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.

Role of Tin+ and Aln+ ion irradiation (n = 1, 2) during Ti1-xAlxN alloy film growth in a hybrid HIPIMS/magnetron mode

Abstract: Metastable Ti1-xAlxN (0.4 less than= x less than= 0.76) films are grown using a hybrid approach in which high-power pulsed magnetron sputtering (HIPIMS) is combined with dc magnetron sputtering (DC ...

Synthesis of the new MAX phase Zr2AlC

Abstract: This study reports on the first experimental evidence of the existence of the Zr2AlC MAX phase, synthesised by means of reactive hot pressing of a ZrH2, Al and C powder mixture. The crystal structu ...

Designing MOFs-Derived FeS2@Carbon Composites for High-Rate Sodium Ion Storage with Capacitive Contributions.

Abstract: Sodium-ion batteries suffer the disadvantages of poor rate performance and cycling stability due to its sluggish sodiation kinetics. A rational design strategy for both materials compositions and structures has been proposed to meet these challenges. Herein, a triple-component composite derived from metal–organic frameworks, comprising FeS2, nitrogen–sulfur co-doped porous carbon, and reduced graphene oxide (FeS2@NSC/G), has been successfully synthesized. With the capacities contributions from different sodium storage routes (diffusion-controlled processes and surface capacitive processes) at varies rate conditions, it is aiming to make full use of each component in the electrode composite and their unique porous structures. Expected electrode properties have been achieved and related electrochemical behaviors have also been investigated. The strategy would present a promising thought for composites design, which could enhance high-rate electrochemical energy storage performances.

Structural basis for a Munc13-1 homodimer to Munc13-1/RIM heterodimer switch.

Abstract: C(2) domains are well characterized as Ca(2+)/phospholipid-binding modules, but little is known about how they mediate protein-protein interactions. In neurons, a Munc13-1 C(2)A-domain/RIM zinc-finger domain (ZF) heterodimer couples synaptic vesicle priming to presynaptic plasticity. We now show that the Munc13-1 C(2)A domain homodimerizes, and that homodimerization competes with Munc13-1/RIM heterodimerization. X-ray diffraction studies guided by nuclear magnetic resonance (NMR) experiments reveal the crystal structures of the Munc13-1 C(2)A-domain homodimer and the Munc13-1 C(2)A-domain/RIM ZF heterodimer at 1.44 A and 1.78 A resolution, respectively. The C(2)A domain adopts a beta-sandwich structure with a four-stranded concave side that mediates homodimerization, leading to the formation of an eight-stranded beta-barrel. In contrast, heterodimerization involves the bottom tip of the C(2)A-domain beta-sandwich and a C-terminal alpha-helical extension, which wrap around the RIM ZF domain. Our results describe the structural basis for a Munc13-1 homodimer-Munc13-1/RIM heterodimer switch that may be crucial for vesicle priming and presynaptic plasticity, uncovering at the same time an unexpected versatility of C(2) domains as protein-protein interaction modules, and illustrating the power of combining NMR spectroscopy and X-ray crystallography to study protein complexes.

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.