skip to main content

Search for: All records

Creators/Authors contains: "Arnold, Daniel"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Free, publicly-accessible full text available May 1, 2025
  2. Free, publicly-accessible full text available May 1, 2025
  3. Free, publicly-accessible full text available October 31, 2024
  4. Free, publicly-accessible full text available September 21, 2024
  5. Abstract

    The spatial organization of cell membrane glycoproteins and glycolipids is critical for mediating the binding of ligands, receptors, and macromolecules on the plasma membrane. However, we currently do not have the methods to quantify the spatial heterogeneities of macromolecular crowding on live cell surfaces. In this work, we combine experiment and simulation to report crowding heterogeneities on reconstituted membranes and live cell membranes with nanometer spatial resolution. By quantifying the effective binding affinity of IgG monoclonal antibodies to engineered antigen sensors, we discover sharp gradients in crowding within a few nanometers of the crowded membrane surface. Our measurements on human cancer cells support the hypothesis that raft-like membrane domains exclude bulky membrane proteins and glycoproteins. Our facile and high-throughput method to quantify spatial crowding heterogeneities on live cell membranes may facilitate monoclonal antibody design and provide a mechanistic understanding of plasma membrane biophysical organization.

    more » « less
  6. Fast and safe voltage regulation algorithms can serve as fundamental schemes for achieving a high level of renewable penetration in modern distribution power grids. Faced with uncertain or even unknown distribution grid models and fast changing power injections, model-free deep reinforcement learning (DRL) algorithms have been proposed to find the reactive power injections for inverters while optimizing the voltage profiles. However, such data-driven controllers can not guarantee the satisfaction of the hard operational constraints, such as maintaining voltage profiles within a certain range of the nominal value. To this end, we propose SAVER: SAfe Voltage Regulator, which is composed of an RL learner and a specifically designed, computationally efficient safety projection layer. SAVER provides a plug-and-play interface for a set of DRL algorithms that guarantees the system voltages are within safe bounds. Numerical simulations on real-world data validate the performance of the proposed algorithm. 
    more » « less
  7. null (Ed.)