Lifeboat Foundation

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.

Continue reading “Dr. Seemay Chou, Ph.D. — CEO, Arcadia Science — Tapping Biological Innovation In Nature For Humanity” »

Date December 5, 2022 December 5, 2022

In a breakthrough study, Japanese researchers at Osaka Metropolitan University have engineered the smallest motile life form ever. They introduced seven bacterial proteins into a synthetic bacterium, allowing it to move independently.

The rise of synthetic biology.

Continue reading “The world’s smallest life form can now move, thanks to genetic engineering” »

Science funders must remember the value of addressing the intrinsic biological questions that help to explain the natural world.

Workshop supported by the Imperial College Physics of Life Network of Excellence.

https://www.imperial.ac.uk/physics-of-life/

Continue reading “Physics of Emergent Behaviour III: from origin of life to multicellularity, 2nd July 2021 (part 2)” »

An algorithm that already predicts how proteins fold might also shed light on the physical principles that dictate this folding.

Proteins control every cell-level aspect of life, from immunity to brain activity. They are encoded by long sequences of compounds called amino acids that fold into large, complex 3D structures. Computational algorithms can model the physical amino-acid interactions that drive this folding [1]. But determining the resulting protein structures has remained challenging. In a recent breakthrough, a machine-learning model called AlphaFold [2] predicted the 3D structure of proteins from their amino-acid sequences. Now James Roney and Sergey Ovchinnikov of Harvard University have shown that AlphaFold has learned how to predict protein folding in a way that reflects the underlying physical amino-acid interactions [3]. This finding suggests that machine learning could guide the understanding of physical processes too complex to be accurately modeled from first principles.

Predicting the 3D structure of a specific protein is difficult because of the sheer number of ways in which the amino-acid sequence could fold. AlphaFold can start its computational search for the likely structure from a template (a known structure for similar proteins). Alternatively, and more commonly, AlphaFold can use information about the biological evolution of amino-acid sequences in the same protein family (proteins with similar functions that likely have comparable folds). This information is helpful because consistent correlated evolutionary changes in pairs of amino acids can indicate that these amino acids directly interact, even though they may be far in sequence from each other [4, 5]. Such information can be extracted from the multiple sequence alignments (MSAs) of protein families, determined from, for example, evolutionary variations of sequences across different biological species.