Goto

Collaborating Authors

 organism


AI-powered platforms uncover proteins that organise cellular compartments

AIHub

Scientists at Nanyang Technological University, Singapore (NTU Singapore) have developed two artificial intelligence (AI)-powered platforms that could enable researchers to more accurately predict proteins that undergo phase separation - a process through which proteins in cells separate like oil droplets in water. The tools are the result of a systematic analysis of predicted phase-separating proteins from various living things, including animals, plants, fungi and single-celled organisms such as bacteria. From their analyses, the scientists also uncovered fundamental insights about the phase separation process. Led by Professor Miao Yansong from the School of Biological Sciences and the Institute for Digital Molecular Analytics and Science (IDMxS) at NTU, in collaboration with Professor Weibo Gao from NTU's School of Electrical and Electronic Engineering, the research could be applied to advance the understanding of diseases and ageing, as well as to improve crops. Both AI platforms and their research findings have been reported in the peer-reviewed journal .


AI dives into a sea of data, from plankton to pollution

AIHub

When asked why Jean-Olivier Irisson, a scientist at Sorbonne Université in Paris, decided to dedicate his life to studying microscopic creatures in the sea, his answer was simple: "They are beautiful." Beauty may not be the first thing that comes to mind when we think of plankton - organisms that drift in water and come in an extraordinary variety of shapes and sizes. But images by Irisson's team tell a different story. Shown in striking blues and oranges, as well as black and white, they reveal an unfamiliar and strangely beautiful world. "This one served as the model for the head of the creature in the Alien movie franchise," Irisson said, pointing to one particularly unusual specimen.


Antarctica's Blood Falls teems with life from unexpected source

Popular Science

Despite being over 20 miles from the ocean, it's organisms are pretty salty. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Iron-rich subglacial brine emerges, creating the striking red outflow. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .


AI labels a lot of stuff as alien life

Popular Science

More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Neural networks trained to spot biosignatures may flag far more results than they should. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy . Don't expect a dramatic, AI-assisted sci-fi encounter if humanity ever definitively detects evidence of intelligent extraterrestrial life .


31 alien-like marine species discovered off the coast of Brazil

Popular Science

More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. A siphonophore--a colonial marine invertebrate related to the venomous stinging Portuguese Man-o-war--is scanned using Deep Particle Image Velocimetry (DeepPIV) at a depth of 350 meters. This species was undescribed prior to this encounter and is likely new to science. DeepPIV is a laser-and optics-based imaging system that quantifies both the motion of liquids and the 3D shape of transparent animals. The imaging system was developed by the Bioinspiration Lab at MBARI (Monterey Bay Aquarium Research Institute) to create 3D models of gelatinous animals.


The future of robot armies is here – and it's not what you think

New Scientist

The future of robot armies is here - and it's not what you think Robots are becoming more a part of our lives every year, and worries about a robot army rising up have long plagued the technology. The robot army that saves the world won't be anything like what you imagine. Nope, they aren't little humanoids who can do synchronised martial arts like the ones who dazzled audiences during New Year's festivities in China . And they won't help you find a can of Coke with embarrassing slowness like the man-shaped beast known as Optimus from Elon Musk's Tesla Inc. Instead, they will be microscopic, and mostly made of algae, bacteria and other single-celled organisms.


Glowing algae could power the lamps of the future

Popular Science

The bioluminescent plants are a potential alternative to electrical light and batteries. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Acidic (top) and basic (bottom) environments trigger different bioluminescent behaviors in algae. Breakthroughs, discoveries, and DIY tips sent six days a week. Bioluminescence is everywhere in nature, but it puts on its biggest light shows underwater .


A Experiment on zero-shot classification

Neural Information Processing Systems

The top two rows show easy cases, while the bottom three rows present hard cases, including crowdedness, complex backgrounds, and tiny objects.


Species196: A One-Million Semi-supervised Dataset for Fine-grained Species Recognition Wei He, Kai Han

Neural Information Processing Systems

The development of foundation vision models has pushed the general visual recognition to a high level, but cannot well address the fine-grained recognition in specialized domain such as invasive species classification. Identifying and managing invasive species has strong social and ecological value.


Automated Classification of Model Errors on ImageNet

Neural Information Processing Systems

While the ImageNet dataset has been driving computer vision research over the past decade, significant label noise and ambiguity have made top-1 accuracy an insufficient measure of further progress.