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#439077 How Scientists Grew Human Muscles in Pig ...
The little pigs bouncing around the lab looked exceedingly normal. Yet their adorable exterior hid a remarkable secret: each piglet carried two different sets of genes. For now, both sets came from their own species. But one day, one of those sets may be human.
The piglets are chimeras—creatures with intermingled sets of genes, as if multiple entities were seamlessly mashed together. Named after the Greek lion-goat-serpent monsters, chimeras may hold the key to an endless supply of human organs and tissues for transplant. The crux is growing these human parts in another animal—one close enough in size and function to our own.
Last week, a team from the University of Minnesota unveiled two mind-bending chimeras. One was joyous little piglets, each propelled by muscles grown from a different pig. Another was pig embryos, transplanted into surrogate pigs, that developed human muscles for more than 20 days.
The study, led by Drs. Mary and Daniel Garry at the University of Minnesota, had a therapeutic point: engineering a brilliant way to replace muscle loss, especially for the muscles around our skeletons that allow us to move and navigate the world. Trauma and injury, such as from firearm wounds or car crashes, can damage muscle tissue beyond the point of repair. Unfortunately, muscles are also stubborn in that donor tissue from cadavers doesn’t usually “take” at the injury site. For now, there are no effective treatments for severe muscle death, called volumetric muscle loss.
The new human-pig hybrids are designed to tackle this problem. Muscle wasting aside, the study also points to a clever “hack” that increases the amount of human tissue inside a growing pig embryo.
If further improved, the technology could “provide an unlimited supply of organs for transplantation,” said Dr. Mary Garry to Inverse. What’s more, because the human tissue can be sourced from patients themselves, the risk of rejection by the immune system is relatively low—even when grown inside a pig.
“The shortage of organs for heart transplantation, vascular grafting, and skeletal muscle is staggering,” said Garry. Human-animal chimeras could have a “seismic impact” that transforms organ transplantation and helps solve the organ shortage crisis.
That is, if society accepts the idea of a semi-humanoid pig.
Wait…But How?
The new study took a page from previous chimera recipes.
The main ingredients and steps go like this: first, you need an embryo that lacks the ability to develop a tissue or organ. This leaves an “empty slot” of sorts that you can fill with another set of genes—pig, human, or even monkey.
Second, you need to fine-tune the recipe so that the embryos “take” the new genes, incorporating them into their bodies as if they were their own. Third, the new genes activate to instruct the growing embryo to make the necessary tissue or organs without harming the overall animal. Finally, the foreign genes need to stay put, without cells migrating to another body part—say, the brain.
Not exactly straightforward, eh? The piglets are technological wonders that mix cutting-edge gene editing with cloning technologies.
The team went for two chimeras: one with two sets of pig genes, the other with a pig and human mix. Both started with a pig embryo that can’t make its own skeletal muscles (those are the muscles surrounding your bones). Using CRISPR, the gene-editing Swiss Army Knife, they snipped out three genes that are absolutely necessary for those muscles to develop. Like hitting a bullseye with three arrows simultaneously, it’s already a technological feat.
Here’s the really clever part: the muscles around your bones have a slightly different genetic makeup than the ones that line your blood vessels or the ones that pump your heart. While the resulting pig embryos had severe muscle deformities as they developed, their hearts beat as normal. This means the gene editing cut only impacted skeletal muscles.
Then came step two: replacing the missing genes. Using a microneedle, the team injected a fertilized and slightly developed pig egg—called a blastomere—into the embryo. If left on its natural course, a blastomere eventually develops into another embryo. This step “smashes” the two sets of genes together, with the newcomer filling the muscle void. The hybrid embryo was then placed into a surrogate, and roughly four months later, chimeric piglets were born.
Equipped with foreign DNA, the little guys nevertheless seemed totally normal, nosing around the lab and running everywhere without obvious clumsy stumbles. Under the microscope, their “xenomorph” muscles were indistinguishable from run-of-the-mill average muscle tissue—no signs of damage or inflammation, and as stretchy and tough as muscles usually are. What’s more, the foreign DNA seemed to have only developed into muscles, even though they were prevalent across the body. Extensive fishing experiments found no trace of the injected set of genes inside blood vessels or the brain.
