How a Nobel-Prize Winner Fought Her Family, Nazis, and Bombs to Change our Understanding of Cells Forever
When Rita Levi-Montalcini decided to become a scientist, she was determined that nothing would stand in her way. And from the beginning, that determination was put to the test. Before Levi-Montalcini became a Nobel Prize-winning neurobiologist, the first to discover and isolate a crucial chemical called Neural Growth Factor (NGF), she would have to battle both the sexism within her own family as well as the racism and fascism that was slowly engulfing her country
Levi-Montalcini was born to two loving parents in Turin, Italy at the turn of the 20th century. She and her twin sister, Paola, were the youngest of the family's four children, and Levi-Montalcini described her childhood as "filled with love and reciprocal devotion." But while her parents were loving, supportive and "highly cultured," her father refused to let his three daughters engage in any schooling beyond the basics. "He loved us and had a great respect for women," she later explained, "but he believed that a professional career would interfere with the duties of a wife and mother."
At age 20, Levi-Montalcini had finally had enough. "I realized that I could not possibly adjust to a feminine role as conceived by my father," she is quoted as saying, and asked his permission to finish high school and pursue a career in medicine. When her father reluctantly agreed, Levi-Montalcini was ecstatic: In just under a year, she managed to catch up on her mathematics, graduate high school, and enroll in medical school in Turin.
By 1936, Levi-Montalcini had graduated medical school at the top of her class and decided to stay on at the University of Turin as a research assistant for histologist and human anatomy professor Guiseppe Levi. Levi-Montalcini started studying nerve cells and nerve fibers – the tiny, slender tendrils that are threaded throughout our nerves and that determine what information each nerve can transmit. But it wasn't long before another enormous obstacle to her scientific career reared its head.
Science Under a Fascist Regime
Two years into her research assistant position, Levi-Montalcini was fired, along with every other "non-Aryan Italian" who held an academic or professional career, thanks to a series of antisemitic laws passed by Italy's then-leader Benito Mussolini. Forced out of her academic position, Levi-Montalcini went to Belgium for a fellowship at a neurological institute in Brussels – but then was forced back to Turin when the German army invaded.
Levi-Montalcini decided to keep researching. She and Guiseppe Levi built a makeshift lab in Levi-Montalcini's apartment, borrowing chicken eggs from local farmers and using sewing needles to dissect them. By dissecting the chicken embryos from her bedroom laboratory, she was able to see how nerve fibers formed and died. The two continued this research until they were interrupted again – this time, by British air raids. Levi-Montalcini fled to a country cottage to continue her research, and then two years later was forced into hiding when the German army invaded Italy. Levi-Montalcini and her family assumed different identities and lived with non-Jewish friends in Florence to survive the Holocaust. Despite all of this, Levi-Montalcini continued her work, dissecting chicken embryos from her hiding place until the end of the war.
"The discovery of NGF really changed the world in which we live, because now we knew that cells talk to other cells, and that they use soluble factors. It was hugely important."
A Post-War Discovery
Several years after the war, when Levi-Montalcini was once again working at the University of Turin, a German embryologist named Viktor Hamburger invited her to Washington University in St. Louis. Hamburger was impressed by Levi-Montalcini's research with her chicken embryos, and secured an opportunity for her to continue her work in America. The invitation would "change the course of my life," Levi-Montalcini would later recall.
During her fellowship, Montalcini grew tumors in mice and then transferred them to chick embryos in order to see how it would affect the chickens. To her surprise, she noticed that introducing the tumor samples would cause nerve fibers to grow rapidly. From this, Levi-Montalcini discovered and was able to isolate a protein that she determined was able to cause this rapid growth. She later named this Nerve Growth Factor, or NGF.
From there, Levi-Montalcini and her team launched new experiments to test NGF, injecting it and repressing it to see the effect it had in a test subject's body. When the team injected NGF into embryonic mice, they observed nerve growth, as well as the mouse pups developing faster – their eyes opening earlier and their teeth coming in sooner – than the untreated group. When the team purified the NGF extract, however, it had no effect, leading the team to believe that something else in the crude extract of NGF was influencing the growth of the newborn mice. Stanley Cohen, Levi-Montalcini's colleague, identified another growth factor called EGF – epidermal growth factor – that caused the mouse pups' eyes and teeth to grow so quickly.
