This level of precision could be a game-changer for therapies that require gene expression in one specific tissue, without impacting others.
By providing more control over where and when genes are activated, these AI-designed CREs could potentially be used in a variety of therapeutic applications, from treating genetic diseases to optimizing tissue regeneration.
As this AI-powered approach to designing CREs matures, the possibilities are vast. Beyond basic research, these synthetic DNA switches could be employed in biomanufacturing or to develop advanced treatments for a range of conditions, offering more effective ways to manipulate genes with unprecedented precision.
Caltech scientists have introduced a revolutionary machine-learning-driven technique for accurately measuring the mass of individual particles using advanced nanoscale devices.
This method could dramatically enhance our understanding of proteomes by allowing for the mass measurement of proteins in their native forms, thus offering new insights into biological processes and disease mechanisms.
Caltech scientists have developed a machine-learning-powered method that enables precise measurement of individual particles and molecules using advanced nanoscale devices. This breakthrough could lead to the use of various devices for mass measurement, which is key to identifying proteins. It also holds the potential to map the complete proteome—the full set of proteins in an organism.
Dr. Sanjeev Namjoshi, a machine learning engineer who recently submitted a book on Active Inference to MIT Press, discusses the theoretical foundations and practical applications of Active Inference, the Free Energy Principle (FEP), and Bayesian mechanics. He explains how these frameworks describe how biological and artificial systems maintain stability by minimizing uncertainty about their environment.
Namjoshi traces the evolution of these fields from early 2000s neuroscience research to current developments, highlighting how Active Inference provides a unified framework for perception and action through variational free energy minimization. He contrasts this with traditional machine learning approaches, emphasizing Active Inference’s natural capacity for exploration and curiosity through epistemic value.
The discussion covers key technical concepts like Markov blankets. generative models, and the distinction between continuous and discrete implementations. Namjoshi explains how Active Inference moved from continuous state-space models (2003−2013) to discrete formulations (2015-present) to better handle planning problems.
He sees Active Inference as being at a similar stage to deep learning in the early 2000s — poised for significant breakthroughs but requiring better tools and wider adoption. While acknowledging current computational challenges, he emphasizes Active Inference’s potential advantages over reinforcement learning, particularly its principled approach to exploration and planning.
Namjoshi advocates for balanced oversight that enables innovation while maintaining appropriate safeguards. He expresses particular concern about the rapid pace of AI development potentially outpacing our understanding of risks and regulatory frameworks.
A way to re grow new parts, perfect DNA match, eventually? Will take Agi / ASI to realize full potential, we ll see.
For this, the researchers have created a compact bioprinter to develop biological tissues with microfilament structures. He is now working to bring this technology to market.
“Our aim is to create human tissue models for high-throughput drug screening and other applications,” Liu said.
The human body is composed of various tissues, each with specific structures and functions. These tissues, like muscles, tendons, connective tissue, and nervous tissue, exhibit organized cellular arrangements. This organization is crucial for their proper functioning.
Scientists have developed a new material: an ultra-thin film that can absorb over 99% of electromagnetic waves.
The Korea Institute of Materials Science (KIMS) states it to be the “world’s first ultra-thin film composite material capable of absorbing over 99% of electromagnetic waves.”
This material is less than half a millimeter thick, but it can effectively shield against a wide range of frequencies, including those used by 5G, 6G, Wi-Fi, and autonomous vehicle radar.
Thanks to an algorithm created by an Idaho State University professor, the way engineers, doctors, and physicists tackle the hard questions in their respective fields could all change.
Emanuele Zappala, an assistant professor of mathematics at ISU, and his colleagues at Yale have developed the Attentional Neural Integral Equations algorithm, or ANIE for short. Their work was recently published in Nature Machine Intelligence and describes how ANIE can model large, complex systems using data alone.
“Natural phenomena–everything from plasma physics to how viruses spread–are all governed by equations which we do not fully understand,” explains Zappala. “One of the main complexities lies in long-distance relations between different data points in the systems over space and time. What ANIE does is it allows us to learn these complex systems using just those known data points.”
