Lifeboat Foundation

A Dyson Sphere is a megastructure that has not yet been built. Scientists conceive of it as a giant shell that encloses the sun.

Hypothetically, the Dyson Sphere will be lined with mirrors and solar panels that will collect the energy from the sun. This would be an unimaginable amount of energy.

In theory, the Dyson Sphere would be large enough that it could be a habitable place for humans and it would act as an artificial biosphere in the case that Earth’s supplies have dwindled. It would be a way to ensure survival for the human race.

The concept of ‘anti-realism’ is widely seen as a fact of life for many physicists studying the mysterious effects of quantum mechanics. However, it also seems to contradict the assumptions of many other fields of research. In his research, Dr William Sulis at McMaster University in Canada explores the issue from a new perspective, by using a novel mathematical toolset named the ‘process algebra model’. In suggesting that reality itself is generated by interacting processes more fundamental than quantum particles, his theories could improve researchers’ understanding of fundamental processes in a wide variety of fields.

The concept of ‘locality’ states that objects and processes can only be influenced by other objects and processes in their immediate surroundings. It is a fundamental aspect of many fields of research and underpins all of the most complex systems we observe in nature, including living organisms. “Biologists and psychologists have known for centuries that the physical world is dominated by processes which are characterized by factors including transformation, interdependence, and information”, Dr Sulis explains. “Organisms are born, develop, continually exchange physical components and information with their environment, and eventually die.”

Beyond biology, the principle of locality also extends to Einstein’s theory of special relativity. Since the speed of light sets a fundamental speed limit on all processes in the universe, the theory states that no process can occur if it has not been triggered by another event in its past, at a close enough distance for light to travel between them within the time separating them. In general, these theories are unified by a concept which physicists call ‘realism’. Yet despite this seemingly intuitive rule, physicists have increasingly come to accept the idea that it doesn’t present a full description of how all processes unfold.

Biological supramolecular assemblies, such as phospholipid bilayer membranes, have been used to demonstrate signal processing via short-term synaptic plasticity (STP) in the form of paired pulse facilitation and depression, emulating the brain’s efficiency and flexible cognitive capabilities. However, STP memory in lipid bilayers is volatile and cannot be stored or accessed over relevant periods of time, a key requirement for learning. Using droplet interface bilayers (DIBs) composed of lipids, water and hexadecane, and an electrical stimulation training protocol featuring repetitive sinusoidal voltage cycling, we show that DIBs displaying memcapacitive properties can also exhibit persistent synaptic plasticity in the form of long-term potentiation (LTP) associated with capacitive energy storage in the phospholipid bilayer. The time scales for the physical changes associated with the LTP range between minutes and hours, and are substantially longer than previous STP studies, where stored energy dissipated after only a few seconds. STP behavior is the result of reversible changes in bilayer area and thickness. On the other hand, LTP is the result of additional molecular and structural changes to the zwitterionic lipid headgroups and the dielectric properties of the lipid bilayer that result from the buildup of an increasingly asymmetric charge distribution at the bilayer interfaces.

Researchers at the Department of Energy’s Oak Ridge National Laboratory, the University of Tennessee and Texas A&M University demonstrated bio-inspired devices that accelerate routes to neuromorphic, or brain-like, computing.

Results published in Nature Communications report the first example of a lipid-based “memcapacitor,” a charge storage component with memory that processes information much like synapses do in the brain. Their discovery could support the emergence of computing networks modeled on biology for a sensory approach to machine learning.

“Our goal is to develop materials and computing elements that work like biological synapses and neurons—with vast interconnectivity and flexibility—to enable autonomous systems that operate differently than current computing devices and offer new functionality and learning capabilities,” said Joseph Najem, a recent postdoctoral researcher at ORNL’s Center for Nanophase Materials Sciences, a DOE Office of Science User Facility, and current assistant professor of mechanical engineering at Penn State.

