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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Quantifying the Biophysical and Socioeconomic Drivers of Changes in Forest and Agricultural Land in South and Southeast Asia

South and Southeast Asia (SSEA) has been a hotspot for land use and land cover change (LULCC) in the past few decades. The identification and quantification of the drivers of LULCC are crucial for improving our understanding of LULCC trends. So far, the biophysical and socioeconomic drivers of forest change have not been quantified at the regional scale, particularly for SSEA. In this study, we quantify the biophysical and socioeconomic drivers of forest change on a country-by-country basis in SSEA using an integrated quantitative methodology, which systematically accounts for previously published driver information and regional datasets. We synthesize more than 200 publications to identify the drivers of the forest change at different spatial scales in SSEA. Subsequently, we collect spatially explicit proxy data to represent the identified drivers. We quantify the dynamics of forest and agricultural land from 1992 to 2015 using the Climate Change Initiative (CCI) land cover data developed by the European Space Agency (ESA). A geographically weighted regression method is employed to quantify the spatially heterogeneous drivers of forest change. Our results show that socioeconomic drivers are more important than biophysical drivers for the conversion of forest to agricultural land in South Asia and maritime Southeast Asia. In contrast, biophysical drivers are more important than socioeconomic drivers for the conversion of agricultural land to forest in maritime Southeast Asia and less important in South Asia. Both biophysical and socioeconomic drivers contribute approximately equally to both changes in the mainland Southeast Asia region. By quantifying the dynamics of forest and agricultural land and the spatially explicit drivers of their changes in SSEA, this study provides a solid foundation for LULCC modeling and projection.

Xu, Xiaoming↗
DOE OSTI2017

Structural and Biophysical Characterization of the Mycobacterium tuberculosis Protein Rv0577, a Protein Associated with Neutral Red Staining of Virulent Tuberculosis Strains and Homologue of the Streptomyces coelicolor Protein KbpA

The 261-residue Mycobacterium tuberculosis protein Rv0577 is a prominent antigen in tuberculosis patients, the responsible component for neutral red staining of virulent strains of M. tuberculosis, a putative component in a methylglyoxal detoxification pathway, and an agonist of toll-like receptor 2. It also has 36% sequence identity to AfsK-binding protein A (KbpA), a component in the complex secondary metabolite pathways in the Streptomycetes genus from which many commercial antibiotics are derived. To gain insight into the biological function of Rv0577 and the family of KpbA kinase regulators, the crystal structure for Rv0577 was determined to a resolution of 1.75 Å (3OXH), binding properties with neutral red and deoxyadenosine (Ado) surveyed, backbone dynamics measured, and thermal stability assayed by CD spectroscopy. The protein is composed of four approximate repeats with an topology arranged radially in consecutive pairs to form two continuous eight-strand -sheets capped on both ends with an -helix. The two -sheets intersect in the center at roughly a right angle and form an asymmetric deep “saddle” on both sides of the protein, saddle one (P11 to A129) and saddle two (L143 to A258), that may serve to bind ligands. NMR chemical shift perturbation experiments show that neutral red binds to Rv0577, further cementing the role of Rv0577 in the neutral red staining of virulent strains of M. tuberculosis. Similar experiments show that adenosine also bind to Rv0577, although less tightly, with estimated dissociation constants of 4.1 ± 0.3 mM for saddle one and > 1 M for saddle two. Heteronuclear steady-state {1H}-15N NOE, T1, and T2 values were generally uniform through-out the sequence with only a few modest pockets of differences suggestive of slightly different motion in loops between -strands in saddle 1. Circular dichroism spectroscopy characterization of the thermal stability of Rv0577 indicated irreversible unfolding upon heating with an estimated melting temperature of 56 °C. While it is not known if Rv0577 has a kinase regulatory role similar to its Streptomyces homolog KbpA, protein kinase and phosphatase signaling help M. tuberculosis adapt to the hostile host environment during infections. Consequently, new anti-tuberculosis drugs targeting Rv0577 may act by interfering with multiple mechanisms; a potential signaling machinery as well as toll-like receptor 2 activation and the methylglyoxal detoxification pathway.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Data for A Hybrid Biophysical-Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing

