The increasing interest in Support Vector Machines (SVMs) over the past 15 years is described. Methods are illustrated using simulated case studies, and 4 experimental case studies, namely mass spectrometry for studying pollution, near infrared analysis of food, thermal analysis of polymers and UV/visible spectroscopy of polyaromatic hydrocarbons. The basis of SVMs as two-class classifiers is shown with extensive visualisation, including learning machines, kernels and penalty functions. The influence of the penalty error and radial basis function radius on the model is illustrated. Multiclass implementations including one vs. all, one vs. one, fuzzy rules and Directed Acyclic Graph (DAG) trees are described. One-class Support Vector Domain Description (SVDD) is described and contrasted to conventional two- or multi-class classifiers. The use of Support Vector Regression (SVR) is illustrated including its application to multivariate calibration, and why it is useful when there are outliers and non-linearities.

Partial least squares discriminant analysis (PLS‐DA) has been available for nearly 20 years yet is poorly understood by most users. By simple examples, it is shown graphically and algebraically that for two equal class sizes, PLS‐DA using one partial least squares (PLS) component provides equivalent classification results to Euclidean distance to centroids, and by using all nonzero components to linear discriminant analysis. Extensions where there are unequal class sizes and more than two classes are discussed including common pitfalls and dilemmas. Finally, the problems of overfitting and PLS scores plots are discussed. It is concluded that for classification purposes, PLS‐DA has no significant advantages over traditional procedures and is an algorithm full of dangers. It should not be viewed as a single integrated method but as step in a full classification procedure. However, despite these limitations, PLS‐DA can provide good insight into the causes of discrimination via weights and loadings, which gives it a unique role in exploratory data analysis, for example in metabolomics via visualisation of significant variables such as metabolites or spectroscopic peaks. Copyright © 2014 John Wiley & Sons, Ltd.

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Haematological and solid cancers catabolise the semi-essential amino acid arginine to drive cell proliferation. However, the resulting low arginine microenvironment also impairs chimeric antigen receptor T cells (CAR-T) cell proliferation, limiting their efficacy in clinical trials against haematological and solid malignancies. T cells are susceptible to the low arginine microenvironment due to the low expression of the arginine re-synthesis enzymes argininosuccinate synthase (ASS) and ornithine transcarbamylase (OTC). We demonstrate T cells can be re-engineered to express functional ASS or OTC enzymes, in concert with different chimeric antigen receptors. Enzyme modifications increase CAR-T cell proliferation, with no loss of CAR cytotoxicity or increased exhaustion. In vivo, enzyme-modified CAR-T cells lead to enhanced clearance of leukaemia or solid tumour burden, providing the first metabolic modification to enhance CAR-T cell therapies.

We present, to the best of our knowledge, the first demonstration of mid-infrared supercontinuum (SC) tissue imaging at wavelengths beyond 5 μm using a fiber-coupled SC source spanning 2-7.5 μm. The SC was generated in a tapered large-mode-area chalcogenide photonic crystal fiber in order to obtain broad bandwidth, high average power, and single-mode output for diffraction-limited imaging performance. Tissue imaging was demonstrated in transmission at selected wavelengths between 5.7 (1754  cm) and 7.3 μm (1370  cm) by point scanning over a sub-millimeter region of colon tissue, and the results were compared to images obtained from a commercial instrument.