Machine learning to predict foodborne salmonellosis outbreaks based on genome characteristics and meteorological trends.

Several studies have shown a correlation between outbreaks of Salmonella enterica and meteorological trends, especially related to temperature and precipitation. Additionally, current studies based on outbreaks are performed on data for the species Salmonella enterica, without considering its intra-species and genetic heterogeneity. In this study, we analyzed the effect of differential gene expression and a suite of meteorological factors on salmonellosis outbreak scale (typified by case numbers) using a combination of machine learning and count-based modeling methods. Elastic Net regularization model was used to identify significant genes from a Salmonella pan-genome, and a multi-variable Poisson regression developed to fit the individual and mixed effects data. The best-fit Elastic Net model (α = 0.50; λ = 2.18) identified 53 significant gene features. The final multi-variable Poisson regression model (χ2 = 5748.22; pseudo R2 = 0.669; probability > χ2 = 0) identified 127 significant predictor terms (p < 0.10), comprising 45 gene-only predictors, average temperature, average precipitation, and average snowfall, and 79 gene-meteorological interaction terms. The significant genes ranged in functionality from cellular signaling and transport, virulence, metabolism, and stress response, and included gene variables not considered as significant by the baseline model. This study presents a holistic approach towards evaluating multiple data sources (such as genomic and environmental data) to predict outbreak scale, which could help in revising the estimates for human health risk.

Effectiveness of BMP plans in different land covers, with random, targeted, and optimized allocation.

The ability of 5 Best Management Practice (BMP) allocation methods that consider 8 pre-selected BMPs, to control 4 Nonpoint Source (NPS) constituents in 4 watersheds with contrasting land covers, is investigated. The methods range from random selection of BMPs on randomly selected sites, to optimized selection of BMPs at optimized locations, and the land covers range from natural to ultra-urban. The optimization methods rely on Genetic Algorithms (GA), and a method that uses expert systems is also applied. Watershed hydrologic and water quality response models are developed, using the Soil Water Assessment Tool (SWAT), to compute baseline outputs from the 4 study watersheds without BMPs, and to obtain predicted reductions in NPS constituent outputs when BMPs are implemented in accordance with the 5 allocation plans. Methods used to represent BMPs in SWAT and to speed up optimization processes are also presented. Results indicate that the most computationally intensive methods produce the best results across landscape types. Results also show that opportunities exist for less intensive methods, particularly in less-built environments. For these, however, siting BMPs to hotspots remains an important requirement. The need to select the most appropriate BMP for each implementation site is observed to increase with the level of urbanization of the landscape. Results indicate that optimized selection of BMPs, sited at optimized locations, results in the highest-performing BMP allocation plans across landscape types. Also, the focus on hotspots has the advantage of resulting in BMP plans requiring involvement of fewer stakeholders than when BMPs are located in non-hotspot zones. This targeted hotspot approach could help reduce cost and increase efficiency of implementation.

Effectiveness of vegetated filter strips in retention of Escherichia coli and Salmonella from swine manure slurry.

Vegetated filter strips (VFS) are commonly recommended as a best management practice to prevent manure-borne microorganisms from reaching surface water resources. However, relatively little is known about the efficacy of VFS in mitigating bacterial runoff from land-applied swine manure. A field lysimeter study was designed to evaluate the effect of surface soil hydrologic conditions and vegetation on the retention of swine manure-borne Escherichia coli and Salmonella under simulated rainfall conditions. Experimental plots (6.5 m × 3.9 m) were set on a 5% slope lysimeter with loamy topsoil, clay loam or loam subsoil and a controllable groundwater level. Three small flow-intercepting miniflumes were installed 4.5 m from the plot's top, while all remaining runoff was collected in a gutter at the bottom. Plots were divided into bare soil and grass vegetation and upper surface soil moisture before rainfall events was controlled by the subsurface groundwater level. Swine manure slurry inoculated with E. coli and Salmonella, and with added bromide tracer, was applied on the top of the plots and simultaneously initiated the simulated rainfall. Runoff was collected and analyzed every 5 min. No substantial differences between retention of E. coli and Salmonella were found. In initially wet soil surface conditions, there was limited infiltration both in bare and in vegetated plots; almost all bromide and about 30% of bacteria were recovered in runoff water. In initially dry soil surface conditions, there were substantial discrepancies between bare and vegetated plots. In bare plots, recoveries of runoff water, bromide and bacteria under dry conditions were comparable to wet conditions. However, in dry vegetated plots, from 50% to 75% of water was lost to infiltration, while bromide recoveries ranged from 14 to 36% and bacteria recovery was only 5%. Substantial intraplot heterogeneity was revealed by the data from miniflumes. GIS analysis of the plot microtopography showed that miniflumes located in the zones of flow convergence collected the majority of bacteria. Overall, the efficiency of VFS, with respect to the retention of swine manure bacteria, varied dramatically depending upon the hydrologic soil surface condition. Consequently, VFS recommendations should account for expected amounts of surface soil water saturation as well as the relative soil water storage capacity of the VFS.

