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At least 253 records · Page 14

Intra-canopy leaf trait variation facilitates high LAI and compensatory growth in a clonal woody encroaching shrub in the tallgrass prairie

This data package contains the data for Tooley et al. (2022) Intra-canopy leaf trait variation facilitates high leaf area index and compensatory growth in a clonal woody encroaching shrub. The purpose of this project was to evaluate the leaf ecophysiological traits of Cornus drummondii C.A. Mey to better understand how C. drummondii maximizes photosynthetic rates across large gradients of light availability in its short, dense canopies. We also evaluate how C. drummondii modifies its allocation of leaf traits in response to grassland disturbance. High LAI canopies and a compensatory growth response to disturbance are two key factors contributing to the success of C. drummondii and other woody species responsible for the encroachment of grasslands.The data package contains ecophysiological measurements, including leaf morphological, physiological, and chemical traits; photosynthetic measurements, leaf carbon and nitrogen stable isotopes, leaf area index, and photosynthetically active radiation. All measurements were taken vertically across canopies of the clonal woody-encroaching shrub Cornus drummondii under browsed, grazed, and control (unbrowsed and ungrazed) conditions.Files:SeasonalMeasurements_Archive.csv - Data for all seasonal measurements used in the study.LeafPhotoSeasonalRaw_Archive.zip - zip file containing raw data from an li-6400 for each sampling period in the study.LAI_Archive.xlsx - Data for LAI Measurements.ACiandLightCurveResults_Archive.xlsx - Contains parameters extracted from A-ci and Light Response curves along with the morphological and chemical traits for each leaf.ACiRaw Archive.xlsx - Raw data from Li-6400 for A-ci response curvesLightCurves_Raw.zip - zip file containing raw data for Li-6400 light response curves. Excel file format: "IslandID"-"Depth"-lc.xlsx (or .csv)DataCollectionMethods.docx - file containing the methods of data collection, processing, and QA/QC for all data files. File also contains a list of excel file headers, abbreviations, and their units.

54 ENVIRONMENTAL SCIENCES↗

Reporting Quality of Randomized, Controlled Trials Evaluating Combined Chemoradiotherapy in Nasopharyngeal Carcinoma

Purpose: To comprehensively assess the reporting quality of randomized, controlled trials (RCTs) in nasopharyngeal carcinoma (NPC), and to identify significant predictors of quality. Methods and Materials: Two investigators searched MEDLINE and EMBASE for RCTs published between January 1988 and December 2015 that assessed the effect of combined chemoradiotherapy for NPC. The overall quality of each report was assessed using a 28-point overall quality score (OQS) based on the 2010 Consolidated Standards of Reporting Trials (CONSORT) statement. To provide baseline data for further evaluation, we also investigated the reporting quality of certain important issues in detail, including key methodologic items (allocation concealment, blinding, intention-to-treat principle), endpoints, follow-up, subgroup analyses, and adverse events. Results: We retrieved 24 relevant RCTs including 6591 patients. Median 2010 OQS was 15.5 (range, 10-24). Half of the items in the 2010 OQS were poorly reported in at least 40% of trials. Multivariable regression models revealed that publication after 2010 and high impact factor were significant predictors of improved 2010 OQS. Additionally, many issues that we consider significant were not reported adequately. Conclusions: Despite publication of the CONSORT statement more than a decade ago, overall reporting quality for RCTs in NPC was unsatisfactory. Additionally, substantial selectivity and heterogeneity exists in reporting of certain crucial issues. This survey provides the first prompt for NPC trial investigators to improve reporting quality according to the CONSORT statement; increased scrutiny and diligence by editors and peer reviewers is also required.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Endophyte mediated Populus trichocarpa water use efficiency is dependent on time of day and plant water status

