Ndr/Lats Kinases Bind Specific Mob-Family Coactivators through a Conserved and Modular Interface
Abstract not provided
Parker, Benjamin W.↗
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Results for “modular interface”
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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An Electrochemical Approach to Accelerate Mechanically Assisted Crevice Corrosion Damage on Modular Implant Interfaces
Not provided.
Materials Science↗
Tribol Interface Physics Library
Tribol is a modular interface physics library featuring Lagrangian contact algorithms for explicit and implicit solid mechanics applications. More broadly, Tribol supports the research, development, and implementation of numerical methods for interface physics.
Settgast, RandolphR↗
SPISEA: A Python-based Simple Stellar Population Synthesis Code for Star Clusters
We present Stellar Population Interface for Stellar Evolution and Atmospheres (SPISEA), an open-source Python package that simulates simple stellar populations. The strength of SPISEA is its modular interface which offers the user control of 13 input properties including (but not limited to) the initial mass function, stellar multiplicity, extinction law, and the metallicity-dependent stellar evolution and atmosphere model grids used. The user also has control over the initial–final mass relation in order to produce compact stellar remnants (black holes, neutron stars, and white dwarfs). We demonstrate several outputs produced by the code, including color–magnitude diagrams, HR-diagrams, luminosity functions, and mass functions. SPISEA is object-oriented and extensible, and we welcome contributions from the community. The code and documentation are available on GitHub (https://github.com/astropy/SPISEA) and ReadtheDocs (https://spisea.readthedocs.io/en/latest/), respectively.
79 ASTRONOMY AND ASTROPHYSICS↗
rNets: a standalone package to visualize reaction networks
In the study of chemical processes, visualizing reaction networks is pivotal for identifying crucial compounds and transformations. Traditional methods, such as network schematics and reaction path linear plots, often struggle to effectively represent complex reaction networks due to their size and intricate connectivity. Alternatives capable of leading with complexity include graph methods, but they are not user-friendly, lacking simplicity and modularity, which hinders their integration with widely-used research software. This work introduces rNets an innovative tool designed for the efficient visualization of reaction networks with a user-friendly interface, modularity, and seamless integration with existing software packages. The effectiveness of rNets is demonstrated through its application in analyzing three catalytic reactions, showcasing its potential to significantly enhance research both in homogeneous and heterogeneous catalysis fields. This tool not only simplifies the visualization process but also opens new avenues for exploring complex reaction networks in diverse research contexts.
Pablo-García, Sergio↗
Modular multi-interface nanocrystals for enhanced ethanol oxidation electrocatalysis
Electrochemical processes that utilize biomass-derived ethanol as a source of electrons and protons offer a sustainable energy strategy, yet their practical implementation is limited by sluggish ethanol oxidation reaction (EOR) kinetics and catalyst poisoning. Here, in this study, we report a modular multi-interface nanocrystal catalyst comprising core/shell Co 2 P/Pd and Pd-Au heterostructured interfaces that exhibit complementary functions for the enhanced EOR catalysis. The Co 2 P/Pd interface boosts Pd atom utilization and lowers the kinetic barriers for ethanol-to-acetate conversion, while the Pd-Au interface effectively alleviates CO poisoning caused by C–C bond cleavage of ethanol. In-depth analyses using in situ attenuated total reflectance-surface-enhanced infrared absorption spectroscopy, differential electrochemical mass spectrometry, and density functional theory calculations elucidate the mechanistic roles of these interfaces. The optimized Co 2 P/Pd-Au 0.08 nanorods achieve an excellent mass activity, underscoring the potential of modular, multi-interface nanocrystals for advancing EOR catalysis and offering a generalizable strategy for broader catalytic innovations.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
The DUNE-DAQ Application Framework
The deep underground neutrino experiment (DUNE) is a next-generation neutrino experiment that will probe the properties of these elusive particles with unparalleled precision. It will also act as an observatory for neutrino bursts caused by nearby supernovae, in the event that one occurs, while the experiment is in operation. Given these goals, the DUNE trigger and DAQ system must be able to maintain extremely high uptime and provide a path for full readout of the detectors for very long times (up to 100 s). To achieve these ends, we have designed the DUNE DAQ system around a flexible “application framework,” which provides a modular interface for specific tasks while handling the interconnections between them. The application framework collects modules into applications, which can then be interacted with as units by the control, configuration, and monitoring systems. One of the key features of the framework is its communication abstraction layer, which allows for modules to interact with both internal queues and external network connections with a single transport-agnostic interface. We will report on the architecture and features of the framework.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
