Reliability theory for microbial water quality and sustainability assessment.

Microbial surface water contamination can disrupt critical ecosystem services such as recreation and drinking water supply. Prediction of water contamination and assessment of sustainability of water resources in the context of water quality are needed but are difficult to achieve - with challenges arising from the complexity of environmental systems, and stochastic variability of processes that drive contaminant fate and transport. In this paper we use reliability theory as a framework to address these issues. We define failure as exceedance of regulatory water contamination limits, and system components as reaches in the surface water network. We then methodically study the reliability of each component in the context of water quality, as well as the impact of individual components on overall water quality and sustainability. We obtain spatially distributed probability- and physics-based sustainability measures of reliability, vulnerability, resilience and the sustainability index. Finally, we use GIS as a platform to present these measures as geospatial products in an effort to foster public acceptance of probability-based methods in contaminant hydrology.

Stochastic reliability-based risk evaluation and mapping for watershed systems and sustainability (STREAMS).

Mitigating water contamination, improving water security, and increasing sustainability involve environmental awareness and conscientious decision-making by denizens and stakeholders. Achieving such awareness requires visually compelling geospatial decision-making tools that take into account the probabilistic and spatially distributed nature of water contamination. Inspired by the success of weather maps, this paper presents a novel STochastic Reliability-based Risk Evaluation And Mapping for watershed Systems and Sustainability (STREAMS) tool that produces and effectively communicates the risk of water contamination as maps. STREAMS is integrated with ArcGIS geoprocessing tools and uses physics-based reliability theory to compute the spatial distribution of risk, which is defined as the probability of exceeding a safety threshold of water contamination within a watershed. A quantitative analysis of the efficacy of mitigation strategies is conducted by estimating risk reduction from best management practices throughout the entire watershed. Two case studies at different spatial scales are presented, demonstrating STREAMS application to watersheds with varied properties.

A digital imaging method for evaluating the kinetics of vapochromic response.

This work describes the novel use of a cell phone camera and the L*a*b method (color space defined by the International Commission on Illumination) to characterize the color change in different vapochromic platinum(II) complexes in order to get quantitative and more reliable data. In this study, we have developed a semi-automatic CCA software that digitally analyzes images (e.g., video frames) collected while a vapochromic material is absorbing vapor and changing its color. The advantages of using this method, compared to reflectance or transmission spectroscopy through a thin film, include its low cost, convenience, portability, ease of sample preparation, the lack of need for specialized equipment, and the possibility of simultaneously collecting data on different samples under identical conditions. The results show that this strategy is effective in producing quantitative information about the kinetics of processes.

Reliability-Based Water Quality Assessment with Load Resistance Factor Design: Application to TMDL.

Effective load reduction strategies rely on an accurate Total Maximum Daily Load (TMDL) calculation, which quantifies contaminant loading from various sources. There is a wide range of methods to consider uncertainties in TMDLs: from simple, conservative assumptions regarding factors that contribute to the TMDL required margin of safety (MOS), to probability-based approaches such as Monte Carlo simulations, which explicitly quantifies TMDL uncertainty. In this paper the authors adapt the Load Resistance Factor Design (LRFD), a rigorous, reliability-based framework, to water quality assessment and the TMDL process. The LFRFD replaces the lumped MOS with design factors that reflect the magnitude and distribution of uncertainty among the various contaminant loads. In addition, it produces load reduction estimates to meet management objectives with a contaminant-specific frequency-based target. The LRFD is computationally efficient and flexible in that, to compute the design factors, the procedure can utilize: measurement data, analytical solutions or model simulation results, as well as full or marginal probability distributions.

A screening-level modeling approach to estimate nitrogen loading and standard exceedance risk, with application to the Tippecanoe River watershed, Indiana.

This paper presents a screening-level modeling approach that can be used to rapidly estimate nutrient loading, assess numerical nutrient standard exceedance risk of surface waters leading to potential classification as impaired for designated use, and explore best management practice (BMP) implementation to reduce loading with a relatively low data requirement. The modeling framework uses a hybrid statistical and process based approach to estimate source of pollutants, their transport and decay in the terrestrial and aquatic parts of watersheds. The framework is developed in the ArcGIS environment and is based on the total maximum daily load (TMDL) balance model. Nitrogen (N) is currently addressed in the framework, referred to as WQM-TMDL-N. Loading for each catchment includes non-point sources (NPS) and point sources (PS). The probability of a nutrient load to exceed a target load is evaluated using probabilistic risk assessment, by including the uncertainty associated with export coefficients of various land uses. In an application of this modeling approach to the Tippecanoe River watershed in Indiana, USA, total nitrogen (TN) loading, confidence interval and risk of standard exceedance leading to potential impairment were estimated. Model results suggest that decay coefficients decrease, and delivery fractions increase with increasing stream order. The spatial distribution pattern of delivered incremental TN yield shows a trend similar to that of the delivery fraction in this watershed. The target TN exceedance risk increases considerably when switching from Indiana draft-N benchmark to far lower EPA-proposed TN criteria, suggesting that load reduction to meet the latter criteria may benefit from BMP implementation through source control and delivery reduction.

