Lifeboat Foundation: Safeguarding Humanity
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The two are worlds apart – and that’s a big problem when it comes to recruitment and retainment. On one side is the need to protect American citizens and data from cyber attackers looking to disrupt our way of life by keeping networks and access locked away in a building. On the other side is the best and brightest talent that will bring innovative solutions to our nation’s defense and security organizations who expect flexible remote access – and can easily find it in the private sector.

To maintain our status as a global world power and stay one step ahead of our adversaries, we are going to have to find a balance between the two. To do that, the way we work across the DoD and IC must change.

The Federal government understands the significance of remote access on meeting mission objectives now and in the future. Agency leaders are looking to the private sector for technology that helps them maintain the highest security levels while meeting the ease-of-access demands of today’s worker – and can be implemented quickly. To support this, the National Security Agency developed the Commercial Solutions for Classified (CSfC) program.

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In our latest article, our Divisional Chief Nurse, Clare, discusses the social effects of friendships for people with learning disabilities and/or autism and the importance of those friendships. She also discusses how COVID-19 and the different restrictions have affected people with learning disabilities and/or autism and how best to support them.

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Graphene scientists from The University of Manchester have created a novel “nano-petri dish” using two-dimensional (2D) materials to create a new method of observing how atoms move in liquid.

Publishing in the journal Nature, the team led by researchers based at the National Graphene Institute (NGI) used stacks of 2D materials like graphene to trap liquid in order to further understand how the presence of liquid changes the behavior of the solid.

The team were able to capture images of single atoms “swimming” in liquid for the first time. The findings could have widespread impact on the future development of green technologies such as hydrogen production.

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MRI, electroencephalography (EEG) and magnetoencephalography have long served as the tools to study brain activity, but new research from Carnegie Mellon University introduces a novel, AI-based dynamic brain imaging technology which could map out rapidly changing electrical activity in the brain with high speed, high resolution, and low cost. The advancement comes on the heels of more than thirty years of research that Bin He has undertaken, focused on ways to improve non-invasive dynamic brain imaging technology.

Brain is distributed over the three-dimensional brain and rapidly changes over time. Many efforts have been made to image and dysfunction, and each method bears pros and cons. For example, MRI has commonly been used to study , but is not fast enough to capture brain dynamics. EEG is a favorable alternative to MRI technology however, its less-than-optimal spatial resolution has been a major hindrance in its wide utility for imaging.

Electrophysiological source imaging has also been pursued, in which scalp EEG recordings are translated back to the brain using and machine learning to reconstruct dynamic pictures of brain activity over time. While EEG source imaging is generally cheaper and faster, specific training and expertise is needed for users to select and tune parameters for every recording. In new published work, He and his group introduce a first of its kind AI-based dynamic brain imaging methodology, that has the potential of imaging dynamics of neural circuits with precision and speed.

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Researchers at the Francis Crick Institute have developed an imaging technique to capture information about the structure and function of brain tissue at subcellular level—a few billionths of a meter, while also capturing information about the surrounding environment.

The unique approach detailed in Nature Communications today (25 May), overcomes the challenges of imaging tissues at different scales, allowing scientists to see the surrounding cells and how they function, so they can build a complete picture of neural networks in the .

Various imaging methods are used to capture information about , cells and subcellular structures. However, a single method can only capture information about either the structure or function of the tissue and looking in detail at a nanometer scale means scientists lose information about the wider surroundings. This means that to gain an overall understanding of the tissue, imaging techniques need to be combined.

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The human brain is less accessible than other organs because it is covered by a thick, hard skull. As a result, researchers have been limited to low-resolution imaging or analysis of brain signals measured outside the skull. This has proved to be a major hindrance in brain research, including research on developmental stages, causes of diseases, and their treatments. Recently, studies have been performed using primary neurons from rats or human-derived induced pluripotent stem cells (iPSCs) to create artificial brain models that have been applied to investigate brain developmental processes and the causes of brain diseases. These studies are expected to play a key role to unlocking the mysteries of the brain.

In the past, artificial models were created and studied in 2D; however, in 2017, a research team from KIST developed a 3D artificial brain model that more closely resembled the real brain. Unfortunately, due to the absence of an analytical framework for studying signals in a 3D brain model, studies were limited to analyses of surface signals or had to reform the 3D structure to a flat shape. As such, tracking in a complex, interconnected artificial network remained a challenge.

The Korea Institute of Science and Technology (KIST) announced that the research teams of Doctors Il-Joo Cho and Nakwon Choi have developed a that can apply precise non-destructive stimuli to a 3D artificial neural circuit and measure neural signals in real-time from multiple locations inside the model at the cellular level.

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