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At least 163 records · Page 9

Reliability metrics and their management implications for open pond algae cultivation

The prevalence of contaminating organisms in outdoor algae cultivation, with the often-associated dramatic crop failures, necessitates the need for metrics describing production system reliability. Standard metrics for algae cultivation reliability are critically needed to be able to map improvements in operational parameters, but do not currently exist. In this work, we present a set of standard metrics including mean time to failure (MTTF) and mean time between failures (MTBF) as the basis for the calculation of pond failure rate (FR) and reliability coefficient (RC). Metrics associated with the numbers of contaminating organisms such as abundance ratio (AR), prevalence of infection (PI), and mean intensity of infection (MII) are also relevant. These metrics, based on measured experimental values of outdoor pond performance during the Algae Testbed Public-Private Partnership (ATP3) Unified Field Studies (UFS), provide a basis for quantifying pond failure and provide insight into potential pond management and contaminant mitigation strategies. From these reliability metrics applied to this dataset, we are able to link operational parameters, such as harvest frequency and inoculum source, to pond reliability for different algae strains and seasons, and from an assessment of AR, provide contamination thresholds beyond which a culture may be unrecoverable. Ultimately, the implementation of widely-practiced and simple-to-calculate algae pond reliability metrics calculated from rapid and easy to collect data or observations will reduce risk and uncertainty in large-scale algae deployment and aid in the development of integrated pest management (IPM) strategies.

09 BIOMASS FUELS↗

Janthinobacter additions reduce rotifer grazing of microalga Microchloropsis salina in biotically complex communities

Grazing and parasitism in algal production systems are significant causes of crop loss. Biological control of algal ponds, such as application of protective bacteria, represents a promising alternative to current mitigation strategies. The major barriers to probiotic methods are the lack of identified protective bacteria and uncertainty of scale-up effects, including potential negative interactions with the resident algal microbiome. Here, we report that application of Janthinobacter lividum affords robust protection against rotifer (Brachionus plicatilis) grazing on Microchloropsis salina. Protection was observed at laboratory scale from live bacteria and solvent extract additions as well as in biologically complex communities from bacterial additions to outdoor mesocosms. J. lividum persisted in outdoor communities for at least 3 days following addition, and did not lead to selective exclusion of specific taxa in the resident microbiome. These results demonstrate that J. lividum can provide protection against grazers in these diverse communities, without severe effects on the resident microbiome. Our results suggest that J. lividum is a promising treatment to mitigate algal crop losses due to pests. Additionally, the study highlights the importance of an ecologically-informed approach to algal crop management that will aid future applications in the biological control of algal ponds.

09 BIOMASS FUELS↗

Grazer-induced changes in molecular signatures of cyanobacteria

Algal biomass production is an emerging renewable source of fuels, nutrients, manufacturing materials, and pharmaceuticals. Industrial-scale production is predominantly performed in open raceway ponds that are inexpensive to build, operate, and maintain compared to closed bioreactors. However, these open pond systems suffer from increased opportunities for biological contamination from predators, pathogens, and competitors, which result in reduced biomass quality and yields and often the complete destruction of the crop over a short period of time. Early detection of contaminants is a necessary step of integrated pest management for triggering and informing interventions to prevent crop losses. To develop a sensitive method of detection utilizing mass spectrometry (MS), we used three methods – imaging mass spectrometry (imaging MS), liquid chromatography MS/MS (LC-MS/MS) combined with molecular networking, and gas chromatography MS/MS (GC–MS/MS) – to observe and identify molecular signatures from a model predator-prey system of the heterolobosean amoeba HGG1 preying on the filamentous cyanobacterium Anabaena sp. PCC 7120. Imaging MS enabled the association of molecules with the crop, the predator, or the activity of protozoan grazing, while LC-MS/MS-based molecular networking identified a subset of the grazing-specific signals as the chlorophyll breakdown products pyropheophytin, pheophorbide A, and pyropheophorbide. Application of MS techniques to other amoebacyanobacterial predator-prey pairs allowed categorization of grazing signatures as universal or specific to the species of prey, predator, or the prey-predator pairing, indicating that MS-based techniques can distinguish crops from competitors and potentially identify predators. Finally, GCMS/MS was shown to be capable of monitoring novel volatile organic compounds (VOCs), including those predicted to be released through chlorophyll breakdown, in the headspace over algal cultures under predation. Furthermore, these results demonstrate that the combination of multiple MS technologies creates a predictive framework to identify and catalog relevant molecular signatures for informing crop protection strategies.

