A research team led by Professor Junsuk Rho, Ph.D. candidate Minkyung Kim, and Dr. Dasol Lee of POSTECH’s departments of mechanical engineering and chemical engineering, and a team led by Professor Heon Lee and Soomin of the Department of Materials Science and Engineering at Korea University have together developed a transparent radiative cooler that transmits visible light while reflecting near-infrared light, and radiates heat in the atmospheric window where the wavelength range is between 8 and 13 micrometers (?m). Recognized for its significance, this study was featured as the front cover paper of the latest issue of Advanced Optical Materials.

Radiative cooling is a technology that lowers the temperature of an object by absorbing less energy from the sun and emitting radiant heat. Conventional radiative cooling materials developed so far have been limited to transparent emitters that transmit all light under direct sunlight and opaque radiative coolers that reflect all solar energy. For this, transparency is an important characteristic in practical applications of radiative cooling, but the transmitted sunlight trapped in an inner space is generally the main cause of the increasing temperature.

The joint research team focused on developing a transparent radiative cooler to solve this problem. Taking advantage of the characteristics of light, the researchers proposed a material that can transmit visible light, reflect near-infrared light, and emit mid-infrared rays.

The radiative cooling material developed by the researchers in this study exhibits both transparency and radiative cooling effect by selectively reflecting near-infrared light. In an outdoor rooftop experiment, the researchers found that the interior temperature of the high-absorbing chamber in the cooler box was lowered by 14.4 °C and the temperature of the material itself was lowered by 10.1 °C even when paint was applied.

“The radiative cooler developed this time can be used as a window of a building or an observatory that needs to maintain transparency, or as a window in an amusement ride or a vehicle,” explained Professor Junsuk Rho of POSTECH. “It can even take on color because it retains its cooling effect when paint is applied to it.”

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A pressing question is how these sequestered chloroplasts retains their photosynthetic capability without algal nuclei. Since the genome of the algal nucleus encodes most of the proteins required for photosynthesis, chloroplasts isolated from algal cells instantly lose their photosynthetic capability. Nevertheless, algae-eating sea slugs retain this photosynthetic capability for months. There have been numerous debates about the mechanisms underlying the phenomenon of sequestered chloroplasts retaining photosynthetic capabilities over the long term. A widely accepted hypothesis accounting for kleptoplasty is the horizontal gene transfer of the photosynthesis genes from algae to sea slug.

A team of researchers at the National Institute for Basic Biology (NIBB), in addition to collaborators from seven other Japanese institutions, have published the genome of the sea slug, Plakobranchus ocellatus type black, in eLife. “Since the sea slug is a non-model organism, its genome analysis was very tough in comparison to model organisms such as the mouse and the fruitfly. Furthermore, there was no high-quality genetic information for them. This situation consequently hindered the verification of the hypothesis of algae-derived horizontal gene transfer,” said Shuji Shigenobu, a genome scientist and professor at NIBB who is the corresponding author of the paper, “But we succeeded in accurately revealing the genome information of the sea slug.” Scientists are ready to settle the arguments concerning the horizontal transfer of algal genes to the animal nucleus based on newly unveiled genome data. “We looked at the genome very carefully, but we found no evidence of photosynthetic genes encoded on the sea slug genome, ” he said.

“We are embarking upon a new challenge to answer the question: how does the sea slug retains this function without horizontal gene transfer?,” said Taro Maeda, the first and co-corresponding author of the paper. “Our genome data also provides clues to this. We have found several candidate genes related to the long-term maintenance of photosynthetic activity. These genes related to protein metabolism, oxidative stress tolerance, and innate immunity should be subsequently highlighted in future studies.”

The mechanisms underlying kleptoplasty are still elusive. Further understanding of this phenomenon may lead us to innovative biotechnologies, which, for example, could bestow photosynthetic abilities to other various animal cells in the future.

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While researchers have intensively studied the social structure of hyenas and other animals, it’s only recently that scientists have begun to investigate how this structure arises. A new study led by Penn biologists, which relies upon 27 years of detailed observations of hyena social behavior collected by researchers at Michigan State University, pulls back the curtain on how social order comes to be.

Their findings show that hyenas inherit their mother’s social networks, so their social connections resemble their mother’s. However, offspring of higher-ranking individuals more faithfully replicate their mother’s interactions, winding up with social networks that more closely resemble their mother’s than do offspring of females that rank lower on the clan’s social ladder. The team reported their findings in the journal Science.

“We knew that the social structure of hyenas is based in part on one’s rank in the agonistic hierarchy, which we know is inherited from mothers” says Erol Akçay, a study coauthor and associate professor in Penn’s School of Arts & Sciences. “But what we found, that affiliative, or friendly interactions, are also inherited, hadn’t been shown.”

