The Fluorescent Enzyme Cascade Detects Low Abundance Protein Modifications Suitable for the Assembly of Functionally Annotated Modificatome Databases.

Pathophysiological functions of proteins critically depend on both their chemical composition, including post-translational modifications, and their three-dimensional structure, commonly referred to as structure-activity relationship. Current analytical methods, like capillary electrophoresis or mass spectrometry, suffer from limitations, such as the detection of unexpected modifications at low abundance and their insensitivity to conformational changes. Building on previous enzyme-based analytical methods, we here introduce a fluorescence-based enzyme cascade (fEC), which can detect diverse chemical and conformational variations in protein samples and assemble them into digital databases. Together with complementary analytical methods an automated fEC analysis established unique modification-function relationships, which can be expanded to a proteome-wide scale, i. e. a functionally annotated modificatome. The fEC offers diverse applications, including hypersensitive biomarker detection in complex samples.

Combined Fully Contactless Finger and Hand Vein Capturing Device with a Corresponding Dataset.

Vascular pattern based biometric recognition is gaining more and more attention, with a trend towards contactless acquisition. An important requirement for conducting research in vascular pattern recognition are available datasets. These datasets can be established using a suitable biometric capturing device. A sophisticated capturing device design is important for good image quality and, furthermore, at a decent recognition rate. We propose a novel contactless capturing device design, including technical details of its individual parts. Our capturing device is suitable for finger and hand vein image acquisition and is able to acquire palmar finger vein images using light transmission as well as palmar hand vein images using reflected light. An experimental evaluation using several well-established vein recognition schemes on a dataset acquired with the proposed capturing device confirms its good image quality and competitive recognition performance. This challenging dataset, which is one of the first publicly available contactless finger and hand vein datasets, is published as well.