MOTIVATION: Modern biological screens yield enormous numbers of measurements, and identifying and interpreting statistically significant associations among features are essential. In experiments featuring multiple high-dimensional datasets collected from the same set of samples, it is useful to identify groups of associated features between the datasets in a way that provides high statistical power and false discovery rate (FDR) control. RESULTS: Here, we present a novel hierarchical framework, HAllA (Hierarchical All-against-All association testing), for structured association discovery between paired high-dimensional datasets. HAllA efficiently integrates hierarchical hypothesis testing with FDR correction to reveal significant linear and non-linear block-wise relationships among continuous and/or categorical data. We optimized and evaluated HAllA using heterogeneous synthetic datasets of known association structure, where HAllA outperformed all-against-all and other block-testing approaches across a range of common similarity measures. We then applied HAllA to a series of real-world multiomics datasets, revealing new associations between gene expression and host immune activity, the microbiome and host transcriptome, metabolomic profiling and human health phenotypes. AVAILABILITY AND IMPLEMENTATION: An open-source implementation of HAllA is freely available at http://huttenhower.sph.harvard.edu/halla along with documentation, demo datasets and a user group. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The increasing complexity of smartphone applications and services necessitate high battery consumption but the growth of smartphones' battery capacity is not keeping pace with these increasing power demands. To overcome this problem, researchers gave birth to the Mobile Cloud Computing (MCC) research area. In this paper we advance on previous ideas, by proposing and implementing the first known Near Field Communication (NFC)-based computation offloading framework. This research is motivated by the advantages of NFC's short distance communication, with its better security, and its low battery consumption. We design a new NFC communication protocol that overcomes the limitations of the default protocol; removing the need for constant user interaction, the one-way communication restraint, and the limit on low data size transfer. We present experimental results of the energy consumption and the time duration of two computationally intensive representative applications: (i) RSA key generation and encryption, and (ii) gaming/puzzles. We show that when the helper device is more powerful than the device offloading the computations, the execution time of the tasks is reduced. Finally, we show that devices that offload application parts considerably reduce their energy consumption due to the low-power NFC interface and the benefits of offloading.

The proliferation of mobile devices combined with advances in the area of low-power wireless communication, such as Wi-Fi Direct and Bluetooth 4.0, gave rise to a new computation paradigm known as Device-to-Device (D2D) offloading. In this scenario, devices collaborate with each other using short wireless links to create ad-hoc P2P networks for distributed task execution. Experiments on human movement, a non-negligible factor in the D2D context, have shown that people move in group or meet frequently, which suggests that D2D is possible. In this work, we examine the case of parallel compression of smartphone recorded videos with the help of nearby devices. First, we present a mathematical formulation of the problem that optimizes the compression time on the number of nearby helping devices, and show that the problem can be mapped as a water-filling problem. Then, we present real results of the compression time and energy when the compression is performed on one device and when it is parallelized among collaborating devices. To obtain these results, we implemented an Android application that is able to detect nearby devices, connect with them using Wi-Fi Direct, send video chunks for compression, receive and merge compressed chunks into one full compressed video.