Levi-Montalcini continued to experiment with NGF for the next several decades at Washington University, illuminating how NGF works in our body. When Levi-Montalcini injected newborn mice with an antiserum for NGF, for example, her team found that it "almost completely deprived the animals of a sympathetic nervous system." Other experiments done by Levi-Montalcini and her colleagues helped show the role that NGF plays in other important biological processes, such as the regulation of our immune system and ovulation.
"The discovery of NGF really changed the world in which we live, because now we knew that cells talk to other cells, and that they use soluble factors. It was hugely important," said Bill Mobley, Chair of the Department of Neurosciences at the University of California, San Diego School of Medicine.
Her Lasting Legacy
After years of setbacks, Levi-Montalcini's groundbreaking work was recognized in 1986, when she was awarded the Nobel Prize in Medicine for her discovery of NGF (Cohen, her colleague who discovered EGF, shared the prize). Researchers continue to study NGF even to this day, and the continued research has been able to increase our understanding of diseases like HIV and Alzheimer's.
Levi-Montalcini never stopped researching either: In January 2012, at the age of 102, Levi-Montalcini published her last research paper in the journal PNAS, making her the oldest member of the National Academy of Science to do so. Before she died in December 2012, she encouraged other scientists who would suffer setbacks in their careers to keep pursuing their passions. "Don't fear the difficult moments," Levi-Montalcini is quoted as saying. "The best comes from them."
Researchers probe extreme gene therapy for severe alcoholism
Story by Freethink
A single shot — a gene therapy injected into the brain — dramatically reduced alcohol consumption in monkeys that previously drank heavily. If the therapy is safe and effective in people, it might one day be a permanent treatment for alcoholism for people with no other options.
The challenge: Alcohol use disorder (AUD) means a person has trouble controlling their alcohol consumption, even when it is negatively affecting their life, job, or health.
In the U.S., more than 10 percent of people over the age of 12 are estimated to have AUD, and while medications, counseling, or sheer willpower can help some stop drinking, staying sober can be a huge struggle — an estimated 40-60 percent of people relapse at least once.
A team of U.S. researchers suspected that an in-development gene therapy for Parkinson’s disease might work as a dopamine-replenishing treatment for alcoholism, too.
According to the CDC, more than 140,000 Americans are dying each year from alcohol-related causes, and the rate of deaths has been rising for years, especially during the pandemic.
The idea: For occasional drinkers, alcohol causes the brain to release more dopamine, a chemical that makes you feel good. Chronic alcohol use, however, causes the brain to produce, and process, less dopamine, and this persistent dopamine deficit has been linked to alcohol relapse.
There is currently no way to reverse the changes in the brain brought about by AUD, but a team of U.S. researchers suspected that an in-development gene therapy for Parkinson’s disease might work as a dopamine-replenishing treatment for alcoholism, too.
To find out, they tested it in heavy-drinking monkeys — and the animals’ alcohol consumption dropped by 90% over the course of a year.
How it works: The treatment centers on the protein GDNF (“glial cell line-derived neurotrophic factor”), which supports the survival of certain neurons, including ones linked to dopamine.
For the new study, a harmless virus was used to deliver the gene that codes for GDNF into the brains of four monkeys that, when they had the option, drank heavily — the amount of ethanol-infused water they consumed would be equivalent to a person having nine drinks per day.
“We targeted the cell bodies that produce dopamine with this gene to increase dopamine synthesis, thereby replenishing or restoring what chronic drinking has taken away,” said co-lead researcher Kathleen Grant.
To serve as controls, another four heavy-drinking monkeys underwent the same procedure, but with a saline solution delivered instead of the gene therapy.
The results: All of the monkeys had their access to alcohol removed for two months following the surgery. When it was then reintroduced for four weeks, the heavy drinkers consumed 50 percent less compared to the control group.
When the researchers examined the monkeys’ brains at the end of the study, they were able to confirm that dopamine levels had been replenished in the treated animals, but remained low in the controls.
The researchers then took the alcohol away for another four weeks, before giving it back for four. They repeated this cycle for a year, and by the end of it, the treated monkeys’ consumption had fallen by more than 90 percent compared to the controls.