Improved agricultural Water management in data-scarce semi-arid watersheds: Value of integrating remotely sensed leaf area index in hydrological modeling.

In watersheds located in semi-arid regions, vegetation dynamics, evapotranspiration (ET), and associated water and energy balances collectively play a major role in controlling hydrological regimes and crop yield. As such, it is challenging to predict the complex hydrological processes and biophysical dynamics. This challenge increases in areas with limited data availability. The key objective of this study was to evaluate the direct integration of remotely sensed Leaf Area Index (LAI) data into a hydrological model to improve streamflow, ET, and crop yield estimates. We also demonstrated how an improved model integrated with remotely sensed LAI data can inform water managers by predicting water productivity (WP) under different irrigation schemes. We took agricultural-dominated San Joaquin Watershed in California, United States, as our testbed and integrated the Moderate Resolution Imaging Spectroradiometer (MODIS) 500-m resolution 4-day total LAI data into the SWAT (Soil and Water Assessment Tool) model. Results showed that, compared to conventional SWAT model that relies on semi-empirical equations and user inputs for simulating biophysical processes, direct LAI integration into SWAT model (SWAT-LAI) notably captured the actual vegetation dynamics and improved ET and crop yield estimations. The WP simulated by the improved SWAT-LAI model for almond and grape yields varied within a range from 0.363 to 3.81 kg/m3 and 0.32 to 4.76 kg/m3 across different irrigation applications. The outcomes of this study showed that deficit irrigation application could be a viable option in water stressed regions, since it can save a substantial amount of irrigation water and maintain the higher water productivity required for both almond and grape yield production. This study shows an evidence of how remotely sensed data integrated into hydrological models can serve as a decision support tool by providing quantitative information on crop water use and crop production.

Phosphorus and the Chesapeake Bay: Lingering Issues and Emerging Concerns for Agriculture.

Hennig Brandt's discovery of phosphorus (P) occurred during the early European colonization of the Chesapeake Bay region. Today, P, an essential nutrient on land and water alike, is one of the principal threats to the health of the bay. Despite widespread implementation of best management practices across the Chesapeake Bay watershed following the implementation in 2010 of a total maximum daily load (TMDL) to improve the health of the bay, P load reductions across the bay's 166,000-km watershed have been uneven, and dissolved P loads have increased in a number of the bay's tributaries. As the midpoint of the 15-yr TMDL process has now passed, some of the more stubborn sources of P must now be tackled. For nonpoint agricultural sources, strategies that not only address particulate P but also mitigate dissolved P losses are essential. Lingering concerns include legacy P stored in soils and reservoir sediments, mitigation of P in artificial drainage and stormwater from hotspots and converted farmland, manure management and animal heavy use areas, and critical source areas of P in agricultural landscapes. While opportunities exist to curtail transport of all forms of P, greater attention is required toward adapting P management to new hydrologic regimes and transport pathways imposed by climate change.

Linking stormwater Best Management Practices to social factors in two suburban watersheds.

