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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 505 records · Page 28

Beyond PCA: Additional Dimension Reduction Techniques to Consider in the Development of Climate Fingerprints

Abstract Dimension reduction techniques are an essential part of the climate analyst’s toolkit. Due to the enormous scale of climate data, dimension reduction methods are used to identify major patterns of variability within climate dynamics, to create compelling and informative visualizations, and to quantify major named modes such as El Niño–Southern Oscillation. Principal components analysis (PCA), also known as the method of empirical orthogonal functions (EOFs), is the most commonly used form of dimension reduction, characterized by a remarkable confluence of attractive mathematical, statistical, and computational properties. Despite its ubiquity, PCA suffers from several difficulties relevant to climate science: high computational burden with large datasets, decreased statistical accuracy in high dimensions, and difficulties comparing across multiple datasets. In this paper, we introduce several variants of PCA that are likely to be of use in climate sciences and address these problems. Specifically, we introduce non-negative , sparse , and tensor PCA and demonstrate how each approach provides superior pattern recognition in climate data. We also discuss approaches to comparing PCA-family results within and across datasets in a domain-relevant manner. We demonstrate these approaches through an analysis of several runs of the E3SM climate model from 1991 to 1995, focusing on the simulated response to the Mt. Pinatubo eruption; our findings are consistent with a recently identified stratospheric warming fingerprint associated with this type of stratospheric aerosol injection.

Weylandt, Michael↗

Complete sequences of conjugal helper plasmids pRK2013 and pEVS104

We present the complete sequences of two commonly used conjugal helper plasmids: pRK2013 and pEVS104. These sequences will enable engineering of custom helper plasmids, for example, with different antibiotic markers or origins of replication. We provide both sequence information and plasmid maps to aid future engineering efforts.

59 BASIC BIOLOGICAL SCIENCES↗

Mixture Effects of Per- and Polyfluoroalkyl Substances on Embryonic and Larval Sheepshead Minnows ( Cyprinodon variegatus )

Per- and polyfluoroalkyl substances (PFAS) are ubiquitous and persistent environmental contaminants originating from many everyday products. Perfluorooctane sulfonic acid (PFOS) and perfluorooctanoic acid (PFOA) are two PFAS that are commonly found at high concentrations in aquatic environments. Both chemicals have previously been shown to be toxic to fish, as well as having complex and largely uncharacterized mixture effects. However, limited information is available on marine and estuarine species. In this study, embryonic and larval sheepshead minnows (Cyprinodon variegatus) were exposed to several PFAS mixtures to assess lethal and sublethal effects. PFOS alone was acutely toxic to larvae, with a 96 h LC50 of 1.97 mg/L (1.64–2.16). PFOS + PFOA resulted in a larval LC 50 of 3.10 (2.62–3.79) mg/L, suggesting an antagonistic effect. These observations were supported by significant reductions in malondialdehyde (105% ± 3.25) and increases in reduced glutathione concentrations (43.8% ± 1.78) in PFOS + PFOA exposures compared to PFOS-only treatments, indicating reduced oxidative stress. While PFOA reduced PFOS-induced mortality (97.0% ± 3.03), perfluorohexanoic acid (PFHxA) and perfluorobutanoic acid (PFBA) did not. PFOS alone did not affect expression of peroxisome proliferator-activated receptor alpha (pparα) but significantly upregulated apolipoprotein A4 (apoa4) (112.4% ± 17.8), a downstream product of pparα, while none of the other individually tested PFAS affected apoa4 expression. These findings suggest that there are antagonistic interactions between PFOA and PFOS that may reduce mixture toxicity in larval sheepshead minnows through reduced oxidative stress. Elucidating mechanisms of toxicity and interactions between PFAS will aid environmental regulation and management of these ubiquitous pollutants.