A Better Human-Pig Hybrid
Confident in their recipe, the team next repeated the experiment with human cells, with a twist. Instead of using controversial human embryonic stem cells, which are obtained from aborted fetuses, they relied on induced pluripotent stem cells (iPSCs). These are skin cells that have been reverted back into a stem cell state.
Unlike previous attempts at making human chimeras, the team then scoured the genetic landscape of how pig and human embryos develop to find any genetic “brakes” that could derail the process. One gene, TP53, stood out, which was then promptly eliminated with CRISPR.
This approach provides a way for future studies to similarly increase the efficiency of interspecies chimeras, the team said.
The human-pig embryos were then carefully grown inside surrogate pigs for less than a month, and extensively analyzed. By day 20, the hybrids had already grown detectable human skeletal muscle. Similar to the pig-pig chimeras, the team didn’t detect any signs that the human genes had sprouted cells that would eventually become neurons or other non-muscle cells.
For now, human-animal chimeras are not allowed to grow to term, in part to stem the theoretical possibility of engineering humanoid hybrid animals (shudder). However, a sentient human-pig chimera is something that the team specifically addressed. Through multiple experiments, they found no trace of human genes in the embryos’ brain stem cells 20 and 27 days into development. Similarly, human donor genes were absent in cells that would become the hybrid embryos’ reproductive cells.
Despite bioethical quandaries and legal restrictions, human-animal chimeras have taken off, both as a source of insight into human brain development and a well of personalized organs and tissues for transplant. In 2019, Japan lifted its ban on developing human brain cells inside animal embryos, as well as the term limit—to global controversy. There’s also the question of animal welfare, given that hybrid clones will essentially become involuntary organ donors.
As the debates rage on, scientists are nevertheless pushing the limits of human-animal chimeras, while treading as carefully as possible.
“Our data…support the feasibility of the generation of these interspecies chimeras, which will serve as a model for translational research or, one day, as a source for xenotransplantation,” the team said.
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#438925 Nanophotonics Could Be the ‘Dark ...
The race to build the first practical quantum computers looks like a two-horse contest between machines built from superconducting qubits and those that use trapped ions. But new research suggests a third contender—machines based on optical technology—could sneak up on the inside.
The most advanced quantum computers today are the ones built by Google and IBM, which rely on superconducting circuits to generate the qubits that form the basis of quantum calculations. They are now able to string together tens of qubits, and while controversial, Google claims its machines have achieved quantum supremacy—the ability to carry out a computation beyond normal computers.
Recently this approach has been challenged by a wave of companies looking to use trapped ion qubits, which are more stable and less error-prone than superconducting ones. While these devices are less developed, engineering giant Honeywell has already released a machine with 10 qubits, which it says is more powerful than a machine made of a greater number of superconducting qubits.
But despite this progress, both of these approaches have some major drawbacks. They require specialized fabrication methods, incredibly precise control mechanisms, and they need to be cooled to close to absolute zero to protect the qubits from any outside interference.
That’s why researchers at Canadian quantum computing hardware and software startup Xanadu are backing an alternative quantum computing approach based on optics, which was long discounted as impractical. In a paper published last week in Nature, they unveiled the first fully programmable and scalable optical chip that can run quantum algorithms. Not only does the system run at room temperature, but the company says it could scale to millions of qubits.
The idea isn’t exactly new. As Chris Lee notes in Ars Technica, people have been experimenting with optical approaches to quantum computing for decades, because encoding information in photons’ quantum states and manipulating those states is relatively easy. The biggest problem was that optical circuits were very large and not readily programmable, which meant you had to build a new computer for every new problem you wanted to solve.
That started to change thanks to the growing maturity of photonic integrated circuits. While early experiments with optical computing involved complex table-top arrangements of lasers, lenses, and detectors, today it’s possible to buy silicon chips not dissimilar to electronic ones that feature hundreds of tiny optical components.
In recent years, the reliability and performance of these devices has improved dramatically, and they’re now regularly used by the telecommunications industry. Some companies believe they could be the future of artificial intelligence too.
