Prehistoric men hunted; prehistoric women gathered. At least this is the standard narrative written by and about men to the exclusion of women.
The idea of “Man the Hunter” runs deep within anthropology, convincing people that hunting made us human, only men did the hunting, and therefore evolutionary forces must only have acted upon men. Such depictions are found not only in media, but in museums and introductory anthropology textbooks, too.
There is a growing body of physiological, anatomical, ethnographic and archaeological evidence to suggest that not only did women hunt in our evolutionary past, but they may well have been better suited for such an endurance-dependent activity.
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We are bothbiological anthropologists. Cara specializes in the physiology of humans living in extreme conditions, using her research to reconstruct how our ancestors may have adapted to different climates. Sarah studies Neanderthal and early modern human health, and excavates at their archaeological sites.
It’s not uncommon for scientists like us – who attempt to include the contributions of all individuals, regardless of sex and gender, in reconstructions of our evolutionary past – to be accused of rewriting the past to fulfill a politically correct, woke agenda. The actual evidence speaks for itself, though: Gendered labor roles did not exist in the Paleolithic era, which lasted from 3.3 million years ago until 12,000 years ago. The story is written in human bodies, now and in the past.
We recognize that biological sex can be defined using multiple characteristics, including chromosomes, genitalia and hormones, each of which exists on a spectrum. Social gender, too, is not a binary category. We use the terms female and male when discussing the physiological and anatomical evidence, as this is what the research literature tends to use.
Female bodies: Adapted for endurance
One of the key arguments put forth by “Man the Hunter” proponents is that females would not have been physically capable of taking part in the long, arduous hunts of our evolutionary past. But a number of female-associated features, which provide an endurance advantage, tell a different story.
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All human bodies, regardless of sex, have and need both the hormones estrogen and testosterone. On average, females have more estrogen and males more testosterone, though there is a great deal of variationand overlap.
Testosterone often gets all the credit when it comes to athletic success. But estrogen – technically the estrogen receptor – is deeply ancient, originating somewhere between 1.2 billion and 600 million years ago. It predates the existence of sexual reproduction involving egg and sperm. The testosterone receptor originated as a duplicate of the estrogen receptor and is only about half as old. As such, estrogen, in its many forms and pervasive functions, seems necessary for life among both females and males.
Estrogen influences athletic performance, particularly endurance performance. The greater concentrations of estrogen that females tend to have in their bodies likely confer an endurance advantage – an ability to exercise for a longer period of time without becoming exhausted.
The hormone estrogen has multiple effects throughout the body and plays a role in people regardless of sex. Cara Ocobock, CC BY-ND
In addition to their estrogen advantage, females have a greater proportion of type I muscle fibers relative to males.
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These are slow oxidative muscle fibers that prefer to metabolize fats. They’re not particularly powerful, but they take awhile to fatigue – unlike the powerful type II fibers that males have more of but that tire rapidly. Doing the same intense exercise, females burn 70% more fats than males do, and unsurprisingly, are less likely to fatigue.
Estrogen also appears to be important for post-exercise recovery. Intense exercise or heat exposure can be stressful for the body, eliciting an inflammatory response via the release of heat shock proteins. Estrogen limits this response, which would otherwise inhibit recovery. Estrogen also stabilizes cell membranes that might otherwise be damaged or rupture due to the stress of exercise. Thanks to this hormone, females incur less damage during exercise and are therefore capable of faster recovery.
A variety of physiological differences add up to an advantage for women in endurance activities. Cara Ocobock, CC BY-ND
Women in the past likely did everything men did
Forget the Flintstones’ nuclear family with a stay-at-home wife. There’s no evidence of this social structure or gendered labor roles during the 2 million years of evolution for the genus Homo until the last 12,000 years, with the advent of agriculture.
This nongendered picture should not be surprising when you imagine small-group living. Everyone needs to contribute to the tasks necessary for group survival – chiefly, producing food and shelter and raising children. Individual mothers are not solely responsible for their children; in foragers, the whole group contributes to child care.
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You might imagine this unified labor strategy then changed in early modern humans, but archaeological and anatomical evidence shows it did not. Upper Paleolithic modern humans leaving Africa and entering Europe and Asia show very few sexed differences in trauma and repetitive motion wear. One difference is more evidence of “thrower’s elbow” in males than females, though some females shared these pathologies.
And this was also the time when people were innovating with hunting technologies like atlatls, fishing hooks and nets, and bow and arrows – alleviating some of the wear and tear hunting would take on their bodies. A recent archaeological experiment found that using atlatls decreased sex differences in the speed of spears thrown by contemporary men and women.