59 BASIC BIOLOGICAL SCIENCES↗

Scaling Equitable Finance

Driven by dramatic declines in up-front cost, the U.S. solar photovoltaics (PV) industry has taken off over the past decade, growing from 1 gigawatt of installed capacity in 2009 to 89 gigawatts in 2020—or enough capacity to power roughly 19 million homes. The industry is expected to double in size over just the next 5 years.1 Much of the growth has been driven by large, utility-scale projects that can produce 5 mega- watts or more of power—enough to power at least 1,000 homes. The cost of electricity produced by these projects has decreased by more than 70 percent since 2010. As of Q3 2020, development costs of large, util- ity-scale solar PV power plants were under $1 per watt, down by more than 70 percent from 2010.2 A robust array of investors has come forward to efficiently deliver capital to these kinds of utility-scale projects including large banks, insurance companies, pension funds, and others. But low- and moderate-income communities, including communities of color, are at risk of being left behind in the transition to clean energy. Mission- driven solar project developers and financial institu- tions have been working alongside energy justice advocates to open up solar access for these communi- ties, using strategies ranging from community solar, to solar installations on affordable multifamily housing, to distributed solar and storage programs, and more. Their goals go beyond simply generating more green energy to advancing social equity by: • empowering communities to control their energy future • stabilizing energy prices, saving money, and build- ing wealth for low-income families • creating quality jobs • improving health by reducing pollution • providing energy resilience for vulnerable communities Mission-driven actors are successfully deploying a wide variety of strategies to meet these goals, from helping low-income homeowners get solar—and some- times battery storage, to developing solar projects serv- ing affordable rental housing and community facilities, to building larger “shared solar” projects to which households from across the community can subscribe. However, the financing ecosystem does not work nearly as well for these “mission driven” solar proj- ects as it does for utility-scale projects. For home rooftop solar, even if low-income consumers have a home and suitable roof, they may fail to qualify for federal tax incentives, lack adequate credit to qualify for a loan—or the mission-driven lenders seeking to serve them may not be adequately capitalized to make long-term loans. For mission-driven commercial or community-scale projects, assembling nearly every component of the project capital stack—whether bridging early-stage costs, attracting tax credit equity investors, securing long-term debt, or coming up with sponsor equity and filling gaps—can present challenges. A variety of obstacles contribute to the scarcity of financing for low-income solar, including small project sizes, lack of developer balance sheet capacity, both real and perceived issues with credit risk, elevated technical assistance needs, and greater subsidy requirements to pursue goals such as deep energy affordability, climate resilience, or job creation. Still other obstacles are regulatory: for example, not all states allow community solar projects or Power Purchase Agreements, common strategies used for providing low-income solar—and the potential for regulations to shift over time creates risks that mission-driven projects can ill afford. This report synthesizes information garnered from 47 key informant interviews, four focus group discus- sions involving 60 stakeholders, and a review of the substantial existing literature on low-income solar finance to assess the current landscape of mission- driven solar development in the United States, examine the roles that community-based financial institutions could play, and recommend public invest- ments and policy changes that could help to scale the provision of equitable solar finance. Key recommen- dations for policymakers and funders in the renew- able energy and community development fields that emerge from this process include the following: • Help to capitalize and support community-based lenders to provide flexible, low-cost, and long- term financing to mission-driven solar projects— including providing guarantees or other forms of credit enhancement. • Provide federal support for equitable solar, including a grant-in-lieu-of-credits option for the Investment Tax Credit to improve access to this critical government subsidy. • Develop pools of government and philanthropic support that can complement financing from community-based lenders to complete the capi- tal stack for mission-driven projects, as well as to support education and technical assistance to both consumers and potential project sponsors. • Create a national Renewable Energy Credits pro- gram that includes social equity targets to provide a baseline of support for clean energy generation. • Change utility regulations to remove barriers to low-income solar projects; lower permitting costs; provide greater certainty for developers, consumers and owners; and measure progress toward equity in renewable energy policy implementation.

14 SOLAR ENERGY↗

Effective Communication of Energy Science and Technology

Although research, development, and deployment of advanced energy technologies are essential for the clean energy transition, communication about these technologies is equally important to their success. Energy is part of everyday life; therefore, changes in energy systems should be accepted by communities and industries. Yet details about energy generation, transmission, and environmental impacts are complex. The combination of commonality and complexity requires communications to use visualization, localization, narrative, and understandable terminology to reach a range of stakeholders. Collaboration between technology experts and communications professionals builds integrity and accessibility of energy information that enables community-based solutions for energy.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Estimation of pipe failure frequencies in the absence of operational experience data: A pilot study

