Lifeboat Foundation

NASA tracks the microbes that live on the space station, and sometimes it discovers new ones. Those hardy bugs may offer clues about surviving long missions.

Robot swarms have, to date, been constructed from artificial materials. Motile biological constructs have been created from muscle cells grown on precisely shaped scaffolds. However, the exploitation of emergent self-organization and functional plasticity into a self-directed living machine has remained a major challenge. We report here a method for generation of in vitro biological robots from frog (Xenopus laevis) cells. These xenobots exhibit coordinated locomotion via cilia present on their surface. These cilia arise through normal tissue patterning and do not require complicated construction methods or genomic editing, making production amenable to high-throughput projects.

A breakthrough in synthetic biology could shed new light on mechanisms controlling the most basic processes of life.

A new, detailed model of the surface of the SARS-CoV-2 spike protein reveals previously unknown vulnerabilities that could inform development of vaccines. Mateusz Sikora of the Max Planck Institute of Biophysics in Frankfurt, Germany, and colleagues present these findings in the open-access journal PLOS Computational Biology.

SARS-CoV-2 is the virus responsible for the COVID-19 pandemic. A key feature of SARS-CoV-2 is its spike protein, which extends from its surface and enables it to target and infect human cells. Extensive research has resulted in detailed static models of the spike protein, but these models do not capture the flexibility of the spike protein itself nor the movements of protective glycans—chains of sugar molecules—that coat it.

To support vaccine development, Sikora and colleagues aimed to identify novel potential target sites on the surface of the spike protein. To do so, they developed molecular dynamics simulations that capture the complete structure of the spike protein and its motions in a realistic environment.

Bacteria have been found exploiting quantum physics to survive.

Oxygen is life to animals like us. But for many species of microbe, the smallest whiff of the highly reactive element puts their delicate chemical machinery at risk of rusting up.

The photosynthesizing bacterium Chlorobium tepidum has evolved a clever way to shield its light-harvesting processes from oxygen’s poisonous effects, using a quantum effect to shift its energy production line into low gear.

Continue reading “Bacteria Could Be The First Organisms Found to Use Quantum Effects to Survive” »

Engineers at Duke University have developed an electronics-free, entirely soft robot shaped like a dragonfly that can skim across water and react to environmental conditions such as pH, temperature or the presence of oil. The proof-of-principle demonstration could be the precursor to more advanced, autonomous, long-range environmental sentinels for monitoring a wide range of potential telltale signs of problems.

The soft robot is described online March 25 in the journal Advanced Intelligent Systems.

Soft robots are a growing trend in the industry due to their versatility. Soft parts can handle delicate objects such as biological tissues that metal or ceramic components would damage. Soft bodies can help robots float or squeeze into tight spaces where rigid frames would get stuck.

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Continue reading “Telomere Length: How Does it Compare Against Other Biological Age Metrics?” »

(From left) Expedition 65 crew members Pyotr Dubrov, Oleg Novitskiy and Mark Vande Hei, pose for a photo during Soyuz qualification exams in Moscow.

The Expedition 64 crew continued researching how microgravity affects biology aboard the International Space Station today. The orbital residents also conducted vein and eye checks and prepared for three new crew members due in early April.

NASA Flight Engineer Shannon Walker joined Russian cosmonauts Sergey Ryzhikov and Sergey Kud-Sverchkov for vein and eye scans on Thursday. Japan Aerospace Exploration Agency astronaut Soichi Noguchi led the effort scanning veins in the trio’s neck, clavicle and shoulder areas using the Ultrasound 2 device in the morning. In the afternoon, Noguchi examined Walker’s eyes using the orbiting lab’s optical coherence tomography gear.

Continue reading “Vein, Eye Scans on Station as Next Crew Nears Launch” »

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).