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  • Communication Reducing Approaches and Shared-Memory Optimizations for the Hierarchical Fast Multipole Method on Distributed and Many-core Systems

    Abduljabbar, Mustafa (2018-12-06)
    We present algorithms and implementations that overcome obstacles in the migration of the Fast Multipole Method (FMM), one of the most important algorithms in computational science and engineering, to exascale computing. Emerging architectural approaches to exascale computing are all characterized by data movement rates that are slow relative to the demand of aggregate floating point capability, resulting in performance that is bandwidth limited. Practical parallel applications of FMM are impeded in their scaling by irregularity of domains and dominance of collective tree communication, which is known not to scale well. We introduce novel ideas that improve partitioning of the N-body problem with boundary distribution through a sampling-based mechanism that hybridizes two well-known partitioning techniques, Hashed Octree (HOT) and Orthogonal Recursive Bisection (ORB). To reduce communication cost, we employ two methodologies. First, we directly utilize features available in parallel runtime systems to enable asynchronous computing and overlap it with communication. Second, we present Hierarchical Sparse Data Exchange (HSDX), a new all-to-all algorithm that inherently relieves communication by relaying sparse data in a few steps of neighbor exchanges. HSDX exhibits superior scalability and improves relative performance compared to the default MPI alltoall and other relevant literature implementations. We test this algorithm alongside others on a Cray XC40 tightly coupled with the Aries network and on Intel Many Integrated Core Architecture (MIC) represented by Intel Knights Corner (KNC) and Intel Knights Landing (KNL) as modern shared-memory CPU environments. Tests include comparisons of thoroughly tuned handwritten versus auto-vectorization of FMM Particle-to-Particle (P2P) and Multipole-to-Local (M2L) kernels. Scalability of task-based parallelism is assessed with FMM’s tree traversal kernel using different threading libraries. The MIC tests show large performance gains after adopting the prescribed techniques, which are inevitable in a world that is moving towards many-core parallelism.
  • Spatio-Temporal Data Analysis by Transformed Gaussian Processes

    Yan, Yuan (2018-12-06)
    In the analysis of spatio-temporal data, statistical inference based on the Gaussian assumption is ubiquitous due to its many attractive properties. However, data collected from different fields of science rarely meet the assumption of Gaussianity. One option is to apply a monotonic transformation to the data such that the transformed data have a distribution that is close to Gaussian. In this thesis, we focus on a flexible two-parameter family of transformations, the Tukey g-and-h (TGH) transformation. This family has the desirable properties that the two parameters g ∈ R and h ≥ 0 involved control skewness and tail-heaviness of the distribution, respectively. Applying the TGH transformation to a standard normal distribution results in the univariate TGH distribution. Extensions to the multivariate case and to a spatial process were developed recently. In this thesis, motivated by the need to exploit wind as renewable energy, we tackle the challenges of modeling big spatio-temporal data that are non-Gaussian by applying the TGH transformation to different types of Gaussian processes: spatial (random field), temporal (time series), spatio-temporal, and their multivariate extensions. We explore various aspects of spatio-temporal data modeling techniques using transformed Gaussian processes with the TGH transformation. First, we use the TGH transformation to generate non-Gaussian spatial data with the Matérn covariance function, and study the effect of non-Gaussianity on Gaussian likelihood inference for the parameters in the Matérn covariance via a sophisticatedly designed simulation study. Second, we build two autoregressive time series models using the TGH transformation. One model is applied to a dataset of observational wind speeds and shows advantaged in accurate forecasting; the other model is used to fit wind speed data from a climate model on gridded locations covering Saudi Arabia and to Gaussianize the data for each location. Third, we develop a parsimonious spatio-temporal model for time series data on a spatial grid and utilize the aforementioned Gaussianized climate model wind speed data to fit the latent Gaussian spatio-temporal process. Finally, we discuss issues under a unified framework of modeling multivariate trans-Gaussian processes and adopt one of the TGH autoregressive models to build a stochastic generator for global wind speed.
  • Molybdenum Disulfide as an Efficient Catalyst for Hydrogen Evolution Reaction

