“If you frame it as an either/or question, it’s too simplistic,” says Utah State University evolutionary biologist Zachariah Gompert. “The answer isn’t ‘completely random’ or ‘completely deterministic and predictable.’ And yet, examining short time scales, we can find predictable, repeatable evolutionary patterns.”

Gompert and colleagues report evidence of repeatable evolution in populations of stick insects in the May 24, 2024, online edition of the American Association for the Advancement of Science’s journal Science Advances. Collaborating authors on the paper include Gompert’s long-time collaborator Patrik Nosil and other researchers from France’s University of Montpelier, Brazil’s Federal University of São Paulo, the University of Nevada, Reno and Notre Dame University. The research is supported by the National Science Foundation and the European Research Council.

The team examined three decades of data on the frequency of cryptic color-pattern morphs in the stick insect species Timema cristinae in ten naturally replicate populations in California. T. cristinae is polymorphic in regard to its body color and pattern. Some insects are green, which allows the wingless, plant-feeding insect to blend in with California lilac (Ceanothus spinosus) shrubs. In contrast, green striped morphs disappear against chamise (Adenostoma fasciculatum) shrubs.

Hiding amongst the plants is one of T. christinae’s key defenses as hungry birds, such as scrub jays, are insatiable predators of the stick insects.

“Bird predation is a constant driver shaping the insects’ organismal traits, including coloration and striped vs. non-striped,” says Gompert, associate professor in USU’s Department of Biology and the USU Ecology Center. “We observed predictable ‘up-and-down’ fluctuations in stripe frequency in all populations, representing repeatable evolutionary dynamics based on standing genetic variation.”

He says a field experiment demonstrates these fluctuations involved negative frequency-dependent natural selection (NFDS), where cryptic color patterns are more beneficial when rare rather than common. This is likely because birds develop a ‘search image’ for very abundant prey.

“At short time scales, evolution involving existing variations can be quite predictable,” says Gompert, who received a National Science Foundation CAREER grant in 2019 to support his research. “You can count on certain drivers always being there, such as birds feeding on the insects.”

But at longer time scales, evolutionary dynamics become less predictable.

“The populations might experience a chance event, such as a severe drought or a flooding event, that disrupts the status quo and thus, the predictable outcomes,” Gompert says.

On long time scales, a new mutation in the species could introduce a rare trait, he says. “That’s about as close to truly random as you can get.”

“Rare things are easily lost by chance, so there’s a strong probability a new mutation could disappear before it gains a stronghold,” he says. “Indeed, another species of Timema stick insect that also feeds on chamise either never had or quickly lost the mutations making the cryptic stripe trait. Thus, the evolution of stripe is not a repeatable outcome of evolution at this long scale.”

Gompert notes replicated, long-term studies from natural populations, including research on the famous Darwin’s finches, are rare.

“Because most of this work is restricted to one or few populations, it is difficult to draw inferences on repeatability among multiple evolutionary independent populations,” he says. “Such studies are challenging to implement not only because they take concerted effort, but also because you can’t rush time.”

Gompert, who is designated a High Ranked Scholar by ScholarGPS, has developed, with USU colleagues, a research-intensive, interactive introductory biology laboratory class to introduce undergraduates to research. He and colleagues also developed an interactive presentation about evolution for all ages, called “Nabokov’s Butterflies,” that was presented at the USU College of Science’s Science Unwrapped public outreach program in 2022.

In a new study of 11 affected individuals, researchers from Newcastle University, the Wellcome Sanger Institute, the Great North Children’s Hospital, and their collaborators were able to link mutations in the NUDCD3 gene to Severe Combined Immunodeficiency and Omenn syndrome¹ – rare and life-threatening immunodeficiency disorders. These mutations prevented the normal development of diverse immune cells needed to combat different pathogens².

The findings, published today (24 May) in Science Immunology, open opportunities for early diagnosis and intervention for this condition.

Severe Combined Immunodeficiency (SCID) and Omenn syndrome are both rare genetic disorders that leave children without a functional immune system and at risk of life-threatening infections. Without urgent treatment, such as stem cell transplants to replace the faulty immune system, many affected will not survive their first year.

While newborn screening methods can flag T cell deficiency, knowledge of the specific genetic cause increases confidence in the diagnosis of SCID and informs the choice of curative therapy. Currently this remains out of reach for at least 1 in 10 affected families.

In this new study, researchers from Newcastle University, the Wellcome Sanger Institute and their collaborators studied 11 children across four families, two of whom had SCID while the other nine had Omenn syndrome. All had inherited mutations that disrupted the function of the NUDCD3 protein, which had not previously been linked to the immune system.

Using detailed studies of patient-derived cells and mouse models, the team demonstrated that NUDCD3 mutations impair a crucial gene-rearranging process called V(D)J recombination, essential for generating the diverse T cell receptors and antibodies needed to recognise and fight different pathogens.

While mice engineered with the same NUDCD3 mutations had milder immune problems, the human patients faced severe, life-threatening consequences. Two patients did survive, however, after receiving a stem cell transplant — reinforcing the importance of early diagnosis and intervention.

