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.

Lateral encroachment of Ni-silicides in the source/drain regions on ultrathin silicon-on-insulator

Abstract: Lateral growth of Ni silicide towards the channel region of metal-oxide-semiconductor transistors (MOSFETs) fabricated on ultrathin silicon-on-insulator (SOI) is characterized using SOI wafers with a 20-nm-thick surface Si layer. With a 10-nm-thick Ni film for silicide formation, p-channel MOSFETs displaying ordinary device characteristics with silicided p+ source/drain regions were demonstrated. No lateral growth of NiSix under gate isolation spacers was found according to electron microscopy. When the Ni film was 20 nm thick, Schottky contact source/drain MOSFETs showing typical ambipolar characteristics were obtained. A severe lateral encroachment of NiSix into the channel region leading to an increased gate leakage was revealed, while no detectable voiding at the silicide front towards the Si channel was observed.

Electrochemically primed functional redox mediator generator from the decomposition of solid state electrolyte.

Abstract: Recent works into sulfide-type solid electrolyte materials have attracted much attention among the battery community. Specifically, the oxidative decomposition of phosphorus and sulfur based solid state electrolyte has been considered one of the main hurdles towards practical application. Here we demonstrate that this phenomenon can be leveraged when lithium thiophosphate is applied as an electrochemically "switched-on" functional redox mediator-generator for the activation of commercial bulk lithium sulfide at up to 70 wt.% lithium sulfide electrode content. X-ray adsorption near-edge spectroscopy coupled with electrochemical impedance spectroscopy and Raman indicate a catalytic effect of generated redox mediators on the first charge of lithium sulfide. In contrast to pre-solvated redox mediator species, this design decouples the lithium sulfide activation process from the constraints of low electrolyte content cell operation stemming from pre-solvated redox mediators. Reasonable performance is demonstrated at strict testing conditions.

Complex oncogene dependence in microRNA-125a-induced myeloproliferative neoplasms

Abstract: Deregulation of microRNA (miRNA) expression can lead to cancer initiation and progression. However, limited information exists on the function of miRNAs in cancer maintenance. We examined these issues in the case of myeloproliferative diseases and neoplasms (MPN), a collection of hematopoietic neoplasms regarded as preleukemic, thereby representing early neoplastic states. We report here that microRNA-125a (miR-125a)–induced MPN display a complex manner of oncogene dependence. Following a gain-of-function genomics screen, we overexpressed candidate miR-125a in vivo, which led to phenotypes consistent with an atypical MPN characterized by leukocytosis, monocytosis, splenomegaly, and progressive anemia. The diseased MPN state could be recapitulated in a doxycycline-inducible mouse model. Upon doxycycline withdrawal, the primary MPN phenotypes rapidly resolved after the discontinuation of miR-125a overexpression. However, reinduction of miR-125a led to complex phenotypes, with some animals rapidly developing lethal anemia with extensive damages in the spleen. Forced expression of miR-125a resulted in elevated cellular tyrosine phosphorylation and hypersensitivity toward hematopoietic cytokines. Furthermore, we demonstrate that miR-125a targets multiple protein phosphatases. Our data demonstrate that miR-125a–induced MPN is addicted to its sustained overexpression, and highlight the complex nature of oncogenic miRNA dependence in an early neoplastic state.

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.