A stochastic multi-scale model of HIV-1 transmission for decision-making: application to a MSM population.

BACKGROUND: In the absence of an effective vaccine against HIV-1, the scientific community is presented with the challenge of developing alternative methods to curb its spread. Due to the complexity of the disease, however, our ability to predict the impact of various prevention and treatment strategies is limited. While ART has been widely accepted as the gold standard of modern care, its timing is debated. OBJECTIVES: To evaluate the impact of medical interventions at the level of individuals on the spread of infection across the whole population. Specifically, we investigate the impact of ART initiation timing on HIV-1 spread in an MSM (Men who have Sex with Men) population. DESIGN AND METHODS: A stochastic multi-scale model of HIV-1 transmission that integrates within a single framework the in-host cellular dynamics and their outcomes, patient health states, and sexual contact networks. The model captures disease state and progression within individuals, and allows for simulation of therapeutic strategies. RESULTS: Early ART initiation may substantially affect disease spread through a population. CONCLUSIONS: Our model provides a multi-scale, systems-based approach to evaluate the broader implications of therapeutic strategies.

Determining hot spots of fecal contamination in a tropical watershed by combining land-use information and meteorological data with source-specific assays.

The objective of this study was to combine knowledge of environmental, topographical, meteorological, and anthropologic factors in the Río Grande de Arecibo (RGA) watershed in Puerto Rico with information provided by microbial source tracking (MST) to map hot spots (i.e., likely sources) of fecal contamination. Water samples were tested for the presence of human and bovine fecal contamination in addition to fecal indicator bacteria and correlated against several land uses and the density of septic tanks, sewers, and latrines. Specifically, human sources were positively correlated with developed (r = 0.68), barren land uses (r = 0.84), density of septic tanks (r = 0.78), slope (r = 0.63), and the proximity to wastewater treatment plants (WWTPs) (r = 0.82). Agricultural land, the number of upstream National Pollution Discharge Elimination System (NPDES) facilities, and density of latrines were positively associated with the bovine marker (r = 0.71; r = 0.74; and r = 0.68, respectively). Using this information, we provided a hot spot map, which shows areas that should be closely monitored for fecal contamination in the RGA watershed. The results indicated that additional bovine assays are needed in tropical regions. We concluded that meteorological, topographical, anthropogenic, and land cover data are needed to evaluate and verify the performance of MST assays and, therefore, to identify important sources of fecal contamination in environmental waters.

Immune activation, CD4+ T cell counts, and viremia exhibit oscillatory patterns over time in patients with highly resistant HIV infection.

The rates of immunologic and clinical progression are lower in patients with drug-resistant HIV compared to wild-type HIV. This difference is not fully explained by viral load. It has been argued that reductions in T cell activation and/or viral fitness might result in preserved target cells and an altered relationship between the level of viremia and the rate of CD4+ T cell loss. We tested this hypothesis over time in a cohort of patients with highly resistant HIV. Fifty-four antiretroviral-treated patients with multi-drug resistant HIV and detectable plasma HIV RNA were followed longitudinally. CD4+ T cell counts and HIV RNA levels were measured every 4 weeks and T cell activation (CD38/HLA-DR) was measured every 16 weeks. We found that the levels of CD4+ T cell activation over time were a strong independent predictor of CD4+ T cell counts while CD8+ T cell activation was more strongly associated with viremia. Using spectral analysis, we found strong evidence for oscillatory (or cyclic) behavior in CD4+ T cell counts, HIV RNA levels, and T cell activation. Each of the cell populations exhibited an oscillatory behavior with similar frequencies. Collectively, these data suggest that there may be a mechanistic link between T cell activation, CD4+ T cell counts, and viremia and lends support for the hypothesis of altered predator-prey dynamics as a possible explanation of the stability of CD4+ T cell counts in the presence of sustained multi-drug resistant viremia.

Teaching hydrogels how to move like an earthworm.

Breaking of symmetry is an important principle employed in nature to achieve directional movement in systems of various dimensional scales. The same principle can be utilized in engineered devices. Here we highlight recent work on directed motion and transport capacity of environmentally sensitive polymer gels. The directional motion of cylindrical hydrogels is driven by spatially and temporally controlled propagation of volume phase transitions along their length. This system could potentially be used in a wide variety of areas including biochemical test systems, targeted drug delivery and "soft" medical tools.