Fourier transform fluorescence recovery after photobleaching (FT-FRAP) is proposed and implemented for quantitatively evaluating diffusion and fractional recovery of proteins in complex matrices. Diffusion characterization of proteins is routinely performed for identification of aggregation and for interrogating molecular interactions with excipients. Conventional FRAP is noninvasive, has low sample volume requirements, and can support short measurement times by performing measurements over distances of only a few micrometers. However, conventional measurements are complicated by the need for precise knowledge of the bleach beam profile and potential errors due to sample inhomogeneity. In FT-FRAP, the time-dependent recovery in fluorescence due to diffusion is measured in the spatial Fourier domain, with substantial improvements in the signal-to-noise ratio, mathematical simplicity, representative sampling, and compatibility with multi-photon excitation. A custom nonlinear-optical beam-scanning microscope enabled patterned illumination for photobleaching a sample through two-photon excitation. The fluorescence recovery produced simple single-exponential decays in the spatial Fourier domain. Measurements in the spatial Fourier domain naturally remove bias from imprecise knowledge of the point spread function and reduce measurement variance from inhomogeneity within samples. Comparison between the fundamental FT frequency and higher harmonics has the potential to yield information about anomalous or spatially dependent diffusion with no increase in measurement time. Initial demonstrations of FT-FRAP using patterned illumination are presented, along with a critical discussion of the figures of merit and future developments.
Raman spectra were perturbed such that an intentional misclassification was induced when using a dimension reduction classifier such as linear discriminate analysis (LDA). These perturbations were primarily targeted at patterning the noise within the spectra such that detection is difficult to detect by visual inspection. Data-intensive decisions are increasingly important to mine the increasing volume of information accessible by modern instrumentation. These decisions are conceptually performed through projection of measurements on high dimensional manifolds to low-dimensional outcomes. This dimension reduction provides suppression of stochastic random noise to better inform the decision. However, non-stochastic patterning of the “noise” can induce intentional misclassification that is difficult to easily detect by visual inspection. Such digital attacks could result in intentional changes in decisions made from many routine automated classifiers. Preliminary results using Raman spectra showed that misclassification can be induced by picking a target classification and patterning the noise in the spectra such that in a reduced dimensional space, it is moved towards the target classification. Development of approaches for optimizing the attacks serves as a prelude for generation of robust classification strategies less susceptible to intentional attacks.