Effects of biofilm heterogeneity on the apparent mechanical properties obtained by shear rheometry.

Rheometry is an experimental technique widely used to determine the mechanical properties of biofilms. However, it characterizes the bulk mechanical behavior of the whole biofilm. The effects of biofilm mechanical heterogeneity on rheometry measurements are not known. We used laboratory experiments and computer modeling to explore the effects of biofilm mechanical heterogeneity on the results obtained by rheometry. A synthetic biofilm with layered mechanical properties was studied, and a viscoelastic biofilm theory was employed using the Kelvin-Voigt model. Agar gels with different concentrations were used to prepare the layered, heterogeneous biofilm, which was characterized for mechanical properties in shear mode with a rheometer. Both experiments and simulations indicated that the biofilm properties from rheometry were strongly biased by the weakest portion of the biofilm. The simulation results using linearly stratified mechanical properties from a previous study also showed that the weaker portions of the biofilm dominated the mechanical properties in creep tests. We note that the model can be used as a predictive tool to explore the mechanical behavior of complex biofilm structures beyond those accessible to experiments. Since most biofilms display some degree of mechanical heterogeneity, our results suggest caution should be used in the interpretation of rheometry data. It does not necessarily provide the "average" mechanical properties of the entire biofilm if the mechanical properties are stratified.

Data-driven modeling of heterogeneous viscoelastic biofilms.

Biofilms are typically heterogeneous in morphology, structure, and composition, resulting in nonuniform mechanical properties. The distribution of mechanical properties, in turn, determines the biofilm behavior, such as deformation and detachment. Most biofilm models neglect biofilm heterogeneity, especially at the microscale. In this study, an image-based modeling approach was developed to transform two-dimensional optical coherence tomography (OCT) biofilm images to a pixel-scale non-Newtonian viscosity map of the biofilm. The map was calibrated using the bulk viscosity data from rheometer tests, based on assumed maximum and minimum viscosities and a relationship between OCT image intensity signals and non-Newtonian viscosity. While not quantitatively measuring biofilm viscosity for each pixel, it allows a rational spatial allocation of viscosities within the biofilm: areas with lower cell density, for example, voids, are assigned lower viscosities, and areas with high cell densities are assigned higher viscosities. The spatial distribution of non-Newtonian viscosity was applied in an established Oldroyd-B constitutive model and implemented using the phase-field continuum approach for the deformation and stress analysis. The heterogeneous model was able to predict deformations more accurately than a homogenous one. Stress distribution in the heterogeneous biofilm displayed better characteristics than that in the homogeneous one, because it is highly dependent on the viscosity distribution. This study, using a pixel-scale, image-based approach to map the mechanical heterogeneity of biofilms for computational deformation and stress analysis, provides a novel modeling approach that allows the consideration of biofilm structural and mechanical heterogeneity. Future research should better characterize the relationship between OCT signal and viscosity, and consider other constitutive models for biofilm mechanical behavior.

A MULTISCALE VISION-ILLUSTRATIVE APPLICATIONS FROM BIOLOGY TO ENGINEERING.

Modeling and simulation have quickly become equivalent pillars of research along with traditional theory and experimentation. The growing realization that most complex phenomena of interest span many orders of spatial and temporal scales has led to an exponential rise in the development and application of multiscale modeling and simulation over the past two decades. In this perspective, the associate editors of the International Journal for Multiscale Computational Engineering and their co-workers illustrate current applications in their respective fields spanning biomolecular structure and dynamics, civil engineering and materials science, computational mechanics, aerospace and mechanical engineering, and more. Such applications are highly tailored, exploit the latest and ever-evolving advances in both computer hardware and software, and contribute significantly to science, technology, and medical challenges in the 21st century.

Predicting biofilm deformation with a viscoelastic phase-field model: Modeling and experimental studies.

Biofilms commonly develop in flowing aqueous environments, where the flow causes the biofilm to deform. Because biofilm deformation affects the flow regime, and because biofilms behave as complex heterogeneous viscoelastic materials, few models are able to predict biofilm deformation. In this study, a phase-field (PF) continuum model coupled with the Oldroyd-B constitutive equation was developed and used to simulate biofilm deformation. The accuracy of the model was evaluated using two types of biofilms: a synthetic biofilm, made from alginate mixed with bacterial cells, and a Pseudomonas aeruginosa biofilm. Shear rheometry was used to experimentally determine the mechanical parameters for each biofilm, used as inputs for the model. Biofilm deformation under fluid flow was monitored experimentally using optical coherence tomography. The comparison between the experimental and modeling geometries, for selected horizontal cross sections, after fluid-driven deformation was good. The relative errors ranged from 3.2 to 21.1% for the synthetic biofilm and from 9.1 to 11.1% for the P. aeruginosa biofilm. This is the first demonstration of the effectiveness of a viscoelastic PF biofilm model. This model provides an important tool for predicting biofilm viscoelastic deformation. It also can benefit the design and control of biofilms in engineering systems.