Endophytes are potential partners for improving the resource use efficiency of bioenergy feedstock systems such as short rotation coppiced Populus. Endophytes isolated from members of the Salicaceae family have broad host compatibility and can improve water use efficiency (WUE) through decreases in stomatal conductance. However, the literature is inconsistent with regards to the environmental conditions and temporal patterns of these benefits. This study investigates how endophyte mediated changes in Populus trichocarpa (Torr. and Gray) ‘Nisqually-1’ stomatal conductance and WUE shift with time and scale in response to water-deficit stress. Leaf gas exchange and above-ground productivity were used to evaluate the carbon and water balances of glasshouse grown plants in response to endophyte inoculation and water-deficit. Differences in stomatal conductance between control and inoculated plants were more pronounced (39.7 % decrease, Welch’s two-sample t(14.34 dfadj) = -2.358, p-value = 0.033) under water-deficit conditions in the late morning during a period of higher light intensity. The decrease in stomatal conductance accompanied a substantial increase in intrinsic WUE (iWUE) for water-deficit inoculated plants. However, increases in iWUE did not result in improvements in aboveground productivity or shoot biomass WUE (WUEsb) for water-deficit inoculated plants. This decoupling between iWUE and aboveground productivity may be an indicator of assimilate allocation to microbial metabolism as an additional carbon sink or a shift in carbon allocation towards belowground biomass. Future work should take a whole-plant approach that accounts for diurnal patterns in incident irradiance to evaluate the impact of endophyte inoculation on host WUE and stress tolerance.

59 BASIC BIOLOGICAL SCIENCES↗

Advanced Instrumentation, Information, and Control Systems Technologies Pathway: FY 2016 External Review

This report describes an External Review conducted by the LWRS Program Advanced Instrumentation, Information, and Control (II&C) Systems Technologies Pathway to solicit feedback on the topics and results of the ongoing II&C research program. This review was held in conjunction with the Nuclear Energy Institute (NEI) Digital I&C Working Group meeting that was held at Idaho National Laboratory (INL) on August 9-10, 2016. Given the opportunity to visit INL and see the pathway research projects, NEI agreed that the Working Group would serve as the External Review panel for the purpose of obtaining expert input on the value and timing of the research projects. This consisted of demonstrations in the Human Systems Simulation Laboratory followed by presentations on the II&C research program in general as well as the five technology development areas. Following the meeting, the presentations were sent to each of the attendees so they could review them in more detail and refer to them in completing the feedback form. Follow-up activities were conducted with the attendees following the meeting to obtain the completed feedback forms. A total of 13 forms were returned. The feedback forms were reviewed by the pathway to compile the data and comments received, which are documented in the report. In all, the feedback provided by the External Review participants is taken to be a strong endorsement of the types of projects being conducted by the pathway, the value they hold for the nuclear plants, and the general timing of need. The feedback aligns well with the priorities, levels of efforts allocated for the research projects, and project schedules. The feedback also represents realistic observations on the practicality of some aspects of implementing these technologies. In some cases, the participants provided thoughtful challenges to certain assumptions in the formulation of the technologies or in deployment plans. These deserve further review and revision of plans if warranted. The pathway will take all of the feedback and address the open issues that have been identified by the participants. This includes 11 actionable items for follow up by the II&C Pathway.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Enhancing Grid Resilience with HIVE: Decentralized V2G Coordination for Black Starts

This paper proposes the HIVE (Harmonized Integration of Vehicle Energy for Grid Support) model, a novel game-theoretic framework for decentralized coordination of electrified vehicles to enable black start and load restoration during grid outages. In the absence of a central controller, HIVE employs a cooperative game to model vehicle interactions, allowing autonomous decision-making while admitting to a Nash equilibrium for grid restoration. The framework addresses the heterogeneity of vehicles and their operational constraints, selecting a lead vehicle for grid-forming and coordinating grid-following vehicles to support prioritized loads. Applied to a hospital blackout scenario, HIVE demonstrates robust performance in forming an islanded microgrid and sustaining critical loads under varying vehicle availability, state of charge, and power constraints. Simulation results highlight the model’s effectiveness in ensuring decentralized coordination of energy allocation and prioritizing loads, offering a scalable solution for resilient grid operations.