The DUNE DAQ Application Framework
The Deep Underground Neutrino Experiment (DUNE) is a next-generation neutrino experiment that will probe the properties of these elusive particles with unparalleled precision. It will also act as an observatory for neutrino bursts caused by nearby supernovae, in the event that one occurs while the experiment is in operation. Given these goals, the DUNE trigger and DAQ system must be able to maintain extremely high uptime and provide a path for full readout of the detectors for very long times (up to 100s). To achieve these ends, we have designed the DUNE DAQ system around a flexible “application framework”, which provides a modular interface for specific tasks while handling the interconnections between them. The application framework collects modules into applications which can then be interacted with as units by the control, configuration and monitoring systems. One of the key features of the framework is its communications abstraction layer, which allows for modules to interact with both internal queues and external network connections with a single transport-agnostic interface. We will report on the architecture and features of the framework.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
plexosdb: A Modular Library for Programmatic PLEXOS Model Construction
plexosdb is a lightweight Python library for constructing PLEXOS models using a SQLite-backed data structure. It provides a clear, modular interface that maps relational data directly to model components. By leveraging SQLite and idiomatic Python, it enables fast iteration and reproducible workflows. The result is a performant, composable foundation for scalable PLEXOS model development.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Engineering modular enzyme assembly: synthetic interface strategies for natural products biosynthesis applications
Covering: 2020 to 2025Natural products remain indispensable sources of therapeutic and bioactive compounds, yet traditional discovery strategies are constrained by compound rediscovery. Modular biosynthetic enzymes, such as type I polyketide synthases (PKSs) and type A non-ribosomal peptide synthetases (NRPSs), offer promising platforms for combinatorial biosynthesis owing to their programmable architectures. However, practical implementation is frequently limited by inter-modular incompatibility and domain-specific interactions. This review highlights recent advances in modular enzyme assembly enabled by synthetic interfaces-including cognate docking domains, synthetic coiled-coils, SpyTag/SpyCatcher, and split inteins-which function as orthogonal, standardized connectors to facilitate post-translational complex formation. These interfaces support rational investigations into substrate specificity, module compatibility, and pathway derivatization as well as general enzyme clustering applications beyond PKS and NRPS systems. Synthetic interfaces can be integrated with computational tools to support a more systematic and scalable framework for modular enzyme engineering by providing predictive insights into domain compatibility and interface design. These approaches within iterative design-build-test-learn workflows can accelerate the programmable assembly of biosynthetic systems and expand the accessible chemical space for natural products.
Kim, Gahyeon↗
Interface Nucleus Templating of Modular Intermetallic Morphologies: Chemical Pressure Complementarity, Columnar Domains, and Complex Disorder in Y 13 Ag 42.7 Zn 29.7
One element of the diversity of intermetallic phases is the formation of complex structures from the assembly of fragments of simpler structures. Recently, we devised the Interface Nuclear Approach as a model for understanding such modular arrangements, in which the intergrowth of different structures is driven by chemical pressure (CP) relief at shared motifs at the domain interfaces, referred to as interface nuclei. In this Article, we present the synthesis, crystal structure, and CP analysis of a new compound that expands on this theme, Y 13 Ag 42.7 Zn 29.7 . Its hexagonal structure contains interpenetrating domains based on the CaPd 5+x and EuMg 5 types. The CaPd 5+x -based regions are reminiscent of the lamellar intergrowth structures previously observed in the Y−Ag−Zn system. In Y 13 Ag 42.7 Zn 29.7 , however, the domains have a different morphology, forming columns that adopt a hexagonal rod-packing. The geometrical features of the remaining spaces are assigned, using the program GrowDomain, to the cores of trigonal units of the EuMg 5 type, while layers of disordered atoms occur at heights along z where the parent structures are mismatched. At the CaPd 5+x -type/EuMg 5 -type interfaces, simple interface nucleus motifs with strong CP-complementarity can be identified, while their distribution within the parent structures supports the notion of templated architectures in modular intermetallics.
