We have investigated thermal conductivity of graphene laminate films deposited on polyethylene terephthalate substrates. Two types of graphene laminate were studied, as deposited and compressed, in ...order to determine the physical parameters affecting the heat conduction the most. The measurements were performed using the optothermal Raman technique and a set of suspended samples with the graphene laminate thickness from 9 to 44 μm. The thermal conductivity of graphene laminate was found to be in the range from 40 to 90 W/mK at room temperature. It was found unexpectedly that the average size and the alignment of graphene flakes are more important parameters defining the heat conduction than the mass density of the graphene laminate. The thermal conductivity scales up linearly with the average graphene flake size in both uncompressed and compressed laminates. The compressed laminates have higher thermal conductivity for the same average flake size owing to better flake alignment. Coating plastic materials with thin graphene laminate films that have up to 600× higher thermal conductivity than plastics may have important practical implications.
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Background and purpose
Besides a distinct spectrum of demyelinating syndromes, encephalitis was observed in patients with myelin oligodendrocyte glycoprotein antibodies (MOG‐abs).
Methods
The ...clinical records of 690 patients with idiopathic demyelinating diseases of the central nervous system seen in our center from June 2015 to December 2017 were retrospectively reviewed. All underwent serum aquaporin 4 antibody (AQP4‐ab) and MOG‐ab detection by cell‐based assays as a routine diagnostic approach. Patients with MOG‐abs or AQP4‐abs who had ever experienced an encephalitis‐like illness during the disease course were identified. Whether diagnoses of possible or definite autoimmune encephalitis could be reached with regard to these particular episodes of encephalitis was determined. The incidence and clinical features of encephalitis in anti‐MOG disease are described in detail and compared with those in anti‐AQP4 disease.
Results
Amongst the 690 patients, 87 were MOG‐ab‐positive whilst 140 were AQP4‐ab‐positive. 20.7% (18/87) of the MOG‐ab‐positive patients had typical presentations of encephalitis. Unique cortical lesions (72.2%, 13/18) were observed; fever (55.6%), intracranial hypertension (41.2%) and cerebrospinal fluid pleocytosis (64.7%) were common during MOG‐ab‐associated encephalitis. Sixteen of the 18 patients fulfilled the criteria of definite autoimmune encephalitis (specific disease with MOG‐ab) during encephalitis, and five patients overlapped with anti‐N‐methyl‐d‐aspartate‐receptor encephalitis (NMDARE). Only 3.6% (5/140) of the AQP4‐ab‐positive patients had encephalitis, and none overlapped with NMDARE. The Expanded Disability Status Scale scores and the Cerebral Functional System Scores at last follow‐up were lower in patients with MOG‐ab‐associated encephalitis than in those with AQP4‐ab‐associated encephalitis.
Conclusions
Encephalitis should be recognized as an important clinical component in anti‐MOG diseases.
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Here we report on the production and tomography of genuinely entangled Greenberger-Horne-Zeilinger states with up to ten qubits connecting to a bus resonator in a superconducting circuit, where the ...resonator-mediated qubit-qubit interactions are used to controllably entangle multiple qubits and to operate on different pairs of qubits in parallel. The resulting 10-qubit density matrix is probed by quantum state tomography, with a fidelity of 0.668±0.025. Our results demonstrate the largest entanglement created so far in solid-state architectures and pave the way to large-scale quantum computation.
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Machine learning, a branch of artificial intelligence, learns from previous experience to optimize performance, which is ubiquitous in various fields such as computer sciences, financial analysis, ...robotics, and bioinformatics. A challenge is that machine learning with the rapidly growing "big data" could become intractable for classical computers. Recently, quantum machine learning algorithms Lloyd, Mohseni, and Rebentrost, arXiv.1307.0411 were proposed which could offer an exponential speedup over classical algorithms. Here, we report the first experimental entanglement-based classification of two-, four-, and eight-dimensional vectors to different clusters using a small-scale photonic quantum computer, which are then used to implement supervised and unsupervised machine learning. The results demonstrate the working principle of using quantum computers to manipulate and classify high-dimensional vectors, the core mathematical routine in machine learning. The method can, in principle, be scaled to larger numbers of qubits, and may provide a new route to accelerate machine learning.
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With the advent of new generation information technologies in industry and product design, the big data-driven product design era has arrived. However, the big data-driven product design mainly ...places emphasis on the analysis of physical data rather than the virtual models, in other words, the convergence between product physical and virtual space is usually absent. Digital twin, a new emerging and fast growing technology which connects the physical and virtual world, has attracted much attention worldwide recently. This paper presents a new method for product design based on the digital twin approach. The development of product design is briefly introduced first. The framework of digital twin-driven product design (DTPD) is then proposed and analysed. A case is presented to illustrate the application of the proposed DTPD method.
