Plant leaves are optically complex, which makes them difficult to image by light microscopy. Careful sample preparation is therefore required to enable researchers to maximize the information gained from advances in fluorescent protein labeling, cell dyes and innovations in microscope technologies and techniques. We have previously shown that mounting leaves in the non-toxic, non-fluorescent perfluorocarbon (PFC), perfluorodecalin (PFD) enhances the optical properties of the leaf with minimal impact on physiology. Here, we assess the use of the PFCs, PFD, and perfluoroperhydrophenanthrene (PP11) for in vivo plant leaf imaging using four advanced modes of microscopy: laser scanning confocal microscopy (LSCM), two-photon fluorescence microscopy, second harmonic generation microscopy, and stimulated Raman scattering (SRS) microscopy. For every mode of imaging tested, we observed an improved signal when leaves were mounted in PFD or in PP11, compared to mounting the samples in water. Using an image analysis technique based on autocorrelation to quantitatively assess LSCM image deterioration with depth, we show that PP11 outperformed PFD as a mounting medium by enabling the acquisition of clearer images deeper into the tissue. In addition, we show that SRS microscopy can be used to image PFCs directly in the mesophyll and thereby easily delimit the “negative space” within a leaf, which may have important implications for studies of leaf development. Direct comparison of on and off resonance SRS micrographs show that PFCs do not to form intracellular aggregates in live plants. We conclude that the application of PFCs as mounting media substantially increases advanced microscopy image quality of living mesophyll and leaf vascular bundle cells.

Abstract-Autoregression (AR) is a tool commonly used to understand and predict time series data. Traditionally the excitation noise is modelled as a Gaussian. However, real-world data may not be Gaussian in nature, and it is known that Gaussian models are adversely affected by the presence of outliers. We introduce a Bayesian AR model in which the excitation noise is assumed to be Student-t distributed. Variational Bayesian approximations to the posterior distributions of the model parameters are used to overcome the intractable integrations inherent in the Bayesian model. Independent Automatic Relevance Determination (ARD) priors over each of the AR coefficients are used to estimate the model order.Using synthetic data we show that the Student-t model performs well against both Gaussian and leptokurtic data, in terms of parameter estimation (including the model order), and is much more robust to outliers than either Gaussian or finite mixtures of Gaussians models.We apply the model to strongly leptokurtic EEG signals and show that the Student-t model makes more accurate one-stepahead predictions than the Gaussian model and provides more consistent estimates of the AR coefficients over simultaneously recorded EEG channels.

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A number of maritime operations can benefit from a short-term deterministic sea wave prediction (DSWP). Conventional X-band radars have recently been shown to provide a low-cost convenient source of two-dimensional wave profile information for DSWP purposes. However, such rotating radars typically introduce temporal smearing into the data, which introduces errors when traditional Fourier transform–based wave prediction methods are used. The authors report on a new approach for DSWP that avoids such errors. Furthermore, it is not susceptible to the condition number problems that arise with any form of direct or indirect inversion-based approaches. Extensive numerical analyses are conducted to illustrate the effect of the mixed space–time nature of the data on DSWP and the efficiency of the proposed technique to handle it.

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We investigate the effects of missing observations on the robust Bayesian model for spectral analysis introduced by Christmas [2013]. The model assumes Student-t distributed noise and uses an automatic relevance determination prior on the precisions of the amplitudes of the component sinusoids and it is not obvious what their effect will be when some of the otherwise temporally uniformly sampled data is missing.

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