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 therapeutic potential of targeting ABC transporters to combat multi-drug resistance

Abstract: Introduction: Most disseminated cancers remain fatal despite the availability of a variety of conventional and novel treatments including surgery, chemotherapy, radiotherapy, immunotherapy, and bio...

Specific roles of cyclooxygenase-1 and cyclooxygenase-2 in lipopolysaccharide-induced fever and Fos expression in rat brain.

Abstract: Fever is a coordinated autonomic, endocrine, and behavioral response mediated by the brain in reaction to inflammatory stimuli. An essential step in transmitting the immune signal to the brain is the formation of prostaglandin E2. Cyclooxygenase (COX) is the critical enzyme in the synthesis of prostaglandins and COX-2, the inducible form of the enzyme, is markedly induced in cells associated with the cerebral blood vessels and the leptomeninges by immune stimuli such as intravenous administration of lipopolysaccharide (LPS). However, the specific roles of COX-1, the constitutive form of cyclooxygenase, and COX-2 in LPS-induced fever are not well understood. We injected LPS i.v. in combination with either a highly selective COX-1 (SC-560) or COX-2 (SC-236) inhibitor to determine the effects of each drug on the subsequent fever response and on the pattern of expression of Fos protein in the brain. The COX-2 inhibitor blocked LPS-induced fever and Fos expression in sites such as the ventromedial preoptic nucleus (VMPO) and the hypothalamic paraventricular nucleus (PVH), although Fos-immunoreactivity in the nucleus of the solitary tract (NTS), ventrolateral medulla (VLM), and parabrachial nucleus (PB) remained. In contrast, the COX-1 inhibitor resulted in a profound hypothermic response to LPS and blocked LPS-induced Fos-immunoreactivity in the PVH, PB, NTS, and VLM, although it had no effect on the VMPO. Although COX-2 plays a dominant role in mediating fever responses to i.v. LPS, at least some components of the response, including avoiding hypothermia and the induction of Fos in the NTS, VLM, PB, and PVH, appear to depend on COX-1. J. Comp. Neurol. 463:3–12, 2003. © 2003 Wiley-Liss, Inc.

Boosting Cell Performance of LiNi 0.8 Co 0.15 Al 0.05 O 2 via Surface Structure Design

Abstract: Although the high energy density and environmental benignancy of LiNi0.8 Co0.15 Al0.05 O2 (NCA) holds promise for use as cathode material in Li-ion batteries, present low rate capabilities, and fast capacity fade limit its broad commercial applications. Here, it is reported that surface modification of NCA cathode (R-3m) with 5 nm-thick nanopillar layers and Fm-3m structures significantly improves electrode structure, morphology, and electrochemical performance. The formation of nanopillar layers increases cycling and working voltage stability of NCA by shielding the host material from hydrofluoric acid and improves structural stability with the electrolyte. The modified NCA cathode exhibits an enhanced 89% capacity retention at a rate of 1 C over that of pristine NCA (75.2%) after 150 cycles and effectively suppresses working voltage fade (a drop of 0.025 V after 300 cycles) during repeated charge-discharge cycles. In addition, the diffusion barrier of Li ions in NCA crystals at 0.80 V is noticeably smaller than that of Li ions in pristine NCA (0.87 eV). These findings demonstrate that this unique surface structure design considerably enhances cycle and rate performance of NCA, which has potential applications in other Ni-rich layered cathode materials.

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.