Background:Next-generation sequencing (NGS) methods pose computational challenges of handling large volumes of data. Although cloud computing offers a potential solution to these challenges, transferring a large data set across the internet is the biggest obstacle, which may be overcome by efficient encoding methods. When encoding is used to facilitate data transfer to the cloud, the time factor is equally as important as the encoding efficiency. Moreover, to take advantage of parallel processing in cloud computing, a parallel technique to decode and split compressed data in the cloud is essential. Hence in this review, we present SOLiDzipper, a new encoding method for NGS data.Methods:The basic strategy of SOLiDzipper is to divide and encode. NGS data files contain both the sequence and non-sequence information whose encoding efficiencies are different. In SOLiDzipper, encoded data are stored in binary data block that does not contain the characteristic information of a specific sequence platform, which means that data can be decoded according to a desired platform even in cases of Illumina, Solexa or Roche 454 data.Results:The main calculation time using Crossbow was 173 minutes when 40 EC2 nodes were involved. In that case, an analysis preparation time of 464 minutes is required to encode data in the latest DNA compression method like G-SQZ and transmit it on a 183 Mbit/s bandwidth. However, it takes 194 minutes to encode and transmit data with SOLiDzipper under the same bandwidth conditions. These results indicate that the entire processing time can be reduced according to the encoding methods used, under the same network bandwidth conditions. Considering the limited network bandwidth, high-speed, high-efficiency encoding methods such as SOLiDzipper can make a significant contribution to higher productivity in labs seeking to take advantage of the cloud as an alternative to local computing.Availability:http://szipper.dinfree.com. Academic/non-profit: Binary available for direct download at no cost. For-profit: Submit request for for-profit license from the web-site.
In recent years, the ALE (Application Level Event) by EPCglobal leads the standard of RFID middleware and that represents minimal interfaces and extension points in order to independently operate it in system infrastructures. However, the extension in functions using the extension point has not standardized in the present time. Also, it demonstrated certain problems that require new codes or modifications to apply the function to the system in a system, which is being operated. This paper proposes a dynamic ALE middleware framework by introducing an AOP (Aspect Oriented Programming) method to solve such problems and designs aspects to implement the proposed framework. The middleware framework and aspect designed in this study are able to provide dynamic functions and that solve the difficulty in the maintaining and management of the conventional ALE-based middleware according to their functional extensions. Also, it makes possible to add new functions as a standardized manner.
서 론최근에However, The PSQI score which was only gleaned through an interview on a questionnaire can not be accurate because it is difficult to remember something about sleep during the last month. In order to resolve this problem, This paper presented the virtual sleep sensor that has a protocol to receive sleep information through physical sensors and smart algorithm. In addition, the virtual sleep sensor can be contributed to a service model for sleep therapy when it is combined with light therapy and aromatherapy. Finally, based on the findings of the experiment, its effectiveness was confirmed in the proposed model.
Background: Because of the importance of early interventions in cognitive decline, cognitive training using electronic devices is becoming popular. However, its effectiveness is not clear. The present study aimed to investigate the effects of smartphone cognitive training on cognitive function and brain connectivity. Methods: Six patients with mild cognitive impairments and one patient with early dementia performed smartphone-based cognitive training twice a week for four weeks. The cognitive training occurred in four cognitive domains (sensory perception, attention, working memory, and reasoning). Language and visual functions were utilized in every domain through the smartphone-based design. Before and after the cognitive training, the patients' cognitive functions were tested, and resting-state
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