Beyond the solar system, the line between planet and star can get blurry. Astronomers call objects that fall between the size ranges of planets and stars substellar objects. One type of substellar object, called a brown dwarf, forms from collapsing gas clouds like a star, but is not quite massive enough to fuse hydrogen in its core. Some can briefly fuse a heavier form of hydrogen, known as deuterium, in their cores, though never enough to produce starlight. Others are small and cold enough to develop complex atmospheres.

Recently, astronomers discovered a new type of substellar object in a star-forming region of the Milky Way Galaxy called IC 348. Using observational data from the JWST’s Near InfraRed Spectrograph (JWST NIRSpec), they found objects 0.2% to 1.2% the mass of the Sun that float freely like stars but have carbon-containing molecules called hydrocarbons in their atmospheres. Because these objects are smaller than known brown dwarfs and have evidence of hydrocarbons, scientists refer to them as H-type objects.

To investigate what H-type objects are and where they fit within the spectrum of substellar objects, researchers Richard J. Parker and Catarina Alves de Oliveira analyzed survey data on stars and substellar objects in IC 348, then conducted follow-up simulations to uncover how H-type objects formed. The main question they asked was whether H-type objects form from collapsing gas clouds, like brown dwarfs and stars, or develop around host stars before being ejected and drifting freely through space, like planets.

First, they reviewed the observational records of IC 348 obtained with the JWST NIRSpec and the near-IR spectrograph SpeX at the NASA Infrared Telescope Facility. IC 348 has 495 stars and brown dwarfs combined, as well as 9 H-type objects. Estimates for the age of this star-forming region range from either 1 to 3 million years or 5 to 6 million years, depending on how different teams calculated it. From there, Parker and Alves de Oliveira compared where the stars, brown dwarfs, and H-type objects were located within the cluster. They found no unique spatial distribution among the 3 types of objects.

Then, the team simulated the evolution of IC 348 over 10 million years to determine which initial conditions would result in a spatial distribution closest to the survey data. They simulated an initial giant gas cloud and used a mathematical function to distribute its mass among the 495 constituent objects. The researchers then placed a Jupiter-sized planet around each simulated star that was approximately the Sun’s mass to determine whether they could grow and escape their home star and become H-type objects. They also repeated the simulations by placing the Jupiter-sized planets at distances from the Sun similar to those of Earth, Jupiter, and Neptune, and varied the sizes and star densities of the simulated IC 348 regions.

They found that if Jupiter-sized planets orbited their stars at roughly Jupiter’s distance from the Sun, then 5 to 15 of them could escape their stars in a region like IC 348, which would roughly match the number of known H-type objects. However, the escaped planets were distinctly more spread-out than the observed H-type objects, which share a similar spatial distribution with the cluster’s stars and brown dwarfs. Since the simulations did not match the observed distribution, the researchers suggested that these H-type objects are likely a new sub-category of very small brown dwarfs rather than escaped planets.

Although this study favors a brown dwarf origin for H-type objects, the exact details of how they formed remain up for debate. Astronomers still have multiple competing theories about how ordinary brown dwarfs form. Perhaps they are ejected from their gas cloud before fully forming, have their outer layers stripped by radiation from a nearby massive star, or simply form like stars on a slightly smaller scale. Regardless, future researchers will continue to explore this murky area of substellar astronomy.

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Since the formation of the Earth 4.2 billion years ago, hot rock from the interior of the planet has cooled off and developed crystals that accumulated over millions of years, forming surface rocks with a layered composition, referred to as continental crust. Geologic evidence of how continental crust formed comes from large, ancient crustal blocks, called cratons. Cratons have existed for billions of years and form the cores of continents. 

Scientists have shown that the upper 10 to 15 kilometers (6 to 9 miles) of continental crust contains high concentrations of elements like uranium and thorium that decay and release heat over time, referred to as radiogenic elements. However, the lower 20 kilometers (12 miles) of crust has very few radiogenic elements. The cause of this planetary-scale differentiation is highly questioned, as geoscientists don’t know what formation processes could produce this unique characteristic. 

Earlier researchers suggested that changes in the concentration of radiogenic elements could occur when tectonic plates collide. Plate collision induces temperatures in the lower crust high enough to melt minerals that contain uranium and thorium, like zircon and monazite. The newly melted minerals form magma, which then rises and crystallizes in the upper crust. To test whether this process formed the layered continental crust, researchers Andrew J. Smye and Peter B. Keleman incorporated geochemical data from studies worldwide into a mathematical model comparing how concentrations of radiogenic elements vary under different temperatures. 

The team separated their global analyses into 2 categories of rocks that were buried deep in the crust and subjected to intense pressures and temperatures, via a process known as metamorphism. The first category included rocks formed from the accumulation of eroded grains buried in the deep crust that are subjected to high pressures and temperatures, called metasedimentary rocks. The second category included rocks formed when magma rises from the deep Earth and solidifies before experiencing high pressures and temperatures, called metaigneous rocks.

The researchers analyzed 3 subcategories from each of the 2 types of deep-crustal rocks. The first was rocks that experienced no intense pressure or temperature, the second was rocks subjected to 650 to 900°C (about 1,200 to 1,650°F), and the third was rocks subjected to 900 to 1,100°C (about 1,650 to 2,000°F). The researchers modeled the geochemistry of the 3 rock types at different temperatures to determine whether ultra-high-temperature metamorphism could cause crustal differentiation of radiogenic elements.

Smye and Keleman found that monazite doesn’t melt in either metasedimentary or metaigneous rocks at temperatures below 900°C, as indicated by the relatively high thorium content in these deep-crustal rocks. Alternatively, they found that above 900°C, uranium and thorium concentrations decreased in metasedimentary rocks, suggesting that minerals like zircon and monazite were melting. The melted minerals enter magma and rise into the upper crust, leaving behind metasedimentary rocks depleted in radiogenic elements by around 0.10 ppm.  

The team concluded that the enrichment of uranium and thorium in the upper continental crust requires temperatures greater than 900°C to initiate the melting of zircon and monazite in the lower crust. Temperatures above 900°C are linked to the movement and collision of continental plates. This result suggests that evidence of Earth’s differentiation has been preserved for more than 3 billion years, spanning most of the planet’s geological history. Since stable continental crust formed, uranium and thorium enrichment in the upper crust has consistently recorded layering produced by ultra-high temperature metamorphism.

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Diseases that cause nerve cells in the brain to die over time are known as neurodegenerative diseases. These cells, known as neurons, are vulnerable to DNA damage because they are highly active cells that require large amounts of energy. Neural DNA damage is a normal part of aging as changes to the DNA sequence, called mutations, build up over time. 

Previous scientists have shown that neurons from people with the neurodegenerative diseases Alzheimer’s disease (AD), frontotemporal dementia (FTD), and amyotrophic lateral sclerosis (ALS) accumulate unusually high numbers of changes in the DNA sequence, called somatic mutations. However, researchers don’t know what causes the high number of mutations or whether the same process causes them in all 3 diseases. 

To answer these questions, researchers at Boston Children’s Hospital and Harvard Medical School analyzed brain tissue from people who died with and without AD, FTD, or ALS. They compared the processes that cause DNA mutations in the 3 diseases by first accessing the DNA inside individual neurons from healthy and diseased postmortem brain tissue. 

To do this, they broke apart the brain tissue and added a fluorescent marker that identified the center of each neuronal cell where DNA is housed, called its nucleus. They used a sorting machine to separate the nuclei from the rest of the cell material to access the neuron’s DNA. Using a DNA sequencing method called single-cell whole genome sequencing, the team then sequenced each neuron’s DNA to identify mutations. 

They analyzed 159 neurons from people with AD, 61 neurons from people with FTD, 77 neurons from people with ALS, and 232 neurons from healthy individuals. This allowed the team to compare the number and types of DNA mutations between diseased and control (non-diseased) neurons. The researchers found that compared to the control neurons, which typically accumulate only 200 to 300 mutations over an 80-year lifespan, neurons from all 3 diseases contained an unusually high number of 2-base-pair deletions, often exceeding 1,000 mutations per cell. 

When a biological process damages DNA, it leaves behind its own unique signature. By identifying these signatures, scientists can identify the biological process that likely caused the damage. Since these 2-base-pair deletions were so frequent in diseased neurons, the researchers wanted to know whether they were left behind by a biological process. The researchers used a machine learning algorithm to scan DNA sequences for recurring mutation patterns and identified a signature in neuronal DNA, which they named ID-4.