32 - ENERGY CONSERVATION, CONSUMPTION, AND UTILIZA↗

Network-Aware and Welfare-Maximizing Dynamic Pricing for Energy Sharing: Preprint

The proliferation of behind-the-meter (BTM) distributed energy resources (DER) within the electrical distribution network presents significant supply and demand flexibilities, but also introduces operational challenges such as voltage spikes and reverse power flows. In response, this paper proposes a network-aware dynamic pricing framework tailored for energy-sharing coalitions that aggregate small, but ubiquitous, BTM DER downstream of a distribution system operator's (DSO) revenue meter that adopts a generic net energy metering (NEM) tariff. By formulating a Stackelberg game between the energy-sharing market leader and its prosumers, we show that the dynamic pricing policy induces the prosumers toward a network-safe operation and decentrally maximizes the energysharing social welfare. The dynamic pricing mechanism involves a combination of a locational ex-ante dynamic price and an ex-post allocation, both of which are functions of the energy sharing's BTM DER. The ex-post allocation is proportionate to the price differential between the DSO NEM price and the energy sharing locational price. Simulation results using real DER data and the IEEE 13-bus test systems illustrate the dynamic nature of network-aware pricing at each bus, and its impact on voltage.

energy communities↗

Convex Q-Learning in Continuous Time with Application to Dispatch of Distributed Energy Resources

Convex Q-learning is a recent approach to reinforcement learning, motivated by the possibility of a firmer theory for convergence, and the possibility of making use of greater a priori knowledge regarding policy or value function structure. This paper explores algorithm design in the continuous time domain, with a finite-horizon optimal control objective. The main contributions are (i) The new Q-ODE: a model-free characterization of the Hamilton-Jacobi-Bellman equation. (ii) A formulation of Convex Q-learning that avoids approximations appearing in prior work. The Bellman error used in the algorithm is defined by filtered measurements, which is necessary in the presence of measurement noise. (iii) Convex Q-learning with linear function approximation is a convex program. It is shown that the constraint region is bounded, subject to an exploration condition on the training input. (iv) The theory is illustrated in application to resource allocation for distributed energy resources, for which the theory is ideally suited.

Lu, Fan↗

Soil fertility controls on tropical forest productivity and mortality: synthesis and roadmap

Tropical forests are highly diverse and productive ecosystems and store nearly 60% of vegetation biomass. Across the tropics, forests span large gradients of soil fertility, from vast regions situated on highly weathered, ancient geological formations to others on young, nutrient-rich landscapes. Tropical forest productivity, mortality, and biomass all vary systematically across these gradients in soil fertility. Aboveground forest productivity tends to increase with higher soil fertility, while counterintuitively, aboveground biomass does not increase proportionally. This disconnect is likely due to coinciding increases in mortality with higher soil fertility. However, we know relatively little about the mechanisms underlying how soil fertility regulates productivity or mortality – two critical determinants of forest biomass – and even less about how these relationships will shape tropical forest responses to global change. Here, we present a mechanistic framework for understanding how soil fertility affects productivity through photosynthesis, carbon allocation, and carbon-use efficiency; how it modulates mortality via direct and indirect interactions with various drivers of mortality; and how global change may alter these relationships. Based on this synthesis, we outline a roadmap to advance our understanding of tropical forests and improve predictions of their responses to global change.

Wong, Michelle Y [Yale University] (ORCID:00000002↗

Development of an Assessment Methodology That Enables the Nuclear Industry to Evaluate Adoption of Advanced Automation

