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A million years after the birth of our sun, the violent explosion of a nearby supernova nearly ended life on Earth before it began. Over the next four and a half billion years, forces of nature shaped our planet and the life it harbored. Barely surviving the traumatic birth of the Moon, buffeted by supernovae, and bombarded by asteroids, the resilient Earth endured. And despite planet-freezing ice ages, devastating mass extinctions, and ever changing climate, life not only survived, it thrived. Today, we are told all life on Earth is threatened by a new peril--human-caused global warming. The Resilient Earth presents the science behind global warming for a general audience, separating fact from fiction and truth from exaggeration.

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Modeling has long been recognized as an invaluable tool for predicting the performance behavior of computer systems. Modeling software, both commercial and open source, is widely used as a guide for the development of new systems and the upgrading of exiting ones. Unfortunately, no set of comprehensive tools exists for modeling complex distributed computing environments such as the ones found in emerging grid deployments. This chapter addresses concepts, methodologies, and tools that are useful when designing, implementing, and tuning the performance in grid and cluster environments.

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The purpose of this study is to develop an adaptive model of a very large scale data processing and storage environment. The target environment includes grid applications such as health-care and finance in which the data may be located primarily within the resources of a worldwide corporation. The approach is to use phase identification techniques that can detect over-utilized grid resources, and then to make dynamic decisions to reassign additional resources to that portion of the application processing. Two phase identification techniques are proposed, a variation technique and a real-time threshold-based technique. The techniques are validated with a simulation model and a case study using measured data from a production grid environment. The case study demonstrates that phase identification techniques can be used as the intelligent component of a reactive mechanism for a grid to adapt to changing environmental conditions by dynamic automatic reconfiguration. Results show that threshold based phase identifying techniques combined with dynamic resource allocation capabilities are effective in alleviating performance hot spots and improving response time in a large scale data grid.

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Proteins are large complex organic molecules that are essential to the existence of life. Decades of study have revealed that proteins having different sequences of amino acids can posses very similar three-dimensional structures. To date, protein structure comparison methods have been accurate but costly in terms of computer time. This dissertation presents a new method for comparing protein structures using dihedral transformations. Atomic XYZ coordinates are transformed into a sequence of dihedral angles, which is then transformed into a sequence of dihedral sectors. Alignment of two sequences of dihedral sectors reveals similarities between the original protein structures. Experiments have shown that this method detects structural similarities between sequences with less than 20% amino acid sequence identity, finding structural similarities that would not have been detected using amino acid alignment techniques. Comparisons can be performed in seconds that had previously taken minutes or hours.

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We describe the design, implementation, and deployment via the Internet of BioSCAN, an application-specific computer system for the rapid determination of statistically significant alignments of biopolymer (DNA, RNA, protein) sequences. BioSCAN continues to outperform other systems designed to perform this basic task of molecular biology, which continues to grow in magnitude and importance. The BioSCAN system is hosted by a general-purpose workstation containing a special-purpose hardware engine that accelerates the core algorithm for comparing two biosequences. Careful partitioning of the computational tasks between hardware and software provides not only high performance but also programmability. The BioSCAN system can compare a sequence of up to 12,992 characters with an arbitrarily large database containing arbitrarily long sequences at a rate of 2 million database characters per second. This rate is nearly 1,000 times greater than the rate achieved by a state-of-the-art workstation using software alone. This network-sharable computational resource is accessible interactively via the World Wide Web using Mosaic, Netscape or other client software.

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We describe a network sharable, interactive computational tool for rapid and sensitive search and analysis of biomolecular sequence databases such as GenBank, GenPept, Protein Identification Resource, and SWISS-PROT. The resource is accessible via the World Wide Web using popular client software such as Mosaic and Netscape. The client software is freely available on a number of computing platforms including Macintosh, IBM-PC, and Unix workstations.

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Rapid growth of protein structures database in recent years requires an effective approach for objective comparison and classification of deposited protein structures. We describe a novel method for structure comparison and classification based on the alignment of one-dimensional structure profiles. These profiles are obtained by calculating the OCCO pseudodihedral angles (formed by O-C-C-O atoms of carbonyl groups of consecutive amino acid residues) from protein three-dimensional coordinates. These angle measurements are then converted into a 24 letter alphabet, and the protein structures are represented by sequences of letter from this alphabet. The BioSCAN parallel computer, designed for primary sequence alignment, is used to rapidly align and classify these one-dimensional structure profiles. We have developed and implemented weighted scoring matrix to identify structural classes based on commonly found structural motifs. The results of our experiments are in good agreement with the traditional protein structure classification schemes. One-dimensional structure profiles significantly improve efficiency of structure comparison and classification.

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The design and implementation of an application-specific, fault-tolerant, and scalable multiprocessor system called BioSCAN (Biological Sequence Comparative Analysis Node) are described. Discussed are system partitioning and integration, functional decomposition between hardware and software, the algorithm and its implementation in VLSI, the early results of using the system, and comparison with other hardware and software solutions for biological sequence analysis.

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This paper is an exploration of the design goals for the Biological Sequence Comparative Analysis Node (BioSCAN) network server software and of the impact that these goals had on the overall structure and implementation of that software. The primary audience for this paper consists of computer scientists and computational biologists involved in developing similar server software. Biologists who are users of the BioSCAN computational node and have a desire for deeper understanding of how the server functions will also find this paper useful. It is assumed that the reader is familiar with UNIX and with basic networking concepts. The peculiarities of implementing a network server for a batch resource will be identified and the solutions chosen by the BioSCAN design team explained. Research for the BioSCAN project, including the design of the server software, was supported in part by NSF grant MIP-9024585.

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This paper compares the performance characteristics of the BioSCAN biological sequence matching algorithm on several different computer architectures. The architectures examined are a conventional {\sc RISC\/} general purpose uni-processor, a vector oriented ``supercomputer'', and a Single Instruction Multi Data ({\sc simd\/}) massively parallel computer. These architectures are represented by the following hardware platforms: a Sun 490 RISC, a Convex 240, and a MasPar MP-1. The performance of these three platforms is compared with that of the custom built BioSCAN hardware.

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