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Extraction of Iron Oxides from Sediments Using Reductive Dissolution by Titanium(III)
- Joseph N. Ryan, Philip M. Gschwend
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- Journal:
- Clays and Clay Minerals / Volume 39 / Issue 5 / October 1991
- Published online by Cambridge University Press:
- 02 April 2024, pp. 509-518
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A new iron oxide dissolution method designed to measure the abundance of “free” Fe oxide phases and associated elements in soils and sediments has been tested. The method employs a ternary complex of Ti(III), citrate, and ethylenediaminetetraacetate (EDTA) as a reductant and bicarbonate as a proton acceptor. The Ti(III)-citrate-EDTA-HCO3 method dissolved more synthetic amorphous ferric oxide and goethite, but less synthetic hematite, than the dithionite-citrate-HCO3 method of Mehra and Jackson. The production of acidity by the dissolution indicated that Ti(IV) is hydrolyzed to TiO2 during the extractions. The heated dithionite method dissolved 3–6 times more Al from kaolinite and nontronite standard clays than room temperature dithionite, and 4–6 times more Al than the Ti(III)-citrate-EDTA-HCO3 method. Furthermore, the release of Fe from the clay mineral samples consistently and rapidly reached a plateau during multiple extractions by the Ti(III)-citrate-EDTA-HCO3 method, indicating that a well-defined Fe oxide fraction was removed. Fe released by the dithionite method continued to increase with each extraction, suggesting that some release of structural Fe occurred. Tests on two natural sediments and one heavy mineral fraction from the Miocene Cohansey Sand in the New Jersey Coastal Plain suggested that the Ti(III)-citrate-EDTA-HCO3 method removed Fe oxides more effectively and more selectively than the dithionite method. The selectivity of the Ti(III)-citrate-EDTA-HCO3 method is enhanced by rapid extractions at room temperature and low free ligand concentrations.
57084 Combining artificial intelligence and robotics: a novel fully automated optical coherence tomography-based approach for eye disease screening
- Ailin Song, Pablo Ortiz, Mark Draelos, Stefanie G. Schuman, Glenn J. Jaffe, Sina Farsiu, Joseph A. Izatt, Ryan P. McNabb, Anthony N. Kuo
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- Journal:
- Journal of Clinical and Translational Science / Volume 5 / Issue s1 / March 2021
- Published online by Cambridge University Press:
- 30 March 2021, p. 122
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ABSTRACT IMPACT: Despite its importance in systemic diseases such as diabetes, the eye is notably difficult to examine for non-specialists; this study introduces a fully automated approach for eye disease screening, coupling a deep learning algorithm with a robotically-aligned optical coherence tomography system to improve eye care in non-ophthalmology settings. OBJECTIVES/GOALS: This study aims to develop and test a deep learning (DL) method to classify images acquired from a robotically-aligned optical coherence tomography (OCT) system as normal vs. abnormal. The long-term goal of our study is to integrate artificial intelligence and robotic eye imaging to fully automate eye disease screening in diverse clinical settings. METHODS/STUDY POPULATION: Between August and October 2020, patients seen at the Duke Eye Center and healthy volunteers (age ≥18) were imaged with a custom, robotically-aligned OCT (RAOCT) system following routine eye exam. Using transfer learning, we adapted a preexisting convolutional neural network to train a DL algorithm to classify OCT images as normal vs. abnormal. The model was trained and validated on two publicly available OCT datasets and two of our own RAOCT volumes. For external testing, the top-performing model based on validation was applied to a representative averaged B-scan from each of the remaining RAOCT volumes. The model’s performance was evaluated against a reference standard of clinical diagnoses by retina specialists. Saliency maps were created to visualize the areas contributing most to the model predictions. RESULTS/ANTICIPATED RESULTS: The training and validation datasets included 87,697 OCT images, of which 59,743 were abnormal. The top-performing DL model had a training accuracy of 96% and a validation accuracy of 99%. For external testing, 43 eyes of 27 subjects were imaged with the robotically-aligned OCT system. Compared to clinical diagnoses, the model correctly labeled 18 out of 22 normal averaged B-scans and 18 out of 21 abnormal averaged B-scans. Overall, in the testing set, the model had an AUC for the detection of pathology of 0.92, an accuracy of 84%, a sensitivity of 86%, and a specificity of 82%. For the correctly predicted scans, saliency maps identified the areas contributing most to the DL algorithm’s predictions, which matched the regions of greatest clinical importance. DISCUSSION/SIGNIFICANCE OF FINDINGS: This is the first study to develop and apply a DL model to images acquired from a self-aligning OCT system, demonstrating the potential of integrating DL and robotic eye imaging to automate eye disease screening. We are working to translate this technology for use in emergency departments and primary care, where it will have the greatest impact.
