To identify gene products that participate in auxin-dependent lateral root formation, a high temporal resolution, genome-wide transcript abundance analysis was performed with auxin-treated Arabidopsis thaliana roots. Data analysis identified 1246 transcripts that were consistently regulated by indole-3-acetic acid (IAA), partitioning into 60 clusters with distinct response kinetics. We identified rapidly induced clusters containing auxin-response functional annotations and clusters exhibiting delayed induction linked to cell division temporally correlated with lateral root induction. Several clusters were enriched with genes encoding proteins involved in cell wall modification, opening the possibility for understanding mechanistic details of cell structural changes that result in root formation following auxin treatment. Mutants with insertions in 72 genes annotated with a cell wall remodeling function were examined for alterations in IAA-regulated root growth and development. This reverse-genetic screen yielded eight mutants with root phenotypes. Detailed characterization of seedlings with mutations in CELLULASE3/GLYCOSYLHYDROLASE9B3 and LEUCINE RICH EXTENSIN2, genes not normally linked to auxin response, revealed defects in the early and late stages of lateral root development, respectively. The genes identified here using kinetic insight into expression changes lay the foundation for mechanistic understanding of auxin-mediated cell wall remodeling as an essential feature of lateral root development.

Osteoarthritis (OA) is characterized by remodeling and degradation of joint tissues. Microarray studies have led to a better understanding of the molecular changes that occur in tissues affected by conditions such as OA; however, such analyses are limited to the identification of a list of genes with altered transcript expression, usually at a single time point during disease progression. While these lists have identified many novel genes that are altered during the disease process, they are unable to identify perturbed relationships between genes and gene products. In this work, we have integrated a time course gene expression data set with network analysis to gain a better systems level understanding of the early events that occur during the development of OA in a mouse model. The subnetworks that were enriched at one or more of the time points examined (2, 4, 8, and 16 weeks after induction of OA) contained genes from several pathways proposed to be important to the OA process, including the extracellular matrix-receptor interaction and the focal adhesion pathways and the Wnt, Hedgehog and TGF-β signaling pathways. The genes within the subnetworks were most active at the 2 and 4 week time points and included genes not previously studied in the OA process. A unique pathway, riboflavin metabolism, was active at the 4 week time point. These results suggest that the incorporation of network-type analyses along with time series microarray data will lead to advancements in our understanding of complex diseases such as OA at a systems level, and may provide novel insights into the pathways and processes involved in disease pathogenesis.

Identifying application types in network traffic is a difficult problem for administrators who must secure and manage network resources, further complicated by the use of encrypted protocols and nonstandard port numbers. This paper takes a unique approach to this problem by modeling and analyzing application graphs, structures which describe the applicationlevel (e.g., HTTP, FTP) communications between hosts. These graphs are searched for motifs: recurring, significant patterns of interconnections that can be used to help determine the network application in use. Motif-based analysis has been applied predominantly to biological networks to hypothesize key functional regulatory units, but never to network traffic as it is here. For the proposed method, a description of each node is generated based on its participation in statistically significant motifs. These descriptions, or profiles, are data points in multidimensional space that are used as input to a k-nearest neighbor (k-NN) classifier to predict the application. This work also compares the performance of motif-based analysis to an alternative profile type based on "traditional" graph measures such as path lengths, clustering coefficients and centrality measures. The results show that motif profiles perform better than traditional profiles, and are able to correctly identify the actions of 85% of the hosts examined across seven protocols.

A Moving Target (MT) defense constantly changes a system's attack surface, in an attempt to limit the usefulness of the reconnaissance the attacker has collected. One approach to this defense strategy is to intermittently change a system's configuration. These changes must maintain functionality and security, while also being diverse. Finding suitable configuration changes that form a MT defense is challenging. There are potentially a large number of individual configurations' settings to consider, without a full understanding of the settings' interdependencies. Evolution-based algorithms, which formulate better solutions from good solutions, can be used to create a MT defense. New configurations are created based on the security of previous configurations and can be periodically implemented to change the system's attack surface. This approach not only has the ability to discover new, more secure configurations, but is also proactive and resilient since it can continually adapt to the current environment in a fashion similar to systems found in nature. This article presents and compares two genetic algorithms to create a MT defense. The primary difference between the two is based on their approaches to mutation. One mutates values, and the other modifies the domains from which values are chosen.

This paper describes a new program for attracting non-traditional students into computer science and retaining them through sustained peer and faculty mentoring. The program is centered on socially-inspired learning, --learning in and for a community. It consists of a STEM Incubator course, hands-on projects with realworld applications, a sandbox lab, and a mentoring system that begins in the STEM Incubator course and continues with students who choose to remain involved in projects and courses. Our program is in its second year. Data collected on enrollment and retention and results of student questionnaires show promise for the success and sustainability of the program.