Chemical structure↗
X‐ray scattering reveals disordered linkers and dynamic interfaces in complexes and mechanisms for DNA double‐strand break repair impacting cell and cancer biology
Abstract Evolutionary selection ensures specificity and efficiency in dynamic metastable macromolecular machines that repair DNA damage without releasing toxic and mutagenic intermediates. Here we examine non‐homologous end joining (NHEJ) as the primary conserved DNA double‐strand break (DSB) repair process in human cells. NHEJ has exemplary key roles in networks determining the development, outcome of cancer treatments by DSB‐inducing agents, generation of antibody and T‐cell receptor diversity, and innate immune response for RNA viruses. We determine mechanistic insights into NHEJ structural biochemistry focusing upon advanced small angle X‐ray scattering (SAXS) results combined with X‐ray crystallography (MX) and cryo‐electron microscopy (cryo‐EM). SAXS coupled to atomic structures enables integrated structural biology for objective quantitative assessment of conformational ensembles and assemblies in solution, intra‐molecular distances, structural similarity, functional disorder, conformational switching, and flexibility. Importantly, NHEJ complexes in solution undergo larger allosteric transitions than seen in their cryo‐EM or MX structures. In the long‐range synaptic complex, X‐ray repair cross‐complementing 4 (XRCC4) plus XRCC4‐like‐factor (XLF) form a flexible bridge and linchpin for DNA ends bound to KU heterodimer (Ku70/80) and DNA‐PKcs (DNA‐dependent protein kinase catalytic subunit). Upon binding two DNA ends, auto‐phosphorylation opens DNA‐PKcs dimer licensing NHEJ via concerted conformational transformations of XLF‐XRCC4, XLF–Ku80, and LigIV BRCT –Ku70 interfaces. Integrated structures reveal multifunctional roles for disordered linkers and modular dynamic interfaces promoting DSB end processing and alignment into the short‐range complex for ligation by LigIV. Integrated findings define dynamic assemblies fundamental to designing separation‐of‐function mutants and allosteric inhibitors targeting conformational transitions in multifunctional complexes.
59 BASIC BIOLOGICAL SCIENCES↗
A Fortran–Python interface for integrating machine learning parameterization into earth system models
Abstract. Parameterizations in earth system models (ESMs) are subject to biases and uncertainties arising from subjective empirical assumptions and incomplete understanding of the underlying physical processes. Recently, the growing representational capability of machine learning (ML) in solving complex problems has spawned immense interests in climate science applications. Specifically, ML-based parameterizations have been developed to represent convection, radiation, and microphysics processes in ESMs by learning from observations or high-resolution simulations, which have the potential to improve the accuracies and alleviate the uncertainties. Previous works have developed some surrogate models for these processes using ML. These surrogate models need to be coupled with the dynamical core of ESMs to investigate the effectiveness and their performance in a coupled system. In this study, we present a novel Fortran–Python interface designed to seamlessly integrate ML parameterizations into ESMs. This interface showcases high versatility by supporting popular ML frameworks like PyTorch, TensorFlow, and scikit-learn. We demonstrate the interface's modularity and reusability through two cases: an ML trigger function for convection parameterization and an ML wildfire model. We conduct a comprehensive evaluation of memory usage and computational overhead resulting from the integration of Python codes into the Fortran ESMs. By leveraging this flexible interface, ML parameterizations can be effectively developed, tested, and integrated into ESMs.
