Abstract: A method and a system for using a learnable augmentation technique to perform few-shot calibration of a model that is designed to generate time series predictions under distribution shifts with improved accuracy are provided. The method includes: receiving first information that relates to a source distribution of a time series and second information that relates to a target distribution of the time series; extracting a latent code from samples of the target distribution; perturbing the latent code by adding random noise in order to generate augmented samples of the target distribution; training a classifier based on samples of the source distribution; adjusting the classifier based on a combination of the samples of the source distribution and the augmented samples of the target distribution; and using the adjusted classifier to train a machine learning model that is usable for making future predictions that relate to the time series.

Abstract: There is provided herein a nanovesicle having a bilayer comprising a saturated first phospholipid and no more than about 15 molar % of a second phospholipid covalently conjugated to a J-aggregate forming dye.

Abstract: There is provided herein a nanovesicle having a bilayer comprising a saturated first phospholipid and no more than about 15 molar % of a second phospholipid covalently conjugated to a J-aggregate forming dye.

Abstract: There is provided herein vesicles comprising a bilayer comprising porphyrin-phospholipid conjugate, wherein the porphyrin-phospholipid conjugate comprises one porphyrin, porphyrin derivative or porphyrin analog covalently attached to a lipid side chain, preferably at the sn-1 or the sn-2 position, of one phospholipids, wherein the vesicle is 1-100 microns in diameter.

Abstract: The present invention relates to a microbubble comprising a monolayer of porphyrin-phospholipid conjugate, said microbubble having encapsulated therein, and to the use of said microbubble in ultrasound imaging of a target area in a subject.