Jun Lu

Bio: Jun Lu is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Medicine & Materials science. The author has an hindex of 135, co-authored 1526 publications receiving 99767 citations. Previous affiliations of Jun Lu include Drexel University & Argonne National Laboratory.

The role of nucleus accumbens core/shell in sleep-wake regulation and their involvement in modafinil-induced arousal.

Abstract: Background We have previously shown that modafinil promotes wakefulness via dopamine receptor D1 and D2 receptors; however, the locus where dopamine acts has not been identified. We proposed that the nucleus accumbens (NAc) that receives the ventral tegmental area dopamine inputs play an important role not only in reward and addiction but also in sleep-wake cycle and in mediating modafinil-induced arousal.

Magnetoelectric effect in NdCrTiO 5

Abstract: We have measured the dielectric constant and the pyroelectric current of orthorhombic (space group $Pbam$) NdCrTiO${}_{5}$ polycrystalline samples. The dielectric constant and the pyroelectric current show features associated with ferroelectric transitions at the antiferromagnetic transition temperature (${T}_{N}$ $=$ 21 K). The effect of magnetic fields is to enhance the features almost linearly up to the maximum measured field (7 T) with a spontaneous polarization value of $\ensuremath{\sim}$3.5 $\ensuremath{\mu}$C/m${}^{2}$. Two possible scenarios, the linear magnetoelectric effect and multiferroicity (antiferromagnetism plus ferroelectricity), are discussed as possible explanations for the observations.

Electrochemical Lithium Doping Induced Property Changes In Halide Perovskite CsPbBr3 Crystal

Abstract: Electrochemical doping was used to dope lithium into perovskite CsPbBr3 crystal. Due to the doping, diamagnetic behavior was observed with a transition temperature (Tc) of 7.17 K. Burstein–Moss effect related photoluminescence (PL) blue-shift was observed as long as 15 nm. UV–vis-NIR spectra indicate that the absorption has been enhanced. The doped CsPbBr3 crystal generated 8 nA photocurrent, which is higher than the undoped CsPbBr3 crystal.

A GaN–SiC hybrid material for high-frequency and power electronics

Abstract: We demonstrate that 3.5% in-plane lattice mismatch between GaN (0001) epitaxial layers and SiC (0001) substrates can be accommodated without triggering extended defects over large areas using a grain-boundary-free AlN nucleation layer (NL). Defect formation in the initial epitaxial growth phase is thus significantly alleviated, confirmed by various characterization techniques. As a result, a high-quality 0.2-μm thin GaN layer can be grown on the AlN NL and directly serve as a channel layer in power devices, like high electron mobility transistors (HEMTs). The channel electrons exhibit a state-of-the-art mobility of >2000 cm2/V-s, in the AlGaN/GaN heterostructures without a conventional thick C- or Fe-doped buffer layer. The highly scaled transistor processed on the heterostructure with a nearly perfect GaN–SiC interface shows excellent DC and microwave performances. A peak RF power density of 5.8 W/mm was obtained at VDSQ = 40 V and a fundamental frequency of 30 GHz. Moreover, an unpassivated 0.2-μm GaN/AlN/SiC stack shows lateral and vertical breakdowns at 1.5 kV. Perfecting the GaN–SiC interface enables a GaN–SiC hybrid material that combines the high-electron-velocity thin GaN with the high-breakdown bulk SiC, which promises further advances in a wide spectrum of high-frequency and power electronics.

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

Abstract: Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Fast parallel algorithms for short-range molecular dynamics

Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.