Memory Performance and Quantitative Neuroimaging Software in Mild Cognitive Impairment: A Concurrent Validity Study
- Laura Glass Umfleet, Alissa M. Butts, Julie K. Janecek, Katherine Reiter, Mohit Agarwal, Benjamin L. Brett, Joseph J. Ryan, James Reuss, Andrew Klein, Anthony N. Correro II, Malgorzata Franczak
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- Journal:
- Journal of the International Neuropsychological Society / Volume 26 / Issue 10 / November 2020
- Published online by Cambridge University Press:
- 28 April 2020, pp. 954-962
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Objective:
This study examined the relationship between patient performance on multiple memory measures and regional brain volumes using an FDA-cleared quantitative volumetric analysis program – Neuroreader™.
Method:Ninety-two patients diagnosed with mild cognitive impairment (MCI) by a clinical neuropsychologist completed cognitive evaluations and underwent MR Neuroreader™ within 1 year of testing. Select brain regions were correlated with three widely used memory tests. Regression analyses were conducted to determine if using more than one memory measures would better predict hippocampal z-scores and to explore the added value of recognition memory to prediction models.
Results:Memory performances were most strongly correlated with hippocampal volumes than other brain regions. After controlling for encoding/Immediate Recall standard scores, statistically significant correlations emerged between Delayed Recall and hippocampal volumes (rs ranging from .348 to .490). Regression analysis revealed that evaluating memory performance across multiple memory measures is a better predictor of hippocampal volume than individual memory performances. Recognition memory did not add further predictive utility to regression analyses.
Conclusions:This study provides support for use of MR Neuroreader™ hippocampal volumes as a clinically informative biomarker associated with memory performance, which is a critical diagnostic feature of MCI phenotype.
Contributors
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- By Douglas L. Arnold, Laura J. Balcer, Amit Bar-Or, Sergio E. Baranzini, Frederik Barkhof, Robert A. Bermel, Francois A. Bethoux, Dennis N. Bourdette, Richard K. Burt, Peter A. Calabresi, Zografos Caramanos, Tanuja Chitnis, Stacey S. Cofield, Jeffrey A. Cohen, Nadine Cohen, Alasdair J. Coles, Devon Conway, Stuart D. Cook, Gary R. Cutter, Peter J. Darlington, Ann Dodds-Frerichs, Ranjan Dutta, Gilles Edan, Michelle Fabian, Franz Fazekas, Massimo Filippi, Elizabeth Fisher, Paulo Fontoura, Corey C. Ford, Robert J. Fox, Natasha Frost, Alex Z. Fu, Siegrid Fuchs, Kazuo Fujihara, Kristin M. Galetta, Jeroen J.G. Geurts, Gavin Giovannoni, Nada Gligorov, Ralf Gold, Andrew D. Goodman, Myla D. Goldman, Jenny Guerre, Stephen L. Hauser, Peter B. Imrey, Douglas R. Jeffery, Stephen E. Jones, Adam I. Kaplin, Michael W. Kattan, B. Mark Keegan, Kyle C. Kern, Zhaleh Khaleeli, Samia J. Khoury, Joep Killestein, Soo Hyun Kim, R. Philip Kinkel, Stephen C. Krieger, Lauren B. Krupp, Emmanuelle Le Page, David