Now, a team of researchers led by Peter Abbamonte, a professor of physics at the University of Illinois Urbana-Champaign, have finally found Pines’ demon 67 years after it was predicted. As the researchers report in the journal Nature, they used a nonstandard experimental technique that directly excites a material’s electronic modes, allowing them to see the demon’s signature in the metal strontium ruthenate.

“Demons have been theoretically conjectured for a long time, but experimentalists never studied them,” Abbamonte said. “In fact, we weren’t even looking for it. But it turned out we were doing exactly the right thing, and we found it.”

The elusive demon

One of the most important discoveries of condensed matter physics is that electrons lose their individuality in solids. Electric interactions make the electrons combine to form collective units. With enough energy, the electrons can even form composite particles called plasmons with a new charge and mass determined by the underlying electric interactions. However, the mass is usually so large that plasmons cannot form with the energies available at room temperature.

Pines found an exception. If a solid has electrons in more than one energy band, as many metals do, he argued that their respective plasmons can combine in an out-of-phase pattern to form a new plasmon that is massless and neutral: a demon. Since demons are massless, they can form with any energy, so they may exist at all temperatures. This has led to speculation that they have important effects on the behavior of multi-band metals.

Demons’ neutrality means that they do not leave a signature in standard condensed matter experiments. “The vast majority of experiments are done with light and measure optical properties, but being electrically neutral means that demons don’t interact with light,” Abbamonte said. “A completely different kind of experiment was needed.”

A serendipitous discovery

Abbamonte recalls that he and his collaborators were studying strontium ruthenate for an unrelated reason — the metal is similar to high-temperature superconductors without being one. Hoping to find clues to why the phenomenon occurs in other systems, they were conducting the first survey of the metal’s electronic properties.

The research group of Yoshi Maeno, a professor of physics at Kyoto University, synthesized high-quality samples of the metal which Abbamonte and former graduate student Ali Husain examined with momentum-resolved electron energy-loss spectroscopy. A nonstandard technique, it uses energy from electrons shot into the metal to directly observe the metal’s features, including plasmons that form. As the researchers were looking through the data, though, they found something unusual: an electronic mode with no mass.

Husain, now a research scientist at Quantinuum, recalled, “At first, we had no idea what it was. Demons are not in the mainstream. The possibility came up early on, and we basically laughed it off. But, as we started ruling things out, we started to suspect that we had really found the demon.”

Edwin Huang, a Moore Postdoctoral Scholar at UIUC and condensed matter theorist, was eventually asked to calculate the features of strontium ruthenate’s electronic structure. “Pines’ prediction of demons necessitates rather specific conditions, and it was not clear to anyone whether strontium ruthenate should have a demon at all,” he said. “We had to perform a microscopic calculation to clarify what was going on. When we did this, we found a particle consisting of two electron bands oscillating out-of-phase with nearly equal magnitude, just like Pines described.”

The importance of just measuring stuff

According to Abbamonte, it was no accident that his group discovered the demon “serendipitously.” He emphasized that he and his group were using a technique that is not widely employed on a substance that has not been well studied. That they found something unexpected and significant is a consequence of simply trying something different, he believes.

“It speaks to the importance of just measuring stuff,” he said. “Most big discoveries are not planned. You go look somewhere new and see what’s there.”

Support was provided by the U.S. Department of Energy, the Japan Society for the Promotion of Science, the National Science Foundation, and the Gordon and Betty Moore Foundation.

Dr Odette Leiter and Dr Tara Walker from UQ’s Queensland Brain Institute led a team which discovered platelets, the tiny blood cells critical for blood clotting, secrete a protein that rejuvenates neurons in aged mice in a similar way to physical exercise.

“We know exercise increases production of new neurons in the hippocampus, the part of the brain important for learning and memory, but the mechanism hasn’t been clear,” Dr Leiter said.

“Our previous research has shown platelets are involved, but this study shows platelets are actually required for this effect in the aged mice.”

The researchers focused on exerkines, the biological compounds released into the bloodstream during exercise, which are believed to stimulate the exercise-induced response in the brain.

“We discovered that the exerkine CXCL4/Platelet factor 4 or PF4, which is released from platelets after exercise, results in regenerative and cognitive improvements when injected into aged mice,” Dr Leiter said.

Dr Walker said the findings have significant implications for the development of drug interventions.

“For a lot of people with health conditions, mobility issues or of advanced age, exercise isn’t possible, so pharmacological intervention is an important area of research,” she said.

“We can now target platelets to promote neurogenesis, enhance cognition and counteract age-related cognitive decline.”

