Haifeng Zhang

Bio: Haifeng Zhang is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Photonic crystal & Materials science. The author has an hindex of 41, co-authored 419 publications receiving 6856 citations. Previous affiliations of Haifeng Zhang include Nanjing University of Posts and Telecommunications & Anhui University.

Epidemic dynamics on scale-free networks with piecewise linear infectivity and immunization.

Abstract: We examine epidemic thresholds for disease spread using susceptible-infected-susceptible models on scale-free networks with variable infectivity. Infectivity between nodes is modeled as a piecewise linear function of the node degree (rather than the less realistic linear transformation considered previously). With this nonlinear infectivity, we derive conditions for the epidemic threshold to be positive. The effects of various immunization schemes including ring and targeted vaccination are studied and compared. We find that both targeted and ring immunization strategies compare favorably to a proportional scheme in terms of effectiveness.

Identifying influential spreaders in complex networks based on gravity formula

Abstract: How to identify the influential spreaders in social networks is crucial for accelerating/hindering information diffusion, increasing product exposure, controlling diseases and rumors, and so on. In this paper, by viewing the k-shell value of each node as its mass and the shortest path distance between two nodes as their distance, then inspired by the idea of the gravity formula, we propose a gravity centrality index to identify the influential spreaders in complex networks. The comparison between the gravity centrality index and some well-known centralities, such as degree centrality, betweenness centrality, closeness centrality, and k-shell centrality, and so forth, indicates that our method can effectively identify the influential spreaders in real networks as well as synthetic networks. We also use the classical Susceptible–Infected–Recovered (SIR) epidemic model to verify the good performance of our method.

Co-contamination of aflatoxin B1 and fumonisin B1 in food and human dietary exposure in three areas of China.

Abstract: Aflatoxins and fumonisins are ubiquitous foodborne toxicants and the co-occurrence of these mycotoxins in human foods represents a significant public health concern, which has been strongly associated with human aflatoxicosis, neural tube defects, as well as many types of primary cancers In this study the co-contamination of aflatoxin B(1) (AFB(1)) and fumonisin B(1) (FB(1)) in food and human dietary exposure was investigated in residents of three different areas of China A total of 209 food samples were measured for AFB(1) and FB(1) The median AFB(1) levels were 135, 23 and 13 µg kg(-1) and the median FB(1) levels were 26, 04 and 03 mg kg(-1) in corn samples collected from Huaian (a high-risk area for oesophageal cancer), Fusui (a high-risk area for liver cancer) and Huantai (a low-risk area for both oesophageal and liver cancers), respectively The median level of AFB(1) in plant oil of Fusui was the highest (523 µg kg(-1)) among all food samples analysed Co-contamination of these two mycotoxins was found in corn, rice and wheat flour Based on measured food consumption data, the averaged daily dietary intake of AFB(1) was 0397 µg (range = 0269-1218 µg) in residents of Huantai, 1723 µg (0224-49772 µg) in Huaian, and 2685 µg (1006-14534 µg) in Fusui The averaged FB(1) daily dietary intake was 924 µg (range = 550-3621 µg) for residents of Huantai, 4600 µg (832-28945 µg) in Huaian, and 1386 µg (300-10,5416 µg) in Fusui These data suggest that the co-exposure to AFB(1) and FB(1) in residents of rural China may contribute to the aetiology of human chronic diseases in high-risk areas

Epidemic spreading on complex networks with general degree and weight distributions.

Abstract: The spread of disease on complex networks has attracted wide attention in the physics community. Recent works have demonstrated that heterogeneous degree and weight distributions have a significant influence on the epidemic dynamics. In this study, a novel edge-weight-based compartmental approach is developed to estimate the epidemic threshold and epidemic size (final infected density) on networks with general degree and weight distributions, and a remarkable agreement with numerics is obtained. Even in complex networks with the strong heterogeneous degree and weight distributions, this approach is used. We then propose an edge-weight-based removal strategy with different biases and find that such a strategy can effectively control the spread of epidemic when the highly weighted edges are preferentially removed, especially when the weight distribution of a network is extremely heterogenous. The theoretical results from the suggested method can accurately predict the above removal effectiveness.

Discovery of a super-strong promoter enables efficient production of heterologous proteins in cyanobacteria

Abstract: Cyanobacteria are oxygenic photosynthetic prokaryotes that play important roles in the global carbon cycle. Recently, engineered cyanobacteria capable of producing various small molecules from CO2 have been developed. However, cyanobacteria are seldom considered as factories for producing proteins, mainly because of the lack of efficient strong promoters. Here, we report the discovery and verification of a super-strong promoter Pcpc560, which contains two predicted promoters and 14 predicted transcription factor binding sites (TFBSs). Using Pcpc560, functional proteins were produced at a level of up to 15% of total soluble protein in the cyanobacterium Synechocystis sp. 6803, a level comparable to that produced in Escherichia coli. We demonstrated that the presence of multiple TFBSs in Pcpc560 is crucial for its promoter strength. Genetically transformable cyanobacteria neither have endotoxins nor form inclusion bodies; therefore, Pcpc560 opens the possibility to use cyanobacteria as alternative hosts for producing heterogeneous proteins from CO2 and inorganic nutrients.

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。

Random graphs

Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.