    Alarawi, Abeer A. (2018-12-02)
    Hydrogen is a carrier energy gas that can be utilized as a clean energy source instead of oil and natural gas. Splitting the water into hydrogen and oxygen is one of the most favorable methods to generate hydrogen. The catalytic properties of molybdenum disulfide (MoS2) could be valuable in this role, particularly due to its unique structure and ability to be chemically modified, enabling its catalytic activity to be further enhanced or made comparable to that of Pt-based materials. In general, these modification strategies may involve either structural engineering of MoS2 or enhancing the kinetics of charge transfer, including by confining to single metal atoms and clusters or integrating with a conductive substrate. We present the results of synergetic integration of MoS2 films with a Si-heterojunction solar cell for generating H2 via the photochemical water splitting approach. The results of the photochemical measurements demonstrated an efficient photocurrent of 36. 3 mA cm-2 at 0 V vs. RHE and an onset potential of 0.56 V vs. RHE. In addition to 25 hours of continuous photon conversion to H2 generation, this study points out that the integration of the Si-HJ with MoS2 is an effective strategy for enhancing the internal conductivity of MoS2 towards efficient and stable hydrogen production. Moreover, we studied the effect of doping an atomic scale of Pt on the catalytic activity of MoS2. The electrochemical results indicated that the optimum single Pt atoms loading amount demonstrated a distinct enhancement in the hydrogen generating, in which the overpotential was minimized to -0.0505 V to reach a current density of 10 mA cm−2 using only 10 ALD cycles of Pt. The Tafel slopes of the ALD Pt/ML-MoS2 electrodes were in the range of 55–120 mV/decade, which indicates a fast improvement in the HER velocity as a result of the increased potential. Stability is another important parameter for evaluating a catalyst. The same (10 ALD cycles) Pt/ML-MoS2 electrode was able to continuously generate hydrogen molecules at for 150 hours. These superior results demonstrate that the low conductivity of semiconductive MoS2 can be enhanced by anchoring the film with Pt SAs and clusters, leading to sufficient charge transport and a decrease in the overpotential.
  • Cylindrical Magnetic Nanowires Towards Three Dimensional Data Storage

    Mohammed, Hanan (2018-12)
    The past few decades have witnessed a race towards developing smaller, faster, cheaper and ultra high capacity data storage technologies. In particular, this race has been accelerated due to the emergence of the internet, consumer electronics, big data, cloud based storage and computing technologies. The enormous increase in data is paving the path to a data capacity gap wherein more data than can be stored is generated and existing storage technologies would be unable to bridge this data gap. A novel approach could be to shift away from current two dimensional architectures and onto three dimensional architectures wherein data can be stored vertically aligned on a substrate, thereby decreasing the device footprint. This thesis explores a data storage concept based on vertically aligned cylindrical magnetic nanowires which are promising candidates due to their low fabrication cost, lack of moving parts as well as predicted high operational speed. In the proposed concept, data is stored in magnetic nanowires in the form of magnetic domains or bits which can be moved along the nanowire to write/read heads situated at the bottom/top of the nanowire using spin polarized current. Cylindrical nanowires generally exhibit a single magnetic domain state i.e. a single bit, thus for these cylindrical nanowire to exhibit high density data storage, it is crucial to pack multiple domains within a nanowire. This dissertation demonstrates that by introducing compositional variation i.e. multiple segments along the nanowire, using materials with differing values of magnetization such as cobalt and nickel, it is possible to incorporate multiple domains in a nanowire. Since the fabrication of cylindrical nanowires is a batch process, examining the properties of a single nanowire is a challenging task. This dissertation deals with the fabrication, characterization and manipulation of magnetic domains in individual nanowires. The various properties of are investigated using electrical measurements, magnetic microscopy techniques and micromagnetic simulations. In addition to packing multiple domains in a cylindrical nanowire, this dissertation reports the current assisted motion of domain walls along multisegmented Co/Ni nanowires, which is a fundamental step towards achieving a high density cylindrical nanowire-based data storage device.
  • Unravelling the Metabolic Interactions of the Aiptasia-Symbiodiniaceae Symbiosis

    Cui, Guoxin (2018-12)
    Many omics-level studies have been undertaken on Aiptasia, however, our understanding of the genes and processes associated with symbiosis regulation and maintenance is still limited. To gain deeper insights into the molecular processes underlying this association, we investigated this relationship using multipronged approaches combining next generation sequencing with metabolomics and immunohistochemistry. We identified 731 high-confident symbiosis-associated genes using meta-analysis. Coupled with metabolomic profiling, we exposed that symbiont-derived carbon enables host recycling of ammonium into nonessential amino acids, which may serve as a regulatory mechanism to control symbiont growth through a carbon-dependent negative feedback of nitrogen availability to the symbiont. We then characterized two symbiosis-associated ammonium transporters (AMTs). Both of the proteins exhibit gastrodermis-specific localization in symbiotic anemones. Their tissuespecific localization consistent with the higher ammonium assimilation rate in gastrodermis of symbiotic Aiptasia as shown by 15N labeling and nanoscale secondary ion mass spectrometry (NanoSIMS). Inspired by the tissue-specific localization of AMTs, we investigated spatial expression of genes in Aiptasia. Our results suggested that symbiosis with Symbiodiniaceae is the main driver for transcriptional changes in Aiptasia. We focused on the phagosome-associated genes and identified several key factors involved in phagocytosis and the formation of symbiosome. Our study provided the first insights into the tissue specific complexity of gene expression in Aiptasia. To investigate symbiosis-induced response in symbiont and to find further evidence for the hypotheses generated from our host-focused analyses, we explored the growth and gene expression changes of Symbiodiniaceae in response to the limitations of three essential nutrients: nitrogen, phosphate, and iron, respectively. Comparisons of the expression patterns of in hospite Symbiodiniaceae to these nutrient limiting conditions showed a strong and significant correlation of gene expression profiles to the nitrogen-limited culture condition. This confirmed the nitrogen-limited growing condition of Symbiodiniaceae in hospite, and further supported our hypothesis that the host limits the availability of nitrogen, possibly to regulate symbiont cell density. In summary, we investigated different molecular aspects of symbiosis from both the host’s and symbiont’s perspective. This dissertation provides novel insights into the function of nitrogen, and the potential underlying molecular mechanisms, in the metabolic interactions between Aiptasia and Symbiodiniaceae.
  • Using single molecule fluorescence to study substrate recognition by a structure-specific 5’ nuclease