While studying how bio-inspired materials might inform the design of next-generation computers, scientists at the Department of Energy’s Oak Ridge National Laboratory achieved a first-of-its-kind result that could have big implications for both edge computing and human health.

Results published in Proceedings of the National Academy of Sciences show that an artificial cell membrane is capable of long-term potentiation, or LTP, a hallmark of biological learning and memory. This is the first evidence that a cell membrane alone—without proteins or other biomolecules embedded within it—is capable of LTP that persists for many hours. It is also the first identified nanoscale structure in which memory can be encoded.

“When facilities were shut down as a result of COVID, this led us to pivot away from our usual membrane research,” said John Katsaras, a biophysicist in ORNL’s Neutron Sciences Directorate specializing in neutron scattering and the study of biological membranes at ORNL.

In the philosophy of mind, the multiple realizability thesis contends that a single mental kind (property, state, event) can be realized by many distinct physical kinds. A common example is pain. Many philosophers have asserted that a wide variety of physical properties, states, or events, sharing no features in common at that level of description, can all realize the same pain. This thesis served as a premise in the most influential argument against early theories that identified mental states with brain states (psychoneural, or mind-brain identity theories). It also served in early arguments for functionalism. Nonreductive physicalists later adopted this premise and these arguments (usually without alteration) to challenge all varieties of psychophysical reductionism. The argument was even used to challenge the functionalism it initially was offered to support. Reductionists (and other critics) quickly offered a number of responses, initially attacking either the anti-reductionist or anti-identity conclusion from the multiple realizability premise, or advocating accounts of the reduction relation that accommodated multiple realizability. More recently it has become fashionable to attack the multiple realizability premise itself. Most recently the first book-length treatment of multiple realizability and its philosophical import has appeared.

This entry proceeds mostly chronologically, to indicate the historical development of the topic. Its principle focus is on philosophy of mind and cognitive science, but it also indicates the more recent shift in emphasis to concerns in the metaphysics of science more generally. It is worth mentioning at the outset that multiple realizability has been claimed in physics (e.g., Batterman 2000), biochemistry (Tahko forthcoming) and synthetic biology (Koskinen 2019a, b). After more than fifty years of detailed philosophical discussion there still seems to be no end in sight for novel ideas about this persistent concern.

In biological imaging, researchers aim to achieve 3D, high-speed, and high-resolution, with low photobleaching and phototoxicity. The light-sheet fluorescence microscope (LSFM) helps meet that aim. Based on a unique excitation and detection scheme, the LSFM can image live specimens with high spatiotemporal resolution and low photobleaching. It has shown great potential for 3D imaging of biological samples.

The principle of LSFM technology is to illuminate the sample with a thin light-sheet and then collect the emitted fluorescence along the axis perpendicular to the transmission of the light-sheet. Therefore, only fluorophores close to the focal plane are excited and detected. Using a thinner light-sheet improves the axial resolution, while a longer light-sheet improves the field of view (FoV) and imaging speed. Tradeoffs are required, as it is difficult to generate a thin, uniform light-sheet.

Multiple light-sheets can be tiled to generate a virtual light-sheet with a higher aspect ratio. However, multiple beams also introduce sidelobes, decreasing the axial resolution and optical sectioning. Axially swept light-sheet microscopy (ASLM) uses a slit to reject the sidelobes. It uses the rolling shutter of the sCMOS, which naturally serves as a slit, to synchronize beam scanning. ASLM can image an arbitrarily large FoV with optimal axial resolution. However, the fluorescence signal outside the rolling shutter will be rejected, so a larger FoV comes at the price of lower photon efficiency.

Tapping Biological Innovation In Nature For Humanity — Dr. Seemay Chou Ph.D., CEO, Arcadia Science

Dr. Seemay Chou, Ph.D. is the Co-Founder, CEO, and Board Member of Arcadia Science (https://www.arcadia.science/), a research and development company focusing on under researched areas in biology, with a specific focus on novel model organisms that haven’t been traditionally studied in the lab.

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Date December 5, 2022 December 5, 2022