Neurotic personality trait a key risk factor for stress perception

While all of the “Big Five” personality traits – agreeableness, conscientiousness, extraversion, neuroticism and openness – are related to experiencing stress, neuroticism showed the strongest link, according to research co-written by Bo Zhang, a professor of labor and employment relations and of psychology at Illinois. 
Photo Credit: Fred Zwicky

A new paper co-written by a team of University of Illinois Urbana-Champaign experts who study the science of personalities points to the important role of personality traits to account for individual differences in experiencing stress.

In a meta-analysis synthesizing more than 1,500 effect sizes from about 300 primary studies, the team showed that while all of the “Big Five” personality traits – agreeableness, conscientiousness, extraversion, neuroticism and openness – are related to experiencing stress, neuroticism showed the strongest link, said Bo Zhang, a professor of labor and employment relations and of psychology at Illinois and a co-author of the paper.

“Stress is a significant mental and physical health issue that affects many people and many important domains of life, and some individuals are more likely to experience or perceive stress disproportionately or more intensely than others, which can then play a role in mental and physical health problems such as anxiety or depression,” he said. “We found that individuals high in neuroticism” – a heightened tendency toward negative affect as well as an exaggerated response to threat, frustration or loss – “demonstrated a relationship with both stressor exposure and perceived stress that was stronger than the other four personality traits.”

Strongest Arctic cyclone on record led to surprising loss of sea ice

A ship-based view of the Arctic Ocean in October 2015, when the ocean’s surface is beginning to freeze. In January, when the massive 2022 cyclone occurred, large sections of the Arctic Ocean would be covered in a layer of sea ice.
Photo Credit: Ed Blanchard-Wrigglesworth/University of Washington

A warming climate is causing a decline in sea ice in the Arctic Ocean, where loss of sea ice has important ecological, economic and climate impacts. On top of this long-term shift due to climate change are weather events that affect the sea ice from week to week.

The strongest Arctic cyclone ever observed poleward of 70 degrees north latitude struck in January 2022 northeast of Greenland. A new analysis led by the University of Washington shows that while weather forecasts accurately predicted the storm, ice models seriously underestimated its impact on the region’s sea ice.

The study, published in October in the Journal of Geophysical Research–Atmospheres, suggests that existing models underestimate the impact of big waves on ice floes in the Arctic Ocean.

“The loss of sea ice in six days was the biggest change we could find in the historical observations since 1979, and the area of ice lost was 30% greater than the previous record,” said lead author Ed Blanchard-Wrigglesworth, a research assistant professor of atmospheric sciences at the UW. “The ice models did predict some loss, but only about half of what we saw in the real world.”

Fertilizing the Ocean to Store Carbon Dioxide

Seeding the oceans with nano-scale fertilizers could create a much-needed, substantial carbon sink.
  Illustration Credit: Stephanie King | Pacific Northwest National Laboratory

The urgent need to remove excess carbon dioxide from Earth’s environment could include enlisting some of our planet’s smallest inhabitants, according to an international research team led by Michael Hochella of the Department of Energy’s Pacific Northwest National Laboratory.

Hochella and his colleagues examined the scientific evidence for seeding the oceans with iron-rich engineered fertilizer particles near ocean plankton. The goal would be to feed phytoplankton, microscopic plants that are a key part of the ocean ecosystem, to encourage growth and carbon dioxide (CO2) uptake. The analysis article appears in the journal Nature Nanotechnology.

“The idea is to augment existing processes,” said Hochella, a Laboratory fellow at Pacific Northwest National Laboratory. “Humans have fertilized the land to grow crops for centuries. We can learn to fertilize the oceans responsibly.”

A pair of lizard ‘kings’ from the old, old West

This photograph shows two blocks containing the holotype of Microteras borealis. It consists of a portion of the snout (left) and the braincase (right).
Resized Image using AI by SFLORG
Photo Credit: Courtesy of the Yale Peabody Museum of Natural History

Yale researchers have identified the oldest-known, definitive members of the lizard crown group that includes all living lizards and their closest extinct relatives.

