• 3D Massive MIMO Systems: Channel Modeling and Performance Analysis

      Nadeem, Qurrat-Ul-Ain (2015-03)
      Multiple-input-multiple-output (MIMO) systems of current LTE releases are capable of adaptation in the azimuth only. More recently, the trend is to enhance the system performance by exploiting the channel's degrees of freedom in the elevation through the dynamic adaptation of the vertical antenna beam pattern. This necessitates the derivation and characterization of three-dimensional (3D) channels. Over the years, channel models have evolved to address the challenges of wireless communication technologies. In parallel to theoretical studies on channel modeling, many standardized channel models like COST-based models, 3GPP SCM, WINNER, ITU have emerged that act as references for industries and telecommunication companies to assess system-level and link-level performances of advanced signal processing techniques over real-like channels. Given the existing channels are only two dimensional (2D) in nature; a large effort in channel modeling is needed to study the impact of the channel component in the elevation direction. The first part of this work sheds light on the current 3GPP activity around 3D channel modeling and beamforming, an aspect that to our knowledge has not been extensively covered by a research publication. The standardized MIMO channel model is presented, that incorporates both the propagation effects of the environment and the radio effects of the antennas. In order to facilitate future studies on the use of 3D beamforming, the main features of the proposed 3D channel model are discussed. A brief overview of the future 3GPP 3D channel model being outlined for the next generation of wireless networks is also provided. In the subsequent part of this work, we present an information-theoretic channel model for MIMO systems that supports the elevation dimension. The model is based on the principle of maximum entropy, which enables us to determine the distribution of the channel matrix consistent with the prior information on the angles of departure and angles of arrival of the propagation paths. Based on this model, an analytical expression for the cumulative density function (CDF) of the mutual information (MI) for systems with a single receive and finite number of transmit antennas in the general signal-to-interference-plus-noise-ratio (SINR) regime is provided. The result is extended to systems with multiple receive antennas in the low SINR regime. A Gaussian approximation to the asymptotic behavior of the MI distribution is derived for the large number of transmit antennas and paths regime. Simulation results study the performance gains realizable through meticulous selection of the transmit antenna down tilt angles, confirming the potential of elevation beamforming to enhance system performance. The results validate the proposed analytical expressions and elucidate the dependence of system performance on azimuth and elevation angular spreads and antenna patterns. We believe that the derived expressions will help evaluate the performance of 3D 5G massive MIMO systems in the future.
    • Abstractocyte: A Visual Tool for Exploring Nanoscale Astroglial Cells

      Mohammed, Haneen (2017-06-12)
      This thesis presents the design and implementation of Abstractocyte, a system for the visual analysis of astrocytes, and their relation to neurons, in nanoscale volumes of brain tissue. Astrocytes are glial cells, i.e., non-neuronal cells that support neurons and the nervous system. Even though glial cells make up around 50 percent of all cells in the mammalian brain, so far they have been far less studied than neurons. Nevertheless, the study of astrocytes has immense potential for understanding brain function. However, the complex and widely-branching structure of astrocytes requires high-resolution electron microscopy imaging and makes visualization and analysis challenging. Using Abstractocyte, biologists can explore the morphology of astrocytes at various visual abstraction levels, while simultaneously analyzing neighboring neurons and their connectivity. We define a novel, conceptual 2D abstraction space for jointly visualizing astrocytes and neurons. Neuroscientists can choose a joint visualization as a specific point in that 2D abstraction space. Dragging this point allows them to smoothly transition between different abstraction levels in an intuitive manner. We describe the design of Abstractocyte, and present three case studies in which neuroscientists have successfully used our system to assess astrocytic coverage of synapses, glycogen distribution in relation to synapses, and astrocytic-mitochondria coverage.
    • Accelerating Monte Carlo Molecular Simulations Using Novel Extrapolation Schemes Combined with Fast Database Generation on Massively Parallel Machines

