• Uncovering the dark matter of the metagenome one read at a time

      Dimonaco, Nicholas; Creevey, Chris; Hoehndorf, Robert; Kulmanov, Maxat; Liuwei, Wang; Clare, Amanda; Aubrey, Wayne; Kenobi, Kim (Access Microbiology, Microbiology Society, 2019-04-24) [Poster]
      Contemporary metagenomic annotation methods have proven insufficient in our attempts to better understand the complex environments around us. We call the yet to be annotated part of a metagenome it’s ‘dark matter’. The Gene Ontology (GO) is a hierarchical vocabulary used to describe gene product function and a large collection of curated genes with GO annotations already exists. DeepGO utilises deep learning to build models from these curated genes and gene products to predict GO categories for novel proteins. One of the major problems with metagenomic studies today is the process of assembling the environmental DNA sequences into their original genomes. This is difficult, with chimeric metagenomically assembled genomes being common. To avoid this and the computational and time expense, we have modified DeepGO to perform protein function prediction directly from sequence reads with limited protein coding sequence prediction. Three independent models were trained as the following; The first 50 amino acids of a protein were used for training, The last 50 amino acids were used for training, A phasing window of 50 amino acids was used to train across the entirety of a protein sequence. These models were chosen to learn from the different parts of a protein sequence we are likely to capture from only the short unassembled sequence reads. We compared the three models by producing a mock metagenomic community consisting of 6 model bacterial genomes. We evaluated the functions predicted from the unassembled sequence reads and the protein coding sequences predicted from the assembled metagenome.
    • High-bitrate visible light communication and high-quality solid-state lighting using superluminescent diode

      Holguin Lerma, Jorge Alberto; Alatawi, Abdullah; Kang, Chun Hong; Shen, Chao; Subedi, Ram Chandra; Albadri, Abdulrahman M.; Alyamani, Ahmed Y.; Ng, Tien Khee; Ooi, Boon S. (2019-02-02) [Poster]
      White light solid-state lighting (SSL) is conventionally based on light-emitting diodes (LEDs). Lately, laser diodes (LDs) have been proposed as a replacement due to their higher device efficiencies; however, laser radiation and eye-safety are always a concern. Superluminescent diodes (SLDs) bring together low coherency and high power, high efficiency and low speckle density, offering an alternative to both LEDs and LDs. Our work compares LED, LD and SLD, and demonstrates high-speed visible light communications and high-quality white light based on c-plane InGaN SLD, constituting a viable innovation for the lighting industry and for the adoption of blue SLD for commercial applications.
    • Computation of Electromagnetic Fields Scattered From Objects With Uncertain Shapes Using Multilevel Monte Carlo Method

      Litvinenko, Alexander; Yucel, Abdulkadir; Bagci, Hakan; Oppelstrup, Jesper; Tempone, Raul; Michielssen, Eric (2019-02-14) [Poster]
      Computational tools for characterizing electromagnetic scattering from objects with uncertain shapes are needed in various applications ranging from remote sensing at microwave frequencies to Raman spectroscopy at optical frequencies. Often, such computational tools use the Monte Carlo (MC) method to sample a parametric space describing geometric uncertainties. For each sample, which corresponds to a realization of the geometry, a deterministic electromagnetic solver computes the scattered fields. However, for an accurate statistical characterization the number of MC samples has to be large. In this work, to address this challenge, the continuation multilevel Monte Carlo (CMLMC) method is used together with a surface integral equation solver. The CMLMC method optimally balances statistical errors due to sampling of the parametric space, and numerical errors due to the discretization of the geometry using a hierarchy of discretizations, from coarse to fine. The number of realizations of finer discretizations can be kept low, with most samples computed on coarser discretizations to minimize computational cost. Consequently, the total execution time is significantly reduced, in comparison to the standard MC scheme.
    • A Case Study of Seismic Wave Propagation with Random Parameters

      Ballesio, Marco; Beck, Joakim; Pandey, Anamika; Parisi, Laura; von Schwerin, Erik; Tempone, Raul (2018-09-07) [Poster]
      We will present results from a case study based on an earthquake with seismograms recorded on a small dense seismic network in the Ngorongoro Conservation Area in Tanzania. We consider forward seismic wave propagation in an inhomogeneous linear viscoelastic media with random wave speeds and densities, subject to deterministic boundary and initial conditions. The random parameters model the inherent uncertainty of the Earth parameters. We use multilevel Monte Carlo simulations for computing statistics of quantities of interest chosen to formulate a suitable loss function for the corresponding source inversion problem. We use recorded seismograms to study a noise model for use in Bayesian inverse problems. This work provides a benchmark for the implementation of Multilevel algorithms to accelerate Seismic Inversion addressing earthquake source estimation as well as inferring Earth structure.
    • Multilevel Monte Carlo (MLMC) Acceleration of Seismic Wave Propagation under Uncertainty

