IEET Fellow Stefan Sorgner: jury member of the Austrian Governmental Award Patent 2018

IEET Fellow and Philosophy Professor Stefan Lorenz Sorgner was invited to being a jury member by the Austrian patent agency to vote for the most innovative inventions of 2018: https://ift.tt/2HNZEIo The jury consists of leading experts in the field of emergent technologies and distinguished policy-maker, e.g. CEO of KTM Stefan Pierer, general director of IBM Austria Patricia Neumann, the rector of the Technical University Vienna Sabine Seidler, Senior Vice President Innovation & Technology of Borealis Maurits van Tol, as well as Business Angel Michael Altrichter: https://ift.tt/2vulKgs

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Superman: The Last Son of Prague

Besides everything else, today Superman is an almost satirically strict exemplar of what a “good” immigrant has to look like: if you can pass as a white able-bodied heterosexual male, were raised in a small Kansas farm, got a prestigious job in a traditional professional field, and save the world two or a hundred times, then you, too, might be accepted (or shot at on sight on account of what a scared cop or soldier thought you might conceivably do, that might happen too). But he was originally something far uncannier and politically disruptive.

Raised in an orphanage, he called himself not a crime-fighter or a world-saver, but a “champion of the oppressed.” Wikipedia has a fascinating list of his feats in the much-coveted but seldom-read Action Comics #1 he

  • violently breaks into the Governor’s mansion in the middle of the night to deliver a confession to stop an execution
  • throws around a man who was beating his wife
  • rescues Lois Lane from a man who abducted her after she rejected him (the man’s a gangster, but the car about to be thrown in the famous cover isn’t “a gangster’s car” but “the car of a man who was about to rape a woman who rejected him”)
  • “forcefully interrogates” a corrupt US Senator to obtain information about his crimes

That’s not the national icon who gets a statue from the thankful government of Metropolis. That’s the creation of a couple of Jewish immigrants and sons of Eastern-European immigrants in a US that wasn’t necessarily welcoming of either group, people who knew first hand that what you needed wasn’t help from alien invasions, but from abusive men and corrupt politicians.

Going slightly back in time, the cultural roots of Superman lie not in Krypton but in the ghetto of Prague, where, in the classic telling of Judah Loew ben Bezalel’s legend, the Rabbi created an invulnerable, super-strong, unstoppable golem — using, in a sense, the advanced technology of a long-dead world — to protect the inhabitants of the ghetto from the many forms of formal and informal violence they were subject to. The legend usually ends up badly in ways that would make Lex Luthor nod in approval, but, long before rich and privileged Victor Frankenstein successfully created life and completely flunked his ethical responsibilities about and towards it, there was already a tradition of non-/super-human life created by the knowledgeable oppressed for specifically ethical and political goals of communal survival. The focus of Superman as immigrant, his tale of his assimilation into and to America, is a relatively later development, as is his deployment as a sort of long-surrendered ideal of what American “hard power” should and should *not* do.

He was to begin with created by and for oppressed groups, using the then-new idioms of science-fiction to retell the story of a supernatural equalizer called forth when the all-too-natural mechanisms of society are overly stacked against you.

There’s no need to remark that all of the crimes Superman fought in Action Comics #1 were and are real and frequent. What might be worth pointing out is that, although our contemporary zeitgeist is one in which the uncanny is becoming increasingly operational and under the control of established powers — where billionaires plan Mars bases, cops in authoritarian countries have cybernetic access to facial recognition databases, and ubiquitously surveilled smart cities are prototyped by companies that are also vying for military contracts for targeting image analysis in, one dreams of being able to hope, completely unrelated developments — there’s an older thread of ideas just below the surface, one in which radical technologies aren’t just deployed by the powerful, but also as forms of both individual and communal resistance.

Captain America is less a metaphor than an entire category of military R&D grants, and more than one billionaire thinks themselves Tony Stark. But, in the inertia and apparent closure of our current narrative about our increasingly weirding, pre-/would-be posthuman future, let’s not forget that the radical application of new technologies can also take place in the political margins. There’s agency there, too, and new potential futures, all built with the age-old goals of community and survival, even if using new and stranger clay.

 

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How Thought Leadership Inspires Action With Ideas

Denise Brosseau believes that leading in today’s complicated world requires clarity of intention, voice, and focus. In essence, it requires thought leadership. In Denise’s view, thought leadership isn’t just about being famous or being known, it’s about getting your ideas out to the world in a way that promotes engagement, connection, and action. And Denise believes thought leadership is a practice that can be learned.

