Quantifying the underlying landscape and paths of cancer

Cancer is a disease regulated by the underlying gene networks. The emergence of normal and cancer states as well as the transformation between them can be thought of as a result of the gene network interactions and associated changes. We developed a global potential landscape and path framework to quantify cancer and associated processes. We constructed a cancer gene regulatory network based on the experimental evidences and uncovered the underlying landscape. The resulting tristable landscape characterizes important biological states: normal, cancer and apoptosis. The landscape topography in terms of barrier heights between stable state attractors quantifies the global stability of the cancer network system. We propose two mechanisms of cancerization: one is by the changes of landscape topography through the changes in regulation strengths of the gene networks. The other is by the fluctuations that help the system to go over the critical barrier at fixed landscape topography. The kinetic paths from least action principle quantify the transition processes among normal state, cancer state and apoptosis state. The kinetic rates provide the quantification of transition speeds among normal, cancer and apoptosis attractors. By the global sensitivity analysis of the gene network parameters on the landscape topography, we uncovered some key gene regulations determining the transitions between cancer and normal states. This can be used to guide the design of new anti-cancer tactics, through cocktail strategy of targeting multiple key regulation links simultaneously, for preventing cancer occurrence or transforming the early cancer state back to normal state.     

An evaluation of the multi-state node networks reliability using the traditional binary-state networks reliability algorithm

A system where the components and system itself are allowed to have a number of performance levels is called the Multi-state system (MSS). A multi-state node network (MNN) is a generalization of the MSS without satisfying the flow conservation law. Evaluating the MNN reliability arises at the design and exploitation stage of many types of technical systems. Up to now, the known existing methods can only evaluate a special MNN reliability called the multi-state node acyclic network (MNAN) in which no cyclic is allowed. However, no method exists for evaluating the general MNN reliability. The main purpose of this article is to show first that each MNN reliability can be solved using any the traditional binary-state networks (TBSN) reliability algorithm with a special code for the state probability. A simple heuristic SDP algorithm based on minimal cuts (MC) for estimating the MNN reliability is presented as an example to show how the TBSN reliability algorithm is revised to solve the MNN reliability problem. To the author's knowledge, this study is the first to discuss the relationships between MNN and TBSN and also the first to present methods to solve the exact and approximated MNN reliability. One example is illustrated to show how the exact MNN reliability is obtained using the proposed algorithm.     

Confidence bounds for the reliability of binary capacitated two-terminal networks

Binary capacitated two-terminal reliability at demand level d (2TR_d) is defined as the probability that network capacity, generated by binary capacitated components, between specified source and sink nodes is greater than or equal to a demand of d units. For the components that comprise these networks, reliability estimates are usually obtained from some source of testing. For these estimates and depending on the type of testing, there is an associated uncertainty that can significantly affect the overall estimation of 2TR_d. That is, an accurate estimate of 2TR_d is highly dependent on the uncertainty associated to the reliability of the network components. Current methods for the estimation of network reliability and associated uncertainty are restricted to the case where the network follows a series-parallel architecture and the components are binary and non-capacitated. For different capacitated network designs, an estimate on 2TR_d can only be approximated for specific scenarios. This paper presents a bounding approach for 2TR_d by explaining how component reliability and associated uncertainty impact estimates at the network level. The proposed method is based on a structured approach that generates a α-level confidence interval (CI) for binary capacitated two-terminal network reliability. Simulation results on different test networks show that the proposed methods can be used to develop very accurate bounds of two-terminal network reliability.     

Impact of scale on the effectiveness of disease control strategies for epidemics with cryptic infection in a dynamical landscape: an example for a crop disease.

We use a spatially explicit, stochastic model to analyse the effectiveness of different scales of local control strategies in containing the long-term, multi-seasonal spread of a crop disease through a dynamically changing population of susceptible crops in which there is cryptic infection. The model distinguishes between susceptible, infested and symptomatic fields. It is motivated by rhizomania disease on sugar beet in the UK as an exemplar of a spatially structured and partially asymptomatic epidemic. Our results show the importance of matching the scales of local control strategies to prevent intensification and regional spread of disease with the inherent temporal and spatial scales of an epidemic. A simple field-scale containment strategy, whereby the susceptible crop is no longer grown on fields showing symptoms, fails for this system with cryptic infection because the locally applied control lags behind the epidemic. A farm-scale strategy, whereby growers respond to the disease status of neighbouring farms by transferring their quota for sugar beet to farmers in regions of reduced risk, succeeds. We conclude that a soil-borne pathogen such as rhizomania could be managed by movement of susceptible crops in the landscape using a strategy that matches the temporal and spatial scales of the epidemic and which take account of risk aversion among growers. We show some parallels and differences in effectiveness between a 'culling' strategy involving crop removal around emerging foci and the local deployment of partially resistant varieties that reduce amplification and transmission of inoculum. Some relationships between the control of plant and livestock diseases are briefly discussed.     