      Ballesio, Marco; Beck, Joakim; Pandey, Anamika; Parisi, Laura; von Schwerin, Erik; Tempone, Raul (2018-04-13) [Poster]
      We consider forward seismic wave propagation in an inhomogeneous linear viscoelastic media with random wave speed subjected to deterministic boundary and initial conditions. Considering random wave speed correspond to the inclusion of inherent uncertainty of the Earth parameters. We propose multilevel Monte Carlo simulation for computing statistics of some given quantities of interest. In this poster, we will present a case study from Tanzania to quantify uncertainty in Earth's parameters. This work provides a benchmark for the implementation of Multilevel algorithms to accelerate Seismic Inversion addressing earthquake source estimation as well as inferring Earth structure.
    • Data mining of Citations in Doctoral Dissertations: Tool for Collection Development and Instructional Services

      Han, Lee Yen; Martin, Jose (2018-12) [Poster]
      Usage statistics, such as access and download data, are a widely used tool in a collection development librarian’s toolkit to assess the relevance and usefulness of a library’s collection to its patrons. The use of citation analysis of students’ theses and dissertations adds another dimension to this evidence-based user-centered approach to assessing collection development activities of the library. In this project, a liaison librarian and a systems specialist teamed up to make use of a systems approach to analyze the citations of doctoral dissertations from the Biological and Environmental Science and Engineering (BESE) Division in a graduate research institution. Making use of KNIME, an open source data-mining software, we created a workflow to examine citation data to discover citation patterns of student dissertations across the different programs within the BESE division and resource usage. This is matched against the current library holdings as well as compared with usage statistics obtained from JUSP. Results suggest that as an academic division, the BESE Division is not a homogenous division and citation patterns are different across the different programs. What and how references are cited are also valuable information to inform, direct and focus our collection development and information literacy program. The use of an open source data-mining software helps to automate the citation analysis process and provides an efficient and replicable framework to analyze citation data to supplement usage statistics. This would be useful for academic libraries planning to conduct similar studies to assess the usefulness of their collection with respect to the research activities of graduate students.
    • DeepExon: Deep Learning Model for Recognition of Acceptor Splice Site

      Albaradei, Somayah; Essack, Magbubah; Magana-Mora, Arturo; Bajic, Vladimir (2018-05-03) [Posters]
    • FindFit: The Fit and Balanced Community of the Future

      Calleja, Maria; Garcias, Neus; Ibrahim, Mahmoud; Torrealba, Victor (2018-05-03) [Posters]
    • Slake the world’s thirst by solar-driven water treatment

      Shi, Le; Shi, Yusuf; Wang, Peng (2018-05-03) [Posters]
    • Solar-Powered Drone for Detection With ADS-B

      Alshanbari, Reem; Hamed, Mishal (2018-05-03) [Posters]
    • A Dream Come True: Saudi Arabia’s First Ecological Park

      Mahalingam, Dinesh; Azan, Ayat Tariq (2018-05-03) [Posters]
    • From Waste to Energy: Utilization of a Common Tropical Fly, Black Soldier Fly

      Hong, Tsufang; Chandiramani, Neil; Restrepo-Cano, Juan Juan; Sarathy, Mani (2018-05-03) [Posters]
    • Laser Scribed 3D Graphene Anodes for Advanced Sodium Ion Batteries

      Zhang, Fan; Alhajji, Eman; Alshareef, Husam N. (2018-05-03) [Posters]
    • Macroalgae, the globefloater

      Ortega, Alejandra; Geraldi, Nathan; Duarte, Carlos M. (2018-05-03) [Posters]
    • A new composition for concrete

      Alsalman, Zainalabdeen Ali (2018-08-28) [Posters]
    • Vortex structures in the near-nozzle field of laminar coflow jets

      Alharbi, Rahaf; Cha, Min Suk (2018-08-28) [Posters]
    • Teaching Cars to Drive

      Aljahdali, Motaz; Ghanem, Bernard; Giancola, Silvio (2018-08-28) [Posters]
    • Critical Parameters Affecting High Frequency Transmission Lines

      Khawaja, Husam; Al-Attar, Talal (2018-08-28) [Posters]