Abstract: Tissue modulating systems, devices, and methods modulate and/or stimulate numerous tissue specimens, independently and in parallel, within a multi-well plate in such a way to allow simultaneous measurement of functional properties of the tissue specimens. Tissue modulating system includes a first plurality of tissue fixtures, with at least one of the first tissue fixtures extending into each well. The system also includes a tissue fixture modulating apparatus including a second plurality of tissue fixtures, with at least one of the second tissue fixtures extending into each well. The tissue fixture modulating apparatus displaces the second tissue fixture in each well.

Abstract: Described herein are compounds and methods for the treatment of coronavirus infection. The compounds can function as an inhibitor of the main protease (Mpro) of coronaviruses. The compounds can include diphenylmethyl piperazine derivatives, diphenylmethyl piperidine derivatives, diphenylmethylidene piperidine derivatives, tricyclo[9.4.0.03,8]pentadeca-1(11),3(8),4,6,12,14-hexaen derivatives, tricyclo[9.4.0.03,8]pentadeca-1(11),3(8),4,6,9,12,14-heptaen derivatives, 6,11-dihydrobenzo[c][1]benzoxepin derivatives, 6,11-dihydrobenzo[c][1]benzothiepin derivatives, 5,5-dioxo-6,11-dihydrobenzo[c][1]benzothiepin derivatives, and 6-oxo-5,11-dihydrobenzo[c][1]benzazepin derivatives, as well as pharmaceutically acceptable salts, hydrates, and prodrugs thereof.

Abstract: Described herein are compounds, and methods for the treatment of coronavirus infection.

Abstract: Systems and methods for generating synthetic data using federated, collaborative, privacy preserving learning models are disclosed. In one embodiment, a method for generating synthetic data from real data for use in a federated learning network may include: (1) conducting, by a backend for a first institution of a plurality of institutions in a federated learning network, a transaction comprising transaction data; (2) generating, by the backend for the first institution, local synthetic data for the transaction data using a local synthetic data generating model; (3) sharing, by the backend for the first institution, the local synthetic data with a plurality of backends for other institutions; (4) receiving, by the backend for the first institution, global synthetic data from the plurality of backends for the other institutions; and (5) training, by the backend for the first institution, a local machine learning model with the global synthetic data.