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At least 721 records · Page 40

Accelerating Traction Motor Optimization Design with AI Surrogate Models

The advancement of artificial intelligence systems enables the use of data-driven physics-based surrogate models to explore design spaces rapidly and deeply for engineering projects. This work presents a surrogate model workflow that accelerates electric traction motor design optimization by replacing finite element analysis (FEA) with an artificial neural network (ANN) and using this model in a genetic algorithm for design optimization. A baseline interior permanent-magnet motor is parameterized and sampled to generate FEA-labeled training data, after which a feed-forward ANN predicts key outputs (e.g., loss components and weight). The validated surrogate enables genetic-algorithm optimization and deep search over the design space without new FEA runs, producing Pareto-optimal trade-offs between weight and losses and set of optimized designs for rapid downselection of manufacturable motor designs.

Ribeiro, Pedro [ORNL] (ORCID:0009000921026641)↗

Data for "Evaluating the industrial potential of emerging biomass pretreatment technologies in bioethanol production and lipid recovery from transgenic sugarcane"

The selection of pretreatment methods is critical to achieving high product yields during bioconversion of lignocellulosic biomass. Hydrothermal, soaking-in-aqueous ammonia, and ionic liquid pretreatment methods are viable candidates for minimizing sugar decomposition, permitting the effective hydrolysis of structural carbohydrates, and producing a fermentable substrate suitable for achieving industrial ethanol titers and yields. In this study, the effect of these three pretreatment methods on non-modified sugarcane cultivar CP88-1762 and two transgenic lipid-accumulating sugarcane lines, oilcane 1565 and oilcane 1566, were investigated and compared in terms of lipid recovery, sugar yield, and ethanol yields within the lignocellulosic biomass conversion pipeline. Fed-batch enzymatic hydrolysis at high solid loading yielded hydrolysates capable of supporting industrial bioethanol titers across all conditions. The highest sugar yields were obtained on ammonia-pretreated biomass hydrolysate (253.73 g L−1), followed by hydrothermally pretreated hydrolysate (213.10 g L−1) and ionic liquid-pretreated hydrolysate (154.20 g L−1). Commercially viable ethanol titers of 100.62 g L−1, 64.47 g L−1, and 52.95 g L−1 were achieved from ammonia, hydrothermal, and ionic liquid pretreated hydrolysate with the corresponding ethanol productivities of 2.08 g L−1 h−1, 0.53 g L−1 h−1, and 0.36 g L−1 h−1. The lower acetic acid concentration in ammonia-pretreated hydrolysate may have enhanced its fermentability relative to the hydrothermal pretreatment condition, as indicated by the differences in ethanol titer and productivity. Lower sugar yields and ethanol productivities under the ionic liquid conditions likely resulted from the inhibitory effect of cholinium lysinate. Oilcane 1565 and oilcane 1566 bagasse accumulated over 16- and 3 times higher lipids than the non-modified sugarcane CP88-1762. The total fatty acid content in the oilcane samples was reduced in ammonia and ionic liquid-pretreated bagasse relative to the hydrothermal pretreatment condition. While all pretreatment techniques tested are industrially viable, the observed differences in titer, productivity, and lipid content indicate that careful selection and validation of upstream processing methods can contribute to improved economic and environmental outcomes.

biomass analytics↗

Nemesis Autonomous Test System

A generalized framework has been developed for systems validation that can be applied to both traditional and autonomous systems. The framework consists of an automated test case generation and execution system called Nemesis that rapidly and thoroughly identifies flaws or vulnerabilities within a system. By applying genetic optimization and goal-seeking algorithms on the test equipment side, a "war game" is conducted between a system and its complementary nemesis. The end result of the war games is a collection of scenarios that reveals any undesirable behaviors of the system under test. The software provides a reusable framework to evolve test scenarios using genetic algorithms using an operation model of the system under test. It can automatically generate and execute test cases that reveal flaws in behaviorally complex systems. Genetic algorithms focus the exploration of tests on the set of test cases that most effectively reveals the flaws and vulnerabilities of the system under test. It leverages advances in state- and model-based engineering, which are essential in defining the behavior of autonomous systems. It also uses goal networks to describe test scenarios.

