This protocol describes how to grow a Pseudomonas aeruginosa biofilm under low fluid shear close to the air-liquid interface using the drip flow reactor (DFR). The DFR can model environments such as food-processing conveyor belts, catheters, lungs with cystic fibrosis and the oral cavity. The biofilm is established by operating the reactor in batch mode for 6 h. A mature biofilm forms as the reactor operates for an additional 48 h with a continuous flow of nutrients. During continuous flow, the biofilm experiences a low shear as the media drips onto a surface set at a 10 degrees angle. At the end of 54 h, biofilm accumulation is quantified by removing coupons from the reactor channels, rinsing the coupons to remove planktonic cells, scraping the biofilm from the coupon surface, disaggregating the clumps, then diluting and plating for viable cell enumeration. The entire procedure takes 13 h of active time that is distributed over 5 d.

Methods validated by a standard setting organization enable public, industry and regulatory stakeholders to make decisions on the acceptability of products, devices and processes. This is because standard methods are demonstrably reproducible when performed in different laboratories by different researchers, responsive to different products, and rugged when small (usually inadvertent) variations from the standard procedure occur. The Single Tube Method (ASTM E2871) is a standard method that measures the efficacy of antimicrobials against biofilm bacteria that has been shown to be reproducible, responsive and rugged. In support of the reproducibility assessment, a six-laboratory study was performed using three antimicrobials: a sodium hypochlorite, a phenolic and a quaternary/alcohol blend, each tested at low and high efficacy levels. The mean log reduction in viable bacteria in this study ranged from 2.32 to 4.58 and the associated reproducibility standard deviations ranged from 0.89 to 1.67. Independent follow-up testing showed that the method was rugged with respect to deviations in sonication duration and sonication power but slightly sensitive to sonicator reservoir degassing and tube location within the sonicator bath. It was also demonstrated that when a coupon was dropped into a test tube, bacteria can splash out of reach of the applied antimicrobials, resulting in substantial bias when estimating log reductions for the products tested. Bias can also result when testing products that hinder the harvesting of microbes from test surfaces. The culmination of this work provided recommended changes to the early version of the standard method E2871-13 (ASTM 2013b) including use of splashguards and microscopy checks. These changes have been incorporated into a revised ASTM 1 ASTM study director, director of all follow-up investigations 2 study design 3 Implementation of all studies 4 Splashguard bias study 5 Imaging 6 Ruggedness study 7 Harvesting bias study 8 Statistical analysis

Beer's Law explains how light attenuates into thick specimens, including thick biofilms. We use a Bayesian optimality criterion, the maximum of the posterior probability distribution, and computationally efficiently fit Beer's Law to the 3D intensity data collected from thick living biofilms by a confocal scanning laser microscope. Using this approach the top surface of the biofilm

Three dimensional confocal scanning laser microscope images offer dramatic visualizations of the action of living biofilms before and after interventions. Here we use confocal microscopy to study the effect of a treatment over time that causes a biofilm to swell and contract due to osmotic pressure changes. From these data, our goal is to reconstruct biofilm surfaces, to estimate the effect of the treatment on the biofilm’s volume, and to quantify the related uncertainties. We formulate the associated massive linear Bayesian inverse problem and then solve it using iterative samplers from large multivariate Gaussians that exploit well-established polynomial acceleration techniques from numerical linear algebra. Because of a general equivalence with linear solvers, these polynomial accelerated iterative samplers have known convergence rates, stopping criteria, and perform well in finite precision. An explicit algorithm is provided, for the first time, for an iterative sampler that is accelerated by the synergistic implementation of preconditioned conjugate gradient and Chebyshev polynomials.

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