Epidemic modelling by ripple-spreading network and genetic algorithm

Mathematical analysis and modelling is central to infectious disease epidemiology. This paper, inspired by the natural ripple-spreading phenomenon, proposes a novel ripple-spreading network model for the study of infectious disease transmission. The new epidemic model naturally has good potential for capturing many spatial and temporal features observed in the outbreak of plagues. In particular, using a stochastic ripple-spreading process simulates the effect of random contacts and movements of individuals on the probability of infection well, which is usually a challenging issue in epidemic modeling. Some ripple-spreading related parameters such as threshold and amplifying factor of nodes are ideal to describe the importance of individuals’ physical fitness and immunity. The new model is rich in parameters to incorporate many real factors such as public health service and policies, and it is highly flexible to modifications. A genetic algorithm is used to tune the parameters of the model by referring to historic data of an epidemic. The well-tuned model can then be used for analyzing and forecasting purposes. The effectiveness of the proposed method is illustrated by simulation results

Productivity Growth of East Asia Economies' Manufacturing: A Decomposition Analysis

Applying a stochastic production frontier to sector-level data within manufacturing, this paper examines total factor productivity (TFP) growth for eight East Asian economies during 1963-1998, using both single country and cross-country regression. The analysis focuses on the trend of technological progress (TP) and technical efficiency change (TEC), and the role of productivity change in economic growth. The empirical results reveal that although input factor accumulation is still the main source for East Asian economies® growth, TFP growth is accounting for an increasing and important proportion of output growth, among which the improved TEC plays a crucial role in productivity growth.total factor productivity, technical efficiency change, technological progress, stochastic production frontier, East Asian economy

Human beta defensin 2 selectively inhibits HIV-1 in highly permissive CCR6+CD4+ T cells

Chemokine receptor type 6 (CCR6)+CD4+ T cells are preferentially infected and depleted during HIV disease progression, but are preserved in non-progressors. CCR6 is expressed on a heterogeneous population of memory CD4+ T cells that are critical to mucosal immunity. Preferential infection of these cells is associated, in part, with high surface expression of CCR5, CXCR4, and α4β7. In addition, CCR6+CD4+ T cells harbor elevated levels of integrated viral DNA and high levels of proliferation markers. We have previously shown that the CCR6 ligands MIP-3α and human beta defensins inhibit HIV replication. The inhibition required CCR6 and the induction of APOBEC3G. Here, we further characterize the induction of apolipoprotein B mRNA editing enzyme (APOBEC3G) by human beta defensin 2. Human beta defensin 2 rapidly induces transcriptional induction of APOBEC3G that involves extracellular signal-regulated kinases 1/2 (ERK1/2) activation and the transcription factors NFATc2, NFATc1, and IRF4. We demonstrate that human beta defensin 2 selectively protects primary CCR6+CD4+ T cells infected with HIV-1. The selective protection of CCR6+CD4+ T cell subsets may be critical in maintaining mucosal immune function and preventing disease progression

Effect of excluded volume on the dipole moments of chain molecules

Dielectric constants have been determined for dimethylsiloxane chains (CH3)3Si[OSi(CH3)2]x OSi(CH3)3 in the thermodynamically good solvent cyclohexane and in the undiluted state, for degrees of polymerization x + 1 ranging from 102 to 103, at a number of temperatures in the range 10–60 °C. These data indicate that at constant temperature the dipole moment ratio 〈μ2〉/nm2〈μ2〉∕nm2 (where 〈μ2〉〈μ2〉 is the mean‐square dipole moment of a chain consisting of n = 2x + 2 bond dipoles of magnitude m) is independent of chain length, as has been predicted for chains of such structural symmetry. Unfortunately, comparison of the experimental values of the dipole moment ratio with those predicted from rotational isomeric state theory is complicated by pronounced specific solvent effects and comparison of experimental and theoretical values of d ln 〈μ2〉/dT〈μ2〉∕dT is also difficult because of the very small magnitude of this coefficient.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/69812/2/JCPSA6-59-7-3825-1.pd

An integrated network approach identifies the isobutanol response network of Escherichia coli

Isobutanol has emerged as a potential biofuel due to recent metabolic engineering efforts. Here we used gene expression and transcription network connectivity data, genetic knockouts, and network component analysis (NCA) to map the initial isobutanol response network of Escherichia coli under aerobic conditions. NCA revealed profound perturbations to respiration. Further investigation showed ArcA as an important mediator of this response. Quinone/quinol malfunction was postulated to activate ArcA, Fur, and PhoB in this study. In support of this hypothesis, quinone-linked ArcA and Fur target expressions were significantly less perturbed by isobutanol under fermentative growth whereas quinol-linked PhoB target expressions remained activated, and isobutanol impeded growth on glycerol, which requires quinones, more than on glucose. In addition, ethanol, n-butanol, and isobutanol response networks were compared. n-Butanol and isobutanol responses were qualitatively similar, whereas ethanol had notable induction differences of pspABCDE and ndh, whose gene products manage proton motive force. The network described here could aid design and comprehension of alcohol tolerance, whereas the approach provides a general framework to characterize complex phenomena at the systems level