Recent Submissions

  • UAV Pathplanning Dataset & Benchmark

    Smith, Neil; Moehrle, Nils; Goesele, Michael; Heidrich, Wolfgang (2018-12-04)
  • Dataset for Figure 1 of "A tutorial on laser-based lighting and visible light communication: device and technology"

    Sun, Xiaobin; Guo, Yujian; Alkhazragi, Omar; Kang, Chun Hong; Shen, Chao; Mao, Yuan; Ng, Tien Khee; Ooi, Boon S. (2018-10-28)
  • Dataset for Figure 2 of "A tutorial on laser-based lighting and visible light communication: device and technology"

    Sun, Xiaobin; Guo, Yujian; Alkhazragi, Omar; Kang, Chun Hong; Shen, Chao; Mao, Yuan; Ng, Tien Khee; Ooi, Boon S. (2018-10-28)
  • Thesis Chapter 3 tables

    Awlia, Mariam (2018-09-24)
  • Supplementary Material for: Observations and cloud-resolving modeling of haboob dust storms over the Arabian Peninsula

    Anisimov, Anatolii; Axisa, Duncan; Kucera, Paul A.; Mostamandi, Suleiman; Stenchikov, Georgiy L. (2018-06-28)
  • Data for "Space-time Tomography for Continuously Deforming Objects"

    Zang, Guangming; Idoughi, Ramzi; Tao, Ran; Lubineau, Gilles; Wonka, Peter; Heidrich, Wolfgang (2018-04-26)
  • Dust impact on the Red Sea modelling framework and supporting data

    Osipov, Sergey; Stenchikov, Georgiy L. (2018-01-08)
  • Data for Figures 6 and 8 for "Time-dependent Pore Filling"

    Jang, Junbong; Santamarina, Carlos; Sun, Zhonghao (2018)
  • Supplementary Material for: Measurements of Positively Charged Ions in Premixed Methane-Oxygen Atmospheric Flames

    Alquaity, Awad B. S.; Han, Jie; Chahine, May; Selim, Hatem; Belhi, Memdouh; Sarathy, Mani; Bisetti, Fabrizio; Farooq, Aamir (Figshare, 2017)
    <p>Cations and anions are formed as a result of chemi-ionization processes in combustion systems. Electric fields can be applied to reduce emissions and improve combustion efficiency by active control of the combustion process. Detailed flame ion chemistry models are needed to understand and predict the effect of external electric fields on combustion plasmas. In this work, a molecular beam mass spectrometer (MBMS) is utilized to measure ion concentration profiles in premixed methane–oxygen argon burner-stabilized atmospheric flames. Lean and stoichiometric flames are considered to assess the dependence of ion chemistry on flame stoichiometry. Relative ion concentration profiles are compared with numerical simulations using various temperature profiles, and good qualitative agreement was observed for the stoichiometric flame. However, for the lean flame, numerical simulations misrepresent the spatial distribution of selected ions greatly. Three modifications are suggested to enhance the ion mechanism and improve the agreement between experiments and simulations. The first two modifications comprise the addition of anion detachment reactions to increase anion recombination at low temperatures. The third modification involves restoring a detachment reaction to its original irreversible form. To our knowledge, this work presents the first detailed measurements of cations and flame temperature in canonical methane–oxygen-argon atmospheric flat flames. The positive ion profiles reported here may be useful to validate and improve ion chemistry models for methane-oxygen flames.</p>
  • CCDC 1477674: Experimental Crystal Structure Determination : (1,3-bis(2,4,6-trimethylphenyl)imidazol-2-ylidene)-trimethyl-gallium

    Wu, Melissa M.; Gill, Arran M.; Yunpeng, Lu; Yongxin, Li; Ganguly, Rakesh; Falivene, Laura; García, Felipe (Cambridge Crystallographic Data Centre, 2017)
    An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
  • CCDC 1477676: Experimental Crystal Structure Determination : (1,3-bis(2,6-diisopropylphenyl)imidazol-2-ylidene)-trimethyl-indium

    Wu, Melissa M.; Gill, Arran M.; Yunpeng, Lu; Yongxin, Li; Ganguly, Rakesh; Falivene, Laura; García, Felipe (Cambridge Crystallographic Data Centre, 2017)
    An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
  • CCDC 1477677: Experimental Crystal Structure Determination : (1,3-bis(2,6-diisopropylphenyl)imidazol-2-ylidene)-trimethyl-gallium

    Wu, Melissa M.; Gill, Arran M.; Yunpeng, Lu; Yongxin, Li; Ganguly, Rakesh; Falivene, Laura; García, Felipe (Cambridge Crystallographic Data Centre, 2017)
    An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
  • CCDC 1477678: Experimental Crystal Structure Determination : (1,3-dimesitylimidazolidin-2-ylidene)-trimethyl-gallium

    Wu, Melissa M.; Gill, Arran M.; Yunpeng, Lu; Yongxin, Li; Ganguly, Rakesh; Falivene, Laura; García, Felipe (Cambridge Crystallographic Data Centre, 2017)
    An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
  • CCDC 1477681: Experimental Crystal Structure Determination : (1,3-bis(2,6-diisopropylphenyl)imidazolidin-2-ylidene)-trimethyl-gallium