09 BIOMASS FUELS↗

Development of integrated screening, cultivar optimization, and verification research (DISCOVR): A coordinated research-driven approach to improve microalgal productivity, composition, and culture stability for commercially viable biofuels production

To address major knowledge gaps and barriers to the commercial development of algal biomass for biofuels and co-products, a collaborative consortium, Development of Integrated Screening, Cultivar Optimization, and Verification Research (DISCOVR), was established in 2016. Funded by the U.S. Department of Energy (DOE) Bioenergy Technologies Office (BETO), this consortium constitutes a partnership between four DOE national laboratories - Pacific Northwest National Laboratory (PNNL), Los Alamos National Laboratory (LANL), the National Renewable Energy Laboratory (NREL), and Sandia National Laboratories (SNL) - and the Arizona Center for Algae Technology and Innovation (AzCATI) at Arizona State University. To address the barriers of strain selection for achieving high seasonal productivities with a suitable composition and culture resilience, a tiered strain down-selection pipeline is implemented. At Tier I, the temperature and salinity tolerance of strains is determined in flask cultures; at Tier II, the areal biomass productivity and composition is determined in climate-simulation photobioreactors; at Tier III, the productivity and culture stability are determined in outdoor raceways. The top performing strains move forward to long-term testing at the algae testbed site at AzCATI to generate annual biomass productivity data. Concurrent to the strain down-selection in the DISCOVR pipeline, hypotheses for increasing biomass productivity, shifting biomass composition to enhance intrinsic value, and improving culture stability and resistance to pests are also tested. Techno-economic analyses are carried out to determine whether promising findings from laboratory studies or proposed modifications in outdoor pond cultivation conditions translate into reductions in the minimum biomass selling price (MBSP). Notably, in the three years following the launch of DISCOVR, annual biomass productivity has increased from 11.7 to 17.6 g m -2 day -1 , resulting in an MBSP decrease from 824 to 611 $ ton -1 .

09 BIOMASS FUELS↗

Laboratory column experiments and transport modeling to evaluate retardation of uranium in an aquifer downgradient of a uranium in-situ recovery site

The purpose of this study was to determine the attenuation potential and retardation of uranium in sediments taken from boreholes at the Smith-Ranch Highland in-situ recovery (ISR) site. Five column experiments with four different sediments were conducted to study the effects of variable mineralogy and alkalinity on uranium breakthrough. Uranium transport was modeled with PHREEQC using a generalized composite surface complexation model (GC SCM) with one, two, and, three generic surfaces, respectively. Reactive surface areas were approximated with PEST using BET derived surface areas to constrain fitting parameters. Uranium breakthrough was delayed by a factor of 1.68, 1.69 and 1.47 relative to the non-reactive tracer for three of the 5 experiments at an alkalinity of 540 mg/l. A sediment containing smectite and kaolinite retained uranium by a factor of 2.80 despite a lower measured BET surface area. Decreasing alkalinity to 360 mg/l from 540 mg/l increased retardation by a factor of 4.26. Model fits correlated well to overall BET surface area in the three columns where clay content was less than 1%. For the sediment with clay, models consistently understated uranium retardation when reactive surface sites were restricted by BET results. Calcite saturation was shown to be a controlling factor for uranium desorption as the pH of the system changes. A pH of 6 during a secondary background water flush remobilized previously sorbed uranium resulting in a secondary uranium peak at twice the influent concentrations. Furthermore, this study demonstrates the potential of GC SCM models to predict uranium transport in sediments with homogenous mineral composition, but highlights the need for further research to understand the role of sediment clay composition and calcite saturation in uranium transport.

