Karen Dobos, Colorado State University and Marcela Henao-Tamayo, Colorado State University

An outbreak of tuberculosis, or TB – a lung disease that is often accompanied by a hacking cough – began in January 2024 in Kansas City, Kansas, and two nearby counties and continues as of early March 2025. To date, 147 people have been reportedly diagnosed with TB in the outbreak, with 67 becoming ill. The remaining 80 people diagnosed with TB in Kansas contracted the illness but showed no symptoms, which is called a latent infection.

TB is the leading infectious cause of death around the world, outpaced only by COVID-19 during the first three years of the pandemic.

The Conversation asked microbiologists Karen Dobos and Marcela Henao-Tamayo, both from Colorado State University, to explain why this ancient disease seems to be making a comeback.

What’s the history of TB?

Mycobacterium tuberculosis is the organism that causes the disease tuberculosis in humans. The disease has been infecting humans for thousands of years. Researchers found evidence of the disease 9,000 years ago in the excavated remains of people who lived in the Eastern Mediterranean region during that time.

Reports of TB date back to around 410-400 B.C.E., when the physician Hippocrates termed the disease phthisis, an archaic word that means a progressive “wasting away,” due to the way people with the disease become emaciated.

TB was also known as consumption for the same reason. Similarly, it was called the white plague or white death – due to anemia from the disease, with people appearing pallid or chalky – leading to near-certain death. Untreated active TB, meaning cases that are symptomatic, is highly lethal.

About half of all people with untreated active TB die from the disease, whereas treatment reduces the death rate to 12%.

One of the more colorful phrases describing TB is “the king’s evil.” This is a form of TB that also causes neck swelling and lesions, a condition called scrofula. During the Middle Ages, people believed that the touch of a king could cure a person from this form of TB through miraculous intervention.

Finally, TB was most ominously called the “robber of youth” due to its historical propensity to afflict people 15 to 30 years old.

In 1865, Jean Antoine Villemin, an army physician in Paris, demonstrated that TB could be transmitted from infected animals to healthy ones through inoculation. Before these studies, the cause of TB was presumed to be primarily constitutional, by either an inherent predisposition or from unhealthy or immoral lifestyles.

The microorganism causing TB was ultimately discovered in 1882 by the German physician Robert Koch. Koch announced his findings on March 24, 1882, a day globally recognized as World TB Day.

How does TB spread?

Tuberculosis is spread by small infectious droplets in the air. A TB patient may emit these droplets by coughing, singing and potentially from regular breathing that occurs during sleep or resting.

One form of TB can be spread through unpasteurized dairy products. While rare, there have been reports of TB transmission through bone graphs, in which healthy, donated bone material is used to replace damaged bones.

The origin of the TB outbreak in Kansas remains unknown as of early March 2025. The outbreak has disproportionately affected those in low-income communities, and two people have died from it.

Importantly, a patient with untreated TB can infect 10 to 15 others.

Could the COVID-19 pandemic be a factor?

The COVID-19 pandemic has played a pivotal role in the resurgence of TB. Cases increased globally by 4.6% from 2020 to 2023, reversing decades of steady declines in the disease. In the U.S. alone, TB cases rose by more than 15% from 2022 to 2023.

During mandatory shutdowns, people were less able to access health care centers for early diagnosis of TB or to fill prescriptions for treatment, perhaps due to the fear of contracting COVID-19 while visiting a medical care facility. COVID-19-related disruptions in care resulted in nearly 700,000 excess deaths from TB.

Access to health care may not be the only factor behind this uptick. Medical supply shortages and delays in shipment may have also played a role. For example, the U.S. experienced shortages of one of the primary TB drugs between 2021 and 2023.

What are the main treatments?

Multidrug treatment is currently the only way to cure TB and stop its spread.

Prior to the late 1930s, when the first antibiotic for TB treatment was developed, TB treatments included bloodletting and consumption of cod liver oil. The most popular treatment involved isolated sanatoriums in high-altitude areas such as the Adirondacks and the Rocky Mountains, where the cold, dry air was believed to be a cure. Scholars at the time suggested that the potential for cure was due to these environments being more invigorating for the body and providing more restful sleep. There is no evidence to support these beliefs.