Thermal infrared-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal infrared data sets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of a ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81–0.94) and H (R2 = 0.46–0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical-machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

AI/ML↗
DOE OSTI2017

Using coherence to enhance function in chemical and biophysical systems

Coherence phenomena arise from interference, or the addition, of wave-like amplitudes with fixed phase differences. Although coherence has been shown to yield transformative ways for improving function, advances have been confined to pristine matter and coherence was considered fragile. However, recent evidence of coherence in chemical and biological systems suggests that the phenomena are robust and can survive in the face of disorder and noise. In this work, we survey the state of recent discoveries, present viewpoints that suggest that coherence can be used in complex chemical systems, and discuss the role of coherence as a design element in realizing function.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Anti-pulmonary fibrotic activity of salvianolic acid B was screened by a novel method based on the cyto-biophysical properties

Various methods have been used to evaluate anti-fibrotic activity of drugs. However, most of them are complicated, labor-intensive and lack of efficiency. This study was intended to develop a rapid method for anti-fibrotic drugs screening based on biophysical properties. A549 cells in vitro were stimulated with transforming growth factor-β1 (TGF-β1), and fibrogenesis was confirmed by conventional immunological assays. Meanwhile, the alterations of cyto-biophysical properties including morphology, roughness and stiffness were measured utilizing atomic force microscopy (AFM). It was found that fibrogenesis was accompanied with changes of cellular biophysical properties. TGF-β1-stimulated A549 cells became remarkably longer, rougher and stiffer than the control. Then, the effect of N-acetyl-L-cysteine (NAC) as a positive drug on ameliorating fibrogenesis in TGF-β1-stimulated A549 cells was verified respectively by immunological and biophysical markers. The result of Principal Component Analysis showed that stiffness was a leading index among all biophysical markers during fibrogenesis. Salvianolic acid B (SalB), a natural anti-oxidant, was detected by AFM to protect TGF-β1-stimulated A549 cells against stiffening. Then, SalB treatment was provided in preventive mode on a rat model of bleomycin (BLM) -induced pulmonary fibrosis. The results showed that SalB treatment significantly ameliorated BLM-induced histological alterations, blocked collagen accumulations and reduced α-SMA expression in lung tissues. All these results revealed the anti-pulmonary fibrotic activity of SalB. Detection of cyto-biophysical properties were therefore recommended as a rapid method for anti-pulmonary fibrotic drugs screening. - Highlights: • Fibrogenesis was accompanied with the changes of cyto-biophysical properties. • Cyto-biophysical properties could be markers for anti-fibrotic drugs screening. • Stiffness is a leading index among all biophysical markers. • SalB was detected to protect TGF-β1-stimulated A549 cells against stiffening. • SalB treatment ameliorated pulmonary fibrosis induced by BLM in rats.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

A Sensitivity Analysis of Surface Biophysical, Carbon, and Climate Impacts of Tropical Deforestation Rates in CCSM4-CNDV*

The biophysical–climate and combined biophysical and carbon–climate feedbacks of tropical deforestation rates are explored through sensitivity analyses using the Community Climate System Model 4 with prognostic carbon–nitrogen and dynamic vegetation. Simulations test 5%, 2%, 1%, and 0.5% annual deforestation rates, each paired with preservation targets of 10% per tropical tree type. Perturbations are applied over pan-tropical land but analyses also explore responses over the subcontinental areas of the Amazon basin, central Africa, and Southeast Asia. Sensitivities [expressed as the change in a variable per million square kilometers (Mkm 2 ) of change in tree cover] and means of selected biophysical, carbon, and climate variables during and after deforestation are compared across rates. The most apparent effect of the rates is in hastening/postponing climate change, but otherwise results show no consistent differences across rates and vary more across subcontinents (with the Amazon basin reflecting highest sensitivities in albedo and ground temperatures, and Southeast Asia for total ecosystem carbon). Furthermore, biophysical feedbacks alone were found to have significant impact on climate over subcontinental scales. In the Amazon, ground temperature increase due to biophysical feedbacks is as much as 55%, and precipitation decrease up to 61%, of combined biophysical and carbon impacts. Replication with other models is required. While it is still unclear whether a slow but prolonged deforestation differs in impacts from one that is rapid but short, the rate can still be relevant to planning with regards to the timing of impacts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Importance of biophysical effects on climate warming mitigation potential of biofuel crops over the conterminous United States