58 GEOSCIENCES↗

Deltamethrin induced RIPK3-mediated caspase-independent non-apoptotic cell death in rat primary hepatocytes

Deltamethrin (DLM), a synthetic pyrethroid insecticide, is used all over the world for indoor and field pest management. In the present study, we investigated the elicited pathogenesis of DLM-induced hepatotoxicity in rat primary hepatocytes. DLM-induced cell death was accompanied with increased ROS generation, decreased mitochondrial membrane potential and G2/M arrest. Pre-treatment with N-acetyl cysteine/butylated hydroxyanisole/IM54 could partly rescue hepatocytes suggesting that ROS might play a role in DLM-induced toxicity. Interestingly, DLM treatment resulted in a caspase-independent but non-apoptotic cell death. Pre-treatment with pan-caspase inhibitor (ZVAD-FMK) could not rescue hepatocytes. Unaltered caspase-3 activity and absence of cleaved caspase-3 also corroborated our findings. Further, LDH release and Transmission electron microscopy (TEM) analysis demonstrated that DLM incites membrane disintegrity and necrotic damage. Immunochemical staining revealed an increased expression of inflammatory markers (TNFα, NFκB, iNOS, COX-2) following DLM treatment. Moreover, the enhanced RIPK3 expression in DLM treated groups and prominent rescue from cell death by GSK-872 indicated that DLM exposure could induce programmed necrosis in hepatocytes. The present study demonstrates that DLM could induce hepatotoxicity via non-apoptotic mode of cell death. - Highlights: • Deltamethrin (DLM) induces caspase-independent death in rat primary hepatocytes. • RIPK3 played a central role in DLM-mediated programmed necrosis in liver cells. • Reactive oxygen species played a role in DLM mediated hepatotoxity.

60 APPLIED LIFE SCIENCES↗

TORC1 signaling regulates DNA replication via DNA replication protein levels

Highlights: • TORC1 protein kinase reduced the levels of proteins critical for DNA replication initiation and retarded DNA replication. • TORC1 inactivation promoted Mcm3 degradation in a manner dependent on Skp1–Cullin–F-box (SCF)-Grr1 and proteasome. • TORC1-downstream factors PP2A-Cdc55 protein phosphatase and protein kinase A regulated Mcm3 degradation. Accurate DNA replication is at the heart of faithful genome transmission in dividing cells. DNA replication is strictly controlled by various factors. However, how environmental stresses such as nutrient starvation impact on these factors and DNA replication is largely unknown. Here we show that DNA replication is regulated by target of rapamycin complex 1 (TORC1) protein kinase, which is a central regulator of cell growth and proliferation in response to nutrients. TORC1 inactivation reduced the levels of various proteins critical for DNA replication initiation, such as Mcm3, Orc3, Cdt1, and Sld2, and retarded DNA replication. TORC1 inactivation promoted proteasome-mediated Mcm3 degradation. Skp1–Cullin–F-box (SCF)-Grr1 and PEST motif mediated Mcm3 degradation. TORC1-downstream factors PP2A-Cdc55 protein phosphatase and protein kinase A regulated Mcm3 degradation. This study showed that TORC1 signaling modulates DNA replication to coordinate cell growth and genome replication in response to nutrient availability.

60 APPLIED LIFE SCIENCES↗

Trait drift in microalgae and applications for strain improvement

Microalgae are increasingly used to generate a wide range of commercial products, and there is growing evidence that microalgae-based products can be produced sustainably. However, industrial production of microalgal biomass is not as developed as other biomanufacturing platform technologies. In addition, results of bench-scale research often fail to translate to large-scale or mass production systems. This disconnect may result from trait drift and evolution occurring, through time, in response to unique drivers in each environment, such as cultivation regimes, weather, and pests. Moreover, outdoor and indoor cultivation of microalgae has the potential to impose negative selection pressures, which makes the maintenance of desired traits a challenge. In this context, this review sheds the light on our current understanding of trait drift and evolution in microalgae. Here, we delineate the basics of phenotype plasticity and evolution, with a focus on how microalgae respond under various conditions. In addition, we review techniques that exploit phenotypic plasticity and evolution for strain improvement in view of industrial commercial applications, highlighting associated advantages and shortcomings. Finally, we suggest future research directions and recommendations to overcome unwanted trait drift and evolution in microalgae cultivation.

