Speciation is characterized by the development of reproductive isolating barriers between diverging groups. Intrinsic post-zygotic barriers of the type envisioned by Bateson, Dobzhansky, and Muller are deleterious epistatic interactions among loci that reduce hybrid fitness, leading to reproductive isolation. The first formal population genetic model of the development of these barriers was published by Orr in 1995, and here we develop a more general model of this process by incorporating finite protein–protein interaction networks, which reduce the probability of deleterious interactions in vivo. Our model shows that the development of deleterious interactions is limited by the density of the protein–protein interaction network. We have confirmed our analytical predictions of the number of possible interactions given the number of allele substitutions by using simulations on the Saccharomyces cerevisiae protein–protein interaction network. These results allow us to define the rate at which deleterious interactions are expected to form, and hence the speciation rate, for any protein–protein interaction network.

In computer-aided diagnosis (CAD), having an accurate ground truth is critical. However, the number of databases containing medical images with diagnostic information is limited. Using pulmonary computed tomography (CT) scans, we develop a content-based image retrieval (CBIR) approach to exploit the limited amount of diagnostically labeled data in order to annotate unlabeled images with diagnoses. By applying this CBIR method iteratively, we expand the set of diagnosed data available for CAD systems. We evaluate the method by implementing a CAD system that uses undiagnosed lung nodules as queries and retrieves similar nodules from the diagnostically labeled dataset. In calculating the precision of this system, radiologist-and computer-predicted malignancy data are used as ground truth for the undiagnosed query nodules. Our results indicate that CBIR expansion is an effective method for labeling undiagnosed images in order to improve the performance of CAD systems.

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InputStage A Stage B Stage C Stage D Piko Frontend Pipeline Schedule Pipeline Description Spatial Bins Multicore CPU Manycore GPU Piko Backend Figure 1: Piko is a framework for designing and implementing programmable graphics pipelines that can be easily retargeted to different application configurations and architectural targets. Piko's input is a functional and structural description of the desired graphics pipeline, augmented with a per-stage grouping of computation into spatial bins (or tiles), and a scheduling preference for these bins. Our compiler generates efficient implementations of the input pipeline for multiple architectures and allows the programmer to tweak these implementations using simple changes in the bin configurations and scheduling preferences. AbstractWe present Piko, a framework for designing, optimizing, and retargeting implementations of graphics pipelines on multiple architectures. Piko programmers express a graphics pipeline by organizing the computation within each stage into spatial bins and specifying a scheduling preference for these bins. Our compiler, Pikoc, compiles this input into an optimized implementation targeted to a massively-parallel GPU or a multicore CPU. Piko manages work granularity in a programmable and flexible manner, allowing programmers to build load-balanced parallel pipeline implementations, to exploit spatial and producer-consumer locality in a pipeline implementation, and to explore tradeoffs between these considerations. We demonstrate that Piko can implement a wide range of pipelines, including rasterization, Reyes, ray tracing, rasterization/ray tracing hybrid, and deferred rendering. Piko allows us to implement efficient graphics pipelines with relative ease and to quickly explore design alternatives by modifying the spatial binning configurations and scheduling preferences for individual stages, all while delivering real-time performance that is within a factor six of state-of-the-art rendering systems.

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