The researchers said the next step is to test the response in Alzheimer diseased mice, before moving towards human trials.

“It’s important to note this is not a replacement for exercise,” Dr Walker said.

“But it could help the very elderly or someone who has had a brain injury or stroke to improve cognition.”

The study is published in Nature Communications.

What if “looking your age” refers not to your face, but to your chest? Osaka Metropolitan University scientists have developed an advanced artificial intelligence (AI) model that utilizes chest radiographs to accurately estimate a patient’s chronological age. More importantly, when there is a disparity, it can signal a correlation with chronic disease. These findings mark a leap in medical imaging, paving the way for improved early disease detection and intervention. The results are set to be published in The Lancet Healthy Longevity.

The research team, led by graduate student Yasuhito Mitsuyama and Dr. Daiju Ueda from the Department of Diagnostic and Interventional Radiology at the Graduate School of Medicine, Osaka Metropolitan University, first constructed a deep learning-based AI model to estimate age from chest radiographs of healthy individuals. They then applied the model to radiographs of patients with known diseases to analyze the relationship between AI-estimated age and each disease. Given that AI trained on a single dataset is prone to overfitting, the researchers collected data from multiple institutions.

For the development, training, internal and external testing of the AI model for age estimation, a total of 67,099 chest radiographs were obtained between 2008 and 2021 from 36,051 healthy individuals who underwent health check-ups at three facilities. The developed model showed a correlation coefficient of 0.95 between the AI-estimated age and chronological age. Generally, a correlation coefficient of 0.9 or higher is considered to be very strong.

To validate the usefulness of AI-estimated age using chest radiographs as a biomarker, an additional 34,197 chest radiographs were compiled from 34,197 patients with known diseases from two other institutions. The results revealed that the difference between AI-estimated age and the patient’s chronological age was positively correlated with a variety of chronic diseases, such as hypertension, hyperuricemia, and chronic obstructive pulmonary disease. In other words, the higher the AI-estimated age compared to the chronological age, the more likely individuals were to have these diseases.

“Chronological age is one of the most critical factors in medicine,” stated Mr. Mitsuyama. “Our results suggest that chest radiography-based apparent age may accurately reflect health conditions beyond chronological age. We aim to further develop this research and apply it to estimate the severity of chronic diseases, to predict life expectancy, and to forecast possible surgical complications.”

These fossils are very important to understand the evolution of sea spiders. They show that the diversity of sea spiders that still exist today had already started to form by the Jurassic.

Lead author Dr Romain Sabroux from the University of Bristol’s School of Earth Sciences, said: “Sea spiders (Pycnogonida), are a group of marine animals that is overall very poorly studied.

“However, they are very interesting to understand the evolution of arthropods [the group that includes insects, arachnids, crustaceans, centipedes and millipedes] as they appeared relatively early in the arthropod tree of life. That’s why we are interested in their evolution.

“Sea spider fossils are very rare, but we know a few of them from different periods. One of the most remarkable fauna, by its diversity and its abundance, is the one of La Voulte-sur-Rhône that dates back to the Jurassic, some 160 million years ago.”

Unlike older sea spider fossils, the La Voulte pycnogonids are morphologically similar (but not identical) to living species, and previous studies suggested they could be closely related to living sea spider families. But these hypotheses were restricted by the limitation of their observation means. As it was impossible to access what was hidden in the rock fossils, Dr Sabroux and his team travelled to Paris and set out to investigate this question with cutting-edge approaches.

Dr Sabroux explained: “We used two methods to reinvestigate the morphology of the fossils: X-ray microtomography, to ‘look inside’ the rock, find morphological features hidden inside and reconstruct a 3D model of the fossilised specimen; and Reflectance Transformation Imaging, a picture technic that relies on varied orientation of the light around the fossil to enhance the visibility of inconspicuous features on their surface.

“From these new insights, we drew new morphological information to compare them with extant species,” explained Dr Sabroux.

This confirmed that these fossils are close relatives to surviving pycnogonids. Two of these fossils belong to two living pycnogonid families: Colossopantopodus boissinensis was a Colossendeidae while another, Palaeoendeis elmii was an Endeidae. The third species, Palaeopycnogonides gracilis, seems to belong to a family that has disappeared today.

“Today, by calculating the difference between the DNA sequences of a sample of species, and using DNA evolution models, we are able to estimate the timing of the evolution that bind these species together,” added Dr Sabroux.

