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In genetics and developmental biology, somatic cell nuclear transfer (SCNT) is a laboratory technique for creating an ovum with a donor nucleus. It can be used in embryonic stem cell research, or in regenerative medicine where it is sometimes referred to as “therapeutic cloning”.

https://www.biointeractive.org/classroom-resources/somatic-c…94yzh8K2uw

LOS ANGELES (AP) — New research gives some biological clues to why women may be more likely than men to develop Alzheimer’s disease and how this most common form of dementia varies by sex.

At the Alzheimer’s Association International Conference in Los Angeles on Tuesday, scientists offered evidence that the disease may spread differently in the brains of women than in men. Other researchers showed that several newly identified genes seem related to the disease risk by sex.

Two-thirds of Alzheimer’s cases in the U.S. are in women and “it’s not just because we live longer,” said Maria Carrillo, the association’s chief science officer. There’s also “a biological underpinning” for sex differences in the disease, she said.

The only thing we can be sure of is our own awareness. That we exist. It is from this knowledge that we can infer that survival is important.


In the past I have written about a vision for human civilizational flourishing, and would like to follow up those thoughts briefly now. More to the point, I wish to offer a deeper or foundational basis for those previous ideas. One might consider this to be a simple philosophical basis for action in the 21st Century. These thoughts are also directly relevant to my most recent post, about The Singularity & Convergent Risk.

Assume Nothing

The simplest, most pure basis for any philosophy – especially one that would be in harmony with empirical science – is to assume nothing. Start at the beginning, examine all assumptions.

A neuromorphic computer that can simulate 8 million neurons is in the news. The term “neuromorphic” suggests a design that can mimic the human brain. And neuromorphic computing? It is described as using very large scale integration systems with electric analog circuits imitating neuro-biological architectures in our system.

This is where Intel steps in, and significantly so. The Loihi chip applies the principles found in biological brains to computer architectures. The payoff for users is that they can process information up to 1,000 times faster and 10,000 times more efficiently than CPUs for specialized applications, e.g., sparse coding, graph search and constraint-satisfaction problems.

Its news release on Monday read “Intel’s Pohoiki Beach, a 64-Chip Neuromorphic System, Delivers Breakthrough Results in Research Tests.” Pohoiki Beach is Intel’s latest neuromorphic system.

Metamaterials are artificial materials engineered to have properties not found in naturally occurring materials, and they are best known as materials for invisibility cloaks often featured in sci-fi novels or games. By precisely designing artificial atoms smaller than the wavelength of light, and by controlling the polarization and spin of light, researchers achieve new optical properties that are not found in nature. However, the current process requires much trial and error to find the right material. Such efforts are time-consuming and inefficient; artificial intelligence (AI) could provide a solution for this problem.

The research group of Prof. Junsuk Rho, Sunae So and Jungho Mun of Department of Mechanical Engineering and Department of Chemical Engineering at POSTECH have developed a design with a higher degree of freedom that allows researchers to choose materials and design photonic structures arbitrarily by using deep learning. Their findings are published in several journals including Applied Materials and Interfaces, Nanophotonics, Microsystems & Nanoengineering, Optics Express, and Scientific Reports.

AI can be trained with a vast amount of data, and it can learn designs of various and the correlation between photonic structures and their optical properties. Using this training process, it can provide a that makes a photonic structure with desired optical properties. Once trained, it can provide a desired design promptly and efficiently. This has already been researched at various institutions in the U.S. such as MIT, Stanford University and Georgia Institute of Technology. However, the previous studies require inputs of materials and structural parameters beforehand, and adjusting photonic structures afterwards.