Lifeboat Foundation

To show the capability of the OrganoidChip in enabling higher-resolution imaging, we used confocal microscopy for several organoids immobilized on the chip. Representative images show improved optical segmentation and the ability to resolve single cells within an organoid (Fig. 4 d). The co-localized EthD-1-and Hoechst-stained nuclei are resolvable and can potentially be used to increase the accuracy of viability measurements. Future implementation of 3D-segmentation using AI-assisted algorithms in the analysis pipeline can provide more accurate estimations of cellular viability in larger screens.

Next, we measured the effect of DOX treatment on the beating kinetics of cardiac organoids. To do this, we relied on calcium fluorescence imaging, as it has been shown to be a good approximation of the cardiomyocytes’ action potentials³². Calcium imaging proved beneficial for beating and contraction parameters since smaller beating portions cannot necessarily be detected from brightfield images, particularly when organoids have been compromised as a result of drug treatment.

When assessing drug effects, we observed some degree of variability in the spontaneous contractile behaviour and beating kinetics between cardiac organoids. Such variability often skews any averaged parameter value across organoids and does not reflect the effect of the treatment conditions on organoid health. To address this challenge, we tracked each individual organoid’s beating off-and on-chip. The drug-induced functionality results are therefore reported as averages of fractional changes of each individual organoid’s beating kinetics parameters, measured at 48 h post-treatment, on both the chamber slide and on the chip, relative to its pre-treatment value (Eq. 3).

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Year 2017 😗😁

Artificial-intelligence program AlphaGo Zero trained in just days, without any human input.

Google’s DeepMind has revealed an AI model capable of teaching itself without human intervention. Find out more.

It’s one of the biggest conferences of the year that almost no one is allowed to go to.

Allen & Co.’s annual conference in Sun Valley, Idaho, often referred to as “summer camp for billionaires,” is a who’s who of the biggest players in media and tech. This year’s attendees include Disney’s Bob Iger, Warner Bros. Discovery’s David Zaslav, Apple’s Tim Cook, and OpenAI’s Sam Altman.

The event even got the “Succession” treatment.

Researchers at New York University (NYU), Columbia University, and the New York Genome Center have developed an artificial intelligence (AI) platform that can predict on-and off-target activity of CRISPR tools that target RNA instead of DNA.

The team paired a deep learning model with CRISPR screens to control the expression of human genes in different ways, akin to either flicking a light switch to shut them off completely or by using a dimmer knob to partially turn down their activity. The resulting neural network, which they called targeted inhibition of gene expression via gRNA design— TIGER—was able to predict efficacy from guide sequence and context. The team suggests the new technology could pave the way to the development of precise gene controls for use in CRISPR-based therapies.

“Our deep learning model can tell us not only how to design a guide RNA that knocks down a transcript completely, but can also ‘tune’ it—for instance, having it produce only 70% of the transcript of a specific gene,” said Andrew Stirn, a PhD student at Columbia Engineering and the New York Genome Center. Stirn is co-first author of the researchers’ published paper in Nature Biotechnology, titled “Prediction of on-target and off-target activity of CRISPR-Cas13D guide RNAs using deep learning.” In their paper, the researchers concluded, “We believe that TIGER predictions will enable ranking and ultimately avoidance of undesired off-target binding sites and nuclease activation, and further spur the development of RNA-targeting therapeutics.”

Companion robots enhanced with artificial intelligence may one day help alleviate the loneliness epidemic, suggests a new report from researchers at Auckland, Duke, and Cornell Universities.

Their report, appearing in the July 12 issue of Science Robotics, maps some of the ethical considerations for governments, policy makers, technologists, and clinicians, and urges stakeholders to come together to rapidly develop guidelines for trust, agency, engagement, and real-world efficacy.

It also proposes a new way to measure whether a companion robot is helping someone.

Computers possess two remarkable capabilities with respect to images: They can both identify them and generate them anew. Historically, these functions have stood separate, akin to the disparate acts of a chef who is good at creating dishes (generation), and a connoisseur who is good at tasting dishes (recognition).

Yet, one can’t help but wonder: What would it take to orchestrate a harmonious union between these two distinctive capacities? Both chef and connoisseur share a common understanding in the taste of the food. Similarly, a unified vision system requires a deep understanding of the visual world.

Now, researchers in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have trained a system to infer the missing parts of an image, a task that requires deep comprehension of the image’s content. In successfully filling in the blanks, the system, known as the Masked Generative Encoder (MAGE), achieves two goals at the same time: accurately identifying images and creating new ones with striking resemblance to reality.

Shah, the 31-year-old CEO and founder of Bengaluru-based Duukan, which helps merchants to set up online stores and sell products digitally, posted that “We had to layoff 90% of our support team because of this AI chatbot. Tough? Yes. Necessary? Absolutely.”

We had to layoff 90% of our support team because of this AI chatbot.

Tough? Yes. Necessary? Absolutely.