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At least 199 records · Page 11

A complex ligase ribozyme evolved in vitro from a group I ribozyme domain

Like most proteins, complex RNA molecules often are modular objects made up of distinct structural and functional domains. The component domains of a protein can associate in alternative combinations to form molecules with different functions. These observations raise the possibility that complex RNAs also can be assembled from preexisting structural and functional domains. To test this hypothesis, an in vitro evolution procedure was used to isolate a previously undescribed class of complex ligase ribozymes, starting from a pool of 10(16) different RNA molecules that contained a constant region derived from a large structural domain that occurs within self-splicing group I ribozymes. Attached to this constant region were three hypervariable regions, totaling 85 nucleotides, that gave rise to the catalytic motif within the evolved catalysts. The ligase ribozymes catalyze formation of a 3',5'-phosphodiester linkage between adjacent template-bound oligonucleotides, one bearing a 3' hydroxyl and the other a 5' triphosphate. Ligation occurs in the context of a Watson-Crick duplex, with a catalytic rate of 0.26 min(-1) under optimal conditions. The constant region is essential for catalytic activity and appears to retain the tertiary structure of the group I ribozyme. This work demonstrates that complex RNA molecules, like their protein counterparts, can share common structural domains while exhibiting distinct catalytic functions.

NASA Discipline Exobiology↗

Intelligent Systems Approach for Automated Identification of Individual Control Behavior of a Human Operator

Results have been obtained using conventional techniques to model the generic human operator?s control behavior, however little research has been done to identify an individual based on control behavior. The hypothesis investigated is that different operators exhibit different control behavior when performing a given control task. Two enhancements to existing human operator models, which allow personalization of the modeled control behavior, are presented. One enhancement accounts for the testing control signals, which are introduced by an operator for more accurate control of the system and/or to adjust the control strategy. This uses the Artificial Neural Network which can be fine-tuned to model the testing control. Another enhancement takes the form of an equiripple filter which conditions the control system power spectrum. A novel automated parameter identification technique was developed to facilitate the identification process of the parameters of the selected models. This utilizes a Genetic Algorithm based optimization engine called the Bit-Climbing Algorithm. Enhancements were validated using experimental data obtained from three different sources: the Manual Control Laboratory software experiments, Unmanned Aerial Vehicle simulation, and NASA Langley Research Center Visual Motion Simulator studies. This manuscript also addresses applying human operator models to evaluate the effectiveness of motion feedback when simulating actual pilot control behavior in a flight simulator.

Zaychik, Kirill B.↗

Structural mode coupling in perovskite oxides using hypothesis-driven active learning

Abstract Finding the ground-state structure with minimum energy is paramount to designing any material. In ABO 3 -type perovskite oxides with Pnma symmetry, the lowest energy phase is driven by an inherent trilinear coupling between the two primary order parameters such as rotation and tilt with antiferroelectric displacement of the A-site cations as established via hybrid improper ferroelectric mechanism. Conventionally, finding the relevant mode coupling driving phase transition requires performing first-principles calculations which is computationally time-consuming as well as expensive. It involves following an intuitive iterative hit and trial method of (a) adding two or multiple mode vectors, followed by (b) evaluating which combination would lead to the ground-state energy. In this study, we show how a hypothesis-driven active learning framework can identify suitable mode couplings within the Landau free energy expansion with minimal information on amplitudes of modes for a series of double perovskite oxides with A-site layered, columnar and rocksalt ordering. This scheme is expected to be applicable universally for understanding atomistic mechanisms derived from various structural mode couplings behind functionalities, for e.g. polarization, magnetization and metal–insulator transitions.