When the researchers compared the ID-4 signature with a database of previously characterized signatures, they found that it was associated with a DNA repair protein called TOP1. TOP1 temporarily cuts and rejoins DNA to relieve the tension that builds up during normal cellular activity. TOP1 typically detaches from the DNA after completing this process. However, if TOP1 fails to detach, it breaks one of the two DNA strands. 

To test whether abnormal TOP1 activity was breaking the DNA, the researchers isolated DNA from control and diseased neurons. They visualized the DNA on a gel, where intact DNA forms bands while broken DNA spreads out into smears. The more fragmented the DNA is, the more intense the smear appears. Using a machine that quantified the intensity of smears, the researchers found that DNA from diseased neurons produced smears that were up to 3.2 times more intense than those from control neurons. They also found that neurons with more DNA fragmentation had more ID-4 signatures. 

Based on these findings, the researchers concluded that abnormal TOP1 activity created frequent single-strand DNA breaks. They proposed that some of these breaks are repaired incorrectly, eventually leading to widespread DNA breaks in all 3 neurodegenerative diseases.

Overall, the researchers found that neurons from these 3 neurodegenerative diseases accumulated excessive DNA mutations despite differences in the disease symptoms and underlying causes. Their findings also provided evidence that abnormal TOP1 activity contributes to this mutation accumulation. Based on these findings, they stated that preventing TOP1 from becoming trapped on DNA, or improving the DNA repair process that removes trapped TOP1, could be a promising way to treat a wide range of neurodegenerative diseases.

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Since the 1850s, plowing and clear-cutting in modern agriculture have increased how fast soil erodes in the Upper Mississippi River Valley. Across the globe, farming strips soil up to 100 times faster than natural processes. It also removes farmable land and clogs waterways, threatening long-term environmental sustainability. However, scientists in the past have struggled to separate the effects of land-use changes from the transition out of the last ice age over the past 20,000 years. 

To determine whether current farming practices are sustainable, researchers from Minnesota recently measured the speed at which soil eroded during the ice age transition and compared it with modern agricultural erosion. They focused on the Trout Creek tributary of the Mississippi River. This tributary has 2 step-like formations, or terraces, on its bank that represent past floodplains before the river eroded to its current level. 

The oldest, upper terrace is about 14 meters (50 feet) above the lower terrace, which is about 8 meters (26 feet) above the modern floodplain. Scientists studying nearby areas have suggested that the terraces contain sediment from the last ice age, 20,000 years ago, referred to as the Last Glacial Maximum, to today. 

In the upper terrace, the researchers pushed an empty cylinder into the ground, like a straw into a layer cake, to collect a cylindrical sample of sediments about 4 centimeters (1.5 inches) wide and 6 meters (20 feet) deep, called a sediment core. They collected slices of this core from 2 meters (6 feet) and 6 meters (20 feet) to find the terrace’s age. 

Some grains in the sediment trap electrons while buried and release them when exposed to light. So the team shone light on the sediment slices to stimulate these electrons and measure the intensity of light emitted, known as their optically stimulated luminescence. Brighter light emissions indicate the sediment is older and was buried for a longer period of time. Using this method, they confirmed that the deep sediment slice was 22,000 years old, and the shallow sediment slice was 20,000 years old, dating the terrace back to the Last Glacial Maximum.

Next, the team drilled into the upper terrace with an auger to collect 25-centimeter (10-inch) wide plastic tubes of sediment. They pulled 4 samples from 0 to 2 meters (0 to 6 feet), and 1 sample from 6 meters (20 feet) deep. They explained that minerals in the sediment called quartz produce the radioactive form of the element beryllium, known as ¹⁰Be, when exposed to the Sun, so low ¹⁰Be means the sediment was buried quickly and had little sun exposure. The team measured the concentration of ¹⁰Be in each sediment sample and used an online calculator to estimate how quickly it eroded. The researchers calculated that during the Last Glacial Maximum, 0.073 millimeters (0.003 inches) of surface sediment were removed per year.

The researchers collected similar samples from the lower terrace and used optically stimulated luminescence to determine that it was between 22,000 and 14,000 years old. This terrace was the floodplain during the transition out of the Last Glacial Maximum, or deglacial period, where temperatures warmed and permafrost retreated. They measured the concentration of ¹⁰Be in samples from this terrace and determined that 0.049 millimeters (0.002 inches) of surface sediment were removed per year. 

Finally, the researchers measured ¹⁰Be in a sediment sample from the modern floodplain, deposited between 14,00 years ago and today. They found that the sediment had eroded 0.053 millimeters (0.002 inches) per year during that interval, at a rate slower than it had during the glacial period, but similar to the deglacial period. 

The researchers compared their findings to the average soil loss caused by modern agriculture, as reported by 4 previous scientists studying the area. They saw that modern farming removes an average of 0.60 millimeters (0.02 inches) of soil per year, which is 8 to 12 times greater than all the natural erosion rates they calculated. 

They concluded that the recent spike in soil loss from agriculture greatly exceeds that caused by the largest natural climate transition in recent Earth history. Human-driven erosion shows an upward “hockey stick” trend like the one seen in global temperatures, suggesting that similar trends could exist throughout Earth systems. The team concluded that modern agricultural practices aren’t currently sustainable. While measures like native plant reintroduction can help curb this trend, more research is required to determine how best to limit long-term erosion.

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In the 1970s, astronomer Vera Rubin analyzed a series of observations of how quickly different galaxies rotated around their centers. She found that these galaxies rotated much faster than they should have, given the amount of material visible through starlight. She concluded that a large fraction of these galaxies’ masses must therefore consist of some kind of unseen material.

Scientists have since observed galaxies to more precisely measure this unseen material, which they now call dark matter. They’ve shown that dark matter moves much more slowly than the speed of light. Unlike most celestial objects, it can’t emit or block any light, meaning it doesn’t interact with the electromagnetic force. Even though it doesn’t interact with a fundamental force in the universe, dark matter constitutes over 80% of the mass of an average galaxy.

In the prevailing model of cosmology, known as ΛCDM (pronounced “Lambda-CDM”), dark matter determined where galaxies formed. The theory is that gravity pulled dark matter together more quickly than regular matter in the primordial universe. This redistribution led to regions of space with greater mass density, which collected chaotic, regular matter into pools that eventually formed galaxies, stars, and planets. 

In the present universe, dark matter and regular matter coexist in galaxies, though generally separated. Regular matter pools in the very center of galaxies, forming the more familiar galactic disks. Dark matter stays towards the edges of galaxies, enveloping the regular matter disks in what are known as dark matter halos. Because these halos make up so much of the mass of the average galaxy, researchers Guangze Sun, Fangzhou Jiang, and Jing Wang conducted a series of simulations to test how dark matter halos affect regular matter in galaxies. Specifically, they examined what characteristics of dark matter halos determine galaxy sizes. 

The force of gravity works such that more massive dark matter halos draw in more regular matter and produce larger galaxies. So these researchers fixed the total mass of the galaxies they simulated at 100 billion times that of the Sun to determine which properties of the dark matter halos would produce the widest galaxies. They selected 4 variables to test: the total density of dark matter in the galactic halo, the density of dark matter in the innermost 1% of the halo, how fast the halo spun, and the ratio of regular matter to dark matter in the entire galaxy. For each variable, they tested 3 to 4 inputs representing low, moderate, and high values against a standard reference value and ran 132 total simulations to generate enough data for statistical analyses.

To simulate the complex physics of dark matter halos, the team used the modeling software GIZMO to model gravity, magnetism, and fluid dynamics. They combined this with a second model, called FIRE-3, which modeled how stars explode at the end of their lives, how much metal these explosions produce, and how gases heat up and cool down over time. The researchers ran the dynamic simulations for 3 billion model years, then used a program they developed based on the existing Pynbody to measure the radii of the simulated galaxies. 

The team found that the faster a dark matter halo spun around a simulated galaxy, the larger the galaxy became, as shown in scenario 1 in the figure below. By contrast, in scenario 2, increasing the halo’s total dark matter concentration caused the central galaxy to shrink. Increasing only the innermost dark matter density in the halo had a smaller effect but generally reduced the galaxy’s size, as shown in scenario 3. And lastly, increasing the ratio of regular matter to dark matter in galaxies resulted in smaller galaxies that formed stars closer to their centers, as shown by a greater concentration of stars in scenario 4. 

This diagram showcases the team’s results across 4 broad scenarios. The left column depicts a default galaxy under the baseline parameters. The right column depicts how the galaxies changed when the team varied a single experimental factor, including 1) increasing how fast the dark matter halo spun, 2) increasing the total density of the dark matter halo, 3) increasing the density of dark matter in the innermost halo, and 4) increasing the ratio of regular matter to dark matter in the entire galaxy. The dark matter haloes are represented as red rings surrounding the galaxies, not to scale. Illustration by Andrew Bizal.

