FIP Virtual Seminar "LIGHTning Talks" from FIP students & postdocs
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Wed, 03/17/2021 - 12:00 to 13:00
Dr. Yijun Bao (BME Postdoc), Vanessa Cupil-Garcia (Chemistry PhD Student) & Erin Viere (Chemistry PhD Student), Duke University
Honorable Mention of FIP Postdoc & Student Speaker Awards.
LIGHTNING TALK Speakers will present their research in 15-minute intervals followed by 5 minutes of Q&A.
Fiberoptics SERS Sensors using Plasmonic Nanostar Probes for Detection of Molecular Biotargets
Vanessa Cupil-Garcia, Chemistry PhD Candidate, Duke University (Prof. Vo-Dinh Lab)
Our group has integrated surface-enhanced Raman scattering (SERS) silver coated gold nanostars on an optical fiber. Fiber-based sensors are an in-situ technology that can simultaneously bring the sensor and light to the sample without disturbing the environment. This technology is a multi-use method that does not require complex sample preparation. Fiber sensors or optrodes, enable the detection of analytes in samples that are difficult to access. Additionally, optrodes allow for specific detection while evading background signals from non-target regions. The fiber-optrode was used to detect miRNA and illegal food additives.
Vanessa Cupil-Garcia is a 4th year Ph.D. Candidate in Dr. Tuan Vo-Dinh’s Group in the Department of Chemistry. She was born in Tabasco, Mexico and came to the U.S.A. as a child. Her research at Duke University under the guidance of Dr. Vo-Dinh focuses on integrating nanotechnology and chemistry to develop diagnostic and therapeutic tools. She is currently synthesizing inorganic and organic nanomaterials for treatment of cancers combined with immunotherapy. She is also adapting inverse molecular sentinel sensors (iMS) for microRNA detection in plants for bioenergy purposes.
Designing conjugated oligomers for NIR fluorescence
Erin Viere, Chemistry PhD Candidate, Duke University (Prof. Therien Lab)
High quantum yield NIR fluorophores are rare. As molecules emit at lower energies, non-radiative rates increase exponentially in accordance with the energy gap law. In (porphinato)zinc(II) arrays, proquinoidal benzothiadiazole building blocks can be utilized to minimize the extent of excited-state structural relaxation relative to the ground-state conformation. By integrating spacers into the backbone of these compositions, we can develop new classes of impressive NIR fluorophores featuring absolute fluorescence quantum yields between 28-36% in toluene solvent over the 700-900 nm window of the NIR. Herein I examine the unique photophysical properties of benzothiadiazole-incorporated PZn arrays through of a series of solvent-dependent fsTA spectroscopy experiments that interrogate the relative roles played by solvent response, nuclear relaxation, and the nature of configuration interaction describing the ultrafast dynamics of these unique systems. By examining the solvent-dependence of these ultrafast decay processes, we can work towards identifying key design principles for the future development of highly efficient NIR fluorophores.
Erin Viere is a Ph.D. candidate in the Therien lab studying inorganic photochemistry. She focuses on the synthesis and spectroscopy of novel conjugated materials for electron transport. Erin graduated from Villanova University with a B.S. in Chemistry, where she worked with Dr. Jared Paul investigating the spectroelectrochemistry of ruthenium polypyridyl complexes. Outside of the lab, Erin is passionate about science communication and outreach.
Fast active neuron segmentation for two-photon imaging videos using a shallow U-Net
Dr. Yijun Bao, BME Postdoctoral Associate, Duke University (Prof. Gong Lab)
Fluorescent genetically encoded calcium indicators and two-photon microscopy help understand brain function by generating large-scale in vivo recordings in multiple animal models. Automatic, fast, and accurate active neuron segmentation is critical when processing these videos. In this work, we developed and characterized a novel method, Shallow U-Net Neuron Segmentation (SUNS), to quickly and accurately segment active neurons from two-photon fluorescence imaging videos. We used temporal filtering and whitening schemes to extract temporal features associated with active neurons, and used a compact shallow U-Net to extract spatial features of neurons. Our method was both more accurate and an order of magnitude faster than state-of-the-art techniques when processing multiple datasets acquired by independent experimental groups. We also developed an online version, potentially enabling real-time feedback neuroscience experiments.
Yijun Bao is a postdoctoral associate in the Department of Biomedical Engineering at Duke University. His research focuses on biomedical video processing, particularly using machine learning techniques to extract active neurons and their activities from fluorescence imaging videos. He earned his BS in physics from Peking University in China and his PhD in electrical engineering from the Georgia Institute of Technology in the United States.