With the demand for donor organs far outstripping the supply, calls for changes in public policy are growing. An opt-out (‘presumed consent’) default policy is often seen as a promising approach. This policy stipulates that all adults are automatically considered potential organ donors after their death, unless they explicitly withdraw their consent during their lifetime. In contrast, the opt-in (‘explicit consent’) system requires potential donors to actively consent to donate their organs after they die. The discussion around implementing an opt-out policy has recently gained traction again in Germany, raising the question of whether such a change in policy would actually lead to an increase in the number of deceased organ donors.

A recent analysis of all member countries of the Organisation for Economic Co-operation and Development (OECD) found no significant difference in deceased donor rates between opt-in and opt-out countries, but significantly fewer living donors — individuals who voluntarily donate organs, like a kidney, while alive — in opt-out countries. However, such cross-sectional analyses cannot control for all country-specific factors like health infrastructure, culture, and religious issues — all of which can influence donation rates.

To address the limitations of prior research, the current study used a longitudinal approach, analyzing changes in deceased donor rates over time in five countries — Argentina, Chile, Sweden, Uruguay, and Wales — that had switched from an opt-in to an opt-out default policy. This method provided a more reliable assessment of the impact of opt-out policies by controlling for long-term trends and country-specific factors.

Data was collected from international databases, including the International Registry in Organ Donation and Transplantation (IRODaT) and the Global Observatory on Donation and Transplantation (GODT). Of the 39 countries that had changed from explicit to presumed consent by December 2019, only five could be included in the analysis due to a lack of historical data for changes made before the IRODaT database was launched in 1996 and because presumed consent practices often existed informally prior to formal legislation.

Consistent with previous cross-sectional analyses, the study found that switching the default from opt-in to opt-out did not lead to any increase in organ donation rates in the five countries considered. Moreover, the results indicated that the opt-out default did not cause even a slight upward curve in organ donations: the long-term trend remained the same, showing no change in the rate following the switch. As expected, the results did show a reduction in deceased donations with the onset of the COVID-19 pandemic, with only a slow recovery observed by 2022.

“Simply switching to an opt-out system does not automatically lead to more organ donations,” states author Mattea Dallacker, who led the project at the Center for Adaptive Rationality at the Max Planck Institute for Human Development. “Without accompanying measures, such as investments in the healthcare system and public awareness campaigns, a shift to an opt-out default is unlikely to increase organ donations. There is no easy solution to the complex challenge of boosting organ donation rates,” she continues.

The study also underscores the crucial role of relatives in organ donation decisions. Even in presumed consent systems, where individuals are considered donors unless they opt out, families are often consulted and can override the presumed consent. Since many people do not talk about their donation wishes with loved ones, presumed consent can lead to uncertainty and hesitation among families, potentially resulting in refusals.

“A possible alternative to the opt-out system is a mandatory choice system,” says Ralph Hertwig, Director at the Center for Adaptive Rationality at the Max Planck Institute for Human Development. “This would allow citizens to explicitly register their consent or objection to organ donation, when applying for a driver’s license or ID card, for example. This active choice system could prompt people to make an informed decision, which would eliminate the perceived ambiguity about their preference that appears to lead to higher family refusal rates. Good and accessible information about organ donation is essential for informed choice,” Hertwig continues.

“It’s a very weird event, called AT 2021hdr, that keeps recurring every few months,” said Lorena Hernández-García, an astrophysicist at the Millennium Institute of Astrophysics, the Millennium Nucleus on Transversal Research and Technology to Explore Supermassive Black Holes, and University of Valparaíso in Chile. “We think that a gas cloud engulfed the black holes. As they orbit each other, the black holes interact with the cloud, perturbing and consuming its gas. This produces an oscillating pattern in the light from the system.”

A paper about AT 2021hdr, led by Hernández-García, was published Nov. 13 in the journal Astronomy and Astrophysics.

