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Category: biological – Page 120

Max Plank AI Researchers Have Developed Bio-Realistic Artificial Neurons That Can Work In A Biological Environment And Can Produce Diverse Spiking Dynamics

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.

Genetics May Explain Link Between Unhealthy Teen Lifestyles and Accelerated Biological Aging

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.

A new large-scale virtual model of the visual cortex is highly successful in solving visual tasks

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 (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 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.

Speaking the same language: How artificial neurons mimic biological neurons

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 , 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 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.

Scientists Engineer Bacteria to Recycle Plastic Waste Into Valuable Chemicals

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.

Dr. James Revill, Ph.D. — Head of Weapons of Mass Destruction & Space Security Programs, UNIDIR

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).

Dr. Revill’s research interests focus on the evolution of the chemical and biological weapons and he has published widely in these areas. He was previously a Research Fellow with the Harvard Sussex Program at the Science Policy Research Unit, University of Sussex and completed research fellowships with the Landau Network Volta Center in Italy and the Bradford Disarmament Research Centre in the UK.

Dr. Revill holds a Ph.D. focused on the evolution of the Biological Weapons Convention from the University of Bradford, UK.

Dr. Revill’s areas of expertise include biological weapons, biosecurity, bioterrorism, chemical weapons, chemical terrorism, chemical weapons convention, compliance, verification, and improvised explosive devices.

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Incredibly, Microbes Inside Our Mouths Turn Into a Superorganism That Moves Around

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Hello and welcome! My name is Anton and in this video, we will talk about unusual discoveries coming directly from within our mouths — biofilm complexity.
Links:
https://en.wikipedia.org/wiki/Biofilm.
https://www.pnas.org/doi/full/10.1073/pnas.2209699119
https://en.wikipedia.org/wiki/Quorum_sensing.
Slime: https://www.youtube.com/watch?v=spZwZLkMsYw.
Biofilm communication and bacterial cities: https://youtu.be/4M872c27bSc.
#biology #dentistry #biofilm.

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Meta AI creates first ever database of 600 million metagenomic structures

‘These structures provide an unprecedented view into the breadth and diversity of nature,’ say the researchers.

In a world first, Meta’s artificial intelligence (AI) has produced the structures of the metagenomic world at the scale of hundreds of millions of proteins, according to a blog by the company published on Tuesday.

“Proteins are complex and dynamic molecules, encoded by our genes, that are responsible for many of the varied and fundamental processes of life. They have an astounding range of roles in biology,” wrote the Meta research team who also published a paper on the matter in the preprint database bioRxiv.

It is common knowledge that a vast number of proteins exist beyond the ones that have been catalogued and annotated in well-studied organisms and now these proteins are coming to the surface.

Researchers From MIT Have Developed A New Machine Learning Based Approach With 90 Percent Accuracy To Screen Candidate Materials If They Are Topological For Next-Generation Computer Chips or Quantum Devices

Topological materials are a special kind of material that have different functional properties on their surfaces than on their interiors. One of these properties is electrical. These materials have the potential to make electronic and optical devices much more efficient or serve as key components of quantum computers. But recent theories and calculations have shown that there can be thousands of compounds that have topological properties, and testing all of them to determine their topological properties through experiments will take years of work and analysis. Hence, there is a dire need for faster methods to test and study topological materials.

A team of researchers from MIT, Harvard University, Princeton University, and Argonne National Laboratory proposed a new approach that is faster at screening the candidate materials and can predict with more than 90 percent accuracy whether a material is topological or not. The traditional way of solving this problem is quite complicated and can be explained as follows: Firstly, a method called density functional theory is used to perform initial calculations, which are then followed by complex experiments that involve cutting a piece of material to atomic-level flatness and probing it with instruments under high vacuum.

The new proposed method is based on how the material absorbs X-rays, which is different from the old methods, which were based on photoemissions or tunneling electrons. There are certain significant advantages to using X-ray absorption data, which can be listed as follows: Firstly, there is no requirement for expensive lab apparatus. X-ray absorption spectrometers are used, which are readily available and can work in a typical environment, hence the low cost of setting up an experiment. Secondly, such measurements have already been done in chemistry and biology for other applications, so the data is already available for numerous materials.

Brain Complexity and Consciousness

Read the accompanying news item: https://www.humanbrainproject.eu/en/follow-hbp/news/ebrains-…disorders/

Using the EBRAINS research infrastructure, scientists of the Human Brain Project have developed multi-scale simulations of the human brain that mimic hallmarks of activity during wake and deep sleep states. Such simulations can lead to a better understanding of biological mechanisms that regulate human consciousness and its disorders, which span from single neurons to whole brain scales.