LLNL’s Distributed Implicit Neural Representation (DINR) is a novel approach to 4D time-space reconstruction of dynamic objects.  DINR is the first technology to enable 4D imaging of dynamic objects at sufficiently high spatial and temporal resolutions that are necessary for real world medical and industrial applications. 

To replicate the physiology and functionality of tissues and organs, LLNL has developed an in vitro device that contains 3D MEAs made from flexible polymeric probes with multiple electrodes along the body of each probe. At the end of each probe body is a specially designed hinge that allows the probe to transition from lying flat to a more upright position when actuated and then…

LLNL’s high throughput method involves proteome-wide screening for linear B-cell epitopes using native proteomes isolated from a pathogen of interest and convalescent sera from immunized animals. LLNL researchers have applied their newly developed generalizable screening method to the identification of pathogenic bacteria by screening linear B-cell epitopes in the proteome of Francisella…

The LiDO code combines finite element analysis, design sensitivity analysis and nonlinear programming in a High-Performance Computing (HPC) environment that enables the solution of large-scale structural optimization problems in a computationally efficient manner. Currently, the code uses topology optimization strategies in which a given material is optimally distributed throughout the domain…

LLNL has developed a novel process of production, isolation, characterization, and functional re-constitution of membrane-associated proteins in a single step. In addition, LLNL has developed a colorimetric assay that indicates production, correct folding, and incorporation of bR into soluble nanolipoprotein particles (NLPs).

LLNL has developed an approach, for formation of NLP/…