It’s an achievement with important implications for scientists studying the brain and working on treatments for a broad range of neurological and neurodevelopmental disorders, such as Alzheimer’s and Parkinson’s disease.

“This could be a hugely powerful model to help us understand how brain cells and parts of the brain communicate in humans,” says Su-Chun Zhang, professor of neuroscience and neurology at UW-Madison’s Waisman Center. “It could change the way we look at stem cell biology, neuroscience, and the pathogenesis of many neurological and psychiatric disorders.”

Printing methods have limited the success of previous attempts to print brain tissue, according to Zhang and Yuanwei Yan, a scientist in Zhang’s lab. The group behind the new 3D-printing process described their method today in the journal Cell Stem Cell.

Instead of using the traditional 3D-printing approach, stacking layers vertically, the researchers went horizontally. They situated brain cells, neurons grown from induced pluripotent stem cells, in a softer “bio-ink” gel than previous attempts had employed.

“The tissue still has enough structure to hold together but it is soft enough to allow the neurons to grow into each other and start talking to each other,” Zhang says.

The cells are laid next to each other like pencils laid next to each other on a tabletop.

“Our tissue stays relatively thin and this makes it easy for the neurons to get enough oxygen and enough nutrients from the growth media,” Yan says.

The results speak for themselves — which is to say, the cells can speak to each other. The printed cells reach through the medium to form connections inside each printed layer as well as across layers, forming networks comparable to human brains. The neurons communicate, send signals, interact with each other through neurotransmitters, and even form proper networks with support cells that were added to the printed tissue.

“We printed the cerebral cortex and the striatum and what we found was quite striking,” Zhang says. “Even when we printed different cells belonging to different parts of the brain, they were still able to talk to each other in a very special and specific way.”

The printing technique offers precision — control over the types and arrangement of cells — not found in brain organoids, miniature organs used to study brains. The organoids grow with less organization and control.

“Our lab is very special in that we are able to produce pretty much any type of neurons at any time. Then we can piece them together at almost any time and in whatever way we like,” Zhang says. “Because we can print the tissue by design, we can have a defined system to look at how our human brain network operates. We can look very specifically at how the nerve cells talk to each other under certain conditions because we can print exactly what we want.”

That specificity provides flexibility. The printed brain tissue could be used to study signaling between cells in Down syndrome, interactions between healthy tissue and neighboring tissue affected by Alzheimer’s, testing new drug candidates, or even watching the brain grow.

“In the past, we have often looked at one thing at a time, which means we often miss some critical components. Our brain operates in networks. We want to print brain tissue this way because cells do not operate by themselves. They talk to each other. This is how our brain works and it has to be studied all together like this to truly understand it,” Zhang says. “Our brain tissue could be used to study almost every major aspect of what many people at the Waisman Center are working on. It can be used to look at the molecular mechanisms underlying brain development, human development, developmental disabilities, neurodegenerative disorders, and more.”

The new printing technique should also be accessible to many labs. It does not require special bio-printing equipment or culturing methods to keep the tissue healthy, and can be studied in depth with microscopes, standard imaging techniques and electrodes already common in the field.

The researchers would like to explore the potential of specialization, though, further improving their bio-ink and refining their equipment to allow for specific orientations of cells within their printed tissue..

“Right now, our printer is a benchtop commercialized one,” Yan says. “We can make some specialized improvements to help us print specific types of brain tissue on-demand.”

This study was supported in part by NIH-NINDS (NS096282, NS076352, NS086604), NICHD (HD106197, HD090256), the National Medical Research Council of Singapore (MOH-000212, MOH-000207), Ministry of Education of Singapore (MOE2018-T2-2-103), Aligning Science Across Parkinson’s (ASAP-000301), the Bleser Family Foundation, and the Busta Foundation.

Due to this enormous data gap, researchers have been skeptical that recent AI advances can tell us much about human learning and development. An ideal test for demonstrating a connection would involve training an AI model, not on massive data from the web, but on only the input that a single child receives. What would the model be able to learn then?

A team of New York University researchers ran this exact experiment. They trained a multimodal AI system through the eyes and ears of a single child, using headcam video recordings from when the child was six months and through their second birthday. They examined if the AI model could learn words and concepts present in a child’s everyday experience.

Their findings, reported in the latest issue of the journal Science, showed that the model, or neural network, could, in fact, learn a substantial number of words and concepts using limited slices of what the child experienced. That is, the video only captured about 1% of the child’s waking hours, but that was sufficient for genuine language learning.

In this video, the researchers describe their work in greater detail.

“We show, for the first time, that a neural network trained on this developmentally realistic input from a single child can learn to link words to their visual counterparts,” says Wai Keen Vong, a research scientist at NYU’s Center for Data Science and the paper’s first author. “Our results demonstrate how recent algorithmic advances paired with one child’s naturalistic experience has the potential to reshape our understanding of early language and concept acquisition.”