Streptomycin was the first antibiotic treatment to become available for TB, in the 1940s. However, the microorganism quickly became drug resistant. A second antibiotic, called isoniazid, was developed as a first-line treatment against TB in the 1950s. Again, the microorganism became drug resistant.

Two- and four-drug combinations are now used to treat both latent infections and active disease. Treatment of active TB requires at least six months of uninterrupted therapy. Disruptions in treatment result in further spread of TB and the emergence of multidrug resistant TB, which requires additional drugs and more than nine months of treatment.

All TB drugs are toxic; the quality of life for TB patients deteriorates during treatment and remains so throughout their lives. Finding cases and treating TB illness early, before symptoms begin, is important because it not only reduces the spread of disease but also greatly reduces drug toxicity.

What should people be aware of?

People should be aware that TB is still a public health problem across the globe. Education on the transmission, treatment and need for active work to eradicate TB is the best defense.

One of the reasons why education and awareness about TB are so important is that a person with latent TB may be unknowingly harboring the microorganism for years. In the absence of symptoms, these people are unlikely to seek care and will not be diagnosed and treated unless identified as part of an outbreak, as was the case for more than half of the patients in Kansas.

Karen Dobos, Professor of Microbiology, Colorado State University and Marcela Henao-Tamayo, Associate Professor of Microbiology & Immunology, Colorado State University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Ambuj Tewari, University of Michigan

Microplastics – the tiny particles of plastic shed when litter breaks down – are everywhere, from the deep sea to Mount Everest, and many researchers worry that they could harm human health.

I am a machine learning researcher. With a team of scientists, I have developed a tool to make identification of microplastics using their unique chemical fingerprint more reliable. We hope that this work will help us learn about the types of microplastics floating through the air in our study area, Michigan.

Microplastics – a global problem

The term plastic refers to a wide variety of artificially created polymers. Polyethylene, or PET, is used for making bottles; polypropylene, or PP, is used in food containers; and polyvinyl chloride, or PVC, is used in pipes and tubes.

Microplastics are small plastic particles that range in size from 1 micrometer to 5 millimeters. The width of a human hair, for comparison, ranges from 20 to 200 micrometers.

Most scientific studies focus on microplastics in water. However, microplastics are also found in the air. Scientists know much less about microplastics in the atmosphere.

When scientists collect samples from the environment to study microplastics, they usually want to know more about the chemical identities of the microplastic particles found in the samples.

Fingerprinting microplastics

Just as fingerprinting uniquely identifies a person, scientists use spectroscopy to determine the chemical identity of microplastics. In spectroscopy, a substance either absorbs or scatters light, depending on how its molecules vibrate. The absorbed or scattered light creates a unique pattern called the spectrum, which is effectively the substance’s fingerprint.

Just like a forensic analyst can match an unknown fingerprint against a fingerprint database to identify the person, researchers can match the spectrum of an unknown microplastic particle against a database of known spectra.

However, forensic analysts can get false matches in fingerprint matching. Similarly, spectral matching against a database isn’t foolproof. Many plastic polymers have similar structures, so two different polymers can have similar spectra. This overlap can lead to ambiguity in the identification process.

So, an identification method for polymers should provide a measure of uncertainty in its output. That way, the user can know how much to trust the polymer fingerprint match. Unfortunately, current methods don’t usually provide an uncertainty measure.

Data from microplastic analyses can inform health recommendations and policy decisions, so it’s important for the people making those calls to know how reliable the analysis is.

Conformal prediction

Machine learning is one tool researchers have started using for microplastic identification.

First, researchers collect a large dataset of spectra whose identities are known. Then, they use this dataset to train a machine learning algorithm that learns to predict a substance’s chemical identity from its spectrum.

Sophisticated algorithms whose inner workings can be opaque make these predictions, so the lack of an uncertainty measure becomes an even greater problem when machine learning is involved.

Our recent work addresses this issue by creating a tool with an uncertainty quantification for microplastic identification. We use a machine learning technique called conformal prediction.

