Hydrodynamic deformation and removal of Staphylococcus epidermidis biofilms treated with urea, chlorhexidine, iron chloride, or DispersinB

The force-deflection and removal characteristics of bacterial biofilm were measured by two different techniques before and after chemical, or enzymatic, treatment. The first technique involved time lapse imaging of a biofilm grown in a capillary flow cell and subjected to a brief shear stress challenge imparted through increased fluid flow. Biofilm removal was determined by calculating the reduction in biofilm area from quantitative analysis of transmission images. The second technique was based on micro-indentation using an atomic force microscope. In both cases, biofilms formed by Staphylococcus epidermidis were exposed to buffer (untreated control), urea, chlorhexidine, iron chloride, or DispersinB. In control experiments, the biofilm exhibited force-deflection responses that were similar before and after the same treatment. The biofilm structure was stable during the post-treatment shear challenge (1% loss). Biofilms treated with chlorhexidine became less deformable after treatment and no increase in biomass removal was seen during the post-treatment shear challenge (2% loss). In contrast, biofilms treated with urea or DispersinB became more deformable and exhibited significant biofilm loss during the post-treatment flow challenge (71% and 40%, respectively). During the treatment soak phase, biofilms exposed to urea swelled. Biofilms exposed to iron chloride showed little difference from the control other than slight contraction during the treatment soak. These observations suggest the following interpretations: (1) chemical or enzymatic treatments, including those that are not frankly antimicrobial, can alter the cohesion of bacterial biofilm; (2) biocidal treatments (e.g., chlorhexidine) do not necessarily weaken the biofilm; and (3) biofilm removal following treatment with agents that make the biofilm more deformable (e.g., urea, DispersinB) depend on interaction between the moving fluid and the biofilm structure. Measurements such as those reported here open the door to development of new technologies for controlling detrimental biofilms by targeting biofilm cohesion rather than killing microorganisms.     

Disturbance Frequency Determines Morphology and Community Development in Multi-Species Biofilm at the Landscape Scale

Many natural and engineered biofilm systems periodically face disturbances. Here we present how the recovery time of a biofilm between disturbances (expressed as disturbance frequency) shapes the development of morphology and community structure in a multi-species biofilm at the landscape scale. It was hypothesized that a high disturbance frequency favors the development of a stable adapted biofilm system while a low disturbance frequency promotes a dynamic biofilm response. Biofilms were grown in laboratory-scale reactors over a period of 55-70 days and exposed to the biocide monochloramine at two frequencies: daily or weekly pulse injections. One untreated reactor served as control. Biofilm morphology and community structure were followed on comparably large biofilm areas at the landscape scale using automated image analysis (spatial gray level dependence matrices) and community fingerprinting (single-strand conformation polymorphisms). We demonstrated that a weekly disturbed biofilm developed a resilient morphology and community structure. Immediately after the disturbance, the biofilm simplified but recovered its initial complex morphology and community structure between two biocide pulses. In the daily treated reactor, one organism largely dominated a morphologically simple and stable biofilm. Disturbances primarily affected the abundance distribution of already present bacterial taxa but did not promote growth of previously undetected organisms. Our work indicates that disturbances can be used as lever to engineer biofilms by maintaining a biofilm between two developmental states.     

Quantifying biofilm structure: facts and fiction

There is no doubt among biofilm researchers that biofilm structure is important to many biofilm processes, such as the transport of nutrients to deeper layers of the biofilm. However, biofilm structure is an elusive term understood only qualitatively, and as such it cannot be directly correlated with any measurable parameters characterizing biofilm performance. To correlate biofilm structure with the parameters characterizing biofilm performance, such as the rate of nutrient transport within the space occupied by the biofilms, biofilm structure must first be quantified and expressed numerically on an appropriate scale. The task of extracting numerical parameters quantifying biofilm structure relies on using biofilm imaging and image analysis. Although defining parameters characterizing biofilm structure is relatively straightforward, and multiple parameters have been described in the computer science literature, interpreting the results of such analyses is not trivial. Existing computer software developed by several research groups, including ours, for the sole purpose of analyzing biofilm images helps quantify parameters from biofilm images but does nothing to help interpret the results of such analyses. Although computing structural parameters from biofilm images permits correlating biofilm structure with other biofilm processes, the meaning of the results is not obvious. The first step to understanding the quantification of biofilm structure, developing image analysis, methods to quantify information from biofilm images, has been made by several research groups. The next step is to explain the meaning of these analyses. This presentation explains the meaning of several parameters commonly used to characterize biofilm structure. It also reviews the authors' research and experience in quantifying biofilm structure and their attempts to quantitatively relate biofilm structure to fundamental biofilm processes.     

