Monitoring of the bacterial and fungal biodiversity and dynamics during Massa Medicata Fermentata fermentation.

The microbial community dynamics play an important role during Massa Medicata Fermentata (MMF) fermentation. In this study, bacterial and fungal communities were investigated based on the culture-dependent method and polymerase chain reaction-denaturing gradient gel electrophoresis analysis. Meanwhile the dynamic changes of digestive enzyme activities were also examined. Plating results showed that MMF fermentation comprised two stages: pre-fermentation stage (0-4 days) was dominated by bacterial community and post-fermentation stage (5-9 days) was dominated by fungal community. The amount of bacteria reached the highest copy number 1.2 × 10(10) CFU/g at day 2, but the fungi counts reached 6.3 × 10(5) CFU/g at day 9. A total of 170 isolates were closely related to genera Enterobacter, Klebsiella, Acinetobacter, Pseudomonas, Mucor, Saccharomyces, Rhodotorula, and Amylomyces. DGGE analysis showed a clear reduction of bacterial and fungal diversity during fermentation, and the dominant microbes belonged to genera Enterobacter, Pediococcus, Pseudomonas, Mucor, and Saccharomyces. Digestive enzyme assay showed filter paper activity; the activities of amylase, carboxymethyl cellulase, and lipase reached a peak at day 4; and the protease activity constantly increased until the end of the fermentation. In this study, we carried out a detailed and comprehensive analysis of microbial communities as well as four digestive enzymes' activities during MMF fermentation process. The monitoring of bacterial and fungal biodiversity and dynamics during MMF fermentation has significant potential for controlling the fermentation process.

Control of epidemic spreading on complex networks by local traffic dynamics.

Despite extensive work on traffic dynamics and epidemic spreading on complex networks, the interplay between these two types of dynamical processes has not received adequate attention. We study the effect of local-routing-based traffic dynamics on epidemic spreading. For the case of unbounded node-delivery capacity, where the traffic is free of congestion, we obtain analytic and numerical results indicating that the epidemic threshold can be maximized by an optimal routing protocol. This means that epidemic spreading can be effectively controlled by local traffic dynamics. For the case of bounded delivery capacity, numerical results and qualitative arguments suggest that traffic congestion can suppress epidemic spreading. Our results provide quantitative insight into the nontrivial role of traffic dynamics associated with a local-routing scheme in the epidemic spreading.

Generalized Eden model with a screening effect.

We generalize the Eden model to take into account the screening effect, i.e., the end point grows much faster than the interior points do. Highly anisotropic clusters are obtained in our generalized Eden model. It is found that the length in the long direction scales differently than that in the short direction does as the number of sites increases.

Transportation dynamics on networks of mobile agents.

Most existing works on transportation dynamics focus on networks of a fixed structure, but networks whose nodes are mobile have become widespread, such as cell-phone networks. We introduce a model to explore the basic physics of transportation on mobile networks. Of particular interest is the dependence of the throughput on the speed of agent movement and the communication range. Our computations reveal a hierarchical dependence for the former, while an algebraic power law is found between the throughput and the communication range with the exponent determined by the speed. We develop a physical theory based on the Fokker-Planck equation to explain these phenomena. Our findings provide insights into complex transportation dynamics arising commonly in natural and engineering systems.

Clinical values of multiple Epstein-Barr virus (EBV) serological biomarkers detected by xMAP technology.

BACKGROUND: Serological examination of Epstein-Barr virus (EBV) antibodies has been performed for screening nasopharyngeal carcinoma (NPC) and other EBV-associated diseases. METHODS: By using xMAP technology, we examined immunoglobulin (Ig) A antibodies against Epstein-Barr virus (EBV) VCA-gp125, p18 and IgA/IgG against EA-D, EBNA1 and gp78 in populations with distinct diseases, or with different genetic or geographic background. Sera from Cantonese NPC patients (n = 547) and healthy controls (n = 542), 90 members of high-risk NPC families and 52 non-endemic healthy individuals were tested. Thirty-five of NPC patients were recruited to observe the kinetics of EBV antibody levels during and after treatment. Patients with other EBV-associated diseases were collected, including 16 with infectious mononucleosis, 28 with nasal NK/T cell lymphoma and 14 with Hodgkin's disease. RESULTS: Both the sensitivity and specificity of each marker for NPC diagnosis ranged 61-84%, but if combined, they could reach to 84.5% and 92.4%, respectively. Almost half of NPC patients displayed decreased EBV immunoactivities shortly after therapy and tumor recurrence was accompanied with high EBV antibody reactivates. Neither the unaffected members from high-risk NPC families nor non-endemic healthy population showed statistically different EBV antibody levels compared with endemic controls. Moreover, elevated levels of specific antibodies were observed in other EBV-associated diseases, but all were lower than those in NPC. CONCLUSION: Combined EBV serological biomarkers could improve the diagnostic values for NPC. Diverse EBV serological spectrums presented in populations with different EBV-associated diseases, but NPC patients have the highest EBV activity.

