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A research team has identified a molecular target that could open up new therapeutic options to treat aging-associated diseases like Parkinson’s. Scientists at the University of Cologne have discovered how cells can eliminate mutated mitochondrial DNA (mtDNA). Mitochondria are the powerhouses of our cells. Due to their evolutionary descent from bacteria, they still have genetic material packaged in chromosome-like structures (nucleoids). They convert the chemical energy in our food into a biologically usable form. A team of researchers from the University of Cologne’s Physiology Centre at the Faculty of Medicine, the Centre for Molecular Medicine Cologne (CMMC) and the CECAD Cluster of Excellence for Aging Research has now shown that mutations of the mtDNA lead to a local rearrangement of proteins in the mitochondrial membrane. The mutated mtDNA is targeted, eliminated, and subjected to autophagy, the cellular ‘waste disposal’. The results have appeared in Nature Communications under the title ‘Mitochondrial membrane proteins and VPS35 orchestrate selective removal of mtDNA’.

In many tissues, mutations in mtDNA accumulate as a result of normal aging. These kinds of mutations are an important cause of many aging-associated diseases. There are thousands of copies mtDNA in every cell, so mitochondrial function is only impaired when the percentage of mutated mtDNA molecules exceeds a certain threshold value. It has long been established that mitochondrial damage, including acute mtDNA damage, triggers the process of mitophagy. In this process, dysfunctional mitochondrial parts are selectively degraded and recycled.

Dr David Pla-Martin, the lead author of the current study, explained the details: ‘What is new in our study is that this mechanism does not affect the cells’ endowment with mitochondria, but only clears out the damaged mtDNA. By labelling neighbouring proteins — so-called proximity labelling — we showed that mtDNA damage leads to the recruitment of endosomes in close proximity to nucleoids.’ Their removal is coordinated by the interaction of the nucleoid protein Twinkle and the mitochondrial membrane proteins SAMM50 and ATAD3 controls their distribution, SAMM50 induces the release and transfer of the nucleoid to the so-called endosomes. ‘This additionally prevents the activation of an immune response. The protein VPS35, the main component of the retromer, mediates the maturation of early endosomes into late autophagy vesicles, where degradation and recycling ultimately take place,’ said Pla-Martin.

At the time, all this was theoretical. But last week, the company announced they’d linked 16 CS-2s together into a world-class AI supercomputer.

Meet Andromeda

The new machine, called Andromeda, has 13.5 million cores capable of speeds over an exaflop (one quintillion operations per second) at 16-bit half precision. Due to the unique chip at its core, Andromeda isn’t easily compared to supercomputers running on more traditional CPUs and GPUs, but Feldman told HPC Wire Andromeda is roughly equivalent to Argonne National Laboratory’s Polaris supercomputer, which ranks 17th fastest in the world, according to the latest Top500 list.

In a conversation right before the 2021 Conference on Neural Information Processing Systems (NeurIPS), Amazon vice president and distinguished scientist Bernhard Schölkopf — according to Google Scholar, the most highly cited researcher in the field of causal inference — said that the next frontier in artificial-intelligence research was causal-representation learning.

Where existing approaches to causal inference use machine learning to discover causal relationships between variables — say, the latencies of various interrelated services on a website — causal-representation learning learns the variables themselves. “These kinds of causal representations will also go toward reasoning, which we will ultimately need if we want to move away from this pure pattern recognition view of intelligence,” Schölkopf said.

Francesco Locatello, a senior applied scientist with Amazon Web Services, leads Amazon’s research on causal-representation learning, and he’s a coauthor on four papers at this year’s NeurIPS.

In a recent study from Yale University, scientists found the factors causing changes in the DNA that contribute most to cancer growth in tumors of most major tumor types. Some of the most prevalent cancers in the United States are known to be highly preventable by human decisions.

Kedar Kulkarni, a 27-year-old from Pune is transforming public urinals into odourless and hygienic urinals, using the waterless technology he innovated.

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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.