In this GEN interview, Nobel Laureate David Baker, PhD, emphasizes that designing proteins from scratch is reality and unpacks what’s needed for AI to transform medicine.
Category: robotics/AI – Page 19
AI nutrition study finds ‘five everyday’ may keep the doctor away
In a new study using AI and machine learning, EPFL researchers have found that it’s not only what we eat, but how consistently we eat it that plays a crucial role in gut health.
The gut microbiota is the community of microorganisms, including bacteria, viruses, fungi and other microbes, that lives in our digestive systems—some of these microbes are helpful and others can be harmful.
Many previous studies have shown that what we eat has an impact on our gut microbiota. Healthy diets rich in fruit, vegetables, fiber and nuts are strongly associated with increased microbial diversity and better stomach health.
Advancing human leukocyte antigen-based cancer immunotherapy: from personalized to broad-spectrum strategies for genetically heterogeneous populations
Human leukocyte antigen (HLA)-based immunotherapeutics, such as tebentafusp-tebn and afamitresgene autoleucel, have expanded the treatment options for HLA-A*02-positive patients with rare solid tumors such as uveal melanoma, synovial sarcoma, and myxoid liposarcoma. Unfortunately, many patients of European, Latino/Hispanic, African, Asian, and Native American ancestry who carry non-HLA-A*02 alleles remain largely ineligible for most current HLA-based immunotherapies. This comprehensive review introduces HLA allotype-driven cancer health disparities (HACHD) as an emerging research focus, and examines how past and current HLA-targeted immunotherapeutic strategies may have inadvertently contributed to cancer health disparities. We discuss several preclinical and clinical strategies, including the incorporation of artificial intelligence (AI), to address HACHD.
New AI model for drug design brings more physics to bear in predictions
When machine learning is used to suggest new potential scientific insights or directions, algorithms sometimes offer solutions that are not physically sound.
Take, for example, AlphaFold, the AI system that predicts the complex ways in which amino acid chains will fold into 3D protein structures. The system sometimes suggests “unphysical” folds—configurations that are implausible based on the laws of physics —especially when asked to predict the folds for chains that are significantly different from its training data.
To limit this type of unphysical result in the realm of drug design, Anima Anandkumar, Bren Professor of Computing and Mathematical Sciences at Caltech, and her colleagues have introduced a new machine learning model called NucleusDiff, which incorporates a simple physical idea into its training, greatly improving the algorithm’s performance.
Chemical networks can mimic nervous systems to power movement in soft materials
What if a soft material could move on its own, guided not by electronics or motors, but by the kind of rudimentary chemical signaling that powers the simplest organisms? Researchers at the University of Pittsburgh Swanson School of Engineering have modeled just that—a synthetic system that on its own directly transforms chemical reactions into mechanical motion, without the need for the complex biochemical machinery present in our bodies.
Just like jellyfish, some of the simplest organisms do not have a centralized brain or nervous system. Instead, they have a “nerve net” which consists of dispersed nerve cells that are interconnected by active junctions, which emit and receive chemical signals. Even without a central “processor,” the chemical signals spontaneously travel through the net and trigger the autonomous motion needed for organisms’ survival.
In a study published in PNAS Nexus, Oleg E. Shklyaev, research assistant, and Anna C. Balazs, Distinguished Professor of Chemical and Petroleum Engineering and the John A. Swanson Chair of Engineering, have developed computer simulations to design a soft material with a “nerve net” that links chemical and mechanical networks in a way that mimics how the earliest and simplest living systems coordinate motion.
Optical system achieves terabit-per-second capacity and integrates quantum cryptography for long-term security
The artificial intelligence (AI) boom has created unprecedented demand for data traffic. But the infrastructure needed to support it faces mounting challenges. AI data centers must deliver faster, more reliable communication than ever before, while also confronting their soaring electricity use and a looming quantum security threat, which could one day break today’s encryption methods.
To address these challenges, a recent study published in Advanced Photonics proposes a quantum-secured architecture that involves minimal digital signal processing (DSP) consumption and meets all the stringent requirements for AI-driven data center optical interconnect (AI–DCI) scenarios. This system enables data to move at terabit-per-second speeds with low power consumption while defending against future quantum threats.
“Our work paves the way for the next generation of secure, scalable, and cost-efficient optical interconnects, protecting AI-driven data centers against quantum security threats while meeting the high demands of modern data-driven applications,” the researchers state in their paper.
How a human ‘jumping gene’ targets structured DNA to reshape the genome
Long interspersed nuclear element-1 (LINE-1 or L1) is the only active, self-copying genetic element in the human genome—comprising about 17% of the genome. It is commonly called a “jumping gene” or “retrotransposon” because it can “retrotranspose” (move) from one genomic location to another.
Researchers from the Institute of Biophysics of the Chinese Academy of Sciences have now unveiled the molecular mechanisms that underlie L1’s retrotransposition and integration into genomic DNA. Their study was published in Science on October 9.
L1 is the only autonomously active retrotransposon in the human genome and serves as the primary vehicle for the mobilization of most other retrotransposons. Its retrotransposition process is mediated by the reverse transcriptase ORF2p through a mechanism known as target-primed reverse transcription (TPRT). Until now, the manner in which ORF2p recognizes DNA targets and mediates integration had remained unclear.
131 Chrome Extensions Caught Hijacking WhatsApp Web for Massive Spam Campaign
Cybersecurity researchers have uncovered a coordinated campaign that leveraged 131 rebranded clones of a WhatsApp Web automation extension for Google Chrome to spam Brazilian users at scale.
The 131 spamware extensions share the same codebase, design patterns, and infrastructure, according to supply chain security company Socket. The browser add-ons collectively have about 20,905 active users.
“They are not classic malware, but they function as high-risk spam automation that abuses platform rules,” security researcher Kirill Boychenko said. “The code injects directly into the WhatsApp Web page, running alongside WhatsApp’s own scripts, automates bulk outreach and scheduling in ways that aim to bypass WhatsApp’s anti-spam enforcement.”
