Nylon Oxygen Barrier Tubing Reduces Biofouling in Beer Draught Lines

Beer draught lines are frequently contaminated with biofilm-forming microorganisms, which forces retailers to spend considerable time and money cleaning and replacing lines. In light of this financial burden, draught tubing composition was examined for its role in the prevention of biofouling in beer lines. Three types of draught tubing - vinyl, polyethylene, and nylon barrier - were inoculated with a combination of biofilm-forming microorganisms (Hafnia paralvei, Raoultella planticola, Pediococcus damnosus and Saccharomyces cerevisiae) and used to simulate a bar environment for sixteen weeks. Following simulation, the degree of biofouling in each draught line was determined by spectrophotometry and microscopy. Absorption values and fluorescence images showed that nylon barrier tubing was superior to the other lines at resisting biofilm maturation.These results suggest that tubing composition plays a significant role in the prevention of biofilm formation in beer draught lines and supports the adoption of nylon barrier tubing as an effective strategy against biofouling in a variety of applications.

Double-Clad Optical Fiber-Based Multi-Contrast Noncontact Photoacoustic and Fluorescence Imaging System

A noncontact photoacoustic and fluorescence dual-modality imaging system is proposed, which integrates a fiber-based fluorescence imaging system with noncontact photoacoustic imaging using a specially fabricated double-cladding fiber (DCF) coupler and a DCF lens. The performance of the DCF coupler and lens was evaluated, and the feasibility of this new imaging system was demonstrated using simple tubing phantoms with black ink and fluorophore. Our imaging results demonstrated that the multimodal imaging technique can simultaneously acquire photoacoustic and fluorescence images without coming into contact with the sample. Consequently, the developed method is the first noncontact scheme among multimodal imaging systems that is integrated with a photoacoustic imaging system, which can provide varied and complementary information about the sample.

Workflow for Segmentation of Caenorhabditis elegans from Fluorescence Images for the Quantitation of Lipids

The small and transparent nematode Caenorhabditis elegans is increasingly employed for phenotypic in vivo chemical screens. The influence of compounds on worm body fat stores can be assayed with Nile red staining and imaging. Segmentation of C. elegans from fluorescence images is hereby a primary task. In this paper, we present an image-processing workflow that includes machine-learning-based segmentation of C. elegans directly from fluorescence images and quantifies their Nile red lipid-derived fluorescence. The segmentation is based on a J48 classifier using pixel entropies and is refined by size-thresholding. The accuracy of segmentation was >90% in our external validation. Binarization with a global threshold set to the brightness of the vehicle control group worms of each experiment allows a robust and reproducible quantification of worm fluorescence. The workflow is available as a script written in the macro language of imageJ, allowing the user additional manual control of classification results and custom specification settings for binarization. Our approach can be easily adapted to the requirements of other fluorescence image-based experiments with C. elegans.

Utilization of fluorescence images in chlorophyll in Cirebon waters, Indonesia

Satellite is one of the tools used to detect chlorophyll concentration. MODIS chlorophyll concentrations appears to be disturbed by colored dissolved organic matter (CDOM). The fluorescence approach can represent the chlorophyll concentration near the coast more accurately. The data for this study was obtained from satellite Aqua MODIS Level 2 which consisted of MODIS chlorophyll, MODIS fluorescence data, and Observation data. The data was taken on 6 September 2020 in Cirebon Waters. Results of the chlorophyll concentration field data ranged from 0.64 mg m-³ - 4.26 mg m-³. Estimation of chlorophyll concentrations using the standard chlorophyll method ranged from 2.55 mg m-³ - 7.20 mg m-³ and the chlorophyll concentrations using the fluorescence method were 2.58 mg m-³ - 3.5 mg m-³. Comparison of field data with satellite images is better with the florescence method than the standard MODIS chlorophyll technique, with an error of 47.8% for fluorescence and 235.5% for the standard MODIS chlorophyll.

BCM3D 2.0: Accurate segmentation of single bacterial cells in dense biofilms using computationally generated intermediate image representations

Accurate detection and segmentation of single cells in three-dimensional (3D) fluorescence time-lapse images is essential for measuring individual cell behaviors in large bacterial communities called biofilms. Recent progress in machine-learning-based image analysis is providing this capability with every increasing accuracy. Leveraging the capabilities of deep convolutional neural networks (CNNs), we recently developed bacterial cell morphometry in 3D (BCM3D), an integrated image analysis pipeline that combines deep learning with conventional image analysis to detect and segment single biofilm-dwelling cells in 3D fluorescence images. While the first release of BCM3D (BCM3D 1.0) achieved state-of-the-art 3D bacterial cell segmentation accuracies, low signal-to-background ratios (SBRs) and images of very dense biofilms remained challenging. Here, we present BCM3D 2.0 to address this challenge. BCM3D 2.0 is completely complementary to the approach utilized in BCM3D 1.0. Instead of training CNNs to perform voxel classification, we trained CNNs to translate 3D fluorescence images into intermediate 3D image representations that are, when combined appropriately later, more amenable to conventional mathematical image processing than a single experimental image. Using this approach, improved segmentation results are obtained even for very low SBRs and/or high cell density biofilm images. The improved cell segmentation accuracies in turn enable improved accuracies of tracking individual cells through 3D space and time, which opens the door to investigating time-dependent phenomena in bacterial biofilms at the cellular level.

Origami: Single-cell 3D shape dynamics oriented along the apico-basal axis of folding epithelia from fluorescence microscopy data

A common feature of morphogenesis is the formation of three-dimensional structures from the folding of two-dimensional epithelial sheets, aided by cell shape changes at the cellular-level. Changes in cell shape must be studied in the context of cell-polarised biomechanical processes within the epithelial sheet. In epithelia with highly curved surfaces, finding single-cell alignment along a biological axis can be difficult to automate in silico. We present ‘Origami’, a MATLAB-based image analysis pipeline to compute direction-variant cell shape features along the epithelial apico-basal axis. Our automated method accurately computed direction vectors denoting the apico-basal axis in regions with opposing curvature in synthetic epithelia and fluorescence images of zebrafish embryos. As proof of concept, we identified different cell shape signatures in the developing zebrafish inner ear, where the epithelium deforms in opposite orientations to form different structures. Origami is designed to be user-friendly and is generally applicable to fluorescence images of curved epithelia.