Claude Sonnet 4.5 is the best coding model in the world, strongest model for building complex agents, and best model at using computers.
“The unique aspect of our BioCog test is that unlike other digital tests, it has been evaluated in a primary care population, i.e. patients seeking treatment at a health centre because they are experiencing cognitive problems, such as memory problems. Combining the results of the digital test and the blood test increases the accuracy of diagnosing Alzheimer’s disease. The purpose of the test is to make things easier for primary care doctors,” says one of the authors.
The digital test is done by the patient individually on a tablet computer. The test measures:
Alzheimer’s disease is the most common cause of dementia. As new disease-modifying treatments for Alzheimer’s disease are now becoming available, both early and accurate diagnosis in a resource-efficient assessment process are becoming increasingly important, as not everyone responds to the new drugs. Seeking medical care for cognitive impairment is not necessarily the result of Alzheimer’s disease – it can for example be caused by depression, fatigue or other dementias.
“Primary care does not have the resources, time or specialist knowledge to investigate possible Alzheimer’s disease in the same way as specialised memory clinics. And this is where a digital cognitive test can make the biggest difference,” says the senior author.
Unlike pen-and-paper tests, which are generally used to assess cognitive impairment, digital tests provide a more detailed picture. More aspects and new variables that could not previously be measured as easily are included.
One often-repeated example illustrates the mind-boggling potential of quantum computing: A machine with 300 quantum bits could simultaneously store more information than the number of particles in the known universe.
Now process this: Harvard scientists just unveiled a system that was 10 times bigger and the first quantum machine able to operate continuously without restarting.
In a paper published in the journal Nature, the team demonstrated a system of more than 3,000 quantum bits (or qubits) that could run for more than two hours, surmounting a series of technical challenges and representing a significant step toward building the super computers, which could revolutionize science, medicine, finance, and other fields.
Inside the microchips powering your devices, atoms aren’t just randomly scattered. They follow a hidden order that can change how semiconductors behave.
A team of researchers from the Lawrence Berkeley National Laboratory (Berkeley Lab) and George Washington University has, for the first time, observed these tiny patterns, called short-range order (SRO), directly in semiconductors.
This discovery is a game-changer, as understanding how atoms naturally arrange themselves could let researchers design materials with desirable electronic properties. Such control could revolutionize quantum computing, neuromorphic devices that mimic the brain, and advanced optical detectors.
Cultured neural tissues have been widely used as a simplified experimental model for brain research. However, existing devices for growing and recording neural tissues, which are manufactured using semiconductor processes, have limitations in terms of shape modification and the implementation of three-dimensional (3D) structures.
By thinking outside the box, a KAIST research team has successfully created a customized 3D neural chip. They first used a 3D printer to fabricate a hollow channel structure, then used capillary action to automatically fill the channels with conductive ink, creating the electrodes and wiring. This achievement is expected to significantly increase the design freedom and versatility of brain science and brain engineering research platforms. The paper is published in the journal Advanced Functional Materials.
A research team led by Professor Yoonkey Nam from the Department of Bio and Brain Engineering has successfully developed a platform technology that overcomes the limitations of traditional semiconductor-based manufacturing. This technology allows for the precise fabrication of a 3D microelectrode array (neural interfaces with multiple microelectrodes arranged in a 3D space to measure and stimulate the electrophysiological signal of neurons) in various customized forms for in vitro culture chips.
The future of pacemaker technologies — wade demmer — VP, R&D, medtronic.
Wade Demmer is Vice President of Research & Development at Medtronic where he is responsible for the development of new generations of pacemakers (https://www.medtronic.com/en-us/l/patients/treatments-therap…ers.html). With extensive expertise in medical technology and innovation, he leads the company’s R&D efforts to develop cutting-edge healthcare solutions and is dedicated to advancing medical advancements that improve patient outcomes and transform healthcare delivery.
Wade began his career at Intel, where he gained valuable experience in technology development and engineering. Building on his technical expertise, he transitioned into the medical device industry, bringing a strong innovation-driven mindset to healthcare solutions.
Wade is best known for his pioneering work on pacemakers, where he contributed to the design and development of advanced cardiac pacing technologies. His innovative approaches have helped improve the reliability, longevity, and patient comfort of pacemaker devices, significantly impacting the field of cardiac care.
Wade received his Bachelor of Engineering (BEng), with a focus on Computer Engineering, from Iowa State University, and his MBA from University of Minnesota Carlson School of Management.
A traditional digital camera splits an image into three channels—red, green and blue—mirroring how the human eye perceives color. But those are just three discrete points along a continuous spectrum of wavelengths. Specialized “spectral” cameras go further by sequentially capturing dozens, or even hundreds, of these divisions across the spectrum.
This process is slow, however, meaning that hyperspectral cameras can only take still images, or videos with very low frame rates, or frames per second (fps). But what if a high-fps video camera could capture dozens of wavelengths at once, revealing details invisible to the naked eye?
Now, researchers at the University of Utah’s John and Marcia Price College of Engineering have developed a new way of taking a high-definition snapshot that encodes spectral data into images, much like a traditional camera encodes color. Instead of a filter that divides light into three color channels, their specialized filter divides it into 25. Each pixel stores compressed spectral information along with its spatial information, which computer algorithms can later reconstruct into a “cube” of 25 separate images—each representing a distinct slice of the visible spectrum.