Biological cloud dynamics

Equations are obtained which relate three important characteristics of a homogeneous biological aerosol; (1) the over-all viable survival; (2) the distribution of ages (times of suspension) of surviving cells; and (3) the dependence of the death rate of individual cells upon their age. The general solution and specific solutions are discussed and allow the determination of the kinetics of death of microorganisms to be determined from the experimentally measured viable cell concentration in the aerosol as a function of time.     

Uncertainties cloud stem cell developments

Scientific promise in the therapeutic potential of early embryonic stem cell research to treat presently intractable diseases is still meeting ethical, regulatory and patent challenges. Nigel Williams reports on recent moves in Europe and the US.     

Genome research in the cloud

High-throughput genome research has long been associated with bioinformatics, as it assists genome sequencing and annotation projects. Along with databases, to store, properly manage, and retrieve biological data, a large number of computational tools have been developed to decode biological information from this data. However, with the advent of next-generation sequencing (NGS) technology the sequence data starts generating at a pace never before seen. Consequently researchers are facing a threat as they are experiencing a potential shortage of storage space and tools to analyze the data. Moreover, the voluminous data increases traffic in the network by uploading and downloading large data sets, and thus consume much of the network's available bandwidth. All of these obstacles have led to the solution in the form of cloud computing.     

Use of cloud computing in biomedicine

Nowadays, biomedicine is characterised by a growing need for processing of large amounts of data in real time. This leads to new requirements for information and communication technologies (ICT). Cloud computing offers a solution to these requirements and provides many advantages, such as cost savings, elasticity and scalability of using ICT. The aim of this paper is to explore the concept of cloud computing and the related use of this concept in the area of biomedicine. Authors offer a comprehensive analysis of the implementation of the cloud computing approach in biomedical research, decomposed into infrastructure, platform and service layer, and a recommendation for processing large amounts of data in biomedicine. Firstly, the paper describes the appropriate forms and technological solutions of cloud computing. Secondly, the high-end computing paradigm of cloud computing aspects is analysed. Finally, the potential and current use of applications in scientific research of this technology in biomedicine is discussed.     

Biogeographical affinities among Neotropical cloud forests

 Biogeographical affinities among cloud forests in the Neotropical region were studied through a track approach, by constructing generalised tracks based on the results of a parsimony analysis of endemicity (PAE). Distributional data on 946 genera and 1,266 species of vascular plants (Pteridophyta, angiosperms, and gymnosperms) from 26 cloud forest patches from Colombia, Costa Rica, Cuba, Honduras, Jamaica, Mexico, Peru, Puerto Rico, and Venezuela were analysed; and four localities from eastern and western United States were also included as outgroups. The track analysis identified six generalised tracks: a first one that includes the majority of the cloud forests of Mexico, Central America, the Antilles, and northern Colombia; a second one that includes southern Mexico and northern Central America; a third one that includes the mountains in northwestern South America; a fourth one that includes the mountains in southwestern South America; and two others in western and eastern United States. It is concluded that the Neotropical cloud forests are closely related and that those of the Caribbean subregion exhibit complex relationships, which could be due to the complex tectonic history of the area. Received February 22, 2001 Accepted May 1, 2001     

Gene set analysis in the cloud

Cloud computing offers low cost and highly flexible opportunities in bioinformatics. Its potential has already been demonstrated in high-throughput sequence data analysis. Pathway-based or gene set analysis of expression data has received relatively less attention. We developed a gene set analysis algorithm for biomarker identification in the cloud. The resulting tool, YunBe, is ready to use on Amazon Web Services. Moreover, here we compare its performance to those obtained with desktop and computing cluster solutions. AVAILABILITY AND IMPLEMENTATION: YunBe is open-source and freely accessible within the Amazon Elastic MapReduce service at s3n://lrcv-crp-sante/app/yunbe.jar. Source code and user's guidelines can be downloaded from http://tinyurl.com/yunbedownload.     

Searching for SNPs with cloud computing

As DNA sequencing outpaces improvements in computer speed, there is a critical need to accelerate tasks like alignment and SNP calling. Crossbow is a cloud-computing software tool that combines the aligner Bowtie and the SNP caller SOAPsnp. Executing in parallel using Hadoop, Crossbow analyzes data comprising 38-fold coverage of the human genome in three hours using a 320-CPU cluster rented from a cloud computing service for about $85. Crossbow is available from .     