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Bimetallic catalysts of nickel(0) with a trivalent rare-earth ion or Ga(III), NiML3 (where L is iPr2PCH2NPh−, and M is Sc, Y, La, Lu, or Ga), were investigated for the selective hydrogenation of ...diphenylacetylene (DPA) to (E)-stilbene. Each bimetallic complex features a relatively short Ni–M bond length, ranging from 2.3395(8) Å (Ni–Ga) to 2.5732(4) Å (Ni–La). The anodic peak potentials of the NiML3 complexes vary from −0.48 V to −1.23 V, where the potentials are negatively correlated with the Lewis acidity of the M(III) ion. Three catalysts, Ni–Y, Ni–Lu, and Ni–Ga, showed nearly quantitative conversions in the semihydrogenation of DPA, with NiYL3 giving the highest selectivity for (E)-stilbene. Initial rate studies were performed on the two tandem catalytic reactions: DPA hydrogenation and (Z)-stilbene isomerization. The catalytic activity in DPA hydrogenation follows the order Ni–Ga > Ni–La > Ni–Y > Ni–Lu > Ni–Sc. The ranking of catalysts by (Z)-stilbene isomerization initial rates is Ni–Ga ≫ Ni–Sc > Ni–Lu > Ni–Y > Ni–La. In operando 31P and 1H NMR studies revealed that in the presence of DPA, the Ni bimetallic complexes supported by Y, Lu, and La form the Ni(η2-alkyne) intermediate, (η2-PhCCPh)Ni(iPr2PCH2NPh)2M(κ2-iPr2PCH2NPh). In contrast, the Ni–Ga resting state is the Ni(η2-H2) species, and Ni–Sc showed no detectable binding of either substrate. Hence, the mechanism of Ni-catalyzed diphenylacetylene semihydrogenation adheres to two different kinetics: an autotandem pathway (Ni–Ga, Ni–Sc) versus temporally separated tandem reactions (Ni–Y, Ni–Lu, Ni–La). Collectively, the experimental results demonstrate that modulating a base-metal center via a covalently appended Lewis acidic support is viable for promoting selective alkyne semihydrogenation.
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Estrogens encompass steroid hormones which display physiological roles not only in the female reproductive system but also in other organ systems of non‐reproductive controls, including the ...peripheral and central nervous systems. Traditionally, estrogen signals in neurons through a “genomic pathway”: binding to estrogen receptors (ERs) which then interact with nuclear estrogen response elements to initiate transcription. This effect is usually delayed at onset (within several hours to days) and prolonged in duration. In addition to these classical ERs, recent data suggest that other ERs function through pregenomic signaling pathways. Estrogen's pregenomic pathways cause intracellular changes within seconds to minutes and go through a novel, 7‐transmembrane spanning G protein‐coupled receptor (GPER, formerly known as GPR30). In this review, we will briefly cover the cellular and molecular mechanisms of GPER and then discuss newly discovered roles of GPER in cognition, depression, homeostasis, pain processing, and other associated neuronal functions.
Estrogens exert physiological effects via genomic pathways by way of estrogen receptors ERα and β as well as the pregenomic pathways by way of the G protein‐coupled estrogen receptor (GPER)—the receptor recognized as responsible for rapid responses to estrogen. The pregenomic effects of GPER on neuronal function include cognition, depression, homeostasis, pain processing, neuroprotection, and intestinal motility. Though preclinical data demonstrate important roles of GPER on neurological functions, GPER's therapeutic potential and its interaction with classical ERs remains to be investigated.
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DOBA, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, SIK, UILJ, UKNU, UL, UM, UPUK
Anyons are exotic quasiparticles obeying fractional statistics, whose behavior can be emulated in artificially designed spin systems. Here we present an experimental emulation of creating anyonic ...excitations in a superconducting circuit that consists of four qubits, achieved by dynamically generating the ground and excited states of the toric code model, i.e., four-qubit Greenberger-Horne-Zeilinger states. The anyonic braiding is implemented via single-qubit rotations: a phase shift of π related to braiding, the hallmark of Abelian 1/2 anyons, has been observed through a Ramsey-type interference measurement.
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GammaLib and ctools Knodlseder, J; Mayer, M; Deil, C ...
Astronomy and astrophysics (Berlin),
09/2016, Volume:
593
Journal Article
Peer reviewed
Open access
The field of gamma-ray astronomy has seen important progress during the last decade, yet to date no common software framework has been developed for the scientific analysis of gamma-ray telescope ...data. We propose to fill this gap by means of the GammaLib software, a generic library that we have developed to support the analysis of gamma-ray event data. GammaLib was written in C++ and all functionality is available in Python through an extension module. Based on this framework we have developed the ctools software package, a suite of software tools that enables flexible workflows to be built for the analysis of Imaging Air Cherenkov Telescope event data. The ctools are inspired by science analysis software available for existing high-energy astronomy instruments, and they follow the modular ftools model developed by the High Energy Astrophysics Science Archive Research Center. The ctools were written in Python and C++, and can be either used from the command line via shell scripts or directly from Python. In this paper we present the GammaLib and ctools software versions 1.0 that were released at the end of 2015. GammaLib and ctools are ready for the science analysis of Imaging Air Cherenkov Telescope event data, and also support the analysis of Fermi-LAT data and the exploitation of the COMPTEL legacy data archive. We propose using ctools as the science tools software for the Cherenkov Telescope Array Observatory.
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