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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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At least 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

p53-Based Strategy for Protection of Bone Marrow From Y-90 Ibritumomab Tiuxetan

Purpose: The main drawbacks of radioimmunotherapy have been severe hematological toxicity and potential development of myelodysplastic syndrome and secondary leukemia. Activation of p53 follows a major pathway by which normal tissues respond to DNA-damaging agents, such as chemotherapy and radiation therapy, that result in injuries and pathological consequences. This pathway is separate from the tumor suppressor pathway of p53. We have previously reported that use of low-dose arsenic (LDA) temporarily and reversibly suppresses p53 activation, thereby ameliorating normal tissue toxicity from exposure to 5-fluorouracil and X rays. We have also demonstrated that LDA-mediated protection requires functional p53 and thus is selective to normal tissues, as essentially every cancer cell has dysfunctional p53. Here we tested the protective efficacy of LDA for bone marrow tissue against radioimmunotherapy through animal experiments. Methods and Materials: Mice were subjected to LDA pretreatment for 3 days, followed by treatment with Y-90 ibritumomab tiuxetan. Both dose course (10, 25, 50, 100, and 200 μCi) and time course (6, 24, and 72 hours and 1 and 2 weeks) experiments were performed. The response of bone marrow cells to LDA was determined by examining the expression of NFκB, Glut1, and Glut3. Staining with hematoxylin and eosin, γ-H2AX, and terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) was used to examine morphology, DNA damage response, and apoptotic cell populations. Results: Elevated levels of NFκB, Glut1, and Glut3 were observed in bone marrow cells after LDA treatment. Bone marrow damage levels induced by Y-90 ibritumomab tiuxetan were greatly reduced by LDA pretreatment. Consistent with this observation, significantly less DNA damage and fewer apoptotic cells were accumulated after Y-90 ibritumomab tiuxetan treatment in LDA-pretreated mice. Furthermore, in the mouse xenograft model implanted with human Karpas-422 lymphoma cells, LDA pretreatment did not have any detectable effect on either tumor growth or Y-90 ibritumomab tiuxetan (200 μCi)-induced tumor suppression. Conclusions: LDA pretreatment protected bone marrow without compromising tumor control caused by Y-90 ibritumomab tiuxetan.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Towards replacement of animal tests with in vitro assays: a gene expression biomarker predicts in vitro and in vivo estrogen receptor activity

High-throughput transcriptomics (HTTr) has the potential to support efforts to reduce or replace some animal tests. In past studies, we described a computational approach utilizing a gene expression biomarker consisting of 46 genes to predict estrogen receptor (ER) activity after chemical exposure in ER-positive human breast cancer cells including the MCF-7 cell line. We hypothesized that the biomarker model could identify ER activities of chemicals examined by Endocrine Disruptor Screening Program (EDSP) Tier 1 screening assays in which transcript profiles of the same chemicals were examined in MCF-7 cells. For the 62 chemicals examined including 5 chemicals examined in this study using RNA-Seq, the ER biomarker model accuracy was 1) 97% for in vitro reference chemicals, 2) 76–85% for guideline uterotrophic assays, and 3) 87–88% for guideline and nonguideline uterotrophic assays. For the same chemicals, these accuracies were similar or slightly better than those of the ToxCast ER model based on 18 in vitro assays. The performance of the ER biomarker model indicates that HTTr interpreted using the ER biomarker correctly identifies active and inactive ER reference chemicals. Finally, as part of the HTTr screening program the approach could rapidly identify chemicals with potential ER bioactivities for additional screening and testing.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Population exposure to hazardous air quality due to the 2015 fires in Equatorial Asia

Vegetation and peatland fires cause poor air quality and thousands of premature deaths across densely populated regions in Equatorial Asia. Strong El-Niño and positive Indian Ocean Dipole conditions are associated with an increase in the frequency and intensity of wildfires in Indonesia and Borneo, enhancing population exposure to hazardous concentrations of smoke and air pollutants. Here we investigate the impact on air quality and population exposure of wildfires in Equatorial Asia during Fall 2015, which were the largest over the past two decades. We performed high-resolution simulations using the Weather Research and Forecasting model with Chemistry based on a new fire emission product. The model captures the spatio-temporal variability of extreme pollution episodes relative to space- and ground-based observations and allows for identification of pollution sources and transport over Equatorial Asia. We calculate that high particulate matter concentrations from fires during Fall 2015 were responsible for persistent exposure of 69 million people to unhealthy air quality conditions. Short-term exposure to this pollution may have caused 11,880 (6,153–17,270) excess mortalities. Results from this research provide decision-relevant information to policy makers regarding the impact of land use changes and human driven deforestation on fire frequency and population exposure to degraded air quality.

