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Snake fungal disease (SFD) is an emerging skin infection of wild snakes in eastern North America. The fungus Ophidiomyces ophiodiicola is frequently associated with the skin lesions that are characteristic of SFD, but a causal relationship between the fungus and the disease has not been established. We experimentally infected captive-bred corn snakes (Pantherophis guttatus) in the laboratory with pure cultures of O. ophiodiicola. All snakes in the infected group (n = 8) developed gross and microscopic lesions identical to those observed in wild snakes with SFD; snakes in the control group (n = 7) did not develop skin infections. Furthermore, the same strain of O. ophiodiicola used to inoculate snakes was recovered from lesions of all animals in the infected group, but no fungi were isolated from individuals in the control group. Monitoring progression of lesions throughout the experiment captured a range of presentations of SFD that have been described in wild snakes. The host response to the infection included marked recruitment of granulocytes to sites of fungal invasion, increased frequency of molting, and abnormal behaviors, such as anorexia and resting in conspicuous areas of enclosures. While these responses may help snakes to fight infection, they could also impact host fitness and may contribute to mortality in wild snakes with chronic O. ophiodiicola infection. This work provides a basis for understanding the pathogenicity of O. ophiodiicola and the ecology of SFD by using a model system that incorporates a host species that is easy to procure and maintain in the laboratory.

The celebrated PageRank algorithm has proved to be a very effective paradigm for ranking results of web search algorithms. In this paper we refine this basic paradigm to take into account several evolving prominent features of the web, and propose several algorithmic innovations. First, we analyze features of the rapidly growing "frontier" of the web, namely the part of the web that crawlers are unable to cover for one reason or another. We analyze the effect of these pages and find it to be significant. We suggest ways to improve the quality of ranking by modeling the growing presence of "link rot" on the web as more sites and pages fall out of maintenance. Finally we suggest new methods of ranking that are motivated by the hierarchical structure of the web, are more efficient than PageRank, and may be more resistant to direct manipulation.

In several crypt,ographic systems, a fixed elcment g of a group (generally z / q z) is repeatedly raised to many different powers. In this paper we present a practical method of speeding u p such systems. using precomputed values to reduce the number of multiplications needed. In practice this provides a substantial improvement over the level of performance that can be obtained using addition chains, and allows the computation of g" for n < N in O(1og Nlloglog N) group multiplications. We also show how these methods can he parallelized. t o c o m p u t e powers in O(1og log iV) group multiplications with o(1og iV/ log log .V) processors.

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Algorithmic tools for searching and mining the Web are becoming increasingly sophisticated and vital. In this context, algorithms that use and exploit structural information about the Web perform better than generic methods in both efficiency and reliability.We present an extensive characterization of the graph structure of the Web, with a view to enabling high-performance applications that make use of this structure. In particular, we show that the Web emerges as the outcome of a number of essentially independent stochastic processes that evolve at various scales. A striking consequence of this scale invariance is that the structure of the Web is "fractal"---cohesive subregions display the same characteristics as the Web at large. An understanding of this underlying fractal nature is therefore applicable to designing data services across multiple domains and scales.We describe potential applications of this line of research to optimized algorithm design for Web-scale data analysis.

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No abstract