Denise is on the faculty of the Stanford Business School and the CEO of the Thought Leadership Lab. She is also the author of the book Ready to Be a Thought Leader? and runs online classes on thought leadership through LinkedIn Learning. I asked Denise to share more about her views on thought leadership and the importance of investing in it as leadership practice and organizational capability.

Lisa Kay Solomon: What is thought leadership? Why is this important these days?

Denise Brosseau: Thought leaders are the informed opinion leaders and the go-to people in their field of expertise. They are trusted sources who move and inspire people with innovative ideas, turn those ideas into reality, and know and show how to replicate their success. Over time, they create a dedicated group of friends, fans, and followers to help them replicate and scale their ideas into sustainable change, not just in one company but in an industry, a niche, or across an entire ecosystem.

Thought leaders are changing the world in meaningful ways and engaging others to join their efforts. They create evolutionary and even revolutionary advancements in their fields, not just by urging others to be open to new ways of thinking, but by creating a blueprint for people to follow. They provide guidelines or a set of best practices.

Thought leaders are all around us—men and women, young and old. They come from every ethnic, cultural, and socioeconomic background. Most thought leaders are change agents, people trying to change the world around a cause they care about. They engage with stakeholders and followers so that together they can bring about long-term sustainable change.

The world is full of challenges and injustices, and we need more leaders who understand what it takes to become a thought leader. We need people who can create change and even build movements that transform laws and attitudes and galvanize others to take action.

LKS:  We often think of thought leaders as people with special credentials or status, and that followership flows from that position. But in your book Ready to Be a Thought Leader?, you state that thought leadership starts with finding your purpose.  Can you share more about that perspective?

DB: To become recognized as a true thought leader takes time and a lot of stick-to-itiveness. This is why I highly recommend you start with what you care about. Choose a niche that is aligned to your purpose. Then you’ll be far more likely to work to build the credentials and expertise needed to be recognized as a thought leader.  After that you can take the steps to gather followers.

Ask yourself, what are you committed to? What do you spend time on when no one is watching you or paying you? What topics get you fired up?

This is usually something you can speak passionately about. It could be the latest tax code changes, the importance of saving the condors, or why women need their seat at the table.

People want to affiliate with those who are well-known and in the know. Thus, thought leadership also leads to invitations to join corporate boards, serve on government commissions, and participate in industry-wide committees—opportunities to raise your profile from the local to the national to the international stage.

Thought leadership is like the ripples in a pond—you start as a leader, encouraging those around you to make change. Then, as you engage people to share your ideas, those ideas reach a wider and wider audience. As your followers grow, you can accelerate change locally and then at a national or even international scale.

LKS: You talk about different types of thought leaders. Can you explain that a little bit more?

DB: While the path to becoming a thought leader is often similar, people who become thought leaders are not all motivated by the same priorities. Some are builders, motivated to create and show a new path.

Others are collaborators who are motivated to create connections between people with the goal of finding and shaping the best solutions for all. Another category is what I call the competitors. They are motivated to be at the top of their niche, to “win” in a sense by differentiating themselves and standing out from the crowd.

I think of some folks as intellectuals. They are motivated to share their research, knowledge, or lessons learned.

Next, there are the provocateurs, who are motivated to shake things up and challenge the status quo. Some are constructive provocateurs who are willing to patiently make incremental change. Others are revolutionaries ready to abandon the present methods and completely start over. And finally, there are the defenders, who want to protect something important from being changed or destroyed.

LKS: You’re now doing work with organizations that aspire to be thought leadership organizations. Can you describe what that is? How does that differ from just strong branding?

DB: Thought leading organizations are usually motivated by far more than building a strong brand. They know their reputation is shaped by more than their products and services, or even by the voice of the CEO. They want to cooperate with a group of stakeholders to advance industry priorities or a shared cause. They work to establish a strong voice and point of view in the marketplace. They strive to build trust among their customers and community.

They empower every employee to share their knowledge and expertise as an ambassador for the organization. Then they can truly stand out from the crowd and accomplish their goals.

Recognized thought leaders will have the power to persuade, the status and authority to move things in a new direction, and the clout to implement real progress and widespread innovation.