Conformal prediction is like a wrapper around an existing, already trained machine learning algorithm that adds an uncertainty quantification. It does not require the user of the machine learning algorithm to have any detailed knowledge of the algorithm or its training data. The user just needs to be able to run the prediction algorithm on a new set of spectra.

To set up conformal prediction, researchers collect a calibration set containing spectra and their true identities. The calibration set is often much smaller than the training data required for training machine learning algorithms. Usually just a few hundred spectra are enough for calibration.

Then, conformal prediction analyzes the discrepancies between the predictions and correct answers in the calibration set. Using this analysis, it adds other plausible identities to the algorithm’s single output on a particular particle’s spectrum. Instead of outputting one, possibly incorrect, prediction like “this particle is polyethylene,” it now outputs a set of predictions – for example, “this particle could be polyethylene or polypropylene.”

The prediction sets contain the true identity with a level of confidence that users can set themselves – say, 90%. Users can then rerun the conformal prediction with a higher confidence – say, 95%. But the higher the confidence level, the more polymer predictions given by the model in the output.

It might seem that a method that outputs a set rather than a single identity isn’t as useful. But the size of the set serves as a way to assess uncertainty – a small set indicates less uncertainty.

On the other hand, if the algorithm predicts that the sample could be many different polymers, there’s substantial uncertainty. In this case, you could bring in a human expert to examine the polymer closely.

Testing the tool

To run our conformal prediction, my team used libraries of microplastic spectra from the Rochman Lab at the University of Toronto as the calibration set.

Once calibrated, we collected samples from a parking lot in Brighton, Michigan, obtained their spectra, and ran them through the algorithm. We also asked an expert to manually label the spectra with the correct polymer identities. We found that conformal prediction did produce sets that included the label the human expert gave it.

Microplastics are an emerging concern worldwide. Some places such as California have begun to gather evidence for future legislation to help curb microplastic pollution.

Evidence-based science can help researchers and policymakers fully understand the extent of microplastic pollution and the threats it poses to human welfare. Building and openly sharing machine learning-based tools is one way to help make that happen.

Ambuj Tewari, Professor of Statistics, University of Michigan

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Seth Blumsack, Penn State

President Donald Trump’s declaration of a “national energy emergency” on his first day in office – and which he reiterated during his address to Congress on March 4, 2025 – might have seemed to echo other national emergencies, like those presidents declared in the wake of the Sept. 11, 2001, terrorist attacks and to deal with the COVID-19 pandemic in 2020.

But there has never before been a national energy emergency. During the energy crises of the 1970s, President Jimmy Carter declared local or regional energy emergencies in a handful of states. These actions suspended some environmental regulations, such as air-pollution limits for coal-fired power plants, for very short periods to make sure those states’ residents had enough electricity.

When a president declares a national emergency, he claims significant powers under the National Emergencies Act, which allow him to take steps to solve the emergency. In this situation, Trump might seek to override environmental regulations, order utility companies to buy power from particular power plants, or invoke the Defense Production Act to secure materials needed for power plant construction.

Six weeks into his presidency, Trump had not taken any action to address this emergency, though during his speech to Congress he said he wants to increase drilling and build a new natural gas pipeline in Alaska. And Trump’s discussion of energy policy has not directly referred to the consumer price hikes expected as a result of the 10% tariffs he imposed on Canadian oil, gas and electricity starting on March 4, 2025.

Critics of the president’s declaration have described it as a “giveaway” to the fossil fuel industry in the form of looser regulations and measures to make it easier to drill for oil on government-owned land. In fact, the executive order’s definition of “energy” excludes energy generated from wind and solar, as well as efforts to conserve energy – all of which were major parts of the Biden administration’s energy strategy.

As someone who has studied energy markets for decades, I have seen several events that might qualify as energy-related emergencies, such as meltdowns at nuclear power plants around the world, shortages of electricity and natural gas, and massive power blackouts.

But over the past 15 years, the United States has become a global energy superpower even without any emergency declarations. The advent of hydraulic fracturing unleashed a wave of oil and gas production, even as U.S. energy demand barely budged. In a time of such energy abundance, there is no clear emergency on the scale of the energy crises of the 1970s. But there are some causes for concern.

