Engineering DNA-Based Self-Assembly Systems to Produce Nanostructures and Chemical Patterns

While commonly known as a material that stores biological information essential for life, few realize that deoxyribonucleic acid (DNA) is also a wonderful building (i.e., physical structures) and computing material. The field of DNA nanotechnology aims to use DNA primarily to build and control matter on the nanoscale. In 2006, a technique known as DNA origami was developed, which allows for the formation of about any shape on the nanoscale. Such DNA origami have been used in many applications: nanodevices, nanotubes, nanoreactors. However, the small surface area of the origami often limits its usefulness. One promising method for building large (micron-sized) DNA origami structures is to self-assemble multiple origami components into well-defined structures. To date, however, such structures suffer low yields, long reaction times and require experimental optimization with no guiding principles. One primary reason is that a governing theory and experimental measurements behind such a self-assembly process are lacking. In this work, we develop coarse-grained computational simulations to describe and understand the self-assembly of finite-sized, multicomponent complexes (e.g., nine different DNA-origami components that form a square grid complex). To help inform the model, we experimentally investigate how various interface architectures between two self-assembling DNA origami components affect the reaction kinetics and thermodynamics. We further develop the accuracy of our simulations by incorporating these measurements and other thermodynamic measurements from our group and implement a computational algorithm that optimizes the interaction strengths between self-assembling components for reaction efficiency (i.e., speed and yield of the complex). With these experimentally-informed simulations, we suggest design improvements and provide yield predictions to an experimentally demonstrated tetrameric complex. Finally, with the overarching idea of using DNA-based components to self-assemble to produce ordered structures and patterns, we build a reaction-diffusion system whose reactions are programmed using DNA strand displacement and diffusion which occurs in a hydrogel, wherein patterns develop, and liquid reservoirs, which are used to supply the high energy components. With this reaction-diffusion system we create stable (i.e., unchanging in space and time) one and two-dimensional patterns of DNA molecules with millimeter-scale features

Observations of the Antarctic polar front during FDRAKE 76 : a cruise report

Figures 37 and 38 have been reduced from their original size for the purpose of scanning.During March/April 1976 the small-scale structure of the Antarctic Polar Front was observed in the Drake Passage. The observations were part of the International Southern Ocean Studies (ISOS) program called FDRAke 76. The purpose of the program was to obtain densely sampled measurements of temperature, salinity, dissolved oxygen, and chemical nutrients in the Polar Front Zone (PFZ) and pilot measurements of horizontal and vertical velocities in order to explain the above scalar variability. The PFZ is a region where Antarctic and sub-Antarctic waters intermingle and presumably mix to affect the properties of Antarctic Intermediate Water. A report on the third leg of Cruise 107 of the R. V. THOMPSON is presented as well as a description of the measurements and a preliminary report of the data. A feature of interest is the pinching off of a northward meander of the circumpolar current system into a cyclonic ring of Antarctic Waters.Prepared for the National Science Foundation, Office for the International Decade of Ocean Exploration, under Grant OCE75-14056 and the International Southern Ocean Studies (ISOS) Program

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Federated learning enables big data for rare cancer boundary detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing