Complete Mapping of Substrate Translocation Highlights the Role of LeuT N-terminal Segment in Regulating Transport Cycle

Neurotransmitter: sodium symporters (NSSs) regulate neuronal signal transmission by clearing excess neurotransmitters from the synapse, assisted by the co-transport of sodium ions. Extensive structural data have been collected in recent years for several members of the NSS family, which opened the way to structure-based studies for a mechanistic understanding of substrate transport. Leucine transporter (LeuT), a bacterial orthologue, has been broadly adopted as a prototype in these studies. This goal has been elusive, however, due to the complex interplay of global and local events as well as missing structural data on LeuT N-terminal segment. We provide here for the first time a comprehensive description of the molecular events leading to substrate/Na+ release to the postsynaptic cell, including the structure and dynamics of the N-terminal segment using a combination of molecular simulations. Substrate and Na+-release follows an influx of water molecules into the substrate/Na+-binding pocket accompanied by concerted rearrangements of transmembrane helices. A redistribution of salt bridges and cation-π interactions at the N-terminal segment prompts substrate release. Significantly, substrate release is followed by the closure of the intracellular gate and a global reconfiguration back to outward-facing state to resume the transport cycle. Two minimally hydrated intermediates, not structurally resolved to date, are identified: one, substrate-bound, stabilized during the passage from outward- to inward-facing state (holo-occluded), and another, substrate-free, along the reverse transition (apo-occluded)

Characterizations of matrix and operator-valued Φ-entropies, and operator Efron-Stein inequalities

© 2016 The Author(s). We derive new characterizations of the matrix Φ-entropy functionals introduced in Chen & Tropp (Chen, Tropp 2014 Electron. J. Prob. 19, 1-30. (doi:10.1214/ejp.v19-2964)). These characterizations help us to better understand the properties of matrix Φ-entropies, and are a powerful tool for establishing matrix concentration inequalities for random matrices. Then, we propose an operator-valued generalization of matrix Φ-entropy functionals, and prove the subadditivity under Löwner partial ordering. Our results demonstrate that the subadditivity of operator-valued Φ-entropies is equivalent to the convexity. As an application, we derive the operator Efron-Stein inequality

Concavity of the Auxiliary Function for Classical-Quantum Channels

© 2016 IEEE. The auxiliary function of a classical channel appears in two fundamental quantities, the random coding exponent and the sphere-packing exponent, which yield upper and lower bounds on the error probability of decoding, respectively. A crucial property of the auxiliary function is its concavity, and this property consequently leads to several important results in finite blocklength analysis. In this paper, we prove that the auxiliary function of a classical-quantum channel also enjoys the same concavity property, extending an earlier partial result to its full generality. We also prove that the auxiliary function satisfies the data-processing inequality, among various other important properties. Furthermore, we show that the concavity property of the auxiliary function enables a geometric interpretation of the random coding exponent and the sphere-packing exponent of a classical-quantum channel. The key component in our proof is an important result from the theory of matrix geometric means

The learnability of unknown quantum measurements

© Rinton Press. In this work, we provide an elegant framework to analyze learning matrices in the Schatten class by taking advantage of a recently developed methodology—matrix concentration inequalities. We establish the fat-shattering dimension, Rademacher/Gaussian complexity, and the entropy number of learning bounded operators and trace class operators. By characterising the tasks of learning quantum states and two-outcome quantum measurements into learning matrices in the Schatten-1 and ∞ classes, our proposed approach directly solves the sample complexity problems of learning quantum states and quantum measurements. Our main result in the paper is that, for learning an unknown quantum measurement, the upper bound, given by the fat-shattering dimension, is linearly proportional to the dimension of the underlying Hilbert space. Learning an unknown quantum state becomes a dual problem to ours, and as a byproduct, we can recover Aaronson’s famous result [Proc. R. Soc. A 463, 3089–3144 (2007)] solely using a classical machine learning technique. In addition, other famous complexity measures like covering numbers and Rademacher/Gaussian complexities are derived explicitly under the same framework. We are able to connect measures of sample complexity with various areas in quantum information science, e.g. quantum state/measurement tomography, quantum state discrimination and quantum random access codes, which may be of independent interest. Lastly, with the assistance of general Bloch-sphere representation, we show that learning quantum measurements/states can be mathematically formulated as a neural network. Consequently, classical ML algorithms can be applied to efficiently accomplish the two quantum learning tasks

A γA-Crystallin Mouse Mutant Secc with Small Eye, Cataract and Closed Eyelid

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Thermodynamics of carrier distribution within localized electronic states with a broad Gaussian energy distribution and its effect on luminescence behavior of localized states

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Fourier domain mode locking laser based on two-pump optical parametric amplification

We present a Fourier domain mode locked (FDML) laser scanning from 1540 to 1570 using two-pump optical parametric amplifier (OPA) as the gain medium. The sweep rate of 39.683 kHz is achieved. © 2010 IEEE.published_or_final_versionThe IEEE Photonics Society Summer Topical Meetings, Playa del Carmen, Mexico, 19-21 July 2010. In Proceedings of PHOSST, 2010, p. 186-18

CHANDRA DETECTION OF A NEW DIFFUSE X-RAY COMPONENT FROM THE GLOBULAR CLUSTER 47 TUCANAE

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Search for pulsed γ-ray emission from globular cluster M28

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Exploring the X-ray and γ-ray properties of the redback millisecond pulsar PSR J1723-2837

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