There is an increasing demand within the humanities and social sciences to use computers to analyze material culture and discover patterns of historical and anthropological significance. Using southern Levantine Iron Age (ca. 1200-500 BCE) ceramics as a test case, the Pottery Informatics Query Database (PIQD) provides a novel solution for constructing regional ceramic typologies. Beyond digitally archiving 2D/3D-scanned ceramics, the PIQD encodes ceramic profiles as mathematical representations. This method of digital preservation enables rapid queries to be conducted in a mathematically grounded approach. In this sense, the queries are similar to online Basic Local Alignment Search Tool searches developed in the field of genetics by rapidly associating large quantities of digital vessel profiles to each other based on similar morphological traits. The PIQD is an open-source online tool that enables scholars and students to test humanities-related hypotheses against ceramic data in ways that conventional publications or other databases cannot provide. Regional spatial patterning of the ceramic data is delivered over a Google Earth-based user interface. In this paper, we present the PIQD as an objective method for developing a comprehensive ceramic typology of an entire region of archaeological study and provide an arena to conduct novel scientific research. We then demonstrate through a case study its analytical capabilities to handle large datasets of 3D scans and digitized 2D ceramic profiles and generate cultural inferences with the ceramic assemblages of the Iron Age II "Edomite" region located in modern southern Jordan. PIQD adds an important methodological tool to the post-excavation cyber-archaeology tool box.

BackgroundMany recent studies have investigated modularity in biological networks, and its role in functional and structural characterization of constituent biomolecules. A technique that has shown considerable promise in the domain of modularity detection is the Newman and Girvan (NG) algorithm, which relies on the number of shortest-paths across pairs of vertices in the network traversing a given edge, referred to as the betweenness of that edge. The edge with the highest betweenness is iteratively eliminated from the network, with the betweenness of the remaining edges recalculated in every iteration. This generates a complete dendrogram, from which modules are extracted by applying a quality metric called modularity denoted by Q. This exhaustive computation can be prohibitively expensive for large networks such as Protein-Protein Interaction Networks. In this paper, we present a novel optimization to the modularity detection algorithm, in terms of an efficient termination criterion based on a target edge betweenness value, using which the process of iterative edge removal may be terminated.ResultsWe validate the robustness of our approach by applying our algorithm on real-world protein-protein interaction networks of Yeast, C.Elegans and Drosophila, and demonstrate that our algorithm consistently has significant computational gains in terms of reduced runtime, when compared to the NG algorithm. Furthermore, our algorithm produces modules comparable to those from the NG algorithm, qualitatively and quantitatively. We illustrate this using comparison metrics such as module distribution, module membership cardinality, modularity Q, and Jaccard Similarity Coefficient.ConclusionsWe have presented an optimized approach for efficient modularity detection in networks. The intuition driving our approach is the extraction of holistic measures of centrality from graphs, which are representative of inherent modular structure of the underlying network, and the application of those measures to efficiently guide the modularity detection process. We have empirically evaluated our approach in the specific context of real-world large scale biological networks, and have demonstrated significant savings in computational time while maintaining comparable quality of detected modules.

Duchenne Muscular Dystrophy (DMD) is an important pathology associated with the human skeletal muscle and has been studied extensively. Gene expression measurements on skeletal muscle of patients afflicted with DMD provides the opportunity to understand the underlying mechanisms that lead to the pathology. Community structure analysis is a useful computational technique for understanding and modeling genetic interaction networks. In this paper, we leverage this technique in combination with gene expression measurements from normal and DMD patient skeletal muscle tissue to study the structure of genetic interactions in the context of DMD. We define a novel framework for transforming a raw dataset of gene expression measurements into an interaction network, and subsequently apply algorithms for community structure analysis for the extraction of topological communities. The emergent communities are analyzed from a biological standpoint in terms of their constituent biological pathways, and an interpretation that draws correlations between functional and structural organization of the genetic interactions is presented. We also compare these communities and associated functions in pathology against those in normal human skeletal muscle. In particular, differential enhancements are observed in the following pathways between pathological and normal cases: Metabolic, Focal adhesion, Regulation of actin cytoskeleton and Cell adhesion, and implication of these mechanisms are supported by prior work. Furthermore, our study also includes a gene-level analysis to identify genes that are involved in the coupling between the pathways of interest. We believe that our results serve to highlight important distinguishing features in the structural/functional organization of constituent biological pathways, as it relates to normal and DMD cases, and provide the mechanistic basis for further biological investigations into specific pathways differently regulated between normal and DMD patients. These findings have the potential to serve as fertile ground for therapeutic applications involving targeted drug development for DMD.

