Interferometry with few photons

Optical phase determination is an important and established tool in diverse fields such as astronomy, biology, or quantum optics. There is increasing interest in using a lower number of total photons. However, different noise sources, such as electronic readout noise in the detector, and shot noise, hamper the phase estimation in regimes of very low illumination. Here we report a study on how the quality of phase determination is affected by these two sources of noise. To that end, we experimentally reconstruct different wavefronts by means of a point diffraction interferometer for different mean intensities of illumination, up to $15\ \mathrm{phot/px}$. Our interferometer features a Skipper-CCD sensor, which allows us to reduce the readout noise arbitrarily, thus enabling us to separate the effect of these two sources of noise. For two cases of interest: a spatial qudit encoding phase, consisting of d = 6 uniform phase regions, and a more general continuous phase, we see that reducing the readout noise leads to a clear improvement in the quality of reconstruction. This can be explained by a simple noise model that allows us to predict the expected fidelity of reconstruction and shows excellent agreement with the measurements

1H NMR metabolomics investigation of an Alzheimer’s disease (AD) mouse model pinpoints important biochemical disturbances in brain and plasma

In this study data generated by H-1 NMR were combined with chemometrics to analyse brain and plasma samples from APP/PS1 and wild type mice with the aim of developing a statistical model capable of predicting the features of Alzheimer's disease (AD) displayed by this animal model. APP/PS1 is a well characterised double transgenic mouse model of AD and the results here demonstrate the potential of NMR technology as a platform for the detecting this disease. Using partial least squares discriminant analysis a model was built using both brain extracts (R-2 = 0.99; Q(2) = 0.66) and a high throughput method of plasma analysis (R-2 = 0.98; Q(2) = 0.75) capable of predicting AD in APP/PS1 mice. Analysis of brain extracts led to the elucidation of 20 metabolites and 16 of these were quantifiable. Relative brain levels of ascorbate, creatine, gamma-aminobutyric acid and N-acetyl aspartic acid were significantly altered in APP/PS1 mice (p <0.05). Analysis of plasma identified 14 metabolites and the levels of acetate, citrate, glutamate, glutamine, methionine, and an unknown signal were significantly altered in APP/PS1 mice (p <0.05). Combining H-1 NMR spectral data with chemometrics has been previously used to study biochemical disturbances in various disease states. This study further indicates the translational potential of this technology for identifying AD in people attending the memory clinic