Athanasios Fevgas

DIANA-TarBase v7.0: indexing more than half a million experimentally supported miRNA:mRNA interactions

microRNAs (miRNAs) are short non-coding RNA species, which act as potent gene expression regulators. Accurate identification of miRNA targets is crucial to understanding their function. Currently, hundreds of thousands of miRNA:gene interactions have been experimentally identified. However, this wealth of information is fragmented and hidden in thousands of manuscripts and raw next-generation sequencing data sets. DIANA-TarBase was initially released in 2006 and it was the first database aiming to catalog published experimentally validated miRNA:gene interactions. DIANA-TarBase v7.0 (http://www.microrna.gr/tarbase) aims to provide for the first time hundreds of thousands of high-quality manually curated experimentally validated miRNA:gene interactions, enhanced with detailed meta-data. DIANA-TarBase v7.0 enables users to easily identify positive or negative experimental results, the utilized experimental methodology, experimental conditions including cell/tissue type and treatment. The new interface provides also advanced information ranging from the binding site location, as identified experimentally as well as in silico, to the primer sequences used for cloning experiments. More than half a million miRNA:gene interactions have been curated from published experiments on 356 different cell types from 24 species, corresponding to 9- to 250-fold more entries than any other relevant database. DIANA-TarBase v7.0 is freely available.

DIANA-miRGen v3.0: accurate characterization of microRNA promoters and their regulators

microRNAs (miRNAs) are small non-coding RNAs that actively fine-tune gene expression. The accurate characterization of the mechanisms underlying miRNA transcription regulation will further expand our knowledge regarding their implication in homeostatic and pathobiological networks. Aim of DIANA-miRGen v3.0 (http://www.microrna.gr/mirgen) is to provide for the first time accurate cell-line-specific miRNA gene transcription start sites (TSSs), coupled with genome-wide maps of transcription factor (TF) binding sites in order to unveil the mechanisms of miRNA transcription regulation. To this end, more than 7.3 billion RNA-, ChIP- and DNase-Seq next generation sequencing reads were analyzed/assembled and combined with state-of-the-art miRNA TSS prediction and TF binding site identification algorithms. The new database schema and web interface facilitates user interaction, provides advanced queries and innate connection with other DIANA resources for miRNA target identification and pathway analysis. The database currently supports 276 miRNA TSSs that correspond to 428 precursors and >19M binding sites of 202 TFs on a genome-wide scale in nine cell-lines and six tissues of Homo sapiens and Mus musculus.

A Platform for Delivering Multimedia Presentations on Cultural Heritage

In this paper we present a platform for delivering multimedia presentations on cultural heritage. The platform aims to enhance cultural knowledge discovery by increasing access to museums’ digital content. The platform generates rich media presentations considering the personal profile of the audience as well as its interests. The presentations may include text, images, video and sound and can be delivered via network. They can be attended either inside the museum or even outside of it e.g. in schools during a preparation class prior to a museum visit. The platform supports creation and editing of slides and presentations, updating existing presentations and projecting them, considering different roles and access levels for archeologists, tourist guides, educators and individuals.

iMuse Mobile Tour: A personalized multimedia museum guide opens to groups

In the recent years, there is a growing interest in exploiting the advances of mobile and pervasive computing to museum environments. A mobile museum guide, named iMuse Mobile Tour is presented in this paper. The guide utilizes UHF RFID technology to provide context aware information services. It comprises predefined and self-defined tours as well as interactive games to stimulate learning. A group support service is introduced, which enables group visitors to exploit guidance services on their private mobile phones. The service provides access to information retrieved by museums RFID enabled devices.

Multiobjective Unfolding of Shared Power Consumption Pattern Using Genetic Algorithm for Estimating Individual Usage in Smart Cities

In smart cities residential homes are fully equipped with information networking and computing technologies and are connected to the power grid via intelligent meters. Connectivity of meters allows formation of groups of residents, which are physically close, and as a result individual consumptions can be aggregated into a shared consumption. In this paper an approach of unfolding shared consumption and making inferences about resident personal usage is presented. The proposed approach tackles the problem of unfolding as a multiobjective problem in which a set of residential profiles is fitted to the measured consumption. A solution to the multiobjective problem is sought by using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) that utilizes the Pareto optimality theory to identify an optimal solution. The approach is applied to a set electricity consumption signals for making inferences about the personal energy usage of residential participants in the shared consumption pattern.