59 BASIC BIOLOGICAL SCIENCES↗

Preface to the Special Issue on TOUGH Symposium 2015

The TOUGH Symposium 2015 was held in Berkeley, California, September 28-30, 2015. The TOUGH family of codes, developed at the Energy Geosciences Division of Lawrence Berkeley National Laboratory (LBNL), is a suite of computer programs for the simulation of multiphase and multicomponent fluid and heat flows in porous and fractured media with applications in many geosciences fields, such as geothermal reservoir engineering, nuclear waste disposal, geological carbon sequestration, oil and gas reservoirs, gas hydrate research, vadose zone hydrology and environmental remediation. Since the first release in the 1980s, many modifications and enhancements have been continuously made to TOUGH and its various descendants (iTOUGH2, TOUGH+, TOUGH-MP, TOUGHREACT, TOUGH+HYDRATE, TMVOC...), at LBNL and elsewhere. Today, these codes are used worldwide in academia, government organizations and private companies in problems involving coupled hydrological, thermal, biogeochemical and geomechanical processes. The Symposia, organized every 2-3 years, bring together developers and users for an open exchange on recent code enhancements and applications. In 2015, the Symposium was attended by one hundred participants, representing thirty-four nationalities. This Special Issue in Computers & Geosciences gathers extended versions of selected Symposium proceedings related to (i) recent enhancements to the TOUGH family of codes and (ii) coupled flow and geomechanics processes modeling. In this special issue, a paper by Jung et al. provides a new base version of TOUGH, TOUGH3. This latest version addresses the computational robustness and efficiency needed for the increasing complexity of the simulated processes and the growing size of model domains. Moreover, it consolidates the serial and parallel versions into a single code, hence, inconsistencies between different versions are less likely to occur, and code maintenance and development become more efficient. Finsterle et al. present a review of the capabilities of iTOUGH2 and illustrate the workflow of an integrated simulation-optimization framework. Liu et al. present an approach to reduce the computational cost of performing Bayesian inverse modeling and demonstrate their approach using a synthetic experiment simulated with TOUGH2. Regarding the mesh generation process, the paper by Doughty presents a generalization of the grid-generation algorithm embedded in TOUGH2 (MESHMAKER) to create one-column grids with fractal dimension, useful for representing flow through fractures or highly heterogeneous media with fractal geometry, and also for inverse methods. Sentís and Gable present a module to interface the open source code Lagrit with TOUGH2, that assists in the processes of mesh generation and model setup, and that can be particularly useful for complex geometries and large models. Bonduà et al. present a pre-processing tool (VORO2MESH) that features a full 3D Voronoi approach for structured and unstructured grids, and an enhanced post-processor (TOUGH2Viewer) for the visualization of 3D Voronoi grids. The literature review shows that coupled fluid flow and geomechanics has gained increasing attention in the last years in many geosciences fields. In this context, several codes of the TOUGH family have been successfully coupled with geomechanics software. The paper by Rutqvist presents a review of some of these TOUGH-based geomechanics models, of which the TOUGH-FLAC simulator – presented for the first time in the TOUGH Symposium 2003 – is well-known worldwide and has been used in a large variety of applications, encompassing the application domains of the TOUGH codes. Blanco-Martín et al. present an extension of TOUGH-FLAC to the finite strain framework, motivated by the existence of large strains associated with rock salt deformation in the context of heat-generating nuclear waste disposal. Following a paper published in the Special Issue of the TOUGH Symposium 2012 in this journal, Kim et al. present the current status of the TOUGH-RSBN simulator for coupled hydro-mechanical modeling of hydraulic fracturing using a discrete fracture network approach, and present some validation and demonstration cases. Rinaldi and Nespoli present TOUGH-SEED, an injection-induced seismicity modeling approach using TOUGH2 and a geomechanics stochastic model, and apply it to the Basel EGS project. Finally, Rinaldi et al. use the PEST protocol implemented in iTOUGH2, combined with TOUGH-FLAC as the forward model, to perform inverse modeling for the In Salah CO2 demonstration site in Algeria. Using pore pressures and InSAR displacements as observations, this approach allows for a multi-physics joint inversion and takes advantage of the inverse capabilities of iTOUGH2 (parameter estimation, sensitivity analysis, uncertainty estimation …), applied to coupled fluid flow and geomechanics. On behalf of the members of the TOUGH Symposium 2015 Organizing Committee, I hope that this collection of papers will provide the readers of Computers & Geosciences with a recent view of the capabilities of the TOUGH family of codes, as well as with current modeling research domains in the geosciences. Last but not least, I would like to express my gratitude to all the reviewers who kindly contributed to this Special Issue, as well as the Journal staff who have made this publication possible.

