The strange science experiment that blew a worm’s head off… and blew our minds.
This interview is an episode from @The-Well, our publication about ideas that inspire a life well-lived, created with the @JohnTempletonFoundation.
“Because of the heterogeneity of this disease, scientists haven’t found good ways of tackling it,” said Olivier Gevaert, PhD, associate professor of biomedical informatics and of data science.
Doctors and scientists also struggle with prognosis, as it can be difficult to parse which cancerous cells are driving each patient’s glioblastoma.
Continue reading “Scientists employ AI to predict brain cancer outcomes” »
The current influenza A vaccines utilize surface proteins as antigens, predominantly hemagglutinin. These antigens change each season, requiring new vaccine formulations and annual administration; thus, development of a universal influenza vaccine is a high priority. In an industry-sponsored phase 2a trial, investigators evaluated a recombinant, nanoparticle-based influenza A vaccine candidate containing influenza nucleoprotein (an invariant protein) and designed to elicit cell-mediated immunity. In all, 137 healthy adults (age range, 18–55) were randomized to receive vaccine (180 µg, 300 µg, or 480 µg) or placebo as a single intramuscular injection.
The vaccine elicited mild-to-moderate local and systemic reactogenicity at all active doses. Cell-mediated responses, as measured by nucleoprotein-specific interferon-gamma ELISpot, showed statistically significant increases compared with baseline in all vaccine groups. In addition to polyfunctional CD4 T-cells and increased antibody levels, the higher doses elicited CD8 T-cell responses. Preliminary evaluation of RT-PCR–positive influenza illness among participants was consistent with vaccine efficacy.
This candidate for a universal influenza A vaccine was safe and showed promise to elicit a strong immune-mediated response. Further studies are needed to evaluate protection against infection and disease compared with the currently available products. However, durability of protection will be the key requirement if a single administration of vaccine is to have a long-lasting effect.
Enter AI. Multiple deep learning methods can already accurately predict protein structures— a breakthrough half a century in the making. Subsequent studies using increasingly powerful algorithms have hallucinated protein structures untethered by the forces of evolution.
Yet these AI-generated structures have a downfall: although highly intricate, most are completely static—essentially, a sort of digital protein sculpture frozen in time.
A new study in Science this month broke the mold by adding flexibility to designer proteins. The new structures aren’t contortionists without limits. However, the designer proteins can stabilize into two different forms—think a hinge in either an open or closed configuration—depending on an external biological “lock.” Each state is analogous to a computer’s “0” or “1,” which subsequently controls the cell’s output.
Part 1 of a 2-part mini-lecture series given by Prof. Leonard Susskind, director of the Stanford Institute for Theoretical Physics.
In many cases, cells are very active in their movement and serve as power generators. The ability of cells to produce physical forces is one of the basic functions of the body. When running, for example, the forces generated in the cells cause the muscles to contract and the breath to work. It has been possible to measure even the forces experienced by individual proteins by force sensors developed in the past, but previously intracellular forces and mechanical strains could not have been measured.
Together with the scientists from The Ohio State University OSU, cell biology researchers at Tampere University have developed a force sensor that can be attached to the side of a mechanically responding protein, allowing it to sense forces and strain on the protein within the cell.
The development of the micro-sized sensor began on a conference travel in December 2019.
A new robotic platform developed at the University of Chicago can adapt to its surroundings in real time for applications in unfamiliar environments.
The platform, dubbed the Granulobot, consists of many identical motorized units each a few centimeters in diameter. The units are embedded with a Wi-Fi microcontroller and sensors and use magnets to engage other units.
Continue reading “Shape-Shifting Robot Swarms Self-Assemble, Adapt to the Unfamiliar” »
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Do Large Language Models really “understand” the world, or just give the appearance of understanding? Evidence (e.g., Othello-GPT) shows LLMs build models of how the world works, which makes me comfortable saying they do understand. More in The Batch: https://t.co/e0JGU2wUbf
https://youtube.com/watch?v=wc8qRKm9MLs
Boltzmann brain is another bizarre consequence of laws of physics. It’s a configuration of matter, similar to our brains; a statistical fluctuation risen out of thermal equilibrium, a conscious observer created by a sudden decrease in entropy, having false memories of a grand structure exactly like our universe.
Given enough time, every single possibility allowed by the physical laws in our most likely closed universe must eventually occur, including one with a fluctuated brain, sitting in the middle of nowhere, having the exact same thoughts that you are having right now.
