The method can detect gravitational waves that oscillate just once every thousand years, 100 times slower than any previously measured gravitational waves.

“These are waves reaching us from the farthest corners of the universe, capable of affecting how light travels,” said Jeff Dror, Ph.D., an assistant professor of physics at the University of Florida and co-author of the new study. “Studying these waves from the early universe will help us build a complete picture of our cosmic history, analogous to previous discoveries of the cosmic microwave background.”

Dror and his co-author, University of California, Santa Cruz postdoctoral researcher William DeRocco, published their findings Feb. 26 in Physical Review Letters.

Gravitational waves are akin to ripples in space. Like sound waves or waves on the ocean, gravitational waves vary in both frequency and amplitude, information that offers insights into their origin and age. Gravitational waves that reach us can be oscillating at extremely low frequencies, much lower than those of sound waves detectable with the human ear. Some of the lowest frequencies detected in the past were as low as one nanohertz.

“For reference,” Dror explained, “the frequency of sound waves created by an alligator roar are about 100 billion times higher than this frequency — these are very low-pitched waves.”

Their new method of detection is based on analyzing pulsars, neutron stars that emit radio waves at highly regular intervals. Dror hypothesized that searching for gradual slowdown in the arrivals of these pulses could reveal new gravitational waves. By studying existing pulsar data, Dror was able to search for gravitational waves with lower frequencies than ever before, increasing our “hearing range” to frequencies as low as 10 picohertz, 100 times lower than previous efforts that detected nanohertz-level waves.

While gravitational waves with frequencies around a nanohertz have been detected before, not much is known about their origin. There are two theories. The leading idea is that these waves are the result of a merger between two supermassive black holes, which, if true, would give researchers a new way to study the behavior of these giant objects that lie at the heart of every galaxy.

The other main theory is that these waves were created by some sort of cataclysmic event early in the universe’s history. By studying gravitational waves at even lower frequencies, they may be able to differentiate these possibilities.

“Looking ahead, the next step is to analyze newer data sets,” Dror said. “The datasets we used were primarily from 2014 and 2015, and a huge number of pulsar observations have been undertaken since that time.”

Dror also plans to run simulations on mock data using UF’s HiPerGator supercomputer to further unravel cosmic history. The supercomputer can efficiently run large, complex simulations, significantly reducing the time required to analyze data.

This study was supported in part by the National Science Foundation and the Department of Energy.

Data on more than 2,700 disadvantaged secondary (high) school students shows that minimizing pupil numbers in classrooms does not lead to better grades. Reducing class sizes could even decrease the odds of children achieving the best results, say the study authors.

The quantity of teachers also does not increase the odds of pupils from the poorest backgrounds achieving academically, despite concerns over staff shortages in schools.

Instead, the researchers say that resilience is guaranteed by the quality of teachers such as those with high discipline standards and who use their expertise to improve learning.

The study authors, who looked at data from China and Japan, are now urging policymakers to invest more in high-quality teachers and not to waste resources on cutting down the number of children in each class.

“This study supports the view that the quality of teachers, rather than the quantity, is the primary guarantee of students’ resilience,” says lead author Professor Tao Jiang, of Taizhou University whose research team also included experts from his institution and other China-based universities Northwest Normal, and Southwest.

“Quality teachers who effectively used teaching methods and managed classroom discipline increased the odds that individuals became resilient students.

“On the other hand, emphasizing the reduction of class sizes in schools may not benefit resilience. Smaller classes either had no relevance to resilience or were disadvantageous for resilience.

“Excessive emphasis on reducing class sizes is unnecessary, as it is detrimental to the emergence of students with high levels of resilience. Instead of allocating financial resources to reduce class sizes, it would be more effective to invest in providing high-quality science teachers.”

Academic resilience is defined as an individual’s ability to resist adversity and do well in school. It’s not fixed, and therefore can be improved, and is linked to what happens in schools and classrooms.