36 MATERIALS SCIENCE↗

High Power Density Thermal Energy Storage With Phase Change Material in Enhanced Compact Heat Exchangers

Abstract Performance of a novel ultracompact thermal energy storage (TES) heat exchanger, designed as a microchannel finned-tube exchanger is presented. With water as the heating–cooling fluid in the microchannels, a salt hydrate phase change material (PCM), lithium nitrate trihydrate (LiNO3 · 3H2O), was encased on the fin side. To establish the hypothesis that small-length-scale encasement (<3 mm) of PCM substantially enhances heat transfer to yield very high power-density energy storage, heat exchanger designs with 10 and 24 fins/inch were considered. They were subjected to thermal cycling, or repeated heating (melting) and cooling (freezing), with inlet fluid flow mimicking diurnal variation between 42 °C and 25 °C (representing typical arid-region conditions) over an accelerated time period. By employing salt self-seeding to obviate subcooling during cooling or recrystallization, the TES was found to exhibit stable long-term (100 heating–cooling cycles) operation with very high PCM-side heat transfer coefficients (∼100–500 W/m2 K) and storage power density (∼160–175 kW/m3). In fact, with optimization of heating–cooling fluid flowrate for given charging–discharging time period and exchanger size, power density >300 kW/m3 can be achieved. The results clearly establish that highly compact heat exchangers used as TES units can provide very high-performance alternatives to conventional ones.

Engineering↗

Independent Fuel Property Effects of Fuel Volatility on Low Temperature Heat Release and Fuel Autoignition (Final Report)

This Cooperative Research and Development Agreement (CRADA) project between Argonne National Laboratory (ANL), Oak Ridge National Laboratory (ORNL), and Shell Global Solutions (Shell) was initiated as part of a Directed Funding Opportunity (DFO) call for proposals from the Co-Optimization of Fuels and Engines (Co-Optima) initiative. Shell had observed that volatile fuels suppress low temperature heat release (LTHR) more than expected based on conventional gasoline autoignition metrics: research octane number (RON) and motor octane number (MON). The role of LTHR contributes to autoignition phenomena for both boosted spark ignition (BSI) and advanced compression ignition (ACI) combustion modes. ACI combustion modes are applicable to large engines in the hard-to-electrify applications such as off-road, rail, and marine. Thus, having a reliable understanding of autoignition phenomena, including being able to accurately account for the effects of fuel volatility, is particularly important as new synthetic and bio-fuel compositions are considered.This CRADA project aimed to test the hypothesis that the decreased LTHR is due to preferential evaporation of multicomponent fuels when using direct injection (DI) fueling technology, creating composition and reactivity stratification. A custom set of fuels was designed and blended to test this hypothesis by Shell, with experimental engine studies at ORNL and engine combustion modeling by ANL. However, the initial experimental findings did not show the expected effect of fuel volatility suppressing LTHR. Instead, the LTHR propensity observed was independent of the fuel volatility. Due to the unexpected experimental result, the remainder of the experimental effort was redirected to study the effect of fuel volatility on emissions under spark-ignited cold-start conditions. However, as with the LTHR experiments, the cold start effort did not show a meaningful effect of fuel volatility on cold start emissions. Meanwhile, improved engine CFD models have been developed for both LTHR and cold start operations for the Shell fuels with different volatilities. While the simulation efforts were not pursued further due to the insignificant effects of fuel volatility as shown in experiments, the models developed can be easily retooled for off-road, rail, and marine applications.