To reduce nutrient pollution in urban watersheds, residents need to voluntarily practice a range of stormwater Best Management Practices (BMPs). However, little is known about the underlying social factors that may act as barriers to BMP implementation. The overall goal of this study was to better understand barriers to BMP implementation by exploring the links among resident demographics, knowledge, and behaviors so that appropriate education can be more effectively developed and targeted. In 2014-2015, a detailed questionnaire was administered door-to-door to 299 randomly selected households in two sub-watersheds of the Chesapeake Bay basin to test relationships among resident demographics, knowledge and attitudes towards water resources and BMPs, and BMP implementation. Multifactor regression models showed that respondents who had greater knowledge of water resources and BMPs lived in households that implemented greater numbers of BMPs. In turn, resident BMP knowledge, or familiarity with BMPs, strongly varied with race and ownership status, with respondents who identified as Caucasian or within a collection of 'Other' races, and who were home owners, having greater BMP knowledge than respondents who identified as African American and who were home renters, respectively. Renters and members of homeowner's associations were also less likely to implement BMPs independent of knowledge, possibly reflecting perceived or real bureaucratic or procedural barriers to good stormwater management. Overall, respondents preferred to receive educational materials on stormwater via pamphlets and YouTube videos. These results suggest that resident ownership status knowledge is important to determining the number of household BMPs, and that education outreach should probably target African American and renting households that have lower BMP knowledge and landlords and administrators of homeowner's associations using well-planned print and video educational media.

A finite element model for protein transport in vivo.

BACKGROUND: Biological mass transport processes determine the behavior and function of cells, regulate interactions between synthetic agents and recipient targets, and are key elements in the design and use of biosensors. Accurately predicting the outcomes of such processes is crucial to both enhancing our understanding of how these systems function, enabling the design of effective strategies to control their function, and verifying that engineered solutions perform according to plan. METHODS: A Galerkin-based finite element model was developed and implemented to solve a system of two coupled partial differential equations governing biomolecule transport and reaction in live cells. The simulator was coupled, in the framework of an inverse modeling strategy, with an optimization algorithm and an experimental time series, obtained by the Fluorescence Recovery after Photobleaching (FRAP) technique, to estimate biomolecule mass transport and reaction rate parameters. In the inverse algorithm, an adaptive method was implemented to calculate sensitivity matrix. A multi-criteria termination rule was developed to stop the inverse code at the solution. The applicability of the model was illustrated by simulating the mobility and binding of GFP-tagged glucocorticoid receptor in the nucleoplasm of mouse adenocarcinoma. RESULTS: The numerical simulator shows excellent agreement with the analytic solutions and experimental FRAP data. Detailed residual analysis indicates that residuals have zero mean and constant variance and are normally distributed and uncorrelated. Therefore, the necessary and sufficient criteria for least square parameter optimization, which was used in this study, were met. CONCLUSION: The developed strategy is an efficient approach to extract as much physiochemical information from the FRAP protocol as possible. Well-posedness analysis of the inverse problem, however, indicates that the FRAP protocol provides insufficient information for unique simultaneous estimation of diffusion coefficient and binding rate parameters. Care should be exercised in drawing inferences, from FRAP data, regarding concentrations of free and bound proteins, average binding and diffusion times, and protein mobility unless they are confirmed by long-range Markov Chain-Monte Carlo (MCMC) methods and experimental observations.

Identification of biomolecule mass transport and binding rate parameters in living cells by inverse modeling.

BACKGROUND: Quantification of in-vivo biomolecule mass transport and reaction rate parameters from experimental data obtained by Fluorescence Recovery after Photobleaching (FRAP) is becoming more important. METHODS AND RESULTS: The Osborne-Moré extended version of the Levenberg-Marquardt optimization algorithm was coupled with the experimental data obtained by the Fluorescence Recovery after Photobleaching (FRAP) protocol, and the numerical solution of a set of two partial differential equations governing macromolecule mass transport and reaction in living cells, to inversely estimate optimized values of the molecular diffusion coefficient and binding rate parameters of GFP-tagged glucocorticoid receptor. The results indicate that the FRAP protocol provides enough information to estimate one parameter uniquely using a nonlinear optimization technique. Coupling FRAP experimental data with the inverse modeling strategy, one can also uniquely estimate the individual values of the binding rate coefficients if the molecular diffusion coefficient is known. One can also simultaneously estimate the dissociation rate parameter and molecular diffusion coefficient given the pseudo-association rate parameter is known. However, the protocol provides insufficient information for unique simultaneous estimation of three parameters (diffusion coefficient and binding rate parameters) owing to the high intercorrelation between the molecular diffusion coefficient and pseudo-association rate parameter. Attempts to estimate macromolecule mass transport and binding rate parameters simultaneously from FRAP data result in misleading conclusions regarding concentrations of free macromolecule and bound complex inside the cell, average binding time per vacant site, average time for diffusion of macromolecules from one site to the next, and slow or rapid mobility of biomolecules in cells. CONCLUSION: To obtain unique values for molecular diffusion coefficient and binding rate parameters from FRAP data, we propose conducting two FRAP experiments on the same class of macromolecule and cell. One experiment should be used to measure the molecular diffusion coefficient independently of binding in an effective diffusion regime and the other should be conducted in a reaction dominant or reaction-diffusion regime to quantify binding rate parameters. The method described in this paper is likely to be widely used to estimate in-vivo biomolecule mass transport and binding rate parameters.