The authors of this study set out to identify the qualities and characteristics of 1,594 disadvantaged science students in Japan and 1,114 in the Macau region of China. Ages ranged from 15 to 16 years approximately and class size from 15 pupils (or fewer) to more than 50.

The participants were grouped into low, medium, or high-level resilience. The researchers looked at what classroom factors, school resources and school culture increased the odds of being in the high-level group.

All students in the study had participated in the 2015 Programme of International Student Assessment (PISA), an international questionnaire-based survey. PISA measures a pupil’s ability to use their reading, mathematics, and science knowledge.

Teacher discipline and support levels were among the many issues assessed by PISA. Pupils were also scored on motivation and how anxious they became during exams.

Results showed overall that a third of students displayed high-level resilience, a quarter low, and the rest medium.

High-resilience students were very positive towards school, science, and their future careers. They also dedicated more time to learning science than others but did experience anxiety about exams.

Science teachers and their teaching methods ‘play a crucial role’ in building students’ resilience, according to the authors. The findings show that these students benefited from classroom discipline, teacher-directed instruction, inquiry-based teaching, and teacher support.

In Japan, the most robust predictor of high-level resilience was inquiry-based teaching, while in Macau, it was teacher-directed instruction.

As for the impact of class size, smaller classes either had no impact on resilience such as in Macau or had a negative effect as in Japan. Conversely, an increase in class size by just one rank raised the odds of disadvantaged pupils in Japan attaining the best rather than the lowest grades by 1.2 times.

Other findings from the study show that misbehavior in the classroom or school in general undermines resilience.

The messages from the study for teachers include the need to guide pupils to conform to rules, to maintain discipline and prepare lessons in line with the needs of students.

The study did not analyze changes in how classrooms are managed now compared with before the pandemic. On this basis, the authors suggest further studies are needed on how COVID-19 has affected teaching in the context of student resilience.

Since a single spike epitope is recognized by multiple T-cell clones, the mRNA vaccination-induced T-cell response may consist of multiple spike-reactive clones. Thus, it is important to understand the mechanism of mRNA vaccination-induced cellular immune response. However, to address this clonal-resolution analysis on T-cell responses to mRNA vaccination has not been performed yet.

To bridge this gap, a team of researchers, led by Associate Professor Satoshi Ueha, including Professor Kouji Matsushima from the Tokyo University of Science (TUS), Japan, Mr. Hiroyasu Aoki from the University of Tokyo, and Professor Toshihiro Ito from Nara Medical University, aimed to develop a kinetic profile of spike-reactive T-cell clones during repetitive mRNA vaccination. For this, they performed a longitudinal TCR sequencing on peripheral T cells of 38 participants who had received the Pfizer vaccine from before the vaccine to after the third vaccination and then analyzed the single-cell gene expression and epitope specificity of the clonotypes.

Their findings, published in Cell Reports on March 7, 2024, revealed that while the primary T-cell response of naïve T cells generally peaked 10-18 days after the first shot, expansion of “early responders” was detected on day 7 after the first shot, suggesting that these early responders contain memory T cells against common cold coronaviruses. They also found a “main responder” that expanded after the second shot and did not expand early after the first shot and a “third responder” that appeared and expanded only after the third shot.

By longitudinally tracking the total frequency of each response pattern, it was observed that, after the second shot, a shift among the clonotypes occurred, wherein the major population changed from early responders to main responders, suggestive of a shift in clonal dominance. A similar shift of responding clones was also observed in CD4⁺ T cells.

Expanding upon the research process, Prof. Ueha says, “We next analyzed the phenotype of main responders after the second and the third vaccination. The results showed that the main responders after the second and third shots mostly consist of effector-memory T cells (T_EM), with more terminally differentiated effector memory-like phenotype after the third shot.”

The researchers then examined the repertoire changes of main responders, revealing that the expansion of main responders, which occurred after the second shot, diminished following the third shot, and the clonal diversity decreased and was partially replaced by the third responders. This may potentially mean that the third vaccination selected better-responding clones.