      Amir, Sahar Z. (2013-05)
      We introduce an efficient thermodynamically consistent technique to extrapolate and interpolate normalized Canonical NVT ensemble averages like pressure and energy for Lennard-Jones (L-J) fluids. Preliminary results show promising applicability in oil and gas modeling, where accurate determination of thermodynamic properties in reservoirs is challenging. The thermodynamic interpolation and thermodynamic extrapolation schemes predict ensemble averages at different thermodynamic conditions from expensively simulated data points. The methods reweight and reconstruct previously generated database values of Markov chains at neighboring temperature and density conditions. To investigate the efficiency of these methods, two databases corresponding to different combinations of normalized density and temperature are generated. One contains 175 Markov chains with 10,000,000 MC cycles each and the other contains 3000 Markov chains with 61,000,000 MC cycles each. For such massive database creation, two algorithms to parallelize the computations have been investigated. The accuracy of the thermodynamic extrapolation scheme is investigated with respect to classical interpolation and extrapolation. Finally, thermodynamic interpolation benefiting from four neighboring Markov chains points is implemented and compared with previous schemes. The thermodynamic interpolation scheme using knowledge from the four neighboring points proves to be more accurate than the thermodynamic extrapolation from the closest point only, while both thermodynamic extrapolation and thermodynamic interpolation are more accurate than the classical interpolation and extrapolation. The investigated extrapolation scheme has great potential in oil and gas reservoir modeling.That is, such a scheme has the potential to speed up the MCMC thermodynamic computation to be comparable with conventional Equation of State approaches in efficiency. In particular, this makes it applicable to large-scale optimization of L-J model parameters for hydrocarbons and other important reservoir species. The efficiency of the thermodynamic dependent techniques is expected to make the Markov chains simulation an attractive alternative in compositional multiphase flow simulation.
    • ACCTuner: OpenACC Auto-Tuner For Accelerated Scientific Applications

      Alzayer, Fatemah (2015-05-17)
      We optimize parameters in OpenACC clauses for a stencil evaluation kernel executed on Graphical Processing Units (GPUs) using a variety of machine learning and optimization search algorithms, individually and in hybrid combinations, and compare execution time performance to the best possible obtained from brute force search. Several auto-tuning techniques – historic learning, random walk, simulated annealing, Nelder-Mead, and genetic algorithms – are evaluated over a large two-dimensional parameter space not satisfactorily addressed to date by OpenACC compilers, consisting of gang size and vector length. A hybrid of historic learning and Nelder-Mead delivers the best balance of high performance and low tuning effort. GPUs are employed over an increasing range of applications due to the performance available from their large number of cores, as well as their energy efficiency. However, writing code that takes advantage of their massive fine-grained parallelism requires deep knowledge of the hardware, and is generally a complex task involving program transformation and the selection of many parameters. To improve programmer productivity, the directive-based programming model OpenACC was announced as an industry standard in 2011. Various compilers have been developed to support this model, the most notable being those by Cray, CAPS, and PGI. While the architecture and number of cores have evolved rapidly, the compilers have failed to keep up at configuring the parallel program to run most e ciently on the hardware. Following successful approaches to obtain high performance in kernels for cache-based processors using auto-tuning, we approach this compiler-hardware gap in GPUs by employing auto-tuning for the key parameters “gang” and “vector” in OpenACC clauses. We demonstrate results for a stencil evaluation kernel typical of seismic imaging over a variety of realistically sized three-dimensional grid configurations, with different truncation error orders in the spatial dimensions. Apart from random walk and historic learning based on nearest neighbor in grid size, most of our heuristics, including the one that proves best, appear to be applied in this context for the first time. This work is a stepping-stone towards an OpenACC auto-tuning framework for more general high-performance numerical kernels optimized for GPU computations.
    • Acoustic Estimates of Distribution and Biomass of Different Acoustic Scattering Types Between the New England Shelf Break and Slope Waters