Leppert, Scott Litwiller, Fred D. Lublin, Henry F. McFarland, Joseph C. McGowan, Don Mahad, Jahangir Maleki, Ruth Ann Marrie, Paul M. Matthews, Francesca Milanetti, Aaron E. Miller, Deborah M. Miller, Xavier Montalban, Charity J. Morgan, Ichiro Nakashima, Sridar Narayanan, Avindra Nath, Paul W. O’Connor, Jorge R. Oksenberg, A. John Petkau, Michael D. Phillips, J. Theodore Phillips, Tammy Phinney, Sean J. Pittock, Sarah M. Planchon, Chris H. Polman, Alexander Rae-Grant, Stephen M. Rao, Stephen C. Reingold, Maria A. Rocca, Richard A. Rudick, Amber R. Salter, Paula Sandler, Jaume Sastre-Garriga, John R. Scagnelli, Dana J. Serafin, Lynne Shinto, Nancy L. Sicotte, Jack H. Simon, Per Soelberg Sørensen, Ryan E. Stagg, James M. Stankiewicz, Lael A. Stone, Amy Sullivan, Matthew Sutliff, Jessica Szpak, Alan J. Thompson, Bruce D. Trapp, Helen Tremlett, Maria Trojano, Orla Tuohy, Rhonda R. Voskuhl, Marc K. Walton, Mike P. Wattjes, Emmanuelle Waubant, Martin S. Weber, Howard L Weiner, Brian G. Weinshenker, Bianca Weinstock-Guttman, Jeffrey L. Winters, Jerry S. Wolinsky, Vijayshree Yadav, E. Ann Yeh, Scott S. Zamvil
- Edited by Jeffrey A. Cohen, Richard A. Rudick
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- Book:
- Multiple Sclerosis Therapeutics
- Published online:
- 05 December 2011
- Print publication:
- 20 October 2011, pp viii-xii
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Contributors
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- By Shamsuddin Akhtar, Greg Albert, Sidney Allison, Muhammad Anwar, Haruo Arita, Amanda Barker, Mary Hanna Bekhit, Jeanna Blitz, Tyson Bolinske, David Burbulys, Asokumar Buvanendran, Gregory Cain, Keith A. Candiotti, Daniel B. Carr, Derek Chalmers, John Charney, Rex Cheng, Roger Chou, Keun Sam Chung, Anna Clebone, Frederick Conlin, Susan Dabu-Bondoc, Tiffany Denepitiya-Balicki, Jeanette Derdemezi, Anahat Kaur Dhillon, Ho Dzung, Juan Jose Egas, Stephen M. Eskaros, Zhuang T. Fang, Claudia R. Fernandez Robles, Victor A. Filadora, Ellen Flanagan, Dan Froicu, Allison Gandey, Nehal Gatha, Boris Gelman, Christopher Gharibo, Muhammad K. Ghori, Brian Ginsberg, Michael E. Goldberg, Jeff Gudin, Thomas Halaszynski, Martin Hale, Dorothea Hall, Craig T. Hartrick, Justin Hata, Lars E. Helgeson, Joe C. Hong, Richard W. Hong, Balazs Horvath, Eric S. Hsu, Gabriel Jacobs, Jonathan S. Jahr, Rongjie Jaing, Inderjeet Singh Julka, Zeev N. Kain, Clinton Kakazu, Kianusch Kiai, Mary Keyes, Michael M. Kim, Peter G. Lacouture, Ryan Lanier, Vivian K. Lee, Mark J. Lema, Oscar A. de Leon-Casasola, Imanuel Lerman, Philip Levin, Steven Levin, JinLei Li, Eric C. Lin, Sharon Lin, David A. Lindley, Ana M. Lobo, Marisa Lomanto, Mirjana Lovrincevic, Brenda C. McClain, Tariq Malik, Jure Marijic, Joseph Marino, Laura Mechtler, Alan Miller, Carly Miller, Amit Mirchandani, Sukanya Mitra, Fleurise Montecillo, James M. Moore, Debra E. Morrison, Philip F. Morway, Carsten Nadjat-Haiem, Hamid Nourmand, Dana Oprea, Sunil J. Panchal, Edward J. Park, Kathleen Ji Park, Kellie Park, Parisa Partownavid, Akta Patel, Bijal Patel, Komal D. Patel, Neesa Patel, Swati Patel, Paul