99 GENERAL AND MISCELLANEOUS↗

Protecting honey bees through microbiome engineering

Honey bees are indispensable insects. Their pollination services support modern agriculture and natural ecosystems. Managed honey bee colonies face increasing threats to their survival, ranging from environmental stressors that include agrochemicals to infestations of arthropod pests and infections with microbial pathogens. Like humans, honey bees have a native gut microbiome that supports their health. However, the bee gut microbiome has a simpler composition than the gut microbiome of mammals, and its main constituent bacterial species can be easily cultured outside of the host. Furthermore, this experimental tractability and the need for new methods for protecting hive health have made honey bees a testbed for synthetic microbiomes augmented with probiotic bacteria and engineered DNA. Here, we discuss the natural benefits of bee gut bacteria, recent progress in genetically modifying these bacteria, and how symbiont-mediated RNA interference and other microbiome engineering approaches can boost bee immunity and suppress bee pathogens and parasites. Finally, we discuss how emerging methods for microbiome engineering and biocontainment could be applied to honey bees and used to address challenges in translating these proof-of-principle achievements into safe and effective technologies for field applications at scale.

Biological and medical sciences↗

The Reservoir Temperature Estimator (RTEst): A multicomponent geothermometry tool

The Reservoir Temperature Estimator (RTEst) is a multicomponent geothermometry tool for estimating reservoir geochemical parameters including reservoir temperature, CO 2 fugacity, mass of water lost or gained, and a reaction factor. It estimates these parameters and their associated uncertainties by minimizing an objective function that is the weighted sum of squares of the saturation indices of a user-selected set of minerals believed to be equilibrated with the reservoir fluid. RTEst accomplishes these estimates by combining the geochemical modeling capabilities of The Geochemist’s Workbench® with the optimization/parameter estimation resources of PEST®. An included interface aids the user in selecting plausible mineral phases to comprise the objective function and calculates their weighting factors. The working principles of RTEst are described and its efficacy is illustrated by presenting results of its application to various geothermal fields with known conditions. These examples show RTEst can account for the alteration of ascending reservoir fluid by mineral (calcite) re-equilibration with changes in temperature, reconstruct waters with CO 2 loss, correct for the deficit of water and other volatiles (CO 2,gas , H 2 S gas ) from boiling, and determine the amount of mixing of thermal and non-thermal waters. RTEst can use data with basis species below detection limit, missing, or unreliable either by assuming equilibrium with a controlling mineral (fixed-analyte method) or by treating the analyte concentration as an optimization parameter. The inverse of variance weighting method included in RTEst provides more representative results than either the normalization or unit weighting methods. Finally, the ability of RTEst to calculate reservoir temperatures, gas fugacity, and mixing fractions demonstrates its usefulness as a tool for evaluating geothermal systems.

15 GEOTHERMAL ENERGY↗

Identifying common stored product insects using automated deep learning methods

Monitoring stored product insect pests is a common practice for post-harvest management of stored grain and grain-based commodities, which helps ensure product quality from harvest to final consumer. Current methods of sampling and monitoring can be time-consuming, labor-intensive, expensive and require expertise in insect identification. Therefore, this study aims to develop an image-based automated identification system for common stored product insect species using deep-learning methods. Top-down images of the common stored product adult insect species of Rhyzopertha dominica, Cryptolestes ferrugineus, Tribolium castaneum, Sitophilus oryzae, and Oryzaephilus surinamensis were acquired and analyzed. Deep learning-based, state-of-the-art Convolutional Neural Networks (CNN) models (ResNet-50, MobileNet-v2, DarkNet-53, and EfficientNet-b0) were fine-tuned with a transfer learning approach to classify the insect species. All models were able to correctly identify the insect species with at least 96% accuracy and with few misclassifications. One issue with trained CNNs is that they do not explain the reasoning for the classification and are often called a “black box”. Therefore, visualization methods called Gradient-weighted Class Activation Mapping (Grad-CAM) were implemented to explore the black box network. The Grad-CAM uses heat maps to highlight the major image features that the network focused on to make insect species predictions. The Grad-CAM verifies the network's prediction and also helps improve network performance. This study contributes to the overall goal of developing a camera-based system for monitoring stored grain insects. As a result, the developed system would empower warehouse, flour mills, and other food facilities with a tool to quickly and accurately identify insect species in stored product environments and could be implemented as part of a close to real-time monitoring system.

