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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 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Filling in the GAPS: evaluating completeness and coverage of open-access biodiversity databases in the United States

Primary biodiversity data constitute observations of particular species at given points in time and space. Open-access electronic databases provide unprecedented access to these data, but their usefulness in characterizing species distributions and patterns in biodiversity depend on how complete species inventories are at a given survey location and how uniformly distributed survey locations are along dimensions of time, space, and environment. Our aim was to compare completeness and coverage among three open-access databases representing ten taxonomic groups (amphibians, birds, freshwater bivalves, crayfish, freshwater fish, fungi, insects, mammals, plants, and reptiles) in the contiguous United States. We compiled occurrence records from the Global Biodiversity Information Facility (GBIF), the North American Breeding Bird Survey (BBS), and federally administered fish surveys (FFS). In this study, we aggregated occurrence records by 0.1° × 0.1° grid cells and computed three completeness metrics to classify each grid cell as well-surveyed or not. Next, we compared frequency distributions of surveyed grid cells to background environmental conditions in a GIS and performed Kolmogorov–Smirnov tests to quantify coverage through time, along two spatial gradients, and along eight environmental gradients. The three databases contributed >13.6 million reliable occurrence records distributed among >190,000 grid cells. The percent of well-surveyed grid cells was substantially lower for GBIF (5.2%) than for systematic surveys (BBS and FFS; 82.5%). Still, the large number of GBIF occurrence records produced at least 250 well-surveyed grid cells for six of nine taxonomic groups. Coverages of systematic surveys were less biased across spatial and environmental dimensions but were more biased in temporal coverage compared to GBIF data. GBIF coverages also varied among taxonomic groups, consistent with commonly recognized geographic, environmental, and institutional sampling biases. Lastly, this comprehensive assessment of biodiversity data across the contiguous United States provides a prioritization scheme to fill in the gaps by contributing existing occurrence records to the public domain and planning future surveys.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Limitations of reanalysis data for wind power applications

Wind energy resource estimates commonly depend on simulated wind speed profiles generated by reanalysis or weather models due to the lack of long time series measurements with sufficient coverage at relevant heights (roughly 90 m above ground). However, modeled data, including reanalyses, can be noisy and display a wide range of biases and errors, variously attributed to terrain effects, poor coverage of assimilated inputs, and model resolution. Wind generation records, if available at high temporal and geographical resolution, can provide a proxy for wind measurements and allow for evaluation of reanalyses and weather model wind time series. We use a 7-year-long data set of hourly, plant-level generation records from over 100 wind plants across Texas to evaluate two commonly used reanalysis data sets (MERRA2 and ERA5). Additionally, we use 1-year of records (2019) to evaluate an operational, high-resolution regional weather modeling product (HRRR v3). We find that across the region, and across all modeling products, the modeled representation of wind generation (i.e., wind speeds at hub heights passed through a power curve) has relatively small mean errors when aggregated daily, but that accuracy and hourly correlation have a strong diurnal sensitivity. Accuracy and correlation systematically decline through the evening and markedly improve after sunrise. These diurnal patterns persist even in the highest resolution model tested (HRRR v3). We hypothesize the nighttime decline in accuracy is mostly due to poorly represented boundary layer conditions, perhaps related to model representation of stability, while other uncertainties (such as wake effects) play a secondary role.

17 WIND ENERGY↗
DOE OSTI2016

Stoichiometric modelling of assimilatory and dissimilatory biomass utilisation in a microbial community

Summary Assimilatory and dissimilatory utilisation of autotroph biomass by heterotrophs is a fundamental mechanism for the transfer of nutrients and energy across trophic levels. Metagenome data from a tractable, thermoacidophilic microbial community in Yellowstone National Park was used to build an in silico model to study heterotrophic utilisation of autotroph biomass using elementary flux mode analysis and flux balance analysis. Assimilatory and dissimilatory biomass utilisation was investigated using 29 forms of biomass‐derived dissolved organic carbon (DOC) including individual monomer pools, individual macromolecular pools and aggregate biomass. The simulations identified ecologically competitive strategies for utilizing DOC under conditions of varying electron donor, electron acceptor or enzyme limitation. The simulated growth environment affected which form of DOC was the most competitive use of nutrients; for instance, oxygen limitation favoured utilisation of less reduced and fermentable DOC while carbon‐limited environments favoured more reduced DOC. Additionally, metabolism was studied considering two encompassing metabolic strategies: simultaneous versus sequential use of DOC. Results of this study bound the transfer of nutrients and energy through microbial food webs, providing a quantitative foundation relevant to most microbial ecosystems.