Science & Technology - Other Topics↗
DOE OSTI2017

ChemNet: A Transferable and Generalizable Deep Neural Network for Small-Molecule Property Prediction

With access to large datasets, deep neural networks through representation learning have been able to identify patterns from raw data, achieving human-level accuracy in image and speech recognition tasks. However, in chemistry, availability of large standardized and labelled datasets is scarce, and with a multitude of chemical properties of interest, chemical data is inherently small and fragmented. In this work, we explore transfer learning techniques in conjunction with the existing Chemception CNN model, to create a transferable and generalizable deep neural network for small-molecule property prediction. Our latest model, ChemNet learns in a semi-supervised manner from inexpensive labels computed from the ChEMBL database. When fine-tuned to the Tox21, HIV and FreeSolv dataset, which are 3 separate chemical tasks that ChemNet was not originally trained on, we demonstrate that ChemNet exceeds the performance of existing Chemception models, contemporary MLP models that trains on molecular fingerprints, and it matches the performance of the ConvGraph algorithm, the current state-of-the-art. Furthermore, as ChemNet has been pre-trained on a large diverse chemical database, it can be used as a universal “plug-and-play” deep neural network, which accelerates the deployment of deep neural networks for the prediction of novel small-molecule chemical properties.

Artificial Intelligence↗
DOE OSTI2025

LASSO for CALPHAD Model Selection Enables Data-Efficient Thermodynamic Modeling: An Application in Thermochemical Hydrogen Production Materials

Phenomenological CALPHAD (CALculation of PHAse Diagrams) models, widely used for multicomponent materials, often contain a considerable number of parameters and require fitting using data from a relatively small number of experimental measurements or theoretical calculations. Sometimes these parameters are introduced for the purpose of improving model fits but without clear physical justification, which leads to overparametrized models with poor generalization performance. Automated approaches for optimal model selection based on the available data therefore become critical. Here, in this work, a least absolute shrinkage and selection operator (LASSO)-based approach is developed for model selection by leveraging the linearity of the CALPHAD model with respect to its parameters to convert the model selection and fitting to a LASSO minimization problem. We demonstrate its utility for thermodynamic modeling of thermochemical hydrogen (TCH) production materials using lanthanum strontium manganite (LSM) as an example. Various TCH-relevant properties, including oxygen stoichiometry as a function of oxygen partial pressure, enthalpy of reduction, and entropy of reduction, are successfully predicted with reasonable accuracy using a minimal set of model parameters. Importantly, the model selection and fitting involve minimal human decision; it can therefore be applied to high-throughput DFT defect calculations and yield efficient workflows for TCH material modeling and optimization.

CALPHAD↗
DOE OSTI2021

Modeling Within-Host Dynamics of SARS-CoV-2 Infection: A Case Study in Ferrets

The pre-clinical development of antiviral agents involves experimental trials in animals and ferrets as an animal model for the study of SARS-CoV-2. Here, we used mathematical models and experimental data to characterize the within-host infection dynamics of SARS-CoV-2 in ferrets. We also performed a global sensitivity analysis of model parameters impacting the characteristics of the viral infection. We provide estimates of the viral dynamic parameters in ferrets, such as the infection rate, the virus production rate, the infectious virus proportion, the infected cell death rate, the virus clearance rate, as well as other related characteristics, including the basic reproduction number, pre-peak infectious viral growth rate, post-peak infectious viral decay rate, pre-peak infectious viral doubling time, post-peak infectious virus half-life, and the target cell loss in the respiratory tract. These parameters and indices are not significantly different between animals infected with viral strains isolated from the environment and isolated from human hosts, indicating a potential for transmission from fomites. While the infection period in ferrets is relatively short, the similarity observed between our results and previous results in humans supports that ferrets can be an appropriate animal model for SARS-CoV-2 dynamics-related studies, and our estimates provide helpful information for such studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

QCalEval: Benchmarking Vision-Language Models for Quantum Calibration Plot Understanding