Probabilistic failure metrics such as leak frequency and rupture frequency are commonly used to characterize piping reliability. The methodologies for calculating the failure metrics rely on a complex set of input parameters. Operating experience data and experimental data play an important role in informing the different input parameters. The paper describes results and conclusions of a coordinated research project to benchmark three different reliability models using a four-step procedure: reference case definition of relevance to advanced reactor designs, input parameter calibration, validation of results, and application of different methodologies upon completion of the calibration and validation steps. The reference case is a weld consisting of nickel-base alloy 152/52 and located within a primary pressure boundary of an advanced reactor. This alloy is a class of structural materials known to be highly resistant to stress corrosion cracking. Synergies between the different methods are noted and the importance of a multi-disciplinary approach to input parameter development is underscored. A key conclusion is that the three methods are equally suitable for estimating failure frequencies. In any specific application, a selection of the most practical or effective computational tool can be considered. The comparison of alternative models confirms and helps to gain confidence in the computed failure frequency estimates. The study was part of a coordinated research project organized by the International Atomic Energy Agency.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Design and Characterization of a Transcriptional Repression Toolkit for Plants

Regulation of gene expression is essential for all life. Tools to manipulate the gene expression level have therefore proven to be very valuable in efforts to engineer biological systems. However, there are few well-characterized genetic parts that reduce gene expression in plants, commonly known as transcriptional repressors. We characterized the repression activity of a library consisting of repression motifs from approximately 25% of the members of the largest known family of repressors. Combining sequence information with our trans-regulatory function data, we next generated a library of synthetic transcriptional repression motifs with function predicted in advance. After characterizing our synthetic library, we demonstrated not only that many of our synthetic constructs were functional as repressors but also that our advance predictions of repression strength were better than random guesses. Finally, we assessed the functionality of known transcriptional repression motifs from a wide range of eukaryotes. Our study represents the largest plant repressor motif library experimentally characterized to date, providing unique opportunities for tuning transcription in plants.

59 BASIC BIOLOGICAL SCIENCES↗

Identifying heavy-flavor jets using vectors of locally aggregated descriptors

Abstract Jets of collimated particles serve a multitude of purposes in high energy collisions. Recently, studies of jet interaction with the quark-gluon plasma (QGP) created in high energy heavy ion collisions are of growing interest, particularly towards understanding partonic energy loss in the QGP medium and its related modifications of the jet shower and fragmentation. Since the QGP is a colored medium, the extent of jet quenching and consequently, the transport properties of the medium are expected to be sensitive to fundamental properties of the jets such as the flavor of the parton that initiates the jet. Identifying the jet flavor enables an extraction of the mass dependence in jet-QGP interactions. We present a novel approach to tagging heavy-flavor jets at collider experiments utilizing the information contained within jet constituents via the model architecture. We show the performance of this model in proton-proton collisions at center of mass energy √(s) = 200 GeV as characterized by common metrics and showcase its ability to extract high purity heavy-flavor jet sample at various jet momenta and realistic production cross-sections including a brief discussion on the impact of out-of-time pile-up. Such studies open new opportunities for future high purity heavy-flavor measurements at jet energies accessible at current and future collider experiments.

Instruments & Instrumentation↗

Network science can improve the sustainable development of solar energy

Abstract The recent emergence of agrivoltaic and ecovoltaic approaches to ground-mounted photovoltaic (PV) energy provides a much-needed alternative to the environmentally disruptive practices employed in utility-scale solar development. Research on such land-sharing approaches has grown rapidly, with an emphasis on characterizing how PV arrays impact ecosystem processes and agricultural productivity. Although these studies have done well to quantify a variety of dual-use solar practices by employing site-specific sampling designs, this approach has limited our ability to synthesize results across sites, regions, and globally. We call for a network science approach for improved cross-site synthesis of dual-use solar research. We contend that a common approach for data collection and synthesis will facilitate a more rigorous investigation of the agricultural and ecological impacts of PV development across space and over time. The products of this scientifically informed approach can be directly applied to improve sustainable land management.

