Virus Microneutralization Assay Data Analysis for Vaccine Development, Enhancement and Efficacy Improvement

This CDC generated invention entails improved methods of analyzing microneutralization assays, especially for the purposes of determining specific antibody concentrations and optimizing vaccine formulation. More specifically, the invention is a set of SAS based programs using 4-parameter logistic curve fitting algorithms to interpolate between individual data points, allowing for enhanced accuracy and precision when establishing neutralization titers.

Therapeutic, Bifunctional Janus Microparticles with Spatially Segregated Surface Proteins and Methods of Production

CDC researchers have developed a fabrication process to create bifunctional microparticles displaying two distinct proteins that are spatially segregated onto a single hemispheric surface. At present, there is no described way of producing biological microparticles with two distinct types of separated proteins. Bifunctional Janus particles generated by the CDC approach possess biologically relevant, native conformation proteins attached to a biologically unreactive and safe substrate.

Fluorescent Nanodiamonds as Fiducial Markers for Microscopy

The invention relates to fluorescent nanodiamonds (FNDs) and their uses as fiducial markers for microscopy. FNDs are bright fluorescent probes that do not blink or bleach and have broad fluorescence excitation and emission peaks. The fluorescence intensity can be readily controlled by the size of the FND, the number of fluorescent centers produced in the nanodiamonds, or in situ through the application of a weak magnetic field.

Autodock Vina Software Process for Efficient Large-Scale Cognate Ligand Screening

The invention pertains to software processes, additions, and docking approaches to Autodock Vina that speeds the rate and efficiency of analyzing ligand interactions with a receptor by cognate ligands and rewards conformations in the scoring algorithm for residue interactions that are based on the biological data. The score is multiplied by a weighting factor to control the degree of ligand-residue interactions that are considered. This multiplier is then added to the docking score for confirmation.

DeePlexing – Extending Imaging Multiplexity Using Machine Learning

Spatial proteomics and transcriptomics are fast-emerging fields with the potential to revolutionize various branches of biology. In the last five years, various multiplex immunofluorescence and immunohistochemistry imaging methods have been developed to stain 5-60 different protein markers in a given tissue. Nonetheless, most of these techniques are iterative and can image a maximum of 3-8 markers in a single cycle, resulting in processing time of several hours to days.

General-purpose Deep Learning Image Denoising Based on Magnetic Resonance Imaging Physics

This technology includes a novel method to train deep learning convolution neural network model to improve the signal-noise-ratio for the magnetic resonance (MR) imaging. The novelty lies on the fact that actual MR imaging physics information is used in the deep learning training. The resulting model achieves significant signal-to-noise ratio (SNR) improved for different acceleration factors in MR imaging. The resulting model can be used for many body anatomies (e.g., brain, heart, liver, spine, etc.) to significantly improve the SNR.

Free Breathing Motion Corrected Pixel-wise MRI Myocardial T1 Parameter Mapping for Clinical Cardiac Imaging

This technology includes a method for performing cardiac imaging without the need for the patient to hold their breath. Free breathing pixel-wise myocardial T1 parameter mapping includes performing a free-breathing scan of a cardiac region at a plurality of varying saturation recovery times to acquire a k-space dataset; generating an image dataset based on the k-space dataset; and performing a respiratory motion correction process on the image dataset.

System for Automated Anatomical Structures Segmentation of Contrast-Enhanced Cardiac Computed Tomography Images

This technology includes a fully automatic 3D image processing system to segment the heart as well as other organs from contrast-enhanced cardiac computed tomography (CCT) images. Our method detects four cardiac chambers including left ventricle, right ventricle, left atrium, right atrium, as well as the ascending aorta and left ventricular myocardium. It also classifies noncardiac tissue structures in the CCT images such as lung, chest wall, spine, descending aorta, and liver.

Methods and Systems for Automatically Determining Magnetic Field Inversion Time of a Tissue Species

This technology includes a computer-implemented method for determining magnetic field inversion time of a tissue species using a T1-mapping image, information about the region of interest, and a tissue classification algorithm. This method includes T1-mapping image comprising a plurality of T1 values within an expected range of T1 values for the tissue of interest. An image mask is created based on predetermined identification information about the tissue of interest. Next, an updated image mask is created based on a largest connected region in the image mask.

Prior Enhanced Compressed Sensing (PRINCE-CS) Reconstruction for Dynamic 2D-radial Cardiac MRI

This technology includes a method to reduce scanning time while retaining high image quality during MRI scans. A reconstructed image is rendered from a set of MRI data by first estimating an image with an area which does not contain artifacts or has an artifact with a relatively small magnitude. Corresponding data elements in the estimated image and a trial image are processed, for instance by multiplication, to generate an intermediate data set.

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