“This is a very simple process of social inheritance that we show works very, very well,” says Amiyaal Ilany, a senior lecturer at Israel’s Bar-Ilan University. “Individuals that were born to higher rank are more accurate in their inheritance, and they have good reason to do so. It fits well with what is already known about inheritance of rank. There are very strict rules about what place you sit in the hierarchy if you are a hyena.”

The work builds on a theoretical model of social network inheritance Akçay and Ilany developed in 2016. According to that simple framework, animals establish their networks by “social inheritance,” or copying their mother’s behaviors. The model fit well with snapshots of real-world social networks from not only hyenas but also three other social species: bottle-nosed dolphins, rock hyrax, and sleepy lizards.

In the new work, the team aimed to refine their model to better understand the intricacies of social inheritance in hyenas. They were fortunate to have a robust dataset collected by Akçay and Ilany’s coauthor, zoologist Kay Holekamp of Michigan State University, consisting of 27 years of detailed accounting of a clan’s social interactions.

“We realized we could use that dataset to directly test our model, to see if social ties are inherited or not,” Akçay says.

Field biologists from Holekamp’s research group had meticulously tracked how hyenas in a clan interacted, including who spent time with whom as well as the social rank of each member. To do so, researchers spent months getting to know each member of the clan by sight.

“They are there year-round, every day, identifying individuals by their specific spot patterns and other characteristics,” Ilany says.

These observations allowed Akçay, Ilany, and Holekamp to map out hyenas’ social networks based on which individuals spent time close together.

“This use of proximity to track social networks isn’t possible with humans, as two strangers might randomly get into an elevator together,” Ilany says. “But with hyenas, if one individual gets within a few meters of another, that suggests that they have a social connection.”

With this picture of each individual’s social affiliations in hand, the researchers compared the social networks of mothers to their offspring. “We developed a new metric to measure social inheritance, to track how faithfully an offspring’s network reproduces its mother’s network,” Akçay says.

Hyena cubs stick close to their mothers for the first couple years of life, so the networks of mothers and their offsprings were quite similar to start. However, the researchers noticed that even as the young stopped spending so much time in close proximity to their mothers they still sustained quite similar networks, particularly for female offspring, who generally remain members of the clan for life. “We have data in some cases showing that the network similarity between mothers and offspring, especially female offspring, was still very high after six or so years,” says Ilany. “You may not be seeing your mother as often, or she even may have died, but you still have similar friends.”

This pattern was especially strong for the higher-ranking mothers, for whom social inheritance was the strongest in the group.

“That is kind of intuitive because things like that happen in human society as well,” Akçay says. “It happens so much we take it for granted. We inherit social connections, and there’s a lot of social science research that shows that this has a huge influence on people’s life trajectory.”

Offspring of lower-ranking mothers were less likely to reproduce their mother’s social networks, perhaps trying to compensate for their more lowly origins by associating with a greater variety of individuals.

There is no genetic inheritance of rank or close associates in this species, so in Holekamp’s opinion one of the most remarkable things about the phenomenon documented here is that the youngsters’ relationships with their mothers’ close associates are all learned very early in life. One explanation for why inheritance of social networks works better for high- than for low-ranking hyenas may be that low-ranking females tend to go off on their own more often to avoid competition with higher-ranking hyenas, so their cubs have fewer learning opportunities than cubs of high-ranking females.

Mother-offspring pairs with more similar social networks also lived longer, the team found. This effect on survivorship may owe to the fact that offspring who spend more time with their mothers and thus replicate their social networks benefit from the increased care.

Social rank also had an effect on survivorship and reproductive success.

“Rank is super important,” says Akçay. “If you’re born to a lower-ranked mother, you are less likely to survive and to reproduce.”

The researchers note that social network inheritance likely contributes to a group’s stability and also has implications for how behaviors are learned and spread through groups.

The study also underscores how factors other than genetics hold sway in key evolutionary outcomes, including reproductive success and overall survival. “A lot of things that are considered by default to be genetically determined may depend on environmental and social processes,” says Ilany.

The achievement, which was developed in collaboration with the first participant of a clinical research trial, builds on more than a decade of effort by UCSF neurosurgeon Edward Chang, MD, to develop a technology that allows people with paralysis to communicate even if they are unable to speak on their own. The study appears July 15 in the New England Journal of Medicine.

“To our knowledge, this is the first successful demonstration of direct decoding of full words from the brain activity of someone who is paralyzed and cannot speak,” said Chang, the Joan and Sanford Weill Chair of Neurological Surgery at UCSF, Jeanne Robertson Distinguished Professor, and senior author on the study. “It shows strong promise to restore communication by tapping into the brain’s natural speech machinery.”

