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Topics: New Product Ideas; NASA TU Services; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences.
Topics: New Product Ideas; NASA TU Services; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences.
Topics: New Product Ideas; NASA TU Services; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences.
Topics: New Product Ideas; NASA TU Services; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences.
Topics: New Product Ideas; NASA TU Services; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences.
Topics: New Product Ideas; NASA TU Services; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences.
Topics: New Product Ideas; NASA TU Services; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences.
Topics: New Product Ideas; NASA TU Services; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences.
Topics covered include: Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; and Life Sciences.
Topics: New Product Ideas; NASA TU Services; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences.
This edition of LASER Tech briefs contains a feature on photonics. The other topics include: Electronic Components and Circuits. Electronic Systems, Physical Sciences, Materials, Computer Programs, Mechanics, Machinery, Fabrication Technology, Mathematics and Information Sciences, Life Sciences and books and reports.
Topics include: Electronic Components and Circuits. Electronic Systems, Physical Sciences, Materials, Computer Programs, Mechanics, Machinery, Fabrication Technology, Mathematics and Information Sciences, Life Sciences, and Books and reports
Topics include: New Product Ideas; NASA TU Services; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences.
A long-standing challenge in the metallic glass (MG) community has been how to quantitatively gauge the influence of the intricate local packing environment on the response (such as the propensity for atomic rearrangement) of the atomic configuration to external stimuli. Here we establish this structure–property relation by representing the complex amorphous structure using a single, flexibility-orientated structural quantity. This structural flexibility (SF) couples to a bona fide structural representation, the pair distribution function (PDF) of individual atoms, through a weighting function that reflects what matters in the static atomic configuration to dynamic responses. Machine learning is used, employing microscopic flexibility volume as the supervisory signal, to establish via direct regression an optimized weighting vector, which is proven robust for all quenching rates, deformation conditions, and different compositions in a given (e.g., Cu x Zr 100-x ) alloy system. Additionally, the SF is evaluated solely from the particle positions (PDF), for any structure variation, from the atomic scale up to sample average. Strong correlations are demonstrated between SF and a broad range of properties, including vibrational, diffusional, as well as elastic and plastic relaxation responses.
Machine learning (ML) is transforming materials research, yet potential for biopolymer discovery remains constrained by fragmented data and nonstandardized reporting. Biopolymers differ significantly from synthetic polymers, requiring specialized approaches to represent their biosynthetic origins, hierarchical structures, and application-specific metrics. In this Perspective, we identify three core challenges limiting biopolymer representation: information encoding, data quality, and data sharing. We describe the most pressing issues and propose commensurate approaches to address each key challenge. Recommendations include the design and adoption of biopolymer-specific fingerprinting and representation frameworks, development of hybrid human-large language model (LLM) data extraction strategies, and expanding Findable, Accessible, Interoperable, Reusable (FAIR)-compliant repositories. We propose a robust foundation to define interoperable, high-quality data sets that capture the full context of biopolymer materials. Standardized metadata, shared ontologies, and community-driven infrastructure would enable scalable, reproducible workflows and accelerate the ML-driven development of biopolymers.
Intrinsically disordered regions (IDR) and short linear motifs (SLiMs) play pivotal roles in the intricate signaling networks governed by phosphatases and kinases. B56δ (encoded by PPP2R5D) is a regulatory subunit of protein phosphatase 2A (PP2A) with long IDRs that harbor a substrate-mimicking SLiM and multiple phosphorylation sites. De novo missense mutations in PPP2R5D cause intellectual disabilities (ID), macrocephaly, Parkinsonism, and a broad range of neurological symptoms. Our single-particle cryo-EM structures of the PP2A-B56δ holoenzyme reveal that the long, disordered arms at the B56δ termini fold against each other and the holoenzyme core. This architecture suppresses both the phosphatase active site and the substrate-binding protein groove, thereby stabilizing the enzyme in a closed latent form with dual autoinhibition. The resulting interface spans over 190 Å and harbors unfavorable contacts, activation phosphorylation sites, and nearly all residues with ID-associated mutations. Our studies suggest that this dynamic interface is coupled to an allosteric network responsive to phosphorylation and altered globally by mutations. Furthermore, we found that ID mutations increase the holoenzyme activity and perturb the phosphorylation rates, and the severe variants significantly increase the mitotic duration and error rates compared to the normal variant.
This dataset presents a suite of downscaled hydro-climate projections over the conterminous United States (CONUS) based on multiple selected Global Climate Models (GCMs) from the Coupled Models Intercomparison Project phase 6 (CMIP6). The CMIP6 GCMs are downscaled dynamically using Regional Climate Model version 4 (RegCM4). Each ensemble member covers the 1980-2019 baseline and 2020-2059 near-future periods under the high-end (SSP585) emission scenario. This dataset is formulated to support the SECURE Water Act Section 9505 Assessment for the US Department of Energy (DOE) Water Power Technologies Office (WPTO). For further details on this dataset, please refer to Kao et al. (2022) and Rastogi et al. (2022).
The Hybrid Optimization Software Suite (HOSS) modeling framework and capabilities are described.