Change is necessary in a biological engineering curriculum.

Success of a Biological Engineering undergraduate educational program can be measured in a number of ways, but however it is measured, a presently successful program can translate into an unsuccessful program if it cannot adjust to different conditions posed by technical advances, student characteristics, and academic pressures. Described in this paper is a Biological Engineering curriculum that has changed significantly since its transformation from Agricultural Engineering in 1993. As a result, student numbers have continued to climb, specific objectives have emerged, and unique courses have been developed. The Biological Resources Engineering program has evolved into a program that emphasizes breadth, fundamentals, communications skills, diversity, and practical engineering judgment.

Modeling Sustainability: Population, Inequality, Consumption, and Bidirectional Coupling of the Earth and Human Systems.

Over the last two centuries, the impact of the Human System has grown dramatically, becoming strongly dominant within the Earth System in many different ways. Consumption, inequality, and population have increased extremely fast, especially since about 1950, threatening to overwhelm the many critical functions and ecosystems of the Earth System. Changes in the Earth System, in turn, have important feedback effects on the Human System, with costly and potentially serious consequences. However, current models do not incorporate these critical feedbacks. We argue that in order to understand the dynamics of either system, Earth System Models must be coupled with Human System Models through bidirectional couplings representing the positive, negative, and delayed feedbacks that exist in the real systems. In particular, key Human System variables, such as demographics, inequality, economic growth, and migration, are not coupled with the Earth System but are instead driven by exogenous estimates, such as UN population projections. This makes current models likely to miss important feedbacks in the real Earth-Human system, especially those that may result in unexpected or counterintuitive outcomes, and thus requiring different policy interventions from current models. The importance and imminence of sustainability challenges, the dominant role of the Human System in the Earth System, and the essential roles the Earth System plays for the Human System, all call for collaboration of natural scientists, social scientists, and engineers in multidisciplinary research and modeling to develop coupled Earth-Human system models for devising effective science-based policies and measures to benefit current and future generations.

Atrazine distribution measured in soil and leachate following infiltration conditions.

Atrazine transport through packed 10 cm soil columns representative of the 0-10 cm soil horizon was observed by measuring the atrazine recovery in the total leachate volume, and upper and lower soil layers following infiltration of 7.5 cm water using a mechanical vacuum extractor (MVE). Measured recoveries were analyzed to understand the influence of infiltration rate and delay time on atrazine transport and distribution in the column. Four time periods (0.28, 0.8, 1.8, and 5.5 h) representing very high to moderate infiltration rates (26.8, 9.4, 4.2, and 1.4 cm/h) were used. Replicate soil columns were tested immediately and following a 2-d delay after atrazine application. Results indicate atrazine recovery in leachate was independent of infiltration rate, but significantly lower for infiltration following a 2-d delay. Atrazine distribution in the 0-1 and 9-10 cm soil layers was affected by both infiltration rate and delay. These results are in contrast with previous field and laboratory studies that suggest that atrazine recovery in the leachate increases with increasing infiltration rate. It appears that the difference in atrazine recovery measured using the MVE and other leaching experiments using intact soil cores from this field site and the rain simulation equipment probably illustrates the effect of infiltrating water interacting with the atrazine present on the soil surface. This work suggests that atrazine mobilization from the soil surface is also dependent on interactions of the infiltrating water with the soil surface, in addition to the rate of infiltration through the surface soil.

