Scientists revealed that the flower-shaped geometry causes the field lines of an external magnetic field to concentrate in the centre of the device, resulting in a greatly intensified magnetic field.
Novel artificial neurons learn independently and are more strongly modeled on their biological counterparts. A team of researchers from the Göttingen Campus Institute for Dynamics of Biological Networks (CIDBN) at the University of Göttingen and the Max Planck Institute for Dynamics and Self-Organization (MPI-DS) has programmed these infomorphic neurons and constructed artificial neural networks from them. The special feature is that the individual artificial neurons learn in a self-organized way and draw the necessary information from their immediate environment in the network.
The results were published in PNAS (“A general framework for interpretable neural learning based on local information-theoretic goal functions”).
Both, human brain and modern artificial neural networks are extremely powerful. At the lowest level, the neurons work together as rather simple computing units. An artificial neural network typically consists of several layers composed of individual neurons. An input signal passes through these layers and is processed by artificial neurons in order to extract relevant information. However, conventional artificial neurons differ significantly from their biological models in the way they learn.
494,000 Americans Affected As Massive Data Breach Exposes Names, Financial Records, Medical Data, Social Security Numbers and More: Report
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A cybersecurity incident affecting nearly half a million people has exposed personal, financial and medical information.
The mobility and assistive solutions provider Numotion says 494,000 customers are affected by a data breach witnessed between September 2nd, 2024, and November 18th, 2024, reports Security Week.
Numotion says an unknown entity managed to access the email accounts of the firm’s employees without authorization several times.
In a remarkable leap forward for science, researchers at CERN have successfully created and observed top quarks—one of nature’s most elusive and unstable particles—inside a lab for the very first time. This breakthrough, announced by the ATLAS team at the Large Hadron Collider (LHC), promises to reshape our understanding of the early Universe and the fundamental makeup of matter.
Glucose is life’s main energy source. But a Stanford Medicine study reveals a surprising role as a master manipulator of tissue maturation, hinting at its importance in diabetes and cancer.
With the U.S. significantly downsizing its foreign aid, a key question is whether China will exploit the opportunity and fill the void.
Past neuroscience and psychology studies have shown that after the human brain encodes specific events or information, it can periodically reactivate them to facilitate their retention, via a process known as memory consolidation. The reactivation of memories has been specifically studied in the context of sleep or rest, with findings suggesting that during periods of inactivity, the brain reactivates specific memories, allowing people to remember them in the long term.
Researchers at the University of Pennsylvania and other institutions in the United States recently conducted a study exploring the possibility that the brain engages in a similar reactivation process during wakefulness to store important information for shorter periods of time. Their findings, published in Nature Neuroscience, suggest that the spontaneous reactivation of specific stimuli in the brain during the brief intervals between their encoding predicts the accuracy with which people remember them at the end of a memory task.
“Mike Kahana and I were both quite interested in the long history of thinking about rehearsal and its effects on the way in which people later recalled things,” Dr. David Halpern, the first author of the paper, told Medical Xpress. “Rehearsal is challenging to study since people often do it without any overt behavior (unless we ask them to rehearse out loud).”
In quantum mechanics, skyrmions can appear as stable, wave-like structures that help protect quantum information.
To adapt the existing software to microscopy, the research team first evaluated it on a large set of open-source data, which showed the model’s potential for microscopy segmentation. To improve quality, the team retrained it on a large microscopy dataset. This dramatically improved the model’s performance for the segmentation of cells, nuclei and tiny structures in cells known as organelles.
The team then created their software, μSAM, which enables researchers and medical doctors to analyze images without the need to first manually paint structures or train a specific AI model. The software is already in wide use internationally, for example to analyze nerve cells in the ear as part of a project on hearing restoration, to segment artificial tumor cells for cancer research, or to analyze electron microscopy images of volcanic rocks.
“Analyzing cells or other structures is one of the most challenging tasks for researchers working in microscopy and is an important task for both basic research in biology and medical diagnostics,” says the author.
Identifying and delineating cell structures in microscopy images is crucial for understanding the complex processes of life. This task is called “segmentation” and it enables a range of applications, such as analysing the reaction of cells to drug treatments, or comparing cell structures in different genotypes. It was already possible to carry out automatic segmentation of those biological structures but the dedicated methods only worked in specific conditions and adapting them to new conditions was costly.
An international research team has now developed a method by retraining the existing AI-based software Segment Anything on over 17,000 microscopy images with over 2 million structures annotated by hand.
Their new model is called Segment Anything for Microscopy and it can precisely segment images of tissues, cells and similar structures in a wide range of settings. To make it available to researchers and medical doctors, they have also created μSAM, a user-friendly software to “segment anything” in microscopy images. Their work was published in Nature Methods.