AI & Environment Resource HubSania Jevtic; Colleen Drapek; Christophe Gaillochet; Andrew Brockman; James Cadman; Timo Flesch; Nicolas Kral · 2024-12-09
The CRE.AI.TIVE platform uses machine learning to enhance plant gene regulation, enabling scalable gene upregulation without prior knowledge of regulatory elements.
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Sania Jevtic; Colleen Drapek; Christophe Gaillochet; Andrew Brockman; James Cadman; Timo Flesch; Nicolas Kral. 2024-12-09. A Scalable Method for Modulating Plant Gene Expression Using a Multispecies Genomic Model and Protoplast-Based Massively Parallel Reporter Assay. https://www.biorxiv.org/content/10.1101/2024.12.05.626999v1 (AI & Environment Resource Hub; record paper-036; collection snapshot 2026-10-04).
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Source sheet: Scientific Paper · Row 40 · Original ID: paper-036.
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Shared topics: Biodiversity and ecosystems
Improved machine learning techniques enhance bird call detection in passive acoustic monitoring, boosting precision and biodiversity tracking in climate research.
Shared topics: Biodiversity and ecosystems
This study proposes a low-cost IoT-based TinyML model that intelligently activates mosquito repellent only when an Anopheles mosquito is detected, using a 1D-CNN for efficient classification of mosquito wingbeat sounds, reducing energy waste and potential health risks.
Shared topics: Biodiversity and ecosystems
A hybrid machine learning model improves soil respiration predictions, enhancing carbon cycle modeling and greenhouse gas management strategies.
Shared topics: Biodiversity and ecosystems
A deep learning-based bioacoustic AI tool enables citizen scientists and law enforcement to accurately identify traded songbird species in Asian markets, achieving 91.6% accuracy and supporting conservation efforts against overexploitation and illegal wildlife trade.