Evaluation of antibodies against different Epstein-Barr virus nuclear antigen 1 peptides in diagnosis of nasopharyngeal carcinoma.

Epstein-Barr virus nuclear antigen 1 (EBNA1) is a protein expressed consistently in nasopharyngeal carcinoma (NPC). Although antibody levels against three different EBNA1 peptides were all high in NPC patients, the correlation between any two biomarkers was low. Therefore, the selection of distinct EBNA1 peptides could render different results in serological detection for individuals with NPC.

Evaluation of a multianalyte profiling assay and an enzyme-linked immunosorbent assay for serological examination of Epstein-Barr virus-specific antibody responses in diagnosis of nasopharyngeal carcinoma.

Assessment of antibody responses to Epstein-Barr virus (EBV) antigens has been used to assist in nasopharyngeal carcinoma (NPC) diagnosis by several methods. In this study, we evaluated an in-house Luminex multianalyte profiling (xMAP) technology and commercial enzyme-linked immunosorbent assay (ELISA) kits for serological examination of EBV-specific antibody responses in 135 NPC patients and 130 healthy controls. Four EBV biomarkers were measured: immunoglobulin A (IgA) against viral capsid antigen (VCA), EBV nuclear antigen 1 (EBNA1), diffused early antigen (EA-D), and IgG against EA-D. The sensitivities and specificities of the four markers ranged between 71.5 and 90% for xMAP assays and 80 and 92% for ELISA. Logistic regression analysis revealed that the combined markers in the xMAP assay had overall sensitivity and specificity values of 82% and 92%, respectively. The correlation coefficient (r) values for the xMAP assay and ELISA were lowest for IgA-VCA (0.468) and highest for IgA-EBNA1 (0.846); for IgA-EA-D and IgG-EA-D, the r values were 0.719 and 0.798, respectively. The concordances of the two methods for NPC discrimination were good (79 to 88%). Our results suggest that both the xMAP assay and ELISA are satisfactory for EBV antibody evaluation when multiple antigens are included.

Antibodies against Epstein-Barr virus gp78 antigen: a novel marker for serological diagnosis of nasopharyngeal carcinoma detected by xMAP technology.

Immunoglobulin (Ig) A and/or IgG reactivities to several Epstein-Barr virus (EBV) antigens have been used to facilitate diagnosis of nasopharyngeal carcinoma (NPC). However, antibodies against gp78, an EBV membrane glycoprotein, have not been examined to this day. In this study, we utilized Luminex multi-analyte profiling (xMAP) technology to analyse antibody responses to a synthetic peptide of gp78 in sera samples from 95 NPC patients and 91 healthy controls. Our results showed the sensitivity and specificity of IgA-gp78 for NPC diagnosis were 79 and 71 %, respectively, while those of IgG-gp78 were 74 and 73 %, respectively. The IgA and IgG responses to different EBV antigens were not identical within an individual and IgA-gp78 and IgG-gp78 could be complementary to antibodies against viral capsid antigen (VCA), the diffused early antigen (EA-D) and the nuclear antigen EBNA1 for NPC diagnosis. When the six EBV parameters for NPC prediction, i.e. IgA-gp78, IgG-gp78, IgA-VCA, IgA-EBNA1, IgA-EA-D and IgG-EA-D, are combined, the combined predictors were able to reach overall sensitivity and specificity of 91 and 95 %, respectively. Thus, simultaneous detection of these EBV serological markers could improve the predictive values of NPC using xMAP technology.

Optimal contact process on complex networks.