Quantum computing calibration depends on interpreting experimental data, and calibration plots provide the most universal human-readable representation for this task, yet no systematic evaluation exists of how well vision-language models (VLMs) interpret them. We introduce QCalEval, the first VLM benchmark for quantum calibration plots: 243 samples across 87 scenario types from 22 experiment families, spanning superconducting qubits and neutral atoms, evaluated on six question types in both zero-shot and in-context learning settings. The best general-purpose zero-shot model reaches a mean score of 72.3, and many open-weight models degrade under multi-image in-context learning, whereas frontier closed models improve substantially. A supervised fine-tuning ablation at the 9-billion-parameter scale shows that SFT improves zero-shot performance but cannot close the multimodal in-context learning gap. As a reference case study, we release NVIDIA Ising Calibration 1, an open-weight model based on Qwen3.5-35B-A3B that reaches 74.7 zero-shot average score.

Cao, Shuxiang↗
DOE OSTI2025

A Scalable Hardware-and-Human-in-the-Loop Grid-interactive Efficient Building Equipment Performance Dataset

This project developed a publicly available, high-fidelity dataset about the interactions among humans, homes, and heat pumps supporting grid interactive efficient buildings to balance demand on the grid with comfort for occupants. Laboratory measurements and simulations of the hardware capture the second-scale electric power dynamics of heat pumps providing grid services like load shifting and load shedding. Field measurements, behavior tracking, and qualitative surveys of people in their homes over multiple years—including experimentally adjusting the heating and cooling system to provide grid services—to capture the reciprocal effect of human behavior on grid services, and grid services on human comfort. Taken together, these data capture the complete Hardware and Human in the loop system for residential heat pumps, reducing large uncertainties in simulation for design, and models for control of heat pumps, and grid-interactive buildings.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Life cycle assessment of fuel ethanol produced from soluble sugar in sweet sorghum stalks in North China

This paper describes the results of a life cycle assessment of sweet sorghum stalk (SSS)-based ethanol in North China. We determined the environmental performance of SSS-based ethanol and examined its advantages and disadvantages, as compared to gasoline, focusing on the life cycle of feedstock production, transportation, ethanol production and distribution, and use. The GREET transportation model and the method developed by the Centre of Environmental Sciences at Leiden University (CML method) were used to compile a life cycle inventory and to assess environmental impacts. Results indicate that SSS-based ethanol has advantages in terms of energy consumption, with a well to wheel decrease of 85% fossil energy and 44% global warming potential, as compared with gasoline. Abiotic depletion potential, acidification potential, and photochemical ozone creation potential were also 50–90% lower than in the case of gasoline, while human health toxic potential was 36% lower. However, SSS-based sorghum did not have advantages over gasoline in terms of life cycle cost, land use, and water consumption. Results indicate that such an evaluation cannot just consider a few types of environmental impacts, researchers should promote systematic and comprehensive life cycle assessment of ethanol to guide the development of an energy strategy for China.

Environment impact↗
DOE OSTI2024

Harnessing large language models’ zero-shot and few-shot learning capabilities for regulatory research

Abstract Large language models (LLMs) are sophisticated AI-driven models trained on vast sources of natural language data. They are adept at generating responses that closely mimic human conversational patterns. One of the most notable examples is OpenAI's ChatGPT, which has been extensively used across diverse sectors. Despite their flexibility, a significant challenge arises as most users must transmit their data to the servers of companies operating these models. Utilizing ChatGPT or similar models online may inadvertently expose sensitive information to the risk of data breaches. Therefore, implementing LLMs that are open source and smaller in scale within a secure local network becomes a crucial step for organizations where ensuring data privacy and protection has the highest priority, such as regulatory agencies. As a feasibility evaluation, we implemented a series of open-source LLMs within a regulatory agency’s local network and assessed their performance on specific tasks involving extracting relevant clinical pharmacology information from regulatory drug labels. Our research shows that some models work well in the context of few- or zero-shot learning, achieving performance comparable, or even better than, neural network models that needed thousands of training samples. One of the models was selected to address a real-world issue of finding intrinsic factors that affect drugs' clinical exposure without any training or fine-tuning. In a dataset of over 700 000 sentences, the model showed a 78.5% accuracy rate. Our work pointed to the possibility of implementing open-source LLMs within a secure local network and using these models to perform various natural language processing tasks when large numbers of training examples are unavailable.