“This is what we call a molecular clock analysis. But quite like a real clock, it needs to be calibrated. Basically, we need to tell the clock: ‘we know that at that time, that group was already there.’ Thanks to our work, we now know that Colossendeidae, and Endeidae were already ‘there’ by the Jurassic.”

Now, the team can use these minimal ages as calibrations for the molecular clock, and investigate the timing of Pycnogonida evolution. This can help them understand, for example, how their diversity was impacted by the different biodiversity crises that distributes over the Earth history.

They also plan to investigate other pycnogonid fossil faunae such as the fauna of Hunsrück Slate, in Germany, which dates from the Devonian, some 400 million years ago.

With the same approach, they will aim to redescribe these species and understand their affinities with extant species; and finally, to replace in the tree of life of Pycnogonida all the pycnogonid fossils from all periods.

Dr Sabroux added: “These fossils give us an insight of sea spiders living 160 million years ago.

“This is very exciting when you have been working on the living pycnogonids for years.

“It is fascinating how these pycnogonids look both very familiar, and very exotic. Familiar, because you can definitely recognize some of the families that still exist today, and exotic because of small differences like the size of the legs, the length of the body, and some other morphological characteristics that you do not find in modern species.

“Now we look forward to the next fossil discoveries — from the Jurassic and other geological periods — so that we can complete the picture!”

“Stem cell research has enabled the formation of models capable of organizing into structures that rudimentarily resemble embryos and reflect various degrees of completeness and developmental stages,” says first author Nicolas Rivron, a development biologist at the Austrian Academy of Sciences. “These new propositions are part of an effort to bring clarity to ongoing research — to better classify the types of structures formed in the laboratory, to refine the legal definition of human embryos, and to pinpoint what currently makes models and embryos different from the legal standpoint.”

These new propositions build on the latest formal guidelines from the International Society for Stem Cell Research (ISSCR), which were issued in 2021 and dealt with emerging advances in the field, including stem cell-based embryo models, human embryo research, chimeras, organoids, and genome editing. Discussions about updated guidelines started after mouse embryo models were formed, in anticipation of major advances in human counterparts.

“It’s important to frequently refine these ethical guidelines and gradually adapt ethical oversights as science advances,” Rivron says. “Here, we propose a refined definition of the human embryo that focuses on what it can become rather than how it came to be. This definition allows us to think about the conditions under which models, if improved, might eventually pass a tipping point and be legally considered embryos.”

The authors propose this definition to be “a group of human cells supported by elements fulfilling extra-embryonic and uterine functions that, combined, have the potential to form a fetus.” They note that current models do not meet legal definitions, but that it’s important to define how to evaluate whether they have passed that tipping point in the future.

Decisions about how to regulate embryo research are guided by scientific societies but are ultimately the responsibility of local authorities. “Some nations ask their ethical committees to adapt or implement the ethical guidelines as established by scientific societies without legislating, while others prefer to engrave decisions in a regulatory context, for example the UK’s rule that restricts the culturing of embryos to 14 days,” Rivron says. “Different approaches allow for a different level of flexibility as science progresses.”

In addition, the authors also reiterate that, according to the ISSCR’s Fundamental Principles, it is the duty of scientists to ensure accurate public understanding and perception of human embryology using embryo models.

“Appropriate, trustworthy, and timely public communication is necessary,” Rivron says. For the structures currently being formed, the terms “embryo models,” “embryonic models,” and “stem cell-derived embryo models” are preferable to the term “synthetic embryos,” which could imply that synthetic elements are being used, rather than the natural cells and developmental programs that are at play.

“The reality is that these embryo models cannot form neonates, but they help us fill an important knowledge gap in our basic understanding of how humans form — something that is normally hidden in the womb,” he says. “We hope that in the future this knowledge will benefit society by supporting the development of medicines to combat infertility and early pregnancy loss and by leading to a better understanding of the origins of congenital malformations and diseases. The field is still in its infancy, but it is opening important and previously inaccessible avenues for science, ethics, and medicine.”

While the cost of installing a solar energy system has dropped by more than 99 percent since 1980, this new analysis shows that “soft technology” features, such as the codified permitting practices, supply chain management techniques, and system design processes that go into deploying a solar energy plant, contributed only 10 to 15 percent of total cost declines. Improvements to hardware features were responsible for the lion’s share.

But because soft technology is increasingly dominating the total costs of installing solar energy systems, this trend threatens to slow future cost savings and hamper the global transition to clean energy, says the study’s senior author, Jessika Trancik, a professor in MIT’s Institute for Data, Systems, and Society (IDSS).