The dual black holes are in the center of a galaxy called 2MASX J21240027+3409114, located 1 billion light-years away in the northern constellation Cygnus. The pair are about 16 billion miles (26 billion kilometers) apart, close enough that light only takes a day to travel between them. Together they contain 40 million times the Sun’s mass.

Scientists estimate the black holes complete an orbit every 130 days and will collide and merge in approximately 70,000 years.

AT 2021hdr was first spotted in March 2021 by the Caltech-led ZTF (Zwicky Transient Facility) at the Palomar Observatory in California. It was flagged as a potentially interesting source by ALeRCE (Automatic Learning for the Rapid Classification of Events). This multidisciplinary team combines artificial intelligence tools with human expertise to report events in the night sky to the astronomical community using the mountains of data collected by survey programs like ZTF.

“Although this flare was originally thought to be a supernova, outbursts in 2022 made us think of other explanations,” said co-author Alejandra Muñoz-Arancibia, an ALeRCE team member and astrophysicist at the Millennium Institute of Astrophysics and the Center for Mathematical Modeling at the University of Chile. “Each subsequent event has helped us refine our model of what’s going on in the system.”

Since the first flare, ZTF has detected outbursts from AT 2021hdr every 60 to 90 days.

Hernández-García and her team have been observing the source with Swift since November 2022. Swift helped them determine that the binary produces oscillations in ultraviolet and X-ray light on the same time scales as ZTF sees them in the visible range.

The researchers conducted a Goldilocks-type elimination of different models to explain what they saw in the data.

Initially, they thought the signal could be the byproduct of normal activity in the galactic center. Then they considered whether a tidal disruption event — the destruction of a star that wandered too close to one of the black holes — could be the cause.

Finally, they settled on another possibility, the tidal disruption of a gas cloud, one that was bigger than the binary itself. When the cloud encountered the black holes, gravity ripped it apart, forming filaments around the pair, and friction started to heat it. The gas got particularly dense and hot close to the black holes. As the binary orbits, the complex interplay of forces ejects some of the gas from the system on each rotation. These interactions produce the fluctuating light Swift and ZTF observe.

Hernández-García and her team plan to continue observations of AT 2021hdr to better understand the system and improve their models. They’re also interested in studying its home galaxy, which is currently merging with another one nearby — an event first reported in their paper.

“As Swift approaches its 20th anniversary, it’s incredible to see all the new science it’s still helping the community accomplish,” said S. Bradley Cenko, Swift’s principal investigator at NASA’s Goddard Space Flight Center in Greenbelt, Maryland. “There’s still so much it has left to teach us about our ever-changing cosmos.”

High ocean temperatures cause coral bleaching, which results from the disruption of the relationship between corals and their symbiotic algae, an increasing concern as global temperatures rise. However, relatively little research has examined the effects of high temperatures during early life stages of corals.

In this study, Huffmyer and colleagues exposed coral larvae to high temperatures at the Hawai’i Institute of Marine Biology. For three days during their first week of development, the larvae and their algal symbionts were treated to temperatures 2.5 degrees Celsius above ambient temperature, similar to expected changes in seawater due to climate change. The coral larvae showed no signs of bleaching in the heated water, and they were able to maintain rates of algal photosynthesis and the supply of carbon-based nutrition from the algae to the host. However, there was a 19% reduction in coral metabolism, as well as increased uptake and storage of nitrogen by the coral, both of which are apparent strategies that improve coral survival.

Reduced metabolism allows the coral to conserve energy and resources, also seen in adult corals during bleaching. The change in nitrogen cycling seems to be an adaptation by the coral to limit the amount of nitrogen available to the algae, thus preventing algal overgrowth and the destabilization of the coral-algae relationship.

It remains unclear how effective these strategies are at higher temperatures and for longer durations. Further research into the details and limitations of coral reaction to high temperatures will provide crucial knowledge for predicting coral response and protecting coral reefs as global temperatures continue to rise.

The authors add, “This research reveals that coral larvae must invest in their nutritional partnership with algae to withstand stress, offering key insights into strategies to avoid bleaching in earliest life stages of corals.”

