Rational Design and Application of Genetically Encoded Fluorescent Reporters in Cellular Physiology

Fluorescent protein based genetically encoded fluorescent reporters play an improtant role in understanding the cellular physiology by directly monitoring real-time cellular signaling pathways with fluorescent microscope. Quantitative analysis of Ca2+ fluctuations in the endoplasmic/sarcoplasmic reticulum (ER/SR) is essential to defining the mechanisms of Ca2+-dependent signaling under physiological and pathological conditions. Here, we developed a novel class of genetically encoded indicators by designing a Ca2+ binding site in the enhanced green fluorescent protein (EGFP). One of them, CatchER (Calcium sensor for detecting high concentration in the ER), exhibits unprecedented Ca2+ release kinetics with an off-rate estimated at around 700 s-1 and appropriate Ca2+ binding affinity, likely due to local, Ca2+-induced conformational changes around the designed Ca2+ binding site and reduced chemical exchange between two chromophore states. CatchER reported considerable differences in ER Ca2+ dynamics and concentration among epithelial HeLa, kidney HEK 293, and muscle C2C12 cells, enabling us to monitor SR luminal Ca2+ in flexor digitorum brevis (FDB) muscle fibers to determine the mechanism of diminished SR Ca2+ release in aging mice. Moreover, the structure of CatchER has been investigated by nuclear magnetic resonance spectroscope (NMR) and high-resolution X-ray crystal structures to understand the novel mechanism of Ca2+ induced fluorescent enhancement of GFP. It is crucial to investigate the metal selectivity of Ca2+/Mg2+ of these metalloproteins to understand cellular physiology. The major Mg2+ binding sites of proteins have been reviewed and classified based on structural differences, and identified several key factors to determine Mg2+/Ca2+ selectivity with binding constants difference up to 104 in several types of metalloproteins. Thrombin is involved in numerous cellular signaling pathways and plays a crucial role in blood coagulation. I designed a novel class of single EGFP-based thrombin sensors by inserting a thirty-amino acid short peptide with a thrombin cleavage site into the fluorescent sensitive location of EGFP. These designed protease sensors exhibited optimized kcat/Km up to 104 magnitudes higher than that of small peptide based absorption indicator EGR-pNA. The measured Km value is in below 10 mM, in the same magnitude as that of natural thrombin substrate Fibrinogen A

Real-Time Misbehavior Detection in IEEE 802.11e Based WLANs

The Enhanced Distributed Channel Access (EDCA) specification in the IEEE 802.11e standard supports heterogeneous backoff parameters and arbitration inter-frame space (AIFS), which makes a selfish node easy to manipulate these parameters and misbehave. In this case, the network-wide fairness cannot be achieved any longer. Many existing misbehavior detectors, primarily designed for legacy IEEE 802.11 networks, become inapplicable in such a heterogeneous network configuration. In this paper, we propose a novel real-time hybrid-share (HS) misbehavior detector for IEEE 802.11e based wireless local area networks (WLANs). The detector keeps updating its state based on every successful transmission and makes detection decisions by comparing its state with a threshold. We develop mathematical analysis of the detector performance in terms of both false positive rate and average detection rate. Numerical results show that the proposed detector can effectively detect both contention window based and AIFS based misbehavior with only a short detection window.Comment: Accepted to IEEE Globecom 201

Learning the Joint Representation of Heterogeneous Temporal Events for Clinical Endpoint Prediction

The availability of a large amount of electronic health records (EHR) provides huge opportunities to improve health care service by mining these data. One important application is clinical endpoint prediction, which aims to predict whether a disease, a symptom or an abnormal lab test will happen in the future according to patients' history records. This paper develops deep learning techniques for clinical endpoint prediction, which are effective in many practical applications. However, the problem is very challenging since patients' history records contain multiple heterogeneous temporal events such as lab tests, diagnosis, and drug administrations. The visiting patterns of different types of events vary significantly, and there exist complex nonlinear relationships between different events. In this paper, we propose a novel model for learning the joint representation of heterogeneous temporal events. The model adds a new gate to control the visiting rates of different events which effectively models the irregular patterns of different events and their nonlinear correlations. Experiment results with real-world clinical data on the tasks of predicting death and abnormal lab tests prove the effectiveness of our proposed approach over competitive baselines.Comment: 8 pages, this paper has been accepted by AAAI 201