The team’s results suggest a previously unknown connection between the properties of dark matter halos and galactic disks. However, they cautioned that their simulations were all run on galaxies in isolation, unlike in the actual ΛCDM universe, where their interactions are complicated by galaxies tugging on one another or by material moving from one to another. To further test their results for single galaxies, the researchers plan to run follow-up experiments with halos of different sizes, as well as with galaxies with different shapes and features, such as clumps, gaps, and lopsidedness.

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Temperatures in the Arctic are increasing at a rate 4 times faster than the average global temperature. Scientists have documented the global effects of climate change, but there are still regions where these effects are less well known. This is the case for freshwater ecosystems in the Arctic.  

When the ground has been permanently frozen for 2 years, scientists call it permafrost. Permafrost contains soil, sand, rocks, and organic matter held together by ice. As temperatures rise, permafrost thaws, which can change groundwater flow paths. It can also alter a stream’s temperature, flow patterns, and nutrient loads by introducing new material from the permafrost.  

To determine how thawing permafrost affects Arctic ecosystems, scientists from the U.S. Geological Survey recently studied 10 headwater streams within the Brooks Range in the Noatak National Preserve of Alaska. The scientists studied streams with different amounts of permafrost and ice to test whether connections exist between permafrost, food webs, and populations of fish called the Dolly Varden and Arctic Graylings. They focused on these 2 species of fish because their migrations often indicate ecological shifts in the area.    

The scientists collected fish from the streams by temporarily stunning them, using a method called electrofishing, or by catching them in minnow traps. They manually identified each species they collected, then froze the fish and shipped them to a lab. Once in the lab, the scientists measured the length of each fish and used documented growth patterns to estimate its biomass. To calculate fish abundances, they divided their total biomass by the time spent electrofishing or trapping. Finally, they measured the energy density of each fish by burning it in a device called a bomb calorimeter. 

At each stream, the scientists took manual temperature measurements and deployed loggers that recorded the temperature every 15 minutes. They also deployed equipment at the bottom of each stream to record water pressure in 15-minute intervals and converted the data to depth, since pressure increases with depth. Then, they used wading rods to measure how much water was flowing through each stream at a given spot. 

They combined the depth and wading rod data to determine the flow rate of water in the streams over time. They used these data to identify unstable streams where water rose and fell rapidly. They also collected water samples from each stream and measured their nutrient concentrations, including dissolved organic carbon, nitrogen, and phosphorus, to determine water quality.   

Finally, they measured 2 types of smaller organisms that allow energy to flow through the food web to the fish, called microbial biofilms and macroinvertebrates. They used a hand-held device to estimate the types and amounts of biofilms, and nets to collect macroinvertebrates.

Once they had compiled these data, the team conducted a statistical analysis to compare the studied parameters and fish populations to the extent of permafrost in each area. They found that streams with more permafrost in the area had warmer temperatures, because the permafrost prevented water from infiltrating the soil to cool off. The streams with warmer temperatures, in turn, had lower abundances and biomass of Dolly Varden fish and Arctic Grayling with lower energy densities. They suggested that these results meant that the high temperatures had pushed the fish above their optimum temperature range. 

They also learned that the limited infiltration in permafrost areas caused rainfall to flow straight into the stream. This process created instability as the stream levels rose and fell rapidly. Fish in these unstable streams had lower energy densities, likely due to the harsher environment. 

Areas with greater permafrost also had higher concentrations of dissolved organic carbon and phosphorus, which stimulated the formation of biofilms at the bottom of the food chain. They noted that areas with more biofilms also had more macroinvertebrates and more Dolly Varden biomass. Since the Dolly Varden eats macroinvertebrates, this finding suggested that the lowest levels of the food web shape the health of species higher up. This is called the bottom-up effect, and means that areas with more biofilms and macroinvertebrates will likely have healthier and larger fish populations.  

The scientists explained that knowing how these factors influence Arctic fish populations can help researchers understand how thawing permafrost might affect Arctic ecosystems in the future. They anticipated that their data could help future researchers predict how these and other ecosystems will be impacted by changing landscape dynamics caused by global warming. 

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Have you ever experienced a severe weather event, like a hurricane or a tornado? Researchers have shown that climate change is causing such weather events to become more extreme and more frequent, destroying communities and infrastructure. To determine which areas are most at risk, researchers from Canada studied the environmental effects of a powerful flooding event that occurred in southwestern British Columbia (BC) in November 2021. 

They focused on a type of severe flooding event that is characteristic of the North American Pacific coastal region, called an atmospheric river. An atmospheric river occurs when a long, narrow vapor cloud flows toward land from the Pacific Ocean, causing heavy rainstorms. In November 2021, an atmospheric river in southwestern BC destroyed numerous regional highways, pipelines, power lines, and railways within its path. Over 18,000 people were evacuated due to flooding, and 6 people died. 

The team explained that before the storm, BC experienced a severe drought and an unprecedented heatwave, followed by widespread thunderstorm activity. In 2021, lightning and human activity caused nearly 1,000 wildfires, which led to the third-worst wildfire season ever recorded in BC, partially because trees grow back slowly in forests that have been cut down or harvested. Poor vegetation recovery makes harvested forests more vulnerable to wildfires than old-growth forests. The November atmospheric river occurred shortly after this wildfire season. 

To determine how the atmospheric river impacted southwestern BC, the researchers analyzed approximately 70,000 square kilometers (27,027 square miles) of affected area. They compared satellite images of southwestern BC from Google Earth, Sentinel, and Landsat taken before and after the event to detect changes in river channels and identify geohazards, like landslides and debris flows. They also used satellite images and Canadian government data to identify which forests had been harvested within the study area. The researchers then traversed the area to confirm their remote analyses. 

The researchers used a type of 3D remote sensing, called light detection and ranging, or lidar, to measure how the 2021 atmospheric river redistributed sediment in the Coquihalla and Coldwater River valleys. With the help of lidar, the researchers discovered that both rivers had severely unstable banks due to erosion from the flooding event. Along the Coquihalla River, approximately 53,000 cubic meters (1,871,677 cubic feet) of sediment were eroded, while nearly 50,000 cubic meters (1,765,733 cubic feet) of sediment were eroded along the Coldwater River. 

The researchers also found that hillslope geohazards increased the volume of sediment in both rivers. And in some cases, upstream areas with a history of forest harvesting and wildfires also affected stable banks downstream. For example, they identified debris flows on an unstable hillslope that triggered a landslide on a riverbank downstream. They observed that the river and stream channels shifted due to a sudden addition of sediment from hillslope geohazards and floodwater during the event.

In total, the researchers documented over 1,300 geohazards along 47 kilometers (about 30 miles) of the Coquihalla River and 19 kilometers (about 12 miles) of the Coldwater River and nearby creeks. Nearly half of the geohazards originated in an area with a history of wildfires or forest harvesting. Of the 155 geohazards at the Coquilhalla River, 43% occurred below forest harvesting resource roads, and of the 117 geohazards observed at the Coldwater River, 29% originated in areas with past wildfires.

The researchers suggested that their findings could help scientists predict where severe geohazards will occur during extreme weather events, since both watersheds had geohazards originating in harvested forests. Before 2000, clear-cutting was a widespread practice in BC, and the team’s results demonstrate that unsustainable harvesting practices like this can have lasting environmental impacts.

The researchers concluded that both forest harvesting and wildfires contributed to the severe impacts of the November 2021 atmospheric river, causing catastrophic landslides and debris flows to occur in traditionally dry regions. They suggested that more accurate risk assessments can better equip communities for severe weather events like this. While the next atmospheric river that lands in the Pacific coastal region may be worse than the last, this study can inform scientists and communities about which areas it might affect most.

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Horseshoe crabs are marine animals more closely related to spiders than crabs. These animals have existed for more than 450 million years, and are sometimes called “living fossils” because they appear to have changed very little throughout their evolutionary history. However, horseshoe crabs have a poor fossil record, so this apparent lack of change could also be due to a lack of evidence, particularly when it comes to their reproduction. Scientists want to know: Are horseshoe crabs truly living fossils?

Modern horseshoe crabs reproduce by releasing hundreds of eggs and sperm into tidal waters, via a process called external fertilization. Until now, scientists have assumed that horseshoe crabs have always reproduced this way. But paleontologists have only found 3 fossil horseshoe crabs from the past 252 million years with their reproductive systems preserved, so this assumption is mainly based on an absence of evidence to the contrary. 

