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At least 1,189 records · Page 66

Testing Prospects for Reliable Diatom Nanotechnology in Microgravity

The worldwide effort to grow nanotechnology, rather than use lithography, focuses on diatoms, single cell eukaryotic algae with ornate silica shells, which can be replaced by oxides and ceramics, or reduced to elemental silicon, to create complex nanostructures with compositions of industrial and electronics importance. Diatoms produce an enormous variety of structures, some of which are microtubule dependent and perhaps sensitive to microgravity. The NASA Single Loop for Cell Culture (SLCC) for culturing and observing microorganisms permits inexpensive, low labor in-space experiments. We propose to send up to the International Space Station diatom cultures of the three diatom species whose genomes are being sequenced, plus the giant diatoms of Antarctica (up to 2 mm diameter for a single cell) and the unique colonial diatom, Bacillaria paradoxa. Bacillaria cells move against each other in partial synchrony, like a sliding deck of cards, by a microfluidics mechanism. Will normal diatoms have aberrant pattern and shape or motility compared to ground controls? The generation time is typically one day, so that many generations may be examined from one flight. Rapid, directed evolution may be possible running the SLCC as a compustat. The shell shapes and patterns are preserved in hard silica, so that the progress of normal and aberrant morphogenesis may be followed by drying samples on a moving filter paper "diatom tape recorder". With a biodiversity of 100,000 distinct species, diatom nanotechnology may offer a compact and portable nanotechnology toolkit for exploration anywhere.

Gordon, Richard↗

The Phenix‐AlphaFold webservice: Enabling AlphaFold predictions for use in Phenix

Abstract Advances in machine learning have enabled sufficiently accurate predictions of protein structure to be used in macromolecular structure determination with crystallography and cryo‐electron microscopy data. The Phenix software suite has AlphaFold predictions integrated into an automated pipeline that can start with an amino acid sequence and data, and automatically perform model‐building and refinement to return a protein model fitted into the data. Due to the steep technical requirements of running AlphaFold efficiently, we have implemented a Phenix‐AlphaFold webservice that enables all Phenix users to run AlphaFold predictions remotely from the Phenix GUI starting with the official 1.21 release. This webservice will be improved based on how it is used by the research community and the future research directions for Phenix.

Poon, Billy K.↗

Nonlinear effects in thermal stress analysis of a solid propellant rocket motor

Direct characterization procedures were used to determine the relaxation modulus as a function of time, temperature, and state of strain. Using the quasi-elastic method of linearviscoelasticity, these properties were employed in a finite element computer code to analyze a thick-walled, nonlinear viscoelastic cylinder in the state of plane strain bonded to a thin (but stiff) elastic casing and subjected to slow thermal cooling. The viscoelastic solution is then expressed as a sequence of elastic finite element solutions. The strain-dependent character of the relaxation modulus is included by replacing the single relaxation curve used in the linear viscoelastic theory by a family of relaxation functions obtained at various strain levels. These functions may be regarded as a collection of stress histories or responses to specific loads (in this case, step strains) with which the cooldown solution is made to agree by iterations on the modulus and strain level.

Francis, E. C.↗

Early cratering rates and the nature of the Martian cratered uplands

The cratered uplands of Mars represent a complex sequence of reworked crater ejecta, distant basin ejecta deposits, sedimentary (aeolian and fluvial) deposits, and possible volcanic plains. The heritage of the early period of impact bombardment is documented in the preserved cratering record where the aereal density of large craters (greater than 20 km) exceeds that of the moon and subtle relicts of large impact basins remain. The cratering record provides a means to estimate the early impact flux on Mars, to constrain the direct and indirect effects of the impact process over various scales, and to evaluate the nature of the cratered upland surface materials. Four issues are addressed: the early Martian cratering rate; the role of late-stage atmospheric accretion of volatiles; the relative effects of craters and basins on the global redistribution of crustal materials; and the indirect longer term effects of basin formation.

