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.

Fe2P-decorated N,P Codoped Carbon Synthesized via Direct Biological Recycling for Endurable Sulfur Encapsulation.

Abstract: In spite of the great potential in leading next-generation energy storage technology, Li-S batteries suffer rapid capacity decay arising from the shuttling effect of lithium polysulfides (LiPSs), a major concern that must be addressed before commercialization can be realized. To tackle this challenge, we demonstrate a facile approach to fabricate a hierarchically structured composite of Fe2P@nitrogen, phosphorus codoped carbon (Fe2P@NPC) by direct biological recycling of iron metal from electroplating sludge using bacteria. This material, featuring uniform dispersion of Fe2P nanoparticles (NPs) in porous NPC matrix, effectively adapts volume variation of sulfur upon cycling and simultaneously provides multiple channels for efficient lithium ion transport. In addition, Fe2P NPs with strong adhesion properties of tightly anchored soluble LiPSs formed during discharge can significantly facilitate the decomposition of Li2S during the subsequent charging process. The Li-S cell built on this cathode architecture delivers high specific capacity (1555.7 mAh g-1 at 0.1 C), appreciable rate capability (679.7 mAh g-1 at 10 C), and greatly enhanced cycling performance (761.9 mAh g-1 at 1.0 C after 500 cycles).

Importance-Aware Semantic Segmentation in Self-Driving with Discrete Wasserstein Training

Abstract: Semantic segmentation (SS) is an important perception manner for self-driving cars and robotics, which classifies each pixel into a pre-determined class. The widely-used cross entropy (CE) loss-based deep networks has achieved significant progress w.r.t. the mean Intersection-over Union (mIoU). However, the cross entropy loss can not take the different importance of each class in an self-driving system into account. For example, pedestrians in the image should be much more important than the surrounding buildings when make a decisions in the driving, so their segmentation results are expected to be as accurate as possible. In this paper, we propose to incorporate the importance-aware inter-class correlation in a Wasserstein training framework by configuring its ground distance matrix. The ground distance matrix can be pre-defined following a priori in a specific task, and the previous importance-ignored methods can be the particular cases. From an optimization perspective, we also extend our ground metric to a linear, convex or concave increasing function w.r.t. pre-defined ground distance. We evaluate our method on CamVid and Cityscapes datasets with different backbones (SegNet, ENet, FCN and Deeplab) in a plug and play fashion. In our extenssive experiments, Wasserstein loss demonstrates superior segmentation performance on the predefined critical classes for safe-driving.

Real-Time TEM Study of Nanopore Evolution in Battery Materials and Their Suppression for Enhanced Cycling Performance.

Abstract: Battery materials, which store energy by combining mechanisms of intercalation, conversion, and alloying, provide promisingly high energy density but usually suffer from fast capacity decay due to the drastic volume change upon cycling. Particularly, the significant volume shrinkage upon mass (Li+, Na+, etc.) extraction inevitably leads to the formation of pores in materials and their final pulverization after cycling. It is necessary to explore the failure mechanism of such battery materials from the microscopic level in order to understand the evolution of porous structures. Here, prototyped Sb2Se3 nanowires are targeted to understand the structural failures during repetitive (de)sodiation, which exhibits mainly alloying and conversion mechanisms. The fast growing nanosized pores embedded in the nanowire during desodiation are identified to be the key factor that weakens the mechanical strength of the material and thus cause a rapid capacity decrease. To suppress the pore development, we further limit the cutoff charge voltage in a half-cell against Na below a critical value where the conversion reaction of such a material system is yet happening, the result of which demonstrates significantly improved battery performance with well-maintained structural integrity. These findings may shed some light on electrode failure investigation and rational design of advanced electrode materials with long cycling life.

Exosomal transfer of osteoclast-derived miRNAs to chondrocytes contributes to osteoarthritis progression

Abstract: Osteoarthritis (OA) is a prevalent aging-related joint disease lacking disease-modifying therapies. Here, we identified an upregulation of circulating exosomal osteoclast (OC)-derived microRNAs (OC-miRNAs) during the progression of surgery-induced OA in mice. We found that reducing OC-miRNAs by Cre-mediated excision of the key miRNA-processing enzyme Dicer or blocking the secretion of OC-originated exosomes by short interfering RNA-mediated silencing of Rab27a substantially delayed the progression of surgery-induced OA in mice. Mechanistically, the exosomal transfer of OC-miRNAs to chondrocytes reduced the resistance of cartilage to matrix degeneration, osteochondral angiogenesis and sensory innervation during OA progression by suppressing tissue inhibitor of metalloproteinase-2 (TIMP-2) and TIMP-3. Furthermore, systemic administration of a new OC-targeted exosome inhibitor (OCExoInhib) blunted the progression of surgery-induced OA in mice. We suggest that targeting the exosomal transfer of OC-miRNAs to chondrocytes represents a potential therapeutic avenue to tackle OA progression. The authors show that exosomal transfer of osteoclast-derived microRNAs to chondrocytes decreases the resistance of cartilage to matrix degeneration, angiogenesis and sensory innervation, and promotes osteoarthritis progression in mice.

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.