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A genetic editing system similar to CRISPR-Cas9 has been uncovered for the first time in eukaryotes – the group of organisms that include fungi, plants, and animals. The system, based on a protein called Fanzor, can be guided to precisely target and edit sections of DNA, and that could open up the possibility of its use as a human genome editing tool.

The research team, led by Professor Feng Zhang at the McGovern Institute for Brain Research at MIT and the Broad Institute of MIT and Harvard, began to suspect that Fanzor proteins might act as nucleases – enzymes that can chop up nucleic acids, like DNA – during a previous investigation.

They were looking into the origins of proteins like Cas9. This is the enzyme part of the CRISPR-Cas9 system. CRISPR (short for clustered regularly interspaced short palindromic repeats) sequences are the guide to particular regions of DNA, and Cas9 makes the cut. We hear a lot about CRISPR-Cas systems and their applications in medicine and biotechnology, but you may not be aware that they originate in bacteria, where they play a key role in immunity against viruses.

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Artificial intelligence (AI) can help people shop, plan, and write—but not cook. It turns out humans aren’t the only ones who have a hard time following step-by-step recipes in the correct order, but new research from the Georgia Institute of Technology’s College of Computing could change that.

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Danish architect Bjarke Ingels has collaborated with clothing brand Vollebak to design an entirely self-sufficient, off-grid island home in Nova Scotia, Canada.

Planned for an island within Jeddore Harbour, the house is designed to exemplify the clothing brand’s ideals and Ingels’ studio BIG’s “philosophy of hedonistic sustainability”.

“Vollebak is using technology and material innovation to create clothes that are as sustainable and resilient as they are beautiful,” said Ingels.

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The arrangement of electrons in matter, known as the electronic structure, plays a crucial role in fundamental but also applied research, such as drug design and energy storage. However, the lack of a simulation technique that offers both high fidelity and scalability across different time and length scales has long been a roadblock for the progress of these technologies.

Researchers from the Center for Advanced Systems Understanding (CASUS) at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) in Görlitz, Germany, and Sandia National Laboratories in Albuquerque, New Mexico, U.S., have now pioneered a machine learning–based simulation method that supersedes traditional electronic structure simulation techniques.

Their Materials Learning Algorithms (MALA) software stack enables access to previously unattainable length scales. The work is published in the journal npj Computational Materials.

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Interesting discovery! I’d love to see it in action.

A new ferroelectric polymer that efficiently converts electrical energy into mechanical strain has been developed by Penn State researchers. This material, showing potential for use in medical devices and robotics, overcomes traditional piezoelectric limitations. Researchers improved performance by creating a polymer nanocomposite, significantly reducing the necessary driving field strength, expanding potential applications.

A new type of ferroelectric polymer that is exceptionally good at converting electrical energy into mechanical strain holds promise as a high-performance motion controller or “actuator” with great potential for applications in medical devices, advanced robotics, and precision positioning systems, according to a team of international researchers led by Penn State.

Mechanical strain, how a material changes shape when force is applied, is an important property for an actuator, which is any material that will change or deform when an external force such as electrical energy is applied. Traditionally, these actuator materials were rigid, but soft actuators such as ferrroelectric polymers display higher flexibility and environmental adaptability.

Continue reading “Artificial Muscles Flex for the First Time: Ferroelectric Polymer Innovation in Robotics” | >

Notpla has been announced as the winner of Prince William’s Earthshot Prize, in the category of ‘Build a Waste-Free World’!

https://www.notpla.com/

We visited Notpla, a company that is challenging plastic pollution by creating edible and biodegradable packaging using seaweed.

Continue reading “Seaweed to Replace Plastic | Edible & Biodegradable Packaging | Notpla” | >

FRO is effective against acne

The disc diffusion experiment results indicated that 20 μL FRO successfully suppressed CA growth, producing distinct 13 mm inhibition zones at a concentration of 100 mg/mL. FRO significantly suppressed CA-induced increases in sebum production, thereby slowing or reversing acne onset.

FRO was found to be rich in phenolic compounds, including gallic acid, kaempferol, quercetin, and fisetin. The concentration of total phenolic compounds (TPCs) averaged 118.2 mg gallic acid equivalents (GAEs) for every gram of FRO.

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Reservoir computing is a promising computational framework based on recurrent neural networks (RNNs), which essentially maps input data onto a high-dimensional computational space, keeping some parameters of artificial neural networks (ANNs) fixed while updating others. This framework could help to improve the performance of machine learning algorithms, while also reducing the amount of data required to adequately train them.

RNNs essentially leverage recurrent connections between their different processing units to process sequential data and make accurate predictions. While RNNs have been found to perform well on numerous tasks, optimizing their performance by identifying parameters that are most relevant to the task they will be tackling can be challenging and time-consuming.

Jason Kim and Dani S. Bassett, two researchers at University of Pennsylvania, recently introduced an alternative approach to design and program RNN-based reservoir computers, which is inspired by how programming languages work on computer hardware. This approach, published in Nature Machine Intelligence, can identify the appropriate parameters for a given network, programming its computations to optimize its performance on target problems.

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