Even in death, there are no sexed differences in how Neanderthals or modern humans buried their dead, or the goods affiliated with their graves. These indicators of differential gendered social status do not arrive until agriculture, with its stratified economic system and monopolizable resources.
All this evidence suggests paleolithic women and men did not occupy differing roles or social realms.
Critics might point to recent forager populations and suggest that since they are using subsistence strategies similar to our ancient ancestors, their gendered roles are inherent to the hunter-gatherer lifestyle.
However, there are many flaws in this approach. Foragers are not living fossils, and their social structures and cultural norms have evolved over time and in response to patriarchal agricultural neighbors and colonial administrators. Additionally, ethnographers of the last two centuries brought their sexism with them into the field, and it biased how they understood forager societies. For instance, a recent reanalysis showed that 79% of cultures described in ethnographic data included descriptions of women hunting; however, previous interpretations frequently left them out.
Time to shake these caveman myths
The myth that female reproductive capabilities somehow render them incapable of gathering any food products beyond those that cannot run away does more than just underestimate Paleolithic women. It feeds into narratives that the contemporary social roles of women and men are inherent and define our evolution. Our Paleolithic ancestors lived in a world where everyone in the band pulled their own weight, performing multiple tasks. It was not a utopia, but it was not a patriarchy.
Suggesting that the female body is only designed to gather plants ignores female physiology and the archaeological record. To ignore the evidence perpetuates a myth that only serves to bolster existing power structures.
Many human activities release pollutants into the air, water and soil. These harmful chemicals threaten the health of both people and the ecosystem. According to the World Health Organization, air pollution causes an estimated 4.2 million deaths annually.
I am a materials science and engineering researcher at the University of Tennessee. With the help of robots and artificial intelligence, my colleagues and I are making and testing new photocatalysts with the goal of mitigating air pollution.
Breaking down pollutants
The photocatalysts work by generating charged carriers in the presence of light. These charged carriers are tiny particles that can move around and cause chemical reactions. When they come into contact with water and oxygen in the environment, they produce substances called reactive oxygen species. These highly active reactive oxygen species can bond to parts of the pollutants and then either decompose the pollutants or turn them into harmless – or even useful – products.
To facilitate the photocatalytic reaction, researchers in the Ahmadi lab put plates of perovskite nanocrystals and pollutants under bright light to see whether the reaction breaks down the pollutants. Astita Dubey
But some materials used in the photocatalytic process have limitations. For example, they can’t start the reaction unless the light has enough energy – infrared rays with lower energy light, or visible light, won’t trigger the reaction.
Another problem is that the charged particles involved in the reaction can recombine too quickly, which means they join back together before finishing the job. In these cases, the pollutants either do not decompose completely or the process takes a long time to accomplish.
Additionally, the surface of these photocatalysts can sometimes change during or after the photocatalytic reaction, which affects how they work and how efficient they are.
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To overcome these limitations, scientists on my team are trying to develop new photocatalytic materials that work efficiently to break down pollutants. We also focus on making sure these materials are nontoxic so that our pollution-cleaning materials aren’t causing further pollution.
This plate from the Ahmadi lab is used while testing how perovskite nanocrystals and light break down pollutants, like the blue dye shown. The light blue color indicates partial degradation, while transparent water signifies complete degradation. Astita Dubey
Teeny tiny crystals
Scientists on my team use automated experimentation and artificial intelligence to figure out which photocatalytic materials could be the best candidates to quickly break down pollutants. We’re making and testing materials called hybrid perovskites, which are tiny crystals – they’re about a 10th the thickness of a strand of hair.
These nanocrystals are made of a blend of organic (carbon-based) and inorganic (non-carbon-based) components.
They have a few unique qualities, like their excellent light-absorbing properties, which come from how they’re structured at the atomic level. They’re tiny, but mighty. Optically, they’re amazing too – they interact with light in fascinating ways to generate a large number of tiny charge carriers and trigger photocatalytic reactions.
These materials efficiently transport electrical charges, which allows them to transport light energy and drive the chemical reactions. They’re also used to make solar panels more efficient and in LED lights, which create the vibrant displays you see on TV screens.
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There are thousands of potential types of hybrid nanocrystals. So, my team wanted to figure out how to make and test as many as we can quickly, to see which are the best candidates for cleaning up toxic pollutants.
Bringing in robots
Instead of making and testing samples by hand – which takes weeks or months – we’re using smart robots, which can produce and test at least 100 different materials within an hour. These small liquid-handling robots can precisely move, mix and transfer tiny amounts of liquid from one place to another. They’re controlled by a computer that guides their acceleration and accuracy.