Bacon, Taylor (ORCID:0009000518578569)↗

Fast digital lossy compression for X-ray ptychographic data

Increases in X-ray brightness from synchrotron light sources lead to a requirement for higher frame rates from hybrid pixel array detectors (HPADs), while also favoring charge integration over photon counting. However, transfer of the full uncompressed data will begin to constrain detector design, as well as limit the achievable continuous frame rate. Here a data compression scheme that is easy to implement in a HPAD's application-specific integrated circuit (ASIC) is described, and how different degrees of compression affect image quality in ptychography, a commonly employed coherent imaging method, is examined. Using adaptive encoding quantization, it is shown in simulations that one can digitize signals up to 16383 photons per pixel (corresponding to 14 bits of information) using only 8 or 9 bits for data transfer, with negligible effect on the reconstructed image.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Height Above Nearest Drainage (HAND) and Hydraulic Property Table for CONUS

The continental flood inundation mapping (CFIM) framework is a high-performance computing (HPC)-based computational framework for the Height Above Nearest Drainage (HAND)-based inundation mapping methodology. Using the 10m Digital Elevation Model (DEM) data produced by U.S. Geological Survey (USGS) 3DEP (the 3-D Elevation Program) and the NHDPlus hydrography dataset produced by USGS and the U.S. Environmental Protection Agency (EPA), a hydrological terrain raster called HAND is computed for HUC6 units in the conterminous U.S. (CONUS). The value of each raster cell in HAND is an approximation of the relative elevation between the cell and its nearest water stream. Derived from HAND, a hydraulic property table is established to calculate river geometry properties for each of the 2.7 million river reaches covered by NHDPlus (5.5 million kilometers in total length). This table is a lookup table for water depth given an input stream flow value. Such lookup is available between water depth 0m and 25m at 1-foot interval. The flood inundation map is then computed by using HAND and this lookup table based on the near real-time water forecast from the National Water Model (NWM) at the National Oceanic and Atmospheric Administration (NOAA). HAND and the Hydraulic Property Table version 0.2. is created to correspond to data updates in the USGS 1/3 arcsec DEM, the USGS National Hydrography Dataset, including its Water Boundary Dataset, and the NHDPlus medium resolution dataset. This dataset comprises 331 HUC6 units for CONUS (excluding the five great lakes units), each is a downloadable zip file. Version 0.1 was computed at the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urban-Champaign in 2016 and is currently hosted at the Texas Advanced Computing Center (TACC). Please cite this data DOI and the following publications and data DOIs when you use HAND and the hydraulic property table: Liu, Yan Y., David R. Maidment, David G. Tarboton, Xing Zheng, and Shaowen Wang. 'A CyberGIS integration and computation framework for high-resolution continental-scale flood inundation mapping.' JAWRA Journal of the American Water Resources Association 54, no. 4 (2018): 770-784. DOI: 10.1111/1752-1688.12660 Zheng, Xing, David G. Tarboton, David R. Maidment, Yan Y. Liu, and Paola Passalacqua. 'River channel geometry and rating curve estimation using height above the nearest drainage.' JAWRA Journal of the American Water Resources Association 54, no. 4 (2018): 785-806. DOI: 10.1111/1752-1688.12661 Liu, Y. (2018). Height Above Nearest Drainage (HAND) for CONUS - v0.1, HydroShare, https://doi.org/10.4211/hs.69f7d237675c4c73938481904358c789 LICENSE FOR USE -- MAPS AND DATA DISCLAIMER This resource is shared under the Creative Commons Attribution CC BY, http://creativecommons.org/licenses/by/4.0/ MAPS AND DATA DISCLAIMER The Oak Ridge National Laboratory (ORNL) shall not be held liable for improper or incorrect use of the data described or information contained on this map or associated series of maps. The data and related map graphics are not legal, land survey or engineering documents and are not intended to be used as such. ORNL gives no warranty, express or implied, as to the accuracy, reliability, utility or completeness of this information. The user of these maps and data assumes all responsibility and risk for the use of the maps and data. ORNL disclaims all warranties, representations or endorsements either express or implied, with regard to the information contained in this map product, including, but not limited to, all implied warranties of merchantability, fitness for a particular purpose or non-infringement. This preliminary map product is for research and review purposes only. It is not intended to be used for emergency management operational or life safety decisions at the local or regional governmental level or by the general public. Users requiring information regarding hazardous conditions or meteorological conditions for specific geographic areas should consult directly with their city or county emergency management office.