09 BIOMASS FUELS↗

Comprehensive geoneutrino analysis with Borexino

This paper presents a comprehensive geoneutrino measurement using the Borexino detector, located at Laboratori Nazionali del Gran Sasso (LNGS) in Italy. The analysis is the result of 3262.74 days of data between December 2007 and April 2019. The paper describes improved analysis techniques and optimized data selection, which includes enlarged fiducial volume and sophisticated cosmogenic veto. The reported exposure of ( 1.29 ± 0.05 ) × 10 32 protons × year represents an increase by a factor of two over a previous Borexino analysis reported in 2015. By observing 52.6 - 8.6 + 9.4 ( stat ) - 2.1 + 2.7 ( sys ) geoneutrinos (68% interval) from U 238 and Th 232 , a geoneutrino signal of 47.0 - 7.7 + 8.4 ( stat ) - 1.9 + 2.4 ( sys ) TNU with - 17.2 + 18.3 % total precision was obtained. This result assumes the same Th/U mass ratio as found in chondritic CI meteorites but compatible results were found when contributions from U 238 and Th 232 were both fit as free parameters. Antineutrino background from reactors is fit unconstrained and found compatible with the expectations. The null-hypothesis of observing a geoneutrino signal from the mantle is excluded at a 99.0% C.L. when exploiting detailed knowledge of the local crust near the experimental site. Measured mantle signal of 21.2 - 9.0 + 9.5 ( stat ) - 0.9 + 1.1 ( sys ) TNU corresponds to the production of a radiogenic heat of 24.6 - 10.4 + 11.1 TW (68% interval) from U 238 and Th 232 in the mantle. Assuming 18% contribution of K 40 in the mantle and 8.1 - 1.4 + 1.9 TW of total radiogenic heat of the lithosphere, the Borexino estimate of the total radiogenic heat of the Earth is 38.2 - 12.7 + 13.6 TW , which corresponds to the convective Urey ratio of 0.78 - 0.28 + 0.41 . These values are compatible with different geological predictions, however there is a ~ 2.4 σ tension with those Earth models which predict the lowest concentration of heat-producing elements in the mantle. In addition, by constraining the number of expected reactor antineutrino events, the existence of a hypothetical georeactor at the center of the Earth having power greater than 2.4 TW is excluded at 95% C.L. Particular attention is given to the description of all analysis details which should be of interest for the next generation of geoneutrino measurements using liquid scintillator detectors.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Trust Model System for the Energy Grid of Things Network Communications

Network communication is crucial in the Energy Grid of Things (EGoT). Without a network connection, the energy grid becomes just a power grid where the energy resources are available to the customer uni-directionally. A mechanism to analyze and optimize the energy usage of the grid can only happen through a medium, a communications network, that enables information exchange between the grid participants and the service provider. Security implementers of EGoT network communication take extraordinary measures to ensure the safety of the energy grid, a critical infrastructure, as well as the safety and privacy of the grid participants. With the dynamic nature of network communication of the EGoT, the information provided by the customer or the service provider can be falsified by a malicious attacker. Therefore, a trust model is necessary to monitor any abnormal activities. This paper describes a distributed trust model system that meets the need of the EGoT. This paper describes methods for evaluating and improving the distributed trust model using standard hypothesis testing metrics such as true positive, false positive, true negative, false negative, equal error rate, and F1 score. Example calculations are shown based on generated sample data.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

A Guideline to Mitigate Interfacial Degradation Processes in Solid‐State Batteries Caused by Cross Diffusion

Abstract Diffusion of transition metals across the cathode–electrolyte interface is identified as a key challenge for the practical realization of solid‐state batteries. This is related to the formation of highly resistive interphases impeding the charge transport across the materials. Herein, the hypothesis that formation of interphases is associated with the incorporation of Co into the Li 7 La 3 Zr 2 O 12 lattice representing the starting point of a cascade of degradation processes is investigated. It is shown that Co incorporates into the garnet structure preferably four‐fold coordinated as Co 2+ or Co 3+ depending on oxygen fugacity. The solubility limit of Co is determined to be around 0.16 per formula unit, whereby concentrations beyond this limit causes a cubic‐to‐tetragonal phase transition. Moreover, the temperature‐dependent Co diffusion coefficient is determined, for example, D 700 °C = 9.46 × 10 −14 cm 2 s −1 and an activation energy E a = 1.65 eV, suggesting that detrimental cross diffusion will take place at any relevant process condition. Additionally, the optimal protective Al 2 O 3 coating thickness for relevant temperatures is studied, which allows to create a process diagram to mitigate any degradation with a minimum compromise on electrochemical performance. This study provides a tool to optimize processing conditions toward developing high energy density solid‐state batteries.

Chemistry↗

A Faster Triphosphorylation Ribozyme

In support of the RNA world hypothesis, previous studies identified trimetaphosphate (Tmp) as a plausible energy source for RNA world organisms. In one of these studies, catalytic RNAs (ribozymes) that catalyze the triphosphorylation of RNA 5'-hydroxyl groups using Tmp were obtained by in vitro selection. One ribozyme (TPR1) was analyzed in more detail. TPR1 catalyzes the triphosphorylation reaction to a rate of 0.013 min -1 under selection conditions (50 mM Tmp, 100 mM MgCl 2 , 22°C). To identify a triphosphorylation ribozyme that catalyzes faster triphosphorylation, and possibly learn about its secondary structure TPR1 was subjected to a doped selection. The resulting ribozyme, TPR1e, contains seven mutations relative to TPR1, displays a previously unidentified duplex that constrains the ribozyme's structure, and reacts at a 24-fold faster rate than the parent ribozyme. Under optimal conditions (150 mM Tmp, 650 mM MgCl 2 , 40°C), the triphosphorylation rate of TRP1e reaches 6.8 min -1 .

