How dozens of men across Alaska (and their dogs) teamed up to save one town from a deadly outbreak
During the winter of 1924, Curtis Welch – the only doctor in Nome, a remote fishing town in northwest Alaska – started noticing something strange. More and more, the children of Nome were coming to his office with sore throats.
Initially, Welch dismissed the cases as tonsillitis or some run-of-the-mill virus – but when more kids started getting sick, with some even dying, he grew alarmed. It wasn’t until early 1925, after a three-year-old boy died just two weeks after becoming ill, that Welch realized that his worst suspicions were true. The boy – and dozens of other children in town – were infected with diphtheria.
A DEADLY BACTERIA
Diphtheria is nearly nonexistent and almost unheard of in industrialized countries today. But less than a century ago, diphtheria was a household name – one that struck fear in the heart of every parent, as it was extremely contagious and particularly deadly for children.
Diphtheria – a bacterial infection – is an ugly disease. When it strikes, the bacteria eats away at the healthy tissues in a patient’s respiratory tract, leaving behind a thick, gray membrane of dead tissue that covers the patient's nose, throat, and tonsils. Not only does this membrane make it very difficult for the patient to breathe and swallow, but as the bacteria spreads through the bloodstream, it causes serious harm to the heart and kidneys. It sometimes also results in nerve damage and paralysis. Even with treatment, diphtheria kills around 10 percent of people it infects. Young children, as well as adults over the age of 60, are especially at risk.
Welch didn’t suspect diphtheria at first. He knew the illness was incredibly contagious and reasoned that many more people would be sick – specifically, the family members of the children who had died – if there truly was an outbreak. Nevertheless, the symptoms, along with the growing number of deaths, were unmistakable. By 1925 Welch knew for certain that diphtheria had come to Nome.
In desperation, Welch tried treating an infected seven-year-old girl with some expired antitoxin – but she died just a few hours after he administered it.
AN INACCESSIBLE CURE
A vaccine for diphtheria wouldn’t be widely available until the mid-1930s and early 1940s – so an outbreak of the disease meant that each of the 10,000 inhabitants of Nome were all at serious risk.
One option was to use something called an antitoxin – a serum consisting of anti-diphtheria antibodies – to treat the patients. However, the town’s reserve of diphtheria antitoxin had expired. Welch had ordered a replacement shipment of antitoxin the previous summer – but the shipping port that was set to deliver the serum had been closed due to ice, and no new antitoxin would arrive before spring of 1925. In desperation, Welch tried treating an infected seven-year-old girl with some expired antitoxin – but she died just a few hours after he administered it.
Welch radioed for help to all the major towns in Alaska as well as the US Public Health Service in Washington, DC. His telegram read: An outbreak of diphtheria is almost inevitable here. I am in urgent need of one million units of diphtheria antitoxin. Mail is the only form of transportation.
FOUR-LEGGED HEROES
When the Alaskan Board of Health learned about the outbreak, the men rushed to devise a plan to get antitoxin to Nome. Dropping the serum in by airplane was impossible, as the available planes were unsuitable for flying during Alaska’s severe winter weather, where temperatures were routinely as cold as -50 degrees Fahrenheit.
In late January 1925, roughly 30,000 units of antitoxin were located in an Anchorage hospital and immediately delivered by train to a nearby city, Nenana, en route to Nome. Nenana was the furthest city that was reachable by rail – but unfortunately it was still more than 600 miles outside of Nome, with no transportation to make the delivery. Meanwhile, Welch had confirmed 20 total cases of diphtheria, with dozens more at high risk. Diphtheria was known for wiping out entire communities, and the entire town of Nome was in danger of suffering the same fate.
It was Mark Summer, the Board of Health superintendent, who suggested something unorthodox: Using a relay team of sled-racing dogs to deliver the antitoxin serum from Nenana to Nome. The Board quickly voted to accept Summer’s idea and set up a plan: The thousands of units of antitoxin serum would be passed along from team to team at different towns along the mail route from Nenana to Nome. When it reached a town called Nulato, a famed dogsled racer named Leonhard Seppala and his experienced team of huskies would take the serum more than 90 miles over the ice of Norton Sound, the longest and most treacherous part of the journey. Past the sound, the serum would change hands several times more before arriving in Nome.
Between January 27 and 31, the serum passed through roughly a dozen drivers and their dog sled teams, each of them carrying the serum between 20 and 50 miles to the next destination. Though each leg of the trip took less than a day, the sub-zero temperatures – sometimes as low as -85 degrees – meant that every driver and dog risked their lives. When the first driver, Bill Shannon, arrived at his checkpoint in Tolovana on January 28th, his nose was black with frostbite, and three of his dogs had died. The driver who relieved Bill Shannon, named Edgar Kalland, needed the owner of a local roadhouse to pour hot water over his hands to free them from the sled’s metal handlebar. Two more dogs from another relay team died before the serum was passed to Seppala at a town called Ungalik.
THE FINAL STRETCHES
Seppala and his team raced across the ice of the Norton Sound in the dead of night on January 31, with wind chill temperatures nearing an astonishing -90 degrees. The team traveled 84 miles in a single day before stopping to rest – and once rested, they set off again in the middle of the night through a raging winter storm. The team made it across the ice, as well as a 5,000-foot ascent up Little McKinley Mountain, to pass the serum to another driver in record time. The serum was now just 78 miles from Nome, and the death toll in town had reached 28.
The serum reached Gunnar Kaasen and his team of dogs on February 1st. Balto, Kaasen’s lead dog, guided the team heroically through a winter storm that was so severe Kaasen later reported not being able to see the dogs that were just a few feet ahead of him.
Visibility was so poor, in fact, that Kaasen ran his sled two miles past the relay point before noticing – and not wanting to lose a minute, he decided to forge on ahead rather than doubling back to deliver the serum to another driver. As they continued through the storm, the hurricane-force winds ripped past Kaasen’s sled at one point and toppled the sled – and the serum – overboard. The cylinder containing the antitoxin was left buried in the snow – and Kaasen tore off his gloves and dug through the tundra to locate it. Though it resulted in a bad case of frostbite, Kaasen eventually found the cylinder and kept driving.
Kaasen arrived at the next relay point on February 2nd, hours ahead of schedule. When he got there, however, he found the relay driver of the next team asleep. Kaasen took a risk and decided not to wake him, fearing that time would be wasted with the next driver readying his team. Kaasen, Balto, and the rest of the team forged on, driving another 25 miles before finally reaching Nome just before six in the morning. Eyewitnesses described Kaasen pulling up to the town’s bank and stumbling to the front of the sled. There, he collapsed in exhaustion, telling onlookers that Balto was “a damn fine dog.”
A LIVING LEGACY
Just a few hours after Balto’s heroic arrival in Nome, the serum had been thawed and was ready to administer to the patients with diphtheria. Amazingly, the relay team managed to complete the entire journey in just 127 hours – a world record at the time – without one serum vial damaged or destroyed. The serum shipment that arrived by dogsled – along with additional serum deliveries that followed in the next several weeks – were successful in stopping the outbreak in its tracks.
Balto and several other dogs – including Togo, the lead dog on Seppala’s team – were celebrated as local heroes after the race. Balto died in 1933, while the last of the human serum runners died in 1999 – but their legacy lives on: In early 2021, an all-female team of healthcare workers made the news by braving the Alaskan winter to deliver COVID-19 vaccines to people in rural North Alaska, traveling by bobsled and snowmobile – a heroic journey, and one that would have been unthinkable had Balto, Togo, and the 1925 sled runners not first paved the way.
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.