Scientists in the past interpreted the horseshoe crab’s long history of external fertilization as indicative of its evolutionary consistency. However, James Lamsdell and colleagues recently discovered a new fossil species of horseshoe crab in the 450 million-year-old Big Hill Formation in Michigan that challenges this assumption. 

To study the specimen, the scientists photographed it under 2 types of microscopes. The first type of microscope used polarized light to see structures in the fossil that may have blended into the rock, like putting on special glasses to see subtle textures. The second type of microscope used fluorescent light to reveal soft tissue in the fossil. Only some tissues, like ovarian tissue and preserved eggs, glow under fluorescence, so this method helped the team rule out non-biological structures, like bubbles, cracks, or minerals. 

The researchers found that their analytical techniques revealed remarkable details about the ancient horseshoe crab. One unique feature of the fossil was the shape of its exoskeleton. The fossil’s exoskeleton had a more shovel-like appearance than that of modern horseshoe crabs, which are less elongated and more round. It was also about 11 times smaller than a modern adult horseshoe crab. 

The researchers also discovered that the fossil preserved an intricate network of ovaries containing unfertilized eggs. This ovarian network lined the interior surface of the shell in a band of interconnected tubes, preserving both unfertilized eggs and developing eggs. They noted that the fossil eggs were all about 0.1 to 0.15 millimeters in diameter, about the same width as a human hair. This size consistency suggested that all the eggs developed at the same time, via a process called synchronous development. 

Since the fossil crab’s ovarian tubes contained developing eggs, it was an adult, meaning that the earliest adult horseshoe crabs were significantly smaller and shaped differently than modern adult horseshoe crabs. Despite their differences in size and shape, the fossil horseshoe crab’s reproductive system closely mirrored its modern one, in terms of its ovarian system and synchronous development of eggs. As in previous studies, it showed no evidence of internal fertilization.

The researchers concluded that the earliest horseshoe crabs also reproduced through external fertilization. This fossil horseshoe crab is more than 215 million years older than preexisting fossils preserving evidence of external fertilization, and suggests that the horseshoe crab’s mode of reproduction has been conserved for almost half a billion years!

However, the team’s findings reveal a more nuanced evolutionary history for the horseshoe crab than previously assumed. Although they’ve maintained the same reproductive system for almost half a billion years, the size and shape of their exoskeleton have changed. This discovery reveals that different parts of the horseshoe crab evolved at different rates. It seems that horseshoe crabs are not entirely the living fossils scientists once thought… while their bodies have evolved, their reproduction has endured.

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The economist John Maynard Keynes once remarked, “In the long run we are all dead.” From an astronomical standpoint, this is true for the Earth itself. In roughly 5 billion years, the Sun will exhaust the hydrogen at its core and enter the next phase of its life as a Red Giant, where it will swell up large enough to engulf and destroy the Earth.

Currently, the Sun is still in its main-sequence phase, where it fuses hydrogen into helium in its core. Although it remains relatively stable during this phase, it will gradually grow brighter with time. As the Sun shines brighter, the increased heat and radiation will raise Earth’s surface temperature, potentially pushing it past the threshold for life to survive. 

One of Earth’s geological processes that helps regulate its surface temperature is the carbon cycle. Over millions of years, carbon moves from the Earth’s interior into the atmosphere as carbon dioxide or CO₂, where it functions as a greenhouse gas that traps heat. Plants take in this CO₂ through photosynthesis and move carbon through the biosphere. 

Meanwhile, rain and rock weathering draw carbon into the oceans and rocks. By pulling CO₂ out of the atmosphere, the carbon cycle could mitigate Earth’s warming as the Sun brightens over time. Today, human activities are increasing atmospheric CO₂ and warming the planet. However, over billions of years, the carbon cycle could gradually remove enough CO₂ from the atmosphere that eventually too little would remain to support photosynthesis. 

With these challenges to life on Earth in mind, the researchers Jacob Haqq‐Misra and Eric Wolf investigated how long Earth’s biosphere could last in the distant future. The team used computer models to calculate the balance of energy that Earth would receive and emit over time, accounting for the effects of increasing sunlight and changing atmospheric CO₂ concentrations. They followed this up with calculations using the ExoCAM model to determine the temperature and atmospheric conditions of future Earth across different latitudes. They related these conditions to a habitability metric to determine their impact on the biosphere.

To estimate the biosphere’s maximum possible lifespan, the team conducted 29 simulations representing the planet’s conditions over the next 2 billion years. In one set of simulations, the Earth’s carbon cycle contributed little to regulating surface temperature, with the atmosphere’s CO₂ concentration fixed at approximately modern levels of 400 parts per million (ppm) and surface temperatures continuing to increase. In another, the carbon cycle worked to keep Earth’s average surface temperature fixed at the modern average of approximately 59℉ (15℃), no matter how much it had to reduce CO₂ in the atmosphere. 

In the scenario with a fixed CO₂ concentration, the team found that in 1.7 billion years, Earth’s surface reached an average of 122°F (50°C), which is too hot for most land plants. After 1.9 billion years, it reached 149°F (65°), becoming too hot for all land plants to survive as temperatures continued to rise. 

They also found that in the fixed average surface temperature scenario, atmospheric CO₂ dropped below the 150 ppm level that most plants require for photosynthesis within 500 million years. After 1.4 billion years, CO₂ levels fell below 10 ppm, leaving only a few land photosynthesizers and aquatic plants alive. After about 1.8 billion years, atmospheric CO₂ dropped to 1 ppm, leaving only microbes capable of photosynthesis.

The researchers’ findings generally extend the estimated lifetime of Earth’s future biosphere compared with earlier, simpler models that suggested maximum lifespans of 100 million to 1.5 billion years. However, they clarified that other scientists should verify their results by running tests with other models, especially under extreme conditions of high incoming solar radiation and low atmospheric CO₂. 

Lastly, Haqq‐Misra and Wolf noted that their model did not account for major evolutionary changes that could help life adapt to future conditions. Over time, plants could evolve to tolerate higher temperatures or lower atmospheric CO₂ levels. Future humans or another intelligent species might also engage in large-scale geoengineering projects to prevent the end of life on Earth. If life on Earth proves adaptable enough, the biosphere may endure until a Red Giant fills the daytime sky.

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Globally, about 1 billion people live near glaciers and depend on glacial water. As temperatures continue to climb due to global warming, glaciers can become unstable and hazardous to nearby communities. These communities risk exposure to a series of linked processes, like ice collapse, flooding, and landslides, which could have life-threatening consequences. Scientists call these events catastrophic glacier hazard chains, or CGHC. So, a team of scientists in China sought to understand the driving forces behind these dangerous phenomena. 

The team focused on a CGHC event that occurred in 2018 at the Sedongpu Glacier, located in the Eastern Himalayas. During this event, the glacier obstructed the flow of 30 million square meters (more than 300 million square feet) of the Yarlung Tsanpo River, one of the main rivers within the region. 

To reconstruct the event, the team observed satellite images of the Sedongpu glacier over time, using a process called remote sensing. They used the images to calculate ice volumes, glacier velocities, and glacier movement between 1961 and 2018, to determine the glacier’s long-term physical properties and how they changed over time. They found that from 1968 to 2013, the Sedongpu Glacier shrank, or retreated, by over 6 football fields. It also lost ice and water volume and increased its velocity.

The scientists also measured earthquake waves on the Earth’s surface, or seismicity, to characterize any downslope rock or debris movements caused by the glacial retreat. They measured seismicity across 11 different seismic stations within 580 kilometers (about 350 miles) of the glacier. Then, they used this data to identify 2 distinct phases of the CGHC event on Sedongpu Glacier: the ice-rock avalanche and the debris flow.

When the ice-rock avalanche occurred, the team detected noticeable increases in seismicity, suggesting it lasted over 30 seconds. During those 30 seconds, over 8 million cubic meters (more than 280 million cubic feet) of ice fell onto the main glacier. The team recorded even more increases in seismicity during the debris flow, which lasted nearly 5 minutes. The debris from the avalanche hit the main valley glacier at nearly 300 kilometers (200 miles) per hour.

The researchers combined these data to describe how the 2018 CGHC event unfolded. First, the backward movement and shrinkage of the glacier produced an unstable sheet of ice hanging over a cliff face. The steep slope and large cracks in the ice caused an ice-rock avalanche. Debris from the avalanche then accumulated on the main glacier, causing it to move faster. As the main valley glacier moved downslope, it picked up even more debris from the bottom and sides of the glacial valley. The melting ice also increased the glacier’s velocity, and its movement eventually transitioned into a debris flow, which obstructed the Yarlung Tsangpo River. 

