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.

Metabolic effects of chronic sleep restriction in rats

Abstract: Study Objectives: Chronic partial sleep loss is associated with obesity and metabolic syndrome in humans. We used rats with lesions in the ventrolateral preoptic area (VLPO), which spontaneously sleep about 30% less than intact rats, as an animal model to study the consequences of chronic partial sleep loss on energy metabolism. Participants: Adult male Sprague-Dawley rats (300-365 g). Interventions: We ablated the VLPO in rats using orexin-B-saporin and instrumented them with electrodes for sleep recordings. We monitored their food intake and body weight for the next 60 days and assessed their sleep-wake by 24-h EEG/EMG recordings on day 20 and day 50 postsurgery. On day 60, after blood samples were collected for metabolic profiling, the animals were euthanized and the brains were harvested for histological confirmation of the lesion site. Measurements and Results: VLPO-lesioned animals slept up to 40% less than sham-lesioned rats. However, they showed slower weight gain than sham-lesioned controls, despite having normal food intake. An increase in plasma ghrelin and a decrease in leptin levels were observed, whereas plasma insulin levels remained unaffected. As expected from leaner animals, plasma levels of glucose, cholesterol, triglycerides, and Creactive protein were reduced in VLPO-lesioned animals. Conclusions: Chronic partial sleep loss did not lead to obesity or metabolic syndrome in rats. This finding raises the question whether adverse metabolic outcomes associated with chronic partial sleep loss in humans may be due to factors other than short sleep, such as circadian disruption, inactivity, or diet during the additional waking hours.

Theoretical prediction, synthesis, and crystal structure determination of new MAX phase compound V2SnC

Abstract: Guided by the theoretical prediction, a new MAX phase V2SnC was synthesized experimentally for the first time by reaction of V, Sn, and C mixtures at 1000 °C. The chemical composition and crystal structure of this new compound were identified by the cross-check combination of first-principles calculations, X-ray diffraction (XRD), energy dispersive X-ray spectroscopy (EDS), and high resolution scanning transmission electron microscopy (HR-STEM). The stacking sequence of V2C and Sn layers results in a crystal structure of space group P63/mmc. The a- and c-lattice parameters, which were determined by the Rietveld analysis of powder XRD pattern, are 0.2981(0) nm and 1.3470(6) nm, respectively. The atomic positions are V at 4f (1/3, 2/3, 0.0776(5)), Sn at 2d (2/3, 1/3, 1/4), and C at 2a (0, 0, 0). A new set of XRD data of V2SnC was also obtained. Theoretical calculations suggest that this new compound is stable with negative formation energy and formation enthalpy, satisfied Born-Huang criteria of mechanical stability, and positive phonon branches over the Brillouin zone. It also has low shear deformation resistance c44 (second-order elastic constant, cij) and shear modulus (G), positive Cauchy pressure, and low Pugh’s ratio (G/B = 0.500 < 0.571), which is regarded as a quasi-ductile MAX phase. The mechanism underpinning the quasi-ductility is associated with the presence of a metallic bond.

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.