Gregory F. Dolan↗

Hypothesis testing via AI: Generating physically interpretable models of scientific data with machine learning (Full Technical Report)

Deep learning has demonstrated an exceptional ability to solve complex tasks (an engineering success); however, it has done so at the expense of the ability to generate new knowledge (a scientific failure). We propose an alternative framework—entitled Deep Symbolic Regression (DSR)—in which artificial neural networks (NNs) rapidly generate hypotheses about physical relationships among inputs. This framework bypasses the need to interpret an NN altogether, while still leveraging the representational power of deep learning. The resulting models are tractable mathematical expressions, which are inherently and readily human interpretable and can provide insights into underlying physical phenomena. Further, we fold this methodology into the scientific process by allowing the scientist to directly integrate a priori knowledge and beliefs to accelerate learning. We demonstrate this methodology on symbolic regression—the problem of rediscovering underlying expressions describing a dataset—and achieve state-of-the-art performance across a wide variety of symbolic regression problems. Further, we generalize our DSR framework to apply to the more general class of symbolic optimization problems, in which one seeks to optimize a sequence of symbols or “tokens” under a black-box reward function. Examples of other symbolic optimization problems include neural architecture search and computational antibody design. Our generalized tool, Deep Symbolic Optimization (DSO), has been demonstrated on the task of learning symbolic control policies for reinforcement learning environments, and has been adopted as an enabling capability for computational antibody design.

97 MATHEMATICS AND COMPUTING↗

Declines in ice cover are accompanied by light limitation responses and community change in freshwater diatoms

The rediscovery of diatom blooms embedded within and beneath the Lake Erie ice cover (2007–2012) ignited interest in psychrophilic adaptations and winter limnology. Subsequent studies determined the vital role ice plays in winter diatom ecophysiology as diatoms partition to the underside of ice, thereby fixing their location within the photic zone. Yet, climate change has led to widespread ice decline across the Great Lakes, with Lake Erie presenting a nearly “ice-free” state in several recent winters. It has been hypothesized that the resultant turbid, isothermal water column induces light limitation amongst winter diatoms and thus serves as a competitive disadvantage. To investigate this hypothesis, we conducted a physiochemical and metatranscriptomic survey that spanned spatial, temporal, and climatic gradients of the winter Lake Erie water column (2019–2020). Our results suggest that ice-free conditions decreased planktonic diatom bloom magnitude and altered diatom community composition. Diatoms increased their expression of various photosynthetic genes and iron transporters, which suggests that the diatoms are attempting to increase their quantity of photosystems and light-harvesting components (a well-defined indicator of light limitation). We identified two gene families which serve to increase diatom fitness in the turbid ice-free water column: proton-pumping rhodopsins (a potential second means of light-driven energy acquisition) and fasciclins (a means to “raft” together to increase buoyancy and co-locate to the surface to optimize light acquisition). With large-scale climatic changes already underway, our observations provide insight into how diatoms respond to the dynamic ice conditions of today and shed light on how they will fare in a climatically altered tomorrow.

54 ENVIRONMENTAL SCIENCES↗

The future of self-driving laboratories: from human in the loop interactive AI to gamification

Recent developments in artificial intelligence (AI) and machine learning (ML), implemented through self-driving laboratories (SDLs), are rapidly creating unprecedented opportunities for the accelerated discovery and optimization of materials. This paper provides a joint analysis of SDLs from both academic and industry perspectives, highlighting the importance of integrating human intelligence in these systems. It discusses the necessity of careful planning in SDL design across physical, data, and workflow dimensions, including instrumental setup, experimental workflow, data management, and human–SDL interaction. The significance of integrating human input within SDLs, especially as the focus shifts from individual tools and tasks to the creation and management of complex workflows, is emphasized. The paper stresses on the crucial role of reward function design in developing forward-looking workflows and examines the interplay between hardware evolution, ML application across chemical processes, and the influence of reward systems in research. Ultimately, the article advocates for a future where SDLs blend human intuition in hypothesis formulation with AI's precision, speed, and data-handling capabilities.

