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Category: biological

An AI model created to design proteins simulates 500 million years of protein evolution in developing a previously unknown bright fluorescent protein.

Learn more in a new Science study.

More than three billion years of evolution have produced an image of biology encoded into the space of natural proteins. Here we show that language models trained at scale on evolutionary data can generate functional proteins that are far away from known proteins. We present ESM3, a frontier multimodal generative language model that reasons over the sequence, structure, and function of proteins. ESM3 can follow complex prompts combining its modalities and is highly responsive to alignment to improve its fidelity. We have prompted ESM3 to generate fluorescent proteins. Among the generations that we synthesized, we found a bright fluorescent protein at a far distance (58% sequence identity) from known fluorescent proteins, which we estimate is equivalent to simulating five hundred million years of evolution.

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Researchers at Cornell University on Monday showcased a pair of bio-inspired robotics running on a hydraulic fluid-powered battery. The redox flow battery (RFB) also mimics biological functions, as it releases electrolytic fluids, which dissolve to create energy through chemical reaction.

The first two robots on display are a modular worm and a jellyfish, designed by the Cornell Engineering labs. The batteries powering these systems utilize embodied energy, “an approach that incorporates power sources into the body of a machine, to reduce its weight and cost,” according to the school.

Mechanical and aerospace engineering Professor Rob Shepherd describes the underlying technology thusly: “There are a lot of robots that are powered hydraulically, and we’re the first to use hydraulic fluid as the battery, which reduces the overall weight of the robot, because the battery serves two purposes, providing the energy for the system and providing the force to get it to move.”

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Could we create warp drive someday? In this Star Trek-themed episode, Neil deGrasse Tyson and co-host Chuck Nice team up with astrophysicist Charles Liu to dive into the science, technology, and legacy of one of the most influential sci-fi franchises of all time: Star Trek.

We answer questions about quantum entanglement, the size of electrons, and the real science behind Trek tech or Treknology. How close are we to warp drives, transporters, and subspace communication? You might be surprised to hear what’s theoretically possible and what remains in the realm of science fiction.

We discuss technology that exists already and the solutions to storytelling challenges through warp drives and dilithium crystals. Learn about the show’s physics, from phasers and antimatter to the mycelium network’s fascinating parallels with fungal biology. How do you store antimatter without it annihilating? Plus, find out who everyone’s favorite characters are and who they relate to most.

Continue reading “The Science of Star Trek with Dr. Charles Liu” | >

We report the use of a multiagent generative artificial intelligence framework, the X-LoRA-Gemma large language model (LLM), to analyze, design and test molecular design. The X-LoRA-Gemma model, inspired by biological principles and featuring ~7 billion parameters, dynamically reconfigures its structure through a dual-pass inference strategy to enhance its problem-solving abilities across diverse scientific domains. The model is used to first identify molecular engineering targets through a systematic human-AI and AI-AI self-driving multi-agent approach to elucidate key targets for molecular optimization to improve interactions between molecules. Next, a multi-agent generative design process is used that includes rational steps, reasoning and autonomous knowledge extraction. Target properties of the molecule are identified either using a Principal Component Analysis (PCA) of key molecular properties or sampling from the distribution of known molecular properties. The model is then used to generate a large set of candidate molecules, which are analyzed via their molecular structure, charge distribution, and other features. We validate that as predicted, increased dipole moment and polarizability is indeed achieved in the designed molecules. We anticipate an increasing integration of these techniques into the molecular engineering workflow, ultimately enabling the development of innovative solutions to address a wide range of societal challenges. We conclude with a critical discussion of challenges and opportunities of the use of multi-agent generative AI for molecular engineering, analysis and design.

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A new technique involving terahertz light has enabled the creation of chiral states in non-chiral materials, offering exciting possibilities for future technological applications.

Chirality is a key property of matter that plays a crucial role in many biological, chemical, and physical processes. In chiral solids, this property enables unique interactions with chiral molecules and polarized light, making them valuable for applications in catalysis, sensing, and optical devices. However, chirality in these materials is typically fixed during their formation—once a crystal is grown, its left-and right-handed forms, or enantiomers, cannot be switched without melting and recrystallizing it.

