Lifeboat Foundation

A 2022 pilot proved to officials at Dominion Energy that Spot can help improve efficiency and boost worker safety.

HBP 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 artificial intelligence (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 visual processing 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.

For Lisa Kaltenegger and her generation of exoplanet astronomers, decades of planning have set the stage for an epochal detection.

More than a decade ago, scientists started finding peculiar droplets inside cells. Now researchers are trying to work out how these ubiquitous beads form and what they do.

The obscured “zone of avoidance” in space is a place of mystery, and scientists are peering at what’s inside it.

You can’t move a pharmaceutical scientist from a lab to a kitchen and expect the same research output. Enzymes behave exactly the same: They are dependent upon a specific environment. But now, in a study recently published in ACS Synthetic Biology, researchers from Osaka University have imparted an analogous level of adaptability to enzymes, a goal that has remained elusive for over 30 years.

Enzymes perform impressive functions, enabled by the unique arrangement of their constituent amino acids, but usually only within a specific cellular environment. When you change the cellular environment, the enzyme rarely functions well—if at all. Thus, a long-standing research goal has been to retain or even improve upon the function of enzymes in different environments; for example, conditions that are favorable for biofuel production. Traditionally, such work has involved extensive experimental trial-and-error that might have little assurance of achieving an optimal result.

Artificial intelligence (a computer-based tool) can minimize this trial-and-error, but still relies on experimentally obtained crystal structures of enzymes—which can be unavailable or not especially useful. Thus, “the pertinent amino acids one should mutate in the enzyme might be only best-guesses,” says Teppei Niide, co-senior author. “To solve this problem, we devised a methodology of ranking amino acids that depends only on the widely available amino acid sequence of analogous enzymes from other living species.”

Genetic engineering is a rapidly progressing scientific discipline, with tremendous current application and future potential. It’s a bit dizzying for a science communicator who is not directly involved in genetics research to keep up. I do have some graduate level training in genetics so at least I understand the language enough to try to translate the latest research for a general audience.

Many readers have by now heard of CRISPR – a powerful method of altering or silencing genes that brings down the cost and complexity so that almost any genetics lab can use this technique. CRISPR is actually just the latest of several powerful gene-altering techniques, such as TALEN. CRISPR is essentially a way to target a specific sequence of the DNA, and then deliver a package which does something, like splice the DNA. But you also need to target the correct cells. In a petri dish, this is simple. But in living organism, this is a huge challenge. We have developed several viral vectors that can be targeted to specific cell types in order to deliver the CRIPR (or TALEN), which then targets the specific DNA.

Continue reading “Alternative Gene Splicing — Another Method of Bioengineering” »

ARI gene groups (ARI downregulated genes, those highly expressed in BA17 and BA39/40 relative to other regions in controls; ARI upregulated genes, those expressed at low level in BA17 and BA39/40 relative to other regions in controls) were created through taking the union (without duplicates) across all ten identified ASD-attenuated regional comparisons, and sorting genes into the two groups based on gene-expression profiles across regions. The details of this process are described in the Supplementary Methods, along with functional annotation procedures.

Standard workflows using WGCNA¹⁷ were followed as previously described in Parikshak et al.⁵ and Gandal et al.¹ (with minor modifications) to identify gene and transcript co-expression modules. Details regarding network formation, module identification, and module functional characterization are described in the Supplementary Methods.

Frozen brain samples were placed on dry ice in a dehydrated dissection chamber to reduce degradation effects from sample thawing and/or humidity. Approximately 50 mg of cortex was sectioned, ensuring specific grey matter–white matter boundary. The tissue section was homogenized in RNase-free conditions with a light detergent briefly on ice using a dounce homogenizer, filtered through a 40-μM filter and centrifuged at 1,000 g for 8 min at 4 °C. The pelleted nuclei were then filtered through a two-part micro gradient (30%/50%) for 20 min at 4 °C. Clean nuclei were pelleted away from debris. The nuclei were washed two more times with PBS/1%BSA/RNase and spun down at 500 g for 5 min. Cells were inspected for quality (shape, colour and membrane integrity) and counted on a Countess II instrument. They were then loaded onto the 10X Genomics platform to isolate single nuclei and generate libraries for RNA sequencing on the NovaS4 or NovaS2 Illumina machines.

A team of researchers from Korea University, Ajou University and Hanyang University, all in the Republic of Korea, has created a tiny aquabot propelled by fins made of a porous hydrogel imbued with nanoparticles. In their paper published in the journal Science Robotics, the group describes how the hydrogel works to power a tiny boat and reveals how much voltage was required.

Scientists and engineers have been working for several years to build tiny, soft robots for use in medical applications and have found that hydrogels are quite suitable for the task. Unfortunately, such materials also have undesirable characteristics, most notably, poor electro-connectivity. In this new effort, the researchers took a new approach to making hydrogels more amenable for use with electricity as a power source —adding conductive nanoparticles.

Continue reading “Incorporating nanoparticles into a porous hydrogel to propel an aquabot with minimal voltage” »