Prediction of optimal biofilm thickness for membrane-attached biofilms growing in an extractive membrane bioreactor

This article presents a mathematical model of membrane-attached biofilm (MAB) behavior in a single-tube extractive membrane bioreactor (STEMB). MABs can be used for treatment of wastewaters containing VOCs, treatment of saline wastewaters, and nitrification processes. Extractive membrane bioreactors (EMBs) are employed to prevent the direct contact between a toxic volatile pollutant and the aerated gas by allowing counterdiffusion of substrates; i.e., pollutant diffuses from the tube side into the biofilm, whereas oxygen diffuses from the shell side into the biofilm. This reduces the air stripping problems usually found in conventional bioreactors. In this study, the biodegradation of a toxic VOC (1,2-dichloroethane, DCE) present in a synthetic wastewater has been investigated. An unstructured model is used to describe cell growth and cell decay in the MAB. The model has been verified by comparing model predicted trends with experimental data collected over 5 to 20-day periods, and has subsequently been used to model steady states in biofilm behavior over longer time scales. The model is capable of predicting the correct trends in system variables such as biofilm thickness, DCE flux across the membrane, carbon dioxide evolution, and suspended biomass. Steady states (constant biofilm thickness and DCE flux) are predicted, and factors that affect these steady states, i.e., cell endogeneous decay rate, and biofilm attrition, are investigated. Biofilm attrition does not have a great influence on biofilm behavior at low values of detachment coefficient close to those typically reported in the literature. Steady-state biofilm thickness is found to be an important variable; a thin biofilm results in a high DCE flux across the membrane, but with the penalty of a high loss of DCE via air stripping. The optimal biofilm thickness at steady state can be determined by trading off the decrease in air stripping (desirable) and the decrease in DCE flux (undesirable) which occur simultaneously as the thickness increases. (c) 1996 John Wiley & Sons, Inc.     

Assessing ecological habitat structure from local to landscape scales using synthetic aperture radar

Ecological studies need accurate environmental data such as vegetation characterization, landscape structure and organization, to predict and explain the spatial distribution of biodiversity. Few ecological studies use remote sensing data to assess the biophysical or structural properties of vegetation to understand species distribution. To date, synthetic aperture radar (SAR) data have seldom been used for ecological applications. However, these sensors provide data allowing access to the inner structure of vegetation which is a key information in ecology. The objective of this article is to compare the predictive power of ecological habitat structure variables derived from a TerraSAR-X image, an aerial photograph and a SPOT-5 image for species distribution. The test was run with a hedgerow network in Brittany and assessed the spatial distribution of the forest ground carabid beetles which inhabit these hedgerows. The results confirmed that radar and optical images can be indifferently used to extract hedgerow network and derived landscape metrics (hedgerow density, network grain) useful to explain the spatial distribution of forest carabid beetles. In comparison with passive optical remotely sensed data, VHSR SAR images provide new data to characterize vegetation structure and more particularly hedgerow canopy cover, a variable known to explain the spatial distribution of carabid beetles in an agricultural landscape, but not yet quantified at a fine scale. The hedgerow canopy cover derived from the SAR image is a strong predictor of the abundance of forest carabid beetles at two scales i.e., a local scale and a landscape scale.     