“By using AI models to study the real language-learning problem faced by children, we can address classic debates about what ingredients children need to learn words — whether they need language-specific biases, innate knowledge, or just associative learning to get going,” adds Brenden Lake, an assistant professor in NYU’s Center for Data Science and Department of Psychology and the paper’s senior author. “It seems we can get more with just learning than commonly thought.”

Vong, Lake, and their NYU colleagues, Wentao Wang and Emin Orhan, analyzed a child’s learning process captured on first-person video — via a light, head-mounted camera — on a weekly basis beginning at six months and through 25 months, using more than 60 hours of footage. The footage contained approximately a quarter of a million word instances (i.e., the number of words communicated, many of them repeatedly) that are linked with video frames of what the child saw when those words were spoken and included a wide range of different activities across development, including mealtimes, reading books, and the child playing.

The NYU researchers then trained a multimodal neural network with two separate modules: one that takes in single video frames (the vision encoder) and another that takes in the transcribed child-directed speech (the language encoder). These two encoders were combined and trained using an algorithm called contrastive learning, which aims to learn useful input features and their cross-modal associations. For instance, when a parent says something in view of the child, it is likely that some of the words used are likely referring to something that the child can see, meaning comprehension is instilled by linking visual and linguistic cues.

“This provides the model a clue as to which words should be associated with which objects,” explains Vong. “Combining these cues is what enables contrastive learning to gradually determine which words belong with which visuals and to capture the learning of a child’s first words.”

After training the model, the researchers tested it using the same kinds of evaluations used to measure word learning in infants — presenting the model with the target word and an array of four different image options and asking it to select the image that matches the target word. Their results showed that the model was able to learn a substantial number of the words and concepts present in the child’s everyday experience. Furthermore, for some of the words the model learned, it could generalize them to very different visual instances than those seen at training, reflecting an aspect of generalization also seen in children when they are tested in the lab.

“These findings suggest that this aspect of word learning is feasible from the kind of naturalistic data that children receive while using relatively generic learning mechanisms such as those found in neural networks,” observes Lake.

The work was supported by the U.S. Department of Defense’s Defense Advanced Research Projects Agency (N6600119C4030) and the National Science Foundation (1922658). Participation of the child was approved by the parents and the methodology was approved by NYU’s Institutional Review Board.

“We found that trees in warmer, drier climates are essentially coughing instead of breathing,” said Max Lloyd assistant research professor of geosciences at Penn State and lead author on the study recently published in Proceedings of the National Academy of Sciences. “They are sending CO₂ right back into the atmosphere far more than trees in cooler, wetter conditions.”

Through the process of photosynthesis, trees remove CO₂ from the atmosphere to produce new growth. Yet, under stressful conditions, trees release CO₂ back to the atmosphere, a process called photorespiration. With an analysis of a global dataset of tree tissue, the research team demonstrated that the rate of photorespiration is up to two times higher in warmer climates, especially when water is limited. They found the threshold for this response in subtropical climates begins to be crossed when average daytime temperatures exceed roughly 68 degrees Fahrenheit and worsens as temperatures rise further.

The results complicate a widespread belief about the role of plants in helping to draw down, or use, carbon from the atmosphere, providing new insight into how plants could adapt to climate change. Importantly, the researchers noted that as the climate warms, their findings demonstrate that plants could be less able to draw CO₂ out of the atmosphere and assimilate the carbon necessary to help the planet cool down.

“We have knocked this essential cycle off balance,” Lloyd said. “Plants and climate are inextricably linked. The biggest draw down of CO₂ from our atmosphere is photosynthesizing organisms. It’s a big knob on the composition of the atmosphere, so that means small changes have a large impact.”

Plants currently absorb an estimated 25% of the CO₂ emitted by human activities each year, according to the U.S. Department of Energy, but this percentage is likely to decrease in the future as the climate warms, Lloyd explained, especially if water is scarcer.

“When we think about climate futures, we predict that CO₂ will go up, which in theory is good for plants because those are the molecules they breathe in,” Lloyd said. “But we’ve shown there will be a tradeoff that some prevailing models don’t account for. The world will be getting warmer, which means plants will be less able to draw down that CO₂.”

In the study, the researchers discovered that variation in the abundance of certain isotopes of a part of wood called methoxyl groups serves as a tracer of photorespiration in trees. You can think of isotopes as varieties of atoms, Lloyd explained. Just as you might have vanilla and chocolate versions of ice cream, atoms can have different isotopes with their own unique “flavors” due to variations in their mass. The team studied levels of the methoxyl “flavor” of isotope in wood samples from about thirty specimens of trees from a variety of climates and conditions throughout the world to observe trends in photorespiration. The specimens came from an archive at the University of California, Berkeley, that contains hundreds of wood samples collected in the 1930s and 40s.