Each year, thousands of people lose the ability to speak due to stroke, accident, or disease. With further development, the approach described in this study could one day enable these people to fully communicate.

Translating Brain Signals into Speech

Previously, work in the field of communication neuroprosthetics has focused on restoring communication through spelling-based approaches to type out letters one-by-one in text. Chang’s study differs from these efforts in a critical way: his team is translating signals intended to control muscles of the vocal system for speaking words, rather than signals to move the arm or hand to enable typing. Chang said this approach taps into the natural and fluid aspects of speech and promises more rapid and organic communication.

“With speech, we normally communicate information at a very high rate, up to 150 or 200 words per minute,” he said, noting that spelling-based approaches using typing, writing, and controlling a cursor are considerably slower and more laborious. “Going straight to words, as we’re doing here, has great advantages because it’s closer to how we normally speak.”

Over the past decade, Chang’s progress toward this goal was facilitated by patients at the UCSF Epilepsy Center who were undergoing neurosurgery to pinpoint the origins of their seizures using electrode arrays placed on the surface of their brains. These patients, all of whom had normal speech, volunteered to have their brain recordings analyzed for speech-related activity. Early success with these patient volunteers paved the way for the current trial in people with paralysis.

Previously, Chang and colleagues in the UCSF Weill Institute for Neurosciences mapped the cortical activity patterns associated with vocal tract movements that produce each consonant and vowel. To translate those findings into speech recognition of full words, David Moses, PhD, a postdoctoral engineer in the Chang lab and one of the lead authors of the new study, developed new methods for real-time decoding of those patterns and statistical language models to improve accuracy.

But their success in decoding speech in participants who were able to speak didn’t guarantee that the technology would work in a person whose vocal tract is paralyzed. “Our models needed to learn the mapping between complex brain activity patterns and intended speech,” said Moses. “That poses a major challenge when the participant can’t speak.”

In addition, the team didn’t know whether brain signals controlling the vocal tract would still be intact for people who haven’t been able to move their vocal muscles for many years. “The best way to find out whether this could work was to try it,” said Moses.

The First 50 Words

To investigate the potential of this technology in patients with paralysis, Chang partnered with colleague Karunesh Ganguly, MD, PhD, an associate professor of neurology, to launch a study known as “BRAVO” (Brain-Computer Interface Restoration of Arm and Voice). The first participant in the trial is a man in his late 30s who suffered a devastating brainstem stroke more than 15 years ago that severely damaged the connection between his brain and his vocal tract and limbs. Since his injury, he has had extremely limited head, neck, and limb movements, and communicates by using a pointer attached to a baseball cap to poke letters on a screen.

The participant, who asked to be referred to as BRAVO1, worked with the researchers to create a 50-word vocabulary that Chang’s team could recognize from brain activity using advanced computer algorithms. The vocabulary — which includes words such as “water,” “family,” and “good” — was sufficient to create hundreds of sentences expressing concepts applicable to BRAVO1’s daily life.

For the study, Chang surgically implanted a high-density electrode array over BRAVO1’s speech motor cortex. After the participant’s full recovery, his team recorded 22 hours of neural activity in this brain region over 48 sessions and several months. In each session, BRAVO1 attempted to say each of the 50 vocabulary words many times while the electrodes recorded brain signals from his speech cortex.

Translating Attempted Speech into Text

To translate the patterns of recorded neural activity into specific intended words, the other two lead authors of the study, Sean Metzger, MS and Jessie Liu, BS, both bioengineering doctoral students in the Chang Lab used custom neural network models, which are forms of artificial intelligence. When the participant attempted to speak, these networks distinguished subtle patterns in brain activity to detect speech attempts and identify which words he was trying to say.

To test their approach, the team first presented BRAVO1 with short sentences constructed from the 50 vocabulary words and asked him to try saying them several times. As he made his attempts, the words were decoded from his brain activity, one by one, on a screen.

Then the team switched to prompting him with questions such as “How are you today?” and “Would you like some water?” As before, BRAVO1’s attempted speech appeared on the screen. “I am very good,” and “No, I am not thirsty.”

The team found that the system was able to decode words from brain activity at rate of up to 18 words per minute with up to 93 percent accuracy (75 percent median). Contributing to the success was a language model Moses applied that implemented an “auto-correct” function, similar to what is used by consumer texting and speech recognition software.

Moses characterized the early trial results as a proof of principle. “We were thrilled to see the accurate decoding of a variety of meaningful sentences,” he said. “We’ve shown that it is actually possible to facilitate communication in this way and that it has potential for use in conversational settings.”

Looking forward, Chang and Moses said they will expand the trial to include more participants affected by severe paralysis and communication deficits. The team is currently working to increase the number of words in the available vocabulary, as well as improve the rate of speech.