    Wu, Melissa M.; Gill, Arran M.; Yunpeng, Lu; Yongxin, Li; Ganguly, Rakesh; Falivene, Laura; García, Felipe (Cambridge Crystallographic Data Centre, 2017)
    An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
  • CCDC 1477682: Experimental Crystal Structure Determination : (1,3-bis(2,6-diisopropylphenyl)imidazolidin-2-ylidene)-trimethyl-indium

    Wu, Melissa M.; Gill, Arran M.; Yunpeng, Lu; Yongxin, Li; Ganguly, Rakesh; Falivene, Laura; García, Felipe (Cambridge Crystallographic Data Centre, 2017)
    An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
  • CCDC 1477679: Experimental Crystal Structure Determination : (1,3-dimesitylimidazolidin-2-ylidene)-trimethyl-indium

    Wu, Melissa M.; Gill, Arran M.; Yunpeng, Lu; Yongxin, Li; Ganguly, Rakesh; Falivene, Laura; García, Felipe (Cambridge Crystallographic Data Centre, 2017)
    An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
  • Data from: Using a butterflyfish genome as a general tool for RAD-Seq studies in specialized reef fish

    DiBattista, Joseph; Saenz Agudelo, Pablo; Piatek, Marek J.; Wang, Xin; Aranda, Manuel; Berumen, Michael L. (Dryad Digital Repository, 2017)
  • Supplementary Material for: In silico screening for candidate chassis strains of free fatty acid-producing cyanobacteria

    Motwalli, Olaa Amin; Essack, Magbubah; Jankovic, Boris R.; Ji, Boyang; Liu, Xinyao; Ansari, Hifzur; Hoehndorf, Robert; Gao, Xin; Arold, Stefan T.; Mineta, Katsuhiko; Archer, John; Gojobori, Takashi; Mijakovic, Ivan; Bajic, Vladimir B. (Figshare, 2017)
    Abstract Background Finding a source from which high-energy-density biofuels can be derived at an industrial scale has become an urgent challenge for renewable energy production. Some microorganisms can produce free fatty acids (FFA) as precursors towards such high-energy-density biofuels. In particular, photosynthetic cyanobacteria are capable of directly converting carbon dioxide into FFA. However, current engineered strains need several rounds of engineering to reach the level of production of FFA to be commercially viable; thus new chassis strains that require less engineering are needed. Although more than 120 cyanobacterial genomes are sequenced, the natural potential of these strains for FFA production and excretion has not been systematically estimated. Results Here we present the FFA SC (FFASC), an in silico screening method that evaluates the potential for FFA production and excretion of cyanobacterial strains based on their proteomes. A literature search allowed for the compilation of 64 proteins, most of which influence FFA production and a few of which affect FFA excretion. The proteins are classified into 49 orthologous groups (OGs) that helped create rules used in the scoring/ranking of algorithms developed to estimate the potential for FFA production and excretion of an organism. Among 125 cyanobacterial strains, FFASC identified 20 candidate chassis strains that rank in their FFA producing and excreting potential above the specifically engineered reference strain, Synechococcus sp. PCC 7002. We further show that the top ranked cyanobacterial strains are unicellular and primarily include Prochlorococcus (order Prochlorales) and marine Synechococcus (order Chroococcales) that cluster phylogenetically. Moreover, two principal categories of enzymes were shown to influence FFA production the most: those ensuring precursor availability for the biosynthesis of lipids, and those involved in handling the oxidative stress associated to FFA synthesis. Conclusion To our knowledge FFASC is the first in silico method to screen cyanobacteria proteomes for their potential to produce and excrete FFA, as well as the first attempt to parameterize the criteria derived from genetic characteristics that are favorable/non-favorable for this purpose. Thus, FFASC helps focus experimental evaluation only on the most promising cyanobacteria.
  • Supplementary Material for: Growth curve registration for evaluating salinity tolerance in barley

    Meng, Rui; Saade, Stephanie; Kurtek, Sebastian; Berger, Bettina; Brien, Chris; Pillen, Klaus; Tester, Mark A.; Sun, Ying (Figshare, 2017)
    Abstract Background Smarthouses capable of non-destructive, high-throughput plant phenotyping collect large amounts of data that can be used to understand plant growth and productivity in extreme environments. The challenge is to apply the statistical tool that best analyzes the data to study plant traits, such as salinity tolerance, or plant-growth-related traits. Results We derive family-wise salinity sensitivity (FSS) growth curves and use registration techniques to summarize growth patterns of HEB-25 barley families and the commercial variety, Navigator. We account for the spatial variation in smarthouse microclimates and in temporal variation across phenotyping runs using a functional ANOVA model to derive corrected FSS curves. From FSS, we derive corrected values for family-wise salinity tolerance, which are strongly negatively correlated with Na but not significantly with K, indicating that Na content is an important factor affecting salinity tolerance in these families, at least for plants of this age and grown in these conditions. Conclusions Our family-wise methodology is suitable for analyzing the growth curves of a large number of plants from multiple families. The corrected curves accurately account for the spatial and temporal variations among plants that are inherent to high-throughput experiments.

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