Plants that contain these alkaloids have made it very clear they don’t want to be consumed, but that hasn’t deterred bella moths (Utetheisa ornatrix). These day-flying moths exclusively eat the alkaloid-laden leaves and seeds of rattlebox plants. They then use the toxin to guard their eggs and deter predators in later life stages. They even use it to make pheromones that attract mates.

Exactly how bella moths and related species evolved the ability to safely consume pyrrolizidine alkaloids remains unknown.

In a new study published in the journal PNAS, researchers sequence the bella moth genome, which they used to pinpoint specific genes that may confer immunity to these toxins. They also sequenced genomes from 150 museum specimens — some more than a century old — to determine where bella moths and their close relatives originated. Finally, they combed through genetic data looking for clues that could help explain how the intricate wing patterns of bella moths evolved over time — the first study on moths or butterflies that has done so using dry museum collection specimens.

“We’ve managed to show that you can use museum specimens to answer genetic questions that normally require complicated laboratory techniques,” said study co-author Andrei Sourakov, collections coordinator at the Florida Museum of Natural History’s McGuire Center for Lepidoptera and Biodiversity. “This opens a window for future research of this kind.”

Sourakov has been studying bella moths for 15 years and said sequencing the genome for this species was the natural next step in the research he’s conducted so far. Many of the insights he gained during that time came from his work with undergraduate and high school students, whom he helped conduct short experiments, analyze data for science fairs and interpret the results in peer-reviewed papers.

In one such project, a student set out to determine the average lifespan of bella moth adults and inadvertently stumbled across the Methuselah of the moth world. “To our great surprise, they can live for up to 50 days, which is four to five times longer than the average moth,” Sourakov said.

Longevity is not a critically important trait in most moth species. Many breed once, then die shortly after, either from senescence or predation. But bella moths aren’t limited by the latter, making it more likely that genes conferring increased longevity will be beneficial and passed down to the next generation.

“It makes sense for something that’s chemically defended to live longer, because even if they’re caught, the predator most often lets go, and the moth can continue flying around.”

Bella moths live throughout much of eastern North America, Central America and the Caribbean and are often active during the day. Rather than using darkness as a shroud to avoid predators, bella moths make a point of being seen. Their wings are garbed with radiant pink, pearl, onyx and sulfur yellow scales, which birds and carnivorous insects can easily spot from a distance. Any predator unlucky enough to catch a bella moth quickly corrects its mistake.

“Banana spiders will cut them out of their webs,” Sourakov said, adding that wolf spiders and birds will go out of their way to avoid them. “When caught, they produce foaming liquid that tastes bad made almost entirely out of alkaloids.”

When ready to mate, females release a plume of aerosolized alkaloids derived from the plants they ate as caterpillars. Males are attracted to this scent, which they follow to its source. There, they perform a short but elaborate ritual in which they gently touch the female’s head with two fluffy and retractable structures that bear a strong resemblance to dandelions. Each filament in these structures is laced with pyrrolizidine alkaloids.

If the female decides the male has a sufficient quantity and quality of alkaloids stored up, the pair will mate. When finished, the male leaves behind a parting gift called a spermataphore, which contains sperm and, yes, more alkaloids. The female will use this and alkaloids from her own reserve to infuse the resulting eggs with toxins. This sort of biparental egg protection in insects is rare. In fact, when it was first observed in 1989 among bella moth adults, it was the only known example of a male moth or butterfly that invested any chemical resources in its progeny.

Bella moths are able to avoid the ill effects of pyrrolizidine alkaloids by using a special enzyme that oxidizes the molecule, rendering it harmless. If a predator eats a moth, however, the process is reversed, and the alkaloid regains its potency.

Pyrrolizidine alkaloids likely first evolved as a defense mechanism in plants, which then became a commodity for moths. Sourakov and his colleagues wanted to know how bella moths acquired this detoxifying enzyme and how they maintained it through an arms race millions of years long between plant and moth.

The authors discovered bella moths have not one but two copies of the gene that codes for their unique detoxifying enzyme. They may have acquired the second through a process of gene duplication, whereby other species, including many plants, have evolved new traits.