Biofilm structure differentiation based on multi-resolution analysis

Quantitative parameters for describing the morphology of biofilms are crucial towards establishing the influence of growing conditions on biofilm structure. Parameters used in earlier studies generally fail to differentiate complex three-dimensional structures. This article presents a novel approach of defining a parameter vector based on the energy signature of multi-resolution analysis, which was applied to differentiating biofilm structures from confocal laser scanning microscopy (CLSM) biofilm images. The parameter vector distinguished differences in the spatial arrangements of synthetic images. For real CLSM images, this parameter vector detected subtle differences in biofilm structure for three sample cases: (1) two adjacent images of a CLSM stack; (2) two partial stacks from the same CLSM stack with equal numbers of images but spatially offset by one image; and (3) three complete CLSM stacks from different bacterial strains. It was also observed that filtering the noise in CLSM images enhanced the sensitivity of the differentiation using our parameter vector.     

Biofilm structure differentiation based on multi-resolution analysis

Quantitative parameters for describing the morphology of biofilms are crucial towards establishing the influence of growing conditions on biofilm structure. Parameters used in earlier studies generally fail to differentiate complex three-dimensional structures. This article presents a novel approach of defining a parameter vector based on the energy signature of multi-resolution analysis, which was applied to differentiating biofilm structures from confocal laser scanning microscopy (CLSM) biofilm images. The parameter vector distinguished differences in the spatial arrangements of synthetic images. For real CLSM images, this parameter vector detected subtle differences in biofilm structure for three sample cases: (1) two adjacent images of a CLSM stack; (2) two partial stacks from the same CLSM stack with equal numbers of images but spatially offset by one image; and (3) three complete CLSM stacks from different bacterial strains. It was also observed that filtering the noise in CLSM images enhanced the sensitivity of the differentiation using our parameter vector.     

Three-dimensional biofilm image reconstruction for assessing structural parameters

Parameters representing three-dimensional (3D) biofilm structure are quantified from confocal laser-scanning microscope (CLSM) images. These 3D parameters describe the distribution of biomass pixels within the space occupied by a biofilm; however, they lack a direct connection to biofilm activity. As a result, researchers choose a handful of parameters without there being a consensus on a standard set of parameters. We hypothesized that a select 3D parameter set could be used to reconstruct a biofilm image and that the reconstructed and original biofilm images would have similar activities. To test this hypothesis, an algorithm was developed to reconstruct a biofilm image with parameters identical to those of the original CLSM image. We introduced an objective method to assess the reconstruction algorithm by comparing the activities of the original and reconstructed biofilm images. We found that biofilm images with identical structural parameters showed nearly identical activities and substrate concentration profiles. This implies that the set containing all common structural parameters can successfully describe biofilm structure. This finding is significant, as it opens the door to the next step, of finding a smaller standard set of biofilm structural parameters that can be used to compare biofilm structure.     

Tracer measurements reveal experimental evidence of biofilm consolidation

The ability to simultaneously measure both biofilm thickness and the mass transfer coefficient of an inert tracer through it provides a powerful method to study biofilm development. In this communication previously published data has been collated to interpret global trends in biofilm structure during the transition towards steady-state. It appears that sudden changes in biofilm structure (directly related to the rate of change of biofilm mass transfer resistance) may occur following transitions in rate of biomass production. These observations are consistent with the concept of consolidation, recently introduced into spatially structured biofilm mathematical models to account for structural realignment of the biofilm under dynamic conditions.     

Spatial separation of FtsZ and FtsN during cell division

The division of Escherichia coli is mediated by a collection of some 34 different proteins that are recruited to the division septum and are thought to assemble into a macromolecular complex known as ‘the divisome’. Herein, we have endeavored to better understand the structure of the divisome by imaging two of its core components; FtsZ and FtsN. Super resolution microscopy (SIM and gSTED) indicated that both proteins are localized in large assemblies, which are distributed around the division septum (i.e., forming a discontinuous ring). Although the rings had similar radii prior to constriction, the individual densities were often spatially separated circumferentially. As the cell envelope constricted, the discontinuous ring formed by FtsZ moved inside the discontinuous ring formed by FtsN. The radial and circumferential separation observed in our images indicates that the majority of FtsZ and FtsN molecules are organized in different macromolecular assemblies, rather than in a large super‐complex. This conclusion was supported by fluorescence recovery after photobleaching measurements, which indicated that the dynamic behavior of the two macromolecular assemblies was also fundamentally different. Taken together, the data indicates that constriction of the cell envelope is brought about by (at least) two spatially separated complexes.     

