Validation of a Taxonomy for Player Actions with Latency and Network Games

This project was designed to study the validity of a taxonomy to classify the impact of player actions with latency. We utilized a commercial game to simulate latency in a first person shooter match where participants competed against a computer controlled opponent. The participants utilized three different weapons: a shotgun, a rocket launcher, and a sniper rifle. Each weapon was designed to embody different characteristics of the taxonomy axes: precision, impact, and deadline. Overall, we partially confirmed the validity of a previous taxonomy. Our findings fit the taxonomy in regards to the impact of damage and the weapon’s shooting speed on a player’s performance but the results were inconclusive on other aspects of player actions

New techniques to characterise the vaginal microbiome in pregnancy

Understanding of the vaginal microbiome in health and disease is essential to screen, detect and manage complications in pregnancy. One of the major complications of pregnancy is preterm birth, which is the leading world-wide cause of death and disability in children under five years of age. The aetiology of preterm birth is multifactorial, but a causal link has been established with infection. Despite the importance of understanding the vaginal microbiome in pregnancy in order to evaluate strategies to prevent and manage PTB, currently used culture based techniques provide limited information as not all pathogens are able to be cultured. The implementation of culture-independent high-throughput techniques and bioinformatics tools are advancing our understanding of the vaginal microbiome. New methods employing 16S rRNA and metagenomics analyses make possible a more comprehensive description of the bacteria of the human microbiome. Several studies on the vaginal microbiota of pregnant women have identified a large number of taxa. Studies also suggest reduced diversity of the microbiota in pregnancy compared to non-pregnant women, with a relative enrichment of the overall abundance of Lactobacillus species, and significant differences in the diversity of Lactobacillus spp. A number of advantages and disadvantages of these techniques are discussed briefly. The potential clinical importance of the new techniques is illustrated through recent reports where traditional culture-based techniques failed to identify pathogens in high risk complicated pregnancies whose presence subsequently was established using culture-independent, high-throughput analyses

Diet assessment of the Atlantic Sea Nettle Chrysaora quinquecirrha in Barnegat Bay, New Jersey, using next-generation sequencing

Next-generation sequencing (NGS) methodologies have proven useful in deciphering the food items of generalist predators, but have yet to be applied to gelatinous animal gut and tentacle content. NGS can potentially supplement traditional methods of visual identification. Chrysaora quinquecirrha (Atlantic sea nettle) has progressively become more abundant in Mid-Atlantic United States’ estuaries including Barnegat Bay (New Jersey), potentially having detrimental effects on both marine organisms and human enterprises. Full characterization of this predator’s diet is essential for a comprehensive understanding of its impact on the food web and its management. Here, we tested the efficacy of NGS for prey item determination in the Atlantic sea nettle. We implemented a NGS ‘shotgun’ approach to randomly sequence DNA fragments isolated from gut lavages and gastric pouch/tentacle picks of eight and 84 sea nettles, respectively. These results were verified by visual identification and co-occurring plankton tows. Over 550 000 contigs were assembled from ~110 million paired-end reads. Of these, 100 contigs were confidently assigned to 23 different taxa, including soft-bodied organisms previously undocumented as prey species, including copepods, fish, ctenophores, anemones, amphipods, barnacles, shrimp, polychaete worms, flukes, flatworms, echinoderms, gastropods, bivalves and hemichordates. Our results not only indicate that a ‘shotgun’ NGS approach can supplement visual identification methods, but targeted enrichment of a specific amplicon/gene is not a prerequisite for identifying Atlantic sea nettle prey items