Abstract Current quantification of climate warming mitigation potential ( CWMP ) of biomass‐derived energy has focused primarily on its biogeochemical effects. This study used site‐level observations of carbon, water, and energy fluxes of biofuel crops to parameterize and evaluate the community land model ( CLM ) and estimate CO 2 fluxes, surface energy balance, soil carbon dynamics of corn ( Zea mays ), switchgrass ( Panicum virgatum ), and miscanthus ( Miscanthus × giganteus ) ecosystems across the conterminous United States considering different agricultural management practices and land‐use scenarios. We find that neglecting biophysical effects underestimates the CWMP of transitioning from croplands and marginal lands to energy crops. Biogeochemical effects alone result in changes in carbon storage of −1.9, 49.1, and 69.3 g C m −2 y −1 compared to 20.5, 78.5, and 96.2 g C m −2 y −1 when considering both biophysical and biogeochemical effects for corn, switchgrass, and miscanthus, respectively. The biophysical contribution to CWMP is dominated by changes in latent heat fluxes. Using the model to optimize growth conditions through fertilization and irrigation increases the CWMP further to 79.6, 98.3, and 118.8 g C m −2 y −1 , respectively, representing the upper threshold for CWMP . Results also show that the CWMP over marginal lands is lower than that over croplands. This study highlights that neglecting the biophysical effects of altered surface energy and water balance underestimates the CWMP of transitioning to bioenergy crops at regional scales.

09 BIOMASS FUELS↗
DOE OSTI2013

Enhancing the biophysical properties of mRFP1 through incorporation of fluoroproline

Graphical abstract: Enhancing the biophysical properties of mRFP1 through incorporation of (2S, 4R)-4-fluoroproline at proline residues after mutating non-permissive site Pro63 into Ala. -- Highlights: •We incorporate (4S)-FP into mRFP1 led to insoluble protein. •Whereas, incorporation of (4R)-FP resulted in soluble but lost its fluorescence. •mRFP1-P63A mutant accommodate (4R)-FP and gave soluble protein with fluorescence. •Moreover mRFP1-P63A[(4R)-FP] showed enhanced biophysical properties of protein. -- Abstract: Here we enhanced the stability and biophysical properties of mRFP1 through a combination of canonical and non-canonical amino acid mutagenesis. The global replacement of proline residue with (2S, 4R)-4-fluoroproline [(4R)-FP] into mRFP1 led to soluble protein but lost its fluorescence, whereas (2S, 4S)-4-fluoroproline [(4S)-FP] incorporation resulted in insoluble protein. The bioinformatics analysis revealed that (4R)-FP incorporation at Pro63 caused fluorescence loss due to the steric hindrance of fluorine atom of (4R)-FP with the chromophore. Therefore, Pro63 residue was mutated with the smallest amino acid Ala to maintain non coplanar conformation of the chromophore and helps to retain its fluorescence with (4R)-FP incorporation. The incorporation of (4R)-FP into mRFP1-P63A showed about 2–3-fold enhancement in thermal and chemical stability. The rate of maturation is also greatly accelerated over the presence of (4R)-FP into mRFP1-P63A. Our study showed that a successful enhancement in the biophysical property of mRFP1-P63A[(4R)-FP] using non-canonical amino acid mutagenesis after mutating non-permissive site Pro63 into Ala.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Impacts of climate and biophysical variability on global agriculture markets

Agricultural production is highly sensitive to changes in climate and weather patterns. The focus of the great majority of studies assessing climate impacts on agriculture has been on mean changes in agricultural responses. However, the manner in which global agricultural markets respond to the interannual variability of the climate and biophysical shocks is poorly understood. Here we show a strong transmission of interannual variations in climate-induced biophysical yield shocks to agriculture markets, which is magnified further by endogenous market fluctuations. We demonstrate the importance of imperfect versus perfect expectations of market and weather in generating the market fluctuations that play a key role in transferring the interannual variations to markets. We find that the volatility of market prices and consumption could be potentially reduced on average by 55% and 41%, respectively, with improved expectations, where agricultural producers make better decisions to adapt to climate and biophysical variability. We also find much heterogeneity in interannual variability across crops and regions, which is considerably mediated by trade as part of the economic response. Our study provides new insights on climate impacts on agricultural market variability and lays a foundation for further investigating the full range of climate impacts on biophysical and human systems.

