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.

Anatomical Location of the Mesencephalic Locomotor Region and Its Possible Role in Locomotion, Posture, Cataplexy, and Parkinsonism

Abstract: The mesencephalic (or midbrain) locomotor region (MLR) was first described in 1966 by Shik and colleagues who demonstrated that electrical stimulation of this region induced locomotion in decerebrate (intercollicular transection) cats. The pedunculopontine tegmental nucleus (PPT) cholinergic neurons and midbrain extrapyramidal area (MEA) have been suggested to form the neuroanatomical basis for the MLR, but direct evidence for the role of these structures in locomotor behavior has been lacking. Here we tested the hypothesis that the MLR is composed of non-cholinergic spinally-projecting cells in the lateral pontine tegmentum (LPT). Our results showed that putative MLR neurons medial to the PPT and MEA in rats were non-cholinergic, glutamatergic and express the orexin (hypocretin) type 2 receptors. Fos mapping correlated with motor behaviors revealed that the dorsal and ventral MLR are activated, respectively, in association with locomotion and an erect posture. Consistent with these findings, chemical stimulation of the dorsal MLR produced locomotion whereas stimulation of the ventral MLR caused standing. Lesions of the MLR (dorsal and ventral regions together) resulted in cataplexy and episodic immobility of gait. Finally, trans-neuronal tracing with pseudorabies virus (PRV) demonstrated disynaptic input to the MLR from the substantia nigra via the MEA. These findings offer a new perspective on the neuroanatomic basis of the MLR, and suggest that MLR dysfunction may contribute to the postural and gait abnormalities in Parkinsonism.

Discovery of the ternary nanolaminated compound Nb2GeC by a systematic theoretical-experimental approach.

Abstract: Since the advent of theoretical materials science some 60 years ago, there has been a drive to predict and design new materials in silicio. Mathematical optimization procedures to determine phase stability can be generally applicable to complex ternary or higher-order materials systems where the phase diagrams of the binary constituents are sufficiently known. Here, we employ a simplex-optimization procedure to predict new compounds in the ternary Nb-Ge-C system. Our theoretical results show that the hypothetical Nb2GeC is stable, and excludes all reasonably conceivable competing hypothetical phases. We verify the existence of the Nb2GeC phase by thin film synthesis using magnetron sputtering. This hexagonal nanolaminated phase has a and c lattice parameters of similar to 3.24 angstrom and 12.82 angstrom.

Origin of Chemically Ordered Atomic Laminates (i-MAX): Expanding the Elemental Space by a Theoretical/Experimental Approach

Abstract: With increased chemical diversity and structural complexity comes the opportunities for innovative materials possessing advantageous properties. Herein, we combine predictive first-principles calculations with experimental synthesis, to explore the origin of formation of the atomically laminated i-MAX phases. By probing (Mo2/3M1/32)2AC (where M2 = Sc, Y and A = Al, Ga, In, Si, Ge, In), we predict seven stable i-MAX phases, five of which should have a retained stability at high temperatures. (Mo2/3Sc1/3)2GaC and (Mo2/3Y1/3)2GaC were experimentally verified, displaying the characteristic in-plane chemical order of Mo and Sc/Y and Kagome-like ordering of the A-element. We suggest that the formation of i-MAX phases requires a significantly different size of the two metals, and a preferable smaller size of the A-element. Furthermore, the population of antibonding orbitals should be minimized, which for the metals herein (Mo and Sc/Y) means that A-elements from Group 13 (Al, Ga, In) are favored over Group 14 (S...

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.