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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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DOE OSTI2024

scifi-ATAC-seq: massive-scale single-cell chromatin accessibility sequencing using combinatorial fluidic indexing

Abstract Single-cell ATAC-seq has emerged as a powerful approach for revealing candidate cis-regulatory elements genome-wide at cell-type resolution. However, current single-cell methods suffer from limited throughput and high costs. Here, we present a novel technique called scifi-ATAC-seq, single-cell combinatorial fluidic indexing ATAC-sequencing, which combines a barcoded Tn5 pre-indexing step with droplet-based single-cell ATAC-seq using the 10X Genomics platform. With scifi-ATAC-seq, up to 200,000 nuclei across multiple samples can be indexed in a single emulsion reaction, representing an approximately 20-fold increase in throughput compared to the standard 10X Genomics workflow.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Developing ML/AI Methods for High-Throughput Characterization of Multiple-Sensor Streams of Tokamak Dynamics for High-Speed Control (Final Report)

This project evaluated and developed new mathematical and algorithmic techniques capable of handling (in real-time) the growing amounts of data generated by modern fusion research. While existing numerical linear algebra (NLA) methods provide the backbone to classical data analysis and algorithms, these methods fundamentally do not port to distributed architectures nor do they allow low-latency data reduction for control. Motivated by the needs for modern fusion reactors, this project explored and implemented new numerical methods to characterize plasma dynamics, respond in real-time to discharge evolution, and to process massive-scale data accurately and rapidly more fully. This project links expertise in multiple-sensor diagnostics of tokamak plasma dynamics from Columbia University’s Plasma Physics Laboratory with expertise in massive-scale data reduction and extreme data control algorithms at Columbia University’s Data Science Institute. This interdisciplinary project (i) applied machine learning methods, (ii) implemented a properly-trained neural-network for very fast processing of high-speed plasma videography, and (ii) developed the applied mathematical methods, based on randomized-NLA (rNLA) routines, for data analysis, reduction, and real-time control. The Columbia University High Beta Tokamak-Extended Pulse (HBT-EP) facility provided data to test new algorithms and partnership with Columbia University's Data Sciences Institute evaluated the broader use of new algorithms for many challenging control applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

A genomic perspective on fungal diversity and evolution

Originating from aquatic unicellular ancestors, over the course of ~1 billion years, the fungi have evolved to occupy nearly all aerobic environments on the planet, diversified into millions of different ‘species’ and have developed complex multicellular structures. Their relatively small, simple genomes have facilitated massive-scale sequencing and allowed us to explore genome evolution across an ancient eukaryotic kingdom. With thousands of genomes from diverse lineages now available, this Review will discuss insights into fungal biology and evolution gleaned with genomics and other multi-omics approaches. Using published genomes available through GenBank and the Joint Genome Institute’s MycoCosm platform, we generated kingdom-wide phylogenies and used them to highlight how fungal genomes have changed over time. With this phylogeny as a guide, we also discuss major evolutionary transitions that occurred across the fungal kingdom. Although progress has been made, these efforts are hampered by biases in genome representation and limited characterization of gene functions. Here, in this study, we discuss these challenges and possible future directions to address them, including initiatives to characterize conserved genes of unknown function and scale up sequencing towards 10,000 annotated fungal genomes.

Mondo, Stephen J. [USDOE Joint Genome Institute (J↗