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RNA language models predict mutations that improve RNA function
Structured RNA lies at the heart of many central biological processes, from gene expression to catalysis. RNA structure prediction is not yet possible due to a lack of high-quality reference data associated with organismal phenotypes that could inform RNA function. We present GARNET (Gtdb Acquired RNa with Environmental Temperatures), a new database for RNA structural and functional analysis anchored to the Genome Taxonomy Database (GTDB). GARNET links RNA sequences to experimental and predicted optimal growth temperatures of GTDB reference organisms. Using GARNET, we develop sequence- and structure-aware RNA generative models, with overlapping triplet tokenization providing optimal encoding for a GPT-like model. Leveraging hyperthermophilic RNAs in GARNET and these RNA generative models, we identify mutations in ribosomal RNA that confer increased thermostability to the Escherichia coli ribosome. The GTDB-derived data and deep learning models presented here provide a foundation for understanding the connections between RNA sequence, structure, and function.
Author Correction: A universal language for finding mass spectrometry data patterns
Correction to: Nature Methodshttps://doi.org/10.1038/s41592-025-02660-z, published online 12 May 2025. This article was originally published under standard Springer Nature license (© The Author(s), under exclusive licence to Springer Nature America, Inc.). It is now available as an open-access paper under a Creative Commons Attribution 4.0 International license, © The Author(s). The error has been corrected in the HTML and PDF versions of the article.
Information extraction from historical well records using a large language model
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Solving tricky quantum optics problems with assistance from large language models
The capabilities of modern artificial intelligence (AI) as a “scientific collaborator” are explored by engaging it with three nuanced problems in quantum optics: state populations in optical pumping, resonant transitions between decaying states (the Burshtein effect), and degenerate mirrorless lasing. Through iterative dialogue, the authors observe that AI models–when prompted and corrected–can reason through complex scenarios, refine their answers, and provide expert-level guidance, closely resembling the interaction with an adept colleague. The findings highlight that AI can democratize access to sophisticated modeling and analysis, shifting the focus in scientific practice from technical mastery to the generation and testing of ideas, and reducing the time for completing research tasks from days to minutes.
Back Cover image and language for "Evaluating the Bis-isoxazole Core for Energetic Heterocyclic-Based Oligomers"
Development of energetic polymers is at the forefront of the energetics research enterprise. Formulations having high energy density as well as robust safety are goals that can be realized with implementation of new energetic polymers that target performance and mechanical properties. Presented herein are a series of oligomers synthesized from the small molecule BIDO (1) as our group's first attempt at making heterocyclic based energetic polymers without sensitive explosophores. Crystallographic investigation of the BIDO core with surrogate addends demonstrated the significant stacking ability of the BIDO core. Synthesis of four unique oligomers (3–6) is presented and characterized by spectroscopic and thermochemical measurements. Polymerization of 6 was demonstrated and tested for T g and thermal sensitivity, and Shore A hardness. The polymer 7 exhibits a T g value (-18 °C), close to the operating range of other energetic materials, a Shore A hardness of 81, and thermally stable up to 320 °C.
ProtAgents: protein discovery via large language model multi-agent collaborations combining physics and machine learning
ProtAgents is a de novo protein design platform based on multimodal LLMs, where distinct AI agents with expertise in knowledge retrieval, protein structure analysis, physics-based simulations, and results analysis tackle tasks in a dynamic setting.
On the Language of Reliability: A System Engineer Perspective
In its classical definition, risk is defined by three elements: what can go wrong, what are its consequences, and how likely is it to occur? While this definition makes sense in a regulatory-based framework where for the current fleet of operating light water reactors (LWRs), the risks associated with nuclear power plants typically are characterized in terms of core damage and large early release frequency (LERF), this approach does not provide a useful snapshot of the health of the plant from a broader perspective. This is due to the very narrow context in which the term “risk” typically is defined as nuclear safety aspects that have the potential to impact public health. In this paper, we take the viewpoint of nuclear safety that is reflective of the current fleet of operating LWRs for which core damage frequency and LERF are appropriate metrics. For other advanced reactor designs, other more applicable technology neutral metrics of reactor safety metrics would be specified. A possible alternate path would start by redefining the word risk with a broader meaning that better reflects the needs of a system health and asset management decision-making process. Rather than asking how likely an event could occur (in probabilistic terms), we can ask how far this event is from occurring. Our approach starts by defining and quantifying component and system health in terms of a “distance” between its actual and limiting conditions, i.e., determination of the margin that exists between the current state/condition and the state where the component/system is no longer capable of achieving its intended function. A margin is a measure that is more reflective of the current state or performance of a component, and therefore more closely tied to decisions that are made on an ongoing basis. Further, we will show how, given the data available from plant equipment reliability and monitoring (e.g., pump vibration data) and prognostic (e.g., component remaining useful life estimation) data, a margin can be described and determined for all types of maintenance approaches (e.g., corrective or predictive maintenance). We show how classical reliability models (e.g., fault trees) can be used to quantify the system margin provided component margin values. In the approach described in this paper, the propagation of margin values through classical reliability models are not performed using classical probabilistic calculations applied to sets (as performed in a typical plant probabilistic risk assessment). Instead, we show how it is possible to propagate margin values through Boolean logic gates (i.e., AND and OR operators) through distance-based operations.