Nuclear power has a crucial role in providing safe, reliable, and economical carbon-free electricity for today and the future. For continued operation, many of the existing United States nuclear power plants will begin the subsequent license renewal process for extending their operating license periods. As plants extend their expected operating lifetimes, there is a significant opportunity to modernize. These plants have a much stronger business case with these extended mission periods to modernize and significantly enhance their economic viability in current and future energy markets by implementing digital technologies that support innovation, efficiency gains, and business-model transformation. Ensuring continued safety and reliability is crucial. Transformative digital technologies—including automation—that fundamentally change the concept of operation for the nuclear power plant operating model requires a critical focus on the human and technology integration element. Further, the nuclear industry has historically been reluctant to modernize due to having a risk adverse culture and lack of clarity for a transformative new state vision (Joe & Remer, 2019; Thomas et al., 2020). Common barriers include (1) the perceived value and return on investment (ROI) of digital technology, (2) the perceived risk associated with licensing, regulatory, and cybersecurity, and (3) insufficient guidance for performing digital modifications to power generation systems. This work presents a methodology to address these barriers and support the industry in adopting advanced automation and digital technology through developing a transformative vision and implementation strategy that will address the human and technology integration element. This research leverages previous LWRS Program and industry results. It draws specifically on previous LWRS Program research in the areas of advanced alarm systems, computer-based procedures, model informed decision support, and advanced human-system interface displays (e.g., overviews and task-based). The modernization methodology can be used to guide transformative thinking when integrating a set of vendor-specific capabilities to support a new concept of operations and a utility’s end-state vision. The results of this research are organized into six major sections: - Section 1 introduces the need for supporting large-scale digital modifications that will renew the technology base for extended operating life beyond 60 years - Section 2 describes the challenges that the nuclear industry is enduring with modernizing. - Section 3 summarizes the primary standards and guidance. - Section 4 presents earlier work from the LWRS Program regarding the development of a transformative conceptual design for an advanced control room of a hybrid plants. - Section 5 presents a methodology that is designed at addressing the challenges in the industry today in achieving a transformative new state vision and concept of operations. - Conclusions and next steps of this research are provided in Section 6.

99 GENERAL AND MISCELLANEOUS↗

Beyond Static Benchmarking: Using Experimental Manipulations to Evaluate Land Model Assumptions

Land models are often used to simulate terrestrial responses to future environmental changes, but these models are not commonly evaluated with data from experimental manipulations. Results from experimental manipulations can identify and evaluate model assumptions that are consistent with appropriate ecosystem responses to future environmental change. We conducted simulations using three coupled carbon-nitrogen versions of the Community Land Model (CLM, versions 4, 4.5, and—the newly developed—5), and compared the simulated response to nitrogen (N) and atmospheric carbon dioxide (CO 2 ) enrichment with meta-analyses of observations from similar experimental manipulations. In control simulations, successive versions of CLM showed a poleward increase in gross primary productivity and an overall bias reduction, compared to FLUXNET-MTE observations. Simulations with N and CO 2 enrichment demonstrate that CLM transitioned from a model that exhibited strong nitrogen limitation of the terrestrial carbon cycle (CLM4) to a model that showed greater responsiveness to elevated concentrations of CO 2 in the atmosphere (CLM5). Overall, CLM5 simulations showed better agreement with observed ecosystem responses to experimental N and CO 2 enrichment than previous versions of the model. These simulations also exposed shortcomings in structural assumptions and parameterizations. Specifically, no version of CLM captures changes in plant physiology, allocation, and nutrient uptake that are likely important aspects of terrestrial ecosystems' responses to environmental change. These highlight priority areas that should be addressed in future model developments. Moving forward, incorporating results from experimental manipulations into model benchmarking tools that are used to evaluate model performance will help increase confidence in terrestrial carbon cycle projections.

