Fluorescence has been the de facto method for analyzing biomolecules, but the technique has its limitations. When conditions are right, these fluorescent dyes and proteins will glow brightly, making them easy to detect with conventional tools. More often than not, there is background noise that drowns out the fluorescence and makes detection almost impossible. Not happy with that limitation, scientists have developed a “living laser” technology that uses biological components, usually proteins, to enhance the fluorescent signals and make it possible to detect the molecules even when the signals are weak. In the latest example of a living laser, scientists are pushing the envelope by using blood as a critical component.
Detailed in an article recently published in the Optical Society of America Technical Digest, the blood laser uses a dye called indocyanine green (ICG) that is popular in medical imaging and fluoresces in near-infrared light. On its own, the ICG molecule produces a weak light signal that is difficult to detect in situ. To improve the method, researchers started to search for living components to boost the resulting fluorescent signal and that’s when they stumbled upon blood. When the researchers added blood to the mix, they discovered that the ICG dye bound to the plasma proteins in the blood. This combination generated an intense light that the researchers could detect.
Though promising, the procedure faces scrutiny from the public, which likely will be leery of a technique that combines lasers with blood, a unusual combination that sounds like it was pulled straight from the pages of a sci-fi novel. Researchers also have to tweak the process, so the laser and the resulting fluorescence are within safe limits. “You don’t want to burn the tissue,” said researcher Xudong Fan to New Scientist. (We agree.)
The research team eventually hopes to use this blood-based system in the living tissue of a patient in order to detect cancer. In this future scenario, a doctor could wave a laser wand over a patient’s arm and use the resulting fluorescence to detect whether there was evidence of cancer under the patient’s skin.
AI companies spend enormous amounts of time convincing us their technology is safe and secure. But what happens when those same companies become targets of increasingly sophisticated AI-powered attacks? Anthropic, the company behind Claude, is now facing that question. According to a Washington Post report, cybersecurity startup Cathedral discovered vulnerabilities that let its researchers access sensitive information in Anthropic's internal systems.
The researchers used AI agents to identify potential entry points in less than 12 hours. Interestingly, they didn't need to crack Claude itself. Instead, they exploited weaknesses in third-party business applications that Anthropic uses for everyday operations. Anthropic's security testing program disclosed the findings, which allows outside researchers to identify and report vulnerabilities.
Military working dogs charge into places the rest of us would flee. So, I understand why anyone would want to breed tougher, healthier dogs for the job.
Warhound, a new startup from Colossal Biosciences co-founder Ben Lamm and Anduril co-founder Palmer Luckey, has stepped out of stealth with $200 million to clone and gene-edit working dogs for American troops, police departments, and emergency services (via TechCrunch).
AI can describe a picture, identify objects, and answer questions about what a camera sees. But when a blind user needs a quick answer, getting an entire spoken essay about the scene isn't exactly helpful. Researchers at the University of Maryland (UMD) have found that AI-powered visual assistance can leave blind users doing too much work to get the information they actually need.
The study, titled Say It My Way: Exploring Control in Conversational Visual Question Answering with Blind Users, examined how people interact with Be My AI, an AI-powered feature of the Be My Eyes app. The researchers found that users often received lengthy descriptions that didn't adequately address their immediate goals, forcing them to ask follow-up questions or find ways to steer the AI towards a more useful answer.