Due to the vaccination-induced shift in immunodominance of spike epitopes, the study supports the inter-epitope shift model. In addition, there were intra-epitope shifts of vaccine-responding clonotypes within spike epitopes.

Prof. Ueha explains the significance of these results, “Our analysis suggests that T cells can “re-write” themselves and reshape their memory populations after successive vaccinations. This re-writability not only maintains the number of memory T cells but also maintains diversity that can respond to different variants of pathogens. Moreover, by tuning the replacement of memory cells, more effective vaccines can be developed that can also be tailored to an individual’s unique immune response.”

Overall, this study provides important insights into mRNA vaccine-induced T-cell responses, which will be crucial for developing next-generation vaccines for more effective and broad protection against viruses.

The researchers developed so-called threofuranosyl nucleic acid (TNA) with a new, additional base pair. These are the first steps on the way to fully artificial nucleic acids with enhanced chemical functionalities. The study ‘Expanding the Horizon of the Xeno Nucleic Acid Space: Threose Nucleic Acids with Increased Information Storage’ was published in the Journal of the American Chemical Society.

Artificial nucleic acids differ in structure from their originals. These changes affect their stability and function. “Our threofuranosyl nucleic acid is more stable than the naturally occurring nucleic acids DNA and RNA, which brings many advantages for future therapeutic use,” said Professor Dr Stephanie Kath-Schorr. For the study, the 5-carbon sugar deoxyribose, which forms the backbone in DNA, was replaced by a 4-carbon sugar. In addition, the number of nucleobases was increased from four to six. By exchanging the sugar, the TNA is not recognized by the cell’s own degradation enzymes. This has been a problem with nucleic acid-based therapeutics, as synthetically produced RNA that is introduced into a cell is rapidly degraded and loses its effect. The introduction of TNAs into cells that remain undetected could now maintain the effect for longer.

“In addition, the built-in unnatural base pair enables alternative binding options to target molecules in the cell,” added Hannah Depmeier, lead author of the study. Kath-Schorr is certain that such a function can be used in particular in the development of new aptamers, short DNA or RNA sequences, which can be used for the targeted control of cellular mechanisms. TNAs could also be used for the targeted transport of drugs to specific organs in the body (targeted drug delivery) as well as in diagnostics; they could also be useful for the recognition of viral proteins or biomarkers.

“We found that restored coral reefs can grow at the same speed as healthy coral reefs just four years after coral transplantation,” says Ines Lange of University of Exeter, UK. “This means that they provide lots of habitat for marine life and efficiently protect the adjacent island from wave energy and erosion.”

“The speed of recovery that we saw was incredible,” she says. “We did not expect a full recovery of reef framework production after only four years.”

The work by Lange and her international colleagues represents the first reef carbonate budget trajectories at any coral restoration sites. The study was conducted at the Mars Coral Reef Restoration Programme in South Sulawesi, Indonesia, one of the largest restoration projects in the world. The project relies on transplanting corals and adding substrate to restore reefs badly damaged by blast fishing 30 or 40 years ago. Without human intervention, those reefs had shown no signs of recovering due to the presence of loose coral rubble that prevents young coral larvae from surviving.

The restoration effort has added a continuous network of sand-coated steel structures to consolidate the rubble and offer a structure for transplanting coral fragments. The question was whether and how quickly such restored sites would recover. To find out, the researchers measured the carbonate budgets of 12 sites that had been restored at different times, up to four years ago.

“Corals constantly add calcium carbonate to the reef framework while some fishes and sea urchins erode it away, so calculating the overall carbonate budget basically tells you if the reef as a whole is growing or shrinking,” Lange says. “Positive reef growth is important to keep up with sea-level rise, protect coastlines from storms and erosion, and provide habitat for reef animals.”