Image Credit: iidea studio / Shutterstock.com

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How deep learning is about to transform biomedical science

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Human induced pluripotent stem cell neurons imaged in phase contrast (gray pixels, left) — currently processed manually with fluorescent labels (color pixels) to make them visible. That’s about to radically change. (credit: Gladstone Institutes)

Researchers at Google, Harvard University, and Gladstone Institutes have developed and tested new deep-learning algorithms that can identify details in terabytes of bioimages, replacing slow, less-accurate manual labeling methods.

Deep learning is a type of machine learning that can analyze data, recognize patterns, and make predictions. The new deep-learning approach, which the researchers call “in silico labeling” (ISL), can automatically find and predict features in images of “unlabeled” cells (cells that have not been manually identified by using fluorescent chemicals).

The new deep-learning network can identify whether a cell is alive or dead, and get the answer right 98 percent of the time (humans can typically only identify a dead cell with 80 percent accuracy) — without requiring invasive fluorescent chemicals, which make it difficult to track tissues over time. The deep-learning network can also predict detailed features such as nuclei and cell type (such as neural or breast cancer tissue).

The deep-learning algorithms are expected to make it possible to handle the enormous 3–5 terabytes of data per day generated by Gladstone Institutes’ fully automated robotic microscope, which can track individual cells for up to several months.

The research was published in the April 12, 2018 issue of the journal Cell.

How to train a deep-learning neural network to predict the identity of cell features in microscope images

Using fluorescent labels with unlabeled images to train a deep neural network to bring out image detail. (Left) An unlabeled phase-contrast microscope transmitted-light image of rat cortex — the center image from the z-stack (vertical stack) of unlabeled images. (Right three images) Labeled images created with three different fluorescent labels, revealing invisible details of cell nuclei (blue), dendrites (green), and axons (red). The numbered outsets at the bottom show magnified views of marked subregions of images. (credit: Finkbeiner Lab)

To explore the new deep-learning approach, Steven Finkbeiner, MD, PhD, the director of the Center for Systems and Therapeutics at Gladstone Institutes in San Francisco, teamed up with computer scientists at Google.

“We trained the [deep learning] neural network by showing it two sets of matching images of the same cells: one unlabeled [such as the black and white “phase contrast”microscope image shown in the illustration] and one with fluorescent labels [such as the three colored images shown above],” explained Eric Christiansen, a software engineer at Google Accelerated Science and the study’s first author. “We repeated this process millions of times. Then, when we presented the network with an unlabeled image it had never seen, it could accurately predict where the fluorescent labels belong.” (Fluorescent labels are created by adding chemicals to tissue samples to help visualize details.)

The study used three cell types: human motor neurons derived from induced pluripotent stem cells, rat cortical cultures, and human breast cancer cells. For instance, the deep-learning neural network can identify a physical neuron within a mix of cells in a dish. It can go one step further and predict whether an extension of that neuron is an axon or dendrite (two different but similar-looking elements of the neural cell).

For this study, Google used TensorFlow, an open-source machine learning framework for deep learning originally developed by Google AI engineers. The code for this study, which is open-source on Github, is the result of a collaboration between Google Accelerated Science and two external labs: the Lee Rubin lab at Harvard and the Steven Finkbeiner lab at Gladstone.

Animation showing the same cells in transmitted light (black and white) and fluorescence (colored) imaging, along with predicted fluorescence labels from the in silico labeling model. Outset 2 shows the model predicts the correct labels despite the artifact in the transmitted-light input image. Outset 3 shows the model infers these processes are axons, possibly because of their distance from the nearest cells. Outset 4 shows the model sees the hard-to-see cell at the top, and correctly identifies the object at the left as DNA-free cell debris. (credit: Google)

Transforming biomedical research

“This is going to be transformative,” said Finkbeiner, who is also a professor of neurology and physiology at UC San Francisco. “Deep learning is going to fundamentally change the way we conduct biomedical science in the future, not only by accelerating discovery, but also by helping find treatments to address major unmet medical needs.”

In his laboratory, Finkbeiner is trying to find new ways to diagnose and treat neurodegenerative disorders, such as Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis (ALS). “We still don’t understand the exact cause of the disease for 90 percent of these patients,” said Finkbeiner. “What’s more, we don’t even know if all patients have the same cause, or if we could classify the diseases into different types. Deep learning tools could help us find answers to these questions, which have huge implications on everything from how we study the disease to the way we conduct clinical trials.”