They also found two copies of a gene partially involved in antioxidant production and defense. Sourakov suspects these genes may be related both to the ability of bella moths to detoxify alkaloids and to their remarkable longevity.

“Certain types of stress on biological systems result in a longer lifespan. It could be that the interaction bella moths have with alkaloids is not only the reason why it makes sense for them to live long lives, but also one of the mechanisms behind it.”

AI models learn how to carry out such tasks by using a set of data that are annotated by humans, but the process of distinguishing cells from their background, called “single-cell segmentation,” is both time-consuming and laborious. As a result, there are limited amount of annotated data to use in AI training sets. UC Santa Cruz researchers have developed a method to solve this by building a microscopy image generation AI model to create realistic images of single cells, which are then used as “synthetic data” to train an AI model to better carry out single cell-segmentation.

The new software is described in a new paper published in the journal iScience. The project was led by Assistant Professor of Biomolecular Engineering Ali Shariati and his graduate student Abolfazl Zargari. The model, called cGAN-Seg, is freely available on GitHub.

“The images that come out of our model are ready to be used to train segmentation models,” Shariati said. “In a sense we are doing microscopy without a microscope, in that we are able to generate images that are very close to real images of cells in terms of the morphological details of the single cell. The beauty of it is that when they come out of the model, they are already annotated and labeled. The images show a ton of similarities to real images, which then allows us to generate new scenarios that have not been seen by our model during the training.”

Images of individual cells seen through a microscope can help scientists learn about cell behavior and dynamics over time, improve disease detection, and find new medicines. Subcellular details such as texture can help researchers answer important questions, like if a cell is cancerous or not.

Manually finding and labeling the boundaries of cells from their background is extremely difficult, however, especially in tissue samples where there are many cells in an image. It could take researchers several days to manually perform cell segmentation on just 100 microscopy images.

Deep learning can speed up this process, but an initial data set of annotated images is needed to train the models — at least thousands of images are needed as a baseline to train an accurate deep learning model. Even if the researchers can find and annotate 1,000 images, those images may not contain the variation of features that appear across different experimental conditions.

“You want to show your deep learning model works across different samples with different cell types and different image qualities,” Zargari said. “For example if you train your model with high quality images, it’s not going to be able to segment the low quality cell images. We can rarely find such a good data set in the microscopy field.”

To address this issue, the researchers created an image-to-image generative AI model that takes a limited set of annotated, labeled cell images and generates more, introducing more intricate and varied subcellular features and structures to create a diverse set of “synthetic” images. Notably, they can generate annotated images with a high density of cells, which are especially difficult to annotate by hand and are especially relevant for studying tissues. This technique works to process and generate images of different cell types as well as different imaging modalities, such as those taken using fluorescence or histological staining.

Zargari, who led the development of the generative model, employed a commonly used AI algorithm called a “cycle generative adversarial network” for creating realistic images. The generative model is enhanced with so-called “augmentation functions” and a “style injecting network,” which helps the generator to create a wide variety of high quality synthetic images that show different possibilities for what the cells could look like. To the researchers’ knowledge, this is the first time style injecting techniques have been used in this context.

Then, this diverse set of synthetic images created by the generator are used to train a model to accurately carry out cell segmentation on new, real images taken during experiments.

“Using a limited data set, we can train a good generative model. Using that generative model, we are able to generate a more diverse and larger set of annotated, synthetic images. Using the generated synthetic images we can train a good segmentation model — that is the main idea,” Zagari said.

The researchers compared the results of their model using synthetic training data to more traditional methods of training AI to carry out cell segmentation across different types of cells. They found that their model produces significantly improved segmentation compared to models trained with conventional, limited training data. This confirms to the researchers that providing a more diverse dataset during training of the segmentation model improves performance.

Through these enhanced segmentation capabilities, the researchers will be able to better detect cells and study variability between individual cells, especially among stem cells. In the future, the researchers hope to use the technology they have developed to move beyond still images to generate videos, which can help them pinpoint which factors influence the fate of a cell early in its life and predict their future.

“We are generating synthetic images that can also be turned into a time lapse movie, where we can generate the unseen future of cells,” Shariati said. “With that, we want to see if we are able to predict the future states of a cell, like if the cell is going to grow, migrate, differentiate or divide.”