54 ENVIRONMENTAL SCIENCES↗

Climate change impact on residual contaminants under sustainable remediation

This study investigates the potential impact of climate change on residual contaminants in vadose zones and groundwater. We assume that the effect of climate changes can be represented by perturbations in the natural recharge through the aquifer system. We perform numerical modeling of unsaturated/saturated flow and transport and consider different performance metrics: contaminant concentrations at observation wells and contaminant export at the site's boundary. We evaluate the effect of increasing and decreasing recharge as well as the impact of potential failure of surface capping structures employed to immobilize vadose zone contaminants. Our approach is demonstrated in a real case study by simulating transport of non-reactive radioactive tritium at the U.S. Department of Energy's Savannah River Site. Results show that recharge changes significantly affect well concentrations: after an initial slight dilution we identify a significant concentration increase at different observation wells some years after the recharge increase and/or the cap failure, as a consequence of contaminants' mobilization. This effect is generally emphasized and occurs earlier as the recharge increases. Under decreased aquifers' recharge the concentration could slightly increase for some years, due to a decrease of dilution, depending on the magnitude of the negative recharge shift. We identify trigger levels of recharge above which the concentration/export breakthrough curves and the time of exceedance of the Maximum Contaminant Level for tritium are remarkably affected. Moreover, we observe that the contaminant export at the control plane, identified as the risk pathway to the downgradient population, may only be minimally affected by shifts in the natural recharge regime, except for some extreme cases. We conclude that more frequent sampling and in-situ monitoring near the source zone should be adopted to better explain concentrations' anomalies under changing climatic conditions. Moreover, the maintenance of the cap is critical not only to sequester residual contaminants in the vadose zone, but also to reduce the uncertainty associated with future precipitation changes. Finally, realistic flow and transport simulations achieved through proper calibration processes, rather than conservative modeling, should be adopted to identify non-trivial trade-offs which enable better allocation of resources towards reducing uncertainty in decision making.

54 ENVIRONMENTAL SCIENCES↗

Identification of Critical Switches for Integrating Renewable Distributed Generation by Dynamic Network Reconfiguration

With growing penetration of renewable distributed generations (DGs) in distribution systems, effective integration of DGs has become a major concern. Distribution system dynamic reconfiguration (DSDR), which relies on real-time operations of remote-controlled switches, is potentially an efficient strategy receiving inadequate attention. Moreover, in most DSDR-related publications, normally all switches are assumed remotely controllable, which is not practical. Here we borrow the concept of critical switches to denote the switches that are most effective in accommodating DGs by DSDR. In this regard, the problem of identifying critical switches is not well investigated, although in several related publications, selected switches are assumed remote-controlled based on experience. In this work, we study the application of DSDR for DG integration. Critical switches, which optimally enable intra-day DSDR to minimize DG curtailments, are identified by limiting the number of switches to be operated and the switch-type-dependent operation constraints. Considering uncertainties of loads and DGs, the problem is formulated as a two-stage robust optimization model solved by a nested column-and-constraint generation algorithm. Illustrative cases show that DG curtailments can be significantly reduced by a small number of critical switches that operate only several times in intra-day DSDR. The proposed method can be used to provide insights for switch allocation, maintenance, and operation

Lei, Shunbo (ORCID:0000000181690821)↗

Thermosensitivity of growth is determined by chaperone-mediated proteome reallocation

Maintenance of a properly folded proteome is critical for bacterial survival at notably different growth temperatures. Understanding the molecular basis of thermoadaptation has progressed in two main directions, the sequence and structural basis of protein thermostability and the mechanistic principles of protein quality control assisted by chaperones. Yet we do not fully understand how structural integrity of the entire proteome is maintained under stress and how it affects cellular fitness. To address this challenge, we reconstruct a genome-scale protein-folding network for Escherichia coli and formulate a computational model, FoldME, that provides statistical descriptions of multiscale cellular response consistent with many datasets. FoldME simulations show (i) that the chaperones act as a system when they respond to unfolding stress rather than achieving efficient folding of any single component of the proteome, (ii) how the proteome is globally balanced between chaperones for folding and the complex machinery synthesizing the proteins in response to perturbation, (iii) how this balancing determines growth rate dependence on temperature and is achieved through nonspecific regulation, and (iv) how thermal instability of the individual protein affects the overall functional state of the proteome. Overall, these results expand our view of cellular regulation, from targeted specific control mechanisms to global regulation through a web of nonspecific competing interactions that modulate the optimal reallocation of cellular resources. As a result, the methodology developed in this study enables genome-scale integration of environment-dependent protein properties and a proteome-wide study of cellular stress responses.

