Pattern queries are widely used in complex event processing (CEP) systems. Existing pattern matching techniques, however, can provide only limited performance for expensive queries in real-world applications, which may involve Kleene closure patterns, flexible event selection strategies, and events with imprecise timestamps. To support these expensive queries with high performance, we begin our study by analyzing the complexity of pattern queries, with a focus on the fundamental understanding of which features make pattern queries more expressive and at the same time more computationally expensive. This analysis allows us to identify performance bottlenecks in processing those expensive queries, and provides key insights for us to develop a series of optimizations to mitigate those bottlenecks. Microbenchmark results show superior performance of our system for expensive pattern queries while most state-of-the-art systems suffer from poor performance. A thorough case study on Hadoop cluster monitoring further demonstrates the efficiency and effectiveness of our proposed techniques.

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Loss of dopaminergic (DA) neurons leads to Parkinson's disease; however, the mechanism(s) for the vulnerability of DA neurons is(are) not fully understood. We demonstrate that TRPC1 regulates the L-type Ca channel that contributes to the rhythmic activity of adult DA neurons in the substantia nigra region. Store depletion that activates TRPC1, via STIM1, inhibits the frequency and amplitude of the rhythmic activity in DA neurons of wild-type, but not in TRPC1, mice. Similarly, TRPC1 substantia nigra neurons showed increased L-type Ca currents, decreased stimulation-dependent STIM1-Ca1.3 interaction, and decreased DA neurons. L-type Ca currents and the open channel probability of Ca1.3 channels were also reduced upon TRPC1 activation, whereas increased Ca1.3 currents were observed upon STIM1 or TRPC1 silencing. Increased interaction between Ca1.3-TRPC1-STIM1 was observed upon store depletion and the loss of either TRPC1 or STIM1 led to DA cell death, which was prevented by inhibiting L-type Ca channels. Neurotoxins that mimic Parkinson's disease increased Ca1.3 function, decreased TRPC1 expression, inhibited Tg-mediated STIM1-Ca1.3 interaction, and induced caspase activation. Importantly, restoration of TRPC1 expression not only inhibited Ca1.3 function but increased cell survival. Together, we provide evidence that TRPC1 suppresses Ca1.3 activity by providing an STIM1-based scaffold, which is essential for DA neuron survival. Ca entry serves critical cellular functions in virtually every cell type, and appropriate regulation of Ca in neurons is essential for proper function. In Parkinson's disease, DA neurons are specifically degenerated, but the mechanism is not known. Unlike other neurons, DA neurons depend on Ca1.3 channels for their rhythmic activity. Our studies show that, in normal conditions, the pacemaking activity in DA neurons is inhibited by the TRPC1-STIM1 complex. Neurotoxins that mimic Parkinson's disease target TRPC1 expression, which leads to an abnormal increase in Ca1.3 activity, thereby causing degeneration of DA neurons. These findings link TRPC1 to Ca1.3 regulation and provide important indications about how disrupting Ca balance could have a direct implication in the treatment of Parkinson's patients.

Herein, novel conductive composite hydrogels are developed with high stretchability, ultra-softness, excellent conductivity, and good self-healing ability. The hydrogels are formed in the water/glycerol binary solvent system, in which the polyaniline nanoparticles (PANI-NPs) are incorporated into the poly(poly(ethylene glycol) methacrylate-co-acrylic acid) (P(PEG-co-AA)) scaffolds via the dynamically electrostatic interactions and hydrogen bonds. The PANI-NPs serve as conductive fillers to assign conductivity to the hydrogel, while the enhanced interfacial interactions between the PANI-NPs and P(PEG-co-AA) matrix endow the hydrogel with high stretchability (>1000%), low modulus (≈6 kPa), excellent elasticity (η = 0.07, energy loss coefficient at 500% strain), and fast self-healing ability (93.3% after 10 mins). Particularly, the desirable anti-freezing property is achieved by introducing a binary solvent system. The composite hydrogel-based sensors are proposed, with the states-independent properties, low detection limit (0.5% strain and 25 Pa), highly linear dependence, and excellent anti-fatigue performance (>1000 cycles). In addition, during the practical wearable sensing tests, various external stimulus and human motions can be detected, including speaking, writing, joint movement, or even small water droplets, indicating the potential applications for the next generation of epidermal sensors.

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