In total, the CGHC event in the Sedongpu Glacier travelled over 10 kilometers (6 miles) and moved down in elevation for over 3 kilometers (about 2 miles) at a velocity of about 100 kilometers (about 70 miles) per hour. The glacier accumulated enough debris and ice to fill thousands of Olympic-sized swimming pools. Imagine what this could do to a nearby village!

It’s difficult for scientists to estimate when CGHC events will occur, but this team proposed several factors that could help predict them. These factors included the size of the ice-rock avalanche and the glacier velocity, as well as the occurrence of earthquakes, temperature changes, and the melting of the glacier’s underside. They stated that these factors can combine to create a domino effect that drives the CGHC process. They recommended consistent glacial monitoring as temperatures continue to climb, in the Himalayas and globally. 

The team concluded that understanding CGHC events can help scientists mitigate glacial hazards and keep people safe, especially in mountainous areas such as the Eastern Himalayas. They stated that their findings illustrate the dangerous processes behind glacial melting, but further research is needed to accurately assess those processes and ensure nearby communities remain safe.

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In the Bolivian Altiplano, fewer Aymara farmers are relying on local weather knowledge to schedule farming tasks, like choosing the best dates to plant crops and predicting a frost or drought. At the same time, there is growing doubt about whether these traditional indicators remain relevant today in the context of rapid social, economic, and climatic changes. 

Farmers are struggling because the National Weather Service of Bolivia (SEHNAMI) fails to provide useful weather forecasts due to the geographical complexity of the Andes and a scarcity of weather stations. In addition, many farmers can’t access this information because SEHNAMI lacks local outreach programs to deliver reports directly to them. In a region with a weak technical forecasting system, local knowledge is a vital resource, but its use is declining.

Scientists in the past focused on preserving local weather knowledge or discussing how it might help communities adapt to climate change. However, very few researchers have scientifically validated local weather indicators, since doing so requires many years of historical meteorological data to establish statistical “skill” in local forecasting. 

Faced with this gap, researchers in the US and Bolivia recently set out to validate indigenous indicators in 2 Bolivian municipalities: Umala in the Central Altiplano and Ancoraimes in the Northern Altiplano. To do this, they paired each traditional weather sign with a measurable variable, like temperature, that could approximate it. This approach allowed them to directly compare traditional observations with scientific data.

The researchers wanted to determine whether traditional weather knowledge is still accurate and relevant under current conditions, since validating these methods would strengthen local knowledge and benefit the schools and policies that promote it. To test this, the researchers worked with 6 Aymara communities. First, they conducted workshops with local leaders and experts to compile a set of weather-forecast indicators. Then, they surveyed 95 farmers to rank the reliability of these indicators. 

Through discussions with these communities, the team identified 13 weather indicators in the Central region and 20 in the Northern region. These indicators included specific plants, animals, and physical factors like constellations or winds that the farmers used to decide when and where to plant crops, and to predict weather conditions for the next season. The researchers focused on 2 key indicators that the farmers ranked as most reliable: the presence or absence of moisture under rocks on the Fiesta de San Juan on June 24th, and the flowering patterns of specific plants, called Thola and Sank’ayu. The farmers used the San Juan indicator to predict whether the upcoming year would be rainy or dry, and the plants to determine the most favorable dates for planting to avoid agricultural risks like frosts.

Finally, the researchers compared forecasts based on these 2 signs with over 30 years of meteorological data from nearby weather stations. In both cases, they used the minimum daily temperature as the measurable way to represent these local signs scientifically. Their results showed that the minimum daily temperature on the Fiesta de San Juan could explain more than half (55.5%) of the changes in rainfall during the growing season. They also confirmed that severe frosts that “burned” Thola and Sank’ayuthe flowers were followed by periods without rain, validating the farmers’ strategy of delayed planting.

The researchers concluded that local weather indicators used by Aymara farmers are most reliable for seasonal planning, especially in predicting the start of the rainy season. They stated that this result helps legitimize traditional knowledge and empower the community. For example, these communities now hold regular meetings to discuss forecasts and share this information with their neighbors via WhatsApp groups. 

However, the team acknowledged that local knowledge was limited to short-term forecasting, as it only provided warnings a few hours in advance. Farmers need alerts several days in advance to effectively reduce their vulnerability to events such as frosts or hail. For this reason, the researchers suggested that the next step will be to co-produce forecasts by combining meteorological science with traditional knowledge.

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The spots on a leopard, the stripes on a zebra, and the mask on a raccoon are all types of coloration in mammal fur, referred to as pelage. Scientists have struggled for a long time to understand how these colorations evolved. A team of researchers recently discovered how to reconstruct the colors of ancient mammal fur using cell structures that contain the chemical pigment melanin, known as melanosomes. They hope to use this understanding of ancient mammal pelage to determine how they lived and what their environments were like.

To decipher the color of ancient mammal fur, the researchers constructed a statistical computer model to predict their fur colors based on the shape of their melanosomes. First, they needed to understand how the shapes of melanosomes control fur color in modern mammals. To measure the length and width of the modern melanosomes, the scientists used a beam of electrons like a flashlight to map them in great detail, a technique called scanning electron microscopy.

They measured the length and width of 2,615 melanosomes from the fur of 116 different living mammal species, including a Red Panda, a German Shepard, and one or more species of most types in the mammalian classification. Then they recorded the wavelengths of light reflected off each melanosome using a microspectrophotometer. These wavelengths represented the exact color of hair from each melanosome they measured. This means they now had a quantitative measurement of the color of each hair to compare with its corresponding melanosome length and width.

When they examined their data, the researchers found that spherical melanosomes produced brighter fur colors, like reds and oranges, while elongated melanosomes produced darker fur colors, like browns and grays. The researchers plotted the microspectrophotometric measurements for each melanosome as a graph showing how much light it absorbed across each possible wavelength, creating reflectance curves. Based on these data, the researchers used computer software to create a statistical model that could predict reflectance curves, and therefore color, from melanosome shape alone.

Once the researchers created a model, they needed to find fossilized mammal melanosomes to predict their fur color. Mammal fossils are rare, and pieces of cells are only preserved in the most immaculate fossil deposits. In fact, these researchers only found 6 fossils of this caliber, from Mesozoic rocks deposited around 250 million years ago, soon after mammals evolved. 

The fossils came from 2 world-renowned deposits in Northeastern China, the Yanliao and Jehol biotas. They selected 5 previously studied fossils from these locations, including 1 specimen of Megaconus mammaliaformis, 1 specimen of Vilevolodon diplomylos, 2 specimens from the group Docodonta, and 1 specimen from the group Eutheria. All of these animals were small, rodent-like mammals. The researchers also discovered a new species of tree-dwelling mammal, Arboroharamiya fuscus.

The scientists used scanning electron microscopy to measure a total of 2,159 fossilized melanosomes to refine their color predictions. The fossilized melanosomes were all slightly elongated spheres and had far fewer differences in shape than in modern mammals. The researchers used their model to predict reflectance curves based on the shapes of the fossil melanosomes to determine the ancient mammals’ colors. They found that all the extinct mammals they examined likely had dark brown fur without any color patterns.

Based on these findings, the researchers suggested that most, if not all, early mammals were nocturnal, like many dark-colored mammals today. They suggested that this nocturnal lifestyle probably helped them survive the meteorite impact that wiped out the dinosaurs 100 million years later. While dinosaurs were unable to survive in the post-impact wasteland, these smaller, dark-colored mammals were more resilient and thrived in the aftermath. As mammals filled the ecological roles left by the dinosaurs, they evolved new color patterns, some based solely on camouflage, and others on new factors, like sexual selection and visual communication.

The researchers concluded that their model can predict fossilized mammal colors and help determine how these ancient animals lived. Notably, their data suggest that mammals likely stayed small and nocturnal throughout the Mesozoic until the dinosaurs were gone. Future researchers could use similar methods to uncover exactly how mammals evolved the colors we see today. For example, studying mammals closer to the impact event could reveal if it took a long time to develop diverse fur patterns, or if they began before the dinosaurs’ demise. Ultimately, similar studies could help determine whether the extinction of dinosaurs was necessary for the rise of mammals.

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Have you ever wondered how researchers use artificial intelligence (AI) in environmental science? Certain AI computer models can learn from scientific data and operate without manual programming, in a process called machine learning. Machine learning models can identify patterns in environmental datasets and help scientists assess public health risks linked to environmental conditions.