60 APPLIED LIFE SCIENCES↗

Design of an alumina forming coating for Nb-base refractory alloys

Refractory multi-principal element alloys (RMPEAs) promise to significantly enhance gas turbine engine efficiency, but their poor oxidation performance inhibits their implementation. Alumina-forming bond coat alloys can provide oxidation protection, but discovering suitable chemistries remains a challenge. We employed a design methodology that screens for alumina-formation capability using Al activity and phase constitution predictions from CalPhaD (Thermo-Calc). Alloy down-selection from approximately 7,800 alloys in the Nb-Si-Ti-Al-Hf system was conducted via analysis of calculated thermodynamic properties with number-density topology style maps. This approach is validated by creating and testing the composition Nb 12 Si 23 Ti 24 Al 36 Hf 5 , which forms protective alumina scales up to 1400 °C and resists pesting at 800 °C. Further, the alloy has an average coefficient of thermal expansion of ~10.1 ppm/K, making it well matched to Nb-based refractory alloys. The methodology will be useful for the design of coatings for RMPEAs, enabling their implementation and significant efficiency benefits sooner.

36 MATERIALS SCIENCE↗

Community structure and function during periods of high performance and system upset in a full-scale mixed microalgal wastewater resource recovery facility

Microalgae have the potential to exceed current nutrient recovery limits from wastewater, enabling water resource recovery facilities (WRRFs) to achieve increasingly stringent effluent permits. The use of photobioreactors (PBRs) and the separation of hydraulic retention and solids residence time (HRT/SRT) further enables increased biomass in a reduced physical footprint while allowing operational parameters (e.g., SRT) to select for desired functional communities. However, as algal technology transitions to full-scale, there is a need to understand the effect of operational and environmental parameters on complex microbial dynamics among mixotrophic microalgae, bacterial groups, and pests (i.e., grazers and pathogens) and to implement robust process controls for stable long-term performance. Here, we examine a full-scale, intensive WRRF utilizing mixed microalgae for tertiary treatment in the US (EcoRecover, Clearas Water Recovery Inc.) during a nine-month monitoring campaign. We investigated the temporal variations in microbial community structure (18S and 16S rRNA genes), which revealed that stable system performance of the EcoRecover system was marked by a low-diversity microalgal community (D INVSIMPSON = 2.01) dominated by Scenedesmus sp. (MRA = 55 %-80 %) that achieved strict nutrient removal (effluent TP < 0.04 mg·L -1 ) and steady biomass concentration (TSS monthly avg . = 400–700 mg·L −1 ). Operational variables including pH, alkalinity, and influent ammonium (NH 4 + ), correlated positively (p < 0.05, method = Spearman) with algal community during stable performance. Further, the use of these parameters as operational controls along with N/P loading and SRT allowed for system recovery following upset events. Importantly, the presence or absence of bacterial nitrification did not directly impact algal system performance and overall nutrient recovery, but partial nitrification (potentially resulting from NO 2 − accumulation) inhibited algal growth and should be considered during long-term operation. The microalgal communities were also adversely affected by zooplankton grazers (ciliates, rotifers) and fungal parasites (Aphelidium), particularly during periods of upset when algal cultures were experiencing culture turnover or stress conditions (e.g., nitrogen limitation, elevated temperature). Altogether, the active management of system operation in order to maintain healthy algal cultures and high biomass productivity can result in significant periods (>4 months) of stable system performance that achieve robust nutrient recovery, even in winter months in northern latitudes (WI, USA).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Community Data Mining Approach for Surface Complexation Database Development

