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Materials scientists aim to develop autonomous materials that function beyond stimulus responsive actuation. In a new report in Science Advances, Yang Yang and a research team in the Center for Bioinspired Energy Science at the Northwestern University, U.S., developed photo-and electro-activated hydrogels to capture and deliver cargo and avoid obstacles on return.

To accomplish this, they used two spiropyran monomers (photoswitchable materials) in the hydrogel for photoregulated charge reversal and autonomous behaviors under a constant electric field. The photo/electro-active materials could autonomously perform tasks based on constant external stimuli to develop intelligent materials at the molecular scale.

Soft materials with life-like functionality have promising applications as intelligent, robotic materials in complex dynamic environments with significance in human-machine interfaces and biomedical devices. Yang and colleagues designed a photo-and electro-activated hydrogel to capture and deliver cargo, avoid obstacles, and return to its point of departure, based on constant stimuli of visible light and applied electricity. These constant conditions provided energy to guide the hydrogel.

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Most adult humans are innately able to pick up objects in their environment and hold them in ways that facilitate their use. For instance, when picking up a cooking utensil, they would normally grab it from the side that will not be placed inside the cooking pot or pan.

Robots, on the other hand, need to be trained on how to best pick up and hold objects while completing different tasks. This is often a tricky process, given that the robot might also come across objects that it never encountered before.

The University of Bonn’s Autonomous Intelligent Systems (AIS) research group recently developed a new learning pipeline to improve a robotic arm’s ability to manipulate objects in ways that better support their practical use. Their approach, introduced in a paper published on the pre-print server arXiv, could contribute to the development of robotic assistants that can tackle manual tasks more effectively.

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Human Brain Project (HBP) researchers from Forschungszentrum Jülich and the University of Cologne (Germany) have uncovered how neuron densities are distributed across and within cortical areas in the mammalian brain. They have unveiled a fundamental organizational principle of cortical cytoarchitecture: the ubiquitous lognormal distribution of neuron densities.

Numbers of neurons and their play a crucial role in shaping the ’s structure and function. Yet, despite the wealth of available cytoarchitectonic data, the statistical distributions of neuron densities remain largely undescribed. The new HBP study, published in Cerebral Cortex, advances our understanding of the organization of mammalian brains.

The team based their investigations on nine publicly available datasets of seven species: mouse, marmoset, macaque, galago, owl monkey, baboon and human. After analyzing the cortical areas of each, they found that neuron densities within these areas follow a consistent pattern—a lognormal distribution. This suggests a fundamental organizational principle underlying the densities of neurons in the .

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Cellular solids are materials composed of many cells that have been packed together, such as in a honeycomb. The shape of those cells largely determines the material’s mechanical properties, including its stiffness or strength. Bones, for instance, are filled with a natural material that enables them to be lightweight, but stiff and strong.

Inspired by bones and other cellular solids found in nature, humans have used the same concept to develop architected materials. By changing the geometry of the unit cells that make up these materials, researchers can customize the material’s mechanical, thermal, or acoustic properties. Architected materials are used in many applications, from shock-absorbing packing foam to heat-regulating radiators.

Using , the ancient Japanese art of folding and cutting paper, MIT researchers have now manufactured a type of high-performance architected material known as a plate lattice, on a much larger scale than scientists have previously been able to achieve by additive fabrication. This technique allows them to create these structures from metal or other materials with custom shapes and specifically tailored mechanical properties.

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Professor Ori Bar-Nur and his colleagues at ETH Zurich are pioneering the cultivation of muscle cells in the lab, currently using mouse cells as their primary model. While their current studies are centered on mouse cells, the team is also keen on exploring the potential of human and cow cells. The implications of their research are manifold: lab-cultured human muscle tissue could serve surgical needs, while human muscle stem cells might offer therapeutic solutions for those with muscle diseases. On the other hand, cultivating cow muscle tissue in labs could transform the meat industry by eliminating the necessity of animal slaughter.

For now, however, the ETH team’s research is focused on optimizing the generation of muscle stem cells and making it safer. They have now succeeded in doing so via a new approach.

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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.

Watch Michael Levin’s next interview ► https://youtu.be/XHMyKOpiYjk.

Michael Levin, a developmental biologist at Tufts University, challenges conventional notions of intelligence, arguing that it is inherently collective rather than individual.

Continue reading “The beauty of collective intelligence, explained by a developmental biologist | Michael Levin” | >

“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.

But Stanford Medicine scientists and their colleagues recently developed an artificial intelligence model that assesses stained images of glioblastoma tissue to predict the aggressiveness of a patient’s tumor, determine the genetic makeup of the tumor cells and evaluate whether substantial cancerous cells remain after surgery.

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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.

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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.

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