Scientists at the La Jolla Institute for Immunology (LJI) have identified a potential new target for Parkinson’s disease treatment. Their research highlights the role of a specific brain cell protein in triggering the disease and may explain why Parkinson’s is more prevalent in men.
Recent studies from LJI suggest that autoimmunity plays a key role in Parkinson’s onset. Their latest findings, published in The Journal of Clinical Investigation, reveal that the protein PINK1 may label certain brain cells for attack by the immune system, contributing to disease progression.
“This research allows us to better understand the role of the immune system in Parkinson’s disease,” says LJI Professor Alessandro Sette, Dr. Biol. Sci., senior author of the recent study.
“According to Hooke, microscopes, like telescopes, put us on the cusp of doing what philosophers from Antiquity onwards had always tried to do, namely, understand the fundamental nature of reality,” writes assistant professor in philosophy, Peter West.
The idea that we can discover the fundamental level of reality might be alluring, but it’s based on a faulty philosophy, not science, argues Peter West.
Tap to read more about his beliefs that reality is not revealed by quantum mechanics.
The craze with all things quantum is not just because of its inherent weirdness. It’s motivated by a reductionist impulse that has been animating science from Robert Hooke in the 17th century to Stephen Hawking in the 21st. The idea that we can discover the fundamental level of reality might be alluring, but it’s based on a faulty philosophy, not science, writes Peter West.
A-MEM: A Novel Agentic Memory System for LLM Agents that Enables Dynamic Memory Structuring without Relying on Static, Predetermined Memory Operations
Posted in mathematics, robotics/AI | Leave a Comment on A-MEM: A Novel Agentic Memory System for LLM Agents that Enables Dynamic Memory Structuring without Relying on Static, Predetermined Memory Operations
Paper: https://arxiv.org/abs/2502.12110v1
GitHub Page: https://github.com/WujiangXu/AgenticMemory
Current memory systems for large language model (LLM) agents often struggle with rigidity and a lack of dynamic organization. Traditional approaches rely on fixed memory structures—predefined storage points and retrieval patterns that do not easily adapt to new or unexpected information. This rigidity can hinder an agent’s ability to effectively process complex tasks or learn from novel experiences, such as encountering a new mathematical solution. In many cases, the memory operates more as a static archive than as a living network of evolving knowledge. This limitation becomes particularly apparent during multi-step reasoning tasks or long-term interactions, where flexible adaptation is crucial for maintaining consistency and depth in understanding.
Researchers from Rutgers University, Ant Group, and Salesforce Research have introduced A-MEM, an agentic memory system designed to address these limitations. A-MEM is built on principles inspired by the Zettelkasten method—a system known for its effective note-taking and flexible organization. In A-MEM, each interaction is recorded as a detailed note that includes not only the content and timestamp, but also keywords, tags, and contextual descriptions generated by the LLM itself. Unlike traditional systems that impose a rigid schema, A-MEM allows these notes to be dynamically interconnected based on semantic relationships, enabling the memory to adapt and evolve as new information is processed.
Early-Stage Cholangiocarcinoma Detection Using Surface-Enhanced Raman Spectroscopy and 1D CNN with Discrete Wavelet Transform
Posted in biotech/medical, robotics/AI | Leave a Comment on Early-Stage Cholangiocarcinoma Detection Using Surface-Enhanced Raman Spectroscopy and 1D CNN with Discrete Wavelet Transform
Breakthrough in early detection of cholangiocarcinoma using ai-powered spectroscopy.
In a major advancement for cholangiocarcinoma (CCA) detection, researchers have developed a cutting-edge AI-driven diagnostic method that could revolutionize early cancer screening. Utilizing Surface-Enhanced Raman Spectroscopy (SERS), a powerful non-invasive technique, the team introduced a novel approach combining Discrete Wavelet Transform (DWT) with a one-dimensional Convolutional Neural Network (1D CNN) to distinguish early-stage CCA from precancerous, inflammatory, and healthy conditions.
Unlike traditional Principal Component Analysis (PCA) with Support Vector Machine (SVM), which struggles with nonlinear SERS data and only differentiates late-stage CCA, the new AI-enhanced method provides greater accuracy in detecting early-stage cancer, a crucial factor in improving survival rates. Receiver Operating Characteristic (ROC) curve analysis confirmed its superior performance.
The study, conducted on hamster serum, opens the door for future applications in human diagnostics, potentially transforming cancer detection and treatment. This breakthrough underscores the potential of AI and advanced signal processing in enhancing precision medicine and saving lives through early intervention.
That’s what prompted MIT engineers to create a fabric computer that can be stitched into regular clothes. The device features sensors, processors, memory, batteries, and both optical and Bluetooth communications, allowing networks of these fibers to provide sophisticated whole-body monitoring.
“Our bodies broadcast gigabytes of data through the skin every second in the form of heat, sound, biochemicals, electrical potentials, and light, all of which carry information about our activities, emotions, and health,” MIT professor Yoel Fink, who led the research, said in a press release.
“Wouldn’t it be great if we could teach clothes to capture, analyze, store, and communicate this important information in the form of valuable health and activity insights?”
OpenAi has launched GPT-4.5, which is the company’s largest and best model. The company is releasing a research preview of GPT‑4.5 that’s a step forward in scaling up pre-training and post-training. The latest model improves its ability to recognize patterns, draw connections, and generate creative insights without reasoning.
OpenAI revealed that as per early testing, interacting with GPT‑4.5 feels more natural. It has broader knowledge base, improved ability to follow user intent, and greater “EQ” make it useful for tasks like improving writing, programming, and solving practical problems.
A workshop led by scientists at the Department of Energy’s Oak Ridge National Laboratory sketched a road map toward a longtime goal: development of autonomous, or self-driving, next-generation research laboratories.
Download the report of the “Shaping the Future of Self-Driving Autonomous Laboratories” workshop.
Scientists have dreamed for generations of high-tech laboratories operated via robotics at the push of a button. Recent advancements in artificial intelligence bring those dreams closer to reality than ever before, said Rafael Ferreira da Silva, an ORNL senior research scientist and lead author of the workshop’s report.