Hunt, Kristopher A.↗
DOE OSTI2026

Multi‐Model Ensembles in Ecosystem Modeling: Challenges and Best Practices for Decision‐Making

Ecosystem models are increasingly central to the decision-making for environmental policy, conservation planning, and climate-related investments. Yet, the growing reliance on Multi-Model Ensembles (MMEs) of ecosystem models by practitioners and policymakers, sometimes under tight timelines and imperfect information, has frequently outpaced the scientific rigor required to ensure ensemble reliability. Here, MMEs refer to approaches that combine targeted predictions from multiple models with the expectation of improving robustness and quantifying predictive uncertainty. Poorly designed MMEs may create a false sense of confidence and lead to suboptimal policy and market decisions. This perspective argues that robust decision-making-relevant MMEs must be grounded on two pillars: (1) rigorous Model Intercomparison Projects (MIPs), which identify inter-model agreement and disagreement, characterize model uncertainties, and evaluate robustness with observationally based benchmarks—MIPs' diagnostic evaluation is so critical that it must be needed to drive MME's decision in model selection and weighting, especially when only a limited number of models available; and (2) co-design by both stakeholders and scientists to ensure that scenarios, metrics and uncertainty requirements provide decision-relevant information. Building upon the past success and lessons from the existing MIPs-MMEs efforts (e.g., climate/Earth system/crop), we derived the theoretical basis for MMEs, addressed their specific challenges in ecosystem modeling, and highlighted proper consideration of model numbers and diversity, risk of model inter-dependence, effective calibration of model parameters, possible overdue of some ecosystem model development, critical roles of open benchmark data across a wide range of conditions, and suggested use of Artificial Intelligence to support MIPs-MMEs. We highlighted the under-recognized opportunity for MIPs and MMEs to drive scientific progress and innovation through identifying better performing models, systematic benchmarking, feedback loops, and targeted model improvement. By following actionable best practice guidelines, MMEs can evolve from ad hoc aggregation of models into a trusted backbone of environmental policy and decision-making.

ecosystem modeling↗
DOE OSTI2019

Properties of MgFe{sub 2}O{sub 4} Nanoparticles Synthesized by Ultrasonic Aerosol Pyrolysis for Biomedical Applications

We present the data of studies on the structure, phase states, and magnetic properties of magnetic nanoparticles (MNPs) of magnesium ferrite spinel (MgFe{sub 2}O{sub 4}), synthesized by ultrasonic aerosols pyrolysis. Primary single-phase MNPs with an average size of 9.6, 11.5, and 14.0 nm, synthesized from precursors at concentrations of 0.06, 0.12, and 0.24 M, respectively, agglomerate into tightly aggregated spherical particles (secondary particles) with sizes of 206, 300, and 340 nm, respectively. Primary particles inside the spheres do not interact with each other and are in a superparamagnetic state. There is a layer on the surface of the particles, the magnetic structure of which differs from the structure of the inner part of the MNP; this is explained by the formation of a canted spin structure or a spin glass state in the surface layer of the MNPs. MgFe{sub 2}O{sub 4} nanospheres obtained from a precursor at a concentration of 0.06 M are most promising as valid sources of heat in magnetic hyperthermia therapy.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2013

Assessment of Energy Efficiency Improvement in the United States Petroleum Refining Industry