One well-known public health risk is the rise in antibiotic-resistant bacteria. These bacteria carry genes called antibiotic resistance genes that protect them from antibiotic medicines. As the number of these genes increases, the medicines become less effective, and diseases spread faster.

Agricultural soils are the largest reservoirs of antibiotic resistance genes, because manure from livestock like cows and pigs often contains leftover antibiotics fed to them. When used as fertilizer, this manure transfers antibiotics into the agricultural soil, where bacteria develop antibiotic resistance genes.

To better understand how antibiotic-resistant bacteria will affect global health in the future, scientists want to develop faster ways to detect antibiotic resistance genes. However, current methods of detection are slow and fail to predict future trends. To address this problem, a group of scientists from Algeria used machine learning models to predict the prevalence of antibiotic resistance genes in soil microbes under future climate scenarios, and to identify the key environmental drivers and high-risk areas.

​The team compiled existing microbial data from 3 public databases: the National Center for Biotechnology Information’s Sequence Read Archive, the Metagenomic Rapid Annotations using Subsystems Technology database, and the Joint Genome Institute’s Integrated Microbial Genomes & Microbiomes database. Their final dataset contained about 2,000 agricultural soil samples from 67 countries across 6 continents. These samples represented a wide range of soil characteristics, climate conditions, and different types of antibiotic resistance genes. 

They integrated the microbial dataset with climate data, including global temperature and precipitation measurements from WorldClim, and land use data, including crop types, irrigation systems, and livestock density from the European Space Agency Climate Change Initiative Land Cover maps. The researchers also included data on future climate projections for 2050 and 2070 from the World Climate Research Programme based on low, medium, and high global greenhouse gas emission scenarios. 

After preparing the datasets, the team set up 6 machine learning models that differ in terms of their data size, model complexity, analytical speed, and customization. These models included Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), Deep Neural Network (DNN), and Logistic Regression (LR). 

They ran each model using different combinations of variables from their datasets, including soil properties, microbial community metrics, climate variables, and land use characteristics. To validate the models, the team separated the full dataset into 10 random subsets. They trained the models on 9 of the subsets, then recorded the result of the 10th subset. Each model repeated this process 10 times in a different order so that each subset was evaluated once in the results, using a technique known as stratified 10-fold cross-validation. The researchers scored each model on 5 performance metrics, including precision, sensitivity, and predictive power. Based on these results, they determined that LightGBM was the most accurate model. 

They found that the LightGBM model identified soil temperature as the strongest environmental predictor of antibiotic resistance genes. At soil temperatures above 18°C (about 64°F), the number of antibiotic resistance genes increased dramatically. The model also identified soil acidity, organic carbon content, moisture, and annual precipitation as influential variables, accounting for 71% of the increase in antibiotic resistance genes under future climate scenarios. Likewise, the LightGBM model predicted that the number of high-risk areas would increase by 35% under the high-emissions scenario, with South Asia, Sub-Saharan Africa, and Mediterranean Europe as the most vulnerable regions.

Based on the LightGBM model output, the team concluded that soil conditions will change to favor antibiotic resistance genes under all future climate scenarios. They also suggested that LightGBM is a fast and effective model for environmental predictions that could be used to assess other climate change risks such as natural disasters. They recommended that future researchers explore how to apply machine learning methods to early warning systems and utilize machine learning results in environmental management decisions. 

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Around 4.6 billion years ago, an immense cloud of gas and dust in space collapsed in on itself. As it collapsed, gravity pulled most of the material toward several small points, one of which would eventually become our Sun. The other points formed other stars in what scientists call an open cluster. 

During this early stage of the Sun’s life, it was surrounded by a swirling disk of leftover material aligned with its equator, known as a protoplanetary disk. Eventually, this disk condensed to form the planets that make up our solar system. This process normally results in a system with a star at its center, and the planets orbiting the plane of the star’s equator. 

However, scientists have found that this scenario doesn’t quite hold for our solar system. The planets in our solar system orbit the Sun in a single plane offset from the Sun’s equator by approximately 6°. The fact that the planets are in the same orbital plane suggests that the protoplanetary disk model of solar system formation is correct, since the odds of the Sun capturing 8 rogue planets in this arrangement are astronomically low. So, what threw the planets in our solar system off-kilter?

The plane in which the solar system’s planets orbit the Sun is offset from the Sun’s equator by approximately 6°. Li simulated how the solar system tilted based on its 4 largest planets, from left to right: Neptune, Saturn, Jupiter, and Uranus. Illustration by Andrew Bizal.

To determine what caused the solar system to tilt, Daohai Li at Queen Mary University of London recently used the computer program NBODY6++ to model different versions of the open cluster from which the Sun came. In these simulations, he varied the number of stars in the cluster, from 500 to 2,000 to 8,000. He also varied the radius of the cluster, from 0.25 to 1 to 4 parsecs (8 to 30 to 100 trillion kilometers or 5 to 20 to 70 trillion miles). Finally, he varied the percentage of stars with a binary companion from 10% to 50% to 90%. Li assigned each star a randomly generated initial mass, starting position, and velocity, and simulated their interactions for 400 million years.

First, Li examined the simulations to identify stars with masses approximately equal to the Sun’s. From this group of solar-massed stars, Li found ones that left the cluster after 400 million years as a single star, after either forming alone or having lost a former companion star. He proposed that these stars were the most similar to the Sun and could thus host a simulated solar system.

Li then simulated the planets and their orbits as they would have formed on these potential host stars. To simplify this process, Li only simulated the 4 giant planets, Jupiter, Saturn, Uranus, and Neptune, which dominate planetary behavior in our solar system. He modeled these solar systems independently for 200 million years to ensure they would remain intact and used those that did for statistical analysis of the solar system’s past.

Overall, Li discovered 2 scenarios that could explain the solar system’s tilt. In the first, the primordial solar system was jostled by many distant neighboring stars in its cluster. This jostling tilts the planets’ orbits, but it does so randomly. Therefore, this scenario would make it unlikely for the planets to remain in the same orbital plane. It would also cause them to orbit the Sun faster and more chaotically than in our solar system.

In the second scenario, the Sun had a temporary companion star that it either formed with or briefly encountered before separating. When this occurred, the simulated planets’ orbits all tilted together. Stars that formed with a binary companion had a 10% chance of tilting their planets’ orbits by 6° or more, while stars that formed alone had a less than 0.1% chance. 

Li concluded that the solar system could have tilted as a result of interactions between the Sun and a temporary companion star within its parent cluster. However, he acknowledged that the model strongly depends on how closely a star passes by the Sun, since the closer a star passes, the more it disturbs the solar system. Despite this uncertainty, it’s possible that somewhere in space is the Sun’s twin or former neighbor that we haven’t seen in eons, and that’s what made our solar system crooked!

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If you’ve ever swum in the ocean and been touched by seaweed, there’s a good chance it was Sargassum! This free-floating seaweed can have population explosions in coastal waters, degrading water quality, ecosystems, fisheries, and tourism. However, despite its drawbacks, scientists think that Sargassum could be useful for extracting valuable metals, known as rare earth elements (REEs). 

REEs are classified by their average masses and atomic weights. Light REEs include cerium, praseodymium, and neodymium, middle REEs include gadolinium, terbium, and dysprosium, and heavy REEs include erbium, thulium, and ytterbium. These metals are used in magnets, electronic motors, wind turbines, spacecraft, and more. Our current methods of extracting REEs, open-pit mining and chemical processing, are invasive, causing habitat loss and producing toxic waste. Additionally, REEs are difficult to import due to restricted access worldwide. But what if these metals could be extracted using natural methods? 

Scientists from Woods Hole and UCLA recently tested whether Sargassum could accumulate REEs, creating a sustainable solution to these problems. They harvested Sargassum at Waterfront Park in Woods Hole, rinsed the wet samples, and stored them in sterilized, filtered seawater, which they aerated with 12-hour cycles of light and dark to keep the Sargassum alive. They dried another set of samples in the sun under controlled outdoor conditions, stored them in sealed containers, and later rehydrated them in seawater for 3 hours before the experiments.

For each experiment, they added 5 grams (about 0.2 ounces) of Sargassum to solutions containing a mixture of 9 REEs – cerium, praseodymium, neodymium, gadolinium, terbium, dysprosium, erbium, thulium, and ytterbium – in concentrations totalling 0.1 micromolar (μM) REEs, 11μM REEs, 100 to 110μM REEs, and 575 to 600μM REEs. They performed each experiment 4 times: twice with fresh seaweed and twice with rehydrated seaweed. 