“Drinking went down to almost zero,” said Grant. “For months on end, these animals would choose to drink water and just avoid drinking alcohol altogether. They decreased their drinking to the point that it was so low we didn’t record a blood-alcohol level.”
When the researchers examined the monkeys’ brains at the end of the study, they were able to confirm that dopamine levels had been replenished in the treated animals, but remained low in the controls.
Looking ahead: Dopamine is involved in a lot more than addiction, so more research is needed to not only see if the results translate to people but whether the gene therapy leads to any unwanted changes to mood or behavior.
Because the therapy requires invasive brain surgery and is likely irreversible, it’s unlikely to ever become a common treatment for alcoholism — but it could one day be the only thing standing between people with severe AUD and death.
“[The treatment] would be most appropriate for people who have already shown that all our normal therapeutic approaches do not work for them,” said Grant. “They are likely to create severe harm or kill themselves or others due to their drinking.”
This article originally appeared on Freethink, home of the brightest minds and biggest ideas of all time.
Massive benefits of AI come with environmental and human costs. Can AI itself be part of the solution?
The recent explosion of generative artificial intelligence tools like ChatGPT and Dall-E enabled anyone with internet access to harness AI’s power for enhanced productivity, creativity, and problem-solving. With their ever-improving capabilities and expanding user base, these tools proved useful across disciplines, from the creative to the scientific.
But beneath the technological wonders of human-like conversation and creative expression lies a dirty secret—an alarming environmental and human cost. AI has an immense carbon footprint. Systems like ChatGPT take months to train in high-powered data centers, which demand huge amounts of electricity, much of which is still generated with fossil fuels, as well as water for cooling. “One of the reasons why Open AI needs investments [to the tune of] $10 billion from Microsoft is because they need to pay for all of that computation,” says Kentaro Toyama, a computer scientist at the University of Michigan. There’s also an ecological toll from mining rare minerals required for hardware and infrastructure. This environmental exploitation pollutes land, triggers natural disasters and causes large-scale human displacement. Finally, for data labeling needed to train and correct AI algorithms, the Big Data industry employs cheap and exploitative labor, often from the Global South.
Generative AI tools are based on large language models (LLMs), with most well-known being various versions of GPT. LLMs can perform natural language processing, including translating, summarizing and answering questions. They use artificial neural networks, called deep learning or machine learning. Inspired by the human brain, neural networks are made of millions of artificial neurons. “The basic principles of neural networks were known even in the 1950s and 1960s,” Toyama says, “but it’s only now, with the tremendous amount of compute power that we have, as well as huge amounts of data, that it’s become possible to train generative AI models.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries.
In recent months, much attention has gone to the transformative benefits of these technologies. But it’s important to consider that these remarkable advances may come at a price.
AI’s carbon footprint
In their latest annual report, 2023 Landscape: Confronting Tech Power, the AI Now Institute, an independent policy research entity focusing on the concentration of power in the tech industry, says: “The constant push for scale in artificial intelligence has led Big Tech firms to develop hugely energy-intensive computational models that optimize for ‘accuracy’—through increasingly large datasets and computationally intensive model training—over more efficient and sustainable alternatives.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries. In 2019, Emma Strubell, then a graduate researcher at the University of Massachusetts Amherst, estimated that training a single LLM resulted in over 280,000 kg in CO2 emissions—an equivalent of driving almost 1.2 million km in a gas-powered car. A couple of years later, David Patterson, a computer scientist from the University of California Berkeley, and colleagues, estimated GPT-3’s carbon footprint at over 550,000 kg of CO2 In 2022, the tech company Hugging Face, estimated the carbon footprint of its own language model, BLOOM, as 25,000 kg in CO2 emissions. (BLOOM’s footprint is lower because Hugging Face uses renewable energy, but it doubled when other life-cycle processes like hardware manufacturing and use were added.)
Luckily, despite the growing size and numbers of data centers, their increasing energy demands and emissions have not kept pace proportionately—thanks to renewable energy sources and energy-efficient hardware.
But emissions don’t tell the full story.
