Now showing items 21-40 of 19644

    • 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.
    • 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.
    • 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.
    • The activity of indenylidene derivatives in olefin metathesis catalysts

      Voccia, Maria; Nolan, Steven P.; Cavallo, Luigi; Poater, Albert (Beilstein Institut, 2018-11-30)
      The first turnover event of an olefin metathesis reaction using a new family of homogenous Ru-based catalysts bearing modified indenylidene ligands has been investigated, using methoxyethylene as a substrate. The study is carried out by means of density functional theory (DFT). The indenylidene ligands are decorated with ortho-methyl and isopropyl groups at both ortho positions of their phenyl ring. DFT results highlight the more sterically demanding indenylidenes have to undergo a more exothermic first phosphine dissociation step. Overall, the study emphasises advantages of increased steric hindrance in promoting the phosphine release, and the relative stability of the corresponding metallacycle over classical ylidene ligands. Mayer bond orders and steric maps provide structural reasons for these effects, whereas NICS aromaticity and conceptual DFT confirm that the electronic parameters do not play a significant role.
    • Investigation of the turbulent flame structure and topology at different Karlovitz numbers using the tangential stretching rate index

      Manias, Dimitris M.; Tingas, Alexandros; Hernández Pérez, Francisco E.; Malpica Galassi, Riccardo; Ciottoli, Pietro P.; Valorani, Mauro; Im, Hong G. (Elsevier BV, 2018-11-30)
      Turbulent premixed flames at high Karlovitz numbers exhibit highly complex structures in different reactive scalar fields to the extent that the definition of the flame front in an unambiguous manner is not straightforward. This poses a significant challenge in characterizing the observable turbulent flame behaviour such as the flame surface density, turbulent burning velocity, and so on. Turbulent premixed flames are reactive flows involving physical and chemical processes interacting over a wide range of time scales. By recognizing the multi-scale nature of reactive flows, we analyze the topology and structure of two direct numerical simulation cases of turbulent H2/air premixed flames, in the thin reaction zone and distributed combustion regimes, using tools derived from the computational singular perturbation (CSP) method and augmented by the tangential stretching rate (TSR) analysis. CSP allows to identify the local time scale decomposition of the multi-scale problem in its slow and fast components, while TSR allows to identify the most energetic time scale during both the explosive and dissipative regime of the reactive flow dynamics together with the identification of the flame front in an unambiguous manner. Before facing the complexity of the turbulent flow regime, we carry out a preliminary analysis of a one-dimensional laminar premixed flame in view of highlighting similarities and differences between laminar and turbulent cases. Subsequently, it is shown that the TSR metric provides a reliable way to identify the turbulent flame topologies.
    • Band Gap Control in Bilayer Graphene by Co-Doping with B-N Pairs

      Alattas, Maha Hassan Mohssen; Schwingenschlögl, Udo (Springer Nature, 2018-11-30)
      The electronic band structure of bilayer graphene is studied systematically in the presence of substitutional B and/or N doping, using density functional theory with van der Waals correction. We show that introduction of B-N pairs into bilayer graphene can be used to create a substantial band gap, stable against thermal fluctuations at room temperature, but otherwise leaves the electronic band structure in the vicinity of the Fermi energy largely unaffected. Introduction of B-N pairs into B and/or N doped bilayer graphene likewise hardly modifies the band dispersions. In semiconducting systems (same amount of B and N dopants), however, the size of the band gap is effectively tuned in the presence of B-N pairs.
    • Methane Hydrates: Nucleation in Microporous Materials