Biochemistry & Molecular Biology↗
DOE OSTI2020

Twist again: Dynamically and reversibly controllable chirality in liquid crystalline elastomer microposts

Photoresponsive liquid crystalline elastomers (LCEs) constitute ideal actuators for soft robots because their light-induced macroscopic shape changes can be harnessed to perform specific articulated motions. Conventional LCEs, however, do not typically exhibit complex modes of bending and twisting necessary to perform sophisticated maneuvers. Here, we model LCE microposts encompassing side-chain mesogens oriented along a magnetically programmed nematic director, and azobenzene cross-linkers, which determine the deformations of illuminated posts. On altering the nematic director orientation from vertical to horizontal, the post’s bending respectively changes from light-seeking to light-avoiding. Moreover, both modeling and subsequent experiments show that with the director tilted at 45°, the initially achiral post reversibly twists into a right- or left-handed chiral structure, controlled by the angle of incident light. We exploit this photoinduced chirality to design “chimera” posts (encompassing two regions with distinct director orientations) that exhibit simultaneous bending and twisting, mimicking motions exhibited by the human musculoskeletal system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Global Precipitation Experiment—A New World Climate Research Programme Lighthouse Activity

The future state of the global water cycle and the prediction of freshwater availability for humans around the world remain among the challenges of climate research and are relevant to several United Nations Sustainable Development Goals. The Global Precipitation Experiment (GPEX) takes on the challenge of improving the prediction of precipitation quantity, phase, timing, and intensity, characteristics that are products of a complex integrated system. It will achieve this by leveraging existing World Climate Research Programme (WCRP) activities and community capabilities in satellite, surface-based, and airborne observations, modeling, and experimental research and by conducting new and focused activities. It was launched in October 2023 as a WCRP Lighthouse Activity. Here, we present an overview of the GPEX Science Plan that articulates the primary science questions related to precipitation measurements, process understanding, model performance and improvements, and plans for capacity development. The central phase of GPEX is the WCRP Years of Precipitation for 2–3 years with coordinated global field campaigns focusing on different storm types (atmospheric rivers, mesoscale convective systems, monsoons, and tropical cyclones, among others) over different regions and seasons. Activities are planned over the three phases (before, during, and after the Years of Precipitation) spanning a decade. These include gridded data evaluation and development, advanced modeling, enhanced understanding of processes critical to precipitation, multiscale prediction of precipitation events across scales, and capacity development. These activities will be further developed as part of the GPEX Implementation Plan.

Climate change↗
DOE OSTI2016

Longitudinal Assessment of Lung Cancer Progression in Mice Using the Sodium Iodide Symporter Reporter Gene and SPECT/CT Imaging

Lung cancer has the highest mortality rate of any tissue-specific cancer in both men and women. Research continues to investigate novel drugs and therapies to mitigate poor treatment efficacy, but the lack of a good descriptive lung cancer animal model for preclinical drug evaluation remains an obstacle. Here we describe the development of an orthotopic lung cancer animal model which utilizes the human sodium iodide symporter gene (hNIS; SLC5A5) as an imaging reporter gene for the purpose of non-invasive, longitudinal tumor quantification. hNIS is a glycoprotein that naturally transports iodide (I- ) into thyroid cells and has the ability to symport the radiotracer 99mTc-pertechnetate ( 99mTcO4 - ). A549 lung adenocarcinoma cells were genetically modified with plasmid or lentiviral vectors to express hNIS. Modified cells were implanted into athymic nude mice to develop two tumor models: a subcutaneous and an orthotopic xenograft tumor model. Tumor progression was longitudinally imaged using SPECT/CT and quantified by SPECT voxel analysis. hNIS expression in lung tumors was analyzed by quantitative real-time PCR. Additionally, hematoxylin and eosin staining and visual inspection of pulmonary tumors was performed. We observed that lentiviral transduction provided enhanced and stable hNIS expression in A549 cells. Furthermore, 99mTcO4 - uptake and accumulation was observed within lung tumors allowing for imaging and quantification of tumor mass at twotime points. This study illustrates the development of an orthotopic lung cancer model that can be longitudinally imaged throughout the experimental timeline thus avoiding inter-animal variability and leading to a reduction in total animal numbers. Furthermore, our orthotopic lung cancer animal model is clinically relevant and the genetic modification of cells for SPECT/CT imaging can be translated to other tissue-specific tumor animal models.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Breaking Through Historical Biases in Performance Assessments - 18056

