Showing papers by "Oak Ridge National Laboratory published in 2023"

AI-Accelerated Design of Targeted Covalent Inhibitors for SARS-CoV-2

Abstract: Direct-acting antivirals for the treatment of the COVID-19 pandemic caused by the SARS-CoV-2 virus are needed to complement vaccination efforts. Given the ongoing emergence of new variants, automated experimentation, and active learning based fast workflows for antiviral lead discovery remain critical to our ability to address the pandemic's evolution in a timely manner. While several such pipelines have been introduced to discover candidates with noncovalent interactions with the main protease (Mpro), here we developed a closed-loop artificial intelligence pipeline to design electrophilic warhead-based covalent candidates. This work introduces a deep learning-assisted automated computational workflow to introduce linkers and an electrophilic "warhead" to design covalent candidates and incorporates cutting-edge experimental techniques for validation. Using this process, promising candidates in the library were screened, and several potential hits were identified and tested experimentally using native mass spectrometry and fluorescence resonance energy transfer (FRET)-based screening assays. We identified four chloroacetamide-based covalent inhibitors of Mpro with micromolar affinities (KI of 5.27 μM) using our pipeline. Experimentally resolved binding modes for each compound were determined using room-temperature X-ray crystallography, which is consistent with the predicted poses. The induced conformational changes based on molecular dynamics simulations further suggest that the dynamics may be an important factor to further improve selectivity, thereby effectively lowering KI and reducing toxicity. These results demonstrate the utility of our modular and data-driven approach for potent and selective covalent inhibitor discovery and provide a platform to apply it to other emerging targets.

Numerical assessment of fuel physical properties on high-dilution diesel advanced compression ignition combustion

Abstract: Using traditional lean air-fuel mixture aftertreatment technologies to reduce NOx at low exhaust temperatures is limited below 175°C. Recent low engine load NOx emission regulations encouraged researchers to explore the potential of fuel physical properties to enable low-temperature, low-NOx combustion under challenging conditions in which the selective catalytic reduction device might be unable to treat engine-out NOx. Three diesel range fuels were developed to explore how the distillation profile affects and may enable low-load diesel advanced-compression ignition. The least volatile fuel is a conventional, commercially available diesel #2 fuel with a cetane number of 45.3. The second-most volatile fuel is diesel #1 fuel, which can also be found commercially. The last fuel, which is the most volatile, is a new formulation designed to target a final boiling point approximately 50 K below diesel #1 fuel and was termed diesel #0 fuel. A computational fluid dynamics model was developed and validated against experimental data to investigate the underlying effects of each of the developed fuels in the combustion process. Finally, each fuel physical property (e.g., density, viscosity, etc.) was changed independently to assess its effect under the same operating conditions and identify potential directions for fuel formulation that may enable improved NOx emissions and reduce the reliance on aftertreatment systems for NOx control. The results showed that although changes in the fuel distillation curve and other physical properties affect the air-fuel mixtures substantially, changes were not sufficient to result in a large impact to low-load NOx formation. The maximum predicted NOx reduction was 11% uncorrected by combustion phasing. The results suggest that further research efforts should focus on changing the chemical properties of the fuel, like the cetane number, to allow higher EGR dilution and stable combustion.

Reply on RC1

Abstract: Abstract. Global reactive nitrogen (N) deposition has more than tripled since 1860 and is expected to remain high due to land use changes and fossil fuel consumption. We update the 2010 global deposition budget for nitrogen and sulfur with new regional wet deposition measurements from Asia, improving the ensemble results of eleven global chemistry transport models from the second phase of the United Nation’s Task Force on Hemispheric Transport of Air Pollution (HTAP-II). The observationally adjusted global N deposition budget is 130 Tg-N, representing a 10 % increase and the adjusted global sulfur deposition budget is 80 Tg-S, representing no change. Our study demonstrates that a global measurement-model fusion approach can substantially improve N and S deposition model estimates at a regional scale and represents a step forward toward the World Meteorological Organization’s goal of global fusion products for accurately mapping harmful air pollution.

Reply on RC3

Abstract: Abstract. Global reactive nitrogen (N) deposition has more than tripled since 1860 and is expected to remain high due to land use changes and fossil fuel consumption. We update the 2010 global deposition budget for nitrogen and sulfur with new regional wet deposition measurements from Asia, improving the ensemble results of eleven global chemistry transport models from the second phase of the United Nation’s Task Force on Hemispheric Transport of Air Pollution (HTAP-II). The observationally adjusted global N deposition budget is 130 Tg-N, representing a 10 % increase and the adjusted global sulfur deposition budget is 80 Tg-S, representing no change. Our study demonstrates that a global measurement-model fusion approach can substantially improve N and S deposition model estimates at a regional scale and represents a step forward toward the World Meteorological Organization’s goal of global fusion products for accurately mapping harmful air pollution.

