The Nose Knows: Dogs Are Being Trained to Detect the Coronavirus
Asher is eccentric and inquisitive. He loves an audience, likes keeping busy, and howls to be let through doors. He is a six-year-old working Cocker Spaniel, who, with five other furry colleagues, has now been trained to sniff body odor samples from humans to detect COVID-19 infections.
As the Delta variant and other new versions of the SARS-CoV-2 virus emerge, public health agencies are once again recommending masking while employers contemplate mandatory vaccination. While PCR tests remain the "gold standard" of COVID-19 tests, they can take hours to flag infections. To accelerate the process, scientists are turning to a new testing tool: sniffer dogs.
At the London School of Hygiene and Tropical Medicine (LSHTM), researchers deployed Asher and five other trained dogs to test sock samples from 200 asymptomatic, infected individuals and 200 healthy individuals. In May, they published the findings of the yearlong study in a preprint, concluding that dogs could identify COVID-19 infections with a high degree of accuracy – they could correctly identify a COVID-positive sample up to 94% of the time and a negative sample up to 92% of the time. The paper has yet to be peer-reviewed.
"Dogs can screen lots of people very quickly – 300 people per dog per hour. This means they could be used in places like airports or public venues like stadiums and maybe even workplaces," says James Logan, who heads the Department of Disease Control at LSHTM, adding that canines can also detect variants of SARS-CoV-2. "We included samples from two variants and the dogs could still detect them."
Detection dogs have been one of the most reliable biosensors for identifying the odor of human disease. According to Gemma Butlin, a spokesperson of Medical Detection Dogs, the UK-based charity that trained canines for the LSHTM study, the olfactory capabilities of dogs have been deployed to detect malaria, Parkinson's disease, different types of cancers, as well as pseudomonas, a type of bacteria known to cause infections in blood, lungs, eyes, and other parts of the human body.
COVID-19 has a distinctive smell — a result of chemicals known as volatile organic compounds released by infected body cells, which give off an odor "fingerprint."
"It's estimated that the percentage of a dog's brain devoted to analyzing odors is 40 times larger than that of a human," says Butlin. "Humans have around 5 million scent receptors dedicated to smell. Dogs have 350 million and can detect odors at parts per trillion. To put this into context, a dog can detect a teaspoon of sugar in a million gallons of water: two Olympic-sized pools full."
According to LSHTM scientists, COVID-19 has a distinctive smell — a result of chemicals known as volatile organic compounds released by infected body cells, which give off an odor "fingerprint." Other studies, too, have revealed that the SARS-CoV-2 virus has a distinct olfactory signature, detectable in the urine, saliva, and sweat of infected individuals. Humans can't smell the disease in these fluids, but dogs can.
"Our research shows that the smell associated with COVID-19 is at least partly due to small and volatile chemicals that are produced by the virus growing in the body or the immune response to the virus or both," said Steve Lindsay, a public health entomologist at Durham University, whose team collaborated with LSHTM for the study. He added, "There is also a further possibility that dogs can actually smell the virus, which is incredible given how small viruses are."
In April this year, researchers from the University of Pennsylvania and collaborators published a similar study in the scientific journal PLOS One, revealing that detection dogs could successfully discriminate between urine samples of infected and uninfected individuals. The accuracy rate of canines in this study was 96%. Similarly, last December, French scientists found that dogs were 76-100% effective at identifying individuals with COVID-19 when presented with sweat samples.
Grandjean Dominique, a professor at France's National Veterinary School of Alfort, who led the French study, said that the researchers used two types of dogs — search and rescue dogs, as they can sniff sweat, and explosive detection dogs, because they're often used at airports to find bomb ingredients. Dogs may very well be as good as PCR tests, said Dominique, but the goal, he added, is not to replace these tests with canines.
In France, the government gave the green light to train hundreds of disease detection dogs and deploy them in airports. "They will act as mass pre-test, and only people who are positive will undergo a PCR test to check their level of infection and the kind of variant," says Dominique. He thinks the dogs will be able to decrease the amount of PCR testing and potentially save money.
Since the accuracy rate for bio-detection dogs is fairly high, scientists think they could prove to be a quick diagnosis and mass screening tool, especially at ports, airports, train stations, stadiums, and public gatherings. Countries like Finland, Thailand, UAE, Italy, Chile, India, Australia, Pakistan, Saudi Arabia, Switzerland, and Mexico are already training and deploying canines for COVID-19 detection. The dogs are trained to sniff the area around a person, and if they find the odor of COVID-19 they will sit or stand back from an individual as a signal that they've identified an infection.
While bio-detection dogs seem promising for cheap, large-volume screening, many of the studies that have been performed to date have been small and in controlled environments. The big question is whether this approach work on people in crowded airports, not just samples of shirts and socks in a lab.
"The next step is 'real world' testing where they [canines] are placed in airports to screen people and see how they perform," says Anna Durbin, professor of international health at the John Hopkins Bloomberg School of Public Health. "Testing in real airports with lots of passengers and competing scents will need to be done."
According to Butlin of Medical Detection Dogs, scalability could be a challenge. However, scientists don't intend to have a dog in every waiting room, detecting COVID-19 or other diseases, she said.
"Dogs are the most reliable bio sensors on the planet and they have proven time and time again that they can detect diseases as accurately, if not more so, than current technological diagnostics," said Butlin. "We are learning from them all the time and what their noses know will one day enable the creation an 'E-nose' that does the same job – imagine a day when your mobile phone can tell you that you are unwell."
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.