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Amidst mounting global pressure, governments worldwide have embarked on a collective journey towards decarbonising their economies within the next 30 years. This ambitious goal necessitates the phasing out of naturally occurring hydrocarbons such as coal, natural gas, and oil. However, our analysis of data from academic papers, Australian Federal Government publications, and reports from energy industry bodies and manufacturers of electricity‐generating equipment suggests that such a complete elimination of fossil fuel use is not feasible. The data we've gathered, however, indicate that transitioning to a connected energy island power generation topology could at least create a sustainably robust energy supply capable of propelling Australia towards its environmental targets while bolstering its future economic well‐being. Based on analysis of data drawn from academic papers, the Australian Federal Government, energy industry bodies, and manufacturers of electricity generating equipment, the authors believe at this time, elimination of fossil fuel use is simply not possible. By moving to a connected energy island power generation topology, the data suggest we can: 1. Create a sustainably robust energy supply. 2. Move Australia closer to its environmental targets. 3. Underpin Australia's future economic well‐being. [ABSTRACT FROM AUTHOR]

To assess the usefulness and applications of machine vision (MV) and machine learning (ML) techniques that have been used to develop a single cell-based phenotypic (live and fixed biomarkers) platform that correlates with tumor biological aggressiveness and risk stratification, 100 fresh prostate samples were acquired, and areas of prostate cancer were determined by post-surgery pathology reports logged by an independent pathologist. The prostate samples were dissociated into single-cell suspensions in the presence of an extracellular matrix formulation. These samples were analyzed via live-cell microscopy. Dynamic and fixed phenotypic biomarkers per cell were quantified using objective MV software and ML algorithms. The predictive nature of the ML algorithms was developed in two stages. First, random forest (RF) algorithms were developed using 70% of the samples. The developed algorithms were then tested for their predictive performance using the blinded test dataset that contained 30% of the samples in the second stage. Based on the ROC (receiver operating characteristic) curve analysis, thresholds were set to maximize both sensitivity and specificity. We determined the sensitivity and specificity of the assay by comparing the algorithm-generated predictions with adverse pathologic features in the radical prostatectomy (RP) specimens. Using MV and ML algorithms, the biomarkers predictive of adverse pathology at RP were ranked and a prostate cancer patient risk stratification test was developed that distinguishes patients based on surgical adverse pathology features. The ability to identify and track large numbers of individual cells over the length of the microscopy experimental monitoring cycles, in an automated way, created a large biomarker dataset of primary biomarkers. This biomarker dataset was then interrogated with ML algorithms used to correlate with post-surgical adverse pathology findings. Algorithms were generated that predicted adverse pathology with >0.85 sensitivity and specificity and an AUC (area under the curve) of >0.85. Phenotypic biomarkers provide cellular and molecular details that are informative for predicting post-surgical adverse pathologies when considering tumor biopsy samples. Artificial intelligence ML-based approaches for cancer risk stratification are emerging as important and powerful tools to compliment current measures of risk stratification. These techniques have capabilities to address tumor heterogeneity and the molecular complexity of prostate cancer. Specifically, the phenotypic test is a novel example of leveraging biomarkers and advances in MV and ML for developing a powerful prognostic and risk-stratification tool for prostate cancer patients., (© 2021 MedReviews®, LLC.)