Both said that while the study focused on a single participant and a limited vocabulary, those limitations don’t diminish the accomplishment. “This is an important technological milestone for a person who cannot communicate naturally,” said Moses, “and it demonstrates the potential for this approach to give a voice to people with severe paralysis and speech loss.”

Co-authors on the paper include Sean L. Metzger, MS; Jessie R. Liu; Gopala K. Anumanchipalli, PhD; Joseph G. Makin, PhD; Pengfei F. Sun, PhD; Josh Chartier, PhD; Maximilian E. Dougherty; Patricia M. Liu, MA; Gary M. Abrams, MD; and Adelyn Tu-Chan, DO, all of UCSF. Funding sources included National Institutes of Health (U01 NS098971-01), philanthropy, and a sponsored research agreement with Facebook Reality Labs (FRL), which completed in early 2021.

UCSF researchers conducted all clinical trial design, execution, data analysis and reporting. Research participant data were collected solely by UCSF, are held confidentially, and are not shared with third parties. FRL provided high-level feedback and machine learning advice.

The researchers trained 37 domestic dogs to operate a food dispenser by pressing a button, before separating the button and dispenser in separate enclosures. In the first stage, dogs were paired with two unfamiliar humans one at a time. One human partner was helpful — pressing their button to dispense food in the dog’s enclosure — and one was unhelpful. The researchers also reversed the set-up, with a button in the dog’s enclosure that operated a food dispenser in the human’s enclosure. They found no significant differences in the dogs’ tendency to press the button for helpful or unhelpful human partners, and the human’s behavior in the first stage did not affect the dog’s behavior towards them in free interaction sessions after the trials.

Previous studies have demonstrated that dogs are capable of directing helpful behaviors towards other dogs that have helped them previously — a behavior known as reciprocal altruism — and research suggests dogs are also able to distinguish between cooperative and uncooperative humans. However, the present study failed to find evidence that dogs can combine these capabilities to reciprocate help from humans. This finding may reflect a lack of ability or inclination among dogs to reciprocate, or the experimental design may not have detected it. For example, the authors suggest that the dogs may not have understood the experiment because humans are typically the food-giver in the relationship, not the receiver, or because the dogs failed to recognize the connection between the human’s helpful behavior and the reward.

The authors add: “In our study, pet dogs received food from humans but did not return the favour.”

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Research professor Bruce Wilkinson and professor Linda Ivany, both from the Department of Earth and Environmental Sciences, recently co-authored a paper in the Biological Journal of the Linnean Society where they determined that forecasting the total number of species may never be possible.

When asking the question, ‘how many species?,’ it is important to note that only a fraction of existing species have been named. In order to make a prediction on a total number, researchers project the curve of new species descriptions each year into the future until eventually reaching a point when all species should have been found.

Wilkinson, a geologist, noticed parallels between the discovery curves of new species and the total reservoir size of nonrenewable resources like oil or mineral ores. Similar to the species curve, by extending the oil reservoir curve researchers thought they should be able to estimate the total global reservoir and how long it will take to get to it all. The theory of resource exploitation suggests that the number of discoveries over time follows a bell-shaped curve: The curve rises as production rate increases due to new discoveries and then decreases as production declines, despite all the effort continuing to go into finding the resource. The time of maximum discovery is known as Hubbert’s peak, after M. King Hubbert who predicted it. Following that time, the resource is being evermore depleted until it is used up.

“The problem with using that curve to predict how much is left is that you have to assume that the effort invested and the approach used to discover new oil, or species, is consistent and known,” says Wilkinson. “We used to think we’d gone over the peak for oil and gas around 1972, but then 15 or so years ago someone figured out how to do horizontal drilling and all of the sudden there was a new bump in the amount being discovered.”

Wilkinson and Ivany say that the discovery curve for new species of vertebrate animals shows a similar bump. Like the increase in the oil curve caused by horizontal drilling in the early 2000s, there was a surge in new species discovery beginning around 1950, when new funding was being dedicated to science after the World War II, more scientists were going into biology, and new molecular techniques were leading to an increase in the ability to distinguish species from one another.

In both cases, unforeseen changes in the effort and method of discovering new oil or species altered the way the discovery curves were playing out.

If researchers had estimated the total number of species based on data prior to 1950, their estimates would be much different from any estimate made today, and both would likely be wrong because those new advents cannot be predicted.

In some ways, this is a reflection of the scientific method, in which hypotheses stand until new facts are discovered, which lead to changes in the hypothesis.

“As much as we’d like to know ‘the number,’ the total species richness of the planet will remain an elusive target,” says Ivany.

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Materials provided by Syracuse University. Original written by Dan Bernardi. Note: Content may be edited for style and length.