Optimizing High- Throughput Capabilities by Leveraging Reinforcement Learning Methods with the Bluesky Suite
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36 MATERIALS SCIENCE↗
SEARCH · Engineering Papers
Results for “high throughput”
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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Bacterial and fungal growth on fungal necromass and its diverse components: Shared profiles and divergent constraints revealed by high‐throughput phenotyping
1. While fungal necromass is increasingly recognized as a major source of persistent carbon (C) in soils, the relative functional roles of bacteria and fungi in decomposing necromass are not fully resolved, and the processes that select for necromass decomposer communities from the broader soil microbial community are an emerging area of interest. 2. In this study, we characterized the growth of 52 bacterial and 83 fungal strains isolated from necromass and soil on 22 C substrates, including different necromass phenotypes, fungal cell wall polymers, dimers and monomers. 3. We found that the isolation habitat of the strains used in this experiment (necromass vs. soil) had no effect on the substrates they were able to use. Isolates from both microbial domains were able to grow on different labile carbon substrates, polymers and necromass phenotypes. However, fungal growth was most limited by necromass melanin content, while bacterial growth was more limited by the abundance of cell wall polysaccharides. Additionally, overall differences in substrate use between bacteria and fungi were most pronounced on polymer substrates. 4. Collectively, our results suggest that there is substantial functional overlap in necromass substrate use across microbial domains, but some notable differences in bacterial and fungal utilization of cell wall polymers, which can function as a direct energy source or a means of accessing other compounds within necromass. Future studies assessing bacteria and fungi decomposing necromass together rather than in isolation will help to uncover potential physical and chemical interactions within and between these two domains during the decay of this important source of persistent soil C.
dead fungal biomass↗
Development and validation of time‐domain 1 H‐NMR relaxometry correlation for high‐throughput phenotyping method for lipid contents of lignocellulosic feedstocks
Abstract The bioenergy crops such as energycane, miscanthus, and sorghum are being genetically modified using state of the art synthetic biotechnology techniques to accumulate energy‐rich molecules such as triacylglycerides (TAGs) in their vegetative cells to enhance their utility for biofuel production. During the initial genetic developmental phase, many hundreds of transgenic phenotypes are produced. The efficiency of the production pipeline requires early and minimally destructive determination of oil content in individuals. Current screening methods require time‐intensive sample preparation and extraction with chemical solvents for each plant tissue. A rapid screen will also be needed for developing industrial extraction as these crops become available. In the present study, we have devised a proton relaxation nuclear magnetic resonance ( 1 H‐NMR) method for single‐step, non‐invasive, and chemical‐free characterization of in‐situ lipids in untreated and pretreated lignocellulosic biomass. The systematic evaluation of NMR relaxation time distribution provided insight into the proton environment associated with the lipids in the biomass. It resolved two distinct lipid‐associated subpopulations of proton nuclei that characterize total in‐situ lipids into bound and free oil based on their “molecular tumbling” rate. The T1T2 correlation spectra also facilitated the resolution of the influence of various pretreatment procedures on the chemical composition of molecular and local 1 H population in each sample. Furthermore, we show that hydrothermally pretreated biomass is suitable for direct NMR analysis unlike dilute acid and alkaline pretreated biomass which needs an additional step for neutralization.
pretreatment↗
High-throughput homogenization of a quasi-Gaussian ultrafast laser beam using a combined refractive beam shaper and spatial light modulator
Efficiently shaping femtosecond, transverse Gaussian laser beams to flat-top beams with flat wavefronts is critical for large-scale material processing and manufacturing. Existing beam shaping devices fall short either in final beam homogeneity or efficiency. Here, we present an approach that uses refractive optics to perform the majority of the beam shaping and then uses a fine-tune device (spatial light modulator) to refine the intensity profile. For the beam that we selected, circularly asymmetric with intensity fluctuations, our method achieved a uniformity of 0.055 within 90% of the beam area at 92% efficiency. The optimization involved an iterative beam shaping process that converged to optimum within 10 iterations.
43 PARTICLE ACCELERATORS↗
A versatile high-speed x-ray microscope for high-throughput sub-10nm Imaging
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36 MATERIALS SCIENCE↗
Structural genomics of bacterial drug targets: Application of a high-throughput pipeline to solve 58 protein structures from pathogenic and related bacteria
Antibiotic resistance remains a leading cause of severe infections worldwide. Small changes in protein sequence can impact antibiotic efficacy. Here, we report deposition of 58 X-ray crystal structures of bacterial proteins that are known targets for antibiotics, which expands knowledge of structural variation to support future antibiotic discovery or modifications.
PDB↗
MetaFunPrimer: an Environment-Specific, High-Throughput Primer Design Tool for Improved Quantification of Target Genes
Amplification-based gene characterization allows for sensitive and specific quantification of functional genes. There is often a large diversity of genes represented for functional gene groups, and multiple primers may be necessary to target associated genes.
59 BASIC BIOLOGICAL SCIENCES↗
A High-Throughput Method for Identifying Novel Genes That Influence Metabolic Pathways Reveals New Iron and Heme Regulation in Pseudomonas aeruginosa
The ability to simultaneously and more directly correlate genes with metabolite levels on a global level would provide novel information for many biological platforms yet has thus far been challenging. Here, we describe a method to help address this problem, which we dub “Met-Seq” ( met abolite-coupled Tn seq uencing).
59 BASIC BIOLOGICAL SCIENCES↗
High-Throughput Virtual Screening of Small Molecule Inhibitors for SARS-CoV-2 Protein Targets with Deep Fusion Models
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Chemistry, Biological and medical sciences, Mathem↗
ML4PD (Machine Learning for High Throughput Process Design) [SWR-22-62]
ML4PD is a code base developed to use machine learning(ML)-based modular process units that can be combined to create whole process models. The package is developed within python and leverages tensorflow for the ML components.
