Rate of growth of the amount of data generated using the high throughput sequencing (HTS) platforms now exceeds the growth stipulated by Moore's Law. The HTS data is expected to surpass those of other "big data" domains such as astronomy, before the year 2025. In addition to sequencing genomes for research purposes, genome and exome sequencing in clinical settings will be a routine part of health care. The analysis of such large amounts of data, however, is not without computational challenges. This burden is even more increased due to the periodic updates to reference genomes, which typically require re-analysis of existing data. Here we propose Coin-Application Mediator Interface (Coinami) to distribute the workload for mapping reads to reference genomes using a volunteer grid computer approach similar to Berkeley Open Infrastructure for Network Computing (BOINC). However, since HTS read mapping requires substantial computational resources and fast analysis turnout is desired, Coinami uses the HTS read mapping as proof-of-work to generate valid blocks to main its own cryptocurrency system, which may help motivate volunteers to dedicate more resources. The Coinami protocol includes mechanisms to ensure that jobs performed by volunteers are correct, and provides genomic data privacy. The prototype implementation of Coinami is available at http://coinami.github.io/.

In cloud manufacturing, manufacturing business processes are virtualized and encapsulated into manufacturing services. Enterprises can customize their manufacturing business applications on cloud manufacturing platform. To facilitate the customization, a global manufacturing business process is generally built and transformed into a feature model. However, since most global manufacturing business processes have a large number of nodes and complex dependency relationships, it is difficult to transform them into feature models. Therefore, we propose a novel complex manufacturing business process decomposition approach in cloud manufacturing. The approach is beneficial to construct a desired feature model. Firstly, combined with the domain engineering theory, the complex manufacturing business process customization approach in cloud manufacturing is elaborated. Secondly, the complex manufacturing business process decomposition approach is proposed. A global manufacturing business process network (GMBPN) is constructed according to the input and output relationships of manufacturing business activities. Based on the GMBPN, a two-phase decomposition algorithm is presented. In the community detection phase, the nodes in the GMBPN with functional similarity are clustered into the same community with an adaptive strategy. In the sub-process reconstruction phase, the corresponding sub-process is reconstructed in each community. We evaluate the decomposition algorithm with an actual elevator design business process. Experimental results show the proposed algorithm is effective to find the optimal decomposition results.

Computer game playing has been suggested to be an effective training to enhance perceptual and cognitive abilities. Focusing on potential improvements in auditory selective spatial attention induced by computer gaming, we compared a passive waiting-control group with two gaming groups, playing either a first-person audio-only action game requiring spatial attention and sound localization or a platform side-scroller video game without audiospatial components, which has been shown to improve cognitive performance in previous studies. Prior to and immediately after game training for 1 month for at least 30 min per day (total training time ≥15 h), healthy young adults were tested in an audiospatial task simulating a “cocktail-party” situation with multiple speakers at different positions. The proportion of correct target localizations was significantly increased after audio and video gaming compared with the control group. However, there were no significant differences between gaming groups, with similarly strong effects of action audio game and non-action video game trainings on auditory selective spatial attention. Thus, it seems as if successful training of “cocktail-party” listening can be induced not only by modality-specific near-transfer learning within the audiospatial domain, but also by far transfer of trained cognitive skills across sensory modalities, which may enhance domain-general processes supporting selective attention.