      McLaren, Alexander (2011-11)
      Due to their great ecological significance, mesopelagic fishes are attracting a wider audience on account of the large biomass they represent. Data from the National Marine Fisheries Service (NMFS) provided the opportunity to explore an unknown region of the North-West Atlantic, adjacent to one of the most productive fisheries in the world. Acoustic data collected during the cruise required the identification of acoustically distinct scattering types to make inferences on the migrations, distributions and biomass of mesopelagic scattering layers. Six scattering types were identified by the proposed method in our data and traces their migrations and distributions in the top 200m of the water column. This method was able to detect and trace the movements of three scattering types to 1000m depth, two of which can be further subdivided. This process of identification enabled the development of three physically-derived target-strength models adapted to traceable acoustic scattering types for the analysis of biomass and length distribution to 1000m depth. The abundance and distribution of acoustic targets varied closely in relation to varying physical environments associated with a warm core ring in the New England continental Shelf break region. The continental shelf break produces biomass density estimates that are twice as high as the warm core ring and the surrounding continental slope waters are an order of magnitude lower than either estimate. Biomass associated with distinct layers is assessed and any benefits brought about by upwelling at the edge of the warm core ring are shown not to result in higher abundance of deepwater species. Finally, asymmetric diurnal migrations in shelf break waters contrasts markedly with the symmetry of migrating layers within the warm ring, both in structure and density estimates, supporting a theory of predatorial and nutritional constraints to migrating pelagic species.
    • Acoustic Monitoring of a Previously Unstudied Whale Shark Aggregation in the Red Sea

      Cochran, Jesse (2012-01)
      The whale shark (Rhincodon, typus), is a large, pelagic, filter feeder for which the available information is limited. The Red Sea populations in particular are practically unstudied. An aggregation site was recently discovered off the western coast of Saudi Arabia. We report the use of passive acoustic monitoring to assess the spatial and temporal behavior patterns of whale sharks in this new site. The aggregation occurs in the spring and peaks in April/ May. Whale sharks showed a preference for a single near shore reef and even a specific area within it. There is no evidence of sexual segregation as the genders were present in roughly equal proportion and used the same habitat at similar times. This information can be used to guide future studies in the area and to inform local management. Continued study will add to the collective knowledge on Red Sea whale sharks, including the population dynamics within the region and how they interact with the global whale shark community.
    • An Adaptive SPARQL Engine with Dynamic Partitioning for Distributed RDF Repositories

      Ibrahim, Yasser E. (2012-07)
      The tremendous increase in the semantic data is driving the demand for efficient query engines. RDF data being generated at an unprecedented rate introduces a storage, indexing, and querying challenge. Due to the size of the data and the federated nature of the semantic web, it is in many cases impractical to assume a central repository, and more attention is being given to distributed RDF stores. This work is motivated by two major drawbacks of current solutions: 1) pre-processing part is very expensive and takes prohibitively long time for large datasets, and 2) current distributed systems assume that a static partitioning of the data should perform well for all kinds of queries, and do not consider fluctuations in the queryload. In this paper we propose PHD-Store, an in-memory SPARQL engine for distributed RDF repositories. Our system does not assume any particular initial placement of the data and does not require pre-processing before running the first query. It analyzes incoming queries and adjusts data placement dynamically in such a way that communication among repositories is minimized for future queries. To achieve this flexibility, frequent query patterns are detected, and data are redistributed through a Propagating Hash Distribution (PHD) algorithm to ensure optimal placement for frequent query patterns. Our experiments with large RDF graphs verify that PHD-Store scales well and executes complex queries more efficiently than existing systems.
    • Adsorption Characteristics of Water and Silica Gel System for Desalination Cycle