This allowed the Xanadu researchers to design a silicon chip that implements a complex optical network made up of beam splitters, waveguides, and devices called interferometers that cause light sources to interact with each other.
The chip can generate and manipulate up to eight qubits, but unlike conventional qubits, which can simultaneously be in two states, these qubits can be in any configuration of three states, which means they can carry more information.
Once the light has travelled through the network, it is then fed out to cutting-edge photon-counting detectors that provide the result. This is one of the potential limitations of the system, because currently these detectors need to be cryogenically cooled, although the rest of the chip does not.
But most importantly, the chip is easily re-programmable, which allows it to tackle a variety of problems. The computation can be controlled by adjusting the settings of these interferometers, but the researchers have also developed a software platform that hides the physical complexity from users and allows them to program it using fairly conventional code.
The company announced that its chips were available on the cloud in September of 2020, but the Nature paper is the first peer-reviewed test of their system. The researchers verified that the computations being done were genuinely quantum mechanical in nature, but they also implemented two more practical algorithms: one for simulating molecules and the other for judging how similar two graphs are, which has applications in a variety of pattern recognition problems.
In an accompanying opinion piece, Ulrik Andersen from the Technical University of Denmark says the quality of the qubits needs to be improved considerably and photon losses reduced if the technology is ever to scale to practical problems. But, he says, this breakthrough suggests optical approaches “could turn out to be the dark horse of quantum computing.”
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#438774 The World’s First 3D Printed School ...
3D printed houses have been popping up all over the map. Some are hive-shaped, some can float, some are up for sale. Now this practical, cost-cutting technology is being employed for another type of building: a school.
Located on the island of Madagascar, the project is a collaboration between San Francisco-based architecture firm Studio Mortazavi and Thinking Huts, a nonprofit whose mission is to increase global access to education through 3D printing. The school will be built on the campus of a university in Fianarantsoa, a city in the south central area of the island nation.
According to the World Economic Forum, lack of physical infrastructure is one of the biggest barriers to education. Building schools requires not only funds, human capital, and building materials, but also community collaboration and ongoing upkeep and maintenance. For people to feel good about sending their kids to school each day, the buildings should be conveniently located, appealing, comfortable to spend several hours in, and of course safe. All of this is harder to accomplish than you might think, especially in low-income areas.
Because of its comparatively low cost and quick turnaround time, 3D printing has been lauded as a possible solution to housing shortages and a tool to aid in disaster relief. Cost details of the Madagascar school haven’t been released, but if 3D printed houses can go up in a day for under $10,000 or list at a much lower price than their non-3D-printed neighbors, it’s safe to say that 3D printing a school is likely substantially cheaper than building it through traditional construction methods.
The school’s modular design resembles a honeycomb, where as few or as many nodes as needed can be linked together. Each node consists of a room with two bathrooms, a closet, and a front and rear entrance. The Fianarantsoa school with just have one node to start with, but as local technologists will participate in the building process, they’ll learn the 3D printing ins and outs and subsequently be able to add new nodes or build similar schools in other areas.
Artist rendering of the completed school. Image Credit: Studio Mortazavi/Thinking Huts
The printer for the project is coming from Hyperion Robotics, a Finnish company that specializes in 3D printing solutions for reinforced concrete. The building’s walls will be made of layers of a special cement mixture that Thinking Huts says emits less carbon dioxide than traditional concrete. The roof, doors, and windows will be sourced locally, and the whole process can be completed in less than a week, another major advantage over traditional building methods.
“We can build these schools in less than a week, including the foundation and all the electrical and plumbing work that’s involved,” said Amir Mortazavi, lead architect on the project. “Something like this would typically take months, if not even longer.”
The roof of the building will be equipped with solar panels to provide the school with power, and in a true melding of modern technology and traditional design, the pattern of its walls is based on Malagasy textiles.