54 ENVIRONMENTAL SCIENCES↗
A Fortran-Python Interface for Integrating Machine Learning Parameterization into Earth System Models
Parameterizations in Earth System Models (ESMs) are subject to biases and uncertainties arising from subjective empirical assumptions and incomplete understanding of the underlying physical processes. Recently, the growing representational capability of machine learning (ML) in solving complex problems has spawned immense interests in climate science applications. Specifically, ML-based parameterizations have been developed to represent convection, radiation and microphysics processes in ESMs by learning from observations or high-resolution simulations, which have the potential to improve the accuracies and alleviate the uncertainties. Previous works have developed some surrogate models for these processes using ML. These surrogate models need to be coupled with the dynamical core of ESMs to investigate the effectiveness and their performance in a coupled system. In this study, we present a novel Fortran-Python interface designed to seamlessly integrate ML parameterizations into ESMs. This interface showcases high versatility by supporting popular ML frameworks like PyTorch, TensorFlow, and Scikit-learn. We demonstrate the interface's modularity and reusability through two cases: a ML trigger function for convection parameterization and a ML wildfire model. We conduct a comprehensive evaluation of memory usage and computational overhead resulting from the integration of Python codes into the Fortran ESMs. By leveraging this flexible interface, ML parameterizations can be effectively developed, tested, and integrated into ESMs.
54 ENVIRONMENTAL SCIENCES↗
A2SD: Accelerating Scientific Innovation Through Autonomous Discovery Systems
The 2025 Advancing Autonomous Scientific Discovery (A2SD) workshop convened researchers from academia, national laboratories, and industry to explore the transformative role of autonomy in scientific discovery. The workshop highlighted a convergence of artificial intelligence, robotics, and computational workflows into autonomous systems capable of accelerating the scientific process. Presentations and discussions spanned autonomous experimentation, intelligent workflow orchestration, digital twins, and agent-based systems for managing complex research ecosystems. Key challenges discussed included interoperability across heterogeneous infrastructures, near real-time data management under FAIR principles, reproducibility, and the integration of human oversight. The workshop also emphasized the need for modular software interfaces, federated learning models, and education initiatives to support a next-generation scientific workforce.
Taufer, Michela [University of Tennessee, Knoxvill↗
Lessons Learned from Exploring Safety, Security, and Safeguards Interfaces in Advanced and Small Modular Reactors.
Abstract not provided.
Williams, Adam↗
LESSONS LEARNED FROM EXPLORING SAFETY, SECURITY, AND SAFEGUARDS INTERFACES IN ADVANCED AND SMALL MODULAR REACTOR TECHNOLOGIES.
Abstract not provided.
Williams, Adam↗
SQMBox: Interfacing a semiempirical integral library to modular ab initio electronic structure enables new semiempirical methods
Ab initio and semiempirical electronic structure methods are usually implemented in separate software packages or use entirely different code paths. As a result, it can be time-consuming to transfer an established ab initio electronic structure scheme to a semiempirical Hamiltonian. Here we present an approach to unify ab initio and semiempirical electronic structure code paths based on a separation of the wavefunction ansatz and the needed matrix representations of operators. With this separation, the Hamiltonian can refer to either an ab initio or semiempirical treatment of the resulting integrals. We built a semiempirical integral library and interfaced it to the GPU-accelerated electronic structure code TeraChem. Equivalency between ab initio and semiempirical tight-binding Hamiltonian terms is assigned according to their dependence on the one-electron density matrix. The new library provides semiempirical equivalents of the Hamiltonian matrix and gradient intermediates, corresponding to those provided by the ab initio integral library. This enables the straightforward combination of semiempirical Hamiltonians with the full pre-existing ground and excited state functionality of the ab initio electronic structure code. We demonstrate the capability of this approach by combining the extended tight-binding method GFN1-xTB with both spin-restricted ensemble-referenced Kohn–Sham and complete active space methods. We also present a highly efficient GPU implementation of the semiempirical Mulliken-approximated Fock exchange. The additional computational cost for this term becomes negligible even on consumer-grade GPUs, enabling Mulliken-approximated exchange in tight-binding methods for essentially no additional cost.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