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Nicholas J. Durr
Researcher at Johns Hopkins University
Publications - 124
Citations - 3714
Nicholas J. Durr is an academic researcher from Johns Hopkins University. The author has contributed to research in topics: Speckle pattern & Imaging phantom. The author has an hindex of 22, co-authored 124 publications receiving 2934 citations. Previous affiliations of Nicholas J. Durr include University of Texas at Austin & Harvard University.
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Journal ArticleDOI
Two-photon luminescence imaging of cancer cells using molecularly targeted gold nanorods.
Nicholas J. Durr,Timothy Larson,Danielle K. Smith,Brian A. Korgel,Konstantin V Sokolov,Adela Ben-Yakar +5 more
TL;DR: Their strong signal, resistance to photobleaching, chemical stability, ease of synthesis, simplicity of conjugation chemistry, and biocompatibility make gold nanorods an attractive contrast agent for two-photon imaging of epithelial cancer.
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Thermal Analysis of Gold Nanorods Heated with Femtosecond Laser Pulses
Özgür Ekici,Richard K. Harrison,Nicholas J. Durr,Daniel Eversole,Myoungkyu Lee,Adela Ben-Yakar +5 more
TL;DR: An axisymmetric computational model is presented to study the heating processes of gold nanoparticles, specifically nanorods, in aqueous medium by femtosecond laser pulses, using a two-temperature model for the particle, a heat diffusion equation for the surrounding water, and a thermal interface conductance to describe the coupling efficiency at the particle/water interface.
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Femtosecond laser nanoaxotomy lab-on-a-chip for in vivo nerve regeneration studies
Samuel X. Guo,Frederic Bourgeois,Trushal Vijaykumar Chokshi,Nicholas J. Durr,Massimo A. Hilliard,Nikos Chronis,Adela Ben-Yakar +6 more
TL;DR: Using the 'nanoaxotomy' chip, it is discovered that axonal regeneration occurs much faster than previously described, and notably, the distal fragment of the severed axon regrows in the absence of anesthetics.
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Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training
TL;DR: This work proposes a novel framework that uses a reverse flow, where adversarial training is used to make real medical images more like synthetic images, and clinically-relevant features are preserved via self-regularization to improve structural similarity of endoscopy depth estimation.
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Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images
Faisal Mahmood,Daniel Borders,Richard J. Chen,Gregory N. McKay,Kevan J. Salimian,Alexander S. Baras,Nicholas J. Durr +6 more
TL;DR: In this article, a conditional generative adversarial network (cGAN) was used to segment nuclei mymargin using synthetic and real histopathology data, and the network was trained with spectral normalization and gradient penalty for nuclei segmentation.