Zhao, Xin↗
DOE OSTI2026

Climate-Driven Divergence in Biophysical and Economic Impacts of Agrivoltaics

Increasing global demands for food and energy necessitate innovative land-use solutions. Agrivoltaics, colocating solar photovoltaics with agriculture, shows promise, but its widespread adoption faces complex biophysical and economic trade-offs in a changing climate. Here, we develop an integrated biophysical-economic modeling framework to quantify how agrivoltaics affect biophysical and economic impacts across the Midwestern United States under both current and project climate conditions. We find strong regional divergences driven by climate gradients. In the humid eastern Midwest, solar panel shading limits photosynthesis, leading to reduced yields (maize -24%; soybean -16%) and lower farmers' profitability (maize -16%; soybean -2%) compared to conventional agriculture. Conversely, in the semiarid western region, shading alleviates heat and water stress, moderating yield reductions for maize (-12%) and even boosting soybean yields (+6%), resulting in improved economic returns (-6% for maize; +9% for soybean), for a scenario with 33% photovoltaic ground coverage ratio. Although agrivoltaics generate substantial electrical energy across all regions, high upfront installation costs challenge solar developers compared to standalone solar photovoltaics. However, our analysis identifies “win-win” opportunities where soybean-based agrivoltaics in the semiarid region produce economic benefits for both farmers and solar developers, highlighting the necessity for region-specific designs tailored to local climate conditions. Critically, future climate projections indicate eastward expansion of semiarid conditions, broadening areas where agrivoltaics can mitigate crop yield penalties (even boosting yield) and improve overall profitability, especially under high-emission scenarios. The results provide a mechanistic and economically integrated understanding essential for developing evidence-based and region-specific strategies to scale agrivoltaics in a changing climate.

14 SOLAR ENERGY↗
DOE OSTI2020

Integrated Biophysical Modeling and Image Analysis: Application to Neuro-Oncology

Central nervous system (CNS) tumors come with vastly heterogeneous histologic, molecular, and radiographic landscapes, rendering their precise characterization challenging. The rapidly growing fields of biophysical modeling and radiomics have shown promise in better characterizing the molecular, spatial, and temporal heterogeneity of tumors. Integrative analysis of CNS tumors, including clinically acquired multi-parametric magnetic resonance imaging (mpMRI) and the inverse problem of calibrating biophysical models to mpMRI data, assists in identifying macroscopic quantifiable tumor patterns of invasion and proliferation, potentially leading to improved (a) detection/segmentation of tumor subregions and (b) computer-aided diagnostic/prognostic/predictive modeling. This article presents a summary of (a) biophysical growth modeling and simulation,(b) inverse problems for model calibration, (c) these models' integration with imaging workflows, and (d) their application to clinically relevant studies. We anticipate that such quantitative integrative analysis may even be beneficial in a future revision of the World Health Organization (WHO) classification for CNS tumors, ultimately improving patient survival prospects.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Editorial: Advances in computational molecular biophysics

This special issue of BBA is dedicated to Dr. Jeremy C. Smith on the occasion of his 60th birthday and his more than three decades as a practitioner of computational molecular biophysics. The special issue showcases cutting-edge work in computational molecular biophysics. The senior authors of the manuscripts in this issue are Dr. Smith’s previous or present collaborators. Their articles reflect Dr. Smith’s distinguished career integrating experimental and molecular simulation approaches into a unified description of the function, structure, and dynamics of biomolecules. Each of the articles presented here illustrates one aspect of the broad nature of computational molecular biophysics.

59 BASIC BIOLOGICAL SCIENCES↗