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This thesis creates a Domain-Specific Languages (DSL) implementation and integration inside the iipax case handling software. The implementation investigated advantages with Domain-Specific Languages. Following agile software development methods the paper results in a functioning Domain-Specific Language emulating written English. / I denna rapport utvecklas ett Domän-Specifikt Språk (DSL) och integreras i ärehanterings systemet iipax. Implementationen undersökte fördelar med Domän-Specifika Språk. Rapporten ägnade sig åt agil mjukvaruutveckling och resulterade i ett fungerande Domän-Specifikt Språk som efterliknandar engelska.

Protein-protein interactions are an integral part of virtually all aspects of cellular function. Many of these interactions are mediated by small modular units called protein interaction domains (PIDs). We do not yet understand, however, how much functional information is encoded in these modules. It has previously been shown that Nbp2SH3 and Bem1SH3b domains in S. cerevisiae bind several target peptides with the same consensus sequence, yet display finely tuned affinities for each. In this study, I have shown that there exists an evolutionarily conserved ability of orthologous fungal Nbp2SH3, Bem1SH3b, and Abp1SH3 domains to discriminate between target peptides within the same species. In addition, I have developed a method to quantitatively probe SH3 domain specificity using purified SH3 domains and naturally occurring proline-rich constructs (PRRs) in the context of cell lysate from S. cerevisiae. Expansion of this work may yield valuable insights into intrinsic SH3 domain specificity.

Spam, the distribution of unsolicited bulk email, is a big security threat on the Internet. Recent studies show approximately 70-90% of the worldwide email traffic—about 70 billion messages a day—is spam. Spam consumes resources on the network and at mail servers, and it is also used to launch other attacks on users, such as distributing malware or phishing. Spammers have increased their virulence and resilience by sending spam from large collections of compromised machines (“botnets”). Spammers also make heavy use of URLs and domains to direct victims to point-of-sale Web sites, and miscreants register large number of domains to evade blacklisting efforts. To mitigate the threat of spam, users and network administrators need proactive techniques to distinguish spammers from legitimate senders and to take down online spam-advertised sites. In this dissertation, we focus on characterizing spam-related activities and developing systems to detect them early. Our work builds on the observation that spammers need to acquire attack agility to be profitable, which presents differences in how spammers and legitimate users interact with Internet services and exposes detectable during early period of attack. We examine several important components across the spam life cycle, including spam dissemination that aims to reach users' inboxes, the hosting process during which spammers set DNS servers and Web servers, and the naming process to acquire domain names via registration services. We first develop a new spam-detection system based on network-level features of spamming bots. These lightweight features allow the system to scale better and to be more robust. Next, we analyze DNS resource records and lookups from top-level domain servers during the initial stage after domain registrations, which provides a global view across the Internet to characterize spam hosting infrastructure. We further examine the domain registration process and present the unique registration behavior of spammers. Finally, we build an early-warning system to identify spammer domains at time-of-registration rather than later at time-of-use. We have demonstrated that our detection systems are effective by using real-world datasets. Our work has also had practical impact. Some of the network-level features that we identified have since been incorporated into spam filtering products at Yahoo! and McAfee, and our work on detecting spammer domains at time-of-registration has directly influenced new projects at Verisign to investigate domain registrations.

The yeast Fus1p SH3 domain recognizes non-PXXP motif targets. This is contrary to most SH3 domains, which recognize PXXP-containing sequences. In this thesis, I characterized atypical binding of the Fus1p SH3 domain and provide insight into atypical SH3 domain interactions. I demonstrated that the Fus1p SH3 domain binds R(S/T)(S/T)SL-containing peptides derived from yeast proteins, Bnr1p and Ste5p. Through mutagenesis studies and comparisons to other SH3 domains, I showed that the Fus1p SH3 domain utilizes a portion of the same binding surface as typical SH3 domains. However, the PXXP-binding surface is debilitated in the WT domain by the substitution of unusual residues at three key conserved positions. By replacing these residues, I created a version of the Fus1p SH3 domain that binds a PXXP-containing peptide with extremely high affinity. Based on my data and analysis, I clearly delineated two distinct surfaces comprising the typical SH3 domain binding interface, and show that one of these surfaces is the primary mediator of almost every “non-canonical” SH3 domain-mediated interaction. I demonstrated that the Fus1p SH3 domain also binds Bni1p and Pea2p through yeast two-hybrid experiments, which do not contain either PXXP or R(S/T)(S/T)SL motifs. Through mutagenesis studies and two-hybrid experiments, I showed that the Fus1p SH3 domain utilizes a binding surface comprised of two sub-surfaces to interact with Bni1p and Pea2p. The sub-surfaces include the same portion of the binding surface in typical SH3 domains utilized in R(S/T)(S/T)SL binding of the Fus1p SH3 domain, and a novel binding site. I also showed that the protein binding surface of the Fus1p SH3 domain has a role(s) for efficient mating. Based on my findings in this thesis, I propose that dramatic alterations in SH3 domain specificity can be simply explained as a modulation of the binding strengths of sub-binding sites within the binding surface.

Wavefield reconstruction is a crucial step in the seismic processing flow. For instance, unsuccessful interpolation leads to erroneous multiple predictions that adversely affect the performance of multiple elimination, and to imaging artifacts. We present a new non-parametric transform-based reconstruction method that exploits the compression of seismic data b the recently developed curvelet transform. The elements of this transform, called curvelets, are multi-dimensional, multi-scale, and multi-directional. They locally resemble wavefronts present in the data, which leads to a compressible representation for seismic data. This compression enables us to formulate a new curvelet-based seismic data recovery algorithm through sparsity-promoting inversion (CRSI). The concept of sparsity-promoting inversion is in itself not new to geophysics. However, the recent insights from the field of "compressed sensing" are new since they clearly identify the three main ingredients that go into a successful formulation of a reconstruction problem, namely a sparsifying transform, a sub-Nyquist sampling strategy that subdues coherent aliases in the sparsifying domain, and a data-consistent sparsity-promoting program. After a brief overview of the curvelet transform and our seismic-oriented extension to the fast discrete curvelet transform, we detail the CRSI formulation and illustrate its performance on synthetic and read datasets. Then, we introduce a sub-Nyquist sampling scheme, termed jittered undersampling, and show that, for the same amount of data acquired, jittered data are best interpolated using CRSI compared to regular or random undersampled data. We also discuss the large-scale one-norm solver involved in CRSI. Finally, we extend CRSI formulation to other geophysical applications and present results on multiple removal and migration-amplitude recovery.

Thesis (Ph. D - Soil, Water abd Environmental Science) - University of Arizona. / Includes bibliographical references.

Zugl.: Jena, Univ., Diss., 2004