“The database was originally used to train foresters how to identify trees from different places around the world, so we repurposed it to essentially reconstruct these forests to see how well they were taking in CO₂,” Lloyd said.

Until now, photorespiration rates could only be measured in real time using living plants or well-preserved dead specimens that retained structural carbohydrates, which meant that it was nearly impossible to study the rate at which plants draw down carbon at scale or in the past, Lloyd explained.

Now that the team has validated a way to observe photorespiration rate using wood, he said the method could offer researchers a tool for predicting the how well trees might “breathe” in future and how they fared in past climates.

The amount of carbon dioxide in the atmosphere is rapidly rising; it is already greater than at any time in the last 3.6 million years, according to the National Oceanic and Atmospheric Administration. But that period is relatively recent in geologic time, Lloyd explained.

The team will now work to unearth photorespiration rates in the ancient past, up to tens of millions of years ago, using fossilized wood. The methods will allow researchers to explicitly test existing hypotheses regarding the changing influence of plant photorespiration on climate over geologic time.

“I’m a geologist, I work in the past,” Lloyd said. “So, if we’re interested in these big questions about how this cycle worked when the climate was very different than today, we can’t use living plants. We may have to go back millions of years to better understand what our future might look like.”

Other authors on the paper are Rebekah A. Stein, Daniel A. Stolper, Daniel E. Ibarra and Todd E. Dawson of the University of California, Berkeley; Richard S. Barclay and Scott L. Wing of the Smithsonian National Museum of Natural History and David W. Stahle of the University of Arkansas.

The work was funded in part by the Agouron Institute, the Heising-Simons Foundation, and the U.S. National Science Foundation.

Africa’s Greater Virunga Landscape (GVL) is a 15,700-square-kilometer area of savannas, mountains and lakes in Uganda, Rwanda and the Democratic Republic of Congo. It is home to the largest land animals in Africa, with seven national parks, three tropical high-forest reserves and three wildlife reserves, three of which are world heritage sites, covering 88% of the area. It is also home to a population of African elephants whose numbers have dropped so precipitously over the past century that they are now listed as critically endangered by the International Union for Conservation’s Red List.

Elephants play a key role in modifying and sustaining their landscapes by dispersing the seeds of the plants they feed upon, felling trees and enriching soil fertility with their dung. They also play an important role in many African cultures.

To date, few studies have focused on the dynamics of the environment, climate change, elephant demography and how the changing habitat influences elephants over long periods. To get a clearer picture of what the elephants’ future might look like and what we can do to best ensure their survival, lead author Simon Nampindo, who completed this research as part of his Ph.D. in environmental conservation for UMass Amherst and who is now country director for WCS Uganda, and Timothy Randhir, professor of environmental conservation at UMass Amherst, built a systems dynamic model. “This model,” says Nampindo, “can look at all the different environmental and population dynamics within a system. For the first time, we’re able to get a comprehensive vision of what the future might look like for African elephants in the face of climate change.”

Nampindo and Randhir built their model using data on the numbers of elephants, historical changes in the landscape and different future climate-change scenarios representing 1.6 º, 2.8º and 4.3º Celsius of warming over the next 80 years. Finally, they charted the effect each of the climate scenarios would have on five elephant age brackets: under 10 years old, 11 — 30, 31 — 40, 41 — 50, and more than 50 years old, because, as Randhir puts it “any impact on one age class has a community effect throughout the entire population.”

“We found that the older elephants will be massively affected by warming under every scenario,” says Nampindo. “Elephants are matriarchal — their leaders are the older cows, and the herds depend on their wisdom, long memories and ability to outsmart prey, and if they are lost to changing climate, it will wreak havoc on the surviving, younger herds, as well as change the genetic profiles and structures of the herd. There will also be ripple effects through the GVL’s landscape.”

“But,” says Randhir, “this model not only tells us what the threats are, we can also use it to tell us which policy possibilities will be most effective in helping African elephants to survive.”

In the case of a species like the elephant, which migrates widely across national boundaries, it is especially important to also understand how differing policies could affect future herds so that management agencies can coordinate their responses.

In particular, Nampindo and Randhir find that a coordinated GVL management strategy at the national, regional and local levels is needed to address poaching threats. Well-funded anti-poaching efforts are essential, but they also point to the importance of community-led programs and education in the front-line towns and villages where human-elephant interaction is common. The GVL landscape must also be managed appropriately to reduce the impact of habitat fragmentation, fire and invasive species.

“These results are very important to WCS,” says Nampindo. “If we can do a good job at protecting elephants, our efforts will reverberate to other species, such as lions and mountain gorillas.”

“More broadly,” says Randhir, “the most exciting thing about this systems dynamic modeling is that it can be adapted to any migratory species that move across political boundaries, from fish to birds to lions.”