M. Peloso, Danielle Perret, Anthony DePlato, Marjorie Podraza Stiegler, Despina Psillides, Mamatha Punjala, Johan Raeder, Siamak Rahman, Aziz M. Razzuk, Maggy G. Riad, Kristin L. Richards, R. Todd Rinnier, Ian W. Rodger, Joseph Rosa, Abraham Rosenbaum, Alireza Sadoughi, Veena Salgar, Leslie Schechter, Michael Seneca, Yasser F. Shaheen, James H. Shull, Elizabeth Sinatra, Raymond S. Sinatra, Neil Singla, Neil Sinha, Denis V. Snegovskikh, Dmitri Souzdalnitski, Julie Sramcik, Zoreh Steffens, Alexander Timchenko, Vadim Tokhner, Marc C. Torjman, Co T. Truong, Nalini Vadivelu, Ashley Vaughn, Anjali Vira, Eugene R. Viscusi, Dajie Wang, Shu-ming Wang, J. Michael Watkins-Pitchford, Steven J. Weisman, Ira Whitten, Bryan S. Williams, Jeremy M. Wong, Thomas Wong, Christopher Wray, Yaw Wu, Anthony T. Yarussi, Laurie Yonemoto, Bita H. Zadeh, Jill Zafar, Martha Zegarra, Keren Ziv
- Edited by Raymond S. Sinatra, Jonathan S. Jahr, University of California, Los Angeles, School of Medicine, J. Michael Watkins-Pitchford
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- Book:
- The Essence of Analgesia and Analgesics
- Published online:
- 06 December 2010
- Print publication:
- 14 October 2010, pp xi-xviii
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Fluorination of the high-Tc superconductors YBa2Cu3O7−δ and GdBa2Cu3O7−δ
- Nancy N. Sauer, Eduardo Garcia, Joe A. Martin, Robert R. Ryan, P. Gary Eller, Joseph R. Tesmer, Carl J. Maggiore
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- Journal:
- Journal of Materials Research / Volume 3 / Issue 5 / October 1988
- Published online by Cambridge University Press:
- 31 January 2011, pp. 813-818
- Print publication:
- October 1988
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Powdered samples of the high-temperature superconductors A Ba2Cu3O7−δ (A = Gd,Y) were treated with fluorine gas (100 Torr) at room temperature and 400 °C for varying times (12–64 h). Magnetic shielding measurements on fluorinated products showed that the superconducting volume fraction in treated samples was greatly reduced or even completely eradicated. All samples were structurally characterized by x-ray powder diffraction. Two yttrium samples, one fluorinated at 25 °C and one at 400 °, were also examined by neutron powder diffraction. For samples treated at room temperature, no change in the structure or composition of the products was apparent by either technique. However, samples fluorinated at 400 °C are tetragonal, with a = 3.8641 (3), c = 11.704(1) Å, and bulk composition corresponding to the formula YBa2Cu3F3.5O4.5. Nuclear activation analysis, nuclear reaction analysis, and Auger spectroscopy were used to determine fluorine concentration and distribution in the fluorinated materials. For samples treated at room temperature, fluorine was found primarily within approximately 1 μm of the surface of the product particles. No evidence for a fluorinecontaining superconducting phase was found in any sample; fluorine was found to be detrimental to superconductivity in all cases. These results suggest that the 123 oxides are sensitive to surface effects.