      Cevallos, Oscar R. (2012-07)
      An adsorbent suitable for adsorption desalination cycles is essentially characterized by a hydrophilic and porous structure with high surface area where water molecules are adsorbed via hydrogen bonding mechanism. Silica gel type A++ possesses the highest surface area and exhibits the highest equilibrium uptake from all the silica gels available in the market, therefore being suitable for water desalination cycles; where adsorbent’s adsorption characteristics and water vapor uptake capacity are key parameters in the compactness of the system; translated as feasibility of water desalination through adsorption technologies. The adsorption characteristics of water vapor onto silica gel type A++ over a temperature range of 30 oC to 60 oC are investigated in this research. This is done using water vapor adsorption analyzer utilizing a constant volume and variable pressure method, namely the Hydrosorb-1000 instrument by Quantachrome. The experimental uptake data is studied using numerous isotherm models, i. e. the Langmuir, Tóth, generalized Dubinin-Astakhov (D-A), Dubinin-Astakhov based on pore size distribution (PSD) and Dubinin-Serpinski (D-Se) isotherm for the whole pressure range, and for a pressure range below 10 kPa, proper for desalination cycles; isotherms type V of the International Union of Pure and Applied Chemistry (IUPAC) classification were exhibited. It is observed that the D-A based on PSD and the D-Se isotherm models describe the best fitting of the experimental uptake data for desalination cycles within a regression error of 2% and 6% respectively. All isotherm models, except the D-A based on PSD, have failed to describe the obtained experimental uptake data; an empirical isotherm model is proposed by observing the behavior of Tóth and D-A isotherm models. The new empirical model describes the water adsorption onto silica gel type A++ within a regression error of 3%. This will aid to describe the advantages of silica gel type A++ for the design of adsorption desalination processes where reducing capital cost and footprint area are highly important parameters to take into account.
    • Adsorption of Different Fractions of Organic Matter on the Surface of Metal Oxide

      Zaouri, Noor A (2013-05-18)
      The adsorption of different fractions of organic matter on the surface of Al2O3 and ZrO2 were investigated. The aim was to study the affinity of these fractions on the surface of metal oxide and the effect of several factors. Batch adsorption experiments were conducted with Low molecular weight oxygenated compounds. These chemical compound have been chosen to investigate:1) the aliphatic and aromatic structurer;2)contribution of hydroxyl group and; 3) the number of carboxyl group. HPLC and IC analysis used for determent the concentration of these chemical in the working solution. ATR-FTIR used to distinguish the type of coordination structure with the surface of metal oxide. The results fitted with Langmuir equation. The results showed that the chemical structure and the type and number of attached functional have an impact on the adsorption. Which it was proved via ATR-FTIR where the result showed that each chemical have different coordination structure on the surface of ZrO2 and Al2O3. Different fractions and sources of NOM were used (hydrophobic fraction of Suwannee and Colorado River, biopolymers extracted for the exuded of 2 species of algae, and low molecular acids that do not adsorb in XAD-8 resin). Results showed that these different fractions have different affinity with the surface of Al2O3 and ZrO2. These adsorption behaviors were varying according to the difference in the component of each NOM. Biopolymers showed significant adsorption at acidic pH. These biopolymers are mainly comprised of polysaccharides and this result proved that polysaccharide adsorb on the surface of ZrO2 more than Al2O3.
    • Advanced Monitoring and Characterization of Biofouling in Gravity-driven Membrane Filtration

      Wang, Yiran (2016-05)
      Gravity-driven membrane (GDM) filtration is one of the promising membrane bioreactor (MBR) technologies. It operates at a low pressure by gravity, requiring a minimal energy. Thus, it exhibits a great potential for a decentralized system, conducting household in developing and transition countries. Biofouling is a universal problem in almost all membrane filtration applications, leading to the decrease in flux or the increase in transmembrane pressure depending on different operation mode. Air scoring or regular membrane cleaning has been utilized for fouling mitigation, which requires increased energy consumption as well as complicated operations. Besides, repeating cleaning will trigger the deterioration of membranes and shorten their lifetime, elevating cost expenditures accordingly. In this way, GDM filtration stands out from conventional MBR technologies in a long-term operation with relative stable flux, which has been observed in many studies. The objective of this study was to monitor the biofilm development on a flat sheet membrane submerged in a GDM reactor with constant gravitational pressure. Morphology of biofilm layer in a fixed position was acquired by an in-situ and on-line OCT (optical coherence tomography) scanning at regular intervals for both visual investigation and structure analysis. The calculated thickness and roughness were compared to the variation of flux, fouling resistance and permeate quality, showing expected consistency. At the end of experiment, the morphology of entire membrane surface was scanned and recorded by OCT. Membrane autopsy was carried out for biofilm composition analysis by total organic carbon (TOC) and liquid chromatography with organic carbon detection (LC-OCD). In addition, biomass concentration was obtained by flow cytometer and adenosine tri-phosphate (ATP) method. The data of biofilm components indicated a homogeneous biofilm structure formed after a long-term running of the GDM system, based on the morphology observation by OCT images. The superiority of GDM in both flux maintaining and long-term operation with production of high quality effluent was demonstrated, as well as the suitability of OCT for biofouling monitoring was emphasized.
    • Advanced Nanofabrication Process Development for Self-Powered System-on-Chip

