New tools could catch disease outbreaks earlier - or predict them
Every year, the villages which lie in the so-called ‘Nipah belt’— which stretches along the western border between Bangladesh and India, brace themselves for the latest outbreak. For since 1998, when Nipah virus—a form of hemorrhagic fever most common in Bangladesh—first spilled over into humans, it has been a grim annual visitor to the people of this region.
With a 70 percent fatality rate, no vaccine, and no known treatments, Nipah virus has been dubbed in the Western world as ‘the worst disease no one has ever heard of.’ Currently, outbreaks tend to be relatively contained because it is not very transmissible. The virus circulates throughout Asia in fruit eating bats, and only tends to be passed on to people who consume contaminated date palm sap, a sweet drink which is harvested across Bangladesh.
But as SARS-CoV-2 has shown the world, this can quickly change.
“Nipah virus is among what virologists call ‘the Big 10,’ along with things like Lassa fever and Crimean Congo hemorrhagic fever,” says Noam Ross, a disease ecologist at New York-based non-profit EcoHealth Alliance. “These are pretty dangerous viruses from a lethality perspective, which don’t currently have the capacity to spread into broader human populations. But that can evolve, and you could very well see a variant emerge that has human-human transmission capability.”
That’s not an overstatement. Surveys suggest that mammals harbour about 40,000 viruses, with roughly a quarter capable of infecting humans. The vast majority never get a chance to do so because we don’t encounter them, but climate change can alter that. Recent studies have found that as animals relocate to new habitats due to shifting environmental conditions, the coming decades will bring around 300,000 first encounters between species which normally don’t interact, especially in tropical Africa and southeast Asia. All these interactions will make it far more likely for hitherto unknown viruses to cross paths with humans.
That’s why for the last 16 years, EcoHealth Alliance has been conducting ongoing viral surveillance projects across Bangladesh. The goal is to understand why Nipah is so much more prevalent in the western part of the country, compared to the east, and keep a watchful eye out for new Nipah strains as well as other dangerous pathogens like Ebola.
"There are a lot of different infectious agents that are sensitive to climate change that don't have these sorts of software tools being developed for them," says Cat Lippi, medical geography researcher at the University of Florida.
Until very recently this kind of work has been hampered by the limitations of viral surveillance technology. The PREDICT project, a $200 million initiative funded by the United States Agency for International Development, which conducted surveillance across the Amazon Basin, Congo Basin and extensive parts of South and Southeast Asia, relied upon so-called nucleic acid assays which enabled scientists to search for the genetic material of viruses in animal samples.
However, the project came under criticism for being highly inefficient. “That approach requires a big sampling effort, because of the rarity of individual infections,” says Ross. “Any particular animal may be infected for a couple of weeks, maybe once or twice in its lifetime. So if you sample thousands and thousands of animals, you'll eventually get one that has an Ebola virus infection right now.”
Ross explains that there is now far more interest in serological sampling—the scientific term for the process of drawing blood for antibody testing. By searching for the presence of antibodies in the blood of humans and animals, scientists have a greater chance of detecting viruses which started circulating recently.
Despite the controversy surrounding EcoHealth Alliance’s involvement in so-called gain of function research—experiments that study whether viruses might mutate into deadlier strains—the organization’s separate efforts to stay one step ahead of pathogen evolution are key to stopping the next pandemic.
“Having really cheap and fast surveillance is really important,” says Ross. “Particularly in a place where there's persistent, low level, moderate infections that potentially have the ability to develop into more epidemic or pandemic situations. It means there’s a pathway that something more dangerous can come through."
Scientists are searching for the presence of antibodies in the blood of humans and animals in hopes to detect viruses that recently started circulating.
EcoHealth Alliance
In Bangladesh, EcoHealth Alliance is attempting to do this using a newer serological technology known as a multiplex Luminex assay, which tests samples against a panel of known antibodies against many different viruses. It collects what Ross describes as a ‘footprint of information,’ which allows scientists to tell whether the sample contains the presence of a known pathogen or something completely different and needs to be investigated further.
By using this technology to sample human and animal populations across the country, they hope to gain an idea of whether there are any novel Nipah virus variants or strains from the same family, as well as other deadly viral families like Ebola.
This is just one of several novel tools being used for viral discovery in surveillance projects around the globe. Multiple research groups are taking PREDICT’s approach of looking for novel viruses in animals in various hotspots. They collect environmental DNA—mucus, faeces or shed skin left behind in soil, sediment or water—which can then be genetically sequenced.
Five years ago, this would have been a painstaking work requiring bringing collected samples back to labs. Today, thanks to the vast amounts of money spent on new technologies during COVID-19, researchers now have portable sequencing tools they can take out into the field.
