Visualization of subunit interactions and ternary complexes of protein phosphatase 2A in mammalian cells.

Protein phosphatase 2A (PP2A) is a ubiquitous phospho-serine/threonine phosphatase that controls many diverse cellular functions. The predominant form of PP2A is a heterotrimeric holoenzyme consisting of a scaffolding A subunit, a variable regulatory B subunit, and a catalytic C subunit. The C subunit also associates with other interacting partners, such as α4, to form non-canonical PP2A complexes. We report visualization of PP2A complexes in mammalian cells. Bimolecular fluorescence complementation (BiFC) analysis of PP2A subunit interactions demonstrates that the B subunit plays a key role in directing the subcellular localization of PP2A, and confirms that the A subunit functions as a scaffold in recruiting the B and C subunits to form a heterotrimeric holoenzyme. BiFC analysis also reveals that α4 promotes formation of the AC core dimer. Furthermore, we demonstrate visualization of specific ABC holoenzymes in cells by combining BiFC and fluorescence resonance energy transfer (BiFC-FRET). Our studies not only provide direct imaging data to support previous biochemical observations on PP2A complexes, but also offer a promising approach for studying the spatiotemporal distribution of individual PP2A complexes in cells.

Pervasive brain monitoring and data sharing based on multi-tier distributed computing and linked data technology.

EEG-based Brain-computer interfaces (BCI) are facing basic challenges in real-world applications. The technical difficulties in developing truly wearable BCI systems that are capable of making reliable real-time prediction of users' cognitive states in dynamic real-life situations may seem almost insurmountable at times. Fortunately, recent advances in miniature sensors, wireless communication and distributed computing technologies offered promising ways to bridge these chasms. In this paper, we report an attempt to develop a pervasive on-line EEG-BCI system using state-of-art technologies including multi-tier Fog and Cloud Computing, semantic Linked Data search, and adaptive prediction/classification models. To verify our approach, we implement a pilot system by employing wireless dry-electrode EEG headsets and MEMS motion sensors as the front-end devices, Android mobile phones as the personal user interfaces, compact personal computers as the near-end Fog Servers and the computer clusters hosted by the Taiwan National Center for High-performance Computing (NCHC) as the far-end Cloud Servers. We succeeded in conducting synchronous multi-modal global data streaming in March and then running a multi-player on-line EEG-BCI game in September, 2013. We are currently working with the ARL Translational Neuroscience Branch to use our system in real-life personal stress monitoring and the UCSD Movement Disorder Center to conduct in-home Parkinson's disease patient monitoring experiments. We shall proceed to develop the necessary BCI ontology and introduce automatic semantic annotation and progressive model refinement capability to our system.