Wilson, Nolan↗
AlphaBeta: computational inference of epimutation rates and spectra from high-throughput DNA methylation data in plants
Stochastic changes in DNA methylation (i.e., spontaneous epimutations) contribute to methylome diversity in plants. Here, we describe AlphaBeta, a computational method for estimating the precise rate of such stochastic events using pedigree-based DNA methylation data as input. We demonstrate how AlphaBeta can be employed to study transgenerationally heritable epimutations in clonal or sexually derived mutation accumulation lines, as well as somatic epimutations in long-lived perennials. Application of our method to published and new data reveals that spontaneous epimutations accumulate neutrally at the genome-wide scale, originate mainly during somatic development and that they can be used as a molecular clock for age-dating trees.
59 BASIC BIOLOGICAL SCIENCES↗
METABOLIC: high-throughput profiling of microbial genomes for functional traits, metabolism, biogeochemistry, and community-scale functional networks
Background Advances in microbiome science are being driven in large part due to our ability to study and infer microbial ecology from genomes reconstructed from mixed microbial communities using metagenomics and single-cell genomics. Such omics-based techniques allow us to read genomic blueprints of microorganisms, decipher their functional capacities and activities, and reconstruct their roles in biogeochemical processes. Currently available tools for analyses of genomic data can annotate and depict metabolic functions to some extent; however, no standardized approaches are currently available for the comprehensive characterization of metabolic predictions, metabolite exchanges, microbial interactions, and microbial contributions to biogeochemical cycling. Results We present METABOLIC (METabolic And BiogeOchemistry anaLyses In miCrobes), a scalable software to advance microbial ecology and biogeochemistry studies using genomes at the resolution of individual organisms and/or microbial communities. The genome-scale workflow includes annotation of microbial genomes, motif validation of biochemically validated conserved protein residues, metabolic pathway analyses, and calculation of contributions to individual biogeochemical transformations and cycles. The community-scale workflow supplements genome-scale analyses with determination of genome abundance in the microbiome, potential microbial metabolic handoffs and metabolite exchange, reconstruction of functional networks, and determination of microbial contributions to biogeochemical cycles. METABOLIC can take input genomes from isolates, metagenome-assembled genomes, or single-cell genomes. Results are presented in the form of tables for metabolism and a variety of visualizations including biogeochemical cycling potential, representation of sequential metabolic transformations, community-scale microbial functional networks using a newly defined metric “MW-score” (metabolic weight score), and metabolic Sankey diagrams. METABOLIC takes ~ 3 h with 40 CPU threads to process ~ 100 genomes and corresponding metagenomic reads within which the most compute-demanding part of hmmsearch takes ~ 45 min, while it takes ~ 5 h to complete hmmsearch for ~ 3600 genomes. Tests of accuracy, robustness, and consistency suggest METABOLIC provides better performance compared to other software and online servers. To highlight the utility and versatility of METABOLIC, we demonstrate its capabilities on diverse metagenomic datasets from the marine subsurface, terrestrial subsurface, meadow soil, deep sea, freshwater lakes, wastewater, and the human gut. Conclusion METABOLIC enables the consistent and reproducible study of microbial community ecology and biogeochemistry using a foundation of genome-informed microbial metabolism, and will advance the integration of uncultivated organisms into metabolic and biogeochemical models. METABOLIC is written in Perl and R and is freely available under GPLv3 at https://github.com/AnantharamanLab/METABOLIC.
59 BASIC BIOLOGICAL SCIENCES↗
High-throughput mapping of the phage resistance landscape in E. coli
Not Available
59 BASIC BIOLOGICAL SCIENCES↗
Photonic Curing for High Throughput Halide Perovskite Solar Cell Fabrication
F19 MRS poster on photonic curing of MAPbI3
Xu, Weijie↗
Photonic curing for High Throughput Fabrication of Perovskite Solar cells
2020 Fall MRS talk on photonic curing processing of perovskite solar cells
Xu, Weijie↗
High Throughput expression and characterization of laccases in Saccharomyces cerevisiae
Laccases are oxidative enzymes containing 4 conserved copper heteroatoms. Laccases catalyze cleavage of bonds in lignin using radical chemistry, yet their exact specificity for bonds (such as the β-O-4 or C-C) in lignin remains unknown and may vary with the diversity of laccases across fungi, plants and bacteria. Bond specificity may perhaps even vary for the same enzyme across different reaction conditions. Determining these differences has been difficult due to the fact that heterologous expression of soluble, active laccases has proven difficult. Here we describe the successful heterologous expression of functional laccases in two strains of Saccharomyces cerevisiae, including one we genetically modified with CRISPR. We phylogenically map the enzymes that we successfully expressed, compared to those that did not express. We also describe differences protein sequence differences and pH and temperature profiles and their ability to functionally express, leading to a potential future screening platform for directed evolution of laccases and other ligninolytic enzymes such as peroxidases.
59 BASIC BIOLOGICAL SCIENCES↗
High Throughput expression and characterization of laccases in Saccharomyces cerevisiae [Poster]
We are working to generate fundamental understanding of enzymatic depolymerization of lignin and using this understanding to engineer mixtures of enzymes that catalyze the reactions necessary to efficiently depolymerize lignin into defined fragments. Over the years the enzymes involved in these processes have been difficult to study, because 1) the enzymes thought to be most important, fungal laccases and peroxidases, are very difficult to express in soluble, active form; 2) the full complement of required enzymes and whether or not they act synergistically is not known; 3) analysis of bond cleavage events is difficult due to the lack of analytical tools for measuring bond cleavage events in either polymeric lignin or model lignin-like compounds.
59 BASIC BIOLOGICAL SCIENCES↗