59 BASIC BIOLOGICAL SCIENCES↗

Real-Time Federated Cyber-Transmission-Distribution Testbed Architecture for the Resiliency Analysis

With the ongoing automation driven by the push toward the smart electric grid and the advancement in associated cyber infrastructure, the interaction between physical [electric transmission and distribution (T&D) systems], cyber (communication, automation, and control), and human (grid operators and decision-makers) is increasingly becoming more complex. This creates the requirement of analyzing the effect of the transmission system on the distribution system and vice versa with consideration of the additional complexity of the cyber infrastructure. Such an integrated testbed will also help with resiliency analysis, where resiliency refers to the ability of the system to continue serving energy to the critical loads even with limited extreme contingencies. Interaction of the physical power grid with the cyber layer can be effectively modeled using real-time (RT) simulator for developing and validating various operational and control algorithms. Testbeds using RT simulators with multiple capabilities have been developed at different institutions. Still, no single existing testbed can offer full scalability while simultaneously meeting high fidelity requirements for resiliency experimentation. Co-simulating federated testbed assets can provide a scalable experimentation platform that can be leveraged for verification and validation. In this article, an architecture is developed for federated cyber-physical testbed. A local federation with two real-time simulators is developed: real-time digital simulator (RTDS) and OPAL-RT have been interfaced using VILLAS framework for end-to-end testing. Also, a real-time linear predictor is developed and integrated here to address the communication latency impact on geographically allocated federated RT simulation. Finally, resiliency analysis tools are formulated and utilized for T&D systems. Finally as an illustrative use case, the resiliency of a T&D test system is simulated, and the results are analyzed. A 179-bus Western Electricity Coordinating Council (WECC) transmission system is developed using OPAL-RT/ HYPERSIM, and a modified IEEE 13 node feeder system is modeled in RTDS/RSCAD and interfaced for resiliency analysis.

42 ENGINEERING↗

Treatments with Liquid Smoke and Certain Chemical Constituents Prevalent in Smoke Reduce Phloem Vascular Sectoriality in the Sunflower with Improvement to Growth

Many higher plants possess a physiological organization that is based upon the carbon economy of their parts. While photosynthates are partitioned according to the relative strength of the plant’s sink tissues, in many species there is also a very close relationship between partitioning, phyllotaxy and vascular connectivity giving rise to sectorial patterns of allocation. Here, we examined the influence of smoke and certain chemical constituents prevalent in smoke including, catechol, resorcinol and hydroquinone on phloem vascular sectoriality in common sunflower (Helianthis annuus L.), as a model plant for sectoriality. By administering radioactive carbon-11 to a single source leaf as 11CO2, 11C-photosynthate allocation patterns were examined using autoradiography. A 1:200 aqueous dilution of liquid smoke treated soil caused 2.6-fold and 2.5-fold reductions in phloem sectoriality in sink leaves and roots, respectively. Treatment with catechol (1,2-d ihydroxybenzene) or resorcinol (1,3-dihydroxybenzene), polyphenolic constituents that are prevalent in smoke, caused similar reductions in phloem sectoriality in the same targeted sink tissues. However, treatment with hydroquinone (1,4-dihydroxybenzene) had no effect. Finally, the longer-term effects of smoke exposure on plant growth and performance were examined using outdoor potted plants grown over the 2022 season. Plants exposed to liquid smoke treatments of the soil on a weekly basis had larger thicker leaves possessing 35% greater lignin content than untreated control plants. They also had thicker stems although the lignin content was the same as controls. Additionally, plants exposed to treatment produced twice the number of flowers with no difference in their disk floret diameters as untreated controls. Altogether, loss of phloem sectoriality from exposure to liquid smoke in the sunflower model benefited plant performance.