They wanted to know how long it takes to bring back healthy reef growth and its associated functions. Their data show that rapid growth of transplanted corals supports the recovery of coral cover and carbonate production. In fact, just four years in, the net carbonate budget had tripled such that it matched that at healthy control sites.

There were some important differences, however. Because branched corals had been transplanted preferentially over other corals, the makeup of the restored reef communities differs. The researchers say those differences “may affect habitat provision for some marine species and resilience to future heatwaves, as branching corals are more sensitive to bleaching.”

While longer-term study is necessary to see what happens over time and under stress, the findings show that active management actions can help to boost the resilience of reefs and bring back important ecosystem functions that are critical for marine life and local communities in relatively short periods of time, according to the researchers. They’re hopeful that, over time, restored reefs will naturally recruit a more diverse mix of coral species. However, they note that what will happen in any given location around the world will depend on many factors, including environmental conditions and restoration techniques.

“As is so often the case, there is no one-size-fits-all solution, but we hope that this positive example can be used as inspiration for other reef restoration projects around the world,” Lange says.

“These results give us the encouragement that if we can rapidly reduce emissions and stabilize the climate, we have effective tools to help regrow functioning coral reefs,” says Tim Lamont, a study co-author at the Lancaster Environment Centre, Lancaster University, UK.

Researchers report March 8 in the journal Nexus that they have developed a framework for assessing the relative value of rules and data in “informed machine learning models” that incorporate both. They showed that by doing so, they could help the AI incorporate basic laws of the real world and better navigate scientific problems like solving complex mathematical problems and optimizing experimental conditions in chemistry experiments.

“Embedding human knowledge into AI models has the potential to improve their efficiency and ability to make inferences, but the question is how to balance the influence of data and knowledge,” says first author Hao Xu of Peking University. “Our framework can be employed to evaluate different knowledge and rules to enhance the predictive capability of deep learning models.”

Generative AI models like ChatGPT and Sora are purely data-driven — the models are given training data, and they teach themselves via trial and error. However, with only data to work from, these systems have no way to learn physical laws, such as gravity or fluid dynamics, and they also struggle to perform in situations that differ from their training data. An alternative approach is informed machine learning, in which researchers provide the model with some underlying rules to help guide its training process, but little is known about the relative importance of rules vs data in driving model accuracy.

“We are trying to teach AI models the laws of physics so that they can be more reflective of the real world, which would make them more useful in science and engineering,” says senior author Yuntian Chen of the Eastern Institute of Technology, Ningbo.

To improve the performance of informed machine learning, the team developed a framework to calculate the contribution of an individual rule to a given model’s predictive accuracy. The researchers also examined interactions between different rules because most informed machine learning models incorporate multiple rules, and having too many rules can cause models to collapse.

This allowed them to optimize models by tweaking the relative influence of different rules and to filter out redundant or interfering rules entirely. They also identified some rules that worked synergistically and other rules that were completely dependent on the presence of other rules.

“We found that the rules have different kinds of relationships, and we use these relationships to make model training faster and get higher accuracy,” says Chen.

The researchers say that their framework has broad practical applications in engineering, physics, and chemistry. In the paper, they demonstrated the method’s potential by using it to optimize machine learning models to solve multivariate equations and to predict the results of thin layer chromatography experiments and thereby optimize future experimental chemistry conditions.

Next, the researchers plan to develop their framework into a plugin tool that can be used by AI developers. Ultimately, they also want to train their models so that the models can extract knowledge and rules directly from data, rather than having rules selected by human researchers.

“We want to make it a closed loop by making the model into a real AI scientist,” says Chen. “We are working to develop a model that can directly extract knowledge from the data and then use this knowledge to create rules and improve itself.”

This research was supported by the National Center for Applied Mathematics Shenzhen, the Shenzhen Key Laboratory of Natural Gas Hydrates, the SUSTech — Qingdao New Energy Technology Research Institute, and the National Natural Science Foundation of China.