Assessing the potential application of bacteria-based self-healing cementitious materials for enhancing durability of wastewater treatment infrastructure

Wastewater treatment plants (WWTPs) around the world are mainly built using concrete. The continuous exposure to wastewater affects the durability of concrete structures and requires costly maintenance or replacement. Concrete production and repair represents ∼8% of the global anthropogenic CO2 emissions due to the use of cement, thus contributing to climate change. Developing a more sustainable cementitious material is therefore required for this vital health infrastructure. In this study, the feasibility of using bacteria-based self-healing (BBSH) cementitious materials for WWTPs is assessed by exposing BBSH mortar prisms to a continuous municipal wastewater flow and comparing their self-healing capacity to equivalent mortar prisms exposed to tap water. Microscopy imaging, water-flow tests and micro-CT analyses were performed to evaluate the self-healing efficiency of the mortar prisms, while SEM-EDX and Raman spectroscopy were used to characterise the healing products. Our work represents the first systematic study of the healing potential of BBSH in mortar exposed to wastewater. The results indicate that the purposely added bacteria are able to induce calcium carbonate precipitation when exposed to wastewater conditions. Moreover, if additional sources of calcium and carbon are embedded within the cement matrix, the rich bacterial community inherently present in the wastewater is capable of inducing calcium carbonate precipitation, even if no bacteria are purposely added to the mortar. The results of this study offer promising avenues for the construction of more sustainable wastewater infrastructure, with the potential of significantly reducing costs and simplifying the production process of BBSH concretes for this specific application

Distinct lung cell signatures define the temporal evolution of diffuse alveolar damage in fatal COVID-19

Background Lung damage in severe COVID-19 is highly heterogeneous however studies with dedicated spatial distinction of discrete temporal phases of diffuse alveolar damage (DAD) and alternate lung injury patterns are lacking. Existing studies have also not accounted for progressive airspace obliteration in cellularity estimates. We used an imaging mass cytometry (IMC) analysis with an airspace correction step to more accurately identify the cellular immune response that underpins the heterogeneity of severe COVID-19 lung disease. Methods Lung tissue was obtained at post-mortem from severe COVID-19 deaths. Pathologist-selected regions of interest (ROIs) were chosen by light microscopy representing the patho-evolutionary spectrum of DAD and alternate disease phenotypes were selected for comparison. Architecturally normal SARS-CoV-2-positive lung tissue and tissue from SARS-CoV-2-negative donors served as controls. ROIs were stained for 40 cellular protein markers and ablated using IMC before segmented cells were classified. Cell populations corrected by ROI airspace and their spatial relationships were compared across lung injury patterns. Findings Forty patients (32M:8F, age: 22–98), 345 ROIs and >900k single cells were analysed. DAD progression was marked by airspace obliteration and significant increases in mononuclear phagocytes (MnPs), T and B lymphocytes and significant decreases in alveolar epithelial and endothelial cells. Neutrophil populations proved stable overall although several interferon-responding subsets demonstrated expansion. Spatial analysis revealed immune cell interactions occur prior to microscopically appreciable tissue injury. Interpretation The immunopathogenesis of severe DAD in COVID-19 lung disease is characterised by sustained increases in MnPs and lymphocytes with key interactions occurring even prior to lung injury is established

Anatomical Teaching Tools

Instructions and guidelines for generating an anatomical teaching models, developed in UCD School of Medicin

The Incubot: A 3D Printer-Based Microscope for Long-Term Live Cell Imaging within a Tissue Culture Incubator

The Incubator Microscope is an open-source project developed by the Pickering Lab (University College Dublin) to facilitate long-term physiological imaging of tissue culture monolayers within a commercial incubator. The hardware is constructed of a commercial desktop 3D printer, using 3D printed components and commercially available mechanical components to allow for XYZ imaging over time. A graphic user interface is also included to facilitate use of the hardware by users

OPTIMAL: An OPTimised Imaging Mass cytometry AnaLysis framework for benchmarking segmentation and data exploration.

Funder: Medical Research CouncilFunder: UK Research and Innovation; doi: http://dx.doi.org/10.13039/100014013Funder: JGW Patterson Foundation; doi: http://dx.doi.org/10.13039/100010089Analysis of Imaging Mass Cytometry (IMC) data and other low-resolution multiplexed tissue imaging technologies is often confounded by poor single cell segmentation and sub-optimal approaches for data visualisation and exploration. This can lead to inaccurate identification of cell phenotypes, states or spatial relationships compared to reference data from single cell suspension technologies. To this end we have developed the "OPTIMAL" framework to benchmark any approaches for cell segmentation, parameter transformation, batch effect correction, data visualisation/clustering and spatial neighbourhood analysis. Using a panel of 27 metal-tagged antibodies recognising well characterised phenotypic and functional markers to stain the same FFPE human tonsil sample Tissue Microarray (TMA) over 12 temporally distinct batches we tested several cell segmentation models, a range of different arcsinh cofactor parameter transformation values, five different dimensionality reduction algorithms and two clustering methods. Finally we assessed the optimal approach for performing neighbourhood analysis. We found that single cell segmentation was improved by the use of an Ilastik-derived probability map but that issues with poor segmentation were only really evident after clustering and cell type/state identification and not always evident when using "classical" bi-variate data display techniques. The optimal arcsinh cofactor for parameter transformation was 1 as it maximised the statistical separation between negative and positive signal distributions and a simple Z-score normalisation step after arcsinh transformation eliminated batch effects. Of the five different dimensionality reduction approaches tested, PacMap gave the best data structure with FLOWSOM clustering out-performing Phenograph in terms of cell type identification. We also found that neighbourhood analysis was influenced by the method used for finding neighbouring cells with a "disc" pixel expansion outperforming a "bounding box" approach combined with the need for filtering objects based on size and image-edge location. Importantly OPTIMAL can be used to assess and integrate with any existing approach to IMC data analysis and, as it creates. FCS files from the segmentation output, allows for single cell exploration to be conducted using a wide variety of accessible software and algorithms familiar to conventional flow cytometrists