Performance assessments (referred to as safety assessments internationally), which include mathematical modeling of natural and engineered systems for many hundreds and thousands of years, play an important role in decision-making for disposal of radioactive waste and cleanup of sites and facilities contaminated with radioactive material. These decisions can involve options costing hundreds of millions or even billions of dollars. Thus, it is critical to understand the underlying assumptions behind regulatory and technical aspects of these assessments to better understand and communicate the basis for decisions. Policy and technical biases deliberately built into early assessment approaches allowed safety margin to account for uncertainties known to be present, but not quantitatively assessed. Many of these biases (e.g., regulatory standards, human habits, effectiveness of waste forms, inadvertent intrusion assumptions, etc.) continue to exist in spite of advances in technical approaches and knowledge. These built-in biases, often not effectively communicated, can lead to misleading interpretation of results and potentially influence public confidence and costly regulatory decisions regarding the safety of a disposal facility or a closure action. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

GIScience in the era of Artificial Intelligence: a research agenda towards Autonomous GIS

The advent of generative AI exemplified by large language models (LLMs) opens new ways to represent and compute geographic information and transcends the process of geographic knowledge production, driving geographic information systems (GIS) towards autonomous GIS. Leveraging LLMs as the decision core, autonomous GIS can independently generate and execute geoprocessing workflows to perform spatial analysis. In this vision paper, we further elaborate on the concept of autonomous GIS and present a conceptual framework that defines its five autonomous goals, five levels of autonomy, five core functions, and three operational scales. We demonstrate how autonomous GIS could perform geospatial data retrieval, spatial analysis, and map making with four proof-of-concept GIS agents. We conclude by identifying critical challenges and future research directions, including fine-tuning and self-growing decision-cores, autonomous modelling, and examining the societal and practical implications of autonomous GIS. By establishing the groundwork for a paradigm shift in GIScience, this paper envisions a future where GIS moves beyond traditional workflows to autonomously reason, derive, innovate, and advance geospatial solutions to pressing global challenges. Meanwhile, we emphasize that as we design and deploy increasingly intelligent geospatial systems, we carry a responsibility to ensure they are developed in socially responsible ways, serve the public good, and support the continued value of human geographic insight in an AI-augmented future.

Autonomous GI↗
DOE OSTI2016

Exposure to polychlorinated biphenyls and the thyroid gland – examining and discussing possible longitudinal health effects in humans

Background: Many previous studies have dealt with the effect of polychlorinated biphenyls (PCBs) on the thyroid gland, but their findings are inconsistent. One problem of these studies has been their use of cross-sectional designs. Objectives: The aim of the current study is to investigate longitudinal effects of PCBs on the thyroid gland, focusing on: morphological changes in thyroid tissue (i.e. thyroid volume), changes in thyroid hormones and in thyroid antibodies. Methods: A total of 122 individuals (M{sub age}=44.7) were examined over a period of four years (t{sup 1} until t{sup 4}). Medical history was collected via interviews, an ultrasound examination was performed and blood samples were taken to determine plasma PCB levels, thyroid stimulating hormone (TSH), free triiodthyronine (fT3), free thyroxine (fT4), thyroid peroxidase antibodies (TPOab), thyreoglobulin antibodies (TGab) and thyroid-stimulating hormone receptor antibodies (TSHRab). Rank correlation coefficients and mixed effect models were performed controlling for age and total lipids. Results: There were negative correlations between higher chlorinated biphenyls and fT3, cross-sectionally as well as longitudinally. We also found an interaction effect of higher-chlorinated PCBs over time for fT4 as well as TSHRab. In case of high exposure, a decrease in fT4 and an increase in TSHRab level were found over time. In regards to the other variables, our findings yielded no clear results in the examined time period. Conclusion: This is the first study to shows a PCB-related effect on fT3, fT4 and TSHRab over a four year period. The data also suggest that morphological and antibody findings remain inconsistent and do not allow for unambiguous interpretation. - Highlights: • This is a longitudinal study which includes data from four cross sections. • Higher-chlorinated biphenyls are negatively correlated with fT3. • There are interactions of time and higher-chlorinated biphenyls to TSHRab and fT4. • There is an interaction of time and dioxin-like polychlorinated biphenyls to fT4. • No consistent effects were found for thyroid volume, TSH, TPOab and TGab.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Effects of acute ethanol exposure on cytokine production by primary airway smooth muscle cells

