Lifeboat Foundation

How can artificial intelligence help to improve the accuracy of lung cancer screening among people at high risk of developing the disease? Read to find out.

Lung cancers, the vast majority of which are caused by cigarette smoking, are the leading cause of cancer-related deaths in the United States. Lung cancer kills more people than cancers of the breast, prostate, and colon combined. By the time lung cancer is diagnosed, the disease has often already spread outside the lung. Therefore, researchers have sought to develop methods to screen for lung cancer in high-risk populations before symptoms appear. They are evaluating whether the integration of artificial intelligence – the use of computer programs or algorithms that use data to make decisions or predictions – could improve the accuracy and speed of diagnosis, aid clinical decision-making, and lead to better health outcomes.

Human brains can adapt to an extra body part! Check out a 3D-printed and robotic ‘Third Thumb.’

The interactions of helium atoms with crystalline surfaces are so gentle and subtle that it has been challenging to describe them from first principles.

New theoretical work explores the onset of rigidity in granular materials and other disordered systems by mapping out the edges of rigid regions.

Phase transitions are a common part of our daily lives. Many of them are intuitive: water transforms into steam or ice, birds spontaneously form a flock, and random piles of marbles suddenly jam together into a solid. Possibly the most basic phase transition, however, is a more abstract version called connectivity percolation (CP). In systems displaying CP, individual units such as persons or polymers are mapped by their contacts—or connectors—to a graph consisting of nodes and edges. As the number of connectors increases, the system switches from being disconnected (filled with small, separate clusters) to being connected (spanned by one large cluster). This connectivity phase transition is commonly seen in polymer solutions and pandemic spreading, but researchers have also used the percolation perspective to describe the onset of mechanical rigidity in disordered systems, otherwise known as rigidity percolation (RP).

Human decision-making has been the focus of a wide range of research studies. Collectively, these research efforts could help to understand better how people make different types of everyday choices while also shedding light on the neural processes underpinning these choices.

Findings suggest that while making instantaneous decisions, or in other words, choices that need to be made quickly based on the information available at a given moment, humans greatly rely on contextual information. This contextual information can also guide so-called sequential decisions, which entails making a choice after observing the sequential unfolding of a process.

Researchers at the University of Oxford, the National Research Council in Rome, University College London (UCL), and the Max Planck Institute for Human Development recently carried out a study exploring the impact of context on goal-directed decision-making. Their findings, published in Neuron, suggest that goal-seeking ‘compresses’ spatial maps in the hippocampus and orbitofrontal cortices in the brain.

The possible emission rate of particle-stable tetraneutron, a four-neutron system whose existence has been long debated within the scientific community, has been investigated by researchers from Tokyo Tech. They looked into tetraneutron emission from thermal fission of ²³⁵ U by irradiating a sample of ⁸⁸ SrCO₃ in a nuclear research reactor and analyzing it via γ-ray spectroscopy.

Tetraneutron is an elusive atomic nucleus consisting of four neutrons, whose existence has been highly debated by scientists. This stems primarily from our lack of knowledge about systems consisting of only neutrons, since most atomic nuclei are usually made of a combination of protons and neutrons. Scientists believe that the experimental observation of a tetraneutron could be the key to exploring new properties of atomic nuclei and answering the age-old question: Can a charge-neutral multineutron system ever exist?

Two recent experimental studies reported the presence of tetraneutrons in bound state and resonant state (a state that decays with time but lives long enough to be detected experimentally). However, theoretical studies indicate that tetraneutrons will not exist in a bound state if the interactions between neutrons are governed by our common understanding of two or three-body nuclear forces.

Weather forecasting is important for various sectors, including agriculture, military operations, and aviation, as well as for predicting natural disasters like tornados and cyclones. It relies on predicting the movement of air in the atmosphere, which is characterized by turbulent flows resulting in chaotic eddies of air.

However, accurately predicting this turbulence has remained significantly challenging owing to the lack of data on small-scale turbulent flows, which leads to the introduction of small initial errors. These errors can, in turn, lead to drastic changes in the flow states later, a phenomenon known as the chaotic butterfly effect.

To address the challenge of limited data on small-scale turbulent flows, a data-driven method known as Data Assimilation (DA) has been employed for forecasting. By integrating various sources of information, this approach enables the inference of details about small-scale turbulent eddies from their larger counterparts.

Whether in the brain or in the muscles, synapses are present wherever nerve cells exist. Synapses, the connections between neurons, are fundamental to the process of excitation transmission, which is essentially communication between neurons. As in any communication process, there is a sender and a receiver: Nerve cell processes called axons generate and transmit electrical signals thereby acting as signal senders.

Synapses are points of contact between axonal nerve terminals (the pre-synapse) and post-synaptic neurons. At these synapses, the electrical impulse is converted into chemical messengers that are received and sensed by the post-synapses of the neighboring neuron. The messengers are released from special membrane sacs called synaptic vesicles.

As well as transmitting information, synapses can also store information. While the structure and function of synapses are comparably well understood, little is known about how they are formed.

A new study reveals that magnetic fields are common in star systems with large blue stars, challenging prior beliefs and providing insights into the evolution and explosive nature of these massive stars.

Astronomers from the Leibniz Institute for Astrophysics Potsdam (AIP), the European Southern Observatory (ESO), and the MIT Kavli Institute and Department of Physics have discovered that magnetic fields in multiple star systems with at least one giant, hot blue star, are much more common than previously thought by scientists. The results significantly improve the understanding of massive stars and their role as progenitors of supernova explosions.

Characteristics of O-type Stars.