      Rojas, Jhonathan Prieto (2010-11)
      In this work the development of a Self-Powered System-On-Chip is explored by examining two components of process development in different perspectives. On one side, an energy component is approached from a biochemical standpoint where a Microbial Fuel Cell (MFC) is built with standard microfabrication techniques, displaying a novel electrode based on Carbon Nanotubes (CNTs). The fabrication process involves the formation of a micrometric chamber that hosts an enhanced CNT-based anode. Preliminary results are promising, showing a high current density (113.6mA/m2) compared with other similar cells. Nevertheless many improvements can be done to the main design and further characterization of the anode will give a more complete understanding and bring the device closer to a practical implementation. On a second point of view, nano-patterning through silicon nitride spacer width control is developed, aimed at producing alternative sub-100nm device fabrication with the potential of further scaling thanks to nanowire based structures. These nanostructures are formed from a nano-pattern template, by using a bottom-up fabrication scheme. Uniformity and scalability of the process are demonstrated and its potential described. An estimated area of 0.120μm2 for a 6T-SRAM (Static Random Access Memory) bitcell (6 devices) can be achieved. In summary, by using a novel sustainable energy component and scalable nano-patterning for logic and computing module, this work has successfully collected the essential base knowledge and joined two different elements that synergistically will contribute for the future implementation of a Self-Powered System-on-Chip.
    • Advances in RGB and RGBD Generic Object Trackers

      Bibi, Adel Aamer (2016-04)
      Visual object tracking is a classical and very popular problem in computer vision with a plethora of applications such as vehicle navigation, human computer interface, human motion analysis, surveillance, auto-control systems and many more. Given the initial state of a target in the first frame, the goal of tracking is to predict states of the target over time where the states describe a bounding box covering the target. Despite numerous object tracking methods that have been proposed in recent years [1-4], most of these trackers suffer a degradation in performance mainly because of several challenges that include illumination changes, motion blur, complex motion, out of plane rotation, and partial or full occlusion, while occlusion is usually the most contributing factor in degrading the majority of trackers, if not all of them. This thesis is devoted to the advancement of generic object trackers tackling different challenges through different proposed methods. The work presented propose four new state-of-the-art trackers. One of which is 3D based tracker in a particle filter framework where both synchronization and registration of RGB and depth streams are adjusted automatically, and three works in correlation filters that achieve state-of-the-art performance in terms of accuracy while maintaining reasonable speeds.
    • Agent Based Modeling and Simulation of Pedestrian Crowds In Panic Situations