60 APPLIED LIFE SCIENCES↗

Asparagine synthetase gene regulatory network and plant nitrogen metabolism

This DOE project concerns the molecular mechanisms by which plants coordinate carbon (C) and nitrogen (N) metabolism. Specifically, it focuses on a histone methyltransferase protein that targets C/N-metabolism genes for chromatin modifications. How the dynamic changes of chromatin regulate energy metabolism in response to developmental and environmental cues is a new area of study. We have filled this knowledge-gap with our studies of SDG8, a histone methyltransferase, that targets light, C and N metabolism genes. We aimed to understand how plants allocate C resources to assimilate inorganic N onto C-skeletons -- the most energy-intensive pathway in plants – in order to balance energy production and consumption in a changing environment. In order to achieve more C-efficient N-storage under low energy conditions, plants convert assimilated N from “reactive” Gln (C5:N2) into “inert” Asn (C4:N2) through the enzyme ASN1. To identify regulatory factors in this pathway, we used the ASN1 promoter in a genetic selection and discovered SDG8, a histone methyltransferase. We discovered that histone modification by SDG8 coordinates the expression of genes involved in energy generation (light-harvesting and C fixation) with energy consumption (N assimilation and sulfur metabolism). We used this opportunity to explore how histone modifications control energy metabolism genome-wide. Our aims focus on various levels of the regulation of energy related genes at the level of: 1) Chromatin; 2) Transcription; and 3) Metabolites of the C/N regulatory network. Specifically, we asked: 1) How does histone methylation regulate energy metabolism during the N response? 2) How does histone methylation control transcription of the energy metabolism genes? And 3) how does the histone methyltransferase SDG8 and other TF regulators affect C/N metabolism and N use efficiency? Our study indicates that N supply triggers genome-wide changes in chromatin modification via the histone methyltransferase SDG8 which impacts on gene expression, RNA processing, chlorophyll synthesis, N uptake and metabolism. Our study thus revealed a previously overlooked layer of chromatin regulation during nutrient signaling. In broad terms, our study provides a framework to understand how chromatin modifications influence responses to environmental changes and provides unique opportunities for breeding and engineering resilient crops with better performance in the current climate model. Accomplishments AIM 1 CHROMATIN: Role of histone methylation in the ASN1 energy metabolism network. This aim focuses on understanding the role of chromatin modification in response to the environment. We studied the targeting mechanism of histone methylation by SDG8, in particular the role of the CW domain in reading histone methylation status. We also began to study the effect of nitrogen-treatment on genome-wide chromatin modifications in WT and sdg8 mutant plants. AIM 2 TRANSCRIPTION: Transcription control of the ASN1 energy metabolism network. This aim investigated whether SDG8 and the histone modification it mediates affects mRNA processing. We performed genome-wide profiling that uncovered that histone methylation by SDG8 affects the co-transcriptional RNA processing of downstream genes involved in energy and photosynthesis. AIM 3 METABOLISM: A gene regulatory network controlling energy, C/N metabolism and N use efficiency. In Aim 3A, we showed that SDG8 affects the chromatin modification and expression of a transcription factor, CCT, which in turn affects the regulation of carbon responsive genes. In Aim 3B we showed that C and N metabolism is perturbed in the sdg8 mutant through differences in the chlorophyll content, free amino acids, and N uptake. Project activities In Aim 1, we uncovered that the histone methyltransferase SDG8 affects chromatin modification in response to N treatments. The N treatments causes changes in the H3K36me3 distribution off ~200 genes in WT plants, while the N-triggered changes in H3K36me3 of these genes is abrogated in the sdg8-5 mutant. The functional annotation of these 200 genes shows significant enrichment of N-related pathways. Overall, our study suggests that SDG8 mediates chromatin-level adaption of functionally relevant genes in response to changes in N. In Aim 2, we tested if SDG8 plays a role in transcriptional elongation and/or RNA processing. We detected more N-triggered isoform shifting events in the sdg8-5 mutant than in WT, which provides supports for SDG8 being required for proper RNA processing in response to N signaling. In Aim 3, we identified the gene network controlled by a CCT transcription factor under the epigenetic control of SDG8. Isoform abundance of the CCT protein is regulated by SDG8, which leads to an altered response of energy related genes to N level changes. Using physiological, metabolic and genetic approaches, we found that responses to N, including chlorophyll accumulation, N uptake and free amino acids, are altered in sdg8 mutants. Finally, this project provided training to a high school student who performed experiments on the complex interaction of photosynthesis and N availability. The student was named a semi-finalist in both the Regeneron and Siemens National Science competitions for her work.

