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At least 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

On the energy footprint of I/O management in Exascale HPC systems

The advent of unprecedentedly scalable yet energy hungry Exascale supercomputers poses a major challenge in sustaining a high performance-per-watt ratio. With I/O management acquiring a crucial role in supporting scientific simulations, various I/O management approaches have been proposed to achieve high performance and scalability. But, the details of how these approaches affect energy consumption have not been studied yet. Therefore, this paper aims to explore how much energy a supercomputer consumes while running scientific simulations when adopting various I/O management approaches. In particular, we closely examine three radically different I/O schemes including time partitioning, dedicated cores, and dedicated nodes. To accomplish this, we implement the three approaches within the Damaris I/O middleware and perform extensive experiments with one of the target HPC applications of the Blue Waters sustained-petaflop supercomputer project: the CM1 atmospheric model. Our experimental results obtained on the French Grid'5000 platform highlight the differences among these three approaches and illustrate in which way various configurations of the application and of the system can impact performance and energy consumption. Moreover, we propose and validate a mathematical model that estimates the energy consumption of a HPC simulation under different I/O approaches. This proposed model gives hints to pre-select the most energy-efficient I/O approach for a particular simulation on a particular HPC system and therefore provides a step towards energy-efficient HPC simulations in Exascale systems. To the best of our knowledge, our work provides the first in-depth look into the energy-performance tradeoffs of I/O management approaches.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Tri-level hybrid interval-stochastic optimal scheduling for flexible residential loads under GAN-assisted multiple uncertainties

Various building loads, such as heating, ventilation, and air conditioners (HVACs), electric water heaters (EWHs), and electric vehicles (EVs), can introduce opportunities for improving the flexibility of electricity consumption while satisfying the needs of building owners as well as benefiting the resilience of distribution system. To utilize such flexibility, a tri-level distribution market framework is established, including residential consumers, load aggregators (LAs), and the distribution system operator (DSO). In this work, the uncertainties from all three levels are considered. The random consumption behavior at the consumer level is modeled as a Gaussian noise that is also aggregated and transmitted to the LA level. The weather temperature in the LA level is forecasted as an interval, and the photovoltaic (PV) power in the market-clearing level is modeled by a set of power scenarios generated by Generative Adversarial Networks (GANs). Then, a hybrid interval-stochastic programming is proposed to transform the uncertain problems in the first two levels into deterministic ones. For real-time implementations, a rolling horizon optimization (RHO) scheme is employed to continuously optimize the power consumption based on the latest operating information. Finally, case studies on a modified IEEE 69-bus system validate the effectiveness of the proposed uncertainty modeling strategies and the RHO scheme.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Application of liquid desiccant dehumidification to amine-based carbon capture systems

We report liquid desiccant-based dehumidification with water recovery is an effective and robust method to remove excess moisture from process gas streams and recover relatively high quality product water. A study was undertaken to determine if desiccant-based technology could be applied to the benefit of an amine-based CO 2 capture system since the addition of CO 2 capture to a coal-fired power plant can significantly increase the plant’s consumptive water use. Test data with a pilot flue gas moisture recovery system are presented to illustrate the characteristics of the process, including the water recovery potential, product water quality, and thermal energy consumption. The process is then evaluated theoretically under conditions typical of a monoethanolamine-based CO 2 capture process. The analysis indicates that there is less opportunity for moisture recovery compared to a plant without CO 2 capture because of the cooler processing temperatures associated with CO 2 absorption and stripping. However, a desiccant-based dehumidification system might be useful as a heat-driven moisture management tool since desiccant-based moisture removal is more effective than dew point cooling for a given heat rejection temperature. Dehumidifying the CO 2 product stream prior to compression might offer incremental power consumption savings compared to other options of moisture vapor management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Comparison of well-to-wheels energy use and emissions of a hydrogen fuel cell electric vehicle relative to a conventional gasoline-powered internal combustion engine vehicle

