skip to main content

Search for: All records

Creators/Authors contains: "Liu, Jingjing"

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. Polymer dielectrics have been widely used in electrical and electronic systems for capacitive energy storage and electrical insulation. However, emerging applications such as electric vehicles and hybrid electric aircraft demand improved polymer dielectrics for operation not only under high electric fields and high temperatures, but also extreme conditions, for example, low pressures at high altitudes, with largely increased likelihood of electrical partial discharges. To meet these stringent requirements of grand electrifications for payload efficiency, polymers with enhanced discharge resistance are highly desired. Here, we present a surface-engineering approach for Kapton® coated with self-assembled two-dimensional montmorillonite nanosheets. By suppressing the magnitude of the high-field partial discharges, this nanocoating endows polymers with improved discharge resistance, with satisfactory discharge endurance life of 200 hours at a high electric field of 46 kV mm −1 while maintaining the surface morphology of the polymer. Moreover, the MMT nanocoating can also improve the thermal stability of Kapton®, with significantly suppressed temperature coefficients for both the dielectric constant and dielectric loss over a wide temperature range from 25 to 205 °C. This work provides a practical method of surface nanocoating to explore high-discharge-resistant polymers for harsh condition electrification.
  2. The BEN domain is a recently recognized DNA binding module that is present in diverse metazoans and certain viruses. Several BEN domain factors are known as transcriptional repressors, but, overall, relatively little is known of how BEN factors identify their targets in humans. In particular, X-ray structures of BEN domain:DNA complexes are only known for Drosophila factors bearing a single BEN domain, which lack direct vertebrate orthologs. Here, we characterize several mammalian BEN domain (BD) factors, including from two NACC family BTB-BEN proteins and from BEND3, which has four BDs. In vitro selection data revealed sequence-specific binding activities of isolated BEN domains from all of these factors. We conducted detailed functional, genomic, and structural studies of BEND3. We show that BD4 is a major determinant for in vivo association and repression of endogenous BEND3 targets. We obtained a high-resolution structure of BEND3-BD4 bound to its preferred binding site, which reveals how BEND3 identifies cognate DNA targets and shows differences with one of its non-DNA-binding BEN domains (BD1). Finally, comparison with our previous invertebrate BEN structures, along with additional structural predictions using AlphaFold2 and RoseTTAFold, reveal distinct strategies for target DNA recognition by different types of BEN domain proteins. Together, thesemore »studies expand the DNA recognition activities of BEN factors and provide structural insights into sequence-specific DNA binding by mammalian BEN proteins.« less
  3. Intelligent personal assistant systems, with either text-based or voice-based conversational interfaces, are becoming increasingly popular. Most previous research has used either retrieval-based or generation-based methods. Retrieval-based methods have the advantage of returning fluent and informative responses with great diversity. The retrieved responses are easier to control and explain. However, the response retrieval performance is limited by the size of the response repository. On the other hand, although generation-based methods can return highly coherent responses given conversation context, they are likely to return universal or general responses with insufficient ground knowledge information. In this paper, we build a hybrid neural conversation model with the capability of both response retrieval and generation, in order to combine the merits of these two types of methods. Experimental results on Twitter and Foursquare data show that the proposed model can outperform both retrieval-based methods and generation-based methods (including a recently proposed knowledge-grounded neural conversation model) under both automatic evaluation metrics and human evaluation. Our models and research findings provide new insights on how to integrate text retrieval and text generation models for building conversation systems.