AMADA-Analysis of Multidimensional Astronomical Datasets

We present AMADA, an interactive web application to analyse multidimensional datasets. The user uploads a simple ASCII file and AMADA performs a number of exploratory analysis together with contemporary visualizations diagnostics. The package performs a hierarchical clustering in the parameter space, and the user can choose among linear, monotonic or non-linear correlation analysis. AMADA provides a number of clustering visualization diagnostics such as heatmaps, dendrograms, chord diagrams, and graphs. In addition, AMADA has the option to run a standard or robust principal components analysis, displaying the results as polar bar plots. The code is written in R and the web interface was created using the Shiny framework. AMADA source-code is freely available at https://goo.gl/KeSPue, and the shiny-app at http://goo.gl/UTnU7I.Comment: Accepted for publication in Astronomy & Computin

Computational biology in the 21st century

Computational biologists answer biological and biomedical questions by using computation in support of—or in place of—laboratory procedures, hoping to obtain more accurate answers at a greatly reduced cost. The past two decades have seen unprecedented technological progress with regard to generating biological data; next-generation sequencing, mass spectrometry, microarrays, cryo-electron microscopy, and other highthroughput approaches have led to an explosion of data. However, this explosion is a mixed blessing. On the one hand, the scale and scope of data should allow new insights into genetic and infectious diseases, cancer, basic biology, and even human migration patterns. On the other hand, researchers are generating datasets so massive that it has become difficult to analyze them to discover patterns that give clues to the underlying biological processes.National Institutes of Health. (U.S.) ( grant GM108348)Hertz Foundatio

A Stochastic Geometric Analysis of Device-to-Device Communications Operating over Generalized Fading Channels

Device-to-device (D2D) communications are now considered as an integral part of future 5G networks which will enable direct communication between user equipment (UE) without unnecessary routing via the network infrastructure. This architecture will result in higher throughputs than conventional cellular networks, but with the increased potential for co-channel interference induced by randomly located cellular and D2D UEs. The physical channels which constitute D2D communications can be expected to be complex in nature, experiencing both line-of-sight (LOS) and non-LOS (NLOS) conditions across closely located D2D pairs. As well as this, given the diverse range of operating environments, they may also be subject to clustering of the scattered multipath contribution, i.e., propagation characteristics which are quite dissimilar to conventional Rayeligh fading environments. To address these challenges, we consider two recently proposed generalized fading models, namely $\kappa-\mu$ and $\eta-\mu$, to characterize the fading behavior in D2D communications. Together, these models encompass many of the most widely encountered and utilized fading models in the literature such as Rayleigh, Rice (Nakagami-$n$), Nakagami-$m$, Hoyt (Nakagami-$q$) and One-Sided Gaussian. Using stochastic geometry we evaluate the rate and bit error probability of D2D networks under generalized fading conditions. Based on the analytical results, we present new insights into the trade-offs between the reliability, rate, and mode selection under realistic operating conditions. Our results suggest that D2D mode achieves higher rates over cellular link at the expense of a higher bit error probability. Through numerical evaluations, we also investigate the performance gains of D2D networks and demonstrate their superiority over traditional cellular networks.Comment: Submitted to IEEE Transactions on Wireless Communication

A Tractable Approach to Coverage and Rate in Cellular Networks

Cellular networks are usually modeled by placing the base stations on a grid, with mobile users either randomly scattered or placed deterministically. These models have been used extensively but suffer from being both highly idealized and not very tractable, so complex system-level simulations are used to evaluate coverage/outage probability and rate. More tractable models have long been desirable. We develop new general models for the multi-cell signal-to-interference-plus-noise ratio (SINR) using stochastic geometry. Under very general assumptions, the resulting expressions for the downlink SINR CCDF (equivalent to the coverage probability) involve quickly computable integrals, and in some practical special cases can be simplified to common integrals (e.g., the Q-function) or even to simple closed-form expressions. We also derive the mean rate, and then the coverage gain (and mean rate loss) from static frequency reuse. We compare our coverage predictions to the grid model and an actual base station deployment, and observe that the proposed model is pessimistic (a lower bound on coverage) whereas the grid model is optimistic, and that both are about equally accurate. In addition to being more tractable, the proposed model may better capture the increasingly opportunistic and dense placement of base stations in future networks.Comment: Submitted to IEEE Transactions on Communication