      Alrashed, Mohammed (2016-11)
      The increasing occurrence of panic stampedes during mass events has motivated studying the impact of panic on crowd dynamics and the simulation of pedestrian flows in panic situations. The lack of understanding of panic stampedes still causes hundreds of fatalities each year, not to mention the scarce methodical studies of panic behavior capable of envisaging such crowd dynamics. Under those circumstances, there are thousands of fatalities and twice that many of injuries every year caused be crowd stampede worldwide, despite the tremendous efforts of crowd control and massive numbers of safekeeping forces. Pedestrian crowd dynamics are generally predictable in high-density crowds where pedestrians cannot move freely and thus gives rise to self-propelling interactions between pedestrians. Although every pedestrian has personal preferences, the motion dynamics can be modeled as a social force in such crowds. These forces are representations of internal preferences and objectives to perform certain actions or movements. The corresponding forces can be controlled for each individual to represent a different variety of behaviors that can be associated with panic situations such as escaping danger, clustering, and pushing. In this thesis, we use an agent-based model of pedestrian behavior in panic situations to predict the collective human behavior in such crowd dynamics. The proposed simulations suggests a practical way to alleviate fatalities and minimize the evacuation time in panic situations. Moreover, we introduce contagious panic and pushing behavior, resulting in a more realistic crowd dynamics model. The proposed methodology describes the intensity and spread of panic for each individual as a function of distances between pedestrians.
    • Airborne Prokaryote and Virus abundance over the Red Sea

      Yahya, Razan (2018-07)
      Aeolian dust exerts a notable influence on atmospheric and oceanic conditions and human health, particularly in arid and semi-arid regions like Saudi Arabia. Dust is often characterized by its mineral and chemical composition, but there is a microbiological component of natural aerosols which has received comparatively little attention. Moreover, the amount of materials suspended in the atmosphere is highly variable from day to day. Thus, knowing the loads of dust and suspended microbes and its variability over the year is essential to understand the possible effects of dust on the Red Sea ecosystem. Here, we present the first estimates of dust and microbial loads at a coastal side on the Red Sea over a two-year period supplemented with information from dust samples collected along the Red Sea in offshore water and their variability. Weekly average dust loads ranged from 4.63 to 646.11 μg m-3, while the abundance of airborne prokaryotic cells and viral particles ranged from 31,457 to 608,333 cells m-3 and from 69,615.5 to 3,104,758 particles m-3, respectively. These are the first estimates of airborne microbial abundance that we are aware of in this region. The large number of dust particles and suspended microbes found in the air indicates that airborne microbes may have a large impact on our health and that of the Red Sea ecosystem.
    • All Organic Polymers Based Morphing Skin with Controllable Surface Texture

      Favero Bolson, Natanael (2018-05)
      Smart skins are integrating an increasing number of functionalities in order to improve the interaction between the systems they equip and their ambient environment. Here we have developed an electromechanical soft actuator with controlled surface texture due to applied thermal gradient via electrical voltage. The device was fabricated and integrated with optimized process parameters for a prepared heater element [doped PEDOT: PSS (poly-(3, 4 ethylenedioxythiophene): poly (styrene sulfonic acid))], a soft actuator (Ecoflex 00-50/ethanol) and overall packaging case [PDMS (polydimethylsiloxane)]. To study a potential application of the proposed smart skin, we analyze the fluid drag reduction in a texture controlled water flow unit. As a result, we obtained a reduction of approximately 14% in the skin drag friction coefficient during the actuation. We conclude that the proposed soft actuator device is a preferred option for a texture-controlled skin that reduces the skin drag friction coefficient.
    • Analysis and Modeling of Social In uence in High Performance Computing Workloads

      Zheng, Shuai (2011-06)
      High Performance Computing (HPC) is becoming a common tool in many research areas. Social influence (e.g., project collaboration) among increasing users of HPC systems creates bursty behavior in underlying workloads. This bursty behavior is increasingly common with the advent of grid computing and cloud computing. Mining the user bursty behavior is important for HPC workloads prediction and scheduling, which has direct impact on overall HPC computing performance. A representative work in this area is the Mixed User Group Model (MUGM), which clusters users according to the resource demand features of their submissions, such as duration time and parallelism. However, MUGM has some difficulties when implemented in real-world system. First, representing user behaviors by the features of their resource demand is usually difficult. Second, these features are not always available. Third, measuring the similarities among users is not a well-defined problem. In this work, we propose a Social Influence Model (SIM) to identify, analyze, and quantify the level of social influence across HPC users. The advantage of the SIM model is that it finds HPC communities by analyzing user job submission time, thereby avoiding the difficulties of MUGM. An offline algorithm and a fast-converging, computationally-efficient online learning algorithm for identifying social groups are proposed. Both offline and online algorithms are applied on several HPC and grid workloads, including Grid 5000, EGEE 2005 and 2007, and KAUST Supercomputing Lab (KSL) BGP data. From the experimental results, we show the existence of a social graph, which is characterized by a pattern of dominant users and followers. In order to evaluate the effectiveness of identified user groups, we show the pattern discovered by the offline algorithm follows a power-law distribution, which is consistent with those observed in mainstream social networks. We finally conclude the thesis and discuss future directions of our work.
    • Analysis of Dowlink Macro-Femto Cells Environment Based on Per-Energy Capacity