However, nature has shown that vision doesn’t have to be constrained by light’s limitations — many organisms have evolved ways to perceive their environment without relying on light. Bats navigate using the echoes of sound waves, while sharks hunt by sensing electrical fields from their prey’s movements.

Radio waves, whose wavelengths are orders of magnitude longer than light waves, can better penetrate smoke and fog, and can even see through certain materials — all capabilities beyond human vision. Yet robots have traditionally relied on a limited toolbox: they either use cameras and LiDAR, which provide detailed images but fail in challenging conditions, or traditional radar, which can see through walls and other occlusions but produces crude, low-resolution images.

Now, researchers from the University of Pennsylvania School of Engineering and Applied Science (Penn Engineering) have developed PanoRadar, a new tool to give robots superhuman vision by transforming simple radio waves into detailed, 3D views of the environment.

“Our initial question was whether we could combine the best of both sensing modalities,” says Mingmin Zhao, Assistant Professor in Computer and Information Science. “The robustness of radio signals, which is resilient to fog and other challenging conditions, and the high resolution of visual sensors.”

In a paper to be presented at the 2024 International Conference on Mobile Computing and Networking (MobiCom), Zhao and his team from the Wireless, Audio, Vision, and Electronics for Sensing (WAVES) Lab and the Penn Research In Embedded Computing and Integrated Systems Engineering (PRECISE) Center, including doctoral student Haowen Lai, recent master’s graduate Gaoxiang Luo and undergraduate research assistant Yifei (Freddy) Liu, describe how PanoRadar leverages radio waves and artificial intelligence (AI) to let robots navigate even the most challenging environments, like smoke-filled buildings or foggy roads.

PanoRadar is a sensor that operates like a lighthouse that sweeps its beam in a circle to scan the entire horizon. The system consists of a rotating vertical array of antennas that scans its surroundings. As they rotate, these antennas send out radio waves and listen for their reflections from the environment, much like how a lighthouse’s beam reveals the presence of ships and coastal features.

Thanks to the power of AI, PanoRadar goes beyond this simple scanning strategy. Unlike a lighthouse that simply illuminates different areas as it rotates, PanoRadar cleverly combines measurements from all rotation angles to enhance its imaging resolution. While the sensor itself is only a fraction of the cost of typically expensive LiDAR systems, this rotation strategy creates a dense array of virtual measurement points, which allows PanoRadar to achieve imaging resolution comparable to LiDAR. “The key innovation is in how we process these radio wave measurements,” explains Zhao. “Our signal processing and machine learning algorithms are able to extract rich 3D information from the environment.”

One of the biggest challenges Zhao’s team faced was developing algorithms to maintain high-resolution imaging while the robot moves. “To achieve LiDAR-comparable resolution with radio signals, we needed to combine measurements from many different positions with sub-millimeter accuracy,” explains Lai, the lead author of the paper. “This becomes particularly challenging when the robot is moving, as even small motion errors can significantly impact the imaging quality.”

Another challenge the team tackled was teaching their system to understand what it sees. “Indoor environments have consistent patterns and geometries,” says Luo. “We leveraged these patterns to help our AI system interpret the radar signals, similar to how humans learn to make sense of what they see.” During the training process, the machine learning model relied on LiDAR data to check its understanding against reality and was able to continue to improve itself.

“Our field tests across different buildings showed how radio sensing can excel where traditional sensors struggle,” says Liu. “The system maintains precise tracking through smoke and can even map spaces with glass walls.” This is because radio waves aren’t easily blocked by airborne particles, and the system can even “capture” things that LiDAR can’t, like glass surfaces. PanoRadar’s high resolution also means it can accurately detect people, a critical feature for applications like autonomous vehicles and rescue missions in hazardous environments.

Looking ahead, the team plans to explore how PanoRadar could work alongside other sensing technologies like cameras and LiDAR, creating more robust, multi-modal perception systems for robots. The team is also expanding their tests to include various robotic platforms and autonomous vehicles. “For high-stakes tasks, having multiple ways of sensing the environment is crucial,” says Zhao. “Each sensor has its strengths and weaknesses, and by combining them intelligently, we can create robots that are better equipped to handle real-world challenges.”