    Rashid, Fahad (2018-12)
    Nucleases are integral to all DNA processing pathways. The exact nature of substrate recognition and enzymatic specificity in structure-specific nucleases that are involved in DNA replication, repair and recombination has been under intensive debate. The nucleases that rely on the contours of their substrates, such as 5’ nucleases, hold a distinctive place in this debate. How this seemingly blind recognition takes place with immense discrimination is a thought-provoking question. Pertinent to this question is the observation that even minor variations in the substrate provoke extreme catalytic variance. Increasing structural evidence from 5’ nucleases and other structure-specific nuclease families suggest a common theme of substrate recognition involving distortion of the substrate to orient it for catalysis and protein ordering to assemble active sites. Using three single-molecule (sm)FRET approaches of temporal resolution from milliseconds to sub-milliseconds, along with various supporting techniques, I decoded a highly sophisticated mechanism that show how DNA bending and protein ordering control the catalytic selectivity in the prototypic system human Flap Endonuclease 1 (FEN1). Our results are consistent with a mutual induced-fit mechanism, with the protein bending the DNA and the DNA inducing a protein-conformational change, as opposed to functional or conformational selection mechanism. Furthermore, we show that FEN1 incision on the cognate substrate occurs with high efficiency and without missed opportunity. However, when FEN1 encounters substrates that vary in their physical attributes to the cognate substrate, cleavage happens after multiple trials During the course of my work on FEN1, I found a novel photophysical phenomena of protein-induced fluorescence quenching (PIFQ) of cyanine dyes, which is the opposite phenomenon of the well-known protein-induced fluorescence enhancement (PIFE). Our observation and characterization of PIFQ led us to further investigate the general mechanism of fluorescence modulation and how the initial fluorescence state of the DNA-dye complex plays a fundamental role in setting up the stage for the subsequent modulation by protein binding. Within this paradigm, we propose that enhancement and quenching of fluorescence upon protein binding are simply two different faces of the same process. Our observations and correlations eliminate the current inconvenient arbitrary nature of fluorescence modulation experimental design.
  • The Effect of Increasing Temperature on Greenhouse Gas Emissions by Halophila stipulacea in the Red Sea

    Burkholz, Celina (2018-12)
    Seagrass ecosystems are intense carbon sinks, but they can also emit greenhouse gases (GHG), such as carbon dioxide (CO2) and methane (CH4), to the atmosphere. Yet, GHG emissions by seagrasses are not considered when estimating global CH4 production rates by natural sources, although these estimations will help predict future scenarios and potential changes in CH4 emissions. In addition, the effect of warming on GHG emissions by seagrasses has not yet been reported. The present study aims to assess the CO2 and CH4 production rates by vegetated and adjacent bare sediment of a monospecific seagrass meadow (Halophila stipulacea) located in the central Red Sea. We measured CH4 and CO2 fluxes and their isotopic signatures by cavity ringdown spectroscopy on chambers containing vegetated and bare sediment. The fluxes were measured at temperatures from 25 °C (winter seawater temperature) to 37 °C to cover the natural thermal range and future seawater temperatures in the Red Sea. Additional parameters analyzed included changes in the sediment microbial community composition, sediment organic matter, organic carbon, nitrogen, and phosphorus concentration. We detected up to 100-fold higher CH4 (up tp 571.65 µmol CH4 m−2 d−1) and up to six-fold higher CO2 (up to 13,930.18 µmol CO2 m−2 d−1) fluxes in vegetated sediment compared to bare sediment, and an increase in CH4 and CO2 production with increasing temperature. In contrast, CH4 and CO2 production rates decreased in communities that were maintained at 25 °C, while communities that were exposed to prolonged darkness showed a decrease in CH4 and an increase in CO2 production rates. However, only minor changes were seen in the microbial community composition with increasing temperatures. These results show that GHG emissions by seagrasses might be affected by natural temperature extremes and warming due to climate change in the Red Sea. The findings will have critical implications for the estimation of natural GHG sources, especially when predicting future changes in the global CH4 budget.
  • Modeling and Analysis of Hybrid Aerial-Terrestrial Networks: A Stochastic Geometry Approach