Schultz, P. H.↗

Mesh-based super-resolution of fluid flows with multiscale graph neural networks

A graph neural network (GNN) approach is introduced in this work which enables mesh-based three-dimensional super-resolution of fluid flows. In this framework, the GNN is designed to operate not on the full mesh-based field at once, but on localized meshes of elements (or cells) directly. To facilitate mesh-based GNN representations in a manner similar to spectral (or finite) element discretizations, a baseline GNN layer (termed a message passing layer, which updates local node properties) is modified to account for synchronization of coincident graph nodes, rendering compatibility with commonly used element-based mesh connectivities. Furthermore, the architecture is multiscale in nature, and is comprised of a combination of coarse-scale and fine-scale message passing layer sequences (termed processors) separated by a graph unpooling layer. The coarse-scale processor embeds a query element (alongside a set number of neighboring coarse elements) into a single latent graph representation using coarse-scale synchronized message passing over the element neighborhood, and the fine-scale processor leverages additional message passing operations on this latent graph to correct for interpolation errors. Demonstration studies are performed using hexahedral mesh-based data from Taylor–Green Vortex and backward-facing step flow simulations at Reynolds numbers of 1600 and 3200. Through analysis of both global and local errors, the results ultimately show how the GNN is able to produce accurate super-resolved fields compared to targets in both coarse-scale and multiscale model configurations. Reconstruction errors for fixed architectures were found to increase in proportion to the Reynolds number. Geometry extrapolation studies on a separate cavity flow configuration show promising cross-mesh capabilities of the super-resolution strategy.

Backward-facing step↗

IsoForma: An R Package for Quantifying and Visualizing Positional Isomers in Top-Down LC-MS/MS Data

Proteoforms, the different forms of a protein with sequence variations including post-translational modifications (PTMs), execute vital functions in biological systems such as cell signaling and epigenetic regulation. Precisely defining the stoichiometry of PTMs has been challenging because, in the widely used bottom-up proteomics methods, the detection occurs at the peptide level and thus the link between peptides and their specific modification site is lost, resulting in proteoform ambiguity. Advances in top-down mass spectrometry (MS) technology have permitted the direct characterization of intact proteoforms and their exact number of modification sites, allowing for the relative quantification of positional isomers (PI). Proteins with positional isomers refers to proteoforms with identical total mass and set of modifications but varying PTM site combinations. The relative abundance of PI can be estimated by matching proteoform-specific fragment ions to top-down tandem MS (MS2) data to localize and quantify modifications. However, current approaches heavily rely on manual annotation. Here, we present IsoForma, an open-source R package for relative quantification of PI within a single tool. We benchmarked IsoForma’s performance against two existing workflows and highlight the similarity of the results and improvements in speed. Overall, IsoForma provides a streamlined process, reduces the time of conducting isoform-based analyses, and offers an essential framework for developing customized proteoform analysis workflows. Finally, the software is open source and available at https://github.com/EMSL-Computing/isoforma-lib.

59 BASIC BIOLOGICAL SCIENCES↗

N -Terminal Octylated Peptoid Hydrogels as 3D-Printable Cell Scaffolds and Proteolytically Robust Cargo Depots

Supramolecular hydrogels that mimic the extracellular matrix (ECM) represent promising materials for tissue engineering and drug delivery. However, conventional hydrogels formed via the self-assembly of natural or synthetic building blocks often face a trade-off between biological functionality and biochemical stability, limiting their utility in long-term or protease-rich environments. Peptoids, a class of peptide-inspired, sequence-defined polymers, offer a compelling alternative due to their exceptional proteolytic resistance and bioactivity. Despite this potential, the development of supramolecular peptoid hydrogels has been hindered by the absence of backbone hydrogen bond donors, which limits long-range ordering necessary for efficient hydrogel formation. This work describes a short peptoid functionalized at the N-terminus with an octyl chain that readily self-assembles into hydrogels. Hydrophobic interactions among pendant octyl groups promote directional peptoid packing into highly ordered nanosheets, which interconnect to form a porous hydrogel network. These hydrogels exhibit tunable viscoelasticity, shear-thinning, and self-healing properties, enabling their use as inks for extrusion-based 3D printing. They support NIH-3T3 fibroblast adhesion, spreading, and proliferation, maintaining greater than 95% cell viability over 4 days. Moreover, the hydrogels retain their macroscopic integrity under protease-rich conditions, enabling sustained cargo release and uniform cellular uptake. Together, this study demonstrates a class of supramolecular peptoid hydrogelators that integrate biocompatibility, 3D printability, and proteolytic stability, providing a versatile platform for ECMmimetic scaffolds in regenerative medicine and long-term therapeutic delivery.

cargo delivery↗

SYMBA: symbolic computation of squared amplitudes in high energy physics with machine learning

The cross section is one of the most important physical quantities in high-energy physics and the most time consuming to compute. While machine learning has proven to be highly successful in numerical calculations in high-energy physics, analytical calculations using machine learning are still in their infancy. In this work, we use a sequence-to-sequence model, specifically, a transformer, to compute a key element of the cross section calculation, namely, the squared amplitude of an interaction. We show that a transformer model is able to predict correctly 97.6% and 99% of squared amplitudes of quantum chromodynamics and quantum electrodynamics processes, respectively, at a speed that is up to orders of magnitude faster than current symbolic computation frameworks. We discuss the performance of the current model, its limitations and possible future directions for this work.