The two new species, Eoscincus ornatus and Microteras borealis, fill important gaps in the fossil record and offer tantalizing clues about the complexity and geographic distribution of lizard evolution. The new lizard “kings” are described in a study published in Nature Communications.

“This helps us time out the ages of the major living lizard and snake groups, as well as when their key anatomical features originated,” said Chase Brownstein, first author of the study. Brownstein, a Yale senior, collaborated on the study with Yale paleontologists Jacques Gauthier and Bhart-Anjan S. Bhullar.

Gauthier is a professor of Earth and planetary sciences in Yale’s Faculty of Arts and Science and curator at the Yale Peabody Museum of Natural History. Bhullar is an associate professor of Earth & planetary sciences and an associate curator at the Peabody Museum.

The brain's immune cells can be triggered to slow down Alzheimer's disease

Joana B. Pereira, researcher at Lund University and Karolinska Institutet who is first author of the study.
Photo Credit: Courtesy of Lund University

The brain's big-eating immune cells can slow down the progression of Alzheimer's disease. This is shown by a study that is now published in Nature Aging.

The brain's own immune cells are called microglia and are found in the central nervous system. They are big eaters that kill viruses, damaged cells and infectious agents they come across. It has long been known that microglial cells can be activated in different ways in several neurological diseases such as Alzheimer's and Parkinson's diseases. Depending on how they are activated, they can both drive and slow disease development. Researchers from Lund University and Karolinska Institutet have now shown that a certain type of activation of the microglial cells triggers inflammatory protective mechanisms in the immune system:

“Most people probably think that inflammation in the brain is something bad and that you should inhibit the inflammatory system in case of illness. But inflammation doesn't just have to be negative”, says Joana B. Pereira, researcher at Lund University and Karolinska Institutet who is first author of the study.

Machine learning model builds on imaging methods to better detect ovarian lesions

(From left) The top row shows an ultrasound image of a malignant lesion, the blood oxygen saturation, and hemoglobin concentration. The bottom row is an ultrasound image of a benign lesion, the blood oxygen saturation, and hemoglobin concentration.
Image Credit: Zhu lab

Although ovarian cancer is the deadliest type of cancer for women, only about 20% of cases are found at an early stage, as there are no real screening tests for them and few symptoms to prompt them. Additionally, ovarian lesions are difficult to diagnose accurately — so difficult, in fact that there is no sign of cancer in more than 80% of women who undergo surgery to have lesions removed and tested.

Quing Zhu, the Edwin H. Murty Professor of Biomedical Engineering at Washington University in St. Louis’ McKelvey School of Engineering, and members of her lab have applied a variety of imaging methods to diagnose ovarian cancer more accurately. Now, they have developed a new machine learning fusion model that takes advantage of existing ultrasound features of ovarian lesions to train the model to recognize whether a lesion is benign or cancerous from reconstructed images taken with photoacoustic tomography. Machine learning traditionally has been focused on single modality data. Recent findings have shown that multi-modality machine learning is more robust in its performance over unimodality methods. In a pilot study of 35 patients with more than 600 regions of interest, the model’s accuracy was 90%.

To Battle Climate Change, Scientists Tap into Carbon-Hungry Microorganisms for Clues

Electron microscopy images of 7-nanometer-diameter copper nanoparticles (shown left) and silver nanoparticles (center). At right: Electron microscopy image of ultrathin material synthesized from copper and silver nanoparticles, which could potentially be coupled with light-absorbing silicon nanowires for the design of efficient artificial photosynthesis systems. 
Credit: Peidong Yang/Berkeley Lab; courtesy of Nature Catalysis

Scientists at Lawrence Berkeley National Laboratory (Berkeley Lab) have demonstrated a new technique, modeled after a metabolic process found in some bacteria, for converting carbon dioxide (CO2) into liquid acetate, a key ingredient in “liquid sunlight” or solar fuels produced through artificial photosynthesis.

The new approach, reported in Nature Catalysis, could help advance carbon-free alternatives to fossil fuels linked to global warming and climate change.

The work is also the first demonstration of a device that mimics how these bacteria naturally synthesize acetate from electrons and CO2.