59 BASIC BIOLOGICAL SCIENCES↗

Deeply Rooted: Evaluating Plant Rooting Depth as a Means for Enhanced Soil Carbon Sequestration (Full Technical Report)

Soils store three times as much carbon (C) as the atmosphere, but are not at capacity, and enhanced soil C storage is considered an essential strategy to mitigate rising atmospheric CO 2 levels. Agricultural soils have experienced substantial C loss in the past century due to poor agricultural practices and erosion. A shift towards deep-rooting crops and low-impact soil management could potentially increase long-term sequestration of C fixed by plants and stored in their root tissues, particularly for crops that have naturally deep root systems (>1 meter). A substantial amount of the CO 2 taken up by plants is allocated to their root systems, and because C deposited in deep soil layers has a longer residence time (up to millennia, in contrast to C deposited in topsoils), C increases at depth may have better long-term C sequestration potential than topsoils. However, the accrual, turnover, and stabilization of C in subsoils is a critical knowledge gap. We investigated a deeply rooted plant, switchgrass (Panicum virgatum), as a means of increasing carbon stocks in marginal and agricultural soils. We hypothesized that deep (>30 cm) SOC stocks would be greater under bioenergy crops relative to stocks under shallow-rooted conventional crop cover. To test this hypothesis, we compared soil depth profiles beneath deeply rooted switchgrass (cultivated for 4-30 years) and paired shallowrooted annual controls. We studied 12 field sites, 3 that were collected in 2018 before the start of the project as part a Department of Energy (DOE) Sustainable Biofuels study, and 9 that we collected in 2019 on a national field sampling campaign across the eastern US. In our publication from the 2018 study, which was written in collaboration with the LLNL Soil Microbiome Scientific Focus Area (SFA), we found that C stocks increased under switchgrass, but that the increases were dependent on soil texture. In the 2019 study, we found that carbon accrual tended to occur most consistently in low C soil in the southern US, which could indicate that perennial grasses may be a viable strategy to increase SOC in marginals soils in this region. We have published two studies from the 2019 sampling campaign thus far, we measured microbial growth parameters that will aid in modeling subsoil carbon cycling, and we found that switchgrass appears to move water upward in the soil profile and could promote drought tolerance, which a phenomenon commonly performed by trees known as ‘hydraulic redistribution;’ our study is the first to show this can occur in deep-rooted grasses. Finally, we published a modeling paper in collaboration with the LLNL Soil Microbiome SFA, where we found that poorly crystalline minerals are abundant and strongly correlated with organic C in geographically limited zones with enhanced weathering rates. Our results will inform technological development in the agricultural carbon sequestration sector as well as future negative emissions policies.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

Performance Prediction of Big Data Transfer Through Experimental Analysis and Machine Learning

Big data transfer in next-generation scientific applications is now commonly carried out over connections with guaranteed bandwidth provisioned in High-performance Networks (HPNs) through advance bandwidth reservation. To use HPN resources efficiently, provisioning agents need to carefully schedule data transfer requests and allocate appropriate bandwidths. Such reserved bandwidths, if not fully utilized by the requesting user, could be simply wasted or cause extra overhead and complexity in management due to exclusive access. This calls for the capability of performance prediction to reserve bandwidth resources that match actual needs. Towards this goal, we employ machine learning algorithms to predict big data transfer performance based on extensive performance measurements, which are collected over a span of several years from a large number of data transfer tests using different protocols and toolkits between various end sites on several real-life physical or emulated HPN testbeds. We first identify a comprehensive list of attributes involved in a typical big data transfer process, including end host system configurations, network connection properties, and control parameters of data transfer methods. We then conduct an in-depth exploratory analysis of their impacts on application-level throughput, which provides insights into big data transfer performance and motivates the use of machine learning. We also investigate the applicability of machine learning algorithms and derive their general performance bounds for performance prediction of big data transfer in HPNs. Experimental results show that, with appropriate data preprocessing, the proposed machine learning-based approach achieves 95% or higher prediction accuracy in up to 90% of the cases with very noisy real-life performance measurements.

Yun, Daqing↗