Lifeboat Foundation

Artificial microswimmers that can replicate the complex behavior of active matter are often designed to mimic the self-propulsion of microscopic living organisms. However, compared with their living counterparts, artificial microswimmers have a limited ability to adapt to environmental signals or to retain a physical memory to yield optimized emergent behavior. Different from macroscopic living systems and robots, both microscopic living organisms and artificial microswimmers are subject to Brownian motion, which randomizes their position and propulsion direction. Here, we combine real-world artificial active particles with machine learning algorithms to explore their adaptive behavior in a noisy environment with reinforcement learning. We use a real-time control of self-thermophoretic active particles to demonstrate the solution of a simple standard navigation problem under the inevitable influence of Brownian motion at these length scales. We show that, with external control, collective learning is possible. Concerning the learning under noise, we find that noise decreases the learning speed, modifies the optimal behavior, and also increases the strength of the decisions made. As a consequence of time delay in the feedback loop controlling the particles, an optimum velocity, reminiscent of optimal run-and-tumble times of bacteria, is found for the system, which is conjectured to be a universal property of systems exhibiting delayed response in a noisy environment.

Living organisms adapt their behavior according to their environment to achieve a particular goal. Information about the state of the environment is sensed, processed, and encoded in biochemical processes in the organism to provide appropriate actions or properties. These learning or adaptive processes occur within the lifetime of a generation, over multiple generations, or over evolutionarily relevant time scales. They lead to specific behaviors of individuals and collectives. Swarms of fish or flocks of birds have developed collective strategies adapted to the existence of predators (1), and collective hunting may represent a more efficient foraging tactic (2). Birds learn how to use convective air flows (3). Sperm have evolved complex swimming patterns to explore chemical gradients in chemotaxis (4), and bacteria express specific shapes to follow gravity (5).

Inspired by these optimization processes, learning strategies that reduce the complexity of the physical and chemical processes in living matter to a mathematical procedure have been developed. Many of these learning strategies have been implemented into robotic systems (7–9). One particular framework is reinforcement learning (RL), in which an agent gains experience by interacting with its environment (10). The value of this experience relates to rewards (or penalties) connected to the states that the agent can occupy. The learning process then maximizes the cumulative reward for a chain of actions to obtain the so-called policy. This policy advises the agent which action to take. Recent computational studies, for example, reveal that RL can provide optimal strategies for the navigation of active particles through flows (11–13), the swarming of robots (14–16), the soaring of birds , or the development of collective motion (17).

The complex surface topography on biological tissues presents a major challenge in bio‐electronic interfacing. Taking hairy plants as an example, an ionic electrode based on a thermogel is reported,…

Graph representations can solve complex problems in natural science, as patterns of connectivity can give rise to a magnitude of emergent phenomena. Graph-based approaches are specifically important during quantum communication, alongside quantum search algorithms in highly branched quantum networks. In a new report now published on Science Advances, Max Ehrhardt and a team of scientists in physics, experimental physics and quantum science in Germany introduced a hitherto unidentified paradigm to directly realize excitation dynamics associated with three-dimensional networks. To accomplish this, they explored the hybrid action of space and polarization degrees of freedom of photon pairs inside complex waveguide circuits. The team experimentally explored multiparticle quantum walks on complex and highly connected graphs as testbeds to pave the way to explore the potential applications of fermionic dynamics in integrated photonics.

Complex networks

Complex networks can occur across diverse fields of science, ranging from biological signaling pathways and biochemical molecules to exhibit efficient energy transport to neuromorphic circuits across to social interactions across the internet. Such structures are typically modeled using graphs whose complexity relies on the number of nodes and linkage patterns between them. The physical representation of a graph is limited by their requirement for arrangement in three-dimensional (3D) space. The human brain is a marked example of scaling behavior that is unfavorable for physical simulation due to its staggering number of 80 billion neurons, dwarfed by 100 trillion synapses that allow the flow of signals between them. Despite the number of comparably miniscule volume of nodes, discrete quantum systems faced a number of challenges owing to complex network topologies, efficient multipartite quantum communications and search algorithms.

The menagerie of bacterial and fungal species living among us is ever growing — and this is no exception in low-gravity environments, such as the International Space Station (ISS).

Researchers from the United States and India working with NASA have now discovered four strains of bacteria living in different places in the ISS – three of which were, until now, completely unknown to science.

Three of the four strains were isolated back in 2015 and 2016 – one was found on an overhead panel of the ISS research stations, the second was found in the Cupola, the third was found on the surface of the dining table; the fourth was found in an old HEPA filter returned to Earth in 2011.

Photosynthetic light-harvesting antennae transfer energy toward reaction centers with high efficiency, but in high light or oxidative environments, the antennae divert energy to protect the photosynthetic apparatus. For a decade, quantum effects driven by vibronic coupling, where electronic and vibrational states couple, have been suggested to explain the energy transfer efficiency, but questions remain whether quantum effects are merely consequences of molecular systems. Here, we show evidence that biology tunes interpigment vibronic coupling, indicating that the quantum mechanism is operative in the efficient transfer regime and exploited by evolution for photoprotection. Specifically, the Fenna–Matthews–Olson complex uses redox-active cysteine residues to tune the resonance between its excitons and a pigment vibration to steer excess excitation toward a quenching site.

