AIMC Topic: Saccharomyces cerevisiae

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Fusion of microscopic and diffraction images with VGG net for budding yeast recognition in imaging flow cytometry.

Scientific reports
Microscopic-Diffraction Imaging Flow Cytometry (MDIFC) is a high-throughput, stain-free technology that captures paired microscopic and diffraction images of cellular events, utilizing machine learning for the classification of cell subpopulations. H...

Dynamic mode decomposition for analysis and prediction of metabolic oscillations from time-lapse imaging of cellular autofluorescence.

Scientific reports
Oscillations are a common phenomenon in cell biology. They are based on non-linear coupling of biochemical reactions and can show rich dynamic behavior as found in, for example, glycolysis of yeast cells. Here, we show that dynamic mode decomposition...

Machine learning reveals genes impacting oxidative stress resistance across yeasts.

Nature communications
Reactive oxygen species (ROS) are highly reactive molecules encountered by yeasts during routine metabolism and during interactions with other organisms, including host infection. Here, we characterize the variation in resistance to the ROS-inducing ...

Rapid counting of Kazachstania humilis and Saccharomyces cerevisiae in sourdough by deep learning-based classifier.

Journal of microbiological methods
When maintaining sourdough through backslopping, bakers must ensure that the yeast mycobiota remains stable. By introducing two-staged incubation temperatures for cultivation, we found that the colonies of Kazachstania humilis and Saccharomyces cerev...

Divergence in a eukaryotic transcription factor's co-TF dependence involves multiple intrinsically disordered regions.

Nature communications
Combinatorial control by transcription factors (TFs) is central to eukaryotic gene regulation, yet its mechanism, evolution, and regulatory impact are not well understood. Here we use natural variation in the yeast phosphate starvation (PHO) response...

Data-Augmented Deep Learning Algorithm for Accurate Control of Bioethanol Fermentation Using an Online Raman Analyzer.

Biotechnology and bioengineering
Fed-batch fermentation has become the preferred strategy in many industrial biomanufacturing processes. However, a key challenge remains in optimizing the feeding strategy to achieve stable maximum yields. In this study, we present an online Raman sp...

Natural Language Processing and Machine Learning Techniques for Analyzing Conversations About Nutritional Yeasts in the United States and France: Retrospective Social Media Listening Study.

JMIR infodemiology
BACKGROUND: Nutritional yeast, an inactive form of Saccharomyces cerevisiae, has recently become increasingly popular as a food supplement and healthy ingredient, especially among individuals following plant-based diets. It is valued for its health b...

Definer: A computational method for accurate identification of RNA pseudouridine sites based on deep learning.

PloS one
Pseudouridine is an important modification site, which is widely present in a variety of non-coding RNAs and is involved in a variety of important biological processes. Studies have shown that pseudouridine is important in many biological functions s...

Enhancing yeast cell tracking with a time-symmetric deep learning approach.

NPJ systems biology and applications
Accurate tracking of live cells using video microscopy recordings remains a challenging task for popular state-of-the-art image processing-based object tracking methods. In recent years, many applications have attempted to integrate deep-learning fra...

Multiplexed engineering of cytochrome P450 enzymes for promoting terpenoid synthesis in Saccharomyces cerevisiae cell factories: A review.

Biotechnology advances
Terpenoids, also known as isoprenoids, represent the largest and most structurally diverse family of natural products, and their biosynthesis is closely related to cytochrome P450 enzymes (P450s). Given the limitations of direct extraction from natur...