The results were presented today at the 2024 American Society of Clinical Oncology (ASCO) Annual Meeting by Steven Frank, M.D., professor of Radiation Oncology and executive director of the Particle Therapy Institute at The University of Texas MD Anderson Cancer Center.

With a median follow-up of three years, the progression-free survival (PFS) rate was 83% and 83.5% for IMPT and IMRT, respectively, and IMPT was statistically non-inferior to IMRT. There was a significant reduction of malnutrition with IMPT, with 24% of patients sustaining their nutrition with less than 5% weight loss during treatment compared with 14% of those receiving IMRT. Additionally, there was a significant reduction of feeding-tube dependence with IMPT at 28%, compared to 42% with IMRT.

“The results of this multi-center Phase III randomized trial provide evidence for IMPT as a new standard-of-care treatment approach for the management of head and neck tumors,” Frank said. “This is significant for patients as it represents a curative, de-intensified option compared to traditional radiation therapy.”

Proton therapy has both biological and physical advantages over traditional radiation therapy using photons. Unlike photons, protons have mass and can be stopped by the human body. This allows proton radiation to be delivered specifically to the targeted area, limiting the amount that reaches nearby normal tissues. This trial represents the largest randomized Phase III trial to date to investigate proton therapy in comparison to traditional radiation.

The trial enrolled 440 patients at 21 sites in the U.S., with 219 receiving IMRT and 221 receiving IMPT. Patients were stratified based on human papillomavirus (HPV) status, smoking status and whether they had received induction chemotherapy. The primary endpoint of the study was the PFS rate at three years.

“Historically, these kind of large-scale trials to confirm the benefits of proton therapy have been challenging, due in part to relatively few patients having access to proton therapy centers,” Frank said.” Encouraging results like these demonstrate the benefits of proton therapy and hopefully help pave the way for increased access for patients in need.”

This study was funded by grants from the National Institutes of Health (NIH)/National Cancer Institute (NCI) (U19CA021239, R03CA188162, R56DE025248) and Hitachi. Frank reports proton-related grant funding by Hitachi and honoraria fees from Ion Beam Applications S.A. (IBA). He also has non-proton related health care relationships with Boston Scientific (consulting fees), Affirmed Pharma (NIH grant), and C4 Imaging (founder, scientific advisory committee, patents/royalties, ownership interest). 

But the animals are not the only life being reawakened by the spring sun. Algae lying dormant on the ice starts blooming in spring blackening large areas of the ice.

When the ice blackens it’s ability to reflect the sun diminishes and this accelerates the melting of the ice. Increased melting exacerbates global warming.

But researchers might have found a way to control the snow algae growth — and maybe in the long run reduce some of the ice from melting. Living on the ice alongside the algae, postdoc Laura Perini from the Department of Environmental Science at Aarhus University and her colleagues, have found giant viruses.

She suspects that the viruses feed on the snow algae and could work as a natural control mechanism on the algae blooms.

“We don’t know a lot about the viruses, but I think they could be useful as a way of alleviating ice melting caused by algal blooms. How specific they are and how efficient it would be, we do not know yet. But by exploring them further, we hope to answer some of those questions,” she says.

Bigger than bacteria

Viruses are normally much smaller than bacteria. Regular viruses measure 20-200 nanometers in size, whereas a typical bacteria is 2-3 micrometers. In other words a normal virus is around 1000 times smaller than a bacteria.

That is not the case with giant viruses though.

Giant viruses grow to the size of 2.5 micrometers. That is bigger than most bacteria.

But the giant viruses are not only bigger in size. Their genome is much bigger than regular viruses. Bacteriophages — virus infecting bacteria — have between 100,000 and 200,000 letters in their genome. Giant viruses have around 2,500,000.

Never found on the ice before

Giant viruses were first discovered in 1981, when researchers found them in the ocean. These viruses had specialized in infecting green algae in the sea. Later, giant viruses were found in soil on land and even in humans.

But it’s the first time that giant viruses have been found living on the surface ice and snow dominated by microalgae, Laura Perini explains.

“We analyzed samples from dark ice, red snow and melting holes (cryoconite). In both the dark ice and red snow we found signatures of active giant viruses. And that is the first time they’ve been found on surface ice and snow containing a high abundance of pigmented microalgae.

“A few years ago everyone thought this part of the world to be barren and devoid of life. But today we know that several microorganisms live there — including the giant viruses.”

“There’s a whole ecosystem surrounding the algae. Besides bacteria, filamentous fungi and yeasts, there are protists eating the algae, different species of fungi parasitizing them and the giant viruses that we found, infecting them.

“In order to understand the biological controls acting on the algal blooms, we need to study these last three groups.”

Haven’t seen them with the naked eye

Even though the viruses are giant, they can’t be seen with the naked eye. Laura Perini hasn’t even seen them with a light microscope yet. But she hopes to do so in the future.

“The way we discovered the viruses was by analyzing all the DNA in the samples we took. By sifting through this huge dataset looking for specific marker genes, we found sequences that have high similarity to known giant viruses,” she explains.

