Inhibition of Pseudomonas aeruginosa Biofilm Formation with Surface Modified Polymeric Nanoparticles

The gram-negative bacterial pathogen Pseudomonas aeruginosa represents a prominent clinical concern. Due to the observed high levels of antibiotic resistance, copious biofilm formation, and wide array of virulence factors produced by these bacteria, new treatment technologies are required. Here, we present the development of a series of P. aeruginosa LecA-targeted polymeric nanoparticles and demonstrate the anti-adhesion and biofilm inhibitory properties of these constructs

Inhibition of Pseudomonas aeruginosa Biofilm Formation with Surface Modified Polymeric Nanoparticles

The gram-negative bacterial pathogen Pseudomonas aeruginosa represents a prominent clinical concern. Due to the observed high levels of antibiotic resistance, copious biofilm formation, and wide array of virulence factors produced by these bacteria, new treatment technologies are required. Here, we present the development of a series of P. aeruginosa LecA-targeted polymeric nanoparticles and demonstrate the anti-adhesion and biofilm inhibitory properties of these constructs

Improving gridded snow water equivalent products in British Columbia, Canada: multi-source data fusion by neural network models

Estimates of surface snow water equivalent (SWE) in mixed alpine environments with seasonal melts are particularly difficult in areas of high vegetation density, topographic relief, and snow accumulations. These three confounding factors dominate much of the province of British Columbia (BC), Canada. An artificial neural network (ANN) was created using as predictors six gridded SWE products previously evaluated for BC. Relevant spatiotemporal covariates were also included as predictors, and observations from manual snow surveys at stations located throughout BC were used as target data. Mean absolute errors (MAEs) and interannual correlations for April surveys were found using cross-validation. The ANN using the three best-performing SWE products (ANN3) had the lowest mean station MAE across the province. ANN3 outperformed each product as well as product means and multiple linear regression (MLR) models in all of BC's five physiographic regions except for the BC Plains. Subsequent comparisons with predictions generated by the Variable Infiltration Capacity (VIC) hydrologic model found ANN3 to better estimate SWE over the VIC domain and within most regions. The superior performance of ANN3 over the individual products, product means, MLR, and VIC was found to be statistically significant across the province

Land-cover impacts on streamflow: a change-detection modelling approach that incorporates parameter uncertainty

The effect of land-use or land-cover change on stream runoff dynamics is not fully understood. In many parts of the world, forest management is the major land-cover change agent. While the paired catchment approach has been the primary methodology used to quantify such effects, it is only possible for small headwater catchments where there is uniformity in precipitation inputs and catchment characteristics between the treatment and control catchments. This paper presents a model-based change-detection approach that includes model and parameter uncertainty as an alternative to the traditional paired-catchment method for larger catchments. We use the HBV model and data from the HJ Andrews Experimental Forest in Oregon, USA, to develop and test the approach on two small (1 km2)) headwater catchments (a 100% clear-cut and a control) and then apply the technique to the larger 62 km2 Lookout catchment. Three different approaches are used to detect changes in stream peak flows using: (a) calibration for a period before (or after) change and simulation of runoff that would have been observed without land-cover changes (reconstruction of runoff series); (b) comparison of calibrated parameter values for periods before and after a land-cover change; and (c) comparison of runoff predicted with parameter sets calibrated for periods before and after a land-cover change. Our proof-of-concept change detection modelling showed that peak flows increased in the clear-cut headwater catchment, relative to the headwater control catchment, and several parameter values in the model changed after the clear-cutting. Some minor changes were also detected in the control, illustrating the problem of false detections. For the larger Lookout catchment, moderately increased peak flows were detected. Monte Carlo techniques used to quantify parameter uncertainty and compute confidence intervals in model results and parameter ranges showed rather wide distributions of model simulations. While this makes change detection more difficult, it also demonstrated the need to explicitly consider parameter uncertainty in the modelling approach to obtain reliable results