The operation of hydrogen fuel cell electric vehicles (HFCEVs) is more efficient than that of gasoline conventional internal combustion engine vehicles (ICEVs), and produces zero tailpipe pollutant emissions. However, the production, transportation, and refueling of hydrogen are more energy- and emissions-intensive compared to gasoline. In this research, a well-to-wheels (WTW) energy use and emissions analysis was conducted to compare a HFCEV (Toyota Mirai) with a gasoline conventional ICEV (Mazda 3). Two sets of specific fuel consumption data were used for each vehicle: (1) fuel consumption derived from the U.S. Environmental Protection Agency's (EPA's) window-sticker fuel economy figure, and (2) weight-averaged fuel consumption based on physical vehicle testing with a chassis dynamometer on EPA's five standard driving cycles. The WTW results show that a HFCEV, even fueled by hydrogen from a fossil-based production pathway (via steam methane reforming of natural gas), uses 5%–33% less WTW fossil energy and has 15%–45% lower WTW greenhouse gas emissions compared to a gasoline conventional ICEV. The WTW results are sensitive to the source of electricity used for hydrogen compression or liquefaction.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Assessing microbial systems and process configurations for improved ethanol production from sugary stovers by integrating soluble sugars and holocellulose

Here, this study evaluated microbial systems and technological approaches to configure the whole slurry co-fermentation process for ethanol biosynthesis from a novel stover system rich in sugars. Two approaches, namely separate and simultaneous hydrolysis and co-fermentation (SHCF and SSCF, respectively), were investigated using Escherichia coli monoculture and E. coli-yeast coculture. The SSCF with E. coli monoculture produced 32.75 g/L ethanol, representing only 44.87% yield, which left 65.13 g/L of total sugars unconverted and exhibited limited xylose consumption. Subsequently, a coculture-based SHCF significantly enhanced sugar consumption, leading to increase in ethanol yield and concentration to 66.94% and 48.86 g/L, respectively. Nevertheless, xylose utilization remained minimal due to the preference for glucose and the inhibitory effects of certain compounds. Thereafter, modification of the medium composition by supplementing betaine and sodium metabisulfite improved ethanol production to 53.18 g/L by reducing the toxic effects of inhibitors. Finally, a dual-phase SSCF (DP-SSCF) was explored by allowing the consumption of sugars from the pretreated slurry in the first phase, followed by concurrent cellulose hydrolysis and utilization of the resulting glucose in the second phase. This strategy increased ethanol titer to 63.14 g/L, with 84.3% yield and 0.88 g/L/h productivity.

09 BIOMASS FUELS↗
DOE OSTI2020

Dendrite-free Al recycling via electrodeposition using ionic liquid electrolytes: The effects of deposition temperature and cathode surface roughness

In this report, the electrodeposition of Al from aluminum scrap alloys (A2020) on copper cathode substrates with varied surface roughness under different deposition temperatures was studied using low-temperature AlCl 3 -1-butyl-3-methyl-imidazolium chloride (BMIC) ionic liquid electrolytes. The bulk electrodeposition of Al was carried out under a voltage of 1.5 V at a stirring rate of 120 rpm using a fixed ionic liquid electrolyte concentration (molar ratio AlCl 3 : BMIC = 2:1). The effects of deposition temperature (range from 80 °C to 140 °C) and surface roughness of Cu cathode substrates (polished by 320, 600, 800, 1200 grits SiC sandpapers and mirror polishing process) on the morphology of deposited Al, current density, current efficiency and energy consumption, were investigated. The Al deposits were characterized using scanning electron microscopy (SEM) with energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), profilometer, and electrochemical measurements for current density, current efficiency, and energy consumption. It is demonstrated that the deposition temperature and surface roughness of Cu electrodes play a critical role in the nucleation and growth of Al deposits. Higher deposition temperature promotes the diffusion and/or migration of Al 2 Cl 7 - ions and then enhances the current density and efficiency during the electrodeposition of Al. Smoother surface of Cu electrodes is preferred for the formation of dendrite-free Al deposits. Typically, on the mirror polished Cu electrode, no Al dendrite structure was observed, and only plate-like Al deposits were formed at the deposition temperature of 100 °C. Pure metallic Al was successfully deposited on Cu electrodes in AlCl 3 + BMIC ionic liquid electrolytes for all experiments with a current efficiency range from 72% to 99% and energy consumption of 4.6–6.3 kW h/kg Al.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Pseudospectral convex optimization for on-ramp merging control of connected vehicles