97 MATHEMATICS AND COMPUTING↗

Optimizing the Design of Diatom Biosilica-Targeted Fusion Proteins in Biosensor Construction for Bacillus anthracis Detection

In vivo functionalization of diatom biosilica frustules by genetic manipulation requires careful consideration of the overall structure and function of complex fusion proteins. Although we previously had transformed Thalassiosira pseudonana with constructs containing a single domain antibody (sdAb) raised against the Bacillus anthracis Sterne strain, which detected an epitope of the surface layer protein EA1 accessible in lysed spores, we initially were unsuccessful with constructs encoding a similar sdAb that detected an epitope of EA1 accessible in intact spores and vegetative cells. This discrepancy limited the usefulness of the system as an environmental biosensor for B. anthracis. We surmised that to create functional biosilica-localized biosensors with certain constructs, the biosilica targeting and protein trafficking functions of the biosilica-targeting peptide Sil3T8 had to be uncoupled. We found that retaining the ER trafficking sequence at the N-terminus and relocating the Sil3T8 targeting peptide to the C-terminus of the fusion protein resulted in successful detection of EA1 with both sdAbs. Homology modeling of antigen binding by the two sdAbs supported the hypothesis that the rescue of antigen binding in the previously dysfunctional sdAb was due to removal of steric hindrances between the antigen binding loops and the diatom biosilica for that particular sdAb.

59 BASIC BIOLOGICAL SCIENCES↗

Hypothesis tests on Rayleigh wave radiation pattern shapes: a theoretical assessment of idealized source screening

SUMMARY Shallow seismic sources excite Rayleigh wave ground motion with azimuthally dependent radiation patterns. We place binary hypothesis tests on theoretical models of such radiation patterns to screen cylindrically symmetric sources (like explosions) from non-symmetric sources (like non-vertical dip-slip or non-VDS faults). These models for data include sources with several unknown parameters, contaminated by Gaussian noise and embedded in a layered half-space. The generalized maximum likelihood ratio tests that we derive from these data models produce screening statistics and decision rules that depend on measured, noisy ground motion at discrete sensor locations. We explicitly quantify how the screening power of these statistics increase with the size of any dip-slip and strike-slip components of the source, relative to noise (faulting signal strength) and how they vary with network geometry. As applications of our theory, we apply these tests to (1) find optimal sensor locations that maximize the probability of screening non-circular radiation patterns and (2) invert for the largest non-VDS faulting signal that could be mistakenly attributed to an explosion with damage, at a particular attribution probability. Finally, we quantify how certain errors that are sourced by opening cracks increase screening rate errors. While such theoretical solutions are ideal and require future validation, they remain important in underground explosion monitoring scenarios because they provide fundamental physical limits on the discrimination power of tests that screen explosive from non-VDS faulting sources.

58 GEOSCIENCES↗

Regression Analysis of Top of Descent Location for Idle-thrust Descents

In this paper, multiple regression analysis is used to model the top of descent (TOD) location of user-preferred descent trajectories computed by the flight management system (FMS) on over 1000 commercial flights into Melbourne, Australia. The independent variables cruise altitude, final altitude, cruise Mach, descent speed, wind, and engine type were also recorded or computed post-operations. Both first-order and second-order models are considered, where cross-validation, hypothesis testing, and additional analysis are used to compare models. This identifies the models that should give the smallest errors if used to predict TOD location for new data in the future. A model that is linear in TOD altitude, final altitude, descent speed, and wind gives an estimated standard deviation of 3.9 nmi for TOD location given the trajec- tory parameters, which means about 80% of predictions would have error less than 5 nmi in absolute value. This accuracy is better than demonstrated by other ground automation predictions using kinetic models. Furthermore, this approach would enable online learning of the model. Additional data or further knowl- edge of algorithms is necessary to conclude definitively that no second-order terms are appropriate. Possible applications of the linear model are described, including enabling arriving aircraft to fly optimized descents computed by the FMS even in congested airspace. In particular, a model for TOD location that is linear in the independent variables would enable decision support tool human-machine interfaces for which a kinetic approach would be computationally too slow.

