Lifeboat Foundation: Safeguarding Humanity
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Blog – Page 2186

Over the past decades, scientists have made substantial progress unveiling the underlying mechanisms behind many psychiatric disorders. Every year, new genetic mutations or protein dysregulations are identified as potential culprits for the symptoms and sometimes even the root causes of complex neurological diseases, including autism spectrum disorder (ASD), schizophrenia, and Alzheimer’s.

Despite these efforts, the precise roles of several proteins involved in remain obscure. Such is the case for indoleamine 2,3-dioxygenase 2 (IDO2), an enzyme expressed in the brain and metabolized by the tryptophan–kynurenine pathway (TKP).

Changes in the metabolites of this pathway have already been linked to many , and genetically modified mice have been invaluable tools in such studies. However, the detailed functions of IDO2 in the brain are not known.

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A groundbreaking study by Chalmers University scientists reveals unprecedented molecular details in two early-universe galaxies, advancing our understanding of their star-formation activities.

Two galaxies in the early universe, which contain extremely productive star factories, have been studied by a team of scientists led by Chalmers University of Technology in Sweden. Using powerful telescopes to split the galaxies’ light into individual colors, the scientists were amazed to discover light from many different molecules – more than ever before at such distances. Studies like this could revolutionize our understanding of the lives of the most active galaxies when the universe was young, the researchers believe.

Unveiling the nature of early galaxies.

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In an apparent attempt to turn off a beeping noise he allegedly deemed annoying, a janitor turned off the breaker to a freezer that contained extremely valuable cell cultures, samples, and other research.

The freezer, which was set to minus-112 degrees Fahrenheit, as the Times Union reports, warmed up to a catastrophic minus-25.6 degrees, damaging and destroying much of the decades of work.

At least, that’s what happened according to a lawsuit filed by the Rensselaer Polytechnic Institute lab against the janitor’s employer.

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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.

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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).

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