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The present disclosure provides agricultural compositions and methods of using these compositions to increase plant growth, pathogen resistance and drought tolerance. The agricultural compositions disclosed herein comprise mixtures of mutualistic beneficial fungi such as Laccaria bicolor and Piriformispora indica, and bacterial strains of Pseudomonas.
A non-naturally occurring microbe capable of growing in a medium comprising methanol is provided. The methanol contributes to a significant percentage (e.g., at least 40%) of the carbon source for the non-naturally occurring microbe, which expresses heterologous methanol dehydrogenase (MDH) and heterologous ribulose monophosphate (RuMP) pathway enzymes. Methods for producing liquid fuels and chemicals by the non-naturally occurring microbe and methods for preparing the non-naturally occurring microbe are also provided.
Methods and compositions for the production of monocot plants with increased starch content in stems are provided. In accordance with the invention, novel promoters and regulatory elements with specific temporal and spatial expression patterns are disclosed together with methods for the production of plants having desirable stem composition at harvest.
The present invention provides methods and compositions for targeting enzymes involved in lignin or xylan biosynthesis using genome editing nucleases to specifically reduce content in a desired plant cell type(s).
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Fungi and plants interact in a myriad of ways. These interactions range from mutualism to parasitism, and yet, many plant species appear to use similar tools to deal with both. One recent observation is the role that small RNAs play in mediating the conversation between plant and fungus. Increasingly, many studies demonstrate these RNAs are pivotal modulators, reprogramming gene expression and cellular processes essential to the biogenesis of these inter-kingdom relationships.
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Accurate knowledge of the 237 Np($n$; γ) 238 Np cross section at fast neutron energies is important for applied nuclear science. The presently available experimental data has large disagreements in the fast neutron region. A measurement of the 237 Np($n$; γ) 238 Np cross section was performed using a well characterized fast neutron source and the results was compared with previous measurements and current nuclear data evaluations. This will provide an integral measurement that can be used as a benchmark for current evaluations. Multiple samples of 237 Np were irradiated in the Godiva-IV critical assembly. Following the irradiation, the samples placed in a γ-ray counting setup and the γ-rays emitted from the decay of 238 Np were measured over a time period of approximately 7 days. Multiple γ-ray decay branches of 238 Np were observed. The observed activity of 238 Np was used to calculate the amount of 238 Np produced during the irradiation via the 237 Np($n$; γ) 238 Np and an integral cross section of 359(10) mb was measured. The 238 Np half-life has been measured with a result of 50.34(7) hours. An integral measurement of the 237 Np($n$; γ) 238 Np cross section has been measured. This result has been compared to the calculated integral cross section using the ENDF/BVIII.0 and JENDL-4.0 evaluated cross sections and found to be in good agreement with ENDF/BVIII.0 value of 367 mb. The JENDL-4.0 evaluation yields a value of 312 mb, which is significantly lower than the measured cross section.
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The first “Changing Microbiomes” conference was successfully held at the Pennsylvania State University from May 31st – June 3rd, 2022. The overall goal of the symposium was to provide space for researchers to connect, exchange knowledge, and partner to advance our understanding of the mechanisms and applications of ecological and evolutionary changes in microbiomes across systems. The symposium spanned terrestrial and aquatic contexts and host-associated and free-living microbial communities. As such, the symposium will enable cross-disciplinary knowledge and idea exchange to promote future interdisciplinary collaborations. In addition, the program allowed formal and informal interactions between and among students, postdocs, PIs, and industry scientists.
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At the National Renewable Energy Laboratory (NREL)—a U.S. Department of Energy laboratory—computational science, high-performance computing, applied mathematics, advanced computer science, visualization, and data play a pivotal role in advancing energy abundance, affordability, security, and reliability. From fundamental scientifc discovery to systems engineering and analysis, NREL researchers tackle market-relevant challenges to develop solutions for an independent energy system that is reliable, resilient and secure. Collaborative partnerships with industry, government, and academia ensure that our research remains cutting edge, impactful, applicable, and aligned with real-world energy needs. This special issue of Computing in Science & Engineering highlights exemplary NREL projects where computational tools and methodologies drive discovery and accelerate innovation in scalable and integrated energy systems. The featured articles explore the role of computational modeling, high-performance computing, generative AI, and adaptive computing in advancing independent energy solutions, optimizing sustainability research, and enhancing decision-making for energy solutions using a broad mix of energy technologies. Here, these contributions demonstrate how NREL’s computational research bridges the gap between theoretical advancements and practical implementation, emphasizing interdisciplinary collaboration and a commitment to innovation, with a focus on translating computational excellence into real-world impact, thus accelerate progress toward national energy goals. By showcasing cutting-edge research at the intersection of computational science and energy systems, this issue aims to inspire and inform researchers, practitioners, and policymakers dedicated to shaping a more reliable energy future.
New research goals, mitigation successes, and beneficial collaborations burgeoned from the early critical days of 2020 when LANL (Los Alamos National Laboratory) pivoted to help respond to the pandemic. By applying its considerable capabilities in the fields of science, technology, and engineering (STE), LANL responded to the coronavirus infectious disease (COVID-19) pandemic. This report documents representative examples of that response. Through significant investments in COVID-19 mitigations and ongoing research and development (R&D), important advancements were made to address the pandemic that not only benefited the global population affected by the pandemic, but also protected our staff and collaborators as we executed our Lab’s mission.
Exascale computers will offer transformative capabilities to combine data-driven and learning-based approaches with traditional simulation applications to accelerate scientific discovery and insight. These software combinations and integrations, however, are difficult to achieve due to challenges of coordination and deployment of heterogeneous software components on diverse and massive platforms. We present the ExaWorks project, which can address many of these challenges: ExaWorks is leading a co-design process to create a workflow Software Development Toolkit (SDK) consisting of a wide range of workflow management tools that can be composed and interoperate through common interfaces. We describe the initial set of tools and interfaces supported by the SDK, efforts to make them easier to apply to complex science challenges, and examples of their application to exemplar cases. Furthermore, we discuss how our project is working with the workflows community, large computing facilities as well as HPC platform vendors to sustainably address the requirements of workflows at the exascale.
13C-Metabolic Flux Analysis (13C-MFA) and Flux Balance Analysis (FBA) are widely used to investigate the operation of biochemical networks in both biological and biotechnological research. Both methods use metabolic reaction network models of metabolism operating at steady state so that reaction rates (fluxes) and the levels of metabolic intermediates are constrained to be invariant. They provide estimated (MFA) or predicted (FBA) values of the fluxes through the network in vivo, which cannot be measured directly. These fluxes can shed light on basic biology and have been successfully used to inform metabolic engineering strategies. Several approaches have been taken to test the reliability of estimates and predictions from constraint-based methods and to compare alternative model architectures. Despite advances in other areas of the statistical evaluation of metabolic models, such as the quantification of flux estimate uncertainty, validation and model selection methods have been underappreciated and underexplored. We review the history and state-of-the-art in constraint-based metabolic model validation and model selection. Applications and limitations of the χ 2 -test of goodness-of-fit, the most widely used quantitative validation and selection approach in 13C-MFA, are discussed, and complementary and alternative forms of validation and selection are proposed. A combined model validation and selection framework for 13C-MFA incorporating metabolite pool size information that leverages new developments in the field is presented and advocated for. Finally, we discuss how adopting robust validation and selection procedures can enhance confidence in constraint-based modeling as a whole and ultimately facilitate more widespread use of FBA in biotechnology.