Thinking Huts considered seven different countries for its first school, and ended up choosing Madagascar for the pilot based on its need for education infrastructure, stable political outlook, opportunity for growth, and renewable energy potential. However, the team is hoping the pilot will be the first of many similar projects across multiple countries. “We can use this as a case study,” Mortazavi said. “Then we can go to other countries around the world and train the local technologists to use the 3D printer and start a nonprofit there to be able to build schools.”
Construction of the school will take place in the latter half of this year, with hopes of getting students into the classroom as soon as the pandemic is no longer a major threat to the local community’s health.
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#437978 How Mirroring the Architecture of the ...
While AI can carry out some impressive feats when trained on millions of data points, the human brain can often learn from a tiny number of examples. New research shows that borrowing architectural principles from the brain can help AI get closer to our visual prowess.
The prevailing wisdom in deep learning research is that the more data you throw at an algorithm, the better it will learn. And in the era of Big Data, that’s easier than ever, particularly for the large data-centric tech companies carrying out a lot of the cutting-edge AI research.
Today’s largest deep learning models, like OpenAI’s GPT-3 and Google’s BERT, are trained on billions of data points, and even more modest models require large amounts of data. Collecting these datasets and investing the computational resources to crunch through them is a major bottleneck, particularly for less well-resourced academic labs.
It also means today’s AI is far less flexible than natural intelligence. While a human only needs to see a handful of examples of an animal, a tool, or some other category of object to be able pick it out again, most AI need to be trained on many examples of an object in order to be able to recognize it.
There is an active sub-discipline of AI research aimed at what is known as “one-shot” or “few-shot” learning, where algorithms are designed to be able to learn from very few examples. But these approaches are still largely experimental, and they can’t come close to matching the fastest learner we know—the human brain.
This prompted a pair of neuroscientists to see if they could design an AI that could learn from few data points by borrowing principles from how we think the brain solves this problem. In a paper in Frontiers in Computational Neuroscience, they explained that the approach significantly boosts AI’s ability to learn new visual concepts from few examples.
“Our model provides a biologically plausible way for artificial neural networks to learn new visual concepts from a small number of examples,” Maximilian Riesenhuber, from Georgetown University Medical Center, said in a press release. “We can get computers to learn much better from few examples by leveraging prior learning in a way that we think mirrors what the brain is doing.”
Several decades of neuroscience research suggest that the brain’s ability to learn so quickly depends on its ability to use prior knowledge to understand new concepts based on little data. When it comes to visual understanding, this can rely on similarities of shape, structure, or color, but the brain can also leverage abstract visual concepts thought to be encoded in a brain region called the anterior temporal lobe (ATL).
“It is like saying that a platypus looks a bit like a duck, a beaver, and a sea otter,” said paper co-author Joshua Rule, from the University of California Berkeley.
The researchers decided to try and recreate this capability by using similar high-level concepts learned by an AI to help it quickly learn previously unseen categories of images.
Deep learning algorithms work by getting layers of artificial neurons to learn increasingly complex features of an image or other data type, which are then used to categorize new data. For instance, early layers will look for simple features like edges, while later ones might look for more complex ones like noses, faces, or even more high-level characteristics.
First they trained the AI on 2.5 million images across 2,000 different categories from the popular ImageNet dataset. They then extracted features from various layers of the network, including the very last layer before the output layer. They refer to these as “conceptual features” because they are the highest-level features learned, and most similar to the abstract concepts that might be encoded in the ATL.
They then used these different sets of features to train the AI to learn new concepts based on 2, 4, 8, 16, 32, 64, and 128 examples. They found that the AI that used the conceptual features yielded much better performance than ones trained using lower-level features on lower numbers of examples, but the gap shrunk as they were fed more training examples.
While the researchers admit the challenge they set their AI was relatively simple and only covers one aspect of the complex process of visual reasoning, they said that using a biologically plausible approach to solving the few-shot problem opens up promising new avenues in both neuroscience and AI.
“Our findings not only suggest techniques that could help computers learn more quickly and efficiently, they can also lead to improved neuroscience experiments aimed at understanding how people learn so quickly, which is not yet well understood,” Riesenhuber said.
As the researchers note, the human visual system is still the gold standard when it comes to understanding the world around us. Borrowing from its design principles might turn out to be a profitable direction for future research.
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