Adoption of efficient process technologies is an important approach to reducing CO 2 emissions, in particular those associated with combustion. In many cases, implementing energy efficiency measures is among the most cost-effective approaches that any refiner can take, improving productivity while reducing emissions. Therefore, careful analysis of the options and costs associated with efficiency measures is required to establish sound carbon policies addressing global climate change, and is the primary focus of LBNL’s current petroleum refining sector analysis for the U.S. Environmental Protection Agency. The analysis is aimed at identifying energy efficiency-related measures and developing energy abatement supply curves and CO 2 emissions reduction potential for the U.S. refining industry. A refinery model has been developed for this purpose that is a notional aggregation of the U.S. petroleum refining sector. It consists of twelve processing units and account s for the additional energy requirements from steam generation, hydrogen production and water utilities required by each of the twelve processing units. The model is carbon and energy balanced such that crud e oil inputs and major refinery sector outputs (fuels) are benchmarked to 2010 data. Estimates of the current penetration for the identified energy efficiency measures benchmark the energy requirements to those reported in U.S. DOE 2010 data. The remaining energy efficiency potential for each of the measures is estimated and compared to U.S. DOE fuel prices resulting in estimates of cost- effective energy efficiency opportunities for each of the twelve major processes. A combined cost of conserved energy supply curve is also presented along with the CO 2 emissions abatement opportunities that exist in the U.S. petroleum refinery sector. Roughly 1,200 PJ per year of primary fuels savings and close to 500 GWh per y ear of electricity savings are potentially cost-effective given U.S. DOE fuel price forecasts. This represents roughly 70 million metric tonnes of CO 2 emission reductions assuming 2010 emissions factor for grid electricity. Energy efficiency measures resulting in an additional 400 PJ per year of primary fuels savings and close to 1,700 GWh per year of electricity savings, and an associated 24 million metric tonnes of CO 2 emission reductions are not cost-effective given the same assumption with respect to fuel prices and electricity emissions factors. Compared to the modeled energy requirements for the U.S. petroleum refining sector, the cost effective potential represents a 40% reduction in fuel consumption and a 2% reduction in electricity consumption. The non-cost-effective potential represents an additional 13% reduction in fuel consumption and an additional 7% reduction in electricity consumption. The relative energy reduction potentials are mu ch higher for fuel consumption than electricity consumption largely in part because fuel is the primary energy consumption type in the refineries. Moreover, many cost effective fuel savings measures would increase electricity consumption. The model also has the potential to be used to examine the costs and benefits of the other CO 2 mitigation options, such as combined heat and power (CHP), carbon capture, and the potential introduction of biomass feedstocks. However, these options are not addressed in this report as this report is focused on developing the modeling methodology and assessing fuels savings measures. These opportunities to further reduce refinery sector CO 2 emissions and are recommended for further research and analysis.

25 ENERGY STORAGE↗
DOE OSTI2020

A Comprehensive Approach to Measure the Efficiency of Freight Transport: Freight Mobility Energy Productivity (F-MEP) Metric

Freight travel accounts for a major share of the energy consumed in the transportation sector in any country, and the United States is no exception. Understanding and modeling freight movement is critical, particularly in the context of capturing the impact of emerging technologies on freight travel and its externalities. The domain of freight modeling and forecasting is gaining pace in the recent years, but advancement in comprehensive freight performance metrics is still lagging. Conventional freight performance metrics such as truck-miles, ton-miles, or value-miles are unidimensional and aggregated in nature, making them unsuitable to accurately capture the impact of emerging transportation trends on the performance of freight systems. Addressing this research need, this paper presents the 'Freight Mobility Energy Productivity' metric to quantify freight performance of current as well as future freight systems, accounting for various costs associated with freight transport. The proposed metric was implemented using data from the Freight Analysis Framework along with other published sources, which shows intuitive results in quantifying freight performance. Further, a scenario analysis exercise was conducted to test the capability of the metric in tracking improvements in system-level freight efficiency as a result of vehicle powertrain technology improvement. Results of the scenario analysis reinforce confidence that the proposed metric can be used as a decision support tool in assessing the efficiency of existing as well as future freight trends and technologies.

25 ENERGY STORAGE↗
DOE OSTI2016

Evaluation of NorESM-OC (versions 1 and 1.2), the ocean carbon-cycle stand-alone configuration of the Norwegian Earth System Model (NorESM1)

Idealised and hindcast simulations performed with the stand-alone ocean carbon-cycle configuration of the Norwegian Earth System Model (NorESM-OC) are described and evaluated. We present simulation results of three different model configurations (two different model versions at different grid resolutions) using two different atmospheric forcing data sets. Model version NorESM-OC1 corresponds to the version that is included in the NorESM-ME1 fully coupled model, which participated in CMIP5. The main update between NorESM-OC1 and NorESM-OC1.2 is the addition of two new options for the treatment of sinking particles. We find that using a constant sinking speed, which has been the standard in NorESM's ocean carbon cycle module HAMOCC (HAMburg Ocean Carbon Cycle model), does not transport enough particulate organic carbon (POC) into the deep ocean below approximately 2000 m depth. The two newly implemented parameterisations, a particle aggregation scheme with prognostic sinking speed, and a simpler scheme that uses a linear increase in the sinking speed with depth, provide better agreement with observed POC fluxes. Additionally, reduced deep ocean biases of oxygen and remineralised phosphate indicate a better performance of the new parameterisations. For model version 1.2, a re-tuning of the ecosystem parameterisation has been performed, which (i) reduces previously too high primary production at high latitudes, (ii) consequently improves model results for surface nutrients, and (iii) reduces alkalinity and dissolved inorganic carbon biases at low latitudes. We use hindcast simulations with prescribed observed and constant (pre-industrial) atmospheric CO 2 concentrations to derive the past and contemporary ocean carbon sink. As a result, for the period 1990–1999 we find an average ocean carbon uptake ranging from 2.01 to 2.58 Pg C yr -1 depending on model version, grid resolution, and atmospheric forcing data set.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Stilbene Boronic Acids Form a Covalent Bond with Human Transthyretin and Inhibit Its Aggregation

