AIMC Topic: Photosynthesis

Clear Filters Showing 11 to 20 of 34 articles

Predicting photosynthetic bacteria-derived protein synthesis from wastewater using machine learning and causal inference.

Bioresource technology
Causal inference-assisted machine learning was used to predict photosynthetic bacterial (PSB) protein production capacity and identify key factors. The extreme gradient boosting algorithm effectively predicted protein content, while the gradient boos...

Machine learning reveals the transcriptional regulatory network and circadian dynamics of PCC 7942.

Proceedings of the National Academy of Sciences of the United States of America
is an important cyanobacterium that serves as a versatile and robust model for studying circadian biology and photosynthetic metabolism. Its transcriptional regulatory network (TRN) is of fundamental interest, as it orchestrates the cell's adaptatio...

New Strategies for constructing and analyzing semiconductor photosynthetic biohybrid systems based on ensemble Machine learning Models: Visualizing complex mechanisms and yield prediction.

Bioresource technology
Photosynthetic biohybrid systems (PBSs) composed of semiconductor-microbial hybrids provide a novel approach for converting light into chemical energy. However, comprehending the intricate interactions between materials and microbes that lead to PBSs...

Plastic particles and fluorescent brightener co-modify Chlorella pyrenoidosa photosynthesis and a machine learning approach predict algae growth.

Journal of hazardous materials
Global release of plastics exerts various impacts on the ecological cycle, particularly on primary photosynthesis, while the impacts of plastic additives are unknown. As a carrier of fluorescent brightener, plastic particles co-modify Chlorella pyren...

Understanding the phytotoxic effects of organic contaminants on rice through predictive modeling with molecular descriptors: A data-driven analysis.

Journal of hazardous materials
The widespread introduction of organic compounds into environments poses significant risks to ecosystems. Assessing the adverse effects of organic contaminants on crops is crucial for ensuring food safety. However, laboratory research is often time-c...

Machine learning in photosynthesis: Prospects on sustainable crop development.

Plant science : an international journal of experimental plant biology
Improving photosynthesis is a promising avenue to increase food security. Studying photosynthetic traits with the aim to improve efficiency has been one of many strategies to increase crop yield but analyzing large data sets presents an ongoing chall...

Detection of Tip-Burn Stress on Lettuce Grown in an Indoor Environment Using Deep Learning Algorithms.

Sensors (Basel, Switzerland)
Lettuce grown in indoor farms under fully artificial light is susceptible to a physiological disorder known as tip-burn. A vital factor that controls plant growth in indoor farms is the ability to adjust the growing environment to promote faster crop...

A photosynthetic rate prediction model using improved RBF neural network.

Scientific reports
A photosynthetic prediction rate model is a theoretical basis for light environmental regulation, and the existing photosynthetic rate prediction models are limited by low modeling speed and prediction accuracy. Therefore, this paper analyses effects...

PhotoModPlus: A web server for photosynthetic protein prediction from genome neighborhood features.

PloS one
A new web server called PhotoModPlus is presented as a platform for predicting photosynthetic proteins via genome neighborhood networks (GNN) and genome neighborhood-based machine learning. GNN enables users to visualize the overview of the conserved...

The proteome landscape of the kingdoms of life.

Nature
Proteins carry out the vast majority of functions in all biological domains, but for technological reasons their large-scale investigation has lagged behind the study of genomes. Since the first essentially complete eukaryotic proteome was reported, ...