      Andres-Garcia, Eduardo; Dikhtiarenko, Alla; Fauth, Francois; Silvestre-Albero, Joaquin; Ramos-Fernández, Enrique V.; Gascon, Jorge; Corma, Avelino; Kapteijn, Freek (Elsevier BV, 2018-11-29)
      Clathrates are well-known compounds whose low thermal stability makes them extremely rare and appreciated. Although their formation mechanism is still surrounded by many uncertainties, these ice-like structures have the potential to be an alternative for transport and storage of different gases, especially methane. For the formation of methane clathrates extreme pressure conditions and a narrow temperature window are needed. Microporous materials have been proposed to provide nucleation sites that, theoretically, promote clathrate formation at milder conditions. While activated carbons and Metal-Organic Frameworks (MOFs) have already been studied, very little is known about the role of zeolites in this field. In this work, we study the formation of methane clathrates in the presence of RHO zeolite. Experimental results based on adsorption and operando synchrotron X-Ray diffraction demonstrate the formation of clathrates at the surface of the zeolite crystals and reveal mechanistic aspects of this formation at mild conditions.
    • Claims That Anthropogenic Stressors Facilitate Jellyfish Blooms Have Been Amplified Beyond the Available Evidence: A Systematic Review

      Pitt, Kylie A.; Lucas, Cathy H.; Condon, Robert H.; Duarte, Carlos M.; Stewart-Koster, Ben (Frontiers Media SA, 2018-11-29)
      The perception that anthropogenic stressors cause jellyfish blooms is widespread within the scientific literature and media but robust evidence in support of these claims appears scarce. We used a citation analysis of papers published on “jellyfish blooms” to assess the extent to which such claims are made and the robustness of the evidence cited to support claims. Our search of the Web of Science returned 365 papers on “jellyfish blooms.” Each paper was searched for statements linking jellyfish blooms to specific anthropogenic stressors. For each statement we recorded the affirmation afforded to the claim, identified the stressors purported to cause blooms, the sources cited to support the statement, the type of study cited and the species studied in the cited source. Almost half the papers contained statements claiming that blooms were facilitated by anthropogenic stressors but most (70%) afforded a low degree of affirmation to the claim. We identified three major limitations in the evidence cited to support claims: (1) it was dominated by studies of two wide-spread and highly invasive taxa (Aurelia aurita and Mnemiopsis leidyi) that may not represent the responses of jellyfishes more generally; (2) the empirical evidence cited was dominated by correlative studies which, whilst useful for generating hypotheses, cannot attribute causation; and (3) the reviews most commonly-cited as evidence mostly cited circumstantial evidence and other reviews and provided conceptual models of how stressors could influence blooms, rather than robust evidence. We conclude that, although anthropogenic stressors could enhance jellyfish blooms, robust evidence is limited. Claims that strongly affirm anthropogenic stressors as causes of jellyfish blooms appear to be amplifying the evidence beyond that available. As a community we need to qualify the statements we make about jellyfish to strike a better balance between perpetuating perception and accurately portraying the state of knowledge.
    • DeeReCT-PolyA: a robust and generic deep learning method for PAS identification

      Xia, Zhihao; Li, Yu; Zhang, Bin; Li, Zhongxiao; Hu, Yuhui; Chen, Wei; Gao, Xin (Oxford University Press (OUP), 2018-11-29)
      Motivation \nPolyadenylation is a critical step for gene expression regulation during the maturation of mRNA. An accurate and robust method for poly(A) signals (PAS) identification is not only desired for the purpose of better transcripts’ end annotation, but can also help us gain a deeper insight of the underlying regulatory mechanism. Although many methods have been proposed for PAS recognition, most of them are PAS motif-specific and human-specific, which leads to high risks of overfitting, low generalization power, and inability to reveal the connections between the underlying mechanisms of different mammals. \nResults \nIn this work, we propose a robust, PAS motif agnostic, and highly interpretable and transferrable deep learning model for accurate PAS recognition, which requires no prior knowledge or human-designed features. We show that our single model trained over all human PAS motifs not only outperforms the state-of-theart methods trained on specific motifs, but can also be generalized well to two mouse data sets. Moreover, we further increase the prediction accuracy by transferring the deep learning model trained on the data of one species to the data of a different species. Several novel underlying poly(A) patterns are revealed through the visualization of important oligomers and positions in our trained models. Finally, we interpret the deep learning models by converting the convolutional filters into sequence logos and quantitatively compare the sequence logos between human and mouse datasets.
    • Layer-edge device of two-dimensional hybrid perovskites

