Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “lipids”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

The Preservation of PPARγ Genome Duplicates in Some Teleost Lineages: Insights into Lipid Metabolism and Xenobiotic Exploitation

Three peroxisome proliferator-activated receptor paralogues (PPARα, -β and -γ) are currently recognized in vertebrate genomes. PPARγ is known to modulate nutrition, adipogenesis and immunity in vertebrates. Natural ligands of PPARγ have been proposed; however, the receptor also binds synthetic ligands such as endocrine disruptors. Two paralogues of PPARα and PPARβ have been documented in teleost species, a consequence of the 3R WGD. Recently, two PPARγ paralogue genes were also identified in Astyanax mexicanus. We aimed to determine whether the presence of two PPARγ paralogues is prevalent in other teleost genomes, through genomic and phylogenetic analysis. Our results showed that besides Characiformes, two PPARγ paralogous genes were also identified in other teleost taxa, coinciding with the teleost-specific, whole-genome duplication and with the retention of both genes prior to the separation of the Clupeocephala. To functionally characterize these genes, we used the European sardine (Sardina pilchardus) as a model. PPARγA and PPARγB display a different tissue distribution, despite the similarity of their functional profiles: they are unresponsive to tested fatty acids and other human PPARγ ligands yet yield a transcriptional response in the presence of tributyltin (TBT). This observation puts forward the relevance of comparative analysis to decipher alternative binding architectures and broadens the disruptive potential of man-made chemicals for aquatic species.

Páscoa, Inês↗
DOE OSTI2023

PKA regulatory subunit Bcy1 couples growth, lipid metabolism, and fermentation during anaerobic xylose growth in Saccharomyces cerevisiae

Past work from our lab, focused on engineering the budding yeast Saccharomyces cerevisiae for fermentation of the non-native pentose sugar xylose, discovered that hyperactivation of the RAS/Protein Kinase A (PKA) pathway was needed for rapid anaerobic xylose fermentation. Interestingly, the mechanism of PKA hyperactivation has a dramatic impact on growth and metabolism on xylose; deletion of the RAS inhibitor IRA2 permits rapid growth and fermentation, while deletion of the PKA regulatory subunit BCY1 allows for fermentation without growth on xylose. To understand how a single deletion in the PKA pathway can decouple growth and metabolism, we performed transcriptomic analysis of these strains, predicting that altered PKA activity would impact global gene expression and identify pathways important for growth and metabolism coordination.

Gasch, Audrey↗
DOE OSTI2021

Deep learning for lipid droplet recognition in quantitative phase images

This library of Python code is used for performing semantic segmentation of images using 6 different machine learning methods. Five of the methods are implemented entirely within the scikit-learn framework. The Convolutional Neural Network (CNN) method requires Keras with a TensorFlow backend and generally uses a different set of scripts in order to perform the complete training and evaluation.

Sheneman, Lucas↗
DOE OSTI2022

Conversion of lignin into bioplastics and lipid fuels

The present invention provides methods and compositions for increasing lignin degradation to produce a biological product. Also provided are methods for increasing expression of laccase in a bacterial species to produce increased lignin degradation. Also provided are bacterial cells and commodities or commodity produces produced from such methods.

Yuan, Shuhua↗
DOE OSTI2023

Accurate Prediction of Algal Biomass Lipid, Protein, and Carbohydrate Composition with Machine Learning Regression Modelling of Near-IR Spectra

During large scale algal biomass cultivation, it is difficult to reliably control relative composition to target levels. Rapid determination of chemical composition is feasible by using near infrared (NIR) spectral data. We sought to build and improve on reliable high-throughput screening prediction method based on partial least squares regression (PLSR) by the application of artificial neural networks (ANN) and associated optimization strategies. The algal biomass sample set was designed and created in an iterative process of culturing in physiologically diverse conditions at the GAI field site, followed by compositional analyses at NREL. The workflow allowed us to identify gaps in compositional space for informing the subsequent cultivation and sampling efforts and generated a high quality set of 210 unique samples with chemical analysis results, spectral scanning data, and cultivation metadata. We observed a significant improvement in the performance of carbohydrate content predictions using an optimized ANN model compared to PLSR, with > 16% reduction in mean absolute percent error (MAPE) when tested on the same set of reserved data. The optimized ANN models for FAME and protein prediction performed exceptionally well with 5.99% and 5.09% MAPE, respectively. Application of these methods to detection and quantification of minor biomass constituents that are relevant to certain product streams has shown positive preliminary results, opening the possibility for extensions to the outputs of this powerful data type. All models are accompanied by prediction uncertainties and unsupervised spectral outlier detection to alert an operator to unreliable spectral data. These tools can be deployed for rapid determination of algal culture status, and cultivation and biomass quality improvement.

algal biofuels↗