­­Carbon Costs of Plant Nutrient Acquisition Improve Present-Day Carbon Cycle Estimates and Limit CO2 Fertilization Effect

Abstract: Most Earth system models (ESMs) do not explicitly represent the carbon (C) costs of plant nutrient acquisition, which leads to uncertainty in predictions of the current and future constraints to the land C sink. While plants acquire nutrients through different uptake pathways, such as from mycorrhizae, direct root uptake, retranslocation from senescing tissues, and biological fixation in the case of nitrogen (N), they usually have different associated C costs. Determining the amount of nutrients acquired through each uptake pathway and the associated C cost could increase understanding of the global C and nutrient cycles, as well as the predictive skills of ESMs.Here, we integrate a plant productivity-optimizing nutrient (N and phosphorus (P)) acquisition model (Fixation & Uptake of Nutrients, FUN) into the Energy Exascale Earth System (E3SM) Land Model (ELM) to simulate the global C and nutrient cycles (Braghiere et al., 2022). We benchmarked the model with observations (in-situ, remotely sensed, and integrated using artificial intelligence), and other ESMs from CMIP6; we found significant improvements in present C cycle variables estimates. We also examine the impact of mycorrhizal spatial distributions on the global C cycle, since most plant species predominantly associate with a single type of mycorrhizal fungi and uncertainties in mycorrhizal distributions are non-trivial, with current estimates disagreeing in up to 50% over 40% of the land area (Braghiere et al., 2021). Global Net Primary Productivity (NPP) is reduced by 20% with N costs and 50% with NP costs, while modeled and observed nutrient limitation agreement increases when N and P are considered together. Even though NPP has been growing globally in response to increasing CO2, as soil nutrient progressively becomes more limiting, the costs to NPP for nutrient acquisition have increased at a faster rate. This suggests that nutrient acquisition will increasingly demand a higher portion of assimilated C to support the same productivity.Braghiere, et al. (2022) doi.org/10.1029/2022MS003204Braghiere, et al. (2021) doi.org/10.1029/2021GL094514

Historical dynamics of terrestrial carbon during 1901–2016 as simulated by the CLM-Microbe model

Abstract: Abstract. The CLM-Microbe model was able to reproduce the variations of gross (GPP) and net (NPP) primary productivity, heterotrophic (HR), and soil (SR) respiration, microbial (MBC) biomass C in fungi (FBC) and bacteria (BBC) in the top 30 cm and 1 m, dissolved (DOC) and soil organic C (SOC) in the top 30 cm and 1 m during 2901–2016. During the study period, simulated C variables increased by approximately 30 PgC yr−1 for GPP, 13 PgC yr−1 for NPP, 12 PgC yr−1 for HR, 25 PgC yr−1 for SR, 1.0 PgC for FBC and 0.4 PgC for BBC in 0–30 cm, 1.2 PgC for FBC, 0.7 PgC for BBC, 2.4 PgC for DOC, 34 PgC for SOC, and 4 PgC for litter C in 0–1 m, and 37 PgC for vegetation C. Increases in C fluxes and pools were larger at northern high latitudes and in equatorial regions than at other latitudes; the largest absolute increases of C fluxes and pools were in Asia and South America, particularly in eastern Asia and central and northern South America. However, the largest relative increases of GPP, NPP, HR, and SR in Asia and Europe, FBC (0–30 cm and 0–1 m) in South America, BBC (0–30 cm and 0–1 m) in Europe, DOC (0–1 m) in South America and Europe, SOC (0–1 m) in Africa, and vegetation C and litter C (0–1 m) in Europe. Vegetation productivity was primarily controlled by warming and precipitation, while microbial and soil C was jointly governed by vegetation C input and soil temperature and moisture. This study enhances our understanding of soil microbial roles in the global terrestrial C cycle.

Challenges to Rural Service Transformers on Increased Electric Vehicle Charging Infrastructure

Abstract: As Electric Vehicle (EV) electric charging demand increases so to does the corresponding load requirements for charging the vehicles. The expansion of power distribution assets is therefore a critical issue to address on whether rural service transformers can handle the increase in EV load demand. Large-scale EV deployment is likely to cause problems in the localized distributions systems bringing challenges such as increased load demand, increased system losses, and additional voltage drops. This paper investigates how rural service transformer resiliency plays an important role in these challenges. EV charging can create new load peaks exceeding the service transformer's rated capacity, thereby accelerating equipment aging. EV charging can also both positively and negatively affect transformer aging. The core purpose of this paper is to investigate that change in service life of rural service transformers.

Cover Image

Abstract: The cover image is based on the Original Article An exploratory steady-state redox model of photosynthetic linear electron transport for use in complete modelling of photosynthesis for broad applications by Lianhong Gu et al., https://doi.org/10.1111/pce.14563.