Transthyretin (TTR) is a homotetrameric protein. Its dissociation into monomers leads to the formation of fibrils that underlie human amyloidogenic diseases. The binding of small molecules to the thyroxin-binding sites in TTR stabilizes the homotetramer and attenuates TTR amyloidosis. Herein, we report on boronic acid-substituted stilbenes that limit TTR amyloidosis in vitro. Assays of affinity for TTR and inhibition of its tendency to form fibrils were coupled with X-ray crystallographic analysis of nine TTR·ligand complexes. The ensuing structure–function data led to a symmetrical diboronic acid that forms a boronic ester reversibly with serine 117. This diboronic acid inhibits fibril formation by both wild-type TTR and a common disease-related variant, V30M TTR, as effectively as does tafamidis, a small-molecule drug used to treat TTR-related amyloidosis in the clinic. We conclude these findings establish a new modality for covalent inhibition of fibril formation and illuminate a path for future optimization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

System-of-systems optimization of hydrogen infrastructure for heavy-duty freight corridors: The interstate 10 case study

Medium and heavy-duty freight transportation requires hydrogen energy infrastructure that is cost-effective, operationally reliable, spatially coherent, and resilient to demand variability along major corridors. This paper presents an integrated hydrogen corridor planning framework using Oak Ridge National Laboratory's OR-AGENT that couples freight-driven, route-resolved hydrogen demand modeling with optimized station siting, sizing, and station-level techno-economic analysis. The framework is demonstrated for the Interstate 10 freight corridor and the Houston-to-Los-Angeles region. Hydrogen demand is derived from high-resolution origin–destination freight data, duty-cycle characterization, and physics-based energy consumption modeling. Candidate refueling sites are selected from existing heavy-duty diesel fueling locations and optimized subject to onboard storage and station capacity constraints. Resulting station throughputs are evaluated using established techno-economic models for electrolytic hydrogen production and dispensing infrastructure. Results show that a regional, portfolio-level aggregation, average dispensed electrolytic hydrogen cost of $6.87–$7.26/kg is currently feasible, and is strongly influenced by demand density and utilization.

Sujan, Vivek [ORNL] (ORCID:0000000269882342)↗
DOE OSTI2020

Airborne Release Fractions from Surrogate Nuclear Waste Fires Containing Lanthanide Nitrates and Depleted Uranium Nitrate in 30% Tributyl Phosphate in Kerosene

Airborne contaminants from fires containing nuclear waste represent significant health hazards and shape the design and operation of nuclear facilities. Much of the data used to formulate DOE-HDBK-3010-94, “Airborne Release Fractions/Rates and Respirable Fractions for Nonreactor Nuclear Facilities,” from the U.S. Department of Energy, were taken over 40 years ago. The objectives of this study were to reproduce experiments from Pacific Northwest Laboratories conducted in June 1973 employing current aerosol measurement methods and instrumentation, develop an enhanced understanding of particulate formation and transport from fires containing nuclear waste, and provide modeling and experimental capabilities for updating current standards and practices in nuclear facilities. A special chamber was designed to conduct small fires containing 25 mL of flammable waste containing lutetium nitrate, ytterbium nitrate, or depleted uranium nitrate. Carbon soot aerosols showed aggregates of primary particles ranging from 20 to 60 nm in diameter. In scanning electron microscopy, ~200-nm spheroidal particles were also observed dispersed among the fractal aggregates. The 200-nm spherical particles were composed of metal phosphates. Airborne release fractions (ARFs) were characterized by leaching filter deposits and quantifying metal concentrations with mass spectrometry. The average mass-based ARF for 238 U experiments was 1.0 × 10 -3 with a standard deviation of 7.5 × 10 -4 . For the original experiments, DOE-HDBK-3010-94 states, “Uranium ARFs range from 2 × 10 -4 to 3 × 10 -3 , an uncertainty of approximately an order of magnitude.” Therefore, current measurements were consistent with DOE-HDBK-3010-94 values. ARF values for lutetium and ytterbium were approximately one to two orders of magnitude lower than 238 U. Furthermore, metal nitrate solubility may have varied with elemental composition and temperature, thereby affecting ARF values for uranium surrogates (Yb and Lu). In addition to ARF data, solution boiling temperatures and evaporation rates can also be deduced from experimental data.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Bimolecular crystal instability and morphology of bulk heterojunction blends in organic and perovskite solar cells

