Lifeboat Foundation

In a recent study published in The Lancet Microbe, researchers assessed the role of gut bacterial microbiome assembly and the gut mycobiome in relation to health, pathology, and clinical applications.

Studies have built a framework for investigating how gut fungi are connected to—or perhaps cause—different diseases and how to alter gut fungi to treat diseases by revealing the landscape of gut mycobiome composition in humans. Importantly, available mycobiome discoveries are not extensively applied to clinical practice, and gut fungi are still widely ignored in the context of treatments based on the microbiota.

According to studies conducted on mice, altering the intestinal fungi through oral administration of antifungal medications worsened allergic rhinitis and colitis, indicating that an imbalance in the gut mycobiome may play a role in the pathogenesis of intestinal as well as extra-intestinal diseases. Similar comparisons between the gut mycobiomes of healthy people and patients with various intestinal and extra-intestinal disorders have been documented in many studies.

A team of researchers affiliated with several institutions in Switzerland and the U.S. reports evidence that the genetics of longevity are influenced by both gender and age. In their paper published in the journal Science, the group describes their study of aging in mice and humans. João Pedro de Magalhães, with the University of Birmingham, has published a Perspective piece in the same journal issue outlining the technical challenges to understanding how aging works and the work done by the team on this new effort.

Scientists have been studying the aging process for many years but still do not have a good explanation for why organisms age and why some live longer than others. In this new effort, the researchers wondered if something in the genome plays a role in how long a species lives on average.

Noting that another team had created a very large dataset of information regarding aging in nearly 3,000 mice, the researchers found that it also contained genetic information. After obtaining access to the database, they analyzed that genetic information—more specifically, they conducted quantitative trait locus mapping. They found multiple loci that they could associate with longevity, some that were specific to one or the other gender. They also found that mice who weighed more during their early years or who had small litter sizes tended to die younger. They suggest the same genes that were associated with aging may have also played a role in the other two traits. The researchers also found that the aging-related genes they isolated appeared to remain dormant until the latter stages of a given individual’s life.

With more of us living into old age than at any other time, dementia is increasing steadily worldwide, with major individual, family, societal and economic consequences.

Treatment remains largely ineffective and aspects of the underlying pathophysiology are still unclear. But there is good evidence that neurodegenerative diseases —and their manifestation as dementia—are not an inevitable consequence of aging.

Many causes of dementia, including viral infections, are preventable.

Since the 1990s, scientists have cataloged thousands of planets outside our solar system, called exoplanets. Some of these are massive and gaseous, while others are tiny and rocky like our home world. But a recent analysis suggests that some of these exoplanets might be more dense and have more water than previously thought, which has big implications for alien life.

Without altering the genetic code in the DNA, epigenetic modifications can change how genes are expressed, affecting an organism’s health and development. The once radical idea that such changes in gene expression can be inherited now has a growing body of evidence behind it, but the mechanisms involved remain poorly understood.

A new study by researchers at UC Santa Cruz shows how a common type of epigenetic modification can be transmitted via sperm not only from parents to offspring, but to the next generation (“grandoffspring”) as well. This is called “transgenerational epigenetic inheritance,” and it may explain how a person’s health and development could be influenced by the experiences of his or her parents and grandparents.

The study, published the week of September 26 in the Proceedings of the National Academy of Sciences (PNAS), focused on a particular modification of a histone protein that changes the way DNA is packaged in the chromosomes. This widely studied epigenetic mark (called H3K27me3) is known to turn off or “repress” the affected genes and is found in all multicellular animals—from humans to the nematode worm C. elegans used in this study.

Training the large neural networks behind many modern AI tools requires real computational might: For example, OpenAI’s most advanced language model, GPT-3, required an astounding million billion billions of operations to train, and cost about US $5 million in compute time. Engineers think they have figured out a way to ease the burden by using a different way of representing numbers.

Back in 2017, John Gustafson, then jointly appointed at A*STAR Computational Resources Centre and the National University of Singapore, and Isaac Yonemoto, then at Interplanetary Robot and Electric Brain Co., developed a new way of representing numbers. These numbers, called posits, were proposed as an improvement over the standard floating-point arithmetic processors used today.

Now, a team of researchers at the Complutense University of Madrid have developed the first processor core implementing the posit standard in hardware and showed that, bit-for-bit, the accuracy of a basic computational task increased by up to four orders of magnitude, compared to computing using standard floating-point numbers. They presented their results at last week’s IEEE Symposium on Computer Arithmetic.

A team of researchers from HSE MIEM joined colleagues from the Institute of Non-Classical Chemistry in Leipzig to develop a theoretical model of a polymeric ionic liquid on a charged conductive electrode. They used approaches from polymer physics and theoretical electrochemistry to demonstrate the difference in the behavior of electrical differential capacitance of polymeric and ordinary ionic liquids for the first time. The results of the study were published in Physical Chemistry Chemical Physics.

Polymerized ionic liquids (PIL) are a relatively new class of materials with increasing applications in various fields, from the development of new electrolytes to the creation of solar cells. Unlike ordinary room temperature ionic liquids (liquid organic salts in which cations and anions move freely), in PILs, cations are usually linked in long polymeric chains, while anions move freely. In recent years, PILs have been used (along with ordinary ionic liquids) as a filling in the production of supercapacitors.

Supercapacitors are devices that store energy in an electric double layer on the surface of an electrode (as in electrodes of platinum, gold and carbon, for example). Compared, for example, to an accumulator, supercapacitors accumulate more energy and do so faster. The amount of energy a supercapacitor is able to accumulate is known as its ‘capacitance’.

A method to draw data in an area smaller than 10 nanometers has been proposed in a recent study published in Physical Review Letters

A joint research team led by Professor Daesu Lee (Department of Physics) of POSTECH, Professor Se Young Park (Department of Physics) at Soongsil University, and Dr. Ji Hye Lee (Department of Physics and Astronomy) of Seoul National University has proposed a method for densely storing data by “poking” with a sharp probe. This method utilizes a material in the metastable state, whose properties change easily even with slight stimulation.

A thin film of metastable ferroelectric calcium titanate (CaTiO₃) enables the polarization switching of a material even with a slight pressure of a probe: A very weak force of 100 nanonewtons (nN) is more than enough. The joint research team succeeded in making the width of the polarization path smaller than 10 nm by using this force and found the way to dramatically increase the capacity of data storage. This is because the smaller the size of the path, the more data the single material can store.

Neural networks are learning algorithms that approximate the solution to a task by training with available data. However, it is usually unclear how exactly they accomplish this. Two young Basel physicists have now derived mathematical expressions that allow one to calculate the optimal solution without training a network. Their results not only give insight into how those learning algorithms work, but could also help to detect unknown phase transitions in physical systems in the future.

Neural networks are based on the principle of operation of the brain. Such computer algorithms learn to solve problems through repeated training and can, for example, distinguish objects or process spoken language.

For several years now, physicists have been trying to use neural networks to detect phase transitions as well. Phase transitions are familiar to us from everyday experience, for instance when water freezes to ice, but they also occur in more complex form between different phases of magnetic materials or quantum systems, where they are often difficult to detect.