X-ray nanotomography and focused-ion-beam sectioning for quantitative three-dimensional analysis of nanocomposites.

Knowing the relationship between three-dimensional structure and properties is paramount for complete understanding of material behavior. In this work, the internal nanostructure of micrometer-size (∼10 µm) composite Ni/Al particles was analyzed using two different approaches. The first technique, synchrotron-based X-ray nanotomography, is a nondestructive method that can attain resolutions of tens of nanometers. The second is a destructive technique with sub-nanometer resolution utilizing scanning electron microscopy combined with an ion beam and `slice and view' analysis, where the sample is repeatedly milled and imaged. The obtained results suggest that both techniques allow for an accurate characterization of the larger-scale structures, while differences exist in the characterization of the smallest features. Using the Monte Carlo method, the effective resolution of the X-ray nanotomography technique was determined to be ∼48 nm, while focused-ion-beam sectioning with `slice and view' analysis was ∼5 nm.

Towards discrimination of plant species by machine vision: advanced statistical analysis of chlorophyll fluorescence transients.

Automatic discrimination of plant species is required for precision farming and for advanced environmental protection. For this task, reflected sunlight has already been tested whereas fluorescence emission has been only scarcely considered. Here, we investigated the discriminative potential of chlorophyll fluorescence imaging in a case study using three closely related plant species of the family Lamiaceae. We compared discriminative potential of eight classifiers and four feature selection methods to identify the fluorescence parameters that can yield the highest contrast between the species. Three plant species: Ocimum basilicum, Origanum majorana and Origanum vulgare were grown separately as well as in pots where all three species were mixed. First, eight statistical classifiers were applied and tested in simulated species discrimination. The performance of the Quadratic Discriminant Classifier was found to be the most efficient. This classifier was further applied in combination with four different methods of feature selection. The Sequential Forward Floating Selection was found as the most efficient method for selecting the best performing subset of fluorescence images. The ability of the combinatorial statistical techniques for discriminating the species was also compared to the resolving power of conventional fluorescence parameters and found to be more efficient.

Visualization of dynamics of plant-pathogen interaction by novel combination of chlorophyll fluorescence imaging and statistical analysis: differential effects of virulent and avirulent strains of P. syringae and of oxylipins on A. thaliana.

Pathogen infection leads to defence induction as well as to changes in carbohydrate metabolism of plants. Salicylic acid and oxylipins are involved in the induction of defence, but it is not known if these signalling molecules also mediate changes in carbohydrate metabolism. In this study, the effect of application of salicylic acid and the oxylipins 12-oxo-phytodienoic acid (OPDA) and jasmonic acid on photosynthesis was investigated by kinetic chlorophyll fluorescence imaging and compared with the effects of infection by virulent and avirulent strains of Pseudomonas syringae. Both pathogen strains and OPDA caused a similar change in fluorescence parameters of leaves of Arabidopsis thaliana. The response to OPDA appeared faster compared with that to the pathogens and persisted only for a short time. Infiltration with jasmonic acid or salicylic acid did not lead to a localized and distinct fluorescence response of the plant. To capture the faint early symptoms of the plant response, a novel algorithm was applied identifying the unique fluorescence signature-the set of images that, when combined, yield the highest contrast between control and infected leaf segments. Unlike conventional fluorescence parameters, this non-biased approach indeed detected the infection as early as 6 h after inoculation with bacteria. It was posssible to identify distinct fluorescence signatures characterizing the early and late phases of the infection. Fluorescence signatures of both infection phases were found in leaves infiltrated with OPDA.

Case study of combinatorial imaging: what protocol and what chlorophyll fluorescence image to use when visualizing infection of Arabidopsis thaliana by Pseudomonas syringae?

Localized infection of a plant can be mapped by a sequence of images capturing chlorophyll fluorescence transients in actinic light. Choice of the actinic light protocol co-determines fluorescence contrast between infected leaf segment and surrounding healthy tissue. Frequently, biology cannot predict with which irradiance protocol, in which fluorescence image of the sequence, and in which segment of the image there will be the highest contrast between the healthy and infected tissue. Here, we introduce a new technique that can be applied to identify the combination of chlorophyll fluorescence images yielding the highest contrast. The sets of the most contrasting images vary throughout the progress of the infection. Such specific image sets, stress-revealing fluorescence signatures, can be found for the initial and late phases of the infection. Using these signatures, images can be divided into segments that show tissue in different infection phases. We demonstrate the capacity of the algorithm in an investigation of infection of the model plant Arabidopsis thaliana by the bacterium Pseudomonas syringae. We show that the highest contrast is found with transients elicited by fluctuating, harmonically modulated irradiance with long periods.