This study was conducted at the University of Pennsylvania School of Engineering and Applied Science and supported by a faculty startup fund.

Extreme weather events, such as tropical and extratropical cyclones and tornadoes, can cause widespread damage to forests, leading to environmental and financial losses. When trees fall during these storms, ecosystems might be disrupted, increasing forest management costs. As climate change worsens, severe storms are expected to become more frequent, making it crucial to understand how forests respond to wind stress.

Grasping the mechanisms behind tree failure is key to developing strategies for mitigation. While previous studies have explored how trees react to wind, it is unclear whether these responses remain consistent across different forest configurations — characterized by tree spacing and density — and weather conditions.

In this vein, a team of researchers led by Associate Professor Kana Kamimura from the School of Science and Technology at Shinshu University, Japan, investigated tree movements under various forest configurations and weather conditions, including how trees resist winds. The research team included Kazuki Nanko, Asako Matsumoto, and Saneyoshi Ueno from the Forestry and Forest Products Research Institute, Japan, and Barry Gardiner from the University of Freiburg, Germany, and the Institut Européen de la Forêt Cultivée, France. This paper was made available online on August 27, 2024, and was published on November 1, 2024, in Volume 571 of Forest Ecology and Management.

Explaining their motivation behind the study, Prof. Kamimura says, “Several techniques have been developed to predict wind damage. However, they largely depend on empirical data and parameters, and overlook how wind damage occurs. Our research aims to shed light on how winds directly impact trees and how trees reduce the stress from winds to survive.”

To achieve this, researchers set up two experimental plots of Cryptomeria japonica trees, commonly known as the Japanese cedar, in November 2017 in the experimental forests operated by the Forestry and Forest Products Research Institute, Kasumigaura City, Japan. In the first plot, P-100 consisted of 3,000 trees per hectare, creating a dense forest. In the second plot, P-50, half of the trees were removed for this research, leaving 1,500 trees per hectare to mimic thinning practices. Over two years, the team monitored 24 trees in the dense plot and 12 in the thinned plot, using trunk-mounted sensors to track tree sway during various wind conditions. The monitoring period included multiple typhoons, such as Typhoon Trami, in 2018, which caused significant damage to the thinned plot.

The researchers found that cedar trees exhibit two distinct swaying patterns depending on wind speed. In light winds, the trees swayed at around 2 to 2.3 cycles per second, with their branches absorbing much of the wind energy, protecting the trunks and roots from wind stress. However, at higher wind speeds, the trees shifted to a slower swaying pattern of 0.2 to 0.5 cycles per second. In this phase, the whole tree swayed together, transferring force across the trunk and roots, increasing the probability of breakage or uprooting.

Interestingly, the transition between these two swaying modes occurred at different wind speeds, depending on the forest density. In the dense plot, the trees switched patterns at wind speeds between 1.79 and 7.44 meters per second. In contrast, in the thinned plot, the transition occurred at slightly lower wind speeds, ranging from 1.57 to 5.63 meters per second.

Using an uprooted tree as a reference, researchers assessed the resistance to damage in the thinned P-50 over a 10-minute period during Typhoon Trami. They found that the actual resistance was only 48% of the expected resistance estimated through controlled tree-pulling experiments.

Prof. Kamimura elaborates, “The 52% difference between actual and expected resistance values was likely due to the roots weakening because of strong winds, even before the winds became more severe. This root fatigue occurred because the trees moved more due to less support from nearby trees and more wind penetrating the plot.” This also explains why the trees in the dense P-100 were not damaged during Typhoon Trami.

This study offers valuable insights for balancing thinning with wind resistance in forest management to support sustainable forestry practices, and help forests withstand extreme climate changes. While thinning promotes tree growth, it can also make forests more vulnerable to storms, especially soon after thinning. Prof. Kamimura concludes, “With more frequent storms in a changing climate, forest management practices must adapt to maintain resilience.”