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1.
Genomics ; 116(4): 110871, 2024 07.
Article in English | MEDLINE | ID: mdl-38806102

ABSTRACT

Cassava, a crucial tropical crop, faces challenges from cold stress, necessitating an exploration of its molecular response. Here, we investigated the role of DNA methylation in moderating the response to moderate cold stress (10 °C) in cassava. Using whole-genome bisulfite sequencing, we examined DNA methylation patterns in leaf blades and petioles under control conditions, 5 h, and 48 h of cold stress. Tissue-specific responses were observed, with leaf blades exhibiting subtle changes, while petioles displayed a pronounced decrease in methylation levels under cold stress. We identified cold stress-induced differentially methylated regions (DMRs) that demonstrated both tissue and treatment specificity. Importantly, these DMRs were enriched in genes with altered expression, implying functional relevance. The cold-response transcription factor ERF105 associated with DMRs emerged as a significant and conserved regulator across tissues and treatments. Furthermore, we investigated DNA methylation dynamics in transposable elements, emphasizing the sensitivity of MITEs with bHLH binding motifs to cold stress. These findings provide insights into the epigenetic regulation of response to cold stress in cassava, contributing to an understanding of the molecular mechanisms underlying stress adaptation in this tropical plant.


Subject(s)
Cold-Shock Response , DNA Methylation , Gene Expression Regulation, Plant , Manihot , Plant Proteins , Manihot/genetics , Manihot/metabolism , Plant Proteins/genetics , Plant Proteins/metabolism , Epigenesis, Genetic , Plant Leaves/genetics , Plant Leaves/metabolism , DNA Transposable Elements , Transcription Factors/genetics , Transcription Factors/metabolism
2.
Mar Drugs ; 22(2)2024 Feb 03.
Article in English | MEDLINE | ID: mdl-38393049

ABSTRACT

Eleven new brominated depsidones, namely spiromastixones U-Z5 (1-11) along with five known analogues (12-16), were isolated from a deep-sea-derived fungus Spiromastix sp. through the addition of sodium bromide during fermentation. Their structures were elucidated by extensive analysis of the spectroscopic data including high-resolution MS and 1D and 2D NMR data. Compounds 6-10 and 16 exhibited significant inhibition against Gram-positive bacteria including methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococcus faecium (VRE) with MIC values ranging from 0.5 to 2.0 µM. Particularly, tribrominated 7 displayed the strongest activity against MRSA and VRE with a MIC of 0.5 and 1.0 µM, respectively, suggesting its potential for further development as a new antibacterial agent.


Subject(s)
Depsides , Methicillin-Resistant Staphylococcus aureus , Anti-Bacterial Agents/chemistry , Lactones/pharmacology , Fungi , Microbial Sensitivity Tests
3.
Sensors (Basel) ; 22(18)2022 Sep 19.
Article in English | MEDLINE | ID: mdl-36146430

ABSTRACT

(1) Background: Incontinence and its complications pose great difficulties in the care of the disabled. Currently, invasive incontinence monitoring methods are too invasive, expensive, and bulky to be widely used. Compared with previous methods, bowel sound monitoring is the most commonly used non-invasive monitoring method for intestinal diseases and may even provide clinical support for doctors. (2) Methods: This paper proposes a method based on the features of bowel sound signals, which uses a BP classification neural network to predict bowel defecation and realizes a non-invasive collection of physiological signals. Firstly, according to the physiological function of human defecation, bowel sound signals were selected for monitoring and analysis before defecation, and a portable non-invasive bowel sound collection system was built. Then, the detector algorithm based on iterative kurtosis and the signal processing method based on Kalman filter was used to process the signal to remove the aliasing noise in the bowel sound signal, and feature extraction was carried out in the time domain, frequency domain, and time-frequency domain. Finally, BP neural network was selected to build a classification training method for the features of bowel sound signals. (3) Results: Experimental results based on real data sets show that the proposed method can converge to a stable state and achieve a prediction accuracy of 88.71% in 232 records, which is better than other classification methods. (4) Conclusions: The result indicates that the proposed method could provide a high-precision defecation prediction result for patients with fecal incontinence, so as to prepare for defecation in advance.


Subject(s)
Defecation , Neural Networks, Computer , Algorithms , Humans , Signal Processing, Computer-Assisted , Sound
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