Evaluation of the reliability of transport networks based on the stochastic flow of moving objects

In transport networks, human beings are moving objects whose moving direction is stochastic in emergency situations. Based on this idea, a new model—stochastic moving network (SMN) is proposed. It is different from binary-state networks and stochastic-flow networks. The flow of SMNs has multiple-saturated states, that correspond to different flow values in each arc. In this paper, we try to evaluate the system reliability, defined as the probability that the saturated flow of the network is not less than a given demand d. Based on this new model, we obtain the flow probability distribution of every arc by simulation. An algorithm based on the blocking cutset of the SMN is proposed to evaluate the network reliability. An example is used to show how to calculate the corresponding reliabilities for different given demands of the SMN. Simulation experiments of different size were made and the system reliability precision was calculated. The precision of simulation results also discussed.     

用OBDD算法评估无线传感网的可靠度和结点重要性

基于有序二叉判定图(OBDD),提出用结点扩张(NE)算法来评估无线传感网的可靠度和结点重要性.NE算法执行结点扩张操作来处理不可靠结点,从两方面增强了计算效率:利用OBDD结构表示网络状态,减少了大量冗余的等价状态;利用Hash表存储同构子网的OBDD,减少了同构子网的重复计算.另外,该算法对结点重要性进行了评估,为脆弱结点的保护提供参考.实验结果表明NE算法的计算开销比传统的factoring算法低,能有效评估无线传感网的可靠度.     

Analytical methods for quantifying environmental connectivity for the control and surveillance of infectious disease spread

The sustained transmission and spread of environmentally mediated infectious diseases is governed in part by the dispersal of parasites, disease vectors and intermediate hosts between sites of transmission. Functional geospatial models can be used to quantify and predict the degree to which environmental features facilitate or limit connectivity between target populations, yet typical models are limited in their geographical and analytical approach, providing simplistic, global measures of connectivity and lacking methods to assess the epidemiological implications of fine-scale heterogeneous landscapes. Here, functional spatial models are applied to problems of surveillance and control of the parasitic blood fluke Schistosoma japonicum and its intermediate snail host Oncomelania haupensis in western China. We advance functional connectivity methods by providing an analytical framework to (i) identify nodes of transmission where the degree of connectedness to other villages, and thus the potential for disease spread, is higher than is estimated using Euclidean distance alone and (ii) (re)organize transmission sites into disease surveillance units based on second-order relationships among nodes using non-Euclidean distance measures, termed effective geographical distance (EGD). Functional environmental models are parametrized using ecological information on the target organisms, and pair-wise distributions of inter-node EGD are estimated. A Monte Carlo rank product analysis is presented to identify nearby nodes under alternative distance models. Nodes are then iteratively embedded into EGD space and clustered using a k-means algorithm to group villages into ecologically meaningful surveillance groups. A consensus clustering approach is taken to derive the most stable cluster structure. The results indicate that novel relationships between nodes are revealed when non-Euclidean, ecologically determined distance measures are used to quantify connectivity in heterogeneous landscapes. These connections are not evident when analysing nodes in Euclidean space, and thus surveillance and control activities planned using Euclidean distance measures may be suboptimal. The methods developed here provide a quantitative framework for assessing the effectiveness of ecologically grounded surveillance systems and of control and prevention strategies for environmentally mediated diseases.     

The effect of individual variation on the structure and function of interaction networks in harvester ants

Social insects exhibit coordinated behaviour without central control. Local interactions among individuals determine their behaviour and regulate the activity of the colony. Harvester ants are recruited for outside work, using networks of brief antennal contacts, in the nest chamber closest to the nest exit: the entrance chamber. Here, we combine empirical observations, image analysis and computer simulations to investigate the structure and function of the interaction network in the entrance chamber. Ant interactions were distributed heterogeneously in the chamber, with an interaction hot-spot at the entrance leading further into the nest. The distribution of the total interactions per ant followed a right-skewed distribution, indicating the presence of highly connected individuals. Numbers of ant encounters observed positively correlated with the duration of observation. Individuals varied in interaction frequency, even after accounting for the duration of observation. An ant''s interaction frequency was explained by its path shape and location within the entrance chamber. Computer simulations demonstrate that variation among individuals in connectivity accelerates information flow to an extent equivalent to an increase in the total number of interactions. Individual variation in connectivity, arising from variation among ants in location and spatial behaviour, creates interaction centres, which may expedite information flow.     