Dr Gosia Trynka, author of the study at the Wellcome Sanger Institute and science director at Open Targets, said: “For babies born with high-risk immunodeficiencies, early detection can mean the difference between life and death. These diseases leave newborns essentially defenceless against pathogens that most of us can easily fend off. The identification of this new disease gene will help clinicians to make a prompt molecular diagnosis in affected patients, meaning they can receive life-saving treatments more quickly.”

Professor Sophie Hambleton, senior author of the study at Newcastle University and practicing paediatric immunologist at the Great North Children’s Hospital, said: “SCID and Omenn syndrome are devastating disorders, requiring complex and timely treatments. The more we can understand about its underlying causes, the better we can look after affected babies. Our research is aimed at filling in the gaps so that families can achieve a molecular diagnosis while we continue learning more about how the immune system works in health and disease. We are deeply grateful to the families whose invaluable participation in this study will help future generations.”

Notes

1. Children with severe combined immunodeficiency disorder completely lack the T cells needed to combat infections, while those with Omenn syndrome have abnormal T cells that not only fail to combat infections but also mount attacks on the body’s own tissues. They require urgent infection management and treatment.

2. Specifically, the mutant NUDCD3 could not regulate RAG1, a key enzyme needed in V(D)J recombination. This caused RAG1 to get trapped within cell nucleoli instead of facilitating the gene rearrangements that build immune diversity.

While not possible yet, new research by a team of CU Boulder scientists could potentially lead to such advances.

Published today in the Proceedings of the National Academy of Sciences, researchers in Ankur Gupta’s lab discovered how tiny charged particles, called ions, move within a complex network of minuscule pores. The breakthrough could lead to the development of more efficient energy storage devices, such as supercapacitors, said Gupta, an assistant professor of chemical and biological engineering.

“Given the critical role of energy in the future of the planet, I felt inspired to apply my chemical engineering knowledge to advancing energy storage devices,” Gupta said. “It felt like the topic was somewhat underexplored and as such, the perfect opportunity.”

Gupta explained that several chemical engineering techniques are used to study flow in porous materials such as oil reservoirs and water filtration, but they have not been fully utilized in some energy storage systems.

The discovery is significant not only for storing energy in vehicles and electronic devices but also for power grids, where fluctuating energy demand requires efficient storage to avoid waste during periods of low demand and to ensure rapid supply during high demand.

Supercapacitors, energy storage devices that rely on ion accumulation in their pores, have rapid charging times and longer life spans compared to batteries.

“The primary appeal of supercapacitors lies in their speed,” Gupta said. “So how can we make their charging and release of energy faster? By the more efficient movement of ions.”

Their findings modify Kirchhoff’s law, which has governed current flow in electrical circuits since 1845 and is a staple in high school students’ science classes. Unlike electrons, ions move due to both electric fields and diffusion, and the researchers determined that their movements at pore intersections are different from what was described in Kirchhoff’s law.

Prior to the study, ion movements were only described in the literature in one straight pore. Through this research, ion movement in a complex network of thousands of interconnected pores can be simulated and predicted in a few minutes.

“That’s the leap of the work,” Gupta said. “We found the missing link.”

A University of Washington team has developed an artificial intelligence system that lets a user wearing headphones look at a person speaking for three to five seconds to “enroll” them. The system, called “Target Speech Hearing,” then cancels all other sounds in the environment and plays just the enrolled speaker’s voice in real time even as the listener moves around in noisy places and no longer faces the speaker.

The team presented its findings May 14 in Honolulu at the ACM CHI Conference on Human Factors in Computing Systems. The code for the proof-of-concept device is available for others to build on. The system is not commercially available.

“We tend to think of AI now as web-based chatbots that answer questions,” said senior author Shyam Gollakota, a UW professor in the Paul G. Allen School of Computer Science & Engineering. “But in this project, we develop AI to modify the auditory perception of anyone wearing headphones, given their preferences. With our devices you can now hear a single speaker clearly even if you are in a noisy environment with lots of other people talking.”

To use the system, a person wearing off-the-shelf headphones fitted with microphones taps a button while directing their head at someone talking. The sound waves from that speaker’s voice then should reach the microphones on both sides of the headset simultaneously; there’s a 16-degree margin of error. The headphones send that signal to an on-board embedded computer, where the team’s machine learning software learns the desired speaker’s vocal patterns. The system latches onto that speaker’s voice and continues to play it back to the listener, even as the pair moves around. The system’s ability to focus on the enrolled voice improves as the speaker keeps talking, giving the system more training data.

The team tested its system on 21 subjects, who rated the clarity of the enrolled speaker’s voice nearly twice as high as the unfiltered audio on average.

This work builds on the team’s previous “semantic hearing” research, which allowed users to select specific sound classes — such as birds or voices — that they wanted to hear and canceled other sounds in the environment.

Currently the TSH system can enroll only one speaker at a time, and it’s only able to enroll a speaker when there is not another loud voice coming from the same direction as the target speaker’s voice. If a user isn’t happy with the sound quality, they can run another enrollment on the speaker to improve the clarity.

The team is working to expand the system to earbuds and hearing aids in the future.

Additional co-authors on the paper were Bandhav Veluri, Malek Itani and Tuochao Chen, UW doctoral students in the Allen School, and Takuya Yoshioka, director of research at AssemblyAI. This research was funded by a Moore Inventor Fellow award, a Thomas J. Cabel Endowed Professorship and a UW CoMotion Innovation Gap Fund.