AIMC Topic: Oceans and Seas

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Exploring the roles of ribosomal peptides in prokaryote-phage interactions through deep learning-enabled metagenome mining.

Microbiome
BACKGROUND: Microbial secondary metabolites play a crucial role in the intricate interactions within the natural environment. Among these metabolites, ribosomally synthesized and post-translationally modified peptides (RiPPs) are becoming a promising...

Global marine phytoplankton dynamics analysis with machine learning and reanalyzed remote sensing.

PeerJ
Phytoplankton are the world's largest oxygen producers found in oceans, seas and large water bodies, which play crucial roles in the marine food chain. Unbalanced biogeochemical features like salinity, pH, minerals, ., can retard their growth. With a...

Ocean carbon emission prediction and management measures based on artificial intelligence remote sensing estimation in the context of carbon neutrality.

Environmental research
With rapid economic development, the gradual deterioration of the natural environment has posed unprecedented challenges to human social civilization. The marine economy, as an important part of economic development, is the breakthrough of economic t...

An in situ digital synthesis strategy for the discovery and description of ocean life.

Science advances
Revolutionary advancements in underwater imaging, robotics, and genomic sequencing have reshaped marine exploration. We present and demonstrate an interdisciplinary approach that uses emerging quantitative imaging technologies, an innovative robotic ...

Ocean Stratification Impacts on Dissolved Polycyclic Aromatic Hydrocarbons (PAHs): From Global Observation to Deep Learning.

Environmental science & technology
Ocean stratification plays a crucial role in many biogeochemical processes of dissolved matter, but our understanding of its impact on widespread organic pollutants, such as polycyclic aromatic hydrocarbons (PAHs), remains limited. By analyzing disso...

Prediction of microplastic abundance in surface water of the ocean and influencing factors based on ensemble learning.

Environmental pollution (Barking, Essex : 1987)
Microplastics are regarded as emergent contaminants posing a serious threat to the marine ecosystem. It is time-consuming and labor-intensive to determine the number of microplastics in different seas using traditional sampling and detection methods....

Ocean oil spill detection from SAR images based on multi-channel deep learning semantic segmentation.

Marine pollution bulletin
One of the major threats to marine ecosystems is pollution, particularly, that associated with the offshore oil and gas industry. Oil spills occur in the world's oceans every day, either as large-scale spews from drilling-rig or tanker accidents, or ...

Microplankton life histories revealed by holographic microscopy and deep learning.

eLife
The marine microbial food web plays a central role in the global carbon cycle. However, our mechanistic understanding of the ocean is biased toward its larger constituents, while rates and biomass fluxes in the microbial food web are mainly inferred ...

FathomNet: A global image database for enabling artificial intelligence in the ocean.

Scientific reports
The ocean is experiencing unprecedented rapid change, and visually monitoring marine biota at the spatiotemporal scales needed for responsible stewardship is a formidable task. As baselines are sought by the research community, the volume and rate of...

Multi-input multi-output temporal convolutional network for predicting the long-term water quality of ocean ranches.

Environmental science and pollution research international
The prediction of water quality parameters is of great significance to the control of marine environments and provides a scientific decision-making basis for maintaining the stability of water environments and ensuring the normal survival and growth ...