Three-dimensional Fluorescence Polarization Excitation via Multiview Imaging

This technology includes a method that extends fluorescence polarization imaging so that the dipole moment of a fluorescent dye may be excited regardless of its 3D orientation. By exciting the dipole from multiple directions, we ensure that excitation may occur even if the dipole is unfavorably oriented along the axial (propagation) axis. If the dye can be rigidly attached to the structure of interest, our method also enables the 3D orientation of the structure to be estimated accurately.

Fluorescence Scanning System for Improvement of Analytical Ultracentrifugation

This technology includes improvements in the fluorescence scanner to increase efficiency. This method works by eliminating the need to radially slide the optical assembly during scanning, instead using a galvanometric mirror deflecting a laser beam to different positions in the sample. This allows the scanner to be incorporated into existing commercial analytical ultracentrifugation (AUC) systems with minimal modifications.

Radiotherapy and Imaging Agent-based on Peptide Conjugated to Novel Evans Blue Derivatives with Long Half-life and High Accumulation in Target Tissue

This technology includes a newly designed, truncated Evans Blue (EB) form which allows labeling with metal isotopes for nuclear imaging and radiotherapy. Unlike previous designs, this new form of truncated EB confers site specific mono-labeling of desired molecules. The newly designed truncated EB form can be conjugated to various molecules including small molecules, peptides, proteins and aptamers to improve blood half-life and tumor uptake, and confer better imaging, therapy and radiotherapy.

Producing Isotropic Super-Resolution Images from Line Scanning Confocal Microscopy

This technology includes a microscopy technique that produces super-resolution images from diffraction-limited images obtained from a line scanning confocal microscope. First, the operation of the confocal microscope is modified so that images with sparse line excitation are recorded. Second, these images are processed to increase resolution in one dimension. Third, by taking a series of such super-resolved images from a given sample type, a neural network may be trained to produce images with 1D super-resolution from new diffraction-limited images.