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Determination of proteinases--enzymes that catalyze the hydrolysis of peptide bonds--is often difficult due to the presence of interferences in complex biological media and limited sample size. Capillary electrophoresis (CE), with laser-induced fluorescence (LIF) can serve as a useful tool for such determinations. LIF detection offers the advantages of increased sensitivity and increased selectivity. However, direct LIF detection requires the proteinase analyte to be fluorescently derivatized prior to analysis. A viable alternative is offered by the present work, in which protein substrates are first labeled with BODIPY dye, a relatively pH-insensitive, high-fluorescence quantum yield dye. Upon binding of some 4-10 molecules of dye to a single protein, the dye is effectively fluorescence-quenched. Digestion of the BODIPY--labeled and quenched protein by an unlabeled enzyme yields smaller peptide fragments in which the fluorescence of associated BODIPY tags is restored. We will present how the fragmentation pattern of BODIPY-labeled casein changes as a function of incubation time with trypsin, as well as the effect of varying concentrations of trypsin on the BODIPY-casein digest.

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Magnetic resonance imaging (MRI) has emerged as an important tool to identify intermediate biomarkers of Alzheimer's disease (AD) due to its ability to measure regional changes in the brain that are thought to reflect disease severity and progression. In this paper, we set out a novel pipeline that uses volumetric MRI data collected from different subjects as input and classifies them into one of three classes: AD, mild cognitive impairment (MCI) and cognitively normal (CN). Our pipeline consists of three stages -(1) a segmentation layer where brain MRI data is divided into clinically relevant regions; (2) a classification layer that uses relational learning algorithms to make pairwise predictions between the three classes; and (3) a combination layer that combines the results of the different classes to obtain the final classification. One of the key features of our proposed approach is that it allows for domain expert's knowledge to guide the learning in all the layers. We evaluate our pipeline on 397 patients acquired from the Alzheimer's Disease Neuroimaging Initiative and demonstrate that it obtains state-ofthe-art performance with minimal feature engineering.