Photosynthetic species evolved to protect their light-harvesting apparatus from photoxidative damage driven by intracellular redox conditions or environmental conditions. The Fenna–Matthews–Olson (FMO) pigment–protein complex from green sulfur bacteria exhibits redox-dependent quenching behavior partially due to two internal cysteine residues. Here, we show evidence that a photosynthetic complex exploits the quantum mechanics of vibronic mixing to activate an oxidative photoprotective mechanism. We use two-dimensional electronic spectroscopy (2DES) to capture energy transfer dynamics in wild-type and cysteine-deficient FMO mutant proteins under both reducing and oxidizing conditions. Under reducing conditions, we find equal energy transfer through the exciton 4–1 and 4–2–1 pathways because the exciton 4–1 energy gap is vibronically coupled with a bacteriochlorophyll-a vibrational mode.

As the world fights the SARS-CoV-2 virus causing the COVID-19 pandemic, another group of dangerous pathogens looms in the background. The threat of antibiotic-resistant bacteria has been growing for years and appears to be getting worse. If COVID-19 taught us one thing, it’s that governments should be prepared for more global public health crises, and that includes finding new ways to combat rogue bacteria that are becoming resistant to commonly used drugs.

In contrast to the current pandemic, viruses may be be the heroes of the next epidemic rather than the villains. Scientists have shown that viruses could be great weapons against bacteria that are resistant to antibiotics.

I am a biotechnology and policy expert focused on understanding how personal genetic and biological information can improve human health. Every person interacts intimately with a unique assortment of viruses and bacteria, and by deciphering these complex relationships we can better treat infectious diseases caused by antibiotic-resistant bacteria.

Open AI, the research company founded by Elon Musk, has just discovered that their artificial neural network CLIP shows behavior strikingly similar to a human brain. This find has scientists hopeful for the future of AI networks’ ability to identify images in a symbolic, conceptual and literal capacity.

While the human brain processes visual imagery by correlating a series of abstract concepts to an overarching theme, the first biological neuron recorded to operate in a similar fashion was the “Halle Berry” neuron. This neuron proved capable of recognizing photographs and sketches of the actress and connecting those images with the name “Halle Berry.”

Now, OpenAI’s multimodal vision system continues to outperform existing systems, namely with traits such as the “Spider-Man” neuron, an artificial neuron which can identify not only the image of the text “spider” but also the comic book character in both illustrated and live action form. This ability to recognize a single concept represented in various contexts demonstrates CLIP’s abstraction capabilities. Similar to a human brain, the capacity for abstraction allows a vision system to tie a series of images and text to a central theme.

One of the major unsolved mysteries of biological science concerns the question of where and in what form information is stored in the brain. I propose that memory is stored in the brain in a mechanically encoded binary format written into the conformations of proteins found in the cell-extracellular matrix (ECM) adhesions that organise each and every synapse. The MeshCODE framework outlined here represents a unifying theory of data storage in animals, providing read-write storage of both dynamic and persistent information in a binary format. Mechanosensitive proteins that contain force-dependent switches can store information persistently, which can be written or updated using small changes in mechanical force. These mechanosensitive proteins, such as talin, scaffold each synapse, creating a meshwork of switches that together form a code, the so-called MeshCODE. Large signalling complexes assemble on these scaffolds as a function of the switch patterns and these complexes would both stabilise the patterns and coordinate synaptic regulators to dynamically tune synaptic activity. Synaptic transmission and action potential spike trains would operate the cytoskeletal machinery to write and update the synaptic MeshCODEs, thereby propagating this coding throughout the organism. Based on established biophysical principles, such a mechanical basis for memory would provide a physical location for data storage in the brain, with the binary patterns, encoded in the information-storing mechanosensitive molecules in the synaptic scaffolds, and the complexes that form on them, representing the physical location of engrams. Furthermore, the conversion and storage of sensory and temporal inputs into a binary format would constitute an addressable read-write memory system, supporting the view of the mind as an organic supercomputer.

I would like to propose here a unifying theory of rewritable data storage in animals. This theory is based around the realisation that mechanosensitive proteins, which contain force-dependent binary switches, can store information persistently in a binary format, with the information stored in each molecule able to be written and/or updated via small changes in mechanical force. The protein talin contains 13 of these switches (Yao et al., 2016; Goult et al., 2018; Wang et al., 2019), and, as I argue here, it is my assertion that talin is the memory molecule of animals. These mechanosensitive proteins scaffold each and every synapse (Kilinc, 2018; Lilja and Ivaska, 2018; Dourlen et al., 2019) and have been considered mainly structural. However, these synaptic scaffolds also represent a meshwork of binary switches that I propose form a code, the so-called MeshCODE.