Christopher Jerde, a researcher at the UC Santa Barbara Marine Science Institute, points to the Oxford Nanopore MinION sequencer as one example. “I tried one of the early versions of it four years ago, and it was miserable,” he says. “But they’ve really improved, and what we’re going to be able to do in the next five to ten years will be amazing. Instead of having to carefully transport samples back to the lab, we're going to have cigar box-shaped sequencers that we take into the field, plug into a laptop, and do the whole sequencing of an organism.”
In the past, viral surveillance has had to be very targeted and focused on known families of viruses, potentially missing new, previously unknown zoonotic pathogens. Jerde says that the rise of portable sequencers will lead to what he describes as “true surveillance.”
“Before, this was just too complex,” he says. “It had to be very focused, for example, looking for SARS-type viruses. Now we’re able to say, ‘Tell us all the viruses that are here?’ And this will give us true surveillance – we’ll be able to see the diversity of all the pathogens which are in these spots and have an understanding of which ones are coming into the population and causing damage.”
But being able to discover more viruses also comes with certain challenges. Some scientists fear that the speed of viral discovery will soon outpace the human capacity to analyze them all and assess the threat that they pose to us.
“I think we're already there,” says Jason Ladner, assistant professor at Northern Arizona University’s Pathogen and Microbiome Institute. “If you look at all the papers on the expanding RNA virus sphere, there are all of these deposited partial or complete viral sequences in groups that we just don't know anything really about yet.” Bats, for example, carry a myriad of viruses, whose ability to infect human cells we understand very poorly.
Cultivating these viruses under laboratory conditions and testing them on organoids— miniature, simplified versions of organs created from stem cells—can help with these assessments, but it is a slow and painstaking work. One hope is that in the future, machine learning could help automate this process. The new SpillOver Viral Risk Ranking platform aims to assess the risk level of a given virus based on 31 different metrics, while other computer models have tried to do the same based on the similarity of a virus’s genomic sequence to known zoonotic threats.
However, Ladner says that these types of comparisons are still overly simplistic. For one thing, scientists are still only aware of a few hundred zoonotic viruses, which is a very limited data sample for accurately assessing a novel pathogen. Instead, he says that there is a need for virologists to develop models which can determine viral compatibility with human cells, based on genomic data.
“One thing which is really useful, but can be challenging to do, is understand the cell surface receptors that a given virus might use,” he says. “Understanding whether a virus is likely to be able to use proteins on the surface of human cells to gain entry can be very informative.”
As the Earth’s climate heats up, scientists also need to better model the so-called vector borne diseases such as dengue, Zika, chikungunya and yellow fever. Transmitted by the Aedes mosquito residing in humid climates, these blights currently disproportionally affect people in low-income nations. But predictions suggest that as the planet warms and the pests find new homes, an estimated one billion people who currently don’t encounter them might be threatened by their bites by 2080. “When it comes to mosquito-borne diseases we have to worry about shifts in suitable habitat,” says Cat Lippi, a medical geography researcher at the University of Florida. “As climate patterns change on these big scales, we expect to see shifts in where people will be at risk for contracting these diseases.”
Public health practitioners and government decision-makers need tools to make climate-informed decisions about the evolving threat of different infectious diseases. Some projects are already underway. An ongoing collaboration between the Catalan Institution for Research and Advanced Studies and researchers in Brazil and Peru is utilizing drones and weather stations to collect data on how mosquitoes change their breeding patterns in response to climate shifts. This information will then be fed into computer algorithms to predict the impact of mosquito-borne illnesses on different regions.
The team at the Catalan Institution for Research and Advanced Studies is using drones and weather stations to collect data on how mosquito breeding patterns change due to climate shifts.
Gabriel Carrasco
Lippi says that similar models are urgently needed to predict how changing climate patterns affect respiratory, foodborne, waterborne and soilborne illnesses. The UK-based Wellcome Trust has allocated significant assets to fund such projects, which should allow scientists to monitor the impact of climate on a much broader range of infections. “There are a lot of different infectious agents that are sensitive to climate change that don't have these sorts of software tools being developed for them,” she says.
COVID-19’s havoc boosted funding for infectious disease research, but as its threats begin to fade from policymakers’ focus, the money may dry up. Meanwhile, scientists warn that another major infectious disease outbreak is inevitable, potentially within the next decade, so combing the planet for pathogens is vital. “Surveillance is ultimately a really boring thing that a lot of people don't want to put money into, until we have a wide scale pandemic,” Jerde says, but that vigilance is key to thwarting the next deadly horror. “It takes a lot of patience and perseverance to keep looking.”
This article originally appeared in One Health/One Planet, a single-issue magazine that explores how climate change and other environmental shifts are increasing vulnerabilities to infectious diseases by land and by sea. The magazine probes how scientists are making progress with leaders in other fields toward solutions that embrace diverse perspectives and the interconnectedness of all lifeforms and the planet.
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.