    Alshaikh, Khlod K. (2018-12)
    The ever-increasing demand for better mobile experiences is propelling the research communities to look ahead at how future networks can be geared up to meet such demands. It is likely that the next-generation of wireless communications will be revolutionary, outpacing the current systems capabilities in terms connectivity, reliability and intelligence. These trends and predictions will cause a revolutionary change in the wireless communications. In this context, the concept of Ultra-Dense Network (UDN) is poised to be the cornerstone of the development of fifth generation(5G) systems, whereby a massive number of base stations (BSs) are deployed for enhancing the network performance metrics. Though such densification might be economically viable in urban areas, it is mostly unfavorable in rural ones due to the sheer complexity and the various factors involved the planning and installation processes; all of which trigger the need for cost-effective, flexible and easily-implementable solutions. As a result, unmanned aerial vehicles (UAVs) emerge as a promising alternative solution for enhancing wireless coverage. Due to their mobility capabilities, UAVs are of particular importance in events of (i) terrestrial-based cellular systems dilapidation, (ii) infrastructure absence in remote and suburban areas, or (iii) limited-duration events or activities wherein there is a short-term need for supplementary network resources to handle the overload. While a growing body literature works towards characterizing and providing insights into the performance of UAVs-only networks (serving the first two purposes), understanding the performance of such networks when coupled with existing terrestrial BSs remains a challenging, yet interesting, open research venue. Towards this direction, this thesis provides a rigorous analysis of the downlink coverage probability of hybrid aerial-terrestrial networks using tools from Stochastic Geometry. The thesis presents a mathematical model that characterizes the coverage probability metric under different network environments. The proposed model is validated against intensive simulations so as to substantiate the analytical results. The developed work is essential to understanding the premises of one possible solution to the UDNs of tomorrow, capture its key performance metrics and, most importantly, to uncover key design insights and reveal new directions for the wireless communication industry.
  • A Biocomputational Study of Water-Nucleobase Stacking Contacts in Functional RNAs

    Kalra, Kanav (2018-12)
    Recent structural studies evidenced the presence of a recurring well-known interaction between an oxygen atom and an aromatic nucleobase ring in structural motifs of nucleic acids. In particular, this type of interaction is observed between the O4' atom of the (deoxy)ribose moiety and the aromatic nucleobase in Z-DNA molecules and in a variety of structural RNA molecules. In this thesis, we comprehensively examine the hitherto undetected stacking interactions between an oxygen atom of a water (Ow) molecule and the aromatic nucleobase ring, using structural bioinformatics along with quantum mechanics. On the basis of the structural analysis of the high-resolution X-ray structures, we found out that the stacking distance between the Ow atom and the nucleobase plane varies between 3.1 and 4.0 Å. Further, the contact between the Ow-nucleobase plane can be categorized either as a lonepair-π type, where the Ow atom interacts directly with the aromatic surface of the nucleobase, or as an OH-π interaction, where one of the hydrogen atoms of the Ow points towards the nucleobase. Our quantum chemical analysis evidenced that the OH-π interaction is clearly favored in terms of energetics when compared to the lonepair-π, except for the uracil, where the lonepair-π kind of interaction seems to be energetically more stable, as also supported by electrostatic potential map calculations.
  • Contributions to Computational Methods for Association Extraction from Biomedical Data: Applications to Text Mining and In Silico Toxicology

