The team published their findings in a new study in Plants.

“That finding suggests that azolla is food safe and has the potential to safely feed millions of people due to its rapid growth while free-floating on shallow fresh water without the need for nitrogen fertilizers,” said Daniel Winstead, research technologist in Penn State’s College of Agricultural Sciences and lead author on the earlier study. He works in the labs of Michael Jacobson, professor of ecosystem science and management, and Francesco Di Gioia, assistant professor of vegetable crop science. “Azolla is an amazing plant that can double its biomass in two days and capture nitrogen from the air.”

After the original study published, Winstead said, it was brought to his attention that the cyanobacteria that live inside azolla could produce powerful cyanotoxins that dissuade animals from eating the plant. Cyanotoxins have been linked to neurodegenerative disorders including amyotrophic lateral sclerosis (ALS) and Parkinson’s disease, liver and kidney failure, muscle paralysis and other severe health issues. Despite the threat of the toxins and the use and study of azolla, he explained they learned that no scientists had definitively tested for the presence of these toxins in azolla.

“I felt a sense of responsibility to help answer this question because we had just published about azolla’s nutritional quality,” Winstead said. “I didn’t want to be promoting the consumption of a potentially harmful plant. As I was preparing an experimental design, I was contacted by the Azolla Foundation about that organization’s interest in our research. I reached out to them and asked if they knew anyone who was looking into azolla’s toxicity from cyanotoxins.”

Several weeks later he received an email saying a group of researchers was investigating the cyanobacteria-cyanotoxins in azolla question, and they invited Winstead to be a part of the study.

“Together, we analyzed the results and concluded that azolla, and more specifically a cyanobacterium that lives in cavities in the leaves of azolla, do not produce any of the main cyanotoxins,” he said, explaining that the azolla’s cyanobacterium is Nostoc azollae, an endosymbiont or organism that lives within or on the surface of another organism in a mutually beneficial relationship. “More importantly, the known genes required to make these toxins are not even present within the genome of Nostoc azollae.”

According to Winstead, this discovery adds to a growing body of evidence that azolla could be used broadly to solve several global challenges.

“It could help feed many people in need around the world as well as become a new source of biofertilizer and biodiesel,” he said.

Also on the research team were by Jonatha Bujak and Alexandra Bujak, the Azolla Foundation, Blackpool, United Kingdom; Ana Pereira, Joana Azevedo and Vitor Vasconcelos, University of Porto, Portugal; Victor Leshyk, Azolla Biodesign, Sedona, Arizona; Minh Pham Gia, independent researcher, Hanoi, Vietnam; and Timo Stadtlander, The Research Institute of Organic Agriculture, Frick, Switzerland.

Open Philanthropy, Penn State — Research on Emergency Food Resilience project financially supported this research.

Today, a Project CETI research team led by Stephanie Gil, Assistant Professor of Computer Science at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), have proposed a new reinforcement learning framework with autonomous drones to find sperm whales and predict where they will surface.

The research is published in Science Robotics.

This new study uses various sensing devices, such as Project CETI aerial drones with very high frequency (VHF) signal sensing capability that leverage signal phase along with the drone’s motion to emulate an ‘antenna array in air’ for estimating directionality of received pings from CETI’s on-whale tags. It demonstrates that it’s possible to predict when and where a whale may surface by using these various sensor data as well as predictive models of sperm whales dive behavior. With that information, Project CETI can now design algorithms for the most efficient route for a drone to rendezvous — or encounter — a whale at the surface. This also opens up possible conservation applications to help ships avoid striking whales while at the surface.

Presenting the Autonomous Vehicles for whAle Tracking And Rendezvous by remote Sensing, or AVATARS framework, this study jointly develops two interrelated components of autonomy and sensing: autonomy, which determines the positioning commands of the autonomous robots to maximize visual whale encounters; and sensing, which measures the Angle-of-Arrival (AOA) from whale tags to inform the decision-making process. Measurements from our autonomous drone to surfaced tags, acoustic AOA from existing underwater sensors, and whale motion models from previous biological studies of sperm whales are provided as inputs to the AVATARS autonomous decision-making algorithm, which in turn aims to minimize missed rendezvous opportunities with whales.

AVATARS is the first co-development of VHF sensing and reinforcement learning decision-making for maximizing rendezvous of robots and whales at sea. A well-known application of time-critical rendezvous is used with rideshare apps, which uses real-time sensing to note the dynamic paths and positions of drivers and potential riders. When a rider requests a ride, it can assign a driver to rendezvous with the rider as efficiently and as timely as possible. Project CETI’s case is similar in that they are real-time tracking the whale, with the goal of coordinating the drone’s rendezvous to meet the whale at the surface.

This research advances Project CETI’s goal of obtaining millions to billions of high-quality, highly contextualized whale vocalizations. The addition of diverse types of data will improve location estimates and routing algorithms — helping Project CETI meet that goal more efficiently.

“I’m excited to contribute to this breakthrough for Project CETI. By leveraging autonomous systems and advanced sensor integration, we’re able to solve key challenges in tracking and studying whales in their natural habitats. This is not only a technological advancement, but also a critical step in helping us understand the complex communications and behaviors of these creatures,” said Gil.

“This research is a major milestone for Project CETI’s mission. We can now significantly enhance our ability to gather high-quality and large-scale dataset on whale vocalizations and the associated behavioral context, putting us one step closer to better listening to and translating what sperm whales are saying,” said David Gruber, Founder and Lead of Project CETI.

“‘This research was an amazing opportunity to test our systems and algorithms in a challenging marine environment. This interdisciplinary work, that combines wireless sensing, artificial intelligence and marine biology, is a prime example of how robotics can be part of the solution for further deciphering the social behavior of sperm whales,” said Ninad Jadhav, Harvard University PhD candidate and first author on the paper.