Contact processes on complex networks are a recent subject of study in nonequilibrium statistical physics and they are also important to applied fields such as epidemiology and computer and communication networks. A basic issue concerns finding an optimal strategy for spreading. We provide a universal strategy that, when a basic quantity in the contact process dynamics, the contact probability determined by a generic function of its degree W(k) , is chosen to be inversely proportional to the node degree, i.e., W(k) approximately k;{-1} , spreading can be maximized. Computation results on both model and real-world networks verify our theoretical prediction. Our result suggests the determining role played by small-degree nodes in optimizing spreading, in contrast to the intuition that hub nodes are important for spreading dynamics on complex networks.

Geographical networks evolving with an optimal policy.

In this article we propose a growing network model based on an optimal policy involving both topological and geographical measures. In this model, at each time step, a node, having randomly assigned coordinates in a 1x1 square, is added and connected to a previously existing node i, which minimizes the quantity ri2/kialpha, where ri is the geographical distance, ki the degree, and alpha a free parameter. The degree distribution obeys a power-law form when alpha=1, and an exponential form when alpha=0. When alpha is in the interval (0, 1), the network exhibits a stretched exponential distribution. We prove that the average topological distance increases in a logarithmic scale of the network size, indicating the existence of the small-world property. Furthermore, we obtain the geographical edge length distribution, the total geographical length of all edges, and the average geographical distance of the whole network. Interestingly, we found that the total edge length will sharply increase when alpha exceeds the critical value alphac=1, and the average geographical distance has an upper bound independent of the network size. All the results are obtained analytically with some reasonable approximations, which are well verified by simulations.

Modeling the coevolution of topology and traffic on weighted technological networks.

For many technological networks, the network structures and the traffic taking place on them mutually interact. The demands of traffic increment spur the evolution and growth of the networks to maintain their normal and efficient functioning. In parallel, a change of the network structure leads to redistribution of the traffic. In this paper, we perform an extensive numerical and analytical study, extending results of Wang [Phys. Rev. Lett. 94, 188702 (2005)]. By introducing a general strength-coupling interaction driven by the traffic increment between any pair of vertices, our model generates networks of scale-free distributions of strength, weight, and degree. In particular, the obtained nonlinear correlation between vertex strength and degree, and the disassortative property demonstrate that the model is capable of characterizing weighted technological networks. Moreover, the generated graphs possess both dense clustering structures and an anticorrelation between vertex clustering and degree, which are widely observed in real-world networks. The corresponding theoretical predictions are well consistent with simulation results.

Traffic dynamics based on local routing protocol on a scale-free network.

We propose a packet routing strategy with a tunable parameter based on the local structural information of a scale-free network. As free traffic flow on the communication networks is key to their normal and efficient functioning, we focus on the network capacity that can be measured by the critical point of phase transition from free flow to congestion. Simulations show that the maximal capacity corresponds to alpha= -1 in the case of identical nodes' delivering ability. To explain this, we investigate the number of packets of each node depending on its degree in the free flow state and observe the power law behavior. Other dynamic properties including average packets traveling time and traffic load are also studied. Inspiringly, our results indicate that some fundamental relationships exist between the dynamics of synchronization and traffic on the scale-free networks.

Mutual selection model for weighted networks.

For most networks, the connection between two nodes is the result of their mutual affinity and attachment. In this paper, we propose a mutual selection model to characterize the weighted networks. By introducing a general mechanism of mutual selection, the model can produce power-law distributions of degree, weight, and strength, as confirmed in many real networks. Moreover, we also obtained the nontrivial clustering coefficient C, degree assortativity coefficient r, and degree-strength correlation depending on a single parameter m. These results are supported by present empirical evidence. Studying the degree-dependent average clustering coefficient C(k) and the degree-dependent average nearest neighbors' degree k(nn)(k) also provide us with a better description of the hierarchies and organizational architecture of weighted networks.

Power law distribution of wealth in population based on a modified Equíluz-Zimmermann model.

We propose a money-based model for the power law distribution (PLD) of wealth in an economically interacting population. It is introduced as a modification of the Equíluz and Zimmermann (EZ) model for crowding and information transmission in financial markets. Still, it must be stressed that in the EZ model a PLD without exponential correction is obtained only for a particular parameter, while our pattern will give the exact PLD within a wide range. The PLD exponent depends on the model parameters in a non-trivial way and is exactly calculated in this paper. The numerical results are in excellent agreement with the analytic prediction, and also comparable with empirical data of wealth distribution.