The function and fate of T cells are dictated by their various dynamic interactions with cells and tissues. This Review discusses the recreation of key T cell interfaces using nanotechnologies and microtechnologies for the mechanistic study of T cell biology, as well as the manufacturing and sorting of T cell products.
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Researchers have created atomically thin artificial neurons capable of processing both light and electric signals for computing. The material enables the simultaneous existence of separate feedforward and feedback paths within a neural network, boosting the ability to solve complex problems.
For decades, scientists have been investigating how to recreate the versatile computational capabilities of biological neurons to develop faster and more energy-efficient machine learning systems. One promising approach involves the use of memristors: electronic components capable of storing a value by modifying their conductance and then utilising that value for in-memory processing.
However, a key challenge to replicating the complex processes of biological neurons and brains using memristors has been the difficulty in integrating both feedforward and feedback neuronal signals. These mechanisms underpin our cognitive ability to learn complex tasks, using rewards and errors.
A new computational model of the brain based closely on its biology and physiology has 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, reports a team of scientists at Dartmouth College, MIT, and the State University of New York at Stony Brook.
Professor John Donoghue explains why quantum physics and gravity actually work perfectly together. He tackles quadratic gravity, effective field theory, and random dynamics, arguing that grand unification and naturalness aren’t required for a theory of everything.
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Across physics, chemistry, biology, and engineered systems, the operationally significant questionis often not whether a system will eventually reach a particular state, but whether it can be broughtthere within the time available. This paper establishes a single structural necessity: when causalresponse propagates at finite speed, there exist states that are theoretically admissible but practicallyunreachable within any finite time horizon. We formalize this as the causal accessibility horizon—ageometric boundary determined solely by propagation speed and actuation geometry, beyond whichno control action can have effect by a given time T. This constraint is categorical: it arises fromthe hyperbolic structure of finite-speed dynamics and is logically independent of dissipation, whichgoverns amplitude decay within the accessible region but does not determine its boundary. Theresult reframes questions of control, safety, and stabilization as finite-time reachability problemssubject to irreducible geometric limits.
The familiar fight between “mind as software” and “mind as biology” may be a false choice. This work proposes biological computationalism: the idea that brains compute, but not in the abstract, symbol-shuffling way we usually imagine. Instead, computation is inseparable from the brain’s physical structure, energy constraints, and continuous dynamics. That reframes consciousness as something that emerges from a special kind of computing matter, not from running the right program.