      Cheng, Bin; Li, Ting-You; Wei, Pai-Chun; Yin, Jun; Ho, Kang-Ting; Duran Retamal, Jose Ramon; Mohammed, Omar F.; He, Jr-Hau (Springer Nature, 2018-11-29)
      Two dimensional layered organic-inorganic hybrid perovskites (2D perovskites) are potential candidates for next generation photovoltaic device. Especially, the out-of-plane surface perpendicular to the superlattice plane of 2D perovskites (layer-edge surface) has presented several exotic behaviors, such as layer-edge states which are found to be crucial for improving the efficiency of 2D perovskite solar cells. However, fundamental research on transport properties of layer-edge surface is still absent. In this report, we observe the electronic and opto-electronic behavior in layer-edge device of 2D perovskites. The dark and photo currents are demonstrated to strongly depend on the crystallographic orientation in layer-edge device, and such anisotropic properties, together with photo response, are related to the thickness of inorganic layers. Finally, due to the abundant hydroxyl groups, water molecules are easy to condense on the layer-edge surface, and the conductance is extremely sensitive to the humidity environment, indicating a potential application of humidity sensor.
    • 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.
    • Microbial diversity and biosignatures of amorphous silica deposits in orthoquartzite caves

      Sauro, Francesco; Cappelletti, Martina; Ghezzi, Daniele; Columbu, Andrea; Hong, Pei-Ying; Zowawi, Hosam Mamoon; Carbone, Cristina; Piccini, Leonardo; Vergara, Freddy; Zannoni, Davide; De Waele, Jo (Springer Nature, 2018-11-28)
      Chemical mobility of crystalline and amorphous SiO2 plays a fundamental role in several geochemical and biological processes, with silicate minerals being the most abundant components of the Earth's crust. Although the oldest evidences of life on Earth are fossilized in microcrystalline silica deposits, little is known about the functional role that bacteria can exert on silica mobility at non-thermal and neutral pH conditions. Here, a microbial influence on silica mobilization event occurring in the Earth's largest orthoquartzite cave is described. Transition from the pristine orthoquartzite to amorphous silica opaline precipitates in the form of stromatolite-like structures is documented through mineralogical, microscopic and geochemical analyses showing an increase of metals and other bioessential elements accompanied by permineralized bacterial cells and ultrastructures. Illumina sequencing of the 16S rRNA gene describes the bacterial diversity characterizing the consecutive amorphization steps to provide clues on the biogeochemical factors playing a role in the silica solubilization and precipitation processes. These results show that both quartz weathering and silica mobility are affected by chemotrophic bacterial communities, providing insights for the understanding of the silica cycle in the subsurface.
    • 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.
    • Aerosol water parameterization: long-term evaluation and importance for climate studies

      Metzger, Swen; Abdelkader, Mohamed; Steil, Benedikt; Klingmüller, Klaus (Copernicus GmbH, 2018-11-27)
      We scrutinize the importance of aerosol water for the aerosol optical depth (AOD) calculations using a long-term evaluation of the EQuilibrium Simplified Aerosol Model v4 for climate modeling. EQSAM4clim is based on a single solute coefficient approach that efficiently parameterizes hygroscopic growth, accounting for aerosol water uptake from the deliquescence relative humidity up to supersaturation. EQSAM4clim extends the single solute coefficient approach to treat water uptake of multicomponent mixtures. The gas–aerosol partitioning and the mixed-solution water uptake can be solved analytically, preventing the need for iterations, which is computationally efficient. EQSAM4clim has been implemented in the global chemistry climate model EMAC and compared to ISORROPIA II on climate timescales. Our global modeling results show that (I) our EMAC results of the AOD are comparable to modeling results that have been independently evaluated for the period 2000–2010, (II) the results of various aerosol properties of EQSAM4clim and ISORROPIA II are similar and in agreement with AERONET and EMEP observations for the period 2000–2013, and (III) the underlying assumptions on the aerosol water uptake limitations are important for derived AOD calculations. Sensitivity studies of different levels of chemical aging and associated water uptake show larger effects on AOD calculations for the year 2005 compared to the differences associated with the application of the two gas–liquid–solid partitioning schemes. Overall, our study demonstrates the importance of aerosol water for climate studies.