Lifeboat Foundation

The first recorded brain activity of a person during their death suggests a biological trigger for near-death experiences.

Synthetic biology offers a way to engineer cells to perform novel functions, such as glowing with fluorescent light when they detect a certain chemical. Usually, this is done by altering cells so they express genes that can be triggered by a certain input.

However, there is often a long lag time between an event such as detecting a molecule and the resulting output, because of the time required for cells to transcribe and translate the necessary genes. MIT synthetic biologists have now developed an alternative approach to designing such circuits, which relies exclusively on fast, reversible protein-protein interactions. This means that there’s no waiting for genes to be transcribed or translated into proteins, so circuits can be turned on much faster—within seconds.

“We now have a methodology for designing protein interactions that occur at a very fast timescale, which no one has been able to develop systematically. We’re getting to the point of being able to engineer any function at timescales of a few seconds or less,” says Deepak Mishra, a research associate in MIT’s Department of Biological Engineering and the lead author of the new study.

Imagine a robot that could find human beings after a natural disaster because it has a mosquito’s ability to sense human sweat. Shoji Takeuchi of the University of Tokyo has already made a robotic finger that includes living tissue – here he explores possible applications of combining biological material with artificial materials in robotic systems.

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The development of neuromorphic electronics depends on the effective mimic of neurons. But artificial neurons aren’t capable of operating in biological environments. Organic artificial neurons that work based on conventional circuit oscillators have been created, which require many elements for their implementation. An organic artificial neuron based on a compact nonlinear electrochemical element has been reported. This artificial neuron is sensitive to the concentration of biological species in its surroundings and can also operate in a liquid. The system offers in-situ operation, spiking behavior, and ion specificity in biologically relevant conditions, including normal physiological and pathological concentration ranges. While variations in ionic and biomolecular concentrations regulate the neuronal excitability, small-amplitude oscillations and noise in the electrolytic medium alter the dynamics of the neuron. A biohybrid interface is created in which an artificial neuron functions synergistically with biological membranes and epithelial cells in real-time.

Neurons are the basic units of the nervous system that are used to transmit and process electrochemical signals. They operate in a liquid electrolytic medium and communicate via gaps between the axon of presynaptic neurons and the dendrite of postsynaptic neurons. For effective brain-inspired computing, neuromorphic computing leverages hardware-based solutions that imitate the behavior of synapses and neurons. Neuron like dynamics can be established with conventional microelectronics by using oscillatory circuit topologies to mimic neuronal behaviors. However, these approaches can mimic only specific aspects of neuronal behavior by integrating many transistors and passive electronic components, resulting in a bulky biomemtic circuit unsuitable for direct in situ biointerfacing. Volatile and nonlinear devices based on spin torque oscillators or memristor can increase the integration density and emulate neuronal dynamics.

Summary: The epigenetic clocks of those who indulged in unhealthy behaviors as teens were 1.7 to 3.3 years older than individuals who reported more healthy lifestyles as teens.

Source: eLife.

Biological aging results from damage to cells and tissues in the body that accumulates over time. The results of the study could lead to new ways of identifying young people at risk of developing unhealthy habits that are associated with accelerated biological aging and suggest interventions to prevent poor health outcomes later on.

Human Brain Project researchers have trained a large-scale model of the primary visual cortex of the mouse to solve visual tasks in a highly robust way. The model provides the basis for a new generation of neural network models. Due to their versatility and energy-efficient processing, these models can contribute to advances in neuromorphic computing.

Modeling the brain can have a massive impact on artificial intelligence (AI): Since the brain processes images in a much more energy-efficient way than artificial networks, scientists take inspiration from neuroscience to create neural networks that function similarly to the biological ones to significantly save energy.

In that sense, brain-inspired neural networks are likely to have an impact on future technology, by serving as blueprints for visual processing in more energy-efficient neuromorphic hardware. Now, a study by Human Brain Project (HBP) researchers from the Graz University of Technology (Austria) showed how a large data-based model can reproduce a number of the brain’s visual processing capabilities in a versatile and accurate way. The results were published in the journal Science Advances.

Artificial intelligence has long been a hot topic: a computer algorithm “learns” by being taught by examples: What is “right” and what is “wrong.” Unlike a computer algorithm, the human brain works with neurons—cells of the brain. These are trained and pass on signals to other neurons. This complex network of neurons and the connecting pathways, the synapses, controls our thoughts and actions.

Biological signals are much more diverse when compared with those in conventional computers. For instance, neurons in a biological neural network communicate with ions, biomolecules and neurotransmitters. More specifically, neurons communicate either chemically—by emitting the messenger substances such as neurotransmitters—or via electrical impulses, so-called “action potentials” or “spikes”.

Artificial neurons are a current area of research. Here, the efficient communication between the biology and electronics requires the realization of artificial neurons that emulate realistically the function of their biological counterparts. This means artificial neurons capable of processing the diversity of signals that exist in biology. Until now, most artificial neurons only emulate their biological counterparts electrically, without taking into account the wet biological environment that consists of ions, biomolecules and neurotransmitters.

Plastic waste is clogging up our rivers and oceans and causing long-lasting environmental damage that is only just starting to come into focus. But a new approach that combines biological and chemical processes could greatly simplify the process of recycling it.

While much of the plastic we use carries symbols indicating it can be recycled, and authorities around the world make a big show about doing so, the reality is that it’s easier said than done. Most recycling processes only work on a single type of plastic, but our waste streams are made up of a complex mixture that can be difficult and expensive to separate.

On top of that, most current chemical recycling processes produce end products of significantly worse quality that can’t be recycled themselves, which means we’re still a long way from the goal of a circular economy when it comes to plastics.

Building A More Secure World — Dr. James Revill, Ph.D. — Head of Weapons of Mass Destruction & Space Security Programs, UNIDIR, UN Institute for Disarmament Research United Nations.

Dr. James Revill, Ph.D. (https://unidir.org/staff/james-revill) is the Head of the Weapons of Mass Destruction (WMD) and Space Security Program, at the UN Institute for Disarmament Research (UNIDIR).

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