A unique microbiome that metabolizes lactate rather than ethanol for n-caproate production was obtained from a fermentation pit used for the production of Chinese strong-flavour liquor (CSFL). The microbiome was able to produce n-caproate at concentrations as high as 23.41 g/L at a maximum rate of 2.97 g/L/d in batch trials without in-line extraction. Compared with previous work using ethanol as the electron donor, the n-caproate concentration increased by 82.89%. High-throughput sequencing analysis showed that the microbiome was dominated by a Clostridium cluster IV, which accounted for 79.07% of total reads. A new process for n-caproate production was proposed, lactate oxidation coupled to chain elongation, which revealed new insight into the well-studied lactate conversion and carbon chain elongation. In addition, these findings indicated a new synthesis mechanism of n-caproate in CSFL. We believe that this efficient process will provide a promising opportunity for the innovation of waste recovery as well as for n-caproate biosynthesis.
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http://dx.doi.org/10.1038/srep14360 | DOI Listing |
Front Psychol
January 2025
School of Japanese and International Studies, Beijing Centre for Japanese Studies, Beijing Foreign Studies University, Beijing, China.
Existing research has primarily focused on the influence of the native language on second language (L2) acquisition and processing, with less attention given to whether L2 acquisition affects native language processing. This study examines Chinese learners of Japanese, focusing on the orthographic and phonological similarities between two-character words in Chinese and Japanese. It investigates how these similarities affect native Chinese lexical processing at intermediate and advanced stages of Japanese learning and explores the predictive effect of L2 lexical processing efficiency on native language lexical processing efficiency at different stages of L2 learning.
View Article and Find Full Text PDFFront Artif Intell
January 2025
Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada.
Introduction: Active learning can significantly decrease the labeling cost of deep learning workflows by prioritizing the limited labeling budget to high-impact data points that have the highest positive impact on model accuracy. Active learning is especially useful for semantic segmentation tasks where we can selectively label only a few high-impact regions within these high-impact images. Most established regional active learning algorithms deploy a static-budget querying strategy where a fixed percentage of regions are queried in each image.
View Article and Find Full Text PDFACS Agric Sci Technol
January 2025
Laboratory of Organic Electronics, Department of Science and Technology, Linköping University, SE-60174 Norrköping, Sweden.
Plant infiltration techniques, particularly agroinfiltration, have transformed plant science and biotechnology by enabling transient gene expression for genetic engineering of plants or genomic studies. Recently, the use of infiltration has expanded to introduce nanomaterials and polymers in plants to enable nonnative functionalities. Despite its wide use, the impact of the infiltration process on plant physiology needs to be better understood.
View Article and Find Full Text PDFProc (IEEE Conf Multimed Inf Process Retr)
August 2024
Department of Computer Science, University of Kentucky, Lexington, KY, USA.
Despite the prevalence of images and texts in machine learning, tabular data remains widely used across various domains. Existing deep learning models, such as convolutional neural networks and transformers, perform well however demand extensive preprocessing and tuning limiting accessibility and scalability. This work introduces an innovative approach based on a structured state-space model (SSM), MambaTab, for tabular data.
View Article and Find Full Text PDFIndian Dermatol Online J
December 2024
Financial Research and Executive Insights, Everest Group, Gurugram, Haryana, India.
Background: Artificial intelligence (AI) is revolutionizing healthcare by enabling systems to perform tasks traditionally requiring human intelligence. In healthcare, AI encompasses various subfields, including machine learning, deep learning, natural language processing, and expert systems. In the specific domain of onychology, AI presents a promising avenue for diagnosing nail disorders, analyzing intricate patterns, and improving diagnostic accuracy.
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