    Raies, Arwa B. (2018-11-29)
    The task of association extraction involves identifying links between different entities. Here, we make contributions to two applications related to the biomedical field. The first application is in the domain of text mining aiming at extracting associations between methylated genes and diseases from biomedical literature. Gathering such associations can benefit disease diagnosis and treatment decisions. We developed the DDMGD database to provide a comprehensive repository of information related to genes methylated in diseases, gene expression, and disease progression. Using DEMGD, a text mining system that we developed, and with an additional post-processing, we extracted ~100,000 of such associations from free-text. The accuracy of extracted associations is 82% as estimated on 2,500 hand-curated entries. The second application is in the domain of computational toxicology that aims at identifying relationships between chemical compounds and toxicity effects. Identifying toxicity effects of chemicals is a necessary step in many processes including drug design. To extract these associations, we propose using multi‐label classification (MLC) methods. These methods have not undergone comprehensive benchmarking in the domain of predictive toxicology that could help in identifying guidelines for overcoming the existing deficiencies of these methods. Therefore, we performed extensive benchmarking and analysis of ~19,000 MLC models. We demonstrated variability in the performance of these models under several conditions and determined the best performing model that achieves accuracy of 91% on an independent testing set. Finally, we propose a novel framework, LDR (learning from dense regions), for developing MLC and multi-target regression (MTR) models from datasets with missing labels. The framework is generic, so it can be applied to predict associations between samples and discrete or continuous labels. Our assessment shows that LDR performed better than the baseline approach (i.e., the binary relevance algorithm) when evaluated using four MLC and five MTR datasets. LDR achieved accuracy scores of up to 97% using testing MLC datasets, and R2 scores up to 88% for testing MTR datasets. Additionally, we developed a novel method for minority oversampling to tackle the problem of imbalanced MLC datasets. Our method improved the precision score of LDR by 10%.
  • Preparation of modified DNA molecules for multi-Spectroscopy Application

    zhang, xinyu (2018-11-29)
    Hot Electron Nanoscopy and Spectroscopy (HENs) is a current-sensing AFM technique recently developed in our lab, which have proven a new kind of response on conduction at the nanometer scale, casting a new light for the comprehension of electronic states in nanomaterials. Direct imaging of DNA structure has long been investigated, with the development of HENs technology, more structural information about DNA could be revealed by simultaneous measurements of height, phase, Raman signal, and conductivity. With the aim of applying it for the first time on biological molecules, customized double-stranded DNA sequences, including thiol-modified oligonucleotides are designed to create preferential conductive paths through the basis as a benchmark system for the technique on biomolecules. This work aims to a final goal to characterize hot-electron current between gold tip and thiol modified DNA which ideally is covalently bonded to the gold surface and optimized for the application. In this work, high density of DNA absorbed by SERS active gold surface with atomic flat islands has been prepared for HENs application. The samples have been characterized by AFM, SKPM and Raman Spectroscopy, as non-destructive and controlled interactive image analysis. High-resolution images of DNA have been acquired, S-S and Au-S bonding of DNA anchored on SERS active gold substrate are also visible with Surface-enhanced Raman and Tip-enhanced Raman signals. A submolecular feature has also been found in both topographical and electrical results. Herein, we report the synthesis and characterization steps to obtain the optimized operation standard.
  • Statistical characteristics and mapping of near-surface and elevated wind resources in the Middle East

    Yip, Chak Man Andrew (2018-11-28)
    Wind energy is expected to contribute to alleviating the rise in energy demand in the Middle East that is driven by population growth and industrial development. However, variability and intermittency in the wind resource present significant challenges to grid integration of wind energy systems. The first chapter addresses the issues in current wind resource assessment in the Middle East due to sparse meteorological observations with varying record lengths. The wind field with consistent space-time resolution for over three decades at three hub heights over the whole Arabian Peninsula is constructed using the Modern Era Retrospective-Analysis for Research and Applications (MERRA) dataset. The wind resource is assessed at a higher spatial resolution with metrics of temporal variations in the wind than in prior studies. Previously unrecognized locations of interest with high wind abundance and low variability and intermittency have been identified in this study and confirmed by recent on-site observations. The second chapter explores high-altitude wind resources that may provide alternative energy resources in this fossil-fuel-dependent region. This study identifies areas favorable to the deployment of airborne wind energy (AWE) systems in the Middle East and computes the optimal heights at which such systems would best operate. AWE potential is estimated using realistic AWE system specifications and assumptions about deployment scenarios and is compared with the near-surface wind generation potential concerning diurnal and seasonal variability. The results show the potential utility of AWE in areas in the Middle East where the energy demand is high. The third chapter investigates the potential for wind energy to provide a continuous energy supply in the region. We characterize the wind power variability at various time-scales of power operations to illustrate its effects across the Middle East via spectral analysis and clustering. Using a high-resolution dataset obtained from Weather Forecasting and Research (WRF) model simulations, this study showcases how aggregate variability may impact operation, and informs the planning of large-scale wind power integration in the Middle East in light of the scarcity of observational data.
  • Prediction of Active and Inactive Chemical Compounds from High-Throughput Assays