AI’s hidden human cost
“If historical colonialism annexed territories, their resources, and the bodies that worked on them, data colonialism’s power grab is both simpler and deeper: the capture and control of human life itself through appropriating the data that can be extracted from it for profit.” So write Nick Couldry and Ulises Mejias, authors of the book The Costs of Connection.
The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
Technologies we use daily inexorably gather our data. “Human experience, potentially every layer and aspect of it, is becoming the target of profitable extraction,” Couldry and Meijas say. This feeds data capitalism, the economic model built on the extraction and commodification of data. While we are being dispossessed of our data, Big Tech commodifies it for their own benefit. This results in consolidation of power structures that reinforce existing race, gender, class and other inequalities.
“The political economy around tech and tech companies, and the development in advances in AI contribute to massive displacement and pollution, and significantly changes the built environment,” says technologist and activist Yeshi Milner, who founded Data For Black Lives (D4BL) to create measurable change in Black people’s lives using data. The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
AI’s recent explosive growth spiked the demand for manual, behind-the-scenes tasks, creating an industry described by Mary Gray and Siddharth Suri as “ghost work” in their book. This invisible human workforce that lies behind the “magic” of AI, is overworked and underpaid, and very often based in the Global South. For example, workers in Kenya who made less than $2 an hour, were the behind the mechanism that trained ChatGPT to properly talk about violence, hate speech and sexual abuse. And, according to an article in Analytics India Magazine, in some cases these workers may not have been paid at all, a case for wage theft. An exposé by the Washington Post describes “digital sweatshops” in the Philippines, where thousands of workers experience low wages, delays in payment, and wage theft by Remotasks, a platform owned by Scale AI, a $7 billion dollar American startup. Rights groups and labor researchers have flagged Scale AI as one company that flouts basic labor standards for workers abroad.
It is possible to draw a parallel with chattel slavery—the most significant economic event that continues to shape the modern world—to see the business structures that allow for the massive exploitation of people, Milner says. Back then, people got chocolate, sugar, cotton; today, they get generative AI tools. “What’s invisible through distance—because [tech companies] also control what we see—is the massive exploitation,” Milner says.
“At Data for Black Lives, we are less concerned with whether AI will become human…[W]e’re more concerned with the growing power of AI to decide who’s human and who’s not,” Milner says. As a decision-making force, AI becomes a “justifying factor for policies, practices, rules that not just reinforce, but are currently turning the clock back generations years on people’s civil and human rights.”
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement.
Nuria Oliver, a computer scientist, and co-founder and vice-president of the European Laboratory of Learning and Intelligent Systems (ELLIS), says that instead of focusing on the hypothetical existential risks of today’s AI, we should talk about its real, tangible risks.
“Because AI is a transverse discipline that you can apply to any field [from education, journalism, medicine, to transportation and energy], it has a transformative power…and an exponential impact,” she says.
AI's accountability
“At the core of what we were arguing about data capitalism [is] a call to action to abolish Big Data,” says Milner. “Not to abolish data itself, but the power structures that concentrate [its] power in the hands of very few actors.”
A comprehensive AI Act currently negotiated in the European Parliament aims to rein Big Tech in. It plans to introduce a rating of AI tools based on the harms caused to humans, while being as technology-neutral as possible. That sets standards for safe, transparent, traceable, non-discriminatory, and environmentally friendly AI systems, overseen by people, not automation. The regulations also ask for transparency in the content used to train generative AIs, particularly with copyrighted data, and also disclosing that the content is AI-generated. “This European regulation is setting the example for other regions and countries in the world,” Oliver says. But, she adds, such transparencies are hard to achieve.
Google, for example, recently updated its privacy policy to say that anything on the public internet will be used as training data. “Obviously, technology companies have to respond to their economic interests, so their decisions are not necessarily going to be the best for society and for the environment,” Oliver says. “And that’s why we need strong research institutions and civil society institutions to push for actions.” ELLIS also advocates for data centers to be built in locations where the energy can be produced sustainably.
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement. “The only way to make sense of this data is using machine learning methods,” Oliver says.
Milner believes that the best way to expose AI-caused systemic inequalities is through people's stories. “In these last five years, so much of our work [at D4BL] has been creating new datasets, new data tools, bringing the data to life. To show the harms but also to continue to reclaim it as a tool for social change and for political change.” This change, she adds, will depend on whose hands it is in.