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Tumor cells in the most common pancreatic cancer share nutrients to live and grow. A new discovery by University of California, Irvine biologists and collaborators during a four-year investigation could help lead to better treatments for pancreatic ductal adenocarcinoma, which accounts for over 90 percent of pancreatic cancer cases. The scientists’ paper appears in Nature Cancer. While pancreatic cancer is relatively rare, it is among the leading causes of cancer death in the United States.

One obstacle in treating pancreatic ductal adenocarcinoma, known as PDA, is that it generally does not show early symptoms. Another hurdle is the complexity of its dense and fibrous tumors. Consequently, they do not have fully functioning blood vessels in the tumor. On one front, this makes it difficult to deliver effective chemotherapy. However, it also means the tumors have developed a different kind of metabolism.

“Without blood vessels, PDA cells aren’t getting the normal nutrients they need, so they have come up with other ways to nourish themselves and grow,” said Christopher Halbrook, assistant professor of molecular biology & biochemistry, and lead and co-corresponding author. Understanding this process is essential for devising treatments targeting the cancer’s metabolism.

Studies have shown poor balance can be associated with serious health problems – as well as increased risks of falls as we get older. But there are ways to make yours better.

Large language models have advanced significantly in recent years (LLMs). Impressive LLMs have been revealed one after the other, beginning with OpenAI’s GPT-3, which generates exceptionally correct texts and ends with its open-source counterpart BLOOM. Language-related problems that were previously unsolvable had become simply a challenge for these systems.

All of this progress is made possible by the vast amount of data available on the Internet and the accessibility of powerful GPUs. As appealing as they may sound, training an LLM is an incredibly expensive procedure in terms of both data and technology needs. We’re talking about AI systems with billions of parameters, so feeding these models with enough data isn’t easy. However, once you do it, they give you a stunning performance.

Have you ever wondered where the development of “computing” gadgets began? Why did individuals devote so much time and energy to designing and constructing the first computers? We can presume it was not for the purpose of amusing people with video games or YouTube videos.

A team of scientists led by crystallographers from St Petersburg University has succeeded in synthesizing an analog of the Earth’s most structurally complex mineral, ewingite, in a laboratory. The findings of the research are published in Materials.

Ewingite is a mineral that was discovered in the mid-2010s in the abandoned Plavno uranium mine located in the Czech Republic. It is the most complex mineral known to exist on Earth. Moreover, because of the specific thermodynamic conditions required for its formation, the mineral is considered to be very rare.

The researchers managed to synthesize an analog with a composition and crystal structure similar to that of natural ewingite through a combination of low-temperature hydrothermal synthesis and room-temperature evaporation.

The evolution of a new species by hybridization of two previously described species with no change in chromosomal number is very unusual in the animal world. So far, only a few empirically acknowledged cases of this spontaneous mode of evolution (from one generation to the next) known as homoploid hybridization exist.

A study led by Axel Meyer, Professor of Zoology and Evolutionary Biology at the University of Konstanz, has successfully demonstrated the emergence of a new hybrid species in cichlid fishes. This is likely the first instance of this genetic speciation method in vertebrates. The researchers reveal that a new hybrid species has emerged from the cichlid A. sagittae and A. xiloaensis in the crater lake Xiloá in Nicaragua using whole genome sequencing of more than 120 individuals as well as a number of other techniques.

Their findings were recently published in the journal Nature Communications.

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Dark Matter is both the most abundant and most mysterious substance in the Universe, what properties does it have and what technologies might we create to use it in the future?

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Continue reading “Dark Matter Technologies” »

According to the Standard Model of Particle Physics, the Universe is governed by four fundamental forces: electromagnetism, the weak nuclear force, the strong nuclear force, and gravity. Whereas the first three are described by Quantum Mechanics, gravity is described by Einstein’s Theory of General Relativity. Surprisingly, gravity is the one that presents the biggest challenges to physicists. While the theory accurately describes how gravity works for planets, stars, galaxies, and clusters, it does not apply perfectly at all scales.

While General Relativity has been validated repeatedly over the past century (starting with the Eddington Eclipse Experiment in 1919), gaps still appear when scientists try to apply it at the quantum scale and to the Universe as a whole. According to a new study led by Simon Fraser University, an international team of researchers tested General Relativity on the largest of scales and concluded that it might need a tweak or two. This method could help scientists to resolve some of the biggest mysteries facing astrophysicists and cosmologists today.

The team included researchers from Simon Fraser, the Institute of Cosmology and Gravitation at the University of Portsmouth, the Center for Particle Cosmology at the University of Pennsylvania, the Osservatorio Astronomico di Roma, the UAM-CSIC Institute of Theoretical Physics, Leiden University’s Institute Lorentz, and the Chinese Academy of Sciences (CAS). Their results appeared in a paper titled “Imprints of cosmological tensions in reconstructed gravity,” recently published in Nature Astronomy.

Physicists have long struggled to explain why the Universe started out with conditions suitable for life to evolve. Why do the physical laws and constants take the very specific values that allow stars, planets, and ultimately life to develop?

The expansive force of the Universe, dark energy, for example, is much weaker than theory suggests it should be – allowing matter to clump together rather than being ripped apart.

A common answer is that we live in an infinite multiverse of Universes, so we shouldn’t be surprised that at least one Universe has turned out as ours. But another is that our Universe is a computer simulation, with someone (perhaps an advanced alien species) fine-tuning the conditions.

AI can also be of benefit in the diagnosis and treatment of patients. Tools have been created that help diagnose a patient as well as a human would.

AI isn’t a new technology—it’s been researched and developed since the 1950s and is currently present in many of our daily routines. Most of these applications are so common that we don’t even notice them.

Our lives often depend on the healthcare industry. So, having a technology that allows you to speed up patient registration processes and help diagnose more quickly and effectively is essential. Every health center should consider the use of AI for the benefit of its processes so it can adapt to the modern world and its accelerated pace.