Human vs. machine: Detecting wildlife in camera trap images

As the capacity to collect and store large amounts of data expands, identifying and evaluating strategies to efficiently convert raw data into meaningful information is increasingly necessary. Across disciplines, this data processing task has become a significant challenge, delaying progress and actionable insights. In ecology, the growing use of camera traps (i.e., remotely triggered cameras) to collect information on wildlife has led to an enormous volume of raw data (i.e., images) in need of review and annotation. To expedite camera trap image processing, many have turned to the field of artificial intelligence (AI) and use machine learning models to automate tasks such as detecting and classifying wildlife in images. To contribute understanding of the utility of AI tools for processing wildlife camera trap images, we evaluated the performance of a state-of-the-art computer vision model developed by Microsoft AI for Earth named MegaDetector using data from an ongoing camera trap study in Arctic Alaska, USA. Compared to image labels determined by manual human review, we found MegaDetector reliably determined the presence or absence of wildlife in images generated by motion detection camera settings (≥94.6% accuracy), however, performance was substantially poorer for images collected with time-lapse camera settings (≤61.6% accuracy). By examining time-lapse images where MegaDetector failed to detect wildlife, we gained practical insights into animal size and distance detection limits and discuss how those may impact the performance of MegaDetector in other systems. We anticipate our findings will stimulate critical thinking about the tradeoffs of using automated AI tools or manual human review to process camera trap images and help to inform effective implementation of study designs.     

Increase in regularity of fetal heart rate variability with age.

It is generally assumed that fetal heart rate variability increases with gestation, reflecting prenatal development of the autonomic nervous system. We examined standard measures quantifying fetal heart rate variability, as well as a complexity measure, approximate entropy, in 66 fetal magnetocardiograms recorded from 22 healthy pregnant women between the 16th and 42nd week of gestation. In particular, regularity in the fetal RR interval time series was assessed on the basis of symbolic dynamics. The results showed that, beside an overall increase in fetal heart rate variability and complexity during pregnancy, there was also an increase in specific sets of binary patterns with low approximate entropy, i.e., a high degree of regularity. These sets were characterized by short epochs of heart rate acceleration and deceleration, and comparison with surrogate data confirmed that their random occurrence is rare. The results most likely reflect the influence of increasingly differentiated fetal behavioral states and transitions between them in association with fetal development.     

Detachment of multi species biofilm in circulating fluidized bed bioreactor

In this study, the detachment rates of various microbial species from the aerobic and anoxic biofilms in a circulating fluidized bed bioreactor (CFBB) with two entirely separate aerobic and anoxic beds were investigated. Overall detachment rate coefficients for biomass, determined on the basis of volatile suspended solids (VSS), glucose and protein as well as for specific microbial groups, i.e., for nitrifiers, denitrifiers, and phosphorous accumulating organisms (PAOs), were established. Biomass detachment rates were found to increase with biomass attachment on carrier media in both beds. The detachment rate coefficients based on VSS were significantly affected by shear stress, whereas for protein, glucose and specific microbial groups, no significant effect of shear stress was observed. High detachment rates were observed for the more porous biofilm structure. The presence of nitrifiers in the anoxic biofilm and denitrifiers in the aerobic biofilm was established by the specific activity measurements. Detachment rates of PAOs in aerobic and anoxic biofilms were evaluated.     