54 ENVIRONMENTAL SCIENCES↗

Height Above Nearest Drainage (HAND) and Hydraulic Property Table for CONUS - Version 0.21 (20200601)

The continental flood inundation mapping (CFIM) framework is a high-performance computing (HPC)-based computational framework for the Height Above Nearest Drainage (HAND)-based inundation mapping methodology. Using the 10m Digital Elevation Model (DEM) data produced by U.S. Geological Survey (USGS) 3DEP (the 3-D Elevation Program) and the NHDPlus hydrography dataset produced by USGS and the U.S. Environmental Protection Agency (EPA), a hydrological terrain raster called HAND is computed for HUC6 units in the conterminous U.S. (CONUS). The value of each raster cell in HAND is an approximation of the relative elevation between the cell and its nearest water stream. Derived from HAND, a hydraulic property table is established to calculate river geometry properties for each of the 2.7 million river reaches covered by NHDPlus (5.5 million kilometers in total length). This table is a lookup table for water depth given an input stream flow value. Such lookup is available between water depth 0m and 25m at 1-foot interval. The flood inundation map is then computed by using HAND and this lookup table based on the near real-time water forecast from the National Water Model (NWM) at the National Oceanic and Atmospheric Administration (NOAA). HAND and the Hydraulic Property Table version v0.21 is an upgrade to v0.2. Version 0.21 applies NHD HR etching to DEMs for all HUC6 units before running the HAND workflow. This new version also fixed the issue of void areas along HUC boundary. This dataset comprises 331 HUC6 units for CONUS (excluding the five great lakes units), each is a downloadable zip file. Version 0.1 was computed at the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urban-Champaign in 2016 and is currently hosted at the Texas Advanced Computing Center (TACC). Please cite this data DOI and the following publications and data DOIs when you use HAND and the hydraulic property table: Liu, Yan Y. Height Above Nearest Drainage (HAND) and Hydraulic Property Table for CONUS - Version 0.2. (20200301). Oak Ridge Leadership Computing Facility. (2020). DOI: 10.13139/ORNLNCCS/1608331 Liu, Yan Y., David R. Maidment, David G. Tarboton, Xing Zheng, and Shaowen Wang. 'A CyberGIS integration and computation framework for high-resolution continental-scale flood inundation mapping.' JAWRA Journal of the American Water Resources Association 54, no. 4 (2018): 770-784. DOI: 10.1111/1752-1688.12660 Zheng, Xing, David G. Tarboton, David R. Maidment, Yan Y. Liu, and Paola Passalacqua. 'River channel geometry and rating curve estimation using height above the nearest drainage.' JAWRA Journal of the American Water Resources Association 54, no. 4 (2018): 785-806. DOI: 10.1111/1752-1688.12661 Liu, Y. (2018). Height Above Nearest Drainage (HAND) for CONUS, HydroShare, https://doi.org/10.4211/hs.69f7d237675c4c73938481904358c789 LICENSE FOR USE -- MAPS AND DATA DISCLAIMER This resource is shared under the Creative Commons Attribution CC BY, http://creativecommons.org/licenses/by/4.0/ MAPS AND DATA DISCLAIMER The Oak Ridge National Laboratory (ORNL) shall not be held liable for improper or incorrect use of the data described or information contained on this map or associated series of maps. The data and related map graphics are not legal, land survey or engineering documents and are not intended to be used as such. ORNL gives no warranty, express or implied, as to the accuracy, reliability, utility or completeness of this information. The user of these maps and data assumes all responsibility and risk for the use of the maps and data. ORNL disclaims all warranties, representations or endorsements either express or implied, with regard to the information contained in this map product, including, but not limited to, all implied warranties of merchantability, fitness for a particular purpose or non-infringement. This preliminary map product is for research and review purposes only. It is not intended to be used for emergency management operational or life safety decisions at the local or regional governmental level or by the general public. Users requiring information regarding hazardous conditions or meteorological conditions for specific geographic areas should consult directly with their city or county emergency management office.