“This project provides an excellent opportunity to test our algorithms in the field, where robotics and artificial intelligence can enrich data collection and expedite research for broader science in language processing and marine biology, ultimately protecting the health and habitat of sperm whales,” said Sushmita Bhattacharya, a postdoctoral researcher in Gil’s REACT Lab at SEAS.

More information:

https://www.projectceti.org/

Researchers from UBC’s Okanagan and Vancouver campuses, as well as the University of Calgary, discovered that delivering CBTi during pregnancy significantly reduces postpartum depressive symptoms after a baby arrives.

“Early intervention is crucial for infant and parental mental health,” says Dr. Elizabeth Keys, an Assistant Professor in UBCO’s School of Nursing and a study co-author. “Our research explores how addressing sleep problems like insomnia can lead to better mental health outcomes for families, helping parents and their children thrive.”

CBTi is a therapeutic intervention that identifies thoughts, behaviors and sleep patterns that contribute to insomnia. Treatment includes challenging or reframing misconceptions and restructuring habits to improve sleep quality.

“CBTi is the gold standard for the treatment of insomnia and has consistently been shown to improve symptoms of depression,” says Dr. Keys. “Its treatment effects are similar to antidepressant medications among adults, but with fewer side effects, and is therefore often preferred by pregnant individuals.”

Sixty-two women assessed for insomnia and depressive symptoms participated in the study — with half randomly assigned to an intervention group and half to a control group.

“We found that CBTi during pregnancy significantly improved sleep and reduced postpartum depressive symptoms for participants,” explains Dr. Keys. “These are enormously encouraging results for anyone that has struggled in those early weeks and months with their newborns.”

Results indicate that effective insomnia treatment during pregnancy may serve as a protective factor against postpartum depression.

“Our study adds to the growing evidence that treating insomnia during pregnancy is beneficial for various outcomes,” Dr. Keys says. “It’s time to explore how we can make this treatment more accessible to pregnant individuals across the country to improve sleep health equity.”

The research highlights the interdisciplinary collaborations between researchers across Canada and UBC’s Vancouver and Okanagan campuses. Dr. Elizabeth Keys is from UBCO, while Dr. Lianne M. Tomfohr-Madsen, a Canada Research Chair in Mental Health and Intersectionality, is based at UBC Vancouver.

Dr. Keys and Dr. Tomfohr-Madsen are lead investigators on the Canadian Institutes of Health Research (CIHR) Sleep Equity Reimagined team and Canadian Sleep Research Consortium members.

When light conditions rapidly change, our eyes have to respond to this change in fractions of a second to maintain stable visual processing. This is necessary when, for example, we drive through a forest and thus move through alternating stretches of shadows and clear sunlight. “In situations like these, it is not enough for the photoreceptors to adapt, but an additional corrective mechanism is required,” said Professor Marion Silies of Johannes Gutenberg University Mainz (JGU). “Earlier work undertaken by her research group had already demonstrated that such a corrective ‘gain control’ mechanism exists in the fruit fly Drosophila melanogaster, where it acts directly downstream of the photoreceptors. Silies’ team has now managed to identify the algorithms, mechanisms, and neuronal networks that enable the fly to sustain stable visual processing when light levels change rapidly. The corresponding article has been published recently in Nature Communications.

Rapid changes in luminance challenge stable visual processing

Our vision needs to function accurately in many different situations — when we move in our surroundings as well as when our eyes follow an object that moves from light into shade. This applies to us humans and to many thousand animal species that rely heavily on vision to navigate. Rapid changes in luminance are also a problem in the world of inanimate objects when it comes to information processing by, for example, camera-based navigation systems. Hence, many self-driving cars depend on additional radar- or lidar-based technology to properly compute the contrast of an object relative to its background. “Animals are capable of doing this without such technology. Therefore, we decided to see what we could learn from animals about how visual information is stably processed under constantly changing lighting conditions,” explained Marion Silies the research question.

Combination of theoretical and experimental approaches

The compound eye of Drosophila melanogaster consists of 800 individual units or ommatidia. The contrast between an object and its background is determined postsynaptic of the photoreceptors. However, if luminance conditions suddenly change, as in the case of an object moving into the shadow of a tree, there will be differences in contrast responses. Without gain control, this would have consequences for all subsequent stages of visual processing, resulting in the object appearing different. The recent study with lead author Dr. Burak Gür used two-photon microscopy to describe where in visual circuitry stable contrast responses were first generated. This led to the identification of neuronal cell types that are positioned two synapses behind the photoreceptors.

These cell types respond only very locally to visual information. For the background luminance to be correctly included in computing contrast, this information needs narrow spatial pooling, as revealed by a computational model implemented by co-author Dr. Luisa Ramirez. “We started with a theoretical approach that predicted an optimal radius in images of natural environments to capture the background luminance across a particular region in visual space while, in parallel, we were searching for a cell type that had the functional properties to achieve this,” said Marion Silies, head of the Neural Circuits lab at the JGU Institute of Developmental Biology and Neurobiology (IDN).

Information on luminance is spatially pooled

The Mainz-based team of neuroscientists has identified a cell type that meets all required criteria. These cells, designated Dm12, pool luminance signals over a specific radius, which in turn corrects the contrast response between the object and its background in rapidly changing light conditions. “We have discovered the algorithms, circuits, and molecular mechanisms that stabilize vision even when rapid luminance changes occur,” summarized Silies, who has been investigating the visual system of the fruit fly over the past 15 years. She predicts that luminance gain control in mammals, including humans, is implemented in a similar manner, particularly as the necessary neuronal substrate is available.