Mining experimental data from materials science literature with large language models: an evaluation study
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Quantum corrections for the cubic Galileon in the covariant language
We present for the first time an explicit exposition of quantum corrections within the cubic Galileon theory including the effect of quantum gravity, in a background- and gauge-invariant manner, employing the field-reparametrisation approach of the covariant effective action at 1-loop. We show that the consideration of gravitational effects in combination with the non-linear derivative structure of the theory reveals new interactions at the perturbative level, which manifest themselves as higher-operators in the associated effective action, which' relevance is controlled by appropriate ratios of the cosmological vacuum and the Galileon mass scale. The significance and concept of the covariant approach in this context is discussed, while all calculations are explicitly presented.
The abstract geometry modeling language (AgML): experience and road map toward eRHIC
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Studying language evolution in the age of big data
The increasing availability of large digital corpora of cross-linguistic data is revolutionizing many branches of linguistics. Overall, it has triggered a shift of attention from detailed questions about individual features to more global patterns amenable to rigorous, but statistical, analyses. This engenders an approach based on successive approximations where models with simplified assumptions result in frameworks that can then be systematically refined, always keeping explicit the methodological commitments and the assumed prior knowledge. Therefore, they can resolve disputes between competing frameworks quantitatively by separating the support provided by the data from the underlying assumptions. These methods, though, often appear as a ‘black box’ to traditional practitioners. In fact, the switch to a statistical view complicates comparison of the results from these newer methods with traditional understanding, sometimes leading to misinterpretation and overly broad claims. We describe here this evolving methodological shift, attributed to the advent of big, but often incomplete and poorly curated, data, emphasizing the underlying similarity of the newer quantitative to the traditional comparative methods and discussing when and to what extent the former have advantages over the latter. In this review, we cover briefly both randomization tests for detecting patterns in a largely model-independent fashion and phylolinguistic methods for a more model-based analysis of these patterns. We foresee a fruitful division of labor between the ability to computationally process large volumes of data and the trained linguistic insight declaring worthy prior commitments and interesting hypotheses in need of comparison.
Exaggerated Plurivory of Macrophomina phaseolina : Accounting for the Large Host Range Claim and the Shifting of Scientific Language
Macrophomina phaseolina is a plant pathogenic fungus that is frequently described as having a broad host range encompassing more than 500 species. We noticed that citations provided in support of this statement do not actually demonstrate such a broad host range. To elucidate the true documented host range of this fungus, we initiated a literature meta-analysis of 894 publications on M. phaseolina since 1913. We discovered that the first host range summaries did not require Koch's postulates or other experimental demonstrations of pathogenicity. Most of the available early host claims were based on tenuous associations between the fungus and symptoms, sometimes without reporting isolation or morphological examination in vitro. These statements apparently led to a pattern of increasingly exaggerated host range claims, without support from a primary reference, until the claim that M. phaseolina has 500 hosts became common in the early 2000s. At present, the scientific community typically requires Koch's postulates to characterize pathogenicity on a new host. Among all the available literature, we only found primary experimental evidence for M. phaseolina’s pathogenicity on 97 hosts; 74 hosts confirmed by Koch's postulates and 23 hosts with all steps from Koch's postulates completed except for recovery of the pathogen from symptomatic tissues. This study demonstrates how scientific concepts can change over time and necessitate changes to historic axioms. We propose that the hyperbole surrounding the host range of M. phaseolina has obscured an accurate depiction of its biology.
MPNet-GRUs: Sentiment Analysis With Masked and Permuted Pre-Training for Language Understanding and Gated Recurrent Units
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Evaluating Large Language Models for Enhanced Fuzzing: An Analysis Framework for LLM-Driven Seed Generation
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