Cover Picture: Ultrasonication‐Induced Strong Metal‐Support Interaction Construction in Water Towards Enhanced Catalysis (Angew. Chem. Int. Ed. 20/2023)

Abstract: A strong–metal support interaction construction was induced via ultrasonication in water as described by Zhenzhen Yang, Sheng Dai et al. in their Communication (e202214322). The encapsulation degree of metal nanoparticles by reducible oxide overlayers can be modulated by the ultrasonication parameters. The facile tunability offers enhanced activity and selectivity in diverse catalytic hydrogenation applications. The cover picture by Andy Sproles illustrates ultrasound waves encircling supported metal nanoparticles in water to induce the encapsulation procedure. A strong–metal support interaction construction was induced via ultrasonication in water as described by Zhenzhen Yang, Sheng Dai et al. in their Communication (e202214322). The encapsulation degree of metal nanoparticles by reducible oxide overlayers can be modulated by the ultrasonication parameters. The facile tunability offers enhanced activity and selectivity in diverse catalytic hydrogenation applications. The cover picture by Andy Sproles illustrates ultrasound waves encircling supported metal nanoparticles in water to induce the encapsulation procedure. Polyoxometalates Coordination Compounds Supramolecular Chemistry Biocatalysis

Reply on RC2

Abstract: Abstract. Global reactive nitrogen (N) deposition has more than tripled since 1860 and is expected to remain high due to land use changes and fossil fuel consumption. We update the 2010 global deposition budget for nitrogen and sulfur with new regional wet deposition measurements from Asia, improving the ensemble results of eleven global chemistry transport models from the second phase of the United Nation’s Task Force on Hemispheric Transport of Air Pollution (HTAP-II). The observationally adjusted global N deposition budget is 130 Tg-N, representing a 10 % increase and the adjusted global sulfur deposition budget is 80 Tg-S, representing no change. Our study demonstrates that a global measurement-model fusion approach can substantially improve N and S deposition model estimates at a regional scale and represents a step forward toward the World Meteorological Organization’s goal of global fusion products for accurately mapping harmful air pollution.

Encoding integers and rationals on neuromorphic computers using virtual neuron

Abstract: Neuromorphic computers emulate the human brain while being extremely power efficient for computing tasks. In fact, they are poised to be critical for energy-efficient computing in the future. Neuromorphic computers are primarily used in spiking neural network-based machine learning applications. However, they are known to be Turing-complete, and in theory can perform all general-purpose computation. One of the biggest bottlenecks in realizing general-purpose computations on neuromorphic computers today is the inability to efficiently encode data on the neuromorphic computers. To fully realize the potential of neuromorphic computers for energy-efficient general-purpose computing, efficient mechanisms must be devised for encoding numbers. Current encoding mechanisms (e.g., binning, rate-based encoding, and time-based encoding) have limited applicability and are not suited for general-purpose computation. In this paper, we present the virtual neuron abstraction as a mechanism for encoding and adding integers and rational numbers by using spiking neural network primitives. We evaluate the performance of the virtual neuron on physical and simulated neuromorphic hardware. We estimate that the virtual neuron could perform an addition operation using just 23 nJ of energy on average with a mixed-signal, memristor-based neuromorphic processor. We also demonstrate the utility of the virtual neuron by using it in some of the μ-recursive functions, which are the building blocks of general-purpose computation.

Curvilinear Aperture Monopulse

Abstract: By a symmetry argument, a synthetic aperture radar collection along a linear path does not collect three-dimensional information about the scene. However, it is known that vertical curvature can be used to derive some vertical position information. This paper approaches the problem from a monopulse perspective, resulting in a non-iterative computation that commutes with efficient image formation algorithms.

Reply on RC1

Abstract: Abstract. The importance of carbon (C)-nutrient interactions to the prediction of future C uptake has long been recognized. The Energy Exascale Earth System Model (E3SM) land model (ELM) version 1 is one of the few land surface models that include both N and P cycling and limitation (ELMv1-CNP). Here we provide a global scale evaluation of ELMv1-CNP using International Land Model Benchmarking (ILAMB) system. We show that ELMv1-CNP produces realistic estimates of present-day carbon pools and fluxes. Compared to simulations with optimal P availability, simulations with ELMv1-CNP produces better performance, particularly for simulated biomass, leaf area index (LAI), and global net C balance. We also show ELMv1-CNP simulated N and P cycling are in good agreement with data-driven estimates. We compared ELMv1-CNP simulated response to CO₂ enrichment with meta-analysis of observations from similar manipulation experiments. We show that ELMv1-CNP is able to capture the field observed responses for photosynthesis, growth, and LAI. We investigated the role of P limitation in the historical balance and show that global C sources and sinks are significantly affected by P limitation, as the historical CO₂ fertilization effect was reduced by 20 % and C emission due to land use and land cover change was 11 % lower when P limitation was considered. Our simulations suggest that introduction of P cycle dynamics and C-N-P coupling will likely have substantial consequences for projections of future C uptake.