It can be a daunting task for human drivers to merge into highways because of the intricate vehicle negotiations and potential risk within limited time and space. Connected vehicle (CV) technologies could be a solution to this problem and offer many benefits to the road safety, traffic mobility, and energy efficiency. However, real-time optimal control of CVs is still an open challenge, due to the nonlinear vehicle dynamics, non-convex fuel consumption model, and highly dynamic uncertain inter-vehicle interactions. To tackle these issues, a novel real-time optimal control approach that balances the computational efficiency and solution optimality is proposed for the purpose of onboard application. To this end, the pseudospectral collocation method is integrated with a sequential convex programming approach to develop two new optimization algorithms, which are implemented within a model predictive control (MPC) framework to allow for real-time generation of optimal merging speed profiles. One algorithm leverages the line search technique to improve convergence, and the other benefits from the trust region method for better computational efficiency. The optimality and convergence process of both proposed algorithms are investigated by comparing their solutions with a popular non-linear solver. Furthermore, simulation results show that the proposed methods outperform the benchmark in terms of computational cost, fuel consumption, and traffic efficiency. In particular, the proposed fuel-economy merging rule can save 57.1% fuel consumption on average on four different traffic volumes. Meanwhile, the proposed optimal control algorithms can reduce 2.2% travel time on average comparing to the “first-in-first-out” merging rule.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Investigating uncertainties in human adaptation and their impacts on water scarcity in the Colorado river Basin, United States

The Colorado River Basin (CRB) supports the water supply for seven states and forty million people in the Western United States (US) and has been suffering an extensive drought for more than two decades. As climate change continues to reshape water resources distribution in the CRB, its impact can differ in intensity and location, resulting in variations in human adaptation behaviors. The feedback from human systems in response to the environmental changes and the associated uncertainty is critical to water resources management, especially for water-stressed basins. This paper investigates how human adaptation affects water scarcity uncertainty in the CRB and highlights the uncertainties in human behavior modeling. Our focus is on agricultural water consumption, as approximately 80% of the water consumption in the CRB is used in agriculture. We adopted a coupled agent-based and water resources modeling approach for exploring human-water system dynamics, in which an agent is a human behavior model that simulates a farmer’s water consumption decisions. We examined uncertainties at the system, agent, and parameter levels through uncertainty, clustering, and sensitivity analyses. The uncertainty analysis results suggest that the CRB water system may experience 13 to 30 years of water shortage during the 2019–2060 simulation period, depending on the paths of farmers’ adaptation. The clustering analysis identified three decision-making classes: bold, prudent, and forward-looking, and quantified the probabilities of an agent belonging to each class. The sensitivity analysis results indicated agents whose decision-making models require further investigation and the parameters with the higher uncertainty reduction potentials. Here, by conducting numerical experiments with the coupled model, this paper presents quantitative and qualitative information about farmers’ adaptation, water scarcity uncertainties, and future research directions for improving human behavior modeling.

Agent-based modeling↗
DOE OSTI2017

Aspen-based performance and energy modeling frameworks

Now with the anticipation of exascale architectures, energy consumption is becoming one of the critical design parameters, especially in light of the energy budget of 20-30 Megawatts set by the U.S. Department of Energy. Understanding an application’s execution pattern and its energy footprint is critical to improving application operation on a diverse heterogeneous architecture. Applying application-specific performance optimization can consequently improve energy consumption. However, this approach is only applicable to current systems. As we enter a new era of exascale architectures that is projected to contain more complex memory hierarchies, increased levels of parallelism, heterogeneity in hardware, and complex programming models and techniques, energy and performance management is getting more cumbersome. We therefore propose techniques that predict the energy consumption beforehand or at runtime to enable proactive tuning. Such energy prediction approaches must be generic and adapt themselves at runtime to changing application and hardware configurations. Most existing energy estimation and prediction approaches are empirical in nature and thus tied to current systems. To overcome this limitation, we propose two energy estimation techniques: ACEE (Algorithmic and Categorical Energy Estimation), which uses a combination of analytical and empirical modeling techniques; and AEEM (Aspen’s Embedded Energy Estimation), a system-level analytical energy estimation technique. Both of these models incorporate the Aspen domain specific language for performance modeling. We present the methodologies of these two models and test their accuracy using five proxy applications. We also describe three use cases.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Application of Intelligent Load Control to Manage Building Loads to Support Rapid Growth of Distributed Renewable Generation

