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Category: biological

Beyond 3D: Data scientists introduce novel AI tool to interpret complex biological data

As humans, our eyes take in two-dimensional images that our brains convert to three-dimensional experiences. This ability enables us to be aware of our position in space, judge distances, possess depth perception, and visually examine and enjoy all manner of objects and happenings.

But trying to envision subvisible structures and high-dimensional processes that our human-engineered scopes can’t capture is a challenge for data scientists and visualization experts, who turn to machine learning and AI tools to amplify visual exploration.

“Biological processes are an example of complex, high-dimensional data,” says Kevin Moon, director of USU’s Data Science and Artificial Intelligence (DSAI) Center and associate professor in the Department of Mathematics and Statistics.

Multifunctional Organic Materials, Devices, and Mechanisms for Neuroscience, Neuromorphic Computing, and Bioelectronics

Neuromorphic computing has the potential to overcome limitations of traditional silicon technology in machine learning tasks. Recent advancements in large crossbar arrays and silicon-based asynchronous spiking neural networks have led to promising neuromorphic systems. However, developing compact parallel computing technology for integrating artificial neural networks into traditional hardware remains a challenge. Organic computational materials offer affordable, biocompatible neuromorphic devices with exceptional adjustability and energy-efficient switching. Here, the review investigates the advancements made in the development of organic neuromorphic devices. This review explores resistive switching mechanisms such as interface-regulated filament growth, molecular-electronic dynamics, nanowire-confined filament growth, and vacancy-assisted ion migration, while proposing methodologies to enhance state retention and conductance adjustment. The survey examines the challenges faced in implementing low-power neuromorphic computing, e.g., reducing device size and improving switching time. The review analyses the potential of these materials in adjustable, flexible, and low-power consumption applications, viz. biohybrid spiking circuits interacting with biological systems, systems that respond to specific events, robotics, intelligent agents, neuromorphic computing, neuromorphic bioelectronics, neuroscience, and other applications, and prospects of this technology.

Keywords: Brain-inspired neuromorphic computing; Neuromorphic bioelectronics; Neuroscience; Organic materials; Resistive switching mechanisms.

© 2025. The Author(s).

This microbe turns into a cannibalistic ‘Hulk’

A newly discovered microbe is like a mini version of the Hulk.

Euplotes gigatrox is a single-celled protist that resembles an insect. It grazes on bacteria and other tiny microbes. Sometimes a small number of the protists balloon into “supergiants” more than twice their regular size. The huge cells cannibalize their smaller, genetically identical brethren. The triggers for the change aren’t entirely clear, but it tends to happen when there is plenty of food, researchers reported May 14 in the Proceedings of the National Academy of Sciences.

Takes Back Philosophy’s Questions | Alex Rosenberg

Can biology answer questions that once belonged only to philosophy?

Alex Rosenberg argues that Darwinian biology transformed not only science but also our understanding of morality, meaning, mind, and human purpose, bringing traditionally philosophical questions into the scientific domain.

0:00 What Is the Philosophy of Biology 1:14 How Darwin Changed the Nature of Inquiry 4:27 How Philosophers Help Biologists 6:48 Biology and the Philosophy of Mind 9:43 Can Biology Answer Philosophy’s Biggest Questions.

Alexander Rosenberg is an American philosopher and novelist. He is the R. Taylor Cole Professor of Philosophy at Duke University, well known for contributions to philosophy of biology and philosophy of economics. Rosenberg describes himself as a \.

Distributed Cognition: The New Science of Non-Biological Intelligence

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Hello and welcome! My name is Anton and in this video, we will talk about distributed intelligence and experiments on slime mold and ants.
Links:
https://journals.aps.org/prxlife/pdf/.…
ANT Lab • The odorous house ant trail pheromone depo…
Audrey Dussutour • Blob crawling around.
#inteligence #artificialintelligence #biology.

0:00 Intelligence — what is it?
1:10 Mechanical intelligence in the slime mold.
3:30 How it seems to work.
5:55 Ants and swarm intelligence.
6:45 What is the queen for?
8:35 Other swarm animals.
9:45 Ants vs humans.
11:10 Collective intelligence.
12:00 Implications for AI
13:20 Implications for the existence of alien intelligence.