The Opentrons pipetting robot helps Astita Dubey, a visiting scientist working with the Ahmadi lab, synthesize materials and treat them with organic pollutants to test whether they can break down the pollutants. Jordan Marshall
We also use machine learning to guide this process. Machine learning algorithms can analyze test data quickly and then learn from that data for the next set of experiments executed by the robots. These machine learning algorithms can quickly identify patterns and insights in collected data that would normally take much longer for a human eye to catch.
Our approach aims to simplify and better understand complex photocatalytic systems, helping to create new strategies and materials. By using automated experimentation guided by machine learning, we can now make these systems easier to analyze and interpret, overcoming challenges that were difficult with traditional methods.
The presidential debate on Sept. 10, 2024, did not add much context to what the two candidates would do on health care beyond their own records. Visual China Group/Getty Images
Health care is a defining issue in the 2024 election – Democratic presidential nominee Kamala Harris and Republican contender Donald Trump have starkly different records on the issue. Rather than focusing on what they promise to do, let’s examine what their past actions reveal about their approaches to Medicare, the Affordable Care Act, public health infrastructure, drug policy and child abuse and domestic violence prevention.
As a specialist in public health history and policy, I have carefully examined both candidates’ records on American health care. With years of experience in the health care field and being a cystic fibrosis patient myself, I have developed a deep understanding of our health care system and the political dynamics that shape it.
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For me, as for many other Americans, health care is more than just a political issue; it is a deeply personal one.
Medicare
During Harris’ time in the Senate, she co-sponsored the Medicare for All Act, which aimed to expand Medicare to all Americans, effectively eliminating private insurance.
Harris’s legislative efforts, primarily around the 2017-2020 period, reflect a commitment to broadening access to Medicare and reducing costs for seniors. During that time, Harris advocated for the Medicare program to negotiate drug prices directly with pharmaceutical companies.
The proposed cuts did not take effect because they required Congressional approval, which was not granted. The plan faced significant opposition due to concerns about potential negative impacts on beneficiaries.
Although these efforts ultimately failed in the Senate, Trump succeeded in weakening the ACA by eliminating the individual mandate penalty through the 2017 Tax Cuts and Jobs Act. In the debate against Harris, Trump reiterated his position that the Affordable Care Act “was lousy health care,” though he did not ultimately offer a replacement plan, stating only that he has “concepts of a plan.”
Donald Trump claims that as president, he had an obligation to save Obamacare, otherwise known as the Affordable Care Act, but says it is too expensive. He says he has ‘concepts of a plan’ for something to replace the ACA.
Harris also advocated for more federal funding to address public health emergencies, such as the opioid epidemic and the COVID-19 pandemic.
During Trump’s presidency, however, he made significant cuts to public health programs. The Trump administration proposed budget cuts to the Centers for Disease Control and Prevention and other public health agencies, arguing that they were necessary for fiscal responsibility. These proposals drew criticism for potentially undermining the nation’s ability to respond to public health emergencies, a concern that was underscored by the CDC’s struggles during the early days of the COVID-19 pandemic. Trump frequently has responded to these criticisms by asserting he “cut bureaucratic red tape” rather than essential services.
Drug pricing policy
Harris has also supported legislation to lower drug prices and increase transparency in the pharmaceutical industry. She co-sponsored the Drug Price Relief Act, which aimed to allow the federal government to negotiate drug prices for Medicare directly. She also supported efforts to import cheaper prescription drugs from Canada. Her record reflects a focus on reducing costs for consumers and increasing access to affordable medications.
theconversation.com – Joan Casey, Associate Professor of Environmental and Occupational Health Sciences, University of Washington – 2024-09-16 07:26:33
Kids born in 2020 worldwide will experience twice the number of wildfires during their lifetimes compared with those born in 1960. In California and other western states, frequent wildfires have become as much a part of summer and fall as popsicles and Halloween candy.
Wildfires produce fine particulate matter, or PM₂.₅, that chokes the air and penetrates deep into lungs. Researchers know that short-term exposure to wildfire PM₂.₅ increases acute care visits for cardiorespiratory problems such as asthma. However, the long-term effects of repeated exposure to wildfire PM₂.₅ on chronic health conditions are unclear.
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One reason is that scientists have not decided how best to measure this type of intermittent yet ongoing exposure. Environmental epidemiologists and health scientists like us usually summarize long-term exposure to total PM₂.₅ – which comes from power plants, industry and transportation – as average exposure over a year. This might not make sense when measuring exposure to wildfire. Unlike traffic-related air pollution, for example, levels of wildfire PM₂.₅ vary a lot throughout the year.
To improve health and equity research, our team has developed five metrics that better capture long-term exposure to wildfire PM₂.₅.
Measuring fluctuating wildfire PM₂.₅
To understand why current measurements of wildfire PM₂.₅ aren’t adequately capturing an individual’s long-term exposure, we need to delve into the concept of averages.