Electricity utilities are faced with the mounting challenge of providing a stable supply of power to meet the growing demand while also integrating rapid growth in distributed variable renewable generation. Traditional means of balancing short- and long-term supply and demand imbalance will be expensive. Alternative approaches of using flexible loads in buildings are needed to mitigate the imbalance at a lower cost. This paper shows how the intelligent load control (ILC) process can be used to manage loads in buildings by dynamically prioritizing loads for curtailment using both quantitative and qualitative criteria. The ILC process can be deployed on low-cost computing platforms without the need for any additional sensing. ILC was first validated in a simulation environment to provide two grid service use cases: (1) managing monthly peak electricity demand and (2) managing buildings’ electricity consumption during a capacity bidding event. After being successfully tested in a simulation environment, ILC was deployed on real buildings to manage electricity consumption to provide two different use cases under different outdoor operating conditions. Both the simulation tests and the real building experiments were deployed using VOLTTRON™, a distributed sensing and control platform. The results from the tests and experiments showed that ILC was able to manage the controllable loads (heat pumps) in the building to maintain the electricity consumption at the desired level without a significant impact on occupant comfort. Overall, the results demonstrate that the ILC allows coordination of the controllable loads and provides a more intelligent means of load management than the traditional duty-cycling approach.

Kim, Woohyun↗
DOE OSTI2020

Partitioning sources of CO 2 emission after soil wetting using high-resolution observations and minimal models

Wetting dry soil triggers a pulse of microbial respiration. Respiration pulses after wetting can be complex, evolving through several stages before subsiding—possibly reflecting contributions from multiple carbon (C) sources. In this work we hypothesized that respiration after wetting combines C from cellular sources (notably trehalose, a cellular osmo-protectant) and soluble extracellular C made available by the physical effects of wetting, with cellular sources contributing early during the pulse and extracellular C dominating later. We tested this hypothesis by making measurements of soil respiration, microbial biomass, trehalose, and water extractable organic C after wetting dry soil in the laboratory. We then devised several alternative minimal models to evaluate the potential contributions of trehalose and extracellular C to the respiration pulse. We found that the chloroform-labile fraction of the microbial biomass was comprised of 50% trehalose in dry soil. Both trehalose and water extractable organic C pools declined rapidly after wetting; trehalose disappeared within 3 h of water addition. Chloroform-labile glucose increased 3 h after water addition and then declined, suggesting rapid hydrolysis of trehalose and consumption of the resulting glucose. Respiration dynamics after wetting could be best explained by models that included two C sources, combining rapid metabolism of trehalose with slower consumption of extracellular soluble C. These results suggest that pulses of respiration after wetting reflect both osmolyte consumption within the microbial biomass and enhanced extracellular C availability; hence, models of microbial drought response must account for both processes to fully resolve soil C dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Resolving dynamic mineral-organic interactions in the rhizosphere by combining in-situ microsensors with plant-soil reactive transport modeling

Associations between minerals and organic matter represent one of the most important carbon storage mechanisms in soils. Plant roots are major sources of soil carbon, and resolving the dynamics and dominance of microbial consumption versus mineral sorption of root-derived carbon is critical to understanding soil carbon storage. Here we integrate in-situ rhizosphere microsensor and plant physiological measurements with a 3-D plant-soil reactive transport model to explore the fate of dissolved organic carbon (DOC) in the rhizosphere, particularly its microbial consumption and interaction with Fe oxide minerals. Over several days, a microdialysis probe sampling pore water at the root-soil interface of growing Vicia faba roots in live soil, revealed clear diel patterns of DOC concentration. Daytime DOC spikes coincided with peaks in leaf-level photosynthesis rates and were accompanied by declining redox potential and dissolved oxygen as well as increasing pH in the rhizosphere. Incorporating microsensor data into our modeling framework showed that the measured rapid loss of DOC after each mid-day spike could not be explained by consumption via aerobic respiration, nor via anaerobic respiration dominated by Fe oxide reduction. Rather, in the model, a large fraction of rhizosphere DOC was rapidly immobilized each day by adsorption to Fe oxides. Further, modeled microbial Fe reduction (fueled by DOC) did not mobilize significant organic carbon from Fe oxides during the day. Instead, the model predicted equilibrium desorption of organic carbon from Fe oxides at night. This new mechanistic modeling framework, which couples aboveground plant physiological measurements with non-destructive high-resolution monitoring of rhizosphere processes, has great potential for exploring the dynamics and balance of the various microbial reactions and mineral interactions controlling carbon transformations and storage in soils.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Methane flux from transplanted soil monoliths depends on moisture, but not origin