Barltrop, Kevin J.↗

Nickase fidelity drives EvolvR-mediated diversification in mammalian cells

Abstract In vivo genetic diversifiers have previously enabled efficient searches of genetic variant fitness landscapes for continuous directed evolution. However, existing genomic diversification modalities for mammalian genomic loci exclusively rely on deaminases to generate transition mutations within target loci, forfeiting access to most missense mutations. Here, we engineer CRISPR-guided error-prone DNA polymerases (EvolvR) to diversify all four nucleotides within genomic loci in mammalian cells. We demonstrate that EvolvR generates both transition and transversion mutations throughout a mutation window of at least 40 bp and implement EvolvR to evolve previously unreported drug-resistantMAP2K1variants via substitutions not achievable with deaminases. Moreover, we discover that the nickase’s mismatch tolerance limits EvolvR’s mutation window and substitution biases in a gRNA-specific fashion. To compensate for gRNA-to-gRNA variability in mutagenesis, we maximize the number of gRNA target sequences by incorporating a PAM-flexible nickase into EvolvR. Finally, we find a strong correlation between predicted free energy changes underlying R-loop formation and EvolvR’s performance using a given gRNA. The EvolvR system diversifies all four nucleotides to enable the evolution of mammalian cells, while nuclease and gRNA-specific properties underlying nickase fidelity can be engineered to further enhance EvolvR’s mutation rates.

Science & Technology - Other Topics↗

Heterologous expression of formate dehydrogenase enables photoformatotrophy in the emerging model microalga, Picochlorum renovo

Rising global greenhouse gas emissions and the impacts of resultant climate change necessitate development and deployment of carbon capture and conversion technologies. Amongst the myriad of bio-based conversion approaches under evaluation, a formate bio-economy has recently been proposed, wherein CO 2 -derived formate serves as a substrate for concurrent carbon and energy delivery to microbial systems. To date, this approach has been explored in chemolithotrophic and heterotrophic organisms via native or engineered formatotrophy. However, utilization of this concept in phototrophic organisms has yet to be reported. Herein, we have taken the first steps to establish formate utilization in Picochlorum renovo, a recently characterized eukaryotic microalga with facile genetic tools and promising applied biotechnology traits. Plastidial heterologous expression of a formate dehydrogenase (FDH) enabled P. renovo growth on formate as a carbon and energy source. Further, FDH expression enhanced cultivation capacity on ambient CO 2 , underscoring the potential for bypass of conventional CO 2 capture and concentration limitations. This work establishes a photoformatotrophic cultivation regime that leverages light energy-driven formate utilization. The resultant photosynthetic formate platform has widespread implications for applied phototrophic cultivation systems and the bio-economy at large.

09 BIOMASS FUELS↗

Improve Data Mining and Knowledge Discovery Through the Use of MatLab

Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(R) (MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.

Shaykhian, Gholam Ali↗

Improve Data Mining and Knowledge Discovery through the use of MatLab

Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(TradeMark)(MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.

Shaykahian, Gholan Ali↗

Engineering of holocellulase in biomass-degrading fungi for sustainable biofuel production

Biofuels, such as bioethanol, are a clean and sustainable form of energy and have emerged as a viable alternative to fossil fuels. Plant biomass is an important raw material for the production of clean and renewable energy. The holocellulose contained in the composition of plants may be broken down into simple sugars, such as glucose, which are fermented by yeast to produce bioethanol. The conversion of glucose polymers into fermentable sugars is accomplished by enzymes known as holocellulases, which are produced by lignocellulolytic fungi. These enzymes act synergistically for the efficient degradation of cellulose polymers, and the fine and coordinated regulation of this process is performed by transcription factors (TFs). TFs are regulatory proteins that bind to the promoter region of their target genes (CAZymes, sugar transporters, signaling proteins, other TFs, etc.) to induce or repress their transcription. This review aims to understand the main regulatory mechanisms involved in plant biomass degradation by the most studied lignocellulolytic fungi Trichoderma sp., Aspergillus sp., Penicillium sp., and Neurospora crassa. In this context, the most studied TFs related to holocellulose degradation and genetic modification of TFs or promoters as a valuable tool to improve enzyme production for biotechnological purposes have been discussed. Furthermore, this review enables the expansion of knowledge on the regulation of the cellulolytic system of filamentous fungi and the application of this knowledge to the improvement of numerous bioproducts. Engineering TFs and promoters may yield more efficient strains that may be active in plant biomass hydrolysis. In this way, the technological processes for obtaining ethanol from lignocellulose may become more commercially viable.

