent/progressive disease. It is also costly to detect, treat, and control. Definitive diagnosis is made by examination of urine sediment, imaging, direct visualization (cystoscopy), and invasive biopsy of suspect bladder lesions. There are currently no widely-used BCA-specific biomarker urine screening tests for early BCA or for following patients during/after therapy. Urine metabolomic screening for biomarkers is costly and generally unavailable for clinical use. In response, we developed Raman spectroscopy-based chemometric urinalysis (Rametrix (TM)) as a direct liquid urine screening method for detecting complex molecular signatures in urine associated with BCA and other genitourinary tract pathologies. In particular, the Rametrix(TM)screen used principal components (PCs) of urine Raman spectra to build discriminant analysis models that indicate the presence/absence of disease. The number of PCs included was varied, and all models were cross-validated by leave-one-out analysis. In Study 1 reported here, we tested the Rametrix (TM) screen using urine specimens from 56 consented patients from a urology clinic. This proof-of-concept study contained 17 urine specimens with active BCA (BCA-positive), 32 urine specimens from patients with other genitourinary tract pathologies, seven specimens from healthy patients, and the urinalysis control Surine(TM). Using a model built with 22 PCs, BCA was detected with 80.4% accuracy, 82.4% sensitivity, 79.5% specificity, 63.6% positive predictive value (PPV), and 91.2% negative predictive value (NPV). Based on the number of PCs included, we found the Rametrix(TM)screen could be fine-tuned for either high sensitivity or specificity. In other studies reported here, Rametrix(TM)was also able to differentiate between urine specimens from patients with BCA and other genitourinary pathologies and those obtained from patients with end-stage kidney disease (ESKD). While larger studies are needed to improve Rametrix(TM)models and demonstrate clinical relevance, this study demonstrates the ability of the Rametrix(TM)screen to differentiate urine of BCA-positive patients. Molecular signature variances in the urine metabolome of BCA patients included changes in: phosphatidylinositol, nucleic acids, protein (particularly collagen), aromatic amino acids, and carotenoids.
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    Spectral characteristics of urine specimens from healthy human volunteers analyzed using Raman chemometric urinalysis (Rametrix)
    Senger, Ryan S.; Kavuru, Varun; Sullivan, Meaghan; Gouldin, Austin; Lundgren, Stephanie; Merrifield, Kristen; Steen, Caitlin; Baker, Emily; Vu, Tommy; Agnor, Ben; Martinez, Gabrielle; Coogan, Hannah; Carswell, William; Karageorge, Lampros; Dev, Devasmita; Du, Pang; Sklar, Allan; Orlando, Giuseppe; Pirkle, James, Jr.; Robertson, John L. (PLOS, 2019-09-27)
    Raman chemometric urinalysis (Rametrix™) was used to analyze 235 urine specimens from healthy individuals. The purpose of this study was to establish the “range of normal” for Raman spectra of urine specimens from healthy individuals. Ultimately, spectra falling outside of this range will be correlated with kidney and urinary tract disease. Rametrix™ analysis includes direct comparisons of Raman spectra but also principal component analysis (PCA), discriminant analysis of principal components (DAPC) models, multivariate statistics, and it is available through GitHub as the Rametrix™ LITE Toolbox for MATLAB®. Results showed consistently overlapping Raman spectra of urine specimens with significantly larger variances in Raman shifts, found by PCA, corresponding to urea, creatinine, and glucose concentrations. A 2-way ANOVA test found that age of the urine specimen donor was statistically significant (p < 0.001) and donor sex (female or male identification) was less so (p = 0.0526). With DAPC models and blind leave-one-out build/test routines using the Rametrix™ PRO Toolbox (also available through GitHub), an accuracy of 71% (sensitivity = 72%; specificity = 70%) was obtained when predicting whether a urine specimen from a healthy unknown individual was from a female or male donor. Finally, from female and male donors (n = 4) who contributed first morning void urine specimens each day for 30 days, the co-occurrence of menstruation was found statistically insignificant to Rametrix™ results (p = 0.695). In addition, Rametrix™ PRO was able to link urine specimens with the individual donor with an average of 78% accuracy. Taken together, this study established the range of Raman spectra that could be expected when obtaining urine specimens from healthy individuals and analyzed by Rametrix™ and provides the methodology for linking results with donor characteristics.
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    Two-phase Monte Carlo simulation for partitioning the effects of epistemic and aleatory uncertainty in TMDL modeling
    Mishra, Anurag; Ahmadisharaf, Ebrahim; Benham, Brian L.; Gallagher, Daniel L.; Reckhow, Kenneth H.; Smith, Eric P. (ASCE, 2018-10-29)
    A two-phase Monte Carlo simulation (TPMCS) uncertainty analysis framework is used to analyze epistemic and aleatory uncertainty associated with simulated exceedances of an in-stream fecal coliform (FC) water quality criterion when using the Hydrological Simulation Program-FORTRAN (HSPF). The TPMCS framework is compared with a single-phase or standard Monte Carlo simulation (SPMCS) analysis. Both techniques are used to assess two total maximum daily load (TMDL) pollutant allocation scenarios. The application of TPMCS illustrates that cattle directly depositing FC in the stream is a greater source of epistemic uncertainty than FC loading from cropland overland runoff, the two sources specifically targeted for reduction in the allocation scenario. This distinction is not possible using SPMCS. Although applying the TPMCS framework involves subjective decisions about how selected model parameters are considered within the framework, this uncertainty analysis approach is transparent and the results provide information that can be used by decision makers when considering pollution control measure implementation alternatives, including quantifying the level of confidence in achieving applicable water quality standards. © American Society of Civil Engineers (ASCE).