Soils both produce and consume methane (CH 4 ), a potent greenhouse gas that contributes to climate change. In coastal forests, upland soils are shifting from being CH 4 sinks to sources as sea levels rise, increasingly flooding soils with little prior inundation history. Ecosystem CH 4 budgets are highly uncertain due in part to the difficulty in separating fluxes measured at the soil surface into individual production and consumption processes which are likely to have different responses to future environmental conditions. Here, we measured growing season CH 4 fluxes from soil monoliths transplanted four years prior along an inundation and salinity gradient to determine how changes in abiotic conditions control CH 4 flux rates. To parse net fluxes measured at the soil surface into their component gross rates, we paired field measurements with a stable isotope pool dilution incubation of surface soils. Throughout the growing season, net soil surface CH 4 flux was positively correlated with soil moisture (p < 0.01), with lowland-located soils tending towards CH 4 sources (mean 0.349 ± 1.11 mg CH 4 -C m -2 hr -1 , error is standard deviation) and upland-located soils tending towards CH 4 sinks (-0.003 ± 0.003 mg CH 4 -C m -2 hr -1 ). Transplanted soils’ fluxes were statistically identical to their native neighbors once microtopography-driven differences in soil moisture were controlled for. The pool dilution experiment revealed that production and consumption rates were similar in upland and lowland surface soils (2.82 ± 3.29 µmol CH 4 g -1 dry soil d -1 production, 3.47 ± 2.03 µmol CH 4 g -1 dry soil d -1 consumption), indicating the majority of production likely occurs at depth in lowland soils. Both gross and net fluxes from transplanted soils showed no effect of soil origin after four years, suggesting low resistance of CH 4 cycling to global change drivers. Our results indicate the strength of the coastal forest CH 4 sink is likely to decrease in proportion to sea-level rise.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Intake of high-fat diet stimulates the risk of ultraviolet radiation-induced skin tumors and malignant progression of papillomas to carcinoma in SKH-1 hairless mice

Previously, we showed that administration of a high-fat diet (HF-diet) to C57BL/6 mice exacerbates their response to short-term UVB radiation-induced inflammation in the skin. To explore the effects of an HF-diet on UVB-induced tumorigenesis, we have used the SKH-1 hairless mouse model in which the mice are exposed to UVB radiation (180 mJ/cm{sup 2}) three times a week for 24 weeks. The development of UVB-induced skin tumors was rapid and the tumor multiplicity and tumor size were significantly higher (P < 0.01–0.005) in the mice fed an HF-diet than the mice fed a control-diet (C-diet). Moreover, the malignant progression of UVB-induced papillomas to carcinomas was higher in HF-diet-fed mice. On analysis of tumors and tumor-uninvolved skin samples from the tumor-bearing mice, we found that administration of an HF-diet significantly enhanced the levels of UVB-induced expression of cyclooxygenase-2 (COX-2), prostaglandin E{sub 2} (P < 0.01), and PGE{sub 2} receptors, and activation of NF-κB in the UVB-exposed skin as well as in tumors. In addition the HF-diet enhanced the expression of proinflammatory cytokines, including tumor necrosis factor-α (P < 0.01), interleukin (IL)-1β (P < 0.01) and IL-6 (P < 0.05) in the UVB-exposed skin as well as in tumors. Western blot analysis revealed that HF-diet enhanced the levels of epidermal cell proliferation, phosphatidylinositol 3-kinase and phosphorylation of Akt at Ser{sup 473} in UVB-exposed skin and skin tumors. Collectively, these data demonstrate that the regular consumption of an HF-diet increases the risk of photocarcinogenesis in mice and that this is associated with enhanced expression of inflammatory mediators in the UVB-exposed skin and tumors. - Highlights: • Consumption of high-fat diet increases UVB-induced skin tumor development in mice. • Intake of high-fat diet stimulates progression of UV-induced papilloma to carcinoma. • Intake of high-fat diet enhances inflammation in UV-exposed skin • Regular consumption of low-fat diet may inhibit the risk of photocarcinogenesis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Ethanol metabolism, oxidative stress, and endoplasmic reticulum stress responses in the lungs of hepatic alcohol dehydrogenase deficient deer mice after chronic ethanol feeding