54 ENVIRONMENTAL SCIENCES↗

Road Lidar Dataset for the TxDOT Austin District

This is a road lidar data collection for developing road elevation models and road inundation mapping methodologies, a joint work between ORNL and The University of Texas at Austin. This dataset is generated as part of the flood transportation infrastructure, partly funded by the NOAA CIROH project. ORNL is a project partner for high-performance computing-empowered flood inundation mapping methodology R&D. The dataset is computed using a GPU-accelerated lidar data processing workflow developed at ORNL. The lidar data source is from TxGIO, the state lidar data collection site. The output dataset is in two formats: laz and copc. It is organized by TxDOT's maintenance sections, covering the Austin District. Data size: 3.86 billion road lidar points, 1.67% of the entire lidar data input Projection: EPSG:32614 (WGS84/UTM zone 14N) Website: https://web.corral.tacc.utexas.edu/nfiedata/road3d/austin_district/AustinMaintenanceSections_H_epsg6343_V_epsg5703/ LICENSE FOR USE -- MAPS AND DATA DISCLAIMER This resource is shared under the Creative Commons Attribution CC BY, http://creativecommons.org/licenses/by/4.0/ MAPS AND DATA DISCLAIMER The Oak Ridge National Laboratory (ORNL) shall not be held liable for improper or incorrect use of the data described or information contained on this map or associated series of maps. The data and related map graphics are not legal, land survey or engineering documents and are not intended to be used as such. ORNL gives no warranty, express or implied, as to the accuracy, reliability, utility or completeness of this information. The user of these maps and data assumes all responsibility and risk for the use of the maps and data. ORNL disclaims all warranties, representations or endorsements either express or implied, with regard to the information contained in this map product, including, but not limited to, all implied warranties of merchantability, fitness for a particular purpose or non-infringement. This preliminary map product is for research and review purposes only. It is not intended to be used for emergency management operational or life safety decisions at the local or regional governmental level or by the general public. Users requiring information regarding hazardous conditions or meteorological conditions for specific geographic areas should consult directly with their city or county emergency management office.

54 ENVIRONMENTAL SCIENCES↗

ESS-DIVE reporting format for field measurements of soil respiration

This is a new data and metadata reporting format for soil respiration data, based on engagement with a wide range of researchers in the earth and ecological sciences as well as expert advisory panels. Reporting formats are intended to be ‘bottom-up’, community-developed documents and templates that can enable consistent, findable, accessible, interoperable, and reproducible (FAIR) data; our goal was a reporting format that would be relevant and useful for synthesis activities, optimizing data discoverability and usability while not placing an undue burden on data contributors. The format is split into two levels of information—metadata about chambers (used to measure soil respiration in the field), and the measured data themselves. The file data_reportingformat_template.xlsx contains information on how the data table should be structured, including field names, field descriptions, and units; the chammetadata_reportingformat_template.xlsx file contains similar information for the chamber-level metadata. Fundamentally, this format aims to enable FAIR data through an easy-to-use guide for data providers interested in formatting their data in a common way, and provide ‘future-proofing’ capabilities to support reanalyses of soil respiration data, a globally-important component of the carbon cycle.

54 ENVIRONMENTAL SCIENCES↗

IPCC, 2022: Annex II: Definitions, Units and Conventions

This annex on Definitions, Units and Conventions provides background information on material used in the Working Group III contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6 WGIII). The material presented in this annex documents metrics and common datasets that are typically used across multiple chapters of the report. In a few instances there are no updates to what was adopted by WGIII during the production of the Fifth Assessment Report (AR5), in which case this annex refers to Annex II of AR5 (Krey et al. 2014).

54 ENVIRONMENTAL SCIENCES↗

Specificity and tunability of efflux pumps: A new role for the proton gradient?

Efflux pumps that transport antibacterial drugs out of bacterial cells have broad specificity, commonly leading to broad spectrum resistance and limiting treatment strategies for infections. It remains unclear how efflux pumps can maintain this broad spectrum specificity to diverse drug molecules while limiting the efflux of other cytoplasmic content. We have investigated the origins of this broad specificity using theoretical models informed by the experimentally determined structural and kinetic properties of efflux pumps. We developed a set of mathematical models describing operation of efflux pumps as a discrete cyclic stochastic process across a network of states characterizing pump conformations and the presence/absence of bound ligands and protons. These include a minimal three-state model that lends itself to clear analytic calculations as well as a five-state model that relaxes some of the simpler model’s most strict assumptions. We found that the pump specificity is determined not solely by the drug affinity to the pump–as is commonly assumed–but it is also directly affected by the periplasmic pH and the transmembrane potential. Therefore, changes to the proton concentration gradient and voltage drop across the membrane can influence how effective the pump is at extruding a particular drug molecule. Furthermore, we found that while both the proton concentration gradient across the membrane and the transmembrane potential contribute to the thermodynamic force driving the pump, their effects on the efflux enter not strictly in a combined proton motive force. Rather, they have two distinguishable effects on the overall throughput. These results highlight the unexpected effects of thermodynamic driving forces out of equilibrium and illustrate how efflux pump structure and function are conducive to the emergence of multidrug resistance.

