Detection of constitutive heterodimerization of the integrin Mac-1 subunits by fluorescence resonance energy transfer in living cells

Macrophage differentiation antigen associated with complement three receptor function (Mac-1) belongs to beta(2) subfamily of integrins that mediate important cell-cell and cell-extracellular matrix interactions. Biochemical studies have indicated that Mac-1 is a constitutive heterodimer in vitro. Here, we detected the heterodimerization of Mac-1 subunits in living cells by means of two fluorescence resonance energy transfer (FRET) techniques (fluorescence microscopy and fluorescence spectroscopy) and our results demonstrated that there is constitutive heterodimerization of the Mac-1 subunits and this constitutive heterodimerization of the Mac-1 subunits is cell-type independent. Through FRET imaging, we found that heterodimers of Mac-1 mainly localized in plasma membrane, perinuclear, and Golgi area in living cells. Furthermore, through analysis of the estimated physical distances between cyan fluorescent protein (CFP) and yellow fluorescent protein (YFP) fused to Mac-1 subunits, we suggested that the conformation of Mac-1 subunits is not affected by the fusion of CFP or YFP and inferred that Mac-1 subunits take different conformation when expressed in Chinese hamster ovary (CHO) and human embryonic kidney (HEK) 293T cells, respectively. (c) 2006 Elsevier Inc. All rights reserved.

Detection of constitutive homomeric associations of the integrins Mac-1 subunits by fluorescence resonance energy transfer in living cells

Integrins alpha(M)beta(2) plays important role on leukocytes, such as adhesion, migration, phagocytosis, and apoptosis. It was hypothesized that homomeric associations of integrin subunits provide a driving force for integrins activation, and simultaneously inducing the formation of integrins clusters. However, experimental reports on homomeric associations between integrin subunits are still controversial. Here, we proved the homomeric associations of the isolated Mac-1 subunits in living cells using three-channel fluorescence resonance energy transfer (FRET) microscopy and FRET spectra methods. We found that the extent of homomeric associations between beta(2) subunits is higher than alpha(M) subunits. Furthermore, FRET imaging indicated that the extent of homomeric associations of the Mac-1 subunits is higher along the plasma membrane than in the cytoplasm. Finally, we suggested that homomeric associations of the transmernbrane domains or/and cytoplasmic domains may provide the driving force for the formation of constitutive homomeric associations between alpha(M) or beta(2) subunits. (c) 2006 Elsevier Inc. All rights reserved.

Selective data gathering in community sensor networks

Smartphones and other powerful sensor-equipped consumer devices make it possible to sense the physical world at an unprecedented scale. Nearly 2 million Android and iOS devices are activated every day, each carrying numerous sensors and a high-speed internet connection. Whereas traditional sensor networks have typically deployed a fixed number of devices to sense a particular phenomena, community networks can grow as additional participants choose to install apps and join the network. In principle, this allows networks of thousands or millions of sensors to be created quickly and at low cost. However, making reliable inferences about the world using so many community sensors involves several challenges, including scalability, data quality, mobility, and user privacy.

This thesis focuses on how learning at both the sensor- and network-level can provide scalable techniques for data collection and event detection. First, this thesis considers the abstract problem of distributed algorithms for data collection, and proposes a distributed, online approach to selecting which set of sensors should be queried. In addition to providing theoretical guarantees for submodular objective functions, the approach is also compatible with local rules or heuristics for detecting and transmitting potentially valuable observations. Next, the thesis presents a decentralized algorithm for spatial event detection, and describes its use detecting strong earthquakes within the Caltech Community Seismic Network. Despite the fact that strong earthquakes are rare and complex events, and that community sensors can be very noisy, our decentralized anomaly detection approach obtains theoretical guarantees for event detection performance while simultaneously limiting the rate of false alarms.