09 BIOMASS FUELS↗

CRISPR-COPIES: Web Tool

CRISPR/Cas system has emerged as a powerful genome-editing tool for metabolic engineering and human gene therapy. However, the conundrum of where to integrate heterologous genes on the chromosome using the CRISPR/Cas system remains an open question. Selecting a site for gene integration requires incorporation of complex criteria such as factors involved in CRISPR/Cas-mediated integration, genetic stability, and gene expression and therefore, usually requires strenuous characterization of sites on particular or different chromosomal locations. To address these issues, we developed CRISPR-COPIES, a COmputational Pipeline for the Identification of CRISPR/Cas-facilitated intEgration Sites. The tool applies ScaNN, a state-of-the-art model on the embedding-based nearest neighbor search for fast and accurate off-target search and can identify genome-wide intergenic sites for most bacterial and fungal genomes within minutes. This submission contains the code we developed to create a user-friendly web interface for CRISPR-COPIES (https://biofoundry.web.illinois.edu/copies/). We anticipate CRISPR-COPIES will serve as a useful tool for targeted DNA integration and aid in the characterization of synthetic biology toolkits, rapid strain construction to produce valuable biochemicals, and human gene and cell therapy.

Bioinformatics↗

Kinetic proofreading of lipochitooligosaccharides determines signal activation of symbiotic plant receptors

Plant cell surface receptors perceive carbohydrate signaling molecules and hereby establish communication with surrounding microbes. Genetic studies have identified two different classes of lysin motif receptor kinases as gatekeepers that together trigger the symbiotic pathway in plants; however, no structural or functional data of the perception mechanisms switching these receptors from resting state into activation is known. In this study, we use structural biology, biochemical, and genetic approaches to demonstrate how the NFP/NFR5 class of lipochitooligosaccharide (LCO) receptors discriminate bacterial symbionts based on a kinetic proofreading mechanism that controls receptor activation and signaling specificity. We show that the LCO binding site can be engineered to support symbiotic functions, which greatly advance future opportunities for receptor engineering in legumes and nonlegumes.

59 BASIC BIOLOGICAL SCIENCES↗

Biomaterials Out of Thin Air: in Situ, On-Demand Printing of Advanced Biocomposites

Upmass is the single most significant limitation of our current space mission capability. Although biomaterials and biocomposites have mass, strength, flexibility, and self-healing properties that could significantly reduce upmass, their use is limited by the following drawbacks: Expensive, specific production. Many biomaterials can only be produced as part of significant support ecosystem; Inaccessible functional customization. The grain of wood, the porosity of bone, and so on are an integral part of the materials' desired mechanical properties, but are not deterministic when the material is naturally grown; Limited compositions. Most biomaterials (unlike metal, plastic, etc.) cannot be easily combined or modified to produce new materials. This project builds on recent advances in: Synthetic biology. Libraries of standardized genetic parts which can be used for controlled cellular material production, delivery, and binding; 3D printing. Commercial off-the-shelf components which can be used to make of a pico- to nanoliter cell deposition system; Tissue engineering. Proven cell-compatible support hydrogels and scaffolds can be modified to bind the deposited biomaterials of interest. Objectives: Feasibility and benefit analysis. Two mission contexts span the concept's scope (see below); Proof-of-concept demonstration. A simple grid of two proteins, fluorescent for easy detection, to validate the core technology concept; Proposed implementations for follow-on work. Avenues for future work on each core component (host cell, production control, material delivery, material binding, etc.); Complementary studies exploration. A survey of other emerging areas (in situ resource utilization, protein engineering, etc.) with the potential to multiply our technology's impact. Potential Impacts: This application could dramatically expand manufacturing capabilities on Earth and in space: In situ resource utilization. A far greater range of materials and products will be available from the limited palette offered by in situ resource extraction techniques; Reduced equipment and material upmass for off-Earth habitats. Ready- to-use highly specialized construction materials (radiation hardened, compressive/tensile, light or dense) from an extremely low starting mass; Structured biomaterial production. New ready-to-use macro, micro, and molecular manufacturing techniques for traditional biomaterials such as wood, bone and shell; New and novel biocomposite creation. The ability to create completely novel material composites from any base material that cells can be engineered to produce. Suggested Mission Contexts: ISS part manufacturing. A 'minimal working example' making a finished biomaterial part aboard the International Space Station; A long-term Mars habitat. 'Cradle-to-grave' use at a hypothetical Mars habitat, covering everything from tools to construction materials. Alternate Abstract: Imagine being able to print anything from tools and composite building materials to food and human tissues. Imagine being on Mars with the ability to replace any broken part, whether it's a part of your spacesuit, your habitat, or your own body. We propose a technique that would allow just that. By printing 3D arrays of cells engineered to secrete the necessary materials, the abundant in situ resources of atmosphere and regolith become organic, inorganic, or organic-inorganic composite materials. Such materials include novel, biologically derived materials not previously possible to fabricate.

