Microbial lipids-derived biodiesel is garnering much attention owing to its potential to substitute diesel fuel. In this study, lipid accumulation by Yarrowia lipolytica from volatile fatty acids (VFAs) was studied in a lab-scale stirred tank bioreactor. In batch cultures, Y. lipolytica NCYC 2904 was able to grow in 18 g·L of VFAs (acetate, propionate, and butyrate), and the addition of a co-substrate (glucose) led to a fivefold improvement in lipid concentration. Furthermore, the two-stage batch culture (growth phase in glucose (1st stage) followed by a lipogenic phase in VFAs (2nd stage)) was the best strategy to obtain the highest lipid content in the cells (37%, w/w), with aeration conditions that kept dissolved oxygen concentration between 40% and 50% of saturation during the lipogenic phase. The estimated fuel properties of biodiesel produced from Y. lipolytica NCYC 2904 lipids are comparable with those of the biodiesel produced from vegetable oils and are in accordance with the international standards (EN 14214 and ASTM D6751). The cultivation strategies herein devised enable a sustainable, eco-friendly, and economical production of microbial lipids, based on feedstocks such as VFAs that can be derived from the acidogenic fermentation of organic wastes. KEY POINTS: • Addition of glucose to VFAs enhances lipids in Y. lipolytica in batch cultures • Two-stage batch culture - growth in glucose followed by VFAs pulse - rises lipids • Dissolved oxygen of 40-50% of saturation is crucial at the lipogenic phase.
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http://dx.doi.org/10.1007/s00253-022-11900-7 | DOI Listing |
Neural Netw
January 2025
National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Xi'an, 710054, China; Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, 710054, China.
The presence of substantial similarities and redundant information within video data limits the performance of video object recognition models. To address this issue, a Global-Local Storage Enhanced video object recognition model (GSE) is proposed in this paper. Firstly, the model incorporates a two-stage dynamic multi-frame aggregation module to aggregate shallow frame features.
View Article and Find Full Text PDFInt J Mol Sci
December 2024
School of Computer Science and Artificial Intelligence Aliyun School of Big Data School of Software, Changzhou University, Changzhou 213164, China.
Long non-coding RNA (lncRNA) is a non-coding RNA longer than 200 nucleotides, crucial for functions like cell cycle regulation and gene transcription. Accurate localization prediction from sequence information is vital for understanding lncRNA's biological roles. Computational methods offer an effective alternative to traditional experimental methods for annotating lncRNA subcellular positions.
View Article and Find Full Text PDFChemistry
January 2025
University of Missouri, Chemistry, 601 S. College Ave, 65211, Columbia, UNITED STATES OF AMERICA.
CO2-based hydroesterification is an attractive route to produce value added ester compounds, which could replace CO-based hydroesterification processes if sufficient catalytic technologies are developed. One path to CO2-based hydroesterification is through an organoformate intermediate, which is then used in olefin hydroesterification to generate the desirable esters. This route creates a net CO2-based hydroesterification process using tandem catalytic systems for CO2 hydrogenation to organoformate paired with formate-olefin hydroesterification.
View Article and Find Full Text PDFDiagn Interv Radiol
December 2024
King Saud University, College of Applied Medical Sciences, Department of Radiological Sciences, Riyadh, Saudi Arabia.
Purpose: The purpose of this study was to propose a new computer-assisted two-staged diagnosis system that combines a modified deep learning (DL) architecture (VGG19) for the classification of digital breast tomosynthesis (DBT) images with the detection of tumors as benign or cancerous using the You Only Look Once version 5 (YOLOv5) model combined with the convolutional block attention module (CBAM) (known as YOLOv5-CBAM).
Methods: In the modified version of VGG19, eight additional layers were integrated, comprising four batch normalization layers and four additional pooling layers (two max pooling and two average pooling). The CBAM was incorporated into the YOLOv5 model structure after each feature fusion.
J Fungi (Basel)
November 2024
Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal.
This study evaluated the potential of (CBS 2075 and DSM 8218) to grow in waste motor oil (WMO) and produce valuable compounds, laying the foundation for a sustainable approach to WMO management. Firstly, yeast strains were screened for their growth on WMO (2-10 g·L) in microplate cultures. Despite limited growth, the CBS 2075 strain exhibited comparable growth to control conditions (without WMO), while DSM 8218 growth increased 2- and 3-fold at 5 g·L and 10 g·L WMO, respectively.
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