Yang Yang

Bio: Yang Yang is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 171, co-authored 2644 publications receiving 153049 citations. Previous affiliations of Yang Yang include Zhejiang University & Northwest Normal University.

Apoferritin Nanocage for Brain Targeted Doxorubicin Delivery

Abstract: An ideal brain-targeted nanocarrier must be sufficiently potent to penetrate the blood–brain barrier (BBB) and sufficiently competent to target the cells of interest with adequate optimized physiochemical features and biocompatibility. However, it is an enormous challenge to the researchers to organize the above-mentioned properties into a single nanocarrier particle. New frontiers in nanomedicine are advancing the research of new biomaterials. Herein, we demonstrate a straightforward strategy for brain targeting by encapsulating doxorubicin (DOX) into a naturally available and unmodified apoferritin nanocage (DOX-loaded APO). APO can specifically bind to cells expressing transferrin receptor 1 (TfR1). Because of the high expression of TfR1 in both brain endothelial and glioma cells, DOX-loaded APO can cross the BBB and deliver drugs to the glioma with TfR1. Subsequent research demonstrated that the DOX-loaded APO had good physicochemical properties (particle size of 12.03 ± 0.42 nm, drug encapsulation ef...

Efficient hole transport layers with widely tunable work function for deep HOMO level organic solar cells

Abstract: Hole transport layers (HTLs) with large work function (WF) tuning ability for good energy level alignment with deep highest occupied molecular orbital (HOMO) level donor materials are desirable for high-performance and high open-circuit voltage (VOC) organic solar cells (OSCs). Here, a novel low-temperature and solution-process approach to achieve WF tuning in HTLs is proposed. Specifically, the HTLs made from 2,3,4,5,6-pentafluorobenzylphosphonic acid (F5BnPA) incorporated graphene oxide (GO) and molybdenum oxide (MoOx) solution (representing two possible classes of HTLs where carriers transport via valence and conduction bands, respectively) offer continuous WF tuning (the tuning range as large as 0.81 eV) by controlling F5BnPA's concentration. By employing a deep HOMO donor material, OSCs using the composite HTLs can achieve improved performances with largely increased VOC (0.92 V for GO:F5BnPA versus 0.65 V for pristine GO; 0.91 V for MoOx:F5BnPA versus 0.88 V for pristine MoOx). The enhanced performance can be experimentally and theoretically explained by the decreased hole injection barrier (HIB) for GO or equivalent HIB (i.e. electron extraction barrier) for MoOx and enhanced surface recombination velocity, which contribute to eliminating S-shaped current–voltage characteristics. Consequently, the incorporation of F5BnPA can efficiently tune HTL WF for high VOC OSCs and extend HTL applications in organic electronics.

A Novel Method to Determine Magnetic Fields in Low-density Plasma Facilitated Through Accidental Degeneracy of Quantum States in Fe9+

Abstract: We propose a new method to determine magnetic fields, by using the magnetic-field-induced electric dipole transition 3p(4) 3d D-4(7/2) -> 3p(5) P-3/2 in Fe9+ ions. This ion has a high abundance in astrophysical plasma and is therefore well suited for direct measurements of even rather weak fields in, e.g., solar flares. This transition is induced by an external magnetic field and its rate is proportional to the square of the magnetic field strength. We present theoretical values for what we will label the reduced rate and propose that the critical energy difference between the upper level in this transition and the close-to-degenerate 3p(4) 3d D-4(5/2) should be measured experimentally since it is required to determine the relative intensity of this magnetic line for different magnetic fields. (Less)

Relation-Aware Pedestrian Attribute Recognition with Graph Convolutional Networks

Abstract: In this paper, we propose a new end-to-end network, named Joint Learning of Attribute and Contextual relations (JLAC), to solve the task of pedestrian attribute recognition. It includes two novel modules: Attribute Relation Module (ARM) and Contextual Relation Module (CRM). For ARM, we construct an attribute graph with attribute-specific features which are learned by the constrained losses, and further use Graph Convolutional Network (GCN) to explore the correlations among multiple attributes. For CRM, we first propose a graph projection scheme to project the 2-D feature map into a set of nodes from different image regions, and then employ GCN to explore the contextual relations among those regions. Since the relation information in the above two modules is correlated and complementary, we incorporate them into a unified framework to learn both together. Experiments on three benchmarks, including PA-100K, RAP, PETA attribute datasets, demonstrate the effectiveness of the proposed JLAC.

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.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Electronics and optoelectronics of two-dimensional transition metal dichalcogenides.

Abstract: Single-layer metal dichalcogenides are two-dimensional semiconductors that present strong potential for electronic and sensing applications complementary to that of graphene.