    Islam, Elaf J. (2018-11-28)
    This study considers chemical compounds that can exert their activity by interacting with a target protein or other molecular receptors. Our aim is to develop machine learning models that can predict if a chemical compound will be active in a particular test/assay. We will use data from assays that are present in the PubChem knowledgebase, specifically in its segment called BioAssays which reports the results of many high-throughput screening experiments. PubChem BioAssays is a valuable resource that contains information from a large number of experiments. In one assay, sometimes many hundreds or even many thousands of chemicals are tested. Data from these experimental assays contain information about chemicals that are active as well as chemicals that are not active in the assay. These represent an interesting resource of experimental data that are well suited for classification purposes. We will approach the problem by evaluating different ways that chemical compounds can be numerically described by means of so-called fingerprints, and then apply different machine learning (ML) and deep learning (DL) models to classify active and inactive chemicals for a number of assays. In this study, we will make comprehensive comparisons of the types of ML/DL models and types of fingerprint features that describe chemicals, and evaluate combinations of models and fingerprints that work best for the problem in question. Our focus is on finding those combinations which are useful for distinguishing active from inactive compounds in single PubChem assays. We will evaluate the methods across 10 assays and will examine the effects of 11 types of fingerprints. For example, PubChem fingerprints and MACCS keys fingerprints. For the evaluation, up to now we performed 88 experiments for each dataset and 968 in total for all 10 PubChem assays. These experiments involved approximately 6,000 interactions between chemicals and their targets. The implementation of this project has been done using MATLAB. Based on these and additional experiments, we will be in a position to propose which combination of fingerprints and ML/DL models works best in the above mentioned task. Such modeling will be useful to predict activity for chemicals that are not yet tested.
  • Visualization and Simulation of Variants in Personal Genomes With an Application to Premarital Testing (VSIM)

    Althagafi, Azza Th. (2018-11-28)
    Interpretation and simulation of the large-scale genomics data are very challenging, and currently, many web tools have been developed to analyze genomic variation which supports automated visualization of a variety of high throughput genomics data. We have developed VSIM an automated and easy to use web application for interpretation and visualization of a variety of genomics data, it identifies the candidate diseases variants by referencing to four databases Clinvar, GWAS, DIDA, and PharmGKB, and predicted the pathogenic variants. Moreover, it investigates the attitude towards premarital genetic screening by simulating a population of children and analyze the diseases they might be carrying, based on the genetic factors of their parents taking into consideration the recombination hotspots. VSIM supports output formats based on Ideograms that are easy to interpret and understand, which makes it a biologist-friendly powerful tool for data visualization, and interpretation of personal genomic data. Our results show that VSIM can efficiently identify the causative variants by referencing well-known databases for variants in whole genomes associated with different kind of diseases. Moreover, it can be used for premarital genetic screening by simulating a population of offspring and analyze the disorders they might be carrying. The output format provides a better understanding of such large genomics data. VSIM thus helps biologists and marriage counsellor to visualize a variety of genomic variants associated with diseases seamlessly.
  • Mechanisms of Enhanced Thermoelectricity in Chalcogenides

    Alsaleh, Najebah (2018-11-27)
    Thermoelectric materials can provide solutions to power generation and refrigeration challenges. Layered chalchogenides are of particular interest, with bismuth telluride and lead telluride being the most common compounds. Bismuth telluride is often used for room temperature applications, while its solid solutions with antimony or selenium as well as lead tellurides show better thermoelectric properties at elevated temperatures. Regrettably, the efficiency of the known thermoelectric materials is still low. Evidently, bringing thermoelectric energy harvesting to commercial viability is a materials challenge: How can we obtain materials with figure of merit above 3? This question drives the research community since the successes of nanoengineering in the 1990s. Nowadays, high-pressure technology is a promising frontier for making further advances in thermoelectric material performance. The main goal of this thesis is to understand the electronic and thermoelectric properties of selected materials using density functional theory and semi-classical Boltzmann transport theory. Bulk and monolayer CuSbS2 and CuSbSe2 are studied to clarify the role of the interlayer coupling for the thermoelectric properties. The calculated band gaps of the bulk compounds turn out to be in agreement with experiments and significantly higher than those of the monolayers, which thus show lower Seebeck coefficients. Since also the electrical conductivity is lower, the monolayers are characterised by lower power factors. Therefore, the interlayer coupling, even though it is weak, is found to be essential for the thermoelectric response. We study Cu (Sb,Bi)(S,Se)2 under hydrostatic pressure up to 8 GPa, considering the van der Waals interaction, as these compounds have layered structures. We find an indirect band gap that decreases monotonically with increasing hydrostatic pressure. Only CuBiS2 shows an indirect-indirect band gap transition around 3 GPa, leading to conduction band convergence with a concomitant 20% increase in the Seebeck co-efficient. This enhancement results from a complex interplay between multivalley and multiband effects as well as changes of the band effective masses. The variation of the electronic band structure of AB2Te4 (A = Pb, Sn and B = Bi, Sb) under hydrostatic pressure up to 8 GPa is analyzed in detail and its consequences for the material properties are explained.
  • Proximity Mechanisms in Graphene: Insights from Density Functional Theory

