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from the melting of winter snows. Much of the acidity in rain may be neutralized by dissolving minerals in the soil such as aluminum, calcium, magnesium, sodium, and potassium, which are leached from the soil into surface waters. The ability of the soil to neutralize or buffer the acid rain is very dependent on the alkalinity of the soil. Much of the area in eastern Canada and the northeastern United States is covered by thin soils with low acid neutralizing capacity. In such areas the lakes are more susceptible to the effects of acid deposition leading to a low pH and high levels of aluminum, a combination toxic to many species of sh. A second area of concern is that of reduced tree growth in forests. The leaching of nutrients from the soil by acid deposition may cause a reduction in future growth rates or changes in the type of trees to those able to survive in the altered environment. In addition to the change in soil composition, there are the direct effects on the trees from sulfur and nitrogen oxides as well as ozone.

Self-Similar Network Traf c and Performance Evaluation, Edited by Kihong Park and Walter Willinger Copyright # 2000 by John Wiley & Sons, Inc. Print ISBN 0-471-31974-0 Electronic ISBN 0-471-20644-X

309 The remarkable antiviral activity observed with these compounds on day 1 or 2 postinfection, but not on the day of viral challenge, suggests that these compounds might have additional mechanisms of action, which contribute to the drug s effect after viral replication has begun, but before widespread dissemination of infection and extensive tissues damage have occurred c3-NPC A was shown to induce a massive release of IFN-a when administered to Ebola-infected mice, but not uninfected mice, apparently reversing the virus-induced suppression of the type I.

Figure 11.3 The dose-response relationship. (a) Five segments of the sigmoidal dose-response curve as described in the text. (b) Linearized dose-response relationship through log (dose)-probit (effect) transformations. Locations of the LD50 and LD05 are depicted.

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NETWORK DESIGN AND CONTROL USING ON=OFF AND MULTILEVEL SOURCE TRAFFIC MODELS WITH HEAVY-TAILED DISTRIBUTIONS

In order to help design and control the emerging high-speed communication networks, we want source traf c models (also called offered load models or bandwidth demand models) that can be both realistically t to data and successfully analyzed. Many recent traf c measurements have shown that network traf c is quite complex, exhibiting phenomena such as heavy-tailed probability distributions, longrange dependence, and self similarity; for example, see Caceres et al. [7], Leland et al. [23], Paxson and Floyd [24], and Crovella and Bestavros [10]. In fact, the heavy-tailed distributions may be the cause of all these phenomena, because they tend to cause long-range dependence and (asymptotic) self-similarity. For example, the input and buffer content processes associated with an on=off source exhibit long-range dependence when the on and off times have heavy-tailed probability distributions; for example, see Section 17.9. Heavy-tailed distributions are known to cause self-similarity in models of (asymptotically) aggregated traf c; see Willinger et al. [27]. In this chapter we propose a way to analyze the performance of a network with multiple on=off sources and more general multilevel sources in which the on-time, off-time, and level-holding-time distributions are allowed to have heavy tails. To do

The dose-response line also can be used to estimate the threshold dose. The threshold dose is de ned as the lowest dose of the chemical that would be expected to elicit a response under conditions at which the assay was performed. The threshold dose is often empirically estimated as being a dose less that the lowest dose at which an effect was measured but higher than the greatest dose at which no effect was detected. Conceptually, the threshold dose is de ned as the intercept of segments I and II of the dose-response curve (Figure 11.3a). Statistically, the threshold dose can be estimated from the linearized dose-response curve as the LC05. This value will closely approximate the threshold dose and can be statistically derived from the entire data set (i.e., the dose-response line). However, con dence in this value is greatly compromised, since it is derived from one end of the line (Figure 11.3b).

IFN response.310 However, this effect of IFN stimulation was not observed in similarly treated Ebola-infected monkeys.118 Treatment delayed the onset of viremia and illness but did not protect the monkeys from death.311 In summary, IFN-a is highly effective in cell cultures and in Ebola-infected mice when given as a series of doses beginning on the day of exposure. However, IFN therapy is less effective in primates.311

Self-Similar Network Traf c and Performance Evaluation, Edited by Kihong Park and Walter Willinger ISBN 0-471-31974-0 Copyright # 2000 by John Wiley & Sons, Inc.

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