trajectory prediction↗

The Future of Astronaut Nutrition: Daily Production of Kefir Nutrient Packs in a Lunar Analog Mission

Astronaut nutrition will need to be supplemented with fresh nutrients and probiotics for long duration space travel. Kefir is a fermented milk beverage that provides probiotic bacteria, protein, calcium, and vitamin K. It has been historically cultured for thousands of years and is a modern-day health food. During the BioNutrients-2 experiment, one generation of kefir was cultured by astronauts on the ISS in fluorinated ethylene propylene (FEP) bags. This follow-on experiment aimed to test the hypothesis that kefir can be produced at ambient temperatures and safely passaged to make a series of nutrient packs in an analog environment. The Hawaiian Space Exploration Analog and Simulation (HI-SEAS) is a semi-controlled habitat located on the Mauna Loa volcano on the island of Hawaii at 2,500 m elevation. The recent EMMIHS (EuroMoonMars, International MoonBase Alliance, HI-SEAS) missions comprised of interdisciplinary, international crews for mock lunar missions. During a six-day mission in March 2024, crew members tested continuous passaging of kefir cultures (C-FIR commercial strain). After approximately 24 hours of growth, a portion of culture was used to seed the next generation of culture bags for four passages. A color board and pH indicator allowed the crew to easily determine when the culture had reached optimal pH. Laboratory analysis at NASA Ames Research Center showed that the pH met Kefir standards (<5). The yeast and lactic acid bacteria were in normal range and no contamination was detected in the final passage. By successfully accomplishing this experiment the team has demonstrated the ability to safely produce daily cultures of kefir in a microgravity applicable growth system.

Astronaut nutrition↗

Search for heavy particles in the b -tagged dijet mass distribution with additional b -tagged jets in proton-proton collisions at $\sqrt{s}$ = 13 TeV with the ATLAS experiment

A search optimized for new heavy particles decaying to two b-quarks and produced in association with additional b-quarks is reported. The sensitivity is improved by b-tagging at least one lower-p T jet in addition to the two highest-p T jets. The data used in this search correspond to an integrated luminosity of 103 fb –1 collected with a dedicated trijet trigger during the 2017 and 2018 √s = 13 TeV proton–proton collision runs with the ATLAS detector at the LHC. The search looks for resonant peaks in the b-tagged dijet invariant mass spectrum over a smoothly falling background. The background is estimated with an innovative data-driven method based on orthonormal functions. The observed b-tagged dijet invariant mass spectrum is compatible with the background-only hypothesis. Upper limits at 95% confidence level on a heavy vector-boson production cross section times branching ratio to a pair of b-quarks are derived.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Mesospheric heating due to convectively excited gravity waves - A case study

A series of at least daily rocket soundings of the mesosphere at Wallops Island, Virginia (37 deg 50 min N, 75 deg 29 min W), in August and September 1976 reveal near simultaneity between rapid temperature rises and tropospheric convection in the form of squall lines. A multilevel numerical model is developed to test the hypothesis that the convection and warmings are related via internal gravity waves. Some features of the model are (1) the wave energy source is expressed in terms of cloud-base mass flux, plume diameter and buoyant updraft velocity; (2) the turbulent-viscous gravity wave dissipation is limited to above 55 km and is parameterized on the basis of findings by Hines (1965). Significant findings are: (1) mesospheric heating rates of the same order as those observed result for reasonable values of the convective parameters and in situ dissipation time scales; (2) only gravity waves confined to a well-defined wavelength and frequency interval are able to propagate upward to mesospheric altitudes; (3) heating rates are strongly dependent on plume diameter and updraft velocity; and (4) for a given cloud-base mass flux, heating rates are optimized for a plume updraft velocity of 10 m/s.

Clark, J. H. E.↗