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Summary: Researchers have identified a genetic mechanism that regulates behavioral adaptations to emotional experiences by forming R-loops, unique RNA: DNA structures that activate target genes. The study focused on NPAS4, a gene implicated in stress and drug addiction, showing how blocking R-loops prevents maladaptive behaviors like cocaine seeking and stress-induced anhedonia in mice.
This mechanism demonstrates how emotional experiences influence brain circuits by altering gene expression through enhancer RNA. The findings could pave the way for RNA-based therapies to treat psychiatric disorders linked to stress and substance use.
Conquering space is among the main challenges humanity now has — and the huge number of discoveries made in recent decades suggests that nothing is impossible. Tourism beyond Earth is already gaining popularity, and this sector will evolve soon. However, simply enjoying the space views may become boring shortly, and travellers will search for alternative entertainment options. It’s now pretty early to ask whether people will be able to build a land-based casino on the Moon, but some experiences may become available in the near future. The iGaming sector is rapidly evolving, leaving offline settings behind. We can predict that space gambling will begin from top-rated slot games not on GamStop, which are widely accessible globally. Casino platforms have a vast growth potential nowadays, so what would prevent people from using them when enjoying space tourism?
The first bet beyond the Earth has already been placed — this significant event happened in 2021 when Jared Isaacman, the Inspiration 4x commander, wagered on the NFL. Although it may take a while for such betting experiences to become more widespread, the beginning has already been made.
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Gravity is a fascinating force, it’s what holds everything on our planet together, and it might one day help us travel to other planets.
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Episode 477a; December 15, 2024
Produced, Narrated \& Written: Isaac Arthur.
Graphics: Jeremy Jozwik, Sergio Botero.
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Music Courtesy of Epidemic Sound http://epidemicsound.com/creator.
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What is time? Is it just a ticking clock, or is it something more profound?
In this thought-provoking episode of Into the Impossible, Stephen Wolfram challenges everything we know about time, offering a revolutionary computational perspective that could forever change how we understand the universe.
Stephen Wolfram is a computer scientist, physicist, and businessman. He is the founder and CEO of Wolfram Research and the creator of Mathematica, Wolfram Alpha, and Wolfram Language. Over the course of 4 decades, he has pioneered the development & application of computational thinking. He has been responsible for many discoveries, inventions & innovations in science, technology, and business.
Trying to stay healthy into old age is a better goal than attempting to live as long as possible.
Is your skin itching, breaking out, or acting weird? Not sure what’s causing those problems? Get an overview of symptoms and types of skin conditions.
A Sweden-based firm has launched a plasma confinement project to achieve commercially viable fusion energy.
The TauEB project by Novatron Fusion Group aims to revolutionize plasma confinement and energy containment in fusion reactors.
Novatron’s project will introduce a first-of-its-kind integration of three physical confinement techniques, which will include Magnetic Confinement, Ambipolar Plugging, and Ponderomotive Confinement.
Author(s): Yuki Shizuya Originally published on Towards AI. Photo by gilber franco on UnsplashRecently, I needed to research image similarity search. I wonder if there are any differences among embeddings based on the architecture training methods. However, few blogs compare embeddings among several models. So, in this blog, I will compare the vision embeddings of EfficientNet [1], ViT [2], DINO-v2 [3], CLIP [4], and BLIP-2 [5] for image similarity search using the Flickr dataset [6]. I will mainly use Huggingface and Faiss libraries for implementation. First, I will briefly introduce each deep learning model. Next, I will show you the code implementation and the comparison results.
