Lifeboat Foundation

Under a microscope, mammalian tissues reveal their intricate and elegant architectures. But if you look at the same tissue after tumour formation, you will see bedlam. Itai Yanai, a computational biologist at New York University’s Grossman School of Medicine in New York City, is trying to find order in this chaos. “There is a particular logic to how things are arranged, and spatial transcriptomics is helping us see that,” he says.

‘Spatial transcriptomics’ is a blanket term covering more than a dozen techniques for charting genome-scale gene-expression patterns in tissue samples, developed to complement single-cell RNA-sequencing techniques. Yet these single-cell sequencing methods have a downside — they can rapidly profile the messenger RNA content (or transcriptome) of large numbers of individual cells, but generally require physical disruption of the original tissue, which sacrifices crucial information about how cells are organized and can alter them in ways that might muddy later analyses. Immunologist Ido Amit at the Weizmann Institute of Science in Rehovot, Israel, says that such experiments would sometimes leave his group questioning their results. “Is this really the in situ state, or are we just looking at something which is either not a major [factor] or even not real at all?”

By contrast, spatial transcriptomics allows researchers to study gene expression in intact samples, opening frontiers in cancer research and revealing previously inaccessible biology of otherwise well-characterized tissues. The resulting ‘atlases’ of spatial information can tell scientists which cells make up each tissue, how they are organized and how they communicate. But compiling those atlases isn’t easy, because methods for spatial transcriptomics generally represent a tension between two competing goals: broader transcriptome coverage and tighter spatial resolution. Developments in experimental and computational methods are now helping researchers to balance those aims — and improving cellular resolution in the process.

At present, our brains are mostly dependent on all the stuff below the neck to turn thought into action. But advances in neuroscience are making it easier than ever to hook machines up to minds. See neuroscientists John Donoghue and Sheila Nirenberg, computer scientist Michel Maharbiz, and psychologist Gary Marcus discuss the cutting edge of brain-machine interactions in “Cells to Silicon: Your Brain in 2050,” part of the Big Ideas series at the 2014 World Science Festival.

This program is part of the Big Ideas Series, made possible with support from the John Templeton Foundation.

Continue reading “Cells To Silicon: Your Brain In 2050” »

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A Matrioshka Brain is a supermassive structure in space consisting of processors and connected to each other into a massive computer around a sun harnessing its energy completely. So far we haven’t built one as we don’t have the technology for it but when we do the question will be if people will be lost in the vast computing power of the Matrishka brain.

Watch all 3 videos with Brendan Caulfield:
3. Future of Humanity https://youtu.be/XbhWEDhcdFk.
2. The Rockets of SpaceX 🚀https://youtu.be/VPgVS9qgBEM
1. The CAR company that will take us to SPACE🚀 https://youtu.be/Y0jiGkAH-pE

Continue reading “Humanity getting lost in the MATRIOSHKA Brain🤖” »

Nematodes, a specific sort of microscopic worm, have been proven by Osaka University researchers to be capable of killing cancer cells, according to Interesting Engineering and SciTechDaily.

The study titled “Nematode surface functionalization with hydrogel sheaths tailored in situ” by Wildan Mubarok, Masaki Nakahata, Masaru Kojima and Shinji Sakai showed that Hydrogel-based “sheaths” that can be further modified to transport useful cargo (cancer-killing substances) could be applied to these worms as a coating.

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Often when Dr. Thomas Valley sees a new patient in the intensive care unit at Michigan Medicine in Ann Arbor, he clamps a pulse oximeter on their finger – one of the many devices he uses to gauge their health and what course of care they might require, whether they are a child having seizures, a teenage car accident victim or an older person with Covid-19.

But recently, Valley, an assistant professor in the University of Michigan’s Division of Pulmonary and Critical Care, realized first-hand that the small device may yield less accurate oxygen readings in patients with dark skin.

Continue reading “Scientists are searching for solutions after studies show pulse oximeters don’t work as well for people of color” »

In 2019, astronomers observed the nearest example to date of a star that was shredded, or “spaghettified,” after approaching too close to a massive black hole.

That tidal disruption of a sun-like star by a black hole 1 million times more massive than itself took place 215 million light years from Earth. Luckily, this was the first such event bright enough that astronomers from the University of California, Berkeley, could study the optical light from the stellar death, specifically the light’s polarization, to learn more about what happened after the star was torn apart.

Their observations on Oct. 8, 2019, suggest that a lot of the star’s material was blown away at high speed—up to 10,000 kilometers per second—and formed a spherical cloud of gas that blocked most of the high-energy emissions produced as the black hole gobbled up the remainder of the star.

Researchers from North Carolina State University have developed a new approach to federated learning that allows them to develop accurate artificial intelligence (AI) models more quickly and accurately. The work focuses on a longstanding problem in federated learning that occurs when there is significant heterogeneity in the various datasets being used to train the AI.

Federated learning is an AI training technique that allows AI systems to improve their performance by drawing on multiple sets of data without compromising the privacy of that data. For example, federated learning could be used to draw on privileged patient data from multiple hospitals in order to improve diagnostic AI tools, without the hospitals having access to data on each other’s patients.

Federated learning is a form of machine learning involving multiple devices, called clients. The clients and a centralized server all start with a basic model designed to solve a specific problem. From that starting point, each of the clients then trains its local model using its own data, modifying the model to improve its performance. The clients then send these “updates” to the centralized server. The centralized server draws on these updates to create a hybrid model, with the goal of having the hybrid model perform better than any of the clients on their own. The central server then sends this hybrid model back to each of the clients. This process is repeated until the system’s performance has been optimized or reaches an agreed-upon level of accuracy.