The performance of polymer:fullerene bulk heterojunction (BHJ) solar cells is significantly influenced by the morphology of electron donor and acceptor blends under the premise that electronic structure is complimentary regarding donor and acceptor materials. There are many cases where intimate mixing of donor and acceptor perturbs the electronic structure, which fundamentally changes the transport and photovoltaic characteristics. In this work, we present the bimolecular crystal packing of polymers and fullerenes, and investigate the correlation between device performance and structural details. We find that bimolecular crystallization can reorganize polymer chain packing, leading to poor electron transport and device performance. The size compatibility and the passivation of entropy penalty led to cocrystallization instability, which was clearly seen by in situ GIWAXS data. PC 61 BM is found to intercalate into side chains of PQT by reorganizing both PQT and PC 61 BM, forming stable, highly ordered bimolecular crystals. The poor electron transport in bimolecular crystal domain could account for the poor device performance in both perovskite and organic solar cells. Bis-PC 61 BM, on the other hand, only forms loosely packed aggregates in solution, and then rapidly phase separated into large domains with an edge-on interfacial orientation and performed poorly in solar cells.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Comprehensive Approach to Measure the Mobility Energy Productivity of Freight Transport

Freight travel accounts for a major share of the energy consumed in the transportation sector in any country, and the United States is no exception. Understanding and modeling freight movement are critical, particularly in the context of capturing the impact of emerging technologies on freight travel and its externalities. The domain of freight modeling and forecasting has been gaining pace in recent years, but advancement in comprehensive freight performance metrics is still lagging. Conventional freight performance metrics such as truck-miles, ton-miles, or value-miles are unidimensional and aggregate in nature, making them unsuitable to accurately capture the impact of emerging transportation trends on the performance or productivity of freight systems. Addressing the research need, this paper presents the “Freight Mobility Energy Productivity” metric to quantify freight productivity of current as well as future freight systems, accounting for various costs associated with freight transport. The proposed metric was implemented using data from the Freight Analysis Framework along with other published sources, and shows intuitive results in quantifying freight productivity. Further, a scenario analysis exercise was conducted to test the capability of the metric in tracking improvements in system-level freight productivity as a result of vehicle electrification. The relative differences in Freight Mobility Energy Productivity scores help identify which zones benefit from the vehicle powertrain technology improvement. The results of the scenario analysis reinforce confidence that the proposed metric can be used as a decision support tool in assessing the productivity of existing as well as future freight trends and technologies.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

A Comprehensive Approach to Measure the Mobility Energy Productivity of Freight Transport: Preprint

Freight travel accounts for a major share of the energy consumed in the transportation sector in any country, and the United States is no exception. Understanding and modeling freight movement are critical, particularly in the context of capturing the impact of emerging technologies on freight travel and its externalities. The domain of freight modeling and forecasting is gaining pace in the recent years, but advancement in comprehensive freight performance metrics is still lagging. Conventional freight performance metrics such as truck-miles, ton-miles, or value-miles are unidimensional and aggregate in nature, making them unsuitable to accurately capture the impact of emerging transportation trends on the performance or productivity of freight systems. Addressing the research need, this paper presents the “Freight Mobility Energy Productivity” metric to quantify freight productivity of current as well as future freight systems, accounting for various costs associated with freight transport. The proposed metric was implemented using data from the Freight Analysis Framework along with other published sources, which shows intuitive results in quantifying freight productivity. Further, a scenario analysis exercise was conducted to test the capability of the metric in tracking improvements in system-level freight productivity as a result of vehicle electrification. The relative differences in Freight Mobility Energy Productivity scores help identify which zones benefit from the vehicle powertrain technology improvement. The results of the scenario analysis reinforce confidence that the proposed metric can be used as a decision support tool in assessing the productivity of existing as well as future freight trends and technologies.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Effects of carbon concentration on the local atomic structure of amorphous GST