Surfatron accelerator in the local interstellar cloud

Taking into account results of numerous experiments, the variability of the energy spectra of cosmic rays (protons and helium nuclei) in the energy range of 10 GeV to ~10⁷ GeV is explained on the basis of a hypothesis of the existence of two variable sources close to the Sun. The first (soft) surfatron source (with a size of ~100 AU) is located at the periphery of the heliosphere. The second (hard) surfatron source (with a size of ~1 pc) is situated in the Local Interstellar Cloud (LIC) at a distance of <1 pc. The constant background is described by a power-law spectrum with a slope of ~2.75. The variable heliospheric surfatron source is described by a power-law spectrum with a variable amplitude, slope, and cutoff energy, the maximum cutoff energy being in the range of E _СН/Z < 1000 GeV. The variable surfatron source in the LIC is described by a power-law spectrum with a variable amplitude, slope, and cut-off energy, the maximum cut-off energy being E _СL/Z ≤ 3 × 10⁶ GeV. The proposed model is used to approximate data from several experiments performed at close times. The energy of each cosmic-ray component is calculated. The possibility of surfatron acceleration of Fe nuclei (Z = 26) in the LIC up to an energy of E _CL ~ 10¹⁷ eV and electron and positrons to the “knee” in the energy spectrum is predicted. By numerically solving a system of nonlinear equations describing the interaction between an electromagnetic wave and a charged particle with an energy of up to E/Z ~ 3 × 10⁶ GeV, the possibility of trapping, confinement, and acceleration of charged cosmic-ray particles by a quasi-longitudinal plasma wave is demonstrated.     

Software architecture definition for on-demand cloud provisioning

Cloud computing is a promising paradigm for the provisioning of IT services. Cloud computing infrastructures, such as those offered by the RESERVOIR project, aim to facilitate the deployment, management and execution of services across multiple physical locations in a seamless manner. In order for service providers to meet their quality of service objectives, it is important to examine how software architectures can be described to take full advantage of the capabilities introduced by such platforms. When dealing with software systems involving numerous loosely coupled components, architectural constraints need to be made explicit to ensure continuous operation when allocating and migrating services from one host in the Cloud to another. In addition, the need for optimising resources and minimising over-provisioning requires service providers to control the dynamic adjustment of capacity throughout the entire service lifecycle. We discuss the implications for software architecture definitions of distributed applications that are to be deployed on Clouds. In particular, we identify novel primitives to support service elasticity, co-location and other requirements, propose language abstractions for these primitives and define their behavioural semantics precisely by establishing constraints on the relationship between architecture definitions and Cloud management infrastructures using a model denotational approach in order to derive appropriate service management cycles. Using these primitives and semantic definition as a basis, we define a service management framework implementation that supports on demand cloud provisioning and present a novel monitoring framework that meets the demands of Cloud based applications.     

Biomedical cloud computing with Amazon Web Services

In this overview to biomedical computing in the cloud, we discussed two primary ways to use the cloud (a single instance or cluster), provided a detailed example using NGS mapping, and highlighted the associated costs. While many users new to the cloud may assume that entry is as straightforward as uploading an application and selecting an instance type and storage options, we illustrated that there is substantial up-front effort required before an application can make full use of the cloud's vast resources. Our intention was to provide a set of best practices and to illustrate how those apply to a typical application pipeline for biomedical informatics, but also general enough for extrapolation to other types of computational problems. Our mapping example was intended to illustrate how to develop a scalable project and not to compare and contrast alignment algorithms for read mapping and genome assembly. Indeed, with a newer aligner such as Bowtie, it is possible to map the entire African genome using one m2.2xlarge instance in 48 hours for a total cost of approximately $48 in computation time. In our example, we were not concerned with data transfer rates, which are heavily influenced by the amount of available bandwidth, connection latency, and network availability. When transferring large amounts of data to the cloud, bandwidth limitations can be a major bottleneck, and in some cases it is more efficient to simply mail a storage device containing the data to AWS (http://aws.amazon.com/importexport/). More information about cloud computing, detailed cost analysis, and security can be found in references.     

Replication management in peer-to-peer cloud storage systems

Data availability represents one of the primary functionalities of any cloud storage system since it ensures uninterrupted access to data. A common solution used by service providers that increase data availability and improve cloud performance is data replication. In this paper, we present a dynamic data replication strategy that is based on a hybrid peer-to-peer cloud architecture. Our proposed strategy selects the most popular data for replication. To determine the proper nodes for storing popular data, we employ not only the feature specifications of storage nodes, but also the relevant structural positions in the cloud network. Our simulation results show the impact of using features such as data popularity, and structural characteristics in improving network performance and balancing the storage nodes, and reducing user response time.

     

Adaptive Combiner for MapReduce on cloud computing

MapReduce is a programming model to process a massive amount of data on cloud computing. MapReduce processes data in two phases and needs to transfer intermediate data among computers between phases. MapReduce allows programmers to aggregate intermediate data with a function named combiner before transferring it. By leaving programmers the choice of using a combiner, MapReduce has a risk of performance degradation because aggregating intermediate data benefits some applications but harms others. Now, MapReduce can work with our proposal named the Adaptive Combiner for MapReduce (ACMR) to automatically, smartly, and trainer for getting a better performance without any interference of programmers. In experiments on seven applications, MapReduce can utilize ACMR to get the performance comparable to the system that is optimal for an application.