Both chronic and binge alcohol abuse can be significant risk factors for inflammatory lung diseases such as acute respiratory distress syndrome and chronic obstructive pulmonary disease. However, metabolic basis of alcohol-related lung disease is not well defined, and may include key metabolites of ethanol [EtOH] in addition to EtOH itself. Therefore, we investigated the effects of EtOH, acetaldehyde [ACE], and fatty acid ethyl esters [FAEEs] on oxidative stress, endoplasmic reticulum (ER) stress, AMP-activated protein kinase (AMPK) signaling and nuclear translocation of phosphorylated (p)-NF-κB p65 in primary human airway smooth muscle (HASM) cells stimulated to produce cytokines using LPS exposure. Both FAEEs and ACE induced evidence of cellular oxidative stress and ER stress, and increased p-NF-κB in nuclear extracts. EtOH and its metabolites decreased p-AMPKα activation, and induced expression of fatty acid synthase, and decreased expression of sirtuin 1. In general, EtOH decreased secretion of IP-10, IL-6, eotaxin, GCSF, and MCP-1. However, FAEEs and ACE increased these cytokines, suggesting that both FAEEs and ACE as compared to EtOH itself are proinflammatory. A direct effect of EtOH could be consistent with blunted immune response. Collectively, these two features of EtOH exposure, coupled with the known inhibition of innate immune response in our model might explain some clinical manifestations of EtOH exposure in the lung. - Highlights: • Metabolic basis for EtOH toxicity was studied in human airway smooth muscle (HASM) cells. • In HASM cells, EtOH metabolites were found to be relatively more toxic than EtOH itself. • EtOH metabolites mediate deactivation of AMPK via oxidative stress and ER stress. • EtOH metabolites were found to be more proinflammatory than EtOH itself in HASM cells.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Kinetic and temperature sensitivity properties of soil exoenzymes through the soil profile down to one-meter depth at a temperate coniferous forest (Blodgett, CA)

This dataset contains data on kinetic and temperature sensitivity parameters of the exoenzymes β-glucosidase (BG), leucine/leucyl aminopeptidase (LAP) and acid phosphatase (AP) across a soil profile down to 90 cm depth at Blodgett forest, CA, USA. These data were generated to determine if kinetic and thermal properties of microbial exoenzymes involved in organic matter decomposition varied with soil depth, following variation in soil properties and microbial communities, as part of the study: Alves et al. (2021). Kinetic Properties of Microbial Exoenzymes Vary with Soil Depth but Have Similar Temperature Sensitivities Through the Soil Profile. Frontiers in Microbiology 12:3618. https://doi.org/10.3389/fmicb.2021.735282. This research was performed within the framework of the TES Belowground Biogeochemistry SFA project, in particular association with a long-term field warming experiment of the whole soil profile at Blodgett forest. Samples for this work were collected from locations representative of the field experimental plots. Potential enzyme activity rates were measured using laboratory fluorometric assays with soils collected at 0-10, 10-20, 30-40, 50-60, 60-70 and 80-90 cm deep in biological triplicates (i.e., three soil cores collected at different representative locations). Assays with each soil were conducted over a gradient of eight substrate concentrations per enzyme, and incubated at 4, 10, 16, 25, 35 or 50°C. Enzyme Michaelis-Menten kinetics were modeled over the eight substrate concentrations at each temperature, and the temperature sensitivity of the kinetic parameters was modeled over the six temperatures using linear Arrhenius/Q10 and non-linear Macromolecular Rate Theory (MMRT) models. The dataset includes the fully processed enzyme activity rate data used to model Michaelis-Menten kinetics, calculated kinetic and temperature sensitivity parameters, and basic soil and microbial biomass chemistry for each sample. All data is provided for each individual biological replicate, and kinetic and temperature sensitivity parameters are also provided as means of the biological replicates. The dataset also includes all raw measurement data and code used to parse, combine and perform the analyses described by Alves et al. (2021). For file descriptions, see the file-level metadata files: “enzymes_dataProcessed_flmd.xlsx” (processed data); “enzymes_dataRaw_flmd.xlsx” (compressed raw data and metadata); and “enzymes_code_flmd.xlsx” (compressed code). The experimental design, list of parameters measured, soil and microbial biomass chemistry data, and means of biological replicates for kinetic and temperature sensitivity parameters are also provided as human-readable tables in file “enzymeTraits_design_results_tables.xlsx”.

54 ENVIRONMENTAL SCIENCES↗