97 MATHEMATICS AND COMPUTING↗

Catalytic Upgrading of Renewable Feedstock (Final Technical Report)

The goal of this DOE-funded project was to investigate the fundamental science related to the development of homogeneous (de)hydrogenation catalysts in order to enable energy-relevant transformations of bio-relevant chemical feedstocks including ethanol. The primary focus was to improve the activity of ethanol upgrading catalysts, specifically informed through mechanistic studies, in order to enable rational design optimization strategies. Following in depth mechanistic studies, targeted reaction optimization approaches included ligand redesign to improve catalyst stability, developing new carbon-carbon bond forming reactions using ethanol as a precursor, and examining photochemically-mediated reactions, ultimately to integrate within other reactor designs such as continuous flow reactors. The high modularity of the catalyst components (ligands) has enabled the preparation and analyses of multiple catalyst precursors. These studies uncovered an unexpectedly beneficial substitution pattern of the ligand structure that led to the development of new catalysts that are the best in class for upgrading ethanol to butanol with a turnover number of 155,890 and a turnover frequency of 12,690 h –1 . In addition to upgrading ethanol to butanol, cascade reaction sequences were developed to form new C-C bonds using ethanol as a bio-relevant feedstock, providing access to platform chemicals from renewable sources. As part of the reaction discovery process, new mechanistic details were uncovered that provided insights into: a) catalyst speciation, b) decomposition pathways, c) carbon monoxide releasing pathways, and d) carbon-carbon and carbon hydrogen bond breaking pathways. Most of these outcomes were previously not known; however, they provide important directions for new catalyst design strategies. Finally, use of high throughput and in situ photochemical reaction analyses enabled detailed studies into changes to the catalyst structure upon irradiation. Irradiation was found to improve hydrogen transfer catalysis, by promoting a ligand dissociation event.

09 BIOMASS FUELS↗

Replace Human Intelligence with Fast and Smart Geometric Reasoning and Graph Neural Network to Accelerate Next Gen ModSim Workflows

We present an agent-guided approach to CAD geometry decomposition that automates hex/hybrid meshing with graph neural networks (GNNs) to accelerate next-generation ModSim workflows. Our end-to-end pipeline (i) reduces 3D boundary-representation (B-Rep) models to a 2D chordal axis skeleton (CAT) and then to a 1D bipartite graph of surface and curve nodes, (ii) assigns per node labels as Cubit® WebCut actions, (iii) trains a multi-action GNN under supervised learning, and (iv) predicts five surface-node and three curve-node actions on out-of-distribution test geometries. Each graph node carries geometric, topological, and meshing attributes drawn from the B-Rep “skin” and CAT “skeleton,” with two-way mappings across 3D↔2D↔1D representations to maintain traceability back to 3D CAD. The supervised learning model exhibits stable convergence of the binary cross-entropy loss and achieves 98.7% accuracy on unseen lattice models. To operationalize decision-making, we rank predicted commands by geometric significance and prototyped the agent-guided workflow through the Cubit® Meshing PowerTool GUI. As a stretch goal, we explore reinforcement learning (RL) to reduce or remove label requirements and to learn policies for action sequences that maximize total reward (e.g., size of hex-meshable regions and resulting hex mesh quality). When all-hex meshing is not feasible, the agent assists in producing hybrid meshes—prioritizing hex in critical regions and transitioning to tetrahedral elements (tets) elsewhere—maintaining fidelity while ensuring robustness. The overarching objective is to replace manual, heuristics-based decomposition with data-driven, reproducible automation, cutting meshing turnaround time by orders of magnitude. We anticipate direct impact on simulation workflows through intelligent, scalable decomposition of complex CAD models into hex-meshable subdomains.