BackgroundCiliary defects cause heterogenous phenotypes related to mutation burden which lead to impaired development. A previously reported homozygous deletion in the Man1a2 gene causes lethal respiratory failure in newborn pups and decreased lung ciliation compared with wild type (WT) pups. The effects of heterozygous mutation, and the potential for rescue are not known.PurposeWe hypothesized that survival and lung ciliation, (a) would decrease progressively in Man1a2+/− heterozygous and Man1a2–/– null newborn pups compared with WT, and (b) could be enhanced by gestational treatment with N-Acetyl-cysteine (NAC), an antioxidant.MethodsMan1a2+/– adult mice were fed NAC or placebo from a week before breeding through gestation. Survival of newborn pups was monitored for 24 h. Lungs, liver and tails were harvested for morphology, genotyping, and transcriptional profiling.ResultsSurvival (p = 0.0001, Kaplan-Meier) and percent lung ciliation (p = 0.0001, ANOVA) measured by frequency of Arl13b+ respiratory epithelial cells decreased progressively, as hypothesized. Compared with placebo, gestational NAC treatment enhanced (a) lung ciliation in pups with each genotype, (b) survival in heterozygous pups (p = 0.017) but not in WT or null pups. Whole transcriptome of lung but not liver demonstrated patterns of up- and down-regulated genes that were identical in living heterozygous and WT pups, and completely opposite to those in dead heterozygous and null pups. Systems biology analysis enabled reconstruction of protein interaction networks that yielded functionally relevant modules and their interactions. In these networks, the mutant Man1a2 enzyme contributes to abnormal synthesis of proteins essential for lung development. The associated unfolded protein, hypoxic and oxidative stress responses can be mitigated with NAC. Comparisons with the developing human fetal lung transcriptome show that NAC likely restores normal vascular and epithelial tube morphogenesis in Man1a2 mutant mice.ConclusionSurvival and lung ciliation in the Man1a2 mutant mouse, and its improvement with N-Acetyl cysteine is genotype-dependent. NAC-mediated rescue depends on the central role for oxidative and hypoxic stress in regulating ciliary function and organogenesis during development.

Community detection is a key problem of interest in network analysis, with applications in a variety of domains such as biological networks, social network modeling, and communication pattern analysis. In this paper, we present a novel framework for community detection that is motivated by a physical system analogy. We model a network as a system of point masses, and drive the process of community detection, by leveraging the Newtonian interactions between the point masses. Our framework is designed to be generic and extensible relative to the model parameters that are most suited for the problem domain. We illustrate the applicability of our approach by applying the Newtonian Community Detection algorithm on protein-protein interaction networks of E. coli , C. elegans, and S. cerevisiae. We obtain results that are comparable in quality to those obtained from the Newman-Girvan algorithm, a widely employed divisive algorithm for community detection. We also present a detailed analysis of the structural properties of the communities produced by our proposed algorithm, together with a biological interpretation using E. coli protein network as a case study. A functional enrichment heat map is constructed with the Gene Ontology functional mapping, in addition to a pathway analysis for each community. The analysis illustrates that the proposed algorithm elicits communities that are not only meaningful from a topological standpoint, but also possess biological relevance. We believe that our algorithm has the potential to serve as a key computational tool for driving therapeutic applications involving targeted drug development for personalized care delivery.