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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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At least 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Long-range barcode labeling-sequencing

Methods for sequencing single large DNA molecules by clonal multiple displacement amplification using barcoded primers. Sequences are binned based on barcode sequences and sequenced using a microdroplet-based method for sequencing large polynucleotide templates to enable assembly of haplotype-resolved complex genomes and metagenomes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Beyond Fine Tuning: Adding capacity to leverage few labels

In this paper we present a technique to train neural network models on small amounts of data. Current methods for training neural networks on small amounts of rich data typically rely on strategies such as fine-tuning a pre-trained neural networks or the use of domain-specific hand-engineered features. Here we take the approach of treating network layers, or entire networks, as modules and combine pre-trained modules with untrained modules, to learn the shift in distributions between data sets. The central impact of using a modular approach comes from adding new representations to a network, as opposed to replacing representations via fine-tuning. Using this technique, we are able surpass results using standard fine-tuning transfer learning approaches, and we are also able to significantly increase performance over such approaches when using smaller amounts of data.

data science↗
DOE OSTI2018

Boron-based dual imaging probes, compositions and methods for rapid aqueous F-18 labeling, and imaging methods using same

A composition useful as a PET and/or fluorescence imaging probe a compound a compound of Formula I, including salts, hydrates and solvates thereof: ##STR00001## wherein R.sub.1-R.sub.7 may be independently selected from hydrogen, halogen, hydroxy, alkoxy, nitro, substituted and unsubstituted amino, cycloalkyl, carboxy, carboxylic acids and esters thereof, cyano, haloalkyl, aryl, X is selected from the group consisting of C and N; and A is selected of hydrogen, halogen, hydroxy, alkoxy, nitro, substituted and unsubstituted amino, alkyl, cycloalkyl, carboxy, carboxylic acids and esters thereof, cyano, haloalkyl, aryl, including phenyl and aminophenyl, and heteroaryl.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Characterizing intracellular proteomes for microbes: An experimental approach using label-free protein quantitation

Genomic and transcriptomic studies generate a rich source of molecular information; however, neither a static genome nor the presence of a messenger RNA provides the most direct insight into the functional state of a microbial cell at any given time. Rather, it is the ensemble of proteins (i.e., proteome) that is the primary workhorse for most biological processes that are concurrent and coordinately active in a living cell. Currently, mass-spectrometry-based (MS) technologies provide unprecedented information into the composition of the proteome, shedding light on the numerous complex biological processes actively shaping observed phenotypes. Herein, we detail a protocol for the exploration of intracellular proteomes through the large-scale, unbiased identification of proteins and their relative abundances using liquid chromatography (LC) coupled to tandem mass spectrometry (MS/MS). In general, this protocol is applicable to both microbial and eukaryotic systems.

Abraham, Paul↗
DOE OSTI2020

Track Seeding and Labelling with Embedded-space Graph Neural Networks

To address the unprecedented scale of HL-LHC data, the Exa.TrkX project is investigating a variety of machine learning approaches to particle track reconstruction. The most promising of these solutions, graph neural networks (GNN), process the event as a graph that connects track measurements (detector hits corresponding to nodes) with candidate line segments between the hits (corresponding to edges). Detector information can be associated with nodes and edges, enabling a GNN to propagate the embedded parameters around the graph and predict node-, edge- and graph-level observables. Previously, message-passing GNNs have shown success in predicting doublet likelihood, and we here report updates on the state-of-the-art architectures for this task. In addition, the Exa.TrkX project has investigated innovations in both graph construction, and embedded representations, in an effort to achieve fully learned end-to-end track finding. Hence, we present a suite of extensions to the original model, with encouraging results for hitgraph classification. In addition, we explore increased performance by constructing graphs from learned representations which contain non-linear metric structure, allowing for efficient clustering and neighborhood queries of data points. We demonstrate how this framework fits in with both traditional clustering pipelines, and GNN approaches. The embedded graphs feed into high-accuracy doublet and triplet classifiers, or can be used as an end-to-end track classifier by clustering in an embedded space. A set of post-processing methods improve performance with knowledge of the detector physics. Finally, we present numerical results on the TrackML particle tracking challenge dataset, where our framework shows favorable results in both seeding and track finding.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