The researchers removed 1 gram (about 0.04 ounces) of seaweed from each solution after 1, 3, and 7 days, and dissolved it in acid to release the REEs it had absorbed. They analyzed the concentrations of REEs in each seaweed sample and the leftover solutions using 2 different plasma-based methods. They used these REE concentrations to calculate the ratio of REEs in the Sargassum versus in the solution, known as the bioconcentration factor, to determine how effectively the seaweed absorbed REEs. 

They found that after 7 days at lower REE concentrations (0.1μM), the fresh seaweed had bioconcentration factors of over 1,000. At higher REE concentrations (11μM), their bioconcentration factors ranged from about 400 to 700. However, these levels of REEs were eventually toxic to the fresh Sargassum, so they died after 2 weeks. In addition, the higher REE concentrations of 100 to 600μM were immediately toxic to the fresh Sargassum, which died within 1 day in solution. 

The researchers determined that once the seaweed died, it could no longer actively take up REEs into its tissues but could only accumulate them on its surface or adsorb them. Adsorption can be thought of as a powdered donut – when the surface is covered with powdered sugar, no more powder fits on the donut. Similarly, there was a limit to how many REEs could stick to the Sargassum. Due to this limit, the fresh seaweed samples had bioconcentration factors below 250 in the highest REE concentrations. In contrast, the rehydrated Sargassum consumed less REEs at lower concentrations, but didn’t experience toxicity and continued to accumulate REEs at higher concentrations.

Finally, the team compared Sargassum to a common industrial method of accumulating REEs, known as activated carbon. REEs bond easily to activated carbon because of its abundant surface area and high porosity. Both materials accumulated a similar amount of REEs in solutions of 0.1μM REEs. However, at 575 to 600μM REEs, Sargassum accumulated about twice as many REEs as activated carbon, especially the heavier REEs. 

​The researchers concluded that Sargassum is a natural hyper-accumulator, meaning it successfully concentrates REEs. However, they recommended that future researchers test whether Sargassum performs the same in the natural environment as it does in the lab. If so, Sargassum could be used to accumulate REEs from wastewater, like mine drainage, or other water sources with higher concentrations of REEs. Regardless, they suggested that using Sargassum to obtain REEs could simultaneously expand global access to these metals and mitigate toxic blooms.

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Humans have been around for only about 0.01% of Earth’s history, yet we’ve altered natural landscapes and processes that have remained stable for millions of years. A team of researchers from the University of Glasgow recently studied a formerly sandy beach in the UK that highlights how fast these changes can occur.

Industrial steel and iron furnaces operated extensively in the UK from 1856 until the 1980s. During these decades, workers dumped roughly 335 million cubic meters (90 billion gallons) of molten furnace waste, called slag, across the country. Of that, roughly 27 million cubic meters (6 million gallons) now sits on the coast of Derwent Howe in a massive 30-meter (100-foot), solidified cliff. 

The researchers analyzed this region to understand how man-made, or anthropogenic, materials influence the way rocks form. Rocks naturally form and change into different types of rocks over million-year timescales, through a continuous process called the rock cycle. When the team uncovered a soda can tab manufactured in 1989 within the hardened grains, or clasts, of slag, they concluded that the beach had solidified within the last 35 years. Based on this discovery, they proposed a new term for rocks formed from anthropogenic sediments: the anthropoclastic rock cycle. 

The team collected 1,300 grains of sediment from 13 sites in the study area and found that 83% were slag. Natural beach sediments, like quartz sand, comprised only about 17% of the grains. As wind and waves broke down the cliff, slag clasts fell off and became rounded by friction as tides transported them from the cliff to the beach. 

At the boundary where the slag touched the beach, the researchers also saw evidence that the slag had seeped into the underlying wet sediment. The rocks there resembled a type of rock called peperite, which forms when lava flows over wet, loose sediments.  They concluded that this peperite-like rock formed as the molten slag flowed over the still sandy beach. Slag is much thicker than wet sand, so the resulting rock looked like blobs of slag encased in sandy rock. 

The researchers then visually observed how the slag grains were arranged in the solidified rock to reconstruct the tidewater speeds at different points in the coast’s history. The different ways sediments are arranged within a rock are called sedimentary features, and they reflect the processes that formed the rock. For example, as water flows over sand, it forms small, dune-like ripples. When the sand beds harden into rock, the ripples are preserved as features known as trough cross-bedding. 

Faster water erases any ripple formations and instead forms horizontal layers in the sediment, known as planar lamination. The researchers observed both trough cross-bedding and planar laminations preserved in the slag beach rocks. These sedimentary features indicate that the beach at Derwent Howe turned from loose sediment into rock in a generation. 

To investigate why this beach rapidly solidified, the researchers scanned the surfaces of the beach rock with electron beams and X-rays using a scanning electron microscope, or SEM, equipped with an energy dispersive X-ray, or EDX. Every chemical element emits a unique set of X-rays with different wavelengths when hit with EDX, so the researchers could compare the X-rays from their samples to databases of known mineral X-ray signatures. They identified the primary binding agents as calcium- and iron-rich minerals like calcite, goethite, and brucite. These minerals are called cements because they bind grains of sediment together. 

The researchers expected the slag to produce these cement compositions, since extracting metal ores from rock enriches the resultant slag in calcium, magnesium, manganese, and iron, which readily dissolve in water. When water flowing between closely packed slag grains can no longer hold additional dissolved elements, those elements precipitate as solid mineral cements. Since cements only form when the water has very high concentrations of dissolved elements, the introduction of massive amounts of slag caused the cement to form faster than it would in waters with lower concentrations of elements.

The researchers suspected that the anthropoclastic rock cycle they observed at Derwent Howe is not uncommon. Since slag contains all the elements necessary to form rocks when in contact with seawater and air, slag deposits all over the world could be rapidly influencing their local environments. With this in mind, the team suggested that future researchers update geological models to account for human contributions to the natural rock cycle. 

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Nitrogen is an essential building block of proteins, amino acids, and DNA. Without nitrogen, cells can’t function or reproduce. Nitrogen is abundant in the atmosphere as dinitrogen gas, but plants and animals can’t access atmospheric nitrogen. Instead, we rely on biologically available forms like ammonia and nitrate. 

Specialized microbes can transform atmospheric nitrogen into ammonia via a process called biological nitrogen fixation. Nitrogen-fixing soil bacteria have a unique protein complex, the nitrogenase complex, that facilitates this nitrogen transformation. The nitrogenase complex is activated by a group of genes known as nif genes. 

Legume plants like peanuts, peas, and beans have nitrogen-fixing bacteria in the soil zone around their roots, called the rhizosphere. The symbiotic relationship between nitrogen-fixing soil bacteria and legume plants supplies most of the biologically available nitrogen to life on Earth.

Bacteria attached to plant roots “feed” nitrogen to plants through the soil, and in turn receive organic acids for energy from plants performing photosynthesis. Source: Wikimedia Commons

However, one piece of the puzzle remains unexplored: the role of soil viruses in these nitrogen transformations, referred to as the nitrogen cycle. Viruses are microscopic particles made up of genetic material and proteins, but they can’t reproduce without a host cell. Scientists consider viruses to be biological entities that are halfway between living and non-living.

To reproduce, viruses contact a host cell and inject their genetic material into it. The virus’s genetic material takes over the host cell’s genome and forces the cell to produce proteins that assemble into new viruses. As new viruses are created, they can incorporate genes from the host cell into their own genetic material. Genes that are transferred from a host cell to a virus are known as auxiliary metabolic genes (AMGs). Viruses can carry AMGs and pass them to other hosts or use them to increase their own ecological fitness. 

Scientists from China, Spain, and the Czech Republic set out to investigate whether viruses from legume roots could increase local soil nitrogen fixation by expressing nif AMGs or by transferring nif genes to other bacteria. First, they determined the global distribution of viral nitrogen-fixing genes by analyzing about 8.6 million viral genomes from the Integrated Microbial Genomes/Virus database. They looked for the DNA sequences of known nitrogen-fixing genes and the locations where the viruses were sampled. They found that only 0.003% of the viruses in the database had at least 1 nitrogen-fixing gene, but they clustered in the same locations as nitrogen-fixing bacteria. 

The team also found 3 types of viruses that most commonly carried nitrogen-fixing genes: Kyanoviridae, Nudiviridae, and Bronfenbrennervirinae. Kyanoviridae viruses carried a nif gene called nifU. The researchers used a computer program called DRAM-v to confirm that nifU was a fully functional gene and a real tool that viruses could use. 