habitats↗

Biomaterials Out of Thin Air: in Situ, On-demand Printing of Advanced Biocomposites

Upmass is the single most significant limitation of our current space mission capability. Although biomaterials and biocomposites have mass, strength, flexibility, and self-healing properties that could significantly reduce upmass, their use is limited by the following drawbacks: Expensive, specific production. Many biomaterials can only be produced as part of significant support ecosystem; Inaccessible functional customization. The grain of wood, the porosity of bone, and so on are an integral part of the materials' desired mechanical properties, but are not deterministic when the material is naturally grown; Limited compositions. Most biomaterials (unlike metal, plastic, etc.) cannot be easily combined or modified to produce new materials. This project builds on recent advances in: Synthetic biology. Libraries of standardized genetic parts which can be used for controlled cellular material production, delivery, and binding; 3D printing. Commercial off-the-shelf components which can be used to make of a pico- to nanoliter cell deposition system; Tissue engineering. Proven cell-compatible support hydrogels and scaffolds can be modified to bind the deposited biomaterials of interest. Objectives: Feasibility and benefit analysis. Two mission contexts span the concept's scope (see below); Proof-of-concept demonstration. A simple grid of two proteins, fluorescent for easy detection, to validate the core technology concept; Proposed implementations for follow-on work. Avenues for future work on each core component (host cell, production control, material delivery, material binding, etc.); Complementary studies exploration. A survey of other emerging areas (in situ resource utilization, protein engineering, etc.) with the potential to multiply our technology's impact. Potential Impacts: This application could dramatically expand manufacturing capabilities on Earth and in space: In situ resource utilization. A far greater range of materials and products will be available from the limited palette offered by in situ resource extraction techniques; Reduced equipment and material upmass for off-Earth habitats. Ready- to-use highly specialized construction materials (radiation hardened, compressive/tensile, light or dense) from an extremely low starting mass; Structured biomaterial production. New ready-to-use macro, micro, and molecular manufacturing techniques for traditional biomaterials such as wood, bone and shell; New and novel biocomposite creation. The ability to create completely novel material composites from any base material that cells can be engineered to produce. Suggested Mission Contexts: ISS part manufacturing. A 'minimal working example' making a finished biomaterial part aboard the International Space Station; A long-term Mars habitat. 'Cradle-to-grave' use at a hypothetical Mars habitat, covering everything from tools to construction materials. Alternate Abstract: Imagine being able to print anything from tools and composite building materials to food and human tissues. Imagine being on Mars with the ability to replace any broken part, whether it's a part of your spacesuit, your habitat, or your own body. We propose a technique that would allow just that. By printing 3D arrays of cells engineered to secrete the necessary materials, the abundant in situ resources of atmosphere and regolith become organic, inorganic, or organic-inorganic composite materials. Such materials include novel, biologically derived materials not previously possible to fabricate.

habitats↗

Uncovering Unique Molecular Adaptations in the Arabidopsis Thaliana Cvi-0 Ecotype