This paper presents a comprehensive data-to-model workflow, including a findable, accessible, interoperable, reusable (FAIR) community sorption database (newly developed LLNL Surface Complexation/Ion Exchange (L-SCIE) database) along with a data fitting workflow to efficiently optimize surface complexation reaction constants with multiple surface complexation model (SCM) constructs. This workflow serves as a universal framework to mine, compile, and analyze large numbers of published sorption data as well as to estimate reaction constants for parameterizing reactive transport models. Here the framework includes (1) data digitization from published papers, (2) data unification including unit conversions, and (3) data-model integration and reaction constant estimation using geochemical software PHREEQC coupled with the universal parameter estimation code PEST. We demonstrate our approach using an analysis of U(VI) sorption to quartz based on a first L-SCIE implementation, concluding that a multisite SCM construct with carbonate surface species yielded the best fit to community data. Surface complexation reaction constants extracted from this approach captured all available sorption data available in the literature and provided insight into previously published reaction constants and surface complexation model constructs. The L-SCIE sorption database presented herein allows for automating this approach across a wide range of metals and minerals and implementing novel machine learning approaches to reactive transport in the future.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

Gold-Nanocluster-Mediated Delivery of siRNA to Intact Plant Cells for Efficient Gene Knockdown

RNA interference, which involves the delivery of small interfering RNA (siRNA), has been used to validate target genes, to understand and control cellular metabolic pathways, and to use as a "green" alternative to confer pest tolerance in crops. Conventional siRNA delivery methods such as viruses and Agrobacterium -mediated delivery exhibit plant species range limitations and uncontrolled DNA integration into the plant genome. Here, we synthesize polyethylenimine-functionalized gold nanoclusters (PEI-AuNCs) to mediate siRNA delivery into intact plants and show that these nanoclusters enable efficient gene knockdown. We further demonstrate that PEI-AuNCs protect siRNA from RNase degradation while the complex is small enough to bypass the plant cell wall. Consequently, AuNCs enable gene knockdown with efficiencies of up 76.5 ± 5.9% and 76.1 ± 9.5% for GFP and ROQ1, respectively, with no observable toxicity. Our data suggest that AuNCs can deliver siRNA into intact plant cells for broad applications in plant biotechnology.

59 BASIC BIOLOGICAL SCIENCES↗

A Multiscale Deep Learning Model for Soil Moisture Integrating Satellite and In Situ Data

Deep learning (DL) models trained on hydrologic observations can perform extraordinarily well, but they can inherit deficiencies of the training data, such as limited coverage of in situ data or low resolution/accuracy of satellite data. In this work, we propose a novel multiscale DL scheme learning simultaneously from satellite and in situ data to predict 9 km daily soil moisture (5 cm depth). Based on spatial cross-validation over sites in the conterminous United States, the multiscale scheme obtained a median correlation of 0.901 and root-mean-square error of 0.034 m 3 /m 3 . It outperformed the Soil Moisture Active Passive satellite mission's 9 km product, DL models trained on in situ data alone, and land surface models. Our 9 km product showed better accuracy than previous 1 km satellite downscaling products, highlighting limited impacts of improving resolution. Not only is our product useful for planning against floods, droughts, and pests, our scheme is generically applicable to geoscientific domains with data on multiple scales, breaking the confines of individual data sets.

54 ENVIRONMENTAL SCIENCES↗

A transgenic approach for controlling Lygus in cotton

Lygus species of plant-feeding insects have emerged as economically important pests of cotton in the United States. These species are not controlled by commercial Bacillus thuringiensis (Bt) cotton varieties resulting in economic losses and increased application of insecticide. Previously, a Bt crystal protein (Cry51Aa2) was reported with insecticidal activity against Lygus spp. However, transgenic cotton plants expressing this protein did not exhibit effective protection from Lygus feeding damage. Here we employ various optimization strategies, informed in part by protein crystallography and modelling, to identify limited amino-acid substitutions in Cry51Aa2 that increase insecticidal activity towards Lygus spp. by >200-fold. Transgenic cotton expressing the variant protein, Cry51Aa2.834_16, reduce populations of Lygus spp. up to 30-fold in whole-plant caged field trials. One transgenic event, designated MON88702, has been selected for further development of cotton varieties that could potentially reduce or eliminate insecticide application for control of Lygus and the associated environmental impacts.

59 BASIC BIOLOGICAL SCIENCES↗