      León, Jaime (2012-05)
      Placing smaller cells in a heterogeneous cellular network can be beneficial in terms of energy because better capacities can be obtained for a given energy constraint. These type of deployments not only highlight the need for appropriate metrics to evaluate how well energy is being spent, but also raise important issues that need to be taken into account when analysing the overall use of energy. In this work, handoff strategies, bandwidth allocation, and path loss models in different scenarios, illustrate how energy can be consumed in a more efficient way when cell size is decreased. A handoff strategy based on per-energy capacity is studied in order to give priority to a more energy efficient handoff option. Energy can also be spent more adequately if the transmit power is adjusted as a function of interference. As a result, users can experience higher capacities while spending less energy, depending whether they handoff or not, increasing the overall performance of the network in terms of energy efficiency.
    • Analysis of Exoelectrogenic Bacterial Communities Present in Different Brine Pools of the Red Sea

      Ortiz Medina, Juan F. (2014-05)
      One contemporary issue experienced worldwide is the climate change due to the combustion of fossil fuels. Microbial Electrochemical Systems pose as an alternative for energy generation. In this technology, microorganisms are primarily responsible for electricity production. To improve the performance it is reasonable to think that bacteria from diverse environments, such as the brine pools of the Red Sea, can be utilized in these systems. Samples from three brine pools: Atlantis II, Valdivia, and Kebrit Deeps, were analyzed using Microbial Electrochemical Cells, with a poised potential at +0.2 V (vs. Ag/AgCl) and acetate as electron donor, to evaluate the exoelectrogenic activity by the present microorganisms. Only samples from Valdivia Deep were able to produce a noticeable current of 6 A/m2. This result, along with acetate consumption and changes on the redox activity measured with cyclic voltammetry, provides arguments to con rm the presence of exoelectrogenic bacteria in this environment. Further characterization using microscopy and molecular biology techniques is required, to obtain the most amount of information about these microorganisms and their potential use in bioelectrochemical technologies.
    • Analysis of gene and protein name synonyms in Entrez Gene and UniProtKB resources

      Arkasosy, Basil (2013-05-11)
      Ambiguity in texts is a well-known problem: words can carry several meanings, and hence, can be read and interpreted differently. This is also true in the biological literature; names of biological concepts, such as genes and proteins, might be ambiguous, referring in some cases to more than one gene or one protein, or in others, to both genes and proteins at the same time. Public biological databases give a very useful insight about genes and proteins information, including their names. In this study, we made a thorough analysis of the nomenclatures of genes and proteins in two data sources and for six different species. We developed an automated process that parses, extracts, processes and stores information available in two major biological databases: Entrez Gene and UniProtKB. We analysed gene and protein synonyms, their types, frequencies, and the ambiguities within a species, in between data sources and cross-species. We found that at least 40% of the cross-species ambiguities are caused by names that are already ambiguous within the species. Our study shows that from the six species we analysed (Homo Sapiens, Mus Musculus, Arabidopsis Thaliana, Oryza Sativa, Bacillus Subtilis and Pseudomonas Fluorescens), rice (Oriza Sativa) has the best naming model in Entrez Gene database, with low ambiguities between data sources and cross-species.