Without knowing the classifications of a disease, a drug could be tested on the wrong group of patients and seem ineffective, when it could actually work for different patients. With induced pluripotent stem cell technology, scientists could match patients’ own cells with their clinical information, and the deep network could find relationships between the two datasets to predict connections. This could help identify a subgroup of patients with similar cell features and match them to the appropriate therapy, Finkbeiner suggests.

The research was funded by Google, the National Institute of Neurological Disorders and Stroke of the National Institutes of Health, the Taube/Koret Center for Neurodegenerative Disease Research at Gladstone, the ALS Association’s Neuro Collaborative, and The Michael J. Fox Foundation for Parkinson’s Research.


Abstract of In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images

Microscopy is a central method in life sciences. Many popular methods, such as antibody labeling, are used to add physical fluorescent labels to specific cellular constituents. However, these approaches have significant drawbacks, including inconsistency; limitations in the number of simultaneous labels because of spectral overlap; and necessary pertur-bations of the experiment, such as fixing the cells, to generate the measurement. Here, we show that a computational machine-learning approach, which we call ‘‘in silico labeling’’ (ISL), reliably predicts some fluorescent labels from transmitted-light images of unlabeled fixed or live biological samples. ISL predicts a range of labels, such as those for nuclei, cell type (e.g., neural), and cell state (e.g., cell death). Because prediction happens in silico, the method is consistent, is not limited by spectral overlap, and does not disturb the experiment. ISL generates biological measurements that would otherwise be problematic or impossible to acquire.

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How deep learning is about to transform biomedical science

https://ift.tt/2Hcs1mF

Human induced pluripotent stem cell neurons imaged in phase contrast (gray pixels, left) — currently processed manually with fluorescent labels (color pixels) to make them visible. That’s about to radically change. (credit: Gladstone Institutes)

Researchers at Google, Harvard University, and Gladstone Institutes have developed and tested new deep-learning algorithms that can identify details in terabytes of bioimages, replacing slow, less-accurate manual labeling methods.

Deep learning is a type of machine learning that can analyze data, recognize patterns, and make predictions. The new deep-learning approach, which the researchers call “in silico labeling” (ISL), can automatically find and predict features in images of “unlabeled” cells (cells that have not been manually identified by using fluorescent chemicals).

The new deep-learning network can identify whether a cell is alive or dead, and get the answer right 98 percent of the time (humans can typically only identify a dead cell with 80 percent accuracy) — without requiring invasive fluorescent chemicals, which make it difficult to track tissues over time. The deep-learning network can also predict detailed features such as nuclei and cell type (such as neural or breast cancer tissue).

The deep-learning algorithms are expected to make it possible to handle the enormous 3–5 terabytes of data per day generated by Gladstone Institutes’ fully automated robotic microscope, which can track individual cells for up to several months.

The research was published in the April 12, 2018 issue of the journal Cell.

How to train a deep-learning neural network to predict the identity of cell features in microscope images

Using fluorescent labels with unlabeled images to train a deep neural network to bring out image detail. (Left) An unlabeled phase-contrast microscope transmitted-light image of rat cortex — the center image from the z-stack (vertical stack) of unlabeled images. (Right three images) Labeled images created with three different fluorescent labels, revealing invisible details of cell nuclei (blue), dendrites (green), and axons (red). The numbered outsets at the bottom show magnified views of marked subregions of images. (credit: Finkbeiner Lab)

To explore the new deep-learning approach, Steven Finkbeiner, MD, PhD, the director of the Center for Systems and Therapeutics at Gladstone Institutes in San Francisco, teamed up with computer scientists at Google.

“We trained the [deep learning] neural network by showing it two sets of matching images of the same cells: one unlabeled [such as the black and white “phase contrast”microscope image shown in the illustration] and one with fluorescent labels [such as the three colored images shown above],” explained Eric Christiansen, a software engineer at Google Accelerated Science and the study’s first author. “We repeated this process millions of times. Then, when we presented the network with an unlabeled image it had never seen, it could accurately predict where the fluorescent labels belong.” (Fluorescent labels are created by adding chemicals to tissue samples to help visualize details.)

The study used three cell types: human motor neurons derived from induced pluripotent stem cells, rat cortical cultures, and human breast cancer cells. For instance, the deep-learning neural network can identify a physical neuron within a mix of cells in a dish. It can go one step further and predict whether an extension of that neuron is an axon or dendrite (two different but similar-looking elements of the neural cell).