Trancik’s co-authors include lead author Magdalena M. Klemun, a former IDSS graduate student and postdoc who is now an assistant professor at the Hong Kong University of Science and Technology; Goksin Kavlak, a former IDSS graduate student and postdoc who is now an associate at the Brattle Group; and James McNerney, a former IDSS postdoc and now senior research fellow at the Harvard Kennedy School.

The team created a quantitative model to analyze the cost evolution of solar energy systems, which captures the contributions of both hardware technology features and soft technology features.

The framework shows that soft technology hasn’t improved much over time — and that soft technology features contributed even less to overall cost declines than previously estimated.

Their findings indicate that to reverse this trend and accelerate cost declines, engineers could look at making solar energy systems less reliant on soft technology to begin with, or they could tackle the problem directly by improving inefficient deployment processes.

“Really understanding where the efficiencies and inefficiencies are, and how to address those inefficiencies, is critical in supporting the clean energy transition. We are making huge investments of public dollars into this, and soft technology is going to be absolutely essential to making those funds count,” says Trancik.

“However,” Klemun adds, “we haven’t been thinking about soft technology design as systematically as we have for hardware. That needs to change.”

The hard truth about soft costs

Researchers have observed that the so-called “soft costs” of building a solar power plant — the costs of designing and installing the plant — are becoming a much larger share of total costs. In fact, the share of soft costs now typically ranges from 35 to 64 percent.

“We wanted to take a closer look at where these soft costs were coming from and why they weren’t coming down over time as quickly as the hardware costs,” Trancik says.

In the past, scientists have modeled the change in solar energy costs by dividing total costs into additive components — hardware components and nonhardware components — and then tracking how these components changed over time.

“But if you really want to understand where those rates of change are coming from, you need to go one level deeper to look at the technology features. Then things split out differently,” Trancik says.

The researchers developed a quantitative approach that models the change in solar energy costs over time by assigning contributions to the individual technology features, including both hardware features and soft technology features.

For instance, their framework would capture how much of the decline in system installation costs — a soft cost — is due to standardized practices of certified installers — a soft technology feature. It would also capture how that same soft cost is affected by increased photovoltaic module efficiency — a hardware technology feature.

With this approach, the researchers saw that improvements in hardware had the greatest impacts on driving down soft costs in solar energy systems. For example, the efficiency of photovoltaic modules doubled between 1980 and 2017, reducing overall system costs by 17 percent. But about 40 percent of that overall decline could be attributed to reductions in soft costs tied to improved module efficiency.

The framework shows that, while hardware technology features tend to improve many cost components, soft technology features affect only a few.

“You can see this structural difference even before you collect data on how the technologies have changed over time. That’s why mapping out a technology’s network of cost dependencies is a useful first step to identify levers of change, for solar PV and for other technologies as well,” Klemun notes.

Static soft technology

The researchers used their model to study several countries, since soft costs can vary widely around the world. For instance, solar energy soft costs in Germany are about 50 percent less than those in the U.S.

The fact that hardware technology improvements are often shared globally led to dramatic declines in costs over the past few decades across locations, the analysis showed. Soft technology innovations typically aren’t shared across borders. Moreover, the team found that countries with better soft technology performance 20 years ago still have better performance today, while those with worse performance didn’t see much improvement.

This country-by-country difference could be driven by regulation and permitting processes, cultural factors, or by market dynamics such as how firms interact with each other, Trancik says.

“But not all soft technology variables are ones that you would want to change in a cost-reducing direction, like lower wages. So, there are other considerations, beyond just bringing the cost of the technology down, that we need to think about when interpreting these results,” she says.

Their analysis points to two strategies for reducing soft costs. For one, scientists could focus on developing hardware improvements that make soft costs more dependent on hardware technology variables and less on soft technology variables, such as by creating simpler, more standardized equipment that could reduce on-site installation time.

Or researchers could directly target soft technology features without changing hardware, perhaps by creating more efficient workflows for system installation or automated permitting platforms.

“In practice, engineers will often pursue both approaches, but separating the two in a formal model makes it easier to target innovation efforts by leveraging specific relationships between technology characteristics and costs,” Klemun says.

“Often, when we think about information processing, we are leaving out processes that still happen in a very low-tech way through people communicating with one another. But it is just as important to think about that as a technology as it is to design fancy software,” Trancik notes.

In the future, she and her collaborators want to apply their quantitative model to study the soft costs related to other technologies, such as electrical vehicle charging and nuclear fission. They are also interested in better understanding the limits of soft technology improvement, and how one could design better soft technology from the outset.

This research is funded by the U.S. Department of Energy Solar Energy Technologies Office.