Once dismissed as “junk” DNA, ancient viruses embedded in the human genome play a key role in early human development, research finds.

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Hello and welcome! My name is Anton and in this video, we will talk about a major discovery of most distant objects ever seen coming from the James Webb Space Telescope.
Links:
https://arxiv.org/pdf/2503.15594
https://arxiv.org/pdf/2503.
0:00 JWST breaks its own records.
0:40 Earlier observations and theory behind this.
3:10 New records at redshift of 17 and 25
5:20 What we know about these objects.
7:00 Issue explaining this.
7:50 Could this be black holes?
10:40 What’s next?
11:40 Conclusions.
Enjoy and please subscribe.
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Questions to inspire discussion.
Data and Autonomy.
📊 Q: Why is vision data valuable in AI development? A: Vision data is worth more than zero if you can collect and process yataflops and yataflops of data, but worthless without collection capabilities, making the world’s visual data valuable for those who can collect and process it.
🚗 Q: How does solving autonomy relate to AI development? A: Solving autonomy is crucial and requires tons of real world data, which necessitates tons of robots collecting real world data in the real world, creating a cycle of data collection and AI improvement.
Company-Specific Opportunities.
🔋 Q: What advantage does Tesla have in developing humanoid robots? A: Tesla has essentially built the robot’s brain in their vehicles, allowing them to transplant this brain into humanoid robots, giving them a massive head start in development.
Researchers at Sylvester Comprehensive Cancer Center, part of the University of Miami Miller School of Medicine, have documented their use of a new RNA sequencing technology to uncover molecular drivers of cellular differentiation that could lead to better regenerative therapies.
In addition to being used in the lab, the technique, Rapid Precision Run-On Sequencing (rPRO-seq), has the potential to help doctors understand patients’ disease states and response to treatment in real time.
The findings appear in two published articles in Molecular Cell.
The U.S. National Science Foundation Directorate for Technology, Innovation and Partnerships (NSF TIP) announced an inaugural investment of nearly $32 million to five teams across the U.S. through the NSF Use-Inspired Acceleration of Protein Design (NSF USPRD) initiative. This effort aims to accelerate the translation of artificial intelligence-based approaches to protein design and enable new applications of importance to the U.S. bioeconomy.
“NSF is pleased to bring together experts from both industry and academia to confront and overcome barriers to the widespread adoption of AI-enabled protein design,” said Erwin Gianchandani, NSF assistant director for TIP. “Each of the five awardees will focus on developing novel approaches to translate protein design techniques into practical, market-ready solutions. These efforts aim to unlock new uses for this technology in biomanufacturing, advanced materials, and other critical industries. Simply put, NSF USPRD represents a strategic investment in maintaining American leadership in biotechnology at a time of intense global competition.”
Researchers have made significant progress in predicting the 3D structures of proteins and are now leveraging this knowledge to design proteins with specific, desirable characteristics. These advances have been driven by macromolecular modeling, access to training data, applications of AI and machine learning, and high-throughput methods for protein characterization. The NSF USPRD investment seeks to build on this foundation by bringing together cross-disciplinary and cross-sector experts nationwide. The goal is to extend these advances to enzyme design and accelerate the translation of this work into widespread, real-world applications.