To make sure that the viral DNA didn’t come from long dead microorganisms, but from living and active viruses, they also extracted all the mRNA from the sample.

When the sequences of the DNA that form genes are activated, they are transcribed into single stranded pieces called mRNA. These pieces work as recipes for building the proteins the virus needs. If they are present the virus is alive.

“In the total mRNA sequenced from the samples, we found the same markers as in the total DNA, so we know they have been transcribed. It means that the viruses are living and active on the ice,” she says.

DNA and RNA in viruses

At the center of the giant viruses is a cluster of DNA. That DNA contains all the genetic information or recipes needed to create proteins — the chemical compounds that are doing most of the work in the virus.

But in order to use those recipes, the virus needs to transcribe them from double-stranded DNA to single stranded mRNA.

Normal viruses can’t do that. Instead they have strands of RNA floating around in the cell waiting to be activated, when the virus infects an organism and hijacks its cellular production facilities.

Giant viruses can do that themselves which makes them very different from normal viruses.

Whereas DNA from dead viruses can be found in samples, mRNA is broken down much faster. mRNA is therefore an important marker of viral activity. In other words mRNA-recipes of certain proteins show that the viruses are alive and kicking.

Not sure exactly how they work

Because giant viruses are a relatively new discovery not a lot is known about them. In contrast to most other viruses they have a lot of active genes that enable them to repair, replicate, transcribe and translate DNA.

But why that is and exactly what they use it for is not known.

“Which hosts the giant viruses infect, we can’t link exactly. Some of them may be infecting protists while others attack the snow algae. We simply can’t be sure yet,” Laura Perini says.

She’s working hard on discovering more about the giant viruses and has more research coming out soon.

“We keep studying the giant viruses to learn more about their interactions and what is exactly is their role in the ecosystem. Later this year we’ll release another scientific with some more info on giant viruses infecting a cultivated microalgae thriving on the surface ice of the Greenland Ice Sheet,” she concludes.

The same models can also glean other information from animal vocalizations, such as the animal’s age, breed and sex. A collaboration with Mexico’s National Institute of Astrophysics, Optics and Electronics (INAOE) Institute in Puebla, the study finds that AI models originally trained on human speech can be used as a starting point to train new systems that target animal communication.

The results were presented at the Joint International Conference on Computational Linguistics, Language Resources and Evaluation.

“By using speech processing models initially trained on human speech, our research opens a new window into how we can leverage what we built so far in speech processing to start understanding the nuances of dog barks,” said Rada Mihalcea, the Janice M. Jenkins Collegiate Professor of Computer Science and Engineering, and director of U-M’s AI Laboratory.

“There is so much we don’t yet know about the animals that share this world with us. Advances in AI can be used to revolutionize our understanding of animal communication, and our findings suggest that we may not have to start from scratch.”

One of the prevailing obstacles to developing AI models that can analyze animal vocalizations is the lack of publicly available data. While there are numerous resources and opportunities for recording human speech, collecting such data from animals is more difficult.

“Animal vocalizations are logistically much harder to solicit and record,” said Artem Abzaliev, lead author and U-M doctoral student in computer science and engineering. “They must be passively recorded in the wild or, in the case of domestic pets, with the permission of owners.”

Because of this dearth of usable data, techniques for analyzing dog vocalizations have proven difficult to develop, and the ones that do exist are limited by a lack of training material. The researchers overcame these challenges by repurposing an existing model that was originally designed to analyze human speech.

This approach enabled the researchers to tap into robust models that form the backbone of the various voice-enabled technologies we use today, including voice-to-text and language translation. These models are trained to distinguish nuances in human speech, like tone, pitch and accent, and convert this information into a format that a computer can use to identify what words are being said, recognize the individual speaking, and more.

“These models are able to learn and encode the incredibly complex patterns of human language and speech,” Abzaliev said. “We wanted to see if we could leverage this ability to discern and interpret dog barks.”

The researchers used a dataset of dog vocalizations recorded from 74 dogs of varying breed, age and sex, in a variety of contexts. Humberto Pérez-Espinosa, a collaborator at INAOE, led the team who collected the dataset. Abzaliev then used the recordings to modify a machine-learning model — a type of computer algorithm that identifies patterns in large data sets. The team chose a speech representation model called Wav2Vec2, which was originally trained on human speech data.

With this model, the researchers were able to generate representations of the acoustic data collected from the dogs and interpret these representations. They found that Wav2Vec2 not only succeeded at four classification tasks; it also outperformed other models trained specifically on dog bark data, with accuracy figures up to 70%.

“This is the first time that techniques optimized for human speech have been built upon to help with the decoding of animal communication,” Mihalcea said. “Our results show that the sounds and patterns derived from human speech can serve as a foundation for analyzing and understanding the acoustic patterns of other sounds, such as animal vocalizations.”

In addition to establishing human speech models as a useful tool in analyzing animal communication — which could benefit biologists, animal behaviorists and more — this research has important implications for animal welfare. Understanding the nuances of dog vocalizations could greatly improve how humans interpret and respond to the emotional and physical needs of dogs, thereby enhancing their care and preventing potentially dangerous situations, the researchers said.