Unpaired mesh-to-image translation for 3D fluorescent microscopy images of neurons.

While Generative Adversarial Networks (GANs) can now reliably produce realistic images in a multitude of imaging domains, they are ill-equipped to model thin, stochastic textures present in many large 3D fluorescent microscopy (FM) images acquired in biological research. This is especially problematic in neuroscience where the lack of ground truth data impedes the development of automated image analysis algorithms for neurons and neural populations. We therefore propose an unpaired mesh-to-image translation methodology for generating volumetric FM images of neurons from paired ground truths.

Targeting cancer-specific glycans by cyclic peptide lectinomimics.

The transformation from normal to malignant phenotype in human cancers is associated with aberrant cell-surface glycosylation. Thus, targeting glycosylation changes in cancer is likely to provide not only better insight into the roles of carbohydrates in biological systems, but also facilitate the development of new molecular probes for bioanalytical and biomedical applications. In the reported study, we have synthesized lectinomimics based on odorranalectin 1; the smallest lectin-like cyclic peptide isolated from the frog Odorrana grahami skin, and assessed the ability of these peptides to bind specific carbohydrates on molecular and cellular levels.