    Alattas, Maha H. (2018-11-27)
    One of the challenges in graphene fabrication is the production of large scale, high quality sheets. To study a possible approach to achieve quasi-freestanding graphene on a substrate by the intercalation of alkali metal atoms, Cs intercalation between graphene and Ni(111) is investigated. It is known that direct contact between graphene and Ni(111) perturbs the Dirac states. Cs intercalation restores the linear dispersion characteristic of Dirac fermions, which agrees with experiments, but the Dirac cone is shifted to lower energy, i.e., the graphene sheet is n-doped. Cs decouples the graphene sheet, while the spin polarization of Ni(111) does not extend through the intercalated atoms to the graphene sheet, for which we find virtually spin-degeneracy. In order to employ graphene in electronic applications, one requires a finite band gap. We engineer a band gap in metallic bilayer graphene by substitutional B and/or N doping. Specifically, the introduction of B-N pairs into bilayer graphene can be used to create a band gap that is stable against thermal fluctuations at room temperature. Introduction of B-N pairs into B and/or N doped bilayer graphene likewise hardly modifies the band dispersions, however, the size of the band gap is effectively tuned. We also study the influence of terrace edges on the electronic properties of graphene, considering bare edges and H, F, Cl, NH2 terminations. Periodic structural reconstruction is observed for the Cl and NH2 edge terminations due to interaction between the terminating atoms/groups. We observe that Cl edge termination p-dopes the terraces, while NH2 edge termination results in n-doping.
  • Suspended dsDNA/Rad51 on super-hydrophobic devices: Raman spectroscopy characterization

    Morello, Maria Caterina (2018-11-22)
    The novel method herein proposed, aims to study Deoxyribonucleic acid (DNA) and Rad51 repair protein in its resting state after their interaction by using a combination of biological preparation and physical measures. Rad51 is a highly conserved protein; it is involved in eukaryotes genome stability and can interact with single strand (ss) and double strands (ds) DNA. In our work, a droplet of the solution containing the dsDNA/Rad51 complexes was deposited on micro-fabricated super-hydrophobic substrates (SHS) to obtain self-organized and suspended fibers. The silicon-based SHS were designed to incorporate a regular circular array of pillars and to maintain a high contact angle with the drop. The samples were let dehydrate at controlled temperature and humidity conditions. At the end of the buffer evaporation process, non-suspended material and salt excess are concentrated on the top of a few micro-pillars in a limited area (drop residual) of the device while ordered and self-assembled DNA/Rad51 fibers are suspended between micro-pillars. To find the ideal conditions to obtain and suspend the nucleic acid/protein complexes, several parameters were investigated: saline buffer, DNA and protein concentrations were widely titrated and showed a significant effect on the biomolecule suspension on SHS. The samples were then preliminarily checked by microscopy techniques and then described by the Raman spectra acquired. Several techniques were used: optical microscopy, Energy Dispersive X-Ray Spectroscopy (EDAX), Scanning Electron Microscopy (SEM) and Raman Spectroscopy. Protein expressions, DNA suspension, micro-fabrication and characterization were all performed in KAUST Core Labs and Structural Molecular Imaging Light Enhanced Spectroscopies (SMILEs) Lab. The novel approach presented in this work is highly multidisciplinary and comprises physical measurements (Raman spectroscopy and EM imaging), chemistry and biology. In future the method can be used further expanded supporting the data with HRTEM direct imaging to elucidate the nucleic acids/proteins behavior in the multiple phases of the genome repair processes. Also, it and can serve as a fingerprint of the biological molecules involved in biological interactions, their localization and structural characterization, providing a new tool for structural analysis, screening and diagnostics.
  • Micro-fabricated super-hydrophobic substrate for amyloid fibers characterization

    Ricco, Andrea (2018-11-22)
    In recent years super-hydrophobic micro-patterned substrates (SHS) have been successfully used for the suspension of a few biological molecules, allowing the further characterization in a background-free environment by label-free techniques such as Raman spectroscopy, SEM and TEM in one device. This result is due to the combined action of laminar flow and shear stress exerted on the molecules contained in a drop that is spotted on top of the SHS and slowly evaporates. This new method is here proposed for the label-free formation and background-free characterization of amyloid fibers. Amyloids are insoluble aggregates formed by proteins that convert from a misfolded form into highly-organized β-sheet structures that could accumulate in different organs and compromise their normal physiological functions. Known amyloid-related diseases, named amyloidosis, are for instance Alzheimer, Parkinson, and type 2 diabetes. In classical crystallography, the study of the amyloid aggregates structure is often hampered by the laborious and time consuming sample preparation techniques. Therefore the need of a quick reproducible technique, has emerged. The amyloid fibers investigated in this work are derived from a lysozyme protein and a Tau-derived short peptide, both known to be related to two forms of amyloidosis. With this technique we demonstrate that threads of protein fibers are deposited on the substrate helped by the patterning of the SHS and its properties, and by characterizing them with Raman spectroscopy technique we revealed that they are anisotropic structures of amyloid nature. This type of sample preparation technique arises from the effect of the evaporation on the SHS, and opens up new possibilities for the formation of oriented fibers of amyloids and more in general, of proteins, ready for a substrate-free characterization, while classic crystallographic methods could have a limitation.
  • Towards Rational Design of Biosynthesis Pathways