59 BASIC BIOLOGICAL SCIENCES↗

A Data-Driven Passive Islanding Detection Scheme

This paper proposes a simple yet effective statistical parameters based passive islanding scheme. The proposed scheme relies only the voltage data, measured at the point of common coupling (PCC), and is comprised of three stages. The Stage-1 quickly computes what is termed as mean based coarse islanding detection (MID), Stage-2 computes the decaying DC detector (DDCD), and 3) finally, MID is used in conjunction with DDCD as per the statistical digital relay logic (SRDL) is devised in Stage-3 to distinguish between islanding events from non islanding events. Various tests are investigated on the Banshee's industrial Microgrid (MG), modelled in the Real Time Digital Simulator (RTDS), on the basis of the IEEE-1547, UL 1741 standards. The validation of the proposed islanding detection methodology (PIDM) in RTDS platform ensures the viability of the proposed scheme in practical systems. The test results reveal that the PIDM can detect islanding within 2 cycles, and results in a very small non detection zone (NDZ). Moreover, the scheme can very well be used for single-phase MG too as it relies only on the single phase voltage information.

anti-islanding protection↗

Supporting information for Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa

In this work, we use few-shot learning to segment the body and vein architecture of P. trichocarpa leaves from high-resolution scans obtained in the UC Davis common garden. Leaf and vein segmentation are formulated as separate tasks, in which convolutional neural networks (CNNs) are used to iteratively expand partial segmentations until reaching stopping criteria. Our leaf and vein segmentation approaches use just 50 and 8 manually traced images for training, respectively, and are applied to a set of 2,634 top and bottom leaf scans. We show that both methods achieve high segmentation accuracy, in some cases exceeding even human-level segmentation. The leaf and vein segmentations are subsequently used to extract 68 morphological traits using traditional open-source image processing tools, which are validated using real-world physical measurements. For a biological perspective, we perform a genome-wide association study using the vein density trait to discover novel genetic architectures associated with multiple physiological processes relating to leaf development and function. In addition to sharing all of the few-shot learning code (see https://github.com/jlager/few-shot-leaf-segmentation), we are releasing all images, manual segmentations, model predictions, 68 extracted leaf phenotypes, and a new set of SNPs called against the v4 P. trichocarpa genome for 1,419 genotypes. The data folder includes all images, ground truth segmentations, predicted segmentations, and extracted leaf traits. All images encode the sample ID in the file name by indicating the treatment, block, row, position, and leaf side, respectively. For example, the file, C_1_1_2_bot.jpeg, indicates the control treatment, block 1, row 1, position 2, and the bottom side of the leaf. Tabulated results include position IDs as well as the corresponding genotype IDs. The images folder includes the 2,906 high-resolution leaf scans taken in the field. The leaf_masks folder includes 50 ground truth segmentations used for training the leaf tracing algorithm. The leaf_preds folder includes the 2,906 predicted segmentations from the leaf tracing algorithm. The vein_masks folder includes 8 ground truth segmentations used for training the vein growing algorithm. The vein_preds folder includes the 1,453 predicted segmentations from the vein growing algorithm. The vein_probs folder includes the 1,453 predicted probability maps from the vein growing algorithm before thresholding. The genomes folder includes the set of SNPs called against the v4 P. trichocarpa genome for 1,419 genotypes with a README file detailing the steps taken. The results folder includes: (i) raw values of the 68 predicted leaf traits in digital_traits.tsv, (ii) manually measured values of petiole length and width in manual_traits.tsv, (iii) thin plate spline (TPS) adjusted values of the vein density trait in vein_density_tps_adj.tsv, (iv) best linear unbiased prediction (BLUP) adjusted values of the vein density trait in vein_density_blups.tsv, and (v) GWAS results for the vein density trait, including chromosome positions and corresponding P values, in gwas_results.csv.

09 BIOMASS FUELS↗