For this study, Google used TensorFlow, an open-source machine learning framework for deep learning originally developed by Google AI engineers. The code for this study, which is open-source on Github, is the result of a collaboration between Google Accelerated Science and two external labs: the Lee Rubin lab at Harvard and the Steven Finkbeiner lab at Gladstone.

Animation showing the same cells in transmitted light (black and white) and fluorescence (colored) imaging, along with predicted fluorescence labels from the in silico labeling model. Outset 2 shows the model predicts the correct labels despite the artifact in the transmitted-light input image. Outset 3 shows the model infers these processes are axons, possibly because of their distance from the nearest cells. Outset 4 shows the model sees the hard-to-see cell at the top, and correctly identifies the object at the left as DNA-free cell debris. (credit: Google)

Transforming biomedical research

“This is going to be transformative,” said Finkbeiner, who is also a professor of neurology and physiology at UC San Francisco. “Deep learning is going to fundamentally change the way we conduct biomedical science in the future, not only by accelerating discovery, but also by helping find treatments to address major unmet medical needs.”

In his laboratory, Finkbeiner is trying to find new ways to diagnose and treat neurodegenerative disorders, such as Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis (ALS). “We still don’t understand the exact cause of the disease for 90 percent of these patients,” said Finkbeiner. “What’s more, we don’t even know if all patients have the same cause, or if we could classify the diseases into different types. Deep learning tools could help us find answers to these questions, which have huge implications on everything from how we study the disease to the way we conduct clinical trials.”

Without knowing the classifications of a disease, a drug could be tested on the wrong group of patients and seem ineffective, when it could actually work for different patients. With induced pluripotent stem cell technology, scientists could match patients’ own cells with their clinical information, and the deep network could find relationships between the two datasets to predict connections. This could help identify a subgroup of patients with similar cell features and match them to the appropriate therapy, Finkbeiner suggests.

The research was funded by Google, the National Institute of Neurological Disorders and Stroke of the National Institutes of Health, the Taube/Koret Center for Neurodegenerative Disease Research at Gladstone, the ALS Association’s Neuro Collaborative, and The Michael J. Fox Foundation for Parkinson’s Research.


Abstract of In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images

Microscopy is a central method in life sciences. Many popular methods, such as antibody labeling, are used to add physical fluorescent labels to specific cellular constituents. However, these approaches have significant drawbacks, including inconsistency; limitations in the number of simultaneous labels because of spectral overlap; and necessary pertur-bations of the experiment, such as fixing the cells, to generate the measurement. Here, we show that a computational machine-learning approach, which we call ‘‘in silico labeling’’ (ISL), reliably predicts some fluorescent labels from transmitted-light images of unlabeled fixed or live biological samples. ISL predicts a range of labels, such as those for nuclei, cell type (e.g., neural), and cell state (e.g., cell death). Because prediction happens in silico, the method is consistent, is not limited by spectral overlap, and does not disturb the experiment. ISL generates biological measurements that would otherwise be problematic or impossible to acquire.

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Mini-Brains Just Grew Their Own Blood Vessels—Here’s Why That’s Great News

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If you ever put a brain through a Picasso filter, you’d probably get something close to a brain ball.

When brain balls first hit the neuroscience scene in 2013, they were just a curious oddity. Each smaller than a grain of sand, brain balls are conglomerates of neurons seemingly haphazardly mushed into misshapen blobs. But there’s a serious twist: they’re utterly, completely, absolutely alive.

Yup, that’s right: scientists are making living blobs of brain-like organs floating in a dish.

Rather than just the stuff of nightmares, these little guys soon proved their worth. When grown inside a host mouse (creepy!), brain balls—or as the academic community calls them, cerebral organoids—organize into 3D structures remarkably similar to the developing brain.

They sparkle with electrical activity. They develop retinal cells—the critical component of eyes (creepier!). They’re a cellular bonanza of neurons and non-neural cells called glia. By eight months old, they contain roughly the same density of synapses as a human fetal cortex.

While few are willing to argue that brain balls “think” or “see,” they’re definitely doing something. Add the fact that they’re made up of human cells—rather than animal ones—and many neuroscientists believe they’re our best bet for studying neurodevelopmental disorders like autism or schizophrenia.

They’ve already proven their worth for finding drugs that protect the Zika-infected fetal brain. Some even think they’re the key to cracking glioblastoma, the deadliest type of brain cancer.

There’s just one major problem: brain balls have no heart.