A decision support system for phosphorus management at a watershed scale.

Phosphorus (P) is one of the main nutrients controlling algal production in aquatic systems. Proper management of P in agricultural production systems can greatly enhance our ability to combat pollution of aquatic environments. To address this issue, a decision support system (DSS) consisting of the Maryland Phosphorus Index (PI), diagnosis expert system (ES), prescription ES, and a nonpoint-source pollution model, Ground Water Loading Effects of Agricultural Management Systems (GLEAMS), was developed and applied to an agricultural watershed in southern Sweden. This system can identify critical source areas (CSAs) regarding phosphorus losses within the watershed, make a diagnosis of probable causes, prescribe the most appropriate best management practices (BMPs), and test the environmental effects of the applied BMPs. The PI calculations identified small parts of the watershed as CSAs. Only 10.4% of the total watershed area in 1995 and 5.2% of the total watershed area in 1996 were classed as "high potential P movement." Four probable causes (high P level in soil, excessive P fertilization, stream proximity, and subsurface drainage) and three BMPs (riparian buffer strips, reduced P fertilizer application, and P fertilizer incorporation) were identified by a diagnosis and prescription expert system. The GLEAMS simulations conducted for one selected CSA field for a 24-yr period showed that the recommended BMP reduced runoff P losses by 55% and sediment P losses by 71%, if applied from the first year. Results showed that using DSS may enable us to select a proper BMP implementation strategy and to realize the beneficial effect of BMPs on a long-term basis.

Evaluation of infiltration models in contaminated landscape.

The infiltration models of Kostiakov, Green-Ampt, and Philip (two and three terms equations) were used, calibrated, and evaluated to simulate in-situ infiltration in nine different soil types. The Osborne-Moré modified version of the Levenberg-Marquardt optimization algorithm was coupled with the experimental data obtained by the double ring infiltrometers and the infiltration equations, to estimate the model parameters. Comparison of the model outputs with the experimental data indicates that the models can successfully describe cumulative infiltration in different soil types. However, since Kostiakov's equation fails to accurately simulate the infiltration rate as time approaches infinity, Philip's two-term equation, in some cases, produces negative values for the saturated hydraulic conductivity of soils, and the Green-Ampt model uses piston flow assumptions, we suggest using Philip's three-term equation to simulate infiltration and to estimate the saturated hydraulic conductivity of soils.

A diagnosis-prescription system for nitrogen management in environment.

A decision support system in the framework of the geographic information system (GIS) and subsurface flow model, Hydrosub, were used to identify critical areas from simulated spatial distributions of relative nitrogen export. Diagnosis and prescription Expert Systems (ES) are developed and applied to the identification of probable causes of excessive nitrogen export and selection of appropriate Best Management Practices (BMPs). The result is a spatially distributed set of recommended Best Management Practices that are feasible economically and environmentally. For the study watershed, using catch crops and rhizobium-legume (instead of using conventional commercial fertilizers) were the most recommended Best Management Practices.

Herbicides and nitrates in groundwater of Maryland and childhood cancers: a geographic information systems approach.

This hypothesis-generating study explores spatial patterns of childhood cancers in Maryland and investigates their potential associations with herbicides and nitrates in groundwater. The Maryland Cancer Registry (MCR) provided data for bone and brain cancers, leukemia, and lymphoma, for ages 0-17, during the years 1992-1998. Cancer clusters and relative risks generated in the study indicate higher relative risk areas and potential clusters in several counties. Contingency table analysis indicates a potential association with several herbicides and nitrates. Cancer rates for the four types have a crude odds ratio (OR) = 1.10 (0.78-1.56) in relationship to atrazine, and an OR = 1.54 (1.14-2.07) for metolachlor. Potential association to mixtures of three compounds give an OR = 7.56 (4.16-13.73). A potential association is indicated between leukemia and nitrates, OR = 1.81 (1.35-2.42), and bone cancer with metolachlor, OR = 2.26 (0.97-5.24). These results give insight to generate a hypothesis of the potential association between exposure to these herbicides and nitrates and specific types of childhood cancer.