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The obvious spikes around 0146 s for the CMU dataset and around 0000234 s for the external AT&T dataset are due to protocol dependencies The spike in the CMU dataset is caused almost entirely by the KSHELL protocol Each KSHELL invocation establishes two TCP connections within a time period of roughly 0146 s Since, on this Ethernet segment, it is quite likely that no other TCP connection request is initiated within such a small time window, this creates a spike in the empirical cumulative distribution The spike in the external AT&T dataset is caused almost entirely by World Wide Web accesses that correspond to an HTTP client establishing parallel TCP connections to load inlined HTTP objects such as gif les Since this external AT&T dataset is dominated by HTTP traf c, this spike is much more prevalent here than in the CMU dataset.

For the purpose of the analysis discussed in this chapter we did not exclude any of these observations Next, consider the shape and characteristics of the empirical cumulative distribution of all interarrival times Figure 158(a) shows the empirical cumulative distribution resulting from the empirical density function of Fig 157(a) for a CMU trace together with the tted cumulative distributions of the Weibull, Pareto, lognormal (lnorm), and exponential distributions Figure 158(b) shows the cumulative distributions for the external AT&T trace It is immediately obvious that the exponential, Pareto, and lognormal distributions result in very poor ts, while the agreement between the tted Weibull distribution and the measured interarrival times is quite good It is interesting that the exponential model underestimates short as well as long interarrival times, while the lognormal model overestimates both.

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in adipose tissue of the organism with no resulting overt toxicity. Upon entering the reproductive phase, organisms may mobilize fatty stores, releasing the chemical into the blood stream resulting in overt toxicity including death (Figure 11.1c). One could argue under this scenario that chronic exposure ultimately resulted in acute effects. Lastly, acute exposure during a susceptible window of exposure (i.e., embryo development) may result in reproductive abnormalities and reduced fecundity once the organism has attained reproductive maturity (Figure 11.1d). Thus acute exposure may result in chronic toxicity. An additional consideration is noteworthy when comparing acute and chronic toxicity. All chemicals elicit acute toxicity at a suf ciently high dose, whereas all chemicals do not elicit chronic toxicity. Paracelsus often cited phrase all things are poison . . . the dose determines . . . a poison is clearly in reference to acute toxicity. Even the most benign substances will elicit acute toxicity if administered at a suf ciently high dose. However, raising the dose of a chemical does not ensure that chronic toxicity will ultimately be attained. Since chronic toxicity typically occurs at dosages below those

Type I IFNs activate the JAK/STAT signal transduction pathway by binding to the IFN-a/b receptor (IFNAR) The cytoplasmic tails of IFNAR are associated with tyrosine kinases (JAK and TYK), which phosphorylate signal transducers and activators of transcription (STAT)5 STATs are latent transcription factors that, upon phosphorylation, dimerize and form complexes with interferon response factors (IRFs) These complexes move to the nucleus and bind to interferon-stimulated regulatory elements (ISRE)6 Several genes are then transcriptionally stimulated Of these gene products, many contribute to the antiviral actions of the host immune response PKR is a double-stranded RNA (dsRNA)-dependent serine/threonine kinase In the presence of dsRNA, most likely produced from the viral genome itself or formed from its replication or convergent transcription, PKR phosphorylates the a subunit of the eukaryotic initiation factor eIF-2.

The model based on the Pareto distribution overestimates the medium length interarrival times and yields a very poor t for the shorter interarrival times The observation that the tted Weibull distribution yields a much better t than any of the other ones be con rmed the discrepancy measure l2 Table 155 q can q using 2 2 2 ^ ^ ^ ^ ^ ^ summarizes l v l and l v l2 for all ts and three data sets This corresponds to a 6826% con dence interval Figure 159 visualizes the data from Table 155 for all datasets in a bar plot where the height of the bars corresponds to ^ ^ the value of l2 and the error bars to the standard deviation of l2 or the 6826% con dence intervals.

Xenobiotics can affect allergic disease in one of two ways. They can themselves act as antigens and elicit hypersensitivity responses, or they can enhance the development or expression of allergic responses to commonly encountered allergens, such as dust mite. Chemicals that act as allergens include certain proteins that can by themselves induce an immune response and low molecular weight chemicals (known as haptens) that are too small to induce a speci c immune response but may react with a protein to induce an immune response that is then hapten speci c. Haptens have been associated with both allergic contact dermatitis (ACD), sometimes called contact hypersensitivity

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