Consumption and over-consumption of alcoholic beverages are well-recognized contributors to a variety of pulmonary disorders, even in the absence of intoxication. The mechanisms by which alcohol (ethanol) may produce disease include oxidative stress and prolonged endoplasmic reticulum (ER) stress. Many aspects of these processes remain incompletely understood due to a lack of a suitable animal model. Chronic alcohol over-consumption reduces hepatic alcohol dehydrogenase (ADH), the principal canonical metabolic pathway of ethanol oxidation. We therefore modeled this situation using hepatic ADH-deficient deer mice fed 3.5% ethanol daily for 3 months. Blood ethanol concentration was 180 mg% in ethanol fed mice, compared to < 1.0% in the controls. Acetaldehyde (oxidative metabolite of ethanol) was minimally, but significantly increased in ethanol-fed vs. pair-fed control mice. Total fatty acid ethyl esters (FAEEs, nonoxidative metabolites of ethanol) were 47.6 μg/g in the lungs of ethanol-fed mice as compared to 1.5 μg/g in pair-fed controls. Histological and immunohistological evaluation showed perivascular and peribronchiolar lymphocytic infiltration, and significant oxidative injury, in the lungs of ethanol-fed mice compared to pair-fed controls. Several fold increases for cytochrome P450 2E1, caspase 8 and caspase 3 found in the lungs of ethanol-fed mice as compared to pair-fed controls suggest role of oxidative stress in ethanol-induced lung injury. ER stress and unfolded protein response signaling were also significantly increased in the lungs of ethanol-fed mice. Surprisingly, no significant activation of inositol-requiring enzyme-1α and spliced XBP1 was observed indicating a lack of activation of corrective mechanisms to reinstate ER homeostasis. The data suggest that oxidative stress and prolonged ER stress, coupled with formation and accumulation of cytotoxic FAEEs may contribute to the pathogenesis of alcoholic lung disease. - Highlights: • Chronic ethanol feeding causes oxidative stress, ER stress and inflammation in lungs of ADH– deer mice. • Chronic ethanol feeding generates FAEEs (nonoxidative metabolites of ethanol) in lungs of ADH– deer mice. • Chronic ethanol feeding induces CYP2E1 in the lungs of ADH– deer mice. • Lack of ER homeostasis due to a prolonged ethanol feeding could trigger inflammation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Uterine responses to feeding soy protein isolate and treatment with 17β-estradiol differ in ovariectomized female rats

There are concerns regarding reproductive toxicity from consumption of soy foods, including an increased risk of endometriosis and endometrial cancer, as a result of phytoestrogen consumption. In this study, female rats were fed AIN-93G diets made with casein (CAS) or soy protein isolate (SPI) from postnatal day (PND) 30, ovariectomized on PND 50 and infused with 5 μg/kg/d 17β-estradiol (E2) or vehicle. E2 increased uterine wet weight (P < 0.05). RNAseq analysis revealed that E2 significantly altered expression of 1991 uterine genes (P < 0.05). SPI feeding had no effect on uterine weight and altered expression of far fewer genes than E2 at 152 genes (P < 0.05). Overlap between E2 and SPI genes was limited to 67 genes. Functional annotation analysis indicated significant differences in uterine biological processes affected by E2 and SPI and little evidence for recruitment of estrogen receptor (ER)α to the promoters of ER-responsive genes after SPI feeding. The major E2 up-regulated uterine pathways were carcinogenesis and extracellular matrix organization, whereas SPI feeding up-regulated uterine peroxisome proliferator activated receptor (PPAR) signaling and fatty acid metabolism. The combination of E2 and SPI resulted in significant regulation of 504 fewer genes relative to E2 alone. The ability of E2 to induce uterine proliferation in response to the carcinogen dimethybenz(a)anthracene (DMBA) as measured by expression of PCNA and Ki67 mRNA was suppressed by feeding SPI (P < 0.05). These data suggest that SPI is a selective estrogen receptor modulator (SERM) interacting with a small sub-set of E2-regulated genes and is anti-estrogenic in the presence of endogenous estrogens. - Highlights: • Concerns exist regarding risk of uterine cancer from consumption of soy products. • These concerns are related to potential estrogenicity. • Estradiol and soy protein isolate effects on uterine gene expression were compared. • Soy acts as a selective estrogen receptor modulator not a weak estrogen. • Soy feeding blocked uterine proliferation after treatment with estradiol and DMBA.

60 APPLIED LIFE SCIENCES↗