Say the mean level of PM₂.₅ over a year was 1 microgram per cubic meter. A person could experience that exposure as 1 microgram per cubic meter every day for 365 days, or as 365 micrograms per cubic meter on a single day.
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While these two scenarios result in the same average exposure over a year, they might have very different biological effects. The body might be able to fend off damage from exposure to 1 microgram per cubic meter each day, but be overwhelmed by a huge, single dose of 365 micrograms per cubic meter.
For example, a census tract close to the 2018 Camp Fire experienced an average wildfire PM₂.₅ concentration of 1.2 micrograms per cubic meter between 2006 to 2020. But the actual fire event had a peak exposure of 310 micrograms per cubic meter – the world’s highest level that day.
Classic estimates of average PM₂.₅ levels miss the peak exposure of wildfire events. Angela Weiss/AFP via Getty Images
Scientists want to better understand what such extreme exposures mean for long-term human health. Prior studies on long-term wildfire PM₂.₅ exposure focused mostly on people living close to a large fire, following up years later to check on their health status. This misses any new exposures that took place between baseline and follow-up.
More recent studies have tracked long-term exposure to wildfire PM₂.₅ that changes over time. For example, researchers reported associations between wildfire PM₂.₅ exposure over two years and risk of death from cancer and any other cause in Brazil. This work again relied on long-term average exposure and did not directly capture extreme exposures from intermittent wildfire events. Because the study did not evaluate it, we do not know whether a specific pattern of long-term wildfire PM₂.₅ exposure was worse for health.
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Most days, people experience no wildfire PM₂.₅ exposure. Some days, wildfire exposure is intense. As of now, we do not know whether a few very bad days or many slightly bad days are riskier for health.
A new framework
How can we get more realistic estimates that capture the huge peaks in PM₂.₅ levels that people are exposed to during wildfires?
When thinking about the wildfire PM₂.₅ that people experience, exposure scientists – researchers who study contact between humans and harmful agents in the environment – consider frequency, duration and intensity. These interlocking factors help describe the body’s true exposure during a wildfire event.
In our recent study, our team proposed a framework for measuring long-term exposure to wildfire PM₂.₅ that incorporates the frequency, duration and intensity of wildfire events. We applied air quality models to California wildfire data from 2006 to 2020, deriving new metrics that capture a range of exposure types.
One metric we devised is number of days with any wildfire PM₂.₅ exposure over a long-term period, which can identify even the smallest exposures. Another metric is average concentration of wildfire PM₂.₅ during the peak week of smoke levels over a long period, which highlights locations that experience the most extreme exposures. We also developed several other metrics that may be more useful, depending on what effects are being studied.
Interestingly, these metrics were quite correlated with one another, suggesting places with many days of at least some wildfire PM₂.₅ also had the highest levels overall. Although this can make it difficult to decide between different exposure patterns, the suitability of each metric depends in part on what health effects we are investigating.
Environmental injustice
We also assessed whether certain racial and ethnic groups experienced higher-than-average wildfire PM₂.₅ exposure and found that different groups faced the most exposure depending on the year.
Consider 2018 and 2020, two major wildfire years in California. The most exposed census tracts, by all metrics, were composed primarily of non-Hispanic white individuals in 2018 and Hispanic individuals in 2020. This makes sense, since non-Hispanic white people constitute about 41.6% and Hispanic people 36.4% of California’s population.
To understand whether other groups faced excess wildfire PM₂.₅ exposure, we used relative comparisons. This means we compared the true wildfire PM₂.₅ exposure experienced by each racial and ethnic group with what we would have expected if they were exposed to the state average.
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We found that Indigenous communities had the most disproportionate exposure, experiencing 1.68 times more PM₂.₅ than expected. In comparison, non-Hispanic white Californians were 1.13 times more exposed to PM₂.₅ than expected, and multiracial Californians 1.09 times more exposed than expected.
Better metrics for long-term PM2.5 exposure can help researchers better understand who’s most vulnerable to wildfire smoke. Eric Thayer/Stringer via Getty Images News
Rural tribal lands had the highest mean wildfire PM₂.₅ concentrations – 0.83 micrograms per cubic meter – of any census tract in our study. A large portion of Native American people in California live in rural areas, often with higher wildfire risk due to decades of poor forestry management, including legal suppression of cultural burning practices that studies have shown to aid in reducing catastrophic wildfires. Recent state legislation has removed liability risks of cultural burning on Indigenous lands in California.
Understanding the drivers and health effects of high long-term exposure to wildfire PM₂.₅ among Native American and Alaska Native people can help address substantial health disparities between these groups and other Americans.
