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.

Scaled Self-Aligned N-Polar GaN/AlGaN MIS-HEMTs With $f_{T}$ of 275 GHz

Abstract: In this letter, we demonstrate state-of-the-art performance from N-polar GaN/AlGaN metal-insulator-semiconductor high-electron-mobility transistors. Self-aligned gate-first process was used for the fabrication of transistors. Graded InGaN and InN contact layers were used to achieve low ohmic contact resistance. The GaN channel thickness was scaled to 7 nm from previous generation of N-polar GaN devices to improve the aspect ratio and hence achieve better small-signal performance. The devices reported fT of 210 GHz for LG = 30 nm. To further improve the device performance, SiN sidewall spacers were etched and replaced with air gaps resulting in further boost in fT to a state-of-the-art value of 275 GHz.

Effect of Cold Storage and Reheating of Parboiled Rice on Postprandial Glycaemic Response, Satiety, Palatability and Chewed Particle Size Distribution.

Abstract: Background: Globally, hot cooked refined rice is consumed in large quantities and is a major contributor to dietary glycaemic load. This study aimed to compare the glycaemic potency of hot- and cold-stored parboiled rice to widely available medium-grain white rice. Method: Twenty-eight healthy volunteers participated in a three-treatment experiment where postprandial blood glucose was measured over 120 min after consumption of 140 g of rice. The three rice samples were freshly cooked medium-grain white rice, freshly cooked parboiled rice, and parboiled rice stored overnight at 4 °C. All rice was served warm at 65 °C. Chewing time was recorded. Results: incremental area under the curve (iAUC) of the control rice, freshly cooked medium-grain white rice, was the highest: 1.7-fold higher (1.2, 2.6) than reheated parboiled rice (p < 0.001) and 1.5-fold higher (1.0, 2.2) than freshly cooked parboiled rice (p = 0.001). No significant difference in postprandial glycaemic response was observed between freshly cooked and reheated parboiled rice samples (p = 0.445). Chewing time for 10 g cold-stored parboiled rice was 6 s (25%) longer and was considered more palatable, visually appealing and better tasting than freshly cooked medium-grain (all p < 0.05). Conclusions: For regular consumers of rice, reheating cooked rice after cold storage would lower the dietary glycaemic load and, in the long term, may reduce the risk for type 2 and gestational diabetes. More trials are needed to identify the significance.

Nuclear magnetic resonance spectroscopic studies of pyridine methyl derivatives binding to cytochrome c

Abstract: The binding of pyridine methyl derivatives (2-, 3- and 4-methylpyridine) to horse heart ferricytochrome c (cyt c) by displacing methionine-80 was studied by 1H NMR spectroscopy to elucidate the effects of the different methyl substitution positions on the affinity and kinetics of binding to cytochrome c and the hyperfine-shifted NMR signals of the ligand–cytochrome c complex. Two-dimensional exchange spectroscopy (2D-EXSY) showed that except for 2-methylpyridine (2-mpy) these pyridine derivatives can form stable complexes with cytochrome c. The complexes 3-mpy–cyt c and 4-mpy–cyt c exhibit different hyperfine shift patterns compared to that of py–cyt c. The temperature dependence of the methyl resonances of 3-mpy–cyt c differs from those of 4-mpy– and py–cyt c. Kinetic and equilibrium data for the binding of 3- and 4-mpy to cyt c have been obtained by 2D-EXSY. Based on these data a comprehensive comparison between the binding properties of these pyridine derivatives and those of pyridine towards cyt c was made. The 1H NMR resonances of 3-mpy–cyt c have also been assigned including the heme peripheral protons and some aliphatic and aromatic side chain protons.

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.