Accurate and interpretable classification of microspectroscopy pixels using artificial neural networks.

This paper addresses the problem of classifying materials from microspectroscopy at a pixel level. The challenges lie in identifying discriminatory spectral features and obtaining accurate and interpretable models relating spectra and class labels. We approach the problem by designing a supervised classifier from a tandem of Artificial Neural Network (ANN) models that identify relevant features in raw spectra and achieve high classification accuracy. The tandem of ANN models is meshed with classification rule extraction methods to lower the model complexity and to achieve interpretability of the resulting model. The contribution of the work is in designing each ANN model based on the microspectroscopy hypothesis about a discriminatory feature of a certain target class being composed of a linear combination of spectra. The novelty lies in meshing ANN and decision rule models into a tandem configuration to achieve accurate and interpretable classification results. The proposed method was evaluated using a set of broadband coherent anti-Stokes Raman scattering (BCARS) microscopy cell images (600 000  pixel-level spectra) and a reference four-class rule-based model previously created by biochemical experts. The generated classification rule-based model was on average 85% accurate measured by the DICE pixel label similarity metric, and on average 96% similar to the reference rules measured by the vector cosine metric.

Synergistic roles for lipids and proteins in the permanent adhesive of barnacle larvae.

Thoracian barnacles rely heavily upon their ability to adhere to surfaces and are environmentally and economically important as biofouling pests. Their adhesives have unique attributes that define them as targets for bio-inspired adhesive development. With the aid of multi-photon and broadband coherent anti-Stokes Raman scattering microscopies, we report that the larval adhesive of barnacle cyprids is a bi-phasic system containing lipids and phosphoproteins, working synergistically to maximize adhesion to diverse surfaces under hostile conditions. Lipids, secreted first, possibly displace water from the surface interface creating a conducive environment for introduction of phosphoproteins while simultaneously modulating the spreading of the protein phase and protecting the nascent adhesive plaque from bacterial biodegradation. The two distinct phases are contained within two different granules in the cyprid cement glands, implying far greater complexity than previously recognized. Knowledge of the lipidic contribution will hopefully inspire development of novel synthetic bioadhesives and environmentally benign antifouling coatings.