BACKGROUND: Long interspersed element 1 (LINE-1 or L1) retrotransposons are mobile elements that constitute 17-20% of the human genome. Strong correlations between abnormal L1 expression and several human diseases have been reported. This has motivated increasing interest in accurate quantification of the number of L1 copies present in any given biologic specimen. A main obstacle toward this aim is that L1s are relatively long DNA segments with regions of high variability, or largely present in the human genome as truncated fragments. These particularities render traditional alignment strategies, such as seed-and-extend inefficient, as the number of segments that are similar to L1s explodes exponentially. This study uses the pattern matching methodology for more accurate identification of L1s. We validate experimentally the superiority of pattern matching for L1 detection over alternative methods and discuss some of its potential applications. RESULTS: Pattern matching detected full-length L1 copies with high precision, reasonable computational time, and no prior input information. It also detected truncated and significantly altered copies of L1 with relatively high precision. The method was effectively used to annotate L1s in a target genome and to calculate copy number variation with respect to a reference genome. Crucial to the success of implementation was the selection of a small set of k-mer probes from a set of sequences presenting a stable pattern of distribution in the genome. As in seed-and-extend methods, the pattern matching algorithm sowed these k-mer probes, but instead of using heuristic extensions around the seeds, the analysis was based on distribution patterns within the genome. The desired level of precision could be adjusted, with some loss of recall. CONCLUSION: Pattern matching is more efficient than seed-and-extend methods for the detection of L1 segments whose characterization depends on a finite set of sequences with common areas of low variability. We propose that pattern matching may help establish correlations between L1 copy number and disease states associated with L1 mobilization and evolution.

The problem of deciding whether or not a concentration of messenger RNA corresponds to a gene in expressed state is crucial in the Boolean representation of gene regulatory networks. Since there is no independent mathematical description of gene expressions, the computational answers of this problem are affected by uncertainties in the model used for the design of the computer algorithms, and by the number of concentration measurements available. In this article, we handle these uncertainties by computing their probability distributions and unifying heterogeneous algorithmic models with aggregation rules. This approach renders an algorithmic framework that computes a set of probabilistic strings of states and, within it, the elected string, which results from the collective decision of the algorithms on the state of the gene in each of the concentration measurements. The set of probabilistic strings is endowed with a metric and a pseudo metric function that makes it possible to analyze the elected string against the expected and the highest probability strings in the set, among other considerations. The method is applied on biological data sets and the results are compared with those of four previously published algorithms.

Electronic health records (EHR) and electronic billing systems have been proposed as mechanisms to help curb the rising costs of health care in the United States. Given this scenario, our research efforts have targeted the idea of using open-source cloud computing technologies as the mechanism to build an affordable, secure, and scalable platform that supports billing as well as EHR operations. We call this platform MedBook, and in this paper we present the architecture and implementation status of this system. MedBook provides patients, health care providers, and health care payers a platform for exchange of information about EHR, billing activities, and benefits inquiries. MedBook is a Software-as-a-Service (SaaS) application built on top of open source technologies and running on an Infrastructure-as-a-Service platform. The client applications are mobile apps run from iPhone and iPad devices.

Cloud Computing is emerging as the next big step in the design and deployment of an increasing number of distributed applications. Concurrent with this widespread adoption of cloud technologies, we are witnessing the emergence of mobile devices as the principal tools to accesses online services. Private, public or hybrid clouds will host many (if not most) of the online services consumed by mobile devices. Hence, cloud researchers and engineers must develop appropriate architectures and design patterns to ensure that clouds provide responsive, efficient, and feature-rich services to both native and web-based applications running on mobile devices, desktop or laptop computers. In this paper, we present Open911 as reference architecture for the development of cloud-based applications that feed mobiles devices through either native or web applications. Open911 is built on top of cloud services that run on a hybrid cloud that includes Google services and custom-built services deployed on a private cloud powered by Ubuntu Enterprise Cloud. We discuss the elements of our architecture, the rationale behind our design choices, the strengths of the cloud systems, and the drawbacks or limitations that we found during our implementation effort.

Cloud Computing is a promising paradigm designed to harness the power of networks of computers and communications in a more cost effective way. Clouds provide elastic capacity to serve a wide and constantly expanding range of information processing needs, including government, military, business and education. The Cloud Computing paradigm is maturing rapidly and is being considered for adoption in government and business platforms. Open source systems refer to software systems whose source code is available, allowing for immediate incorporation of improvements and adaptations of the system by its users. This paper reports on an evaluation of open source development tools for Cloud Computing. The main tools examined are Eucalyptus, Apache Hadoop, and the Django-Python stack. These tools were used at different layers in the construction of a notional application for managing weather data. The results of our experience are reported in terms of a capability matrix that grades nine different aspects associated with the use of these tools in the development and deployment of applications in open source Cloud Computing environments.

The mathematical frameworks of two schedulers, SCOW and UMR, are used to tune up parameters that optimize the amount of communications and parallel computations in a single-program multiple-data parallel code, under certain constraints. Comparisons are made with simulated data that is fed into the mathematical models, and with performance data taken from the scheduling of a parallel method for finding a string motif in a family of DNA sequences. The latter compares also the make-spans predicted by the underlying mathematical models with the actual results. Some considerations on the validity of the theoretical frameworks and the potential for a hybrid SCOW-UMR scheduler are presented, as well.

We propose an algebraic framework for the design and implementation of a large class of data-sorting procedures, including all index-digit permutations used in FFTs. We discuss both old and new algorithms in terms of this framework. We show that the algebraic formulation of the new algorithms can be easily encoded using a functional programming language, and that the resulting code introduces no inefficiencies. We present performance results for implementations of three new algorithms for mixed-radix digit-reversal on a Cray C-90 and on a Sun Sparc 5.