Now, researchers from the Max Planck Institute for the Structure and Dynamics of Matter (MPSD) and the University of Oxford have discovered a way to induce chirality in a non-chiral crystal using terahertz light. This breakthrough allows them to create either left-or right-handed enantiomers on demand. Published in Science, this finding opens exciting new possibilities for studying and controlling complex materials in non-equilibrium conditions.

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In biology textbooks, the endoplasmic reticulum is often portrayed as a distinct, compact organelle near the nucleus, and is commonly known to be responsible for protein trafficking and secretion. In reality, the ER is vast and dynamic, spread throughout the cell and able to establish contact and communication with and between other organelles. These membrane contacts regulate processes as diverse as fat metabolism, sugar metabolism, and immune responses.

Exploring how pathogens manipulate and hijack essential processes to promote their own life cycles can reveal much about fundamental cellular functions and provide insight into viable treatment options for understudied pathogens.

New research from the Lamason Lab in the Department of Biology at MIT recently published in the Journal of Cell Biology has shown that Rickettsia parkeri, a bacterial pathogen that lives freely in the cytosol, can interact in an extensive and stable way with the rough , forming previously unseen contacts with the organelle.

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Anthropic CEO Dario Amodei said Thursday (Jan. 23) that accelerated advances in artificial intelligence (AI), particularly in biology, can lead to a doubling of human lifespans in as little as five to 10 years “if we really get this AI stuff right.”

During a panel at the World Economic Forum in Davos, Amodei called this the “grand vision.” He explained that if AI today can shrink a century’s worth of work in biology to five to 10 years, and if one believes it would take 100 years to double the average length of human life, then “a doubling of the human lifespan is not at all crazy, and if AI is able to accelerate that we may be able to get that in five to 10 years.”

Amodei also said that Anthropic is working on a “virtual collaborator,” an AI agent capable of doing higher-level tasks in the workplace such as opening Google Docs, using the Slack messaging channel, and interacting with workers. A manager will only need to check in with this AI agent “once in a while,” similar to what management does with human employees.

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In the quest to take the “forever” out of “forever chemicals,” bacteria might be our ally. Most remediation of per-and polyfluoroalkyl substances (PFAS) involves adsorbing and trapping them, but certain microbes can actually break apart the strong chemical bonds that allow these chemicals to persist for so long in the environment.

Now, a University at Buffalo-led team has identified a strain of bacteria that can break down and transform at least three types of PFAS, and perhaps even more crucially, some of the toxic byproducts of the bond-breaking process.

Published in this month’s issue of Science of the Total Environment, the team’s study found that Labrys portucalensis F11 (F11) metabolized over 90% of perfluorooctane (PFOS) following an exposure period of 100 days. PFOS is one of the most frequently detected and persistent types of PFAS and was designated hazardous by the U.S. Environmental Protection Agency last year.

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Artificial neural networks, central to deep learning, are powerful but energy-consuming and prone to overfitting. The authors propose a network design inspired by biological dendrites, which offers better robustness and efficiency, using fewer trainable parameters, thus enhancing precision and resilience in artificial neural networks.

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Hello and welcome! My name is Anton and in this video, we will talk about an intriguing experiment that created endosymbiosis.
Links:
https://www.nature.com/articles/s41586-024-08010-x.
https://pmc.ncbi.nlm.nih.gov/articles/PMC9040847/
Previous videos: https://youtu.be/GkuAzdS-VwA


#symbiosis #biology #life.

0:00 Endosymbiosis in a nutshell.
1:50 Examples.
3:00 Fungal endosymbiosis.
5:35 Questions that need answering.
6:10 Incredible new experiment.
6:48 What fungus was used.
8:00 What the experiment was trying to do.
9:30 Successful union and reproduction.
11:15 Major discoveries 13:00 Conclusions.

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Continue reading “Groundbreaking Experiment Forced Organisms To Become Symbiotic” | >