17 research outputs found

    A Real-time Non-contact Localization Method for Faulty Electric Energy Storage Components using Highly Sensitive Magnetometers

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    With the wide application of electric energy storage component arrays, such as battery arrays, capacitor arrays, inductor arrays, their potential safety risks have gradually drawn the public attention. However, existing technologies cannot meet the needs of non-contact and real-time diagnosis for faulty components inside these massive arrays. To solve this problem, this paper proposes a new method based on the beamforming spatial filtering algorithm to precisely locate the faulty components within the arrays in real-time. The method uses highly sensitive magnetometers to collect the magnetic signals from energy storage component arrays, without damaging or even contacting any component. The experimental results demonstrate the potential of the proposed method in securing energy storage component arrays. Within an imaging area of 80 mm ×\times 80 mm, the one faulty component out of nine total components can be localized with an accuracy of 0.72 mm for capacitor arrays and 1.60 mm for battery arrays

    In-Band OSNR Monitoring From Stokes Parameters Using Support Vector Regression

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    Palladium-Catalyzed Intramolecular C–H Activation/C–C Bond Formation: A Straightforward Synthesis of Phenanthridines

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    The palladium-catalyzed intramolecular C–H activation/C–C cross-coupling has been developed for a straightforward and efficient synthesis of phenanthridines. With Pd­(OAc)<sub>2</sub> (4 mol %) as the catalyst, PCy<sub>3</sub> (8 mol %) as the ligand, and Cs<sub>2</sub>CO<sub>3</sub> as the base, this protocol was applied to synthesize a small library of phenanthridine derivatives in good yields in THF

    A Central Amygdala-Substantia Innominata Neural Circuitry Encodes Aversive Reinforcement Signals

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    Summary: Aversive stimuli can impact motivation and support associative learning as reinforcers. However, the neural circuitry underlying the processing of aversive reinforcers has not been elucidated. Here, we report that a subpopulation of central amygdala (CeA) GABAergic neurons expressing protein kinase C-delta (PKC-δ+) displays robust responses to aversive stimuli during negative reinforcement learning. Importantly, projections from PKC-δ+ neurons of the CeA to the substantia innominata (SI) could bi-directionally modulate negative reinforcement learning. Moreover, consistent with the idea that SI-projecting PKC-δ+ neurons of the CeA encode aversive information, optogenetic activation of this pathway produces conditioned place aversion, a behavior prevented by simultaneous ablating of SI glutamatergic neurons. Taken together, our data define a cell-type-specific neural circuitry modulating associative learning by encoding aversive reinforcement signals. : Cui et al. show that central amygdala PKC-δ+ neurons can modulate negative reinforcement learning by transmitting aversive signals to the substantia innominata. Keywords: central amygdala, negative reinforcement learning, substantia innominate, aversive signal

    Precise Cerebral Vascular Atlas in Stereotaxic Coordinates of Whole Mouse Brain

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    Understanding amazingly complex brain functions and pathologies requires a complete cerebral vascular atlas in stereotaxic coordinates. Making a precise atlas for cerebral arteries and veins has been a century-old objective in neuroscience and neuropathology. Using micro-optical sectioning tomography (MOST) with a modified Nissl staining method, we acquired five mouse brain data sets containing arteries, veins, and microvessels. Based on the brain-wide vascular spatial structures and brain regions indicated by cytoarchitecture in one and the same mouse brain, we reconstructed and annotated the vascular system atlas of both arteries and veins of the whole mouse brain for the first time. The distributing patterns of the vascular system within the brain regions were acquired and our results show that the patterns of individual vessels are different from each other. Reconstruction and statistical analysis of the microvascular network, including derivation of quantitative vascular densities, indicate significant differences mainly in vessels with diameters less than 8 μm and large than 20 μm across different brain regions. Our precise cerebral vascular atlas provides an important resource and approach for quantitative studies of brain functions and diseases
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