Disease transmission in territorial populations: the small-world network of Serengeti lions

Territoriality in animal populations creates spatial structure that is thought to naturally buffer disease invasion. Often, however, territorial populations also include highly mobile, non-residential individuals that potentially serve as disease superspreaders. Using long-term data from the Serengeti Lion Project, we characterize the contact network structure of a territorial wildlife population and address the epidemiological impact of nomadic individuals. As expected, pride contacts are dominated by interactions with neighbouring prides and interspersed by encounters with nomads as they wander throughout the ecosystem. Yet the pride–pride network also includes occasional long-range contacts between prides, making it surprisingly small world and vulnerable to epidemics, even without nomads. While nomads increase both the local and global connectivity of the network, their epidemiological impact is marginal, particularly for diseases with short infectious periods like canine distemper virus. Thus, territoriality in Serengeti lions may be less protective and non-residents less important for disease transmission than previously considered.     

深孔爆破岩石破碎块度的控制研究

根据施工前进行试爆取得的爆破参数,建立了岩石破碎块度控制模型.按照工程地质条件和施工要求,对爆破参数进行了优化,取得了满意的控制效果.     

The contagious nature of imprisonment: an agent-based model to explain racial disparities in incarceration rates

We build an agent-based model of incarceration based on the susceptible–infected–suspectible (SIS) model of infectious disease propagation. Our central hypothesis is that the observed racial disparities in incarceration rates between Black and White Americans can be explained as the result of differential sentencing between the two demographic groups. We demonstrate that if incarceration can be spread through a social influence network, then even relatively small differences in sentencing can result in large disparities in incarceration rates. Controlling for effects of transmissibility, susceptibility and influence network structure, our model reproduces the observed large disparities in incarceration rates given the differences in sentence lengths for White and Black drug offenders in the USA without extensive parameter tuning. We further establish the suitability of the SIS model as applied to incarceration by demonstrating that the observed structural patterns of recidivism are an emergent property of the model. In fact, our model shows a remarkably close correspondence with California incarceration data. This work advances efforts to combine the theories and methods of epidemiology and criminology.     

The effect of temperature on the behavior of the interphase in polymeric composites

The single-filament fragmentation method for measuring the fiber/matrix stress transfer was used for the identification of interphase perturbations. This technique is based on the measurement of the fiber length resulting from the multiple fracture of a single fiber embedded in a resin specimen during tensile loading. A series of single-fiber fragmentation experiments was conducted over a wide range of temperatures on the AS4-carbon-fiber/Epon-828/PACM20-epoxy-resin system. Critical aspect ratios, the magnitude of which is considered to be inversely proportional to the square root of the matrix modulus, showed a significant increase from ambient to elevated temperatures, at temperature levels much lower than the glass transition point of the bulk matrix. This increase was consistent with the existence of an interphase of lower glass transition temperature than the bulk matrix. A three-concentric-cylinder elastic model was employed to correlated the effect of material properties.     

The role of maternal antibodies in the emergence of severe disease as a result of fragmentation

Population fragmentation is a major problem for the conservation of mammalian species. Since the spread of an infectious disease is related to the intensity of contacts between individuals, fragmentation destabilizes the way the parasites circulate in their host population. Recently, Zinkernagel has proposed that a reduction in the frequency of infections by a parasite could lead to the emergence of severe forms of the disease, previously avoided because the disease was contracted early in life and attenuated by maternal antibodies. However, it is still unclear whether this change in disease expression increases the global mortality it induces because the disease becomes more severe and also less frequent. Here, we use a mathematical model to link population fragmentation with the hypothesis of Zinkernagel. Firstly, we show that there is a change in the severity of the disease during the fragmentation process, especially at a local scale, suggesting that host population fragmentation could be a widespread mechanism of disease emergence. Secondly, we show that the emergence of the severe form of the disease can lead to a significant increase in its induced mortality. Finally, we determine the types of interactions for which the fragmentation of the host population could be the most dangerous.     

A study of the exponential smoothing technique in software reliability growth prediction

Software reliability models can provide quantitative measures of the reliability of software systems which are of growing importance today. Most of the models are parametric ones which rely on the modelling of the software failure process as a Markov or non-homogeneous Poisson process. It has been noticed that many of them do not give a very accurate prediction of future software failures as the focus is on the fitting of past data. In this paper we study the use of the double exponential smoothing technique to predict software failures. The proposed approach is a non-parametric one and has the ability of providing more accurate prediction compared with traditional parametric models because it gives a higher weight to the most recent failure data for a better prediction of future behaviour. The method is very easy to use and requires a very limited amount of data storage and computational effort. It can be updated instantly without much calculation. Hence it is a tool that should be more commonly used in practice. Numerical examples are shown to highlight the applicability. Comparisons with other commonly used software reliability growth models are also presented. © 1997 John Wiley & Sons, Ltd.     