97 MATHEMATICS AND COMPUTING↗

Closing the Loop between In Situ Stress Complexity and EGS Fracture Complexity

We present an agent-guided approach to CAD geometry decomposition that automates hex/hybrid meshing with graph neural networks (GNNs) to accelerate next-generation ModSim workflows. Our end-to-end pipeline (i) reduces 3D boundary-representation (B-Rep) models to a 2D chordal axis skeleton (CAT) and then to a 1D bipartite graph of surface and curve nodes, (ii) assigns per node labels as Cubit® WebCut actions, (iii) trains a multi-action GNN under supervised learning, and (iv) predicts five surface-node and three curve-node actions on out-of-distribution test geometries. Each graph node carries geometric, topological, and meshing attributes drawn from the B-Rep “skin” and CAT “skeleton,” with two-way mappings across 3D↔2D↔1D representations to maintain traceability back to 3D CAD. The supervised learning model exhibits stable convergence of the binary cross-entropy loss and achieves 98.7% accuracy on unseen lattice models. To operationalize decision-making, we rank predicted commands by geometric significance and prototyped the agent-guided workflow through the Cubit® Meshing PowerTool GUI. As a stretch goal, we explore reinforcement learning (RL) to reduce or remove label requirements and to learn policies for action sequences that maximize total reward (e.g., size of hex-meshable regions and resulting hex mesh quality). When all-hex meshing is not feasible, the agent assists in producing hybrid meshes—prioritizing hex in critical regions and transitioning to tetrahedral elements (tets) elsewhere—maintaining fidelity while ensuring robustness. The overarching objective is to replace manual, heuristics-based decomposition with data-driven, reproducible automation, cutting meshing turnaround time by orders of magnitude. We anticipate direct impact on simulation workflows through intelligent, scalable decomposition of complex CAD models into hex-meshable subdomains.

42 ENGINEERING↗

ALTKAL: An optimum linear filter for GEOS-3 altimeter data

ALTKAL is a computer program designed to smooth sea surface height data obtained from the GEOS 3 altimeter, and to produce minimum variance estimates of sea surface height and sea surface slopes, along with their standard derivations. The program operates by processing the data through a Kalman filter in both the forward and backward directions, and optimally combining the results. The sea surface height signal is considered to have a geoid signal, modeled by a third order Gauss-Markov process, corrupted by additive white noise. The governing parameters for the signal and noise processes are the signal correlation length and the signal-to-noise ratio. Mathematical derivations of the filtering and smoothing algorithms are presented. The smoother characteristics are illustrated by giving the frequency response, the data weighting sequence and the transfer function of a realistic steady-state smoother example. Based on nominal estimates for geoidal undulation amplitude and correlation length, standard deviations for the estimated sea surface height and slope are 12 cm and 3 arc seconds, respectively.

Fang, B. T.↗

User-oriented end-to-end transport protocols for the real-time distribution of telemetry data from NASA spacecraft

A set of standard telemetry protocols for downlink data flow facilitating the end-to-end transport of instrument data from the spacecraft to the user in real time is proposed. The direct switching of data by autonomous message 'packets' that are assembled by the source instrument on the spacecraft is discussed. The data system consists thus of a format on a message rather than word basis, and such packet telemetry would include standardized protocol headers. Standards are being developed within the NASA End-to-End Data System (NEEDS) program for the source packet and transport frame protocols. The source packet protocol contains identification of both the sequence number of the packet as it is generated by the source and the total length of the packet, while the transport frame protocol includes a sequence count defining the serial number of the frame as it is generated by the spacecraft data system, and a field specifying any 'options' selected in the format of the frame itself.

Hooke, A. J.↗

Quantifying the Long‐Range Coupling of Electronic Properties in Proteins with ab initio Molecular Dynamics**

Abstract The delicate interplay of covalent and non‐covalent interactions in proteins is inherently quantum mechanical and highly dynamic in nature. To directly interrogate the evolving nature of the electronic structure of proteins, we carry out 100‐ps‐scale ab initio molecular dynamics simulations of three representative small proteins with range‐separated hybrid density functional theory. We quantify the nature and length‐scale of the coupling of residue‐specific charge probability distributions in these proteins. While some nonpolar residues exhibit expectedly narrow charge distributions, most polar and charged residues exhibit broad, multimodal distributions. Even for nonpolar residues, we observe sequence‐specific deviations corresponding to charge accumulation or depletion that would be challenging to capture in a fixed charge force field. We quantify the effect of residue‐residue interactions on charge distributions first with linear cross‐correlations. We then show how additional insight can be gained from evaluating the mutual information of charge distributions. We show that a significant number of residues couple most strongly with residues that are distant in both sequence and space over a range of secondary structures including α‐helical, β‐sheet, disulfide bridging, and lasso motifs. The mutual information analysis is necessary to capture coupling between some polar and charged residues that would be otherwise missed.