Utilizing UHF RFIDs to Enhance Museum Visiting Experience

In the recent years, there is an extremely growing interest in the development of mobile information systems for museum visitors. Many of these systems utilize sensor technologies like HF RFIDs to provide an enhanced experience to the visitors. We explore the usage of UHF RFIDs to enhance museum visits and demonstrate a mobile application for delivering multimedia information to museum visitors.

An innovative e-health network for collaboration on emergency cases

This paper discusses a collaborative and innovative e-health system called EMOSNet, for the support of medical decision making in the case of amputated or mangled extremities. The goal of the proposed system is to provide communication and collaboration channels between orthopedists located in regional hospitals and special surgeons of the University Hospital of Larissa, in order to confront emergency orthopedic incidences.

Grid-File: Towards to a Flash Efficient Multi-dimensional Index

Spatial indexes are of great importance for multidimensional query processing. Traditional data structures have been optimized for magnetic disks in the storage layer. In the recent years flash solid disks are widely utilized, as a result of their exceptional features. However, the specifics of flash memory (asymmetric read/write speeds erase before update, wear out) introduce new challenges. Algorithms and data structures designed for magnetic disks experience reduced performance in flash. Most research efforts for flash-aware spatial indexes concern R-tree and its variants. Distinguishing from previous works we investigate the performance of Grid File in flash and enlighten constrains and opportunities towards a flash efficient Grid File. We conducted experiments on mainstream and high performance SSD devices and Grid File outperforms R\(^*\)-tree in all cases.

Efficient solution of large sparse linear systems in modern hardware

The solution of large-scale sparse linear systems arises in numerous scientific and engineering problems. Typical examples involve study of many real world multi-physics problems and the analysis of electric power systems. The latter involve key functions such as contingency, power flow and state estimation whose analysis amounts at solving linear systems with thousands or millions of equations. As a result, efficient and accurate solution of such systems is of paramount importance. The methods for solving sparse systems are distinguished in two categories, direct and iterative. Direct methods are robust but require large amounts of memory, as the size of the problem grows. On the other hand, iterative methods provide better performance but may exhibit numerical problems. In addition, continuous advances in computer hardware and computational infrastructures imposes new challenges and opportunities. GPUs, multi-core CPUs, late memory and storage technologies (flash and phase change memories) introduce new capabilities to optimizing sparse solvers. This work presents a comprehensive study of the performance of some, state of the art, sparse direct and iterative solvers on modern computer infrastructure and aims to identify the limits of each method on different computing platforms. We evaluated two direct solvers in different hardware configurations, examining their strengths and weaknesses both in main memory (in-core) and secondary memory (out-of-core) execution in a series of representative matrices from multi-physics and electric grid problems. Also, we provide a comparison with an iterative method, utilizing a general purpose preconditioner, implemented both on a GPU and a multi-core processor. Based on the evaluation results, we observe that direct solvers can be as efficient as their iterative counterparts if proper memory optimizations are applied. In addition, we demonstrate that GPUs can be utilized as efficient computational platforms for tackling the analysis of electric power systems.

A Study of Sparse Matrix Methods on New Hardware: Advances and Challenges

Modeling of numerous scientific and engineering problems, such as multi-physic problems and analysis of electrical power systems, amounts to the solution of large scale linear systems. The main characteristics of such systems are the large sparsity ratio and the large number of unknowns that can reach thousands or even millions of equations. As a result, efficient solution of sparse large-scale linear systems is of great importance in order to enable analysis of such problems. Direct and iterative algorithms are the prevalent methods for solution of linear systems. Advances in computer hardware provide new challenges and capabilities for sparse solvers. The authors present a comprehensive evaluation of some, state of the art, sparse methods direct and iterative using modern computing platforms, aiming to determine the performance boundaries of each solver on different hardware infrastructures. By identifying the potential performance bottlenecks of out-of-core direct methods, the authors present a series of optimizations that increase their efficiency on flash-based systems.