Big increases in domestic production

One goal Trump’s declaration sets out is to increase what the executive order calls the nation’s “energy security.” Usually that phrase refers to an ability to operate using energy produced within the U.S. rather than overseas – particularly from countries that have long-standing conflicts or disagreements with the United States.

Based on raw numbers, however, the U.S. is already quite energy secure. In 2023, the nation produced nearly 13 million barrels of oil per day, which is more than any country has ever produced in the history of the oil business. Since 2015, when a federal ban on oil exports was lifted, the U.S. has been increasing the amount of oil it exports every year. And for the past several years, the U.S. has been the world’s leading exporter of gasoline, sending 10% of its total annual production to other countries.

Since the start of the shale-fracking boom in the mid-2000s, U.S. production of natural gas has also been increasing. The country’s natural gas exports have also risen over the past 10 years, though they have been limited by the number of ports that can handle liquefied natural gas cargo.

Still a net importer of oil

The U.S. produces plenty of oil to meet its demands, but not the kinds of oil that American refineries are designed to process into useful fuels.

Therefore, despite the increases in domestic production, the U.S. is still a net importer of crude oil. In 2023, the U.S. imported almost twice as much oil as it exported.

And U.S. refineries’ output of gasoline and heating oil depends on imported oil. Most oil refineries in the U.S. are quite old and were engineered to process so-called “heavy” crude oil produced in countries such as Canada, which is historically the United States’ biggest source of imported oil.

Most of the recent increase in U.S. oil production comes from hydraulic fracturing of shale and is so-called “light” crude oil. Refining light crude would require new refineries or a major reengineering of existing refineries, with new equipment, expanded capacity or both.

Making those changes would be very expensive. So refinery owners are hesitant to make these kinds of investments because there is a risk that the investments won’t pay off. Because U.S. refineries produce so much gasoline and have limited capacity, the U.S. also continues to import some refined petroleum fuels such as jet fuel.

A fragile power grid

Concern over the nation’s aging electric power grid is another focus of Trump’s energy emergency declaration. Experts have been issuing warnings for years. A 2024 study on the national transmission grid commissioned by the U.S. Department of Energy has concluded the U.S. needs to double the size of the grid in the next couple of decades.

For the first time in nearly half a century, the U.S. is facing the prospect of rapidly increasing electricity demand. The demand for power has always gone up and down a bit with population and the health of the economy, but this time is different. Growth in electricity demand is now driven by the construction of massive data centers and by electrification of cars and heating and cooling systems. The Department of Energy reports that data center electricity use in particular has tripled in the past 10 years and could easily double in the next few years. At that rate, data centers could account for over 10% of all electricity demand in the country before 2030.

The U.S. supply of power generation in many regions is not ready for this surge in demand. Many power plants – particularly the older ones and those that burn coal – have shut down in the past several years, driven by a combination of economic pressures and environmental regulations. Building new power plants in many parts of the U.S. has become bogged down in regulatory red tape, public opposition and economic uncertainty. The North American Electric Reliability Corp., which develops standards for grid reliability, has placed over half of U.S. states at some level of risk for not having enough power generation to meet anticipated future demand.

Will declaring an emergency help?

Under Trump’s energy emergency declaration, the administration seems likely to take actions that will make it easier to drill for more oil and gas. And the federal government may also make it easier to build power plants that run on coal, natural gas and possibly nuclear fuel.

But expanded fracking, in and of itself, will probably not address any energy security issues in the U.S., unless there are major investments in refineries to handle the increased oil production. Reducing the barriers to building power plants addresses a much more pressing problem, but the country would still need to expand the transmission grid itself, which does not get as much attention in the president’s declaration.

Time will tell whether the energy emergency declaration will be used to solve real problems in the nation’s energy supplies, or whether it will be used to further bolster oil and gas producers that have already made the U.S. a global energy powerhouse.

Seth Blumsack, Professor of Energy and Environmental Economics and International Affairs, Penn State

This article is republished from The Conversation under a Creative Commons license. Read the original article.