Fernández, Facundo M.

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Now showing 1 - 9 of 9
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    Metabolomics and Proteomics Reveal Impacts of Chemically Mediated Competition on Marine Plankton Dataset
    (Georgia Institute of Technology, 2017-12-22) Poulson-Ellestad, Kelsey L. ; Jones, Christina ; Roy, Jessie ; Viant, Mark ; Fernández, Facundo M. ; Kubanek, Julia ; Nunn, Brook
    Competition is a major force structuring marine planktonic communities. The release of compounds that inhibit competitors, a process known as allelopathy, may play a role in the maintenance of large blooms of the red-tide dinoflagellate Karenia brevis, which produces potent neurotoxins that negatively impact coastal marine ecosystems. K. brevis is variably allelopathic to multiple competitors, typically causing sublethal suppression of growth. We used metabolomic and proteomic analyses to investigate the role of chemically mediated ecological interactions between K. brevis and two diatom competitors, Asterionellopsis glacialis and Thalassiosira pseudonana. The impact of K. brevis allelopathy on competitor physiology was reflected in the metabolomes and expressed proteomes of both diatoms, although the diatom that co-occurs with K. brevis blooms (A. glacialis) exhibited more robust metabolism in response to K. brevis. The observed partial resistance of A. glacialis to allelopathy may be a result of its frequent exposure to K. brevis blooms in the Gulf of Mexico. For the more sensitive diatom, T. pseudonana, which may not have had opportunity to evolve resistance to K. brevis, allelopathy disrupted energy metabolism and impeded cellular protection mechanisms including altered cell membrane components, inhibited osmoregulation, and increased oxidative stress. Allelopathic compounds appear to target multiple physiological pathways in sensitive competitors, demonstrating that chemical cues in the plankton have the potential to alter large-scale ecosystem processes including primary production and nutrient cycling.
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    Metabolomics & Molecular Imaging of Complex Biological Systems
    (Georgia Institute of Technology, 2015-10-13) Fernández, Facundo M.
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    Micromachined ultrasonic electrospray array for mass spectrometry
    (Georgia Institute of Technology, 2010-09-30) Fedorov, Andrei G. ; Fernández, Facundo M. ; Degertekin, F. Levent
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    Bond, Drugs, Pollen and Ions
    (Georgia Institute of Technology, 2009-10-08) Fernández, Facundo M.
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    Ovarian Cancer Detection from Metabolomic Liquid Chromatography/Mass Spectrometry Data by Support Vector Machines
    (Georgia Institute of Technology, 2009-08-22) Guan, Wei ; Zhou, Manshui ; Hampton, Christina Young ; Benigno, Benedict B. ; Walker, L. DeEtte ; Gray, Alexander ; McDonald, John F. ; Fernández, Facundo M.
    Background: The majority of ovarian cancer biomarker discovery efforts focus on the identification of proteins that can improve the predictive power of presently available diagnostic tests. We here show that metabolomics, the study of metabolic changes in biological systems, can also provide characteristic small molecule fingerprints related to this disease. Results: In this work, new approaches to automatic classification of metabolomic data produced from sera of ovarian cancer patients and benign controls are investigated. The performance of support vector machines (SVM) for the classification of liquid chromatography/time-of-flight mass spectrometry (LC/TOF MS) metabolomic data focusing on recognizing combinations or "panels" of potential metabolic diagnostic biomarkers was evaluated. Utilizing LC/TOF MS, sera from 37 ovarian cancer patients and 35 benign controls were studied. Optimum panels of spectral features observed in positive or/and negative ion mode electrospray (ESI) MS with the ability to distinguish between control and ovarian cancer samples were selected using state-of-the-art feature selection methods such as recursive feature elimination and L1-norm SVM. Conclusion: Three evaluation processes (leave-one-out-cross-validation, 12-fold-cross-validation, 52-20-split-validation) were used to examine the SVM models based on the selected panels in terms of their ability for differentiating control vs. disease serum samples. The statistical significance for these feature selection results were comprehensively investigated. Classification of the serum sample test set was over 90% accurate indicating promise that the above approach may lead to the development of an accurate and reliable metabolomic-based approach for detecting ovarian cancer.
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    Ultrasonic micromachined nanospray ion sources
    (Georgia Institute of Technology, 2009-05-01) Fernández, Facundo M.
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    Enabling Mass Spectrometry Technologies for High-Throughput Proteomics and Metabolomics
    (Georgia Institute of Technology, 2008-01-29) Fernández, Facundo M.
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    Counterfeit Drugs
    (Georgia Institute of Technology, 2006-11-25) Ludovice, Peter J. ; Hunt, William D. ; Fernández, Facundo M.
    Dr. Facundo Fernandez will be discussing the conterfeiting of pharmaceuticals and his recent work on antimalarial drugs