A competing risk model for the reliability of cylinder liners in marine Diesel engines

In this paper, a competing risk model is proposed to describe the reliability of the cylinder liners of a marine Diesel engine. Cylinder liners presents two dominant failure modes: wear degradation and thermal cracking. The wear process is described through a stochastic process, whereas the failure time due to the thermal cracking is described by the Weibull distribution. The use of the proposed model allows performing goodness-of-fit test and parameters estimation on the basis of both wear and failure data. Moreover, it enables reliability estimates of the state of the liners to be obtained and the hierarchy of the failure mechanisms to be determined for any given age and wear level of the liner. The model has been applied to a real data set: 33 cylinder liners of Sulzer RTA 58 engines, which equip twin ships of the Grimaldi Group. Estimates of the liner reliability and of other quantities of interest under the competing risk model are obtained, as well as the conditional failure probability and mean residual lifetime, given the survival age and the accumulated wear. Furthermore, the model has been used to estimate the probability that a liner fails due to one of the failure modes when both of these modes act.     

Spatial dynamics of the 1918 influenza pandemic in England,Wales and the United States

There is still limited understanding of key determinants of spatial spread of influenza. The 1918 pandemic provides an opportunity to elucidate spatial determinants of spread on a large scale.To better characterize the spread of the 1918 major wave, we fitted a range of city-to-city transmission models to mortality data collected for 246 population centres in England and Wales and 47 cities in the US. Using a gravity model for city-to-city contacts, we explored the effect of population size and distance on the spread of disease and tested assumptions regarding density dependence in connectivity between cities. We employed Bayesian Markov Chain Monte Carlo methods to estimate parameters of the model for population, infectivity, distance and density dependence. We inferred the most likely transmission trees for both countries.For England and Wales, a model that estimated the degree of density dependence in connectivity between cities was preferable by deviance information criterion comparison. Early in the major wave, long distance infective interactions predominated, with local infection events more likely as the epidemic became widespread. For the US, with fewer more widely dispersed cities, statistical power was lacking to estimate population size dependence or the degree of density dependence, with the preferred model depending on distance only. We find that parameters estimated from the England and Wales dataset can be applied to the US data with no likelihood penalty.     

Vapor-liquid equilibria of ternary mixtures in the critical region on paths of constant temperature and overall composition

High-pressure vapor liquid equilibrium (VLE) data for the ternary mixture ethane +n-butane +n-pentane due to Thodos and co-workers have been correlated by a ternary version of the modified Leung Grifiths model. Data were taken along paths of constant temperature and approximately constant overall composition, and a separate test was made for such constancy. Seventeen different specified overall compositions at four dilTerent temperatures were correlated. In general, agreement between the correlation and data is very good, particularly for those curves that satisfy the test for constant overall composition. The ternary model has been constructed from correlations of each of the three constituent binaries without any further adjustable parameters.Paper presented at the Twelfth Symposium on Thermophysical Properties, June 19–24, 1994. Boulder, Colorado, U.S.A.     

Fabrication of a reticular poly(lactide-co-glycolide) cylindrical scaffold for the in vitro development of microvascular networks

Abstract

The microvascular network is a simple but critical system that is responsible for a range of important biological mechanisms in the bodies of all animals. The ability to generate a functional microvessel not only makes it possible to engineer vital tissue of considerable size but also serves as a platform for biomedical studies. However, most of the current methods for generating microvessel networks in vitro use rectangular channels which cannot represent real vessels in vivo and have dead zones at their corners, hence hindering the circulation of culture medium. We propose a scaffold-wrapping method which enables fabrication of a customized microvascular network in vitro in a more biomimetic way. By integrating microelectromechanical techniques with thermal reflow, we designed and fabricated a microscale hemi-cylindrical photoresist template. A replica mold of polydimethylsiloxane, produced by casting, was then used to generate cylindrical scaffolds with biodegradable poly(lactide-co-glycolide) (PLGA). Human umbilical vein endothelial cells were seeded on both sides of the PLGA scaffold and cultured using a traditional approach. The expression of endothelial cell marker CD31 and intercellular junction vascular endothelial cadherin on the cultured cell demonstrated the potential of generating a microvascular network with a degradable cylindrical scaffold. Our method allows cells to be cultured on a scaffold using a conventional culture approach and monitors cell conditions continuously. We hope our cell-covered scaffold can serve as a framework for building large tissues or can be used as the core of a vascular chip for in vitro circulation studies.     

论包装企业数字资产管理平台的构建

现代包装印刷企业需要处理日益增多的电子文档,如何有效地存储和利用这些数字资产成为一个现实问题.对数字资产管理系统的工作机制、系统组成和平台搭建等方面进行了分析对比,从而为包装印刷企业进行数字资产管理系统的搭建和应用提供有益的参考.