AIMC Topic: Plants

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Interpretable deep learning method to quantify the impact of extreme temperatures on vegetation productivity in China.

Scientific reports
As a key ecological parameter, NPP measures the photosynthetic efficiency of plants in capturing atmospheric carbon. With the warming of the climate, extreme temperature events are frequent, which has exerted a profound influence on NPP. Previous stu...

In silico prediction of variant effects: promises and limitations for precision plant breeding.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
Sequence-based AI models show great potential for prediction of variant effects at high resolution, but their practical value in plant breeding remains to be confirmed through rigorous validation studies. Plant breeding has traditionally relied on ph...

Multifunctional cells based neural architecture search for plant images classification.

Scientific reports
To develop a high-performance convolutional neural network (CNN) model for plant image classification automatically, we propose a neural architecture search (NAS) method tailored to multifunctional cells (MFC), termed MFC-NAS. Initially, a search spa...

Plant attribute extraction: An enhancing three-stage deep learning model for relational triple extraction.

PloS one
Various plant attributes, such as growing environment, growth cycle, and ecological distribution, can provide support to fields like agricultural production and biodiversity. This information is widely dispersed in texts. Manual extraction of this in...

Measuring natural selection on the transcriptome.

The New phytologist
The level and pattern of gene expression is increasingly recognized as a principal determinant of plant phenotypes and thus of fitness. The estimation of natural selection on the transcriptome is an emerging research discipline. We here review recent...

READRetro web: A user-friendly platform for predicting plant natural product biosynthesis.

Molecules and cells
Natural products (NPs), a fundamental class of bioactive molecules with broad applicability, are valuable sources in pharmaceutical research and drug discovery. Despite their significance, the large-scale production of NPs is often limited by their a...

Machine learning-powered activatable NIR-II fluorescent nanosensor for in vivo monitoring of plant stress responses.

Nature communications
Real-time monitoring of plant stress signaling molecules is crucial for early disease diagnosis and prevention. However, existing methods are often invasive and lack sensitivity, rendering them inadequate for continuous monitoring of subtle plant str...

Radiocaesium soil-to-plant transfer: a meta-analysis of key variables and data gaps on a global scale.

Journal of environmental radioactivity
A harmonized, publicly accessible database of worldwide observations and experiments on radiocaesium transfer from soil to plants is lacking. Such a database is needed for evaluating and establishing transfer models, especially for regions with limit...

Harnessing chemistry for plant-like machines: from soft robotics to energy harvesting in the phytosphere.

Chemical communications (Cambridge, England)
Nature, especially plants, can inspire scientists and engineers in the development of bioinspired machines able to adapt and interact with complex unstructured environments. Advances in manufacturing techniques, such as 3D printing, have expanded the...

Machine learning for automated electrical penetration graph analysis of aphid feeding behavior: Accelerating research on insect-plant interactions.

PloS one
The electrical penetration graph (EPG) is a well-known technique that provides insights into the feeding behavior of insects with piercing-sucking mouthparts, mostly hemipterans. Since its inception in the 1960s, EPG has become indispensable in study...