    Alazmi, Meshari (2018-11-19)
    Recent advances in genome editing and metabolic engineering enabled a precise construction of de novo biosynthesis pathways for high-value natural products. One important design decision to make for the engineering of heterologous biosynthesis systems is concerned with which foreign metabolic genes to introduce into a given host organism. Although this decision must be made based on multifaceted factors, a major one is the suitability of pathways for the endogenous metabolism of a host organism, in part because the efficacy of heterologous biosynthesis is affected by competing endogenous pathways. To address this point, we developed an open-access web server called MRE (metabolic route explorer) that systematically searches for promising heterologous pathways by considering competing endogenous reactions in a given host organism. MRE utilizes reaction Gibbs free energy information. However, 25% of the reactions do not have accurate estimations or cannot be estimated. To address this issue, we developed a method called FC (fingerprint contribution) to provide a more accurate and complete estimation of the reaction free energy. To rationally design a productive heterologous biosynthesis system, it is essential to consider the suitability of foreign reactions for the specific endogenous metabolic infrastructure of a host. For a given pair of starting and desired compounds in a given chassis organism, MRE ranks biosynthesis routes from the perspective of the integration of new reactions into the endogenous metabolic system. For each promising heterologous biosynthesis pathway, MRE suggests actual enzymes for foreign metabolic reactions and generates information on competing endogenous reactions for the consumption of metabolites. The URL of MRE is Accurate and wide-ranging prediction of thermodynamic parameters for biochemical reactions can facilitate deeper insights into the workings and the design of metabolic systems. Here, we introduce a machine learning method, referred to as fingerprint contribution (FC), with chemical fingerprint-based features for the prediction of the Gibbs free energy of biochemical reactions. From a large pool of 2D fingerprint-based features, this method systematically selects a small number of relevant ones and uses them to construct a regularized linear model. FC is freely available for download at
  • Multilayer Dielectrics and Semiconductor Channels for Thin Film Transistor Applications

    Alshammari, Fwzah (2018-11-13)
    Emerging transparent conducting and semiconducting oxide (TCO) and (TSO) materials have achieved success in display markets. Due to their excellent electrical performance, TSOs have been chosen to enhance the performance of traditional displays and to evaluate their application in future transparent and flexible displays. This dissertation is devoted to the study ZnO-based thin film transistors (TFTs) using multilayer dielectrics and channel layers. Using multilayers to engineer transistor parameters is a new approach. By changing the thickness, composition, and sequence of the layers, transistor performance can be optimized. In one example, Al2O3/Ta2O5 bilayer gate dielectrics, grown by atomic layer deposition at low temperature were developed. The approach combined high dielectric constant of Ta2O5 and the excellent interface quality of Al2O3/ZnO, resulting in enhanced device performance. Using zinc oxide (ZnO)/hafnium oxide (HfO2) multilayer stack as a TFT channel with tunable layer thicknesses resulted in significant improvement in TFT stability. Atomic layer deposited SnO2 was developed as a gate electrode to replace ITO in thin film transistors and circuits. The SnO2 films deposited at 200 °C show low electrical resistivity of ~3.1×10-3 Ohm-cm with the high transparency of ~93%. TFT fabricated with SnO2 gate show excellent transistor properties. Using results from the above experiments, we have developed a novel process in which thin film transistors (TFTs) are fabricated using one binary oxide for all transistor layers (gate, source/drain, semiconductor channel, and dielectric). In our new process, by simply changing the flow ratio of two chemical precursors, C8H24HfN4 and (C2H5)2Zn, in an ALD system, the electronic properties of the binary oxide HZO were controlled from conducting, to semiconducting, to insulating. A complete study of HZO thin films deposited by (ALD) was performed. The use of the multi-layer (HfO2/ZnO) channel layer plays a key role in improving the bias stability of the devices. The low processing temperature of all materials at 160 °C is an advantage for the fabrication of fully transparent and flexible devices. After precise device engineering, including growth temperature, gate dielectric, electrodes (S/D&G) and semiconductor thickness, TFT with excellent device performance are obtained.

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