Human machines will accelerate our study of nature’s machines
The platypus is one of evolution’s lovable, oddball animals. The creature seems to defy well-understood rules of biology by combining physical traits in a bizarre way. They’re egg-laying mammals with duck bills and beaver-like tails, and the males have venomous spurs on their hind feet. In that regard, it’s only fitting that astronomers describe some newly discovered oddball objects as “Astronomy’s Platypus.”
The discovery consists of nine galaxies that also have unusual properties and seem to defy categorization. The findings were recently presented at the 247th meeting of the American Astronomical Society in Phoenix. The results are also in new research titled “A New Population of Point-like, Narrow-line Objects Revealed by the James Webb Space Telescope,” posted to the arXiv preprint server. The lead author is Haojing Yan from the University of Missouri-Columbia.
“We report a new population of objects discovered using the data from the James Webb Space Telescope, which are characterized by their point-like morphology and narrow permitted emission lines,” the authors write in their research. “Due to the limitation of the current data, the exact nature of this new population is still uncertain.”
Seeing chemistry unfold inside living cells is one of the biggest challenges of modern bioimaging. Raman microscopy offers a powerful way to meet this challenge by reading the unique vibrational signatures of molecules. However, cells are extraordinarily complex environments filled with thousands of biomolecules.
To make specific molecules stand out, researchers often attach small chemical probes, such as alkyne tags, that produce signals in a so-called cell-silent spectral window where native cellular components do not scatter light. This allows Raman microscopes to selectively detect the tagged molecules against an otherwise crowded molecular background. Despite this advantage, the widespread adoption of Raman microscopy in biology has been limited by one fundamental problem: Raman signals are extremely weak.
Together with an international team, researchers from the Molecular Physics Department at the Fritz Haber Institute have revealed how atoms rearrange themselves before releasing low-energy electrons in a decay process initiated by X-ray irradiation. For the first time, they have gained detailed insights into the timing of the process—shedding light on related radiation damage mechanisms. Their research is published in the Journal of the American Chemical Society.
High-energy radiation, for example in the X-ray range, can cause damage to our cells. This is because energetic radiation can excite atoms and molecules, which then often decay—meaning that biomolecules are destroyed and larger biological units can lose their function. There is a wide variety of such decay processes, and studying them is of great interest in order to better understand and avert radiation damage.
In the study, researchers from the Molecular Physics Department, together with international partners, investigated a radiation-induced decay process that plays a key role in radiation chemistry and biological damage processes: electron-transfer-mediated decay (ETMD). In this process, one atom is excited by irradiation. Afterward, this atom relaxes by stealing an electron from a neighbor, while the released energy ionizes yet another nearby atom.
NoteL This is elegant theoretical physics showing an intriguing possibility, not a confirmed biological mechanism. It’s a “what if” scenario that could change how we view enzymes, but only if the controversial premise (EED) turns out to be real.
Enhanced enzyme diffusion (EED), in which the diffusion coefficient of an enzyme transiently increases during catalysis, has been extensively reported experimentally, although its existence remains under debate. In this Letter, we investigate what macroscopic consequences would arise if EED exists. Through numerical simulations and theoretical analysis, we demonstrate that such enzymes can act as Maxwell’s demons: They use their enhanced diffusion as a memory of the previous catalytic reaction, to gain information and drive steady-state chemical concentrations away from chemical equilibrium. Our theoretical analysis identifies the conditions under which this process could operate and discusses its possible biological relevance.
This paper formalizes biological intelligence as search efficiency in multi-scale problem spaces, aiming to resolve epistemic deadlocks in the basal “cognition wars” unfolding in the Diverse Intelligence research program. It extends classical work on symbolic problem-solving to define a novel problem space lexicon and search efficiency metric. Construed as an operationalization of intelligence, this metric is the decimal logarithm of the ratio between the cost of a random walk and that of a biological agent. Thus, the search efficiency measures how many orders of magnitude of dissipative work an agentic policy saves relative to a maximal-entropy search strategy. Empirical models for amoeboid chemotaxis and barium-induced planarian head regeneration show that, under conservative (i.e.
A new computational model of the brain based closely on its biology and physiology not only learned a simple visual category learning task exactly as well as lab animals, but even enabled the discovery of counterintuitive activity by a group of neurons that researchers working with animals to perform the same task had not noticed in their data before, says a team of scientists at Dartmouth College, MIT, and the State University of New York at Stony Brook.
Notably, the model produced these achievements without ever being trained on any data from animal experiments. Instead, it was built from scratch to faithfully represent how neurons connect into circuits and then communicate electrically and chemically across broader brain regions to produce cognition and behavior. Then, when the research team asked the model to perform the same task that they had previously performed with the animals (looking at patterns of dots and deciding which of two broader categories they fit), it produced highly similar neural activity and behavioral results, acquiring the skill with almost exactly the same erratic progress.
“It’s just producing new simulated plots of brain activity that then only afterward are being compared to the lab animals. The fact that they match up as strikingly as they do is kind of shocking,” says Richard Granger, a professor of psychological and brain sciences at Dartmouth and senior author of a new study in Nature Communications that describes the model.
Professor Jeong-Min Baik’s research group of the SKKU School of Advanced Materials Science and Engineering has developed a reusable electrokinetic filtration platform capable of filtering more than 99% of ultrafine nanoplastic particles smaller than 50 nm even under commercial-level high-flow conditions.
Plastic pollution, which has surged in recent years through industrialization and the pandemic era, poses a direct threat to human health. In particular, nanoplastics smaller than 100 nm—thousands of times thinner than a human hair—can readily pass through biological membranes in the body and trigger serious diseases such as immune dysregulation and carcinogenicity.
However, conventional water purification systems have struggled to effectively remove nanoplastics of such small sizes, highlighting technological limitations; studies have even reported the presence of hundreds of thousands of particles in a single bottle of bottled water.