“Although the scientific community has always emphasized the importance of oases, there has not been a clear map of the global distribution of oases,” said Dongwei Gui, a geoscientist at the Chinese Academy of Science who led the study. “Oasis research has both theoretical and practical significance for achieving United Nations Sustainable Development Goals and promoting sustainable development in arid regions.”

The study found that oases around the world grew by more than 220,149 square kilometers (85,000 square miles) from 1995 to 2020, mostly due to intentional oasis expansion projects in Asia. But desertification drove the loss of 134,300 square kilometers (51,854 square miles) of oasis over the same period, also mostly in Asia, leading to a net growth of 86,500 square kilometers (about 33,400 square miles) over the study period.

The findings highlight the risk climate change and anthropogenic stressors pose to these wet sanctuaries and can inform water resource management and sustainable development in arid regions. The study was published in the AGU journal Earth’s Future, which publishes interdisciplinary research on the past, present and future of our planet and its inhabitants.

The birth and death of an oasis

Oases are important sources of water for humans, plants and animals in the world’s drylands, supporting a majority of productivity and life in deserts. They form when groundwater flows and settles into low-lying areas, or when surface meltwater flows downslope from adjacent mountain ranges and pools. The existence of an oasis depends primarily on having a reliable source of water that is not rainfall. Today, oases are found in 37 countries; 77% of oases are located in Asia, and 13% are found in Australia.

Gui and his co-investigators wanted to understand the global distribution and dynamic changes of oases and see how they respond to a changing environment, such as variations in climate, water resources and human activities. Using data from the European Space Agency’s Climate Change Initiative Land Cover Product, the team categorized the land surface into seven categories: forest, grassland, shrub, cropland, water, urban and desert.

The researchers used satellite data to look for green, vegetated areas within dryland areas, indicating an oasis, and tracked changes over 25 years. Changes in the greenness of vegetation indicated changes in land use and oasis health, the latter of which can be influenced by both human activity and climate change. They also looked at changes in land surface type to find conversions of land use.

The researchers found that global oasis area increased by 220,800 square kilometers (85,251 square miles) over the 25-year timeframe. Most of that increase was from humans intentionally converting desert land into oases using runoff water and groundwater pumping, creating grasslands and croplands. The increase was concentrated in China, where management efforts have contributed more than 60% of the growth, Gui said. For example, more than 95% of the population in China’s Xinjiang Uygur Autonomous Region lives within an oasis, motivating conservation and a 16,700 square kilometer (6,448 square mile) expansion of the oasis, Gui said.

Countering human efforts to expand oases, desertification contributed to oasis loss. Worldwide, the researchers found there was a loss of more than 134,000 square kilometers (51,738 square miles) of oasis land over the past 25 years. The researchers estimate that changes to oases have directly affected about 34 million people around the world.

Overall, between gains and losses, oases had a net growth of 86,500 square kilometers (33,397 square miles) from 1995 to 2020 — but most gains were from the artificial expansion of oases, which may not be sustainable in the future.

Long-term oasis sustainability

The study highlighted ways to sustain healthy oases, including suggestions for improving water resource management, promoting sustainable land use and management and encouraging water conservation and efficient use. These efforts are especially important as the climate continues to change, Gui said.

Humans’ overexploitation of dwindling groundwater can limit oasis sustainability, as well as long-term glacier loss. While higher temperatures increase glacier melt, temporarily boosting oases’ water supplies, “as glaciers gradually disappear, the yield of meltwater will eventually decrease, leading to the shrinkage of oases once again,” Gui said.

International cooperation plays a crucial role in oasis sustainability, Gui said.

“Due to the unique mechanism of oasis formation, a river basin often nurtures multiple oases across several countries, making transboundary cooperation key to addressing water scarcity and promoting sustainable development,” he said.

By the numbers, from 1995 to 2020:

• Oases gained 85,000 square miles, mostly from artificial expansion projects
• Oases lost 52,000 square miles from desertification and water scarcity
• Oases gained a net area of about 33,400 square miles, but that mostly artificial growth is not sustainable