MOTIVATION: In bioinformatics, genome-wide experiments look for important biological differences between two groups at a large number of locations in the genome. Often, the final analysis focuses on a P-value-based ranking of locations which might then be investigated further in follow-up experiments. However, this strategy may result in small effect sizes, with low P-values, being ranked more favorably than larger more scientifically important effects. Bayesian ranking techniques may offer a solution to this problem provided a good prior distribution for the collective distribution of effect sizes is available. RESULTS: We develop an Empirical Bayes ranking algorithm, using the marginal distribution of the data over all locations to estimate an appropriate prior. In simulations and analysis using real datasets, we demonstrate favorable performance compared to ordering P-values and a number of other competing ranking methods. The algorithm is computationally efficient and can be used to rank the entirety of genomic locations or to rank a subset of locations, pre-selected via traditional FWER/FDR methods in a 2-stage analysis. AVAILABILITY AND IMPLEMENTATION: An R-package, EBrank, implementing the ranking algorithm is available on CRAN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

This paper presents design rule checking (DRC) methods to address challenges caused by non-Manhattan geometries that are widely present in photonic integrated circuit (PIC) physical designs. Verifying such layouts with traditional DRC tools results in a huge number of false errors that are impossible to debug; and some physical rules are simply uncheckable. We demonstrate the use of an extended DRC technique on photonic designs to tackle these issues. The results show that the technique is well-suited for silicon photonics DRC, enabling false errors to be filtered in a controllable manner, leading to easier debugging with layout annotation; multi-dimensional rule checking to be performed; and mathematical expressions to be used for more accurate descriptions of physical rules.

This paper demonstrates how useful content can be generated as a by-product of an enjoyable mobile multiplayer game. In EyeSpy, players tag geographic locations with photos or text. By locating the places in which other players' tags were created and 'confirming' them, players earn points for themselves and verify the tags' locations. As a side effect of game-play, EyeSpy produces a collection of recognisable and findable geographic details, in the form of photographs and text tags, that can be repurposed to support navigation tasks. Two user trials of the game successfully produced an archive of geo-located photographs and tags, and in a follow-up experiment we compared performance in a navigation task using photographs from the game, with geo-referenced photos collected from the Flickr website. Our experiences with EyeSpy support reflection upon the design challenges presented by 'human computation' and the production of usable by-products through mobile game-play.