Abstract: The presently-disclosed subject matter relates to crosslinkers, compositions, and methods for trapping a target of interest on a substrate of interest. The methods may be used to inhibit and treat pathogen infection and provide contraception. The methods may be used to trap or separate particles and other substances. The subject matter further relates to methods of identifying and preparing optimal crosslinkers and methods for manipulating targets of interest.

Abstract: The presently-disclosed subject matter relates to crosslinkers, compositions, and methods for trapping a target of interest on a substrate of interest. The methods may be used to inhibit and treat pathogen infection and provide contraception. The methods may be used to trap or separate particles and other substances. The subject matter further relates to methods of identifying and preparing optimal crosslinkers and methods for manipulating targets of interest.

Abstract: The presently-disclosed subject matter relates to crosslinkers, compositions, and methods for trapping a target of interest on a substrate of interest. The methods may be used to inhibit and treat pathogen infection and provide contraception. The methods may be used to trap or separate particles and other substances. The subject matter further relates to methods of identifying and preparing optimal crosslinkers and methods for manipulating targets of interest.

Abstract: A method for using a synthetically trained neural network for tracking particle movement in video microscopy data includes receiving, as input, video microscopy data representing images of particles that move between video frames. The method includes using a synthetically trained neural network to track movement of the particles between the video frames, wherein the synthetically trained neural network comprises a neural network that is trained on a plurality of different simulated video microscopy data sets. The method further includes outputting, by the synthetically trained neural network, an indication of movement of the particles between the video frames.

Abstract: A method for using a synthetically trained neural network for tracking particle movement in video microscopy data includes receiving, as input, video microscopy data representing images of particles that move between video frames. The method includes using a synthetically trained neural network to track movement of the particles between the video frames, wherein the synthetically trained neural network comprises a neural network that is trained on a plurality of different simulated video microscopy data sets. The method further includes outputting, by the synthetically trained neural network, an indication of movement of the particles between the video frames.