Estimates of Abundance and Harvest Rates of Female Black Bears Across a Large Spatial Extent

American black bears (Ursus americanus) are an iconic wildlife species in the southern Appalachian highlands of the eastern United States and have increased in number and range since the early 1980s. Given an increasing number of human-bear conflicts in the region, many management agencies have liberalized harvest regulations to reduce bear populations to socially acceptable levels. Wildlife managers need reliable population data for assessing the effects of management actions for this high-profile species. Our goal was to use DNA extracted from hair collected at barbed-wire enclosures (i.e., hair traps) to identify individual bears and then use spatially explicit capture-recapture methods to estimate female black bear density, abundance, and harvest rate. We established 888 hair traps across 66,678 km² of the southern Appalachian highlands in Georgia, North Carolina, South Carolina, and Tennessee, USA, in 2017 and 2018, arranged in 174 clusters of 2–9 traps/cluster. We collected 9,113 hair samples from those sites over 6 weeks of sampling, of which 1,954 were successfully genotyped to 462 individual female bears. Our spatially explicit estimator included a percent forest covariate to explain inhomogeneous bear density across the region. Densities ranged up to 0.410 female bears/km² and regional abundance was 5,950 (95% CI = 4,988–7,098) female bears. Based on hunter kill data from 2016 to 2018, mean annual harvest rates for females were 12.7% in Georgia, 17.6% in North Carolina, 17.6% in South Carolina, and 22.8% in Tennessee. Our estimated harvest rates for most states approached or exceeded theoretical maximum sustainable levels, and population trend data (i.e., bait-station indices) indicated decreasing growth rates since about 2009. These data suggest that the increased harvest goals and poor hard mast production over a series of prior years reduced bear population abundance in many states. We were able to obtain reasonable population abundance and density estimates because of spatially explicit capture-recapture methods, cluster sampling, and a large spatial extent. Continued monitoring of bear populations (e.g., annual bait-station surveys and periodic population estimation using spatially explicit methods) by state jurisdictions would help to ensure that population trajectories are consistent with management goals. © 2021 The Wildlife Society.     

Formats of image data files that can be used in routine digital light micrography. Part two

This is part two of an article that describes the properties of the image data files that are encountered routinely in digital light micrography. In the current part of the article, the differences between saving image data as large intact files and smaller files that have had some information removed, i.e., using lossy compression, are related first. Subsequently, appropriate ways of configuring computers to deal with the large intact image data files are suggested. The structures of the image data files used for recording dynamic sequences and kinematic animations of series of digital light micrographs, i.e., movie formats, are then described. Finally, some information is supplied about choosing file formats for compressing both static and dynamic image data sets.     

Formats of image data files that can be used in routine digital light micrography. Part two

This is part two of an article that describes the properties of the image data files that are encountered routinely in digital light micrography. In the current part of the article, the differences between saving image data as large intact files and smaller files that have had some information removed, i.e., using lossy compression, are related first. Subsequently, appropriate ways of configuring computers to deal with the large intact image data files are suggested. The structures of the image data files used for recording dynamic sequences and kinematic animations of series of digital light micrographs, i.e., movie formats, are then described. Finally, some information is supplied about choosing file formats for compressing both static and dynamic image data sets.     

An ImageJ-based tool for three-dimensional registration between different types of microscopic images

Three-dimensional (3D) registration (i.e., alignment) between two microscopic images is very helpful to study tissues that do not adhere to substrates, such as mouse embryos and organoids, which are often 3D rotated during imaging. However, there is no 3D registration tool easily accessible for experimental biologists. Here we developed an ImageJ-based tool which allows for 3D registration accompanied with both quantitative evaluation of the accuracy and reconstruction of 3D rotated images. In this tool, several landmarks are manually provided in two images to be aligned, and 3D rotation is computed so that the distances between the paired landmarks from the two images are minimized. By simultaneously providing multiple points (e.g., all nuclei in the regions of interest) other than the landmarks in the two images, the correspondence of each point between the two images, i.e., to which nucleus in one image a certain nucleus in another image corresponds, is quantitatively explored. Furthermore, 3D rotation is applied to one of the two images, resulting in reconstruction of 3D rotated images. We demonstrated that this tool successfully achieved 3D registration and reconstruction of images in mouse pre- and post-implantation embryos, where one image was obtained during live imaging and another image was obtained from fixed embryos after live imaging. This approach provides a versatile tool applicable for various tissues and species.