Next, the team collected soil samples from a cowpea field in Nanjing, China, to test whether the cowpea roots influenced the abundance of viruses using nitrogen-fixing genes. They collected soil from the cowpea rhizospheres and soil away from the plants. They analyzed both viral and bacterial RNA in the soil samples to identify active genes, using a sequencing process called metatranscriptomics. They found that the viral nifU gene was actively expressed in the cowpea rhizosphere samples more than in the non-crop soil. Although about 96% of nifU genes were being expressed by bacteria, viruses contributed about 4%. 

The researchers then assessed whether rhizospheric viruses changed the amount of nitrogen being fixed in these soils. They set up small, enclosed containers of sterile soil and added either bacteria or a mixture of bacteria and viruses extracted from the cowpea rhizospheres. They found that soil with viruses had higher total nitrogen (36 milligrams per kilogram, or mg/kg) than soil without viruses (17 mg/kg). 

Finally, they adjusted the air in each container to have a different form of dinitrogen gas. Atoms of the same element that have different atomic masses are known as isotopes. Nitrogen has 2 stable isotopes: lighter nitrogen-14 and heavier nitrogen-15. Bacteria or viruses that fix nitrogen at natural isotope abundances will take up nitrogen-14. But scientists can track who in the population fixes nitrogen by adding nitrogen-15 to the air, a process called nitrogen-15 stable isotope probing. Nitrogen-fixers will incorporate the heavy nitrogen-15 into their biomass over time and increase their weight, allowing researchers to separate and identify them.

The team grew the bacteria and viruses with nitrogen-15 for 35 days, then used stable isotope probing to separate the nitrogen fixers. They detected viral nifU AMGs among the heavier biomass, confirming that rhizospheric viruses were fixing nitrogen. 

The team concluded that rhizospheric viruses and the viral nifU AMG can increase nitrogen fixation in cowpea soils. The team suggested that although viruses with nitrogen-fixing genes are rare, they can influence the soil nitrogen cycle. They recommended that future researchers further test the contribution of viral nifU genes to plant nitrogen fixation with controlled infection experiments.

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In March 2021, the carcasses of at least 260 marine organisms from 81 species, including large animals called megafauna, washed ashore in South Africa. Scientists questioned whether this event was the result of an extreme temperature drop that can happen when deep, cold water rises to the surface along coastlines. Scientists refer to these rapid cooling events as coastal upwelling. 

Climate change is making oceans warmer, but it’s also making coastal upwelling stronger, with unknown consequences for marine megafauna. Scientists recently studied 2 boundary currents that are rapidly warming and creating strong upwelling near the edges of sharks’ geographic ranges: the Agulhas Current in southern Africa and the East Australian Current in eastern Australia. They wanted to determine whether climate-driven changes in upwelling zones are making cold-shock events more frequent and intense, and how these events are pushing sharks beyond their temperature limits. 

First, the researchers analyzed sea surface temperature data from satellites to see if rapid cooling from upwelling caused the mass marine die-off in 2021. They also obtained hourly temperature recordings from temperature loggers to measure subsurface temperature changes during the event. They found that during the March 2021 event, sea surface temperatures along the coast dropped by up to 7°C (12°F) in less than 48 hours, from 24°C (75°F) to below 17°C (63°F). Subsurface temperatures dropped faster, by more than 9°C (16°F) in 24 hours, from 21°C (70°F) to 12°C (54°F). 

Bullsharks experience physiological stress at temperatures below 19°C (66°F), which is near their lower thermal limit, when migrating through rapid cooling zones. Therefore, the researchers deduced that during the 2021 cold-shock event, the sharks were exposed to temperatures several degrees below their thermal limit, possibly more than 5°C (or 10°F) colder. 

The researchers also tagged 66 bull sharks with acoustic and satellite transmitters – including 41 at the Agulhas Current in South Africa and 25 at the East Australian Current in eastern Australia – to determine how they responded near their lower thermal limits in these zones. The acoustic tags record when sharks pass underwater receivers, providing information on their movements, locations, and water depths at those specific locations. In contrast, satellite tags continuously record the water temperatures and depths the sharks experience over time. 

The researchers used these data to compare sharks living in warm subtropical waters with those living in cooler waters at the edge of their temperature range. The researchers found that low temperatures that persisted for long periods prevented the sharks and other megafauna from physiologically recovering. They saw that some sharks try to swim to shallower depths and look for warmer water, but cooling sometimes happened faster than they could respond. 

The researchers also obtained wind records from the South African Weather Service to determine if upwelling-favorable winds happened before the 2021 event. They reported strong easterly winds pushing cold water on the surface along the coastline immediately before the event occurred. Because the cooling, strong winds, and animal deaths happened at the same time and place, the researchers inferred that upwelling was the main reason for the die-off.

Finally, the researchers examined 41 years of sea surface temperature data and used mathematical models to compute changes in the frequency and intensity of these events. Their analysis showed that rapid cooling events linked to upwelling can last a lot longer than the usual 1 to 3 days. For example, the March 2021 event lasted for 7 days. 

They found that these events have increased in both frequency and intensity in the past decades, and produce more extreme temperature drops than typically happen within 24 hours. Their results suggested that the 2021 die-off was part of an increasing trend, where warming oceans allow species to move into different areas, but intense upwelling can suddenly expose them to fatal cold events. This pattern is what scientists call a climate bait-and-switch. 

The researchers concluded that climate change is making coastal upwelling stronger, which can push marine megafauna beyond their thermal limits, even when initial warming appears to be beneficial. Their findings highlight an understudied aspect of climate change in the context of how temperature and upwelling cause shifts in species survival. They concluded their findings could help predict how marine ecosystems respond to future changing ocean conditions.

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When you think of an asteroid impact, you’re probably imagining something like the cataclysmic event that nearly wiped out the dinosaurs. In an astonishingly rare event, a rock the size of the island of Manhattan could come hurtling from deep space towards Earth and destroy the world.

On the contrary, asteroids are generally leftover debris from the primordial solar system that didn’t become part of a larger body, like a planet or moon, and they’re not particularly rare. Scientists estimate that there are about 1 billion asteroids and comets classified as near-Earth objects, or NEOs. NEOs are generally much smaller than the asteroid that killed the dinosaurs. They range from weakly held-together piles of rubble 200 meters (700 feet) across, to the minuscule 2024 BX1, approximately 44 centimeters (17 inches) across. 

For several decades, astronomical surveys of the sky have looked for these asteroids. NASA’s Center for Near-Earth Object Studies (CNEOS) Fireball and Bolide Database has records of over 1,000 incidents of objects colliding with our planet since the mid-1990s. Among these is a set of 11 objects that scientists detected before they hit Earth, known as imminent impactors. Finding imminent impactors allows scientists unique opportunities to study the smallest building blocks of the solar system, enabling them to learn more about asteroid composition, structure, and history.

A team of scientists recently conducted a study on how the newly built Vera Rubin Observatory could detect more imminent impactors before they strike Earth. They examined the CNEOS Fireball and Bolide Database impact records spanning February 1, 1994, to January 1, 2026, for records of the asteroids’ coordinates, velocities, and the energy they released at the time of collision. 

They found that 343 NEOs met this criterion, including 5 known imminent impactors. Using these recorded data, they reconstructed the objects’ pre-impact characteristics. The team predicted the asteroids’ orbital paths from the recorded coordinates and velocity. Assuming the objects were spherical, they estimated the size from velocity and impact energy. From this information about the asteroids’ state before impact, the team could then simulate whether the Rubin Observatory would have detected them. 

When small asteroids hit Earth, they mostly burn up in the atmosphere, producing a bright flash that is relatively easy to spot compared to when they are in space. The Rubin Observatory can see objects up to 10 billion times dimmer than the brightest star in the night sky, Sirius. To predict if the Rubin Observatory can detect dim NEOs, the team used the solar system simulator SORCHA. SORCHA takes an observatory’s specifications and the physical properties of the NEOs and uses these to determine whether the observatory could detect them. It also estimates the measured sizes and speeds of the NEOs, and predicts when the observatory would have seen them. 

At the Rubin Observatory, an imminent impactor is considered discovered if it’s observed at least 3 separate times over 15 days before hitting Earth. So this team applied this same criterion to their simulation. They found that of their 343 impactors, SORCHA estimated that the Rubin Observatory would have discovered 14 of them before impact. 

They also found that SORCHA predicted 4 additional impactors that could have been observed at least once, potentially allowing scientists to search other telescope data for evidence of their existence in pre-collision images. Based on these results, the