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Purpose: We investigated if a neural network could be used to predict the change in mean heart dose when a patient's heart deviates from its planned position during radiotherapy treatment., Methods: Predictions were made based on parameters available at the time of treatment planning. The dose prescription, deep inspiration breath-hold (DIBH) amplitude, heart volume, lung volume, V90% and mean heart dose were used to predict the increase in dose to the heart when a shift towards the treatment field was undertaken. The network was trained using 3 mm, 5 mm and 7 mm shifts in heart positions for 50 patients' giving 150 data points in total. The neural network architecture was also varied to find the most optimal network design. The final neural network was then tested using cross-validation to evaluate the model's ability to generalise to new data., Results: The optimal neural network found was comprised of a single hidden layer of 30 neurons. Based on twenty train/test splits, 94% of all prediction errors were below 0.2 Gy, 97.3% were below 0.3 Gy and 100% were below 0.5 Gy. The average RMSE and maximum prediction error over all train/test splits were 0.13 Gy and 0.5 Gy respectively., Conclusions: Our approach using a neural network provides a clinically acceptable estimate of the increase in Mean Heart Dose (MHD), without the need for further imaging, contouring or evaluation. The trained neural network gives clinicians the information and tools required to evaluate what shift in heart position would be acceptable and which scenarios require immediate action before treatment continues., (Copyright © 2019 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.)

Riboswitches are structural genetic regulatory elements that directly couple the sensing of small molecules to gene expression. They have considerable potential for applications throughout synthetic biology and bio-manufacturing as they are able to sense a wide range of small molecules and regulate gene expression in response. Despite over a decade of research they have yet to reach this considerable potential as they cannot yet be treated as modular components. This is due to several limitations including sensitivity to changes in genetic context, low tunability, and variability in performance. To overcome the associated difficulties with riboswitches, we have designed and introduced a novel genetic element called a ribo-attenuator in Bacteria. This genetic element allows for predictable tuning, insulation from contextual changes, and a reduction in expression variation. Ribo-attenuators allow riboswitches to be treated as truly modular and tunable components, thus increasing their reliability for a wide range of applications.

Effective application of whole-cell devices in synthetic biology and biocatalysis will always require consideration of the uptake of molecules of interest into the cell. Here we demonstrate that the AlkL protein from Pseudomonas putida GPo1 is an alkane import protein capable of industrially relevant rates of uptake of C7-C16 n-alkanes. Without alkL expression, native E.coli n-alkane uptake was the rate-limiting step in both the whole-cell bioconversion of C7-C16 n-alkanes and in the activation of a whole-cell alkane biosensor by C10 and C11 alkanes. By coexpression of alkL as a transporter plug-in, specific yields improved by up to 100-fold for bioxidation of >C12 alkanes to fatty alcohols and acids. The alkL protein was shown to be toxic to the host when overexpressed but when expressed from a vector capable of controlled induction, yields of alkane oxidation were improved a further 10-fold (8 g/L and 1.7 g/g of total oxidized products). Further testing of activity on n-octane with the controlled expression vector revealed the highest reported rates of 120 μmol/min/g and 1 g/L/h total oxidized products. This is the first time AlkL has been shown to directly facilitate enhanced uptake of C10-C16 alkanes and represents the highest reported gain in product yields resulting from its use.