Yang, Zhongyue↗

Populus MYC2 orchestrates root transcriptional reprogramming of defence pathway to impair Laccaria bicolor ectomycorrhizal development

Summary The jasmonic acid (JA) signalling pathway plays an important role in the establishment of the ectomycorrhizal symbiosis. The Laccaria bicolor effector MiSSP7 stabilizes JA corepressor JAZ6, thereby inhibiting the activity of Populus MYC2 transcription factors. Although the role of MYC2 in orchestrating plant defences against pathogens is well established, its exact contribution to ECM symbiosis remains unclear. This information is crucial for understanding the balance between plant immunity and symbiotic relationships. Transgenic poplars overexpressing or silencing for the two paralogues of MYC2 transcription factor (MYC2s) were produced, and their ability to establish ectomycorrhiza was assessed. Transcriptomics and DNA affinity purification sequencing were performed. MYC2s overexpression led to a decrease in fungal colonization, whereas its silencing increased it. The enrichment of terpene synthase genes in the MYC2‐regulated gene set suggests a complex interplay between the host monoterpenes and fungal growth. Several root monoterpenes have been identified as inhibitors of fungal growth and ECM symbiosis. Our results highlight the significance of poplar MYC2s and terpenes in mutualistic symbiosis by controlling root fungal colonization. We identified poplar genes which direct or indirect control by MYC2 is required for ECM establishment. These findings deepen our understanding of the molecular mechanisms underlying ECM symbiosis.

Marqués‐Gálvez, José Eduardo↗

SPANet: Generalized permutationless set assignment for particle physics using symmetry preserving attention

The creation of unstable heavy particles at the Large Hadron Collider is the most direct way to address some of the deepest open questions in physics. Collisions typically produce variable-size sets of observed particles which have inherent ambiguities complicating the assignment of observed particles to the decay products of the heavy particles. Current strategies for tackling these challenges in the physics community ignore the physical symmetries of the decay products and consider all possible assignment permutations and do not scale to complex configurations. Attention based deep learning methods for sequence modelling have achieved state-of-the-art performance in natural language processing, but they lack built-in mechanisms to deal with the unique symmetries found in physical set-assignment problems. We introduce a novel method for constructing symmetry-preserving attention networks which reflect the problem's natural invariances to efficiently find assignments without evaluating all permutations. This general approach is applicable to arbitrarily complex configurations and significantly outperforms current methods, improving reconstruction efficiency between 19% - 35% on typical benchmark problems while decreasing inference time by two to five orders of magnitude on the most complex events, making many important and previously intractable cases tractable. A full code repository containing a general library, the specific configuration used, and a complete dataset release, are available at https://github.com/Alexanders101/SPANet

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Programmable DMA controller

In a data system having a memory, plural input/output (I/O) devices and a bus connecting each of the I/O devices to the memory, a direct memory access (DMA) controller regulating access of each of the I/O devices to the bus, including a priority register storing priorities of bus access requests from the I/O devices, an interrupt register storing bus access requests of the I/O devices, a resolver for selecting one of the I/O devices to have access to the bus, a pointer register storing addresses of locations in the memory for communication with the one I/O device via the bus, a sequence register storing an address of a location in the memory containing a channel program instruction which is to be executed next, an ALU for incrementing and decrementing addresses stored in the pointer register, computing the next address to be stored in the sequence register, computing an initial contents of each of the register. The memory contains a sequence of channel program instructions defining a set up operation wherein the contents of each of the registers in the channel register is initialized in accordance with the initial contents computed by the ALU and an access operation wherein data is transferred on the bus between a location in the memory whose address is currently stored in the pointer register and the one I/O device enabled by the resolver.

Hendry, David F.↗

Microstructure of Al2O3 scales formed on NiCrAl alloys

The structure of transient scales formed on pure and Y or Zr-doped Ni-15Cr-13Al alloys oxidized for 0.1 hr at 1100 C was studied by the use of transmission electron microscopy. Crystallographically oriented scales were found on all three alloys, but especially for the Zr-doped NiCrAl. The oriented scales consisted of alpha-(Al,Cr)2O3, Ni(Al,Cr)2O4 and gamma-Al2O3. They were often found in intimate contact with each other such that the close-packed planes and directions of one oxide phase were aligned with those of another. The prominent structural features of the oriented scales were approximately equal to micrometer subgrains; voids, antiphase domain boundaries and aligned precipitates were also prevalent. Randomly oriented alpha-Al2O3 was also found and was the only oxide ever observed at the immediate oxide metal interface. These approximately 0.15 micrometer grains were populated by intragranular voids which decreased in size and number towards the oxide metal interface. A sequence of oxidation was proposed in which the composition of the growing scale changed from oriented oxides rich in Ni and Cr to oriented oxides rich in Al. At the same time the structure changed from cubic spinels to hexagonal corundums with apparent precipitates of one phase in the matrix of the other. Eventually randomly oriented pure alpha-Al2O3 formed as the stable oxide with an abrupt transition: there was no gradual loss of orientation, no gradual compositional change or no gradual decrease in precipitate density.

Smialek, J. L.↗