This research proposal aims to investigate the unique molecular adaptations exhibited by Arabidopsis Thaliana, specifically focusing on the Cape Verde Islands (Cvi-0) ecotype, in response to microgravity conditions. The study examines data from NASA’s Open Science Data Repository and applies a multifaceted RNAseq analysis pipeline using tools in the UseGalaxy.org open platform. Through transcriptomic analysis, differential gene expression patterns were identified in Cvi-0, revealing an absence of heat shock protein (HSP) upregulation and an upregulation of Rubisco Activase (RCA) and chloroplast-related pathways. To test the hypothesis that these adaptations may contribute to Cvi-0’s increased adaptability in microgravity, a three-fold experimental design is proposed. Four experimental groups will be cultivated under simulated microgravity and ground control conditions, including Cvi-0, Col-0, and genetically modified Col-0 with silenced HSP genes, and genetically modified Col-0 with upregulated RCA gene. Growth parameters will be measured to assess plant resilience, and RNA sequencing will provide transcriptomic data for pathway analysis. Anticipated outcomes include improved markers of plant health (mass, growth, etc.) of Cvi-0 in simulated microgravity and enhanced resilience in genetically altered Col-0 variants, providing insights into potential mechanisms of adaptation. This research would bear significance for space agriculture, nutrition for extended space missions, and sustainable terrestrial crop enhancement. Moreover, the insights gained could reshape crop engineering on Earth, enhancing robustness to climate induced stresses and bolstering global food security. The proposal’s trajectory blends scientific curiosity with practical applicability, forging a path towards sustainable food production and improving human exploration beyond our planet.

GL4HS↗

Plant-Enhanced Degradation Of Munitions by Engineered TERrestrial microbes (PEDOMETER) (Final Report)

LLNL led two major Technical Areas (TAs) within the PEDOMETER program: TA3 focused on biocontainment and TA4 focused on developing electrochemical TNT degradation sensors and testbeds. A key takeaway from our work is the importance of chassis host strain choice for kill switch design and actuator choice. Genetic instability of the kill switch circuit is a major barrier toward establishing a kill switch, which is host dependent. Addition of a host down-selection step to regulator and actuator screening stages would be beneficial to hasten kill switch development.

59 BASIC BIOLOGICAL SCIENCES↗

Investigation of the Unusual Ability of the [FeFe] Hydrogenase from Clostridium beijerinckii to Access an O 2 -Protected State

[FeFe] hydrogenases are enzymes capable of producing and oxidizing H 2 at staggering submillisecond time scales. A major limitation in applying these enzymes for industrial hydrogen production is their irreversible inactivation by oxygen. Recently, an [FeFe] hydrogenase from Clostridium beijerinckii (CbHydA1) was reported to regain its catalytic activity after exposure to oxygen. In this report, we have determined that artificially matured CbHydA1 is indeed oxygen tolerant in the absence of reducing agents and sulfides by means of reaching an O 2 -protected state (Hinact). We were also able to generate the H inact state anaerobically via both chemical and electrochemical oxidation. We use a combination of spectroscopy, electrochemistry, and density functional theory (DFT) to uncover intrinsic properties of the active center of CbHydA1, leading to its unprecedented oxygen tolerance. We have observed that reversible, low-potential oxidation of the active center leads to the protection against O 2 -induced degradation. The transition between the active oxidized state (H ox ) and the H inact state appears to proceed without any detectable intermediates. We found that the Hinact state is stable for more than 40 h in air, highlighting the remarkable resilience of CbHydA1 to oxygen. Using a combination of DFT and FTIR, we also provide a hypothesis for the chemical identity of the H inact state. These results demonstrate that CbHydA1 has remarkable stability in the presence of oxygen, which will drive future efforts to engineer more robust catalysts for biofuel production.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Mild hybridisation of turboprop engine with high-power-density integrated electric drives

Here this paper shares with the aerospace community a case study of turboprop mild hybridisation using a recently developed integrated drive system in the University of Nottingham, UK, within the ACHIEVE project under EU H2020 CleanSky 2 program (project No. 737814). The developed drive system enables green taxiing of a turboprop aircraft while on the ground with its engine off, and as an electrical generator when the turboprop is in the air. The entire system is designed to be able to integrate within the power auxiliary gear box (PAGB) of a turboprop aircraft. Some of the key features of the developed system include a high-speed permanent magnet machine (up to 14,200rpm) with dual three-phase design, SiC-based high power density (11.8kW/L for the power converter, 35.3kW/L and 7.2kW/kg for the machine active parts), integrated cooling design for high-temperature operation (>130°C ambient temperature), fault tolerance consideration with dual channel operation capabilities and sensorless control for entire operational conditions. This paper is giving an overview of the design process of the electrical machine, power converters, and its cooling of the entire drive. Numerical analysis (FEM and CFD) and some experimental results are presented to demonstrate the effectiveness and the desired performance of the developed integrated drive system.

42 ENGINEERING↗