Because they lack their own blood supply, brain balls have no way to actively shuttle oxygen and nutrients to their dense inner core. Without vessels, these neurons drown in their own metabolic waste and gradually wither and die. Take away blood, and there’s no way for brain balls to mature past the equivalent of a fetal second trimester.

Now, for the first time, a team from UC Davis managed to insert a homegrown blood vessel network into maturing brain balls. The trick was to embed baby brain balls into a nutritional gel together with endothelial cells—the cells that line blood vessels.

organoid-cross-section-mini-brain-blood-vessels
A cross-section of a mini-brain. Blood vessels, here in red, penetrate the outer layer into the middle core. Image Credit: UC Davis Institute For Regenerative Cures

When transplanted into a mouse, the brain organoids sprouted extensive vasculature similar to the shape of multi-layered petals in rose buds. These vessels reached far and wide throughout the brain blob, penetrating deep into the core. The team published their results in this month’s NeuroReport.

“Vascularization…is considered a crucial next step in the bioengineering of complex human brain tissue,” the authors wrote. A blood supply gives them the chance to mature further into the complex computational organ found in our heads.

“It’s a big deal,” said Dr. Christof Koch, president of the Allen Institute for Brain Science who was not involved in the work. Because the point of organoids is to recapitulate a patient’s brain injuries inside the lab, the closer they are to the real thing, the more valuable they become.

Skin to Brains

The team tweaked a well-known recipe for making brain balls.

They got their idea from studying the developing brain. The brain itself doesn’t have cells that directly mature into blood vessels, they explained. That means the vessels have to come from outside the brain—so if we put a proto-brain together with blood vessel cells, that might work.

Someone’s already tested the trick in mice, though—up until now—no one’s attempted it with human organoids.

Taking a small sample of a patient’s skin, the team transformed the cells into induced pluripotent stem cells (iPSCs). Similar to naturally-occurring stem cells, iPSCs have the vast ability to convert into almost any type of mature cells in the body.

The team painstakingly grew the patient’s iPSCs for five weeks inside a nutritious chemical cocktail designed to push them into baby brain organoids. They then isolated each organoid and gently embedded it into a soft gel.

At the same time, the team saved a small portion of iPSCs and transformed them into endothelial cells—cells that line the inside of blood vessels. They laid roughly 250,000 of these guys into the gel, so that they pillowed the nascent brain organoids.

After cohabiting for three to five weeks, the team then stuck the vascular brain balls into the ultimate incubator: a living rodent’s brain, from which they had carefully removed a small chunk to make room for their brain organoid. Naked brain balls grown without blood vessel cells were also transplanted as a control.

Two weeks later, the naked organoids died off, whereas the ones grown with blood vessels thrived. When the team studied their bizarre organoids under the microscope, they found massive networks of blood vessels and capillaries blooming throughout the mini-brain, even at the normally dead center core.

Mouse With Human Brains

Waldau isn’t the first to tackle the blood vessel problem.

Last year, at the Society for Neuroscience annual meeting, scientists from the Salk Institute and the University of Pennsylvania both reported vascularized mini-brains. When transplanted into rodent brains, the rodents’ own vessels tunneled inside the human brain transplants to bathe them in nutrient-rich blood.

That’s definitely one way towards more complex brain organoids. But many in the field are iffy about potential consequences. The transplanted organoids reached out extensive neuronal branches into the rodent brain, and formed functional synapses with their host. When stimulated, those synapses lit up like fireworks.

“It brings up some pretty interesting questions about what allows us, ethically, to do research on mice in the first place—namely, that they’re not human,” said Dr. Josephine Johnston, a bioethicist at the Hastings Center at the time. “If we give them human cerebral organoids, what does that do to their intelligence, their level of consciousness, even their species identity?”

Brain organoids are reaching a tipping point, one that bioethicists and scientists aren’t ready to deal with. Waldau’s self-vascularized brain balls offer a way out.

If brain balls can grow their own blood vessels, scientists could potentially take rodent hosts (at least partially) out of the picture. One potential way is to hook them up to microfluidic pumps: a brain inside a vat, while often a sci-fi trope, is far less skin-crawling than a rodent with human brain blobs.

Scientists can use these so-called “ex vivo” mini-brains to screen for neurological drugs. These vascularized blobs are especially useful for conditions like stroke, Alzheimer’s, and brain injury, because they often cause the brain’s vasculature to reorganize.

Talk about expanding mini-brains in an entirely new—cough—vein.

Image Credit: UC Davis Institute For Regenerative Cures

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