Benson, Erik | Engineering Papers

DOE OSTI2024

Fair Bagging Boosting Models [SWR-24-38]

Fair Bagging Boosting Models is a software implementation of a framework for building, measuring bias and correcting bias in 3 popular forest machine learning models: gradient boosted trees (GBT), random forest (RF), and XGBoost models, using the XGBoost library. The framework takes advantage of the flexibility in XGBoost library to represent gradient boosted tree and random forest models, as well as the ability to use custom loss function.

Ugirumurera, Juliette↗

DOE OSTI2023

Multilayer DNA Origami with Terminal Interfaces That Are Flat and Wide-Area

DNA origami is a popular nanofabrication strategy that employs self-assembly of a long single scaffold strand, typically less than 10 kilobases in length, with hundreds of shorter staple strands into a desired shape. In particular, origami arranged as a single-layer rectangle has proven popular as flat pegboards that can display functionalities at staple-strand breakpoints, off the sides of the constituent double helices, with a ~5.3 nm rhombic-lattice spacing. For applications that demand tighter spacing, functionalities can be displayed instead on the termini of helices of multilayer DNA origami. However, pegboards with the greatest addressable surface area are often found to be the most versatile. Given the practical limitations of the length of the scaffold that can be easily realized, designs that minimize the length of each helix would have advantages for maximizing the number of helices and therefore the number of addressable pixels on each terminal surface. Here we present an architecture for multilayer DNA origami displaying flush terminal interfaces from over 200 helices that each are only 5.3 turns in length. As a result, we characterize an example using cryo-EM imaging paired with single-particle analysis for further analysis of the global structure.

59 BASIC BIOLOGICAL SCIENCES↗