Lifeboat Foundation

APT C-23 hacker group uses a new Android spyware to target Middle Eastern users.

Since artificial intelligence pioneer Marvin Minsky patented the principle of confocal microscopy in 1957, it has become the workhorse standard in life science laboratories worldwide, due to its superior contrast over traditional wide-field microscopy. Yet confocal microscopes aren’t perfect. They boost resolution by imaging just one, single, in-focus point at a time, so it can take quite a while to scan an entire, delicate biological sample, exposing it light dosages that can be toxic.

To push confocal imaging to an unprecedented level of performance, a collaboration at the Marine Biological Laboratory (MBL) has invented a “kitchen sink” confocal platform that borrows solutions from other high-powered imaging systems, adds a unifying thread of “Deep Learning” artificial intelligence algorithms, and successfully improves the confocal’s volumetric resolution by more than 10-fold while simultaneously reducing phototoxicity. Their report on the technology, called “Multiview Confocal Super-Resolution Microscopy,” is published online this week in Nature.

“Many labs have confocals, and if they can eke more performance out of them using these artificial intelligence algorithms, then they don’t have to invest in a whole new microscope. To me, that’s one of the best and most exciting reasons to adopt these AI methods,” said senior author and MBL Fellow Hari Shroff of the National Institute of Biomedical Imaging and Bioengineering.

Mapping the human connectomics.

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Continue reading “Mapping the Human Brain — (Intro)” »

Artificial neural networks are famously inspired by their biological counterparts. Yet compared to human brains, these algorithms are highly simplified, even “cartoonish.”

Can they teach us anything about how the brain works?

For a panel at the Society for Neuroscience annual meeting this month, the answer is yes. Deep learning wasn’t meant to model the brain. In fact, it contains elements that are biologically improbable, if not utterly impossible. But that’s not the point, argues the panel. By studying how deep learning algorithms perform, we can distill high-level theories for the brain’s processes—inspirations to be further tested in the lab.

Researchers at a Seattle A.I. lab say they have built a system that makes ethical judgments. But its judgments can be as confusing as those of humans.

Most human diseases can be traced to malfunctioning parts of a cell—a tumor is able to grow because a gene wasn’t accurately translated into a particular protein or a metabolic disease arises because mitochondria aren’t firing properly, for example. But to understand what parts of a cell can go wrong in a disease, scientists first need to have a complete list of parts.

By combining microscopy, biochemistry techniques and artificial intelligence, researchers at University of California San Diego School of Medicine and collaborators have taken what they think may turn out to be a significant leap forward in the understanding of human cells.

The technique, known as Multi-Scale Integrated Cell (MuSIC), is described November 24, 2021 in Nature.

Sooo… the inimitable Russell Brand posted a video a few weeks ago saying some amusing but largeuly inaccurate and misleading things about the Grace humanoid eldercare robot we’re making in our Awakening Health project (http://awakening.health).

Russell’s video is here: https://www.youtube.com/watch?v=SDD7M1OWBDg.

Continue reading “Why We’re Making the Grace Humanoid Eldercare Robot: Response to Russell Brand” »

Research led by UT Southwestern and the University of Washington could lead to a wealth of drug targets.

UT Southwestern and University of Washington researchers led an international team that used artificial intelligence (AI) and evolutionary analysis to produce 3D models of eukaryotic protein interactions. The study, published in Science, identified more than 100 probable protein complexes for the first time and provided structural models for more than 700 previously uncharacterized ones. Insights into the ways pairs or groups of proteins fit together to carry out cellular processes could lead to a wealth of new drug targets.

“Our results represent a significant advance in the new era in structural biology in which computation plays a fundamental role,” said Qian Cong, Ph.D., Assistant Professor in the Eugene McDermott Center for Human Growth and Development with a secondary appointment in Biophysics.

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