Progress and challenges in exploring aquatic microbial communities using non-targeted metabolomics

The ISME Journal, Journal Year: 2023, Volume and Issue: 17(12), P. 2147 - 2159

Published: Oct. 19, 2023

Abstract Advances in bioanalytical technologies are constantly expanding our insights into complex ecosystems. Here, we highlight strategies and applications that make use of non-targeted metabolomics methods aquatic chemical ecology research discuss opportunities remaining challenges mass spectrometry-based to broaden understanding environmental systems.

Language: Английский

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PHE-SICH-CT-IDS: A benchmark CT image dataset for evaluation semantic segmentation, object detection and radiomic feature extraction of perihematomal edema in spontaneous intracerebral hemorrhage

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 173, P. 108342 - 108342

Published: March 20, 2024

Language: Английский

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Progress in the discovery and development of anticancer agents from marine cyanobacteria

Natural Product Reports, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

We describe the pipeline of anticancer agents from marine cyanobacteria, highlighting critical steps discovery towards development, including identification molecular target and mechanism action, solving supply problem.

Language: Английский

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Lutein: A natural defence combating age-related macular degeneration

Phytomedicine, Journal Year: 2025, Volume and Issue: unknown, P. 156578 - 156578

Published: March 1, 2025

Language: Английский

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Marine-Based Bioactive Compounds in Healthcare and Wellness Industries

Published: Jan. 1, 2025

Language: Английский

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Discovery of novel CDK4/6 inhibitors from fungal secondary metabolites

International Journal of Biological Macromolecules, Journal Year: 2024, Volume and Issue: unknown, P. 136807 - 136807

Published: Oct. 1, 2024

Language: Английский

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Deep pan-cancer analysis and multi-omics evidence reveal that ALG3 inhibits CD8+ T cell infiltration by suppressing chemokine secretion and is associated with 5-fluorouracil sensitivity

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 177, P. 108666 - 108666

Published: May 28, 2024

Language: Английский

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Dissolved organic nitrogen

Elsevier eBooks, Journal Year: 2024, Volume and Issue: unknown, P. 343 - 404

Published: Jan. 1, 2024

Language: Английский

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DECIPHERING THE DEEP: MACHINE LEARNING APPROACHES TO UNDERSTANDING OCEANIC ECOSYSTEMS

ГРААЛЬ НАУКИ, Journal Year: 2024, Volume and Issue: 36, P. 526 - 534

Published: Feb. 26, 2024

This paper presents a detailed exploration of the transformative role Machine Learning (ML) in oceanographic research, encapsulating paradigm shift towards more efficient and comprehensive analysis marine ecosystems. It delves into multifaceted applications ML, ranging from predictive modeling ocean currents to in-depth biodiversity deciphering complexities deep-sea ecosystems through advanced computer vision techniques. The discussion extends challenges opportunities that intertwine with integration AI ML oceanography, emphasizing need for robust data collection, interdisciplinary collaboration, ethical considerations. Through series case studies thematic discussions, this underscores profound potential revolutionize our understanding preservation oceanic ecosystems, setting new frontier future research conservation strategies realm oceanography.

Language: Английский

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Enhancing cellular production of fucoxanthin through machine learning assisted predictive approach inIsochrysis galbana

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: April 29, 2024

Abstract The marine microalgae, Isochrysis galbana is a prolific producer of fucoxanthin which xanthophyll carotenoid with substantial global market value boasting extensive applications in the food, nutraceutical, pharmaceutical, and cosmetic industries. Although supplementation different phytohormones to medium enhances production, quantification pigment by conventional means time-consuming labor-intensive. This study addressed multiple methodological limitations HPLC-based emphasized need develop Machine Learning (ML) model as optimization precise prediction remain challenging task. Hence, an integrated experimental approach coupled ML models was employed predict yield various phytohormones. accuracy excluding including hormone descriptors compared evaluated using namely Random Forest (RF), Linear Regression (LR), Artificial Neural Network (ANN), Support Vector (SVM). RF provided most accurate coefficient determination ( R 2 = 0.809) root-mean-square error RMSE 0.776) followed ANN 0.722) 0.937). inclusion for training pre-processing data further improved 0.839) 0.712) 0.738) 0.909). These results indicated that combination low-cost, Ultraviolet (UV) spectrometric-based algorithms can be efficiently used reliable enhanced therefore highlighting promising furnishing invaluable insights towards commercialization microalgal production.

Language: Английский

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