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In an effort to make them useless to poachers, researchers are implanting radioactive isotopes into the horns of rhinos in South Africa.
The unusual material would “render the horn useless… essentially poisonous for human consumption,” James Larkin, professor and dean of science at the University of the Witwatersrand in Johannesburg, told Agence France-Presse.
The isotopes would also be “strong enough to set off detectors that are installed globally,” Larkin added, referring to hardware that was originally installed to “prevent nuclear terrorism.”
Our cells and the machinery inside them are engaged in a constant dance. This dance involves some surprisingly complicated choreography within the lipid bilayers that comprise cell membranes and vesicles — structures that transport waste or food within cells.
In a recent ACS Nano paper (“The Secret Ballet Inside Multivesicular Bodies”), Luis Mayorga and Diego Masone shed some light on how these vesicles self-assemble, knowledge that could help scientists design bio-inspired vesicles for drug-delivery or inspire them to create life-like synthetic materials.
A representation of multilayer lipid vesicles inspired by “Color Study: Squares with Concentric Circles,” by the artist Wassily Kandinsky. (Image: ACS Nano 2024, DOI: 10.1021/acsnano.4c01590)
“Surfaces were invented by the devil” — this quote is attributed to the theoretical physicist Wolfgang Pauli, who taught at ETH Zurich for many years and in 1945 received the Nobel Prize in physics for his contributions to quantum mechanics. Researchers do, indeed, struggle with surfaces. On the one hand they are extremely important both in animate and inanimate nature, but on the other hand it can be devilishly difficult to study them with conventional methods.
An interdisciplinary team of materials scientists and electrical engineers led by Lukas Novotny, Professor of Photonics at ETH Zurich, together with colleagues at Humboldt-Universität zu Berlin has now developed a method that will make the characterization of surfaces considerably easier in the future.
They recently published the results of their research, which is based on an extremely thin gold membrane, in the scientific journal Nature Communications (“Bulk-suppressed and surface-sensitive Raman scattering by transferable plasmonic membranes with irregular slot-shaped nanopores”).
About 170 billion cells are in the brain, and as they go about their regular tasks, they produce waste — a lot of it.
The brain appears to rely on synchronized waves to wash out waste products, including toxins associated with Alzheimer’s disease.
Neurogenetic disorders, such as neurofibromatosis type 1 (NF1), are diseases caused by a defect in one or more genes, which can sometimes result in cognitive and motor impairments. Better understanding the neural underpinning of these disorders and how they affect motor and cognitive abilities could contribute to the development of new treatment strategies.
Researchers at Stanford University and Washington University School of Medicine recently performed a study on mice aimed at investigating the impact of Nf1 gene mutations, which cause the NF1 neurogenetic disorder, on oligodendroglial plasticity, an adaptive brain process known to contribute to cognitive and motor functions.
Their findings, published in Nature Neuroscience, provide strong evidence that Nf1 mutations delay the development of oligodendroglia, a type of glial cells that support the functioning of the central nervous system, causing disruptions in motor learning.
This fleshy, pink smiling face is made from living human skin cells, and was created as part of an experiment to let robots show emotion.
How would such a living tissue surface, whatever its advantages and disadvantages, attach to the mechanical foundation of a robot’s limb or “face”?
In humans and…
Numerous electrophysiological experiments have reported that the prefrontal cortex (PFC) is involved in the process of working memory. PFC neurons continue firing to maintain stimulus information in the delay period without external stimuli in working memory tasks. Further findings indicate that while the activity of single neurons exhibits strong temporal and spatial dynamics (heterogeneity), the activity of population neurons can encode spatiotemporal information of stimuli stably and reliably. From the perspective of neural networks, the computational mechanism underlying this phenomenon is not well demonstrated. The main purpose of this paper is to adopt a new strategy to explore the neural computation mechanism of working memory. We used reinforcement learning to train a recurrent neural network model to learn a spatial working memory task.
This paper proposes a new four-dimensional chaotic system that consists of two active magnetically controlled memristors. The dynamic characteristics of the system, including equilibrium points, Lyapunov exponent spectrum, bifurcation diagram, double-parameter Lyapunov exponent, and attractor basin, are analyzed. The results indicate that the Lyapunov exponents of the system undergo abrupt changes. The bifurcation diagrams reveal the occurrence of sudden cusp bifurcations, and the diverse manifestations of two-parameter Lyapunov exponents under different parameter combinations further underscore the system’s complexity and variability. This chaotic system also possesses an infinite number of equilibrium points and coexisting attractors, demonstrating multiple stable states.