Ge-Sb-Te (GST) alloys are leading phase-change materials for data storage due to the fast phase transition between amorphous and crystalline states. Ongoing research aims at improving the stability of the amorphous phase to improve retention. This can be accomplished by the introduction of carbon as a dopant to Ge 2 Sb 2 Te 5 , which is known to alter the short- and mid-range structure of the amorphous phase and form covalently bonded C clusters, both of which hinder crystallization. The relative importance of these processes as a function of C concentration is not known. Here we used molecular dynamics simulation based on density functional theory to study how carbon doping affects the atomic structure of GST-C. Carbon doping results in an increase in tetrahedral coordination, especially of Ge atoms, and this is known to stabilize the amorphous phase. We observe an unexpected, non-monotonous trend in the number of tetrahedral bonded Ge with the amount of carbon doping. Our simulations show an increase in the number of tetrahedral bonded Ge up to 5 at.% C, after which the number saturates and begins to decrease above 14 at.% C. The carbon atoms aggregate into clusters, mostly in the form of chains and graphene flakes, leaving less carbon to disrupt the GST matrix at higher carbon concentrations. Different degrees of carbon clustering can explain divergent experimental results for recrystallization temperature for carbon doped GST.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Confirming a pyrolitic lower mantle using self-consistent pressure scales and new constraints on CaSiO 3 perovskite

In this study, we have examined the lower mantle composition and mineralogy by modeling the density ( ρ ), bulk sound velocity ( V Φ ), and dln ρ /dln V Φ profiles of candidate lower mantle minerals using literature and new experimental equation of state (EoS) results. Additionally, for CaSiO 3 perovskite, complimentary synchrotron X-ray diffraction measurements in a laser-heated diamond anvil cell were conducted up to 156 GPa between 1200 K and 2600 K to provide more reliable constraints on the thermal EoS parameters. These new experimental results as well as literature P-V-T data sets are systematically analyzed using an internally self-consistent pressure scale. We have modeled ρ , V Φ , and dln ρ /dln V Φ profiles of the lower mantle with representative pyrolitic and chondritic compositional models in which the effect of Fe spin transition in ferropericlase is also taken into account. Our modeling results show that a pyrolitic lower mantle with an aggregate mineralogy of 75 vol % bridgmanite, 17 vol % ferropericlase, and 8 vol % CaSiO 3 perovskite produces ρ and V Φ profiles in better agreement with preliminary reference Earth model than a lower mantle with a chondritic composition. The modeled ρ , V Φ , and dln ρ /dln V Φ are mainly affected by the relative ratio of bridgmanite and ferropericlase but are not sensitive to the variation of the CaSiO 3 perovskite content. In addition, the spin crossover of Fe in ferropericlase can greatly raise the value of dln ρ /dln V Φ in the middle lower mantle, which is useful in detecting the presence of ferropericlase in the region. Based on these new mineral physical constraints and radial seismic structure, our study suggests the lower mantle is pyrolitic, which is chemically indistinguishable from the upper mantle.

58 GEOSCIENCES↗
DOE OSTI2016

PRKCI negatively regulates autophagy via PIK3CA/AKT–MTOR signaling

The atypical protein kinase C isoform PRKC iota (PRKCI) plays a key role in cell proliferation, differentiation, and carcinogenesis, and it has been shown to be a human oncogene. Here, we show that PRKCI overexpression in U2OS cells impaired functional autophagy in normal or cell stress conditions, as characterized by decreased levels of light chain 3B-II protein (LC3B-II) and weakened degradation of endogenous and exogenous autophagic substrates. Conversely, PRKCI knockdown by small interference RNA resulted in opposite effects. Additionally, we identified two novel PRKCI mutants, PRKCI{sup L485M} and PRKCI{sup P560R}, which induced autophagy and exhibited dominant negative effects. Further studies indicated that PRKCI knockdown–mediated autophagy was associated with the inactivation of phosphatidylinositol 3-kinase alpha/AKT–mammalian target of rapamycin (PIK3CA/AKT–MTOR) signaling. These data underscore the importance of PRKCI in the regulation of autophagy. Moreover, the finding may be useful in treating PRKCI-overexpressing carcinomas that are characterized by increased levels of autophagy. - Highlights: • The atypical protein kinase C iota isoform (PRKCI) is a human oncogene. • PRKCI overexpression impairs functional autophagy in U2OS cells. • It reduces LC3B-II levels and weakens SQSTM1 and polyQ80 aggregate degradation. • PRKCI knockdown has the opposite effect. • The effect of PRKCI knockdown is related to PIK3CA/AKT–MTOR signaling inactivation.

60 APPLIED LIFE SCIENCES↗