“What’s amazing is that we learned how to selectively convert carbon dioxide into acetate by mimicking how these little microorganisms do it naturally,” said senior author Peidong Yang, who holds titles of senior faculty scientist in Berkeley Lab’s Materials Sciences Division and professor of chemistry and materials science and engineering at UC Berkeley.

Breaking the scaling limits of analog computing

MIT researchers have developed a technique that greatly reduces the error in an optical neural network, which uses light to process data instead of electrical signals. With their technique, the larger an optical neural network becomes, the lower the error in its computations. This could enable them to scale these devices up so they would be large enough for commercial uses.
Credit: SFLORG stock photo

As machine-learning models become larger and more complex, they require faster and more energy-efficient hardware to perform computations. Conventional digital computers are struggling to keep up.

An analog optical neural network could perform the same tasks as a digital one, such as image classification or speech recognition, but because computations are performed using light instead of electrical signals, optical neural networks can run many times faster while consuming less energy.

However, these analog devices are prone to hardware errors that can make computations less precise. Microscopic imperfections in hardware components are one cause of these errors. In an optical neural network that has many connected components, errors can quickly accumulate.

Even with error-correction techniques, due to fundamental properties of the devices that make up an optical neural network, some amount of error is unavoidable. A network that is large enough to be implemented in the real world would be far too imprecise to be effective.

MIT researchers have overcome this hurdle and found a way to effectively scale an optical neural network. By adding a tiny hardware component to the optical switches that form the network’s architecture, they can reduce even the uncorrectable errors that would otherwise accumulate in the device.

Cognitive flexibility enhances mathematical reasoning

Multiple categorizations involves presenting students with mathematical problems that can be solved from different perspectives.
Illustration Credit: Calliste Scheibling-Sève

At school or in everyday life, proportional reasoning is essential for many activities. This type of reasoning allows us to adapt the quantity of ingredients in a recipe or to calculate the distance traveled as a function of speed by relying on ratios and proportions. In school settings, certain intuitive conceptions of proportions can mislead students and hinder their learning. A team from the University of Geneva (UNIGE) shows that multiple categorizations in mathematical problems - the ability to adopt several points of view on the same problem - makes it possible to go past this obstacle. These results open up new perspectives for the learning of mathematics but also for other disciplines. They can be found in the Journal of Numerical Cognition.

Proportional reasoning is a cognitive process that involves ratios and proportions to solve a mathematical problem. This reasoning is regularly practiced and applied in school, but it is also very useful in our daily lives. It allows us to calculate the price of certain products when we shop, to adapt the quantity of ingredients in a recipe, and to convert foreign currencies. It is at play when we understand that a speed of 30mph is equivalent to a distance of 15 miles travelled in 30 minutes. It is also involved in assessing our risk-taking: for example, when we make choices about our health by weighing the effectiveness of a treatment or vaccine against the risks of the disease.

Lychee Peel Powder Can Remove Persistent Dye from Wastewater

The peel of the lychee makes up about 15% of the weight of the fruit.
Photo Credit: Jamie Trinh

The international team of scientists, which includes chemists from the Ural Federal University, found out that chemically modified lychee peel eliminates a very persistent red dye from wastewater. The researchers have developed a new method that can be used to clean wastewater near textile production facilities in an environmentally friendly and cheap way. By doing so, it can prevent disease in humans and save animals, fish and birds that interact with dyed water. It will also help make the world's dirtiest rivers cleaner: the Buringanga River in Bangladesh, the Ganges in India, and the Chintarum in Indonesia, for example. A description of the new method and the results of the experiments were published in the Journal of Molecular Liquids.

"Red dyes emitted in various industries such as textiles, cosmetics, leather, food and plastic are dangerous environmental pollutants. From 20 to 40% of persistent dyes remain in wastewater and cause a critical increase in its acidity and alkalinity. The key factor here is the nature of these dyes. They contribute to increased deposition of calcium salts in organs, are considered highly toxic and pose a serious threat to humans, causing various cancers and mutagenic phenomena at cellular and molecular levels," explains Grigory Zyryanov, Professor of the Department of Organic and Biomolecular Chemistry at the Ural Federal University.