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Meet EcoBOT: The Autonomous Lab Standardizing Plant-Microbe Research

To harness biological systems (plants and microbes) for next-generation energy production and advanced materials, researchers are looking to beneficial plant-microbe interactions. Because these are complex systems, it has proven difficult to reproducibly control exactly which microbes are present. And, subtle differences in materials, methods, or even the hands of the researchers themselves can lead to inconsistent results. This makes it difficult to replicate previous work, significantly slowing the leap from scientific discovery to practical application.

Researchers at Lawrence Berkeley National Laboratory (Berkeley Lab) are overcoming this bottleneck by addressing a multi-layered challenge: building reliable physical hardware, engineering accurate visual sensors, and developing predictive algorithms. Their solution, EcoBOT, stands out from typical plant phenotyping facilities by integrating these distinct components into a reliably automated workflow under strictly sterile conditions.

EcoBOT takes specialized growth chambers, called EcoFABs, and integrates them with machine-learning tools that autonomously guide the discovery cycle. This system uses advanced imaging to regularly scan the entire plant—from the tips of its leaves to the bottom of its roots. By using Gaussian Process models and AI analysis tools, it can quickly analyze and model this visual data to calculate the most informative next steps. This directs the automated hardware to determine exactly how plants adapt to environmental stressors, establishing the crucial microbe-free baseline needed to eventually study plant-microbe interactions and engineer better bioenergy crops.

Intelligence Without Brains: A Radical New Idea

What if intelligence doesn’t require a brain? Biologist Michael Levin argues that intelligence is not confined to neurons, but exists on a continuum of goal-directed behavior and problem-solving across a wide range of species and systems. Using a framework he calls the “cognitive light cone,” Levin explores diverse forms of intelligence extending all the way down to the cellular level. His research suggests that cells communicate through electrical networks, enabling them to make collective decisions and adapt to unexpected challenges, evidenced by engineered tadpoles capable of seeing through eyes located on their tails. Levin radically challenges the conventional wisdom even further, proposing that forms of intelligence may extend beyond biology to molecular systems and maybe even the weather.

00:00 What is intelligence?
01:03 The field of diverse intelligence.
01:33 Intelligence at the cellular level.
02:08 The cognitive light cone.
03:01 The intelligence of groups of cells.
03:52 The bioelectric language of cells.
04:20 The mind of the body.
04:23 Cells that solve problems.
05:17 The tadpole experiment.
06:25 The cognitive spectrum.
06:48 Can you train a hurricane?
07:03 A new science of intelligence.
07:28 Beyond human biases.

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Quanta Magazine is an editorially independent publication supported by the Simons Foundation. We focus on developments in mathematics, theoretical physics, theoretical computer science and the basic life sciences.

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Linear-time prediction of proteome-scale microbial protein interactions

Protein–protein interactions (PPIs) underpin biological function, yet proteome-scale interaction prediction remains bottlenecked by the quadratic computational complexity of all-vs.-all pairwise comparisons. Here, we present FlashPPI, a contrastive learning framework, grounded in residue-level interactions, that enables linear-time prediction of physical protein interfaces across a microbial proteome. By leveraging a genomic language model that captures cross-protein coevolutionary signals from metagenomic sequences, FlashPPI aligns interacting partners in a shared latent space. We demonstrate a four-fold performance increase over existing sequence-based methods, while reducing proteome-wide screening time from days to minutes. Crucially, FlashPPI achieves comparable screening performance to state-of-the-art structure-folding models at a fraction of the computational cost. Finally, we integrate FlashPPI into an interactive web platform that combines predicted networks with functional annotations and genomic context, making proteome-wide network analysis rapid and accessible for microbial discovery.

Scientists Finally Figured Out Why 90% of Humans Are Right-Handed

Not to toot my own horn or anything, but I can extend my empathy beyond myself just enough to imagine someone else’s perspective, fully knowing I’ll never completely understand the texture of their experience. But as a right-handed person, I will never, ever be able to do that for left-handed people. There’s just something in my brain preventing me from understanding how someone can navigate the world primarily using the hand I mostly rely on to accidentally test the sharpness of kitchen knives.

So naturally, it always made sense to me that around 90 percent of humans are right-handed. What never made sense was why. According to new research published in PLOS Biology, we may have finally figured it out: humans became overwhelmingly right-handed because we started walking upright and developed massive brains.

Researchers from the University of Oxford analyzed more than 2,000 primates across 41 species, comparing handedness with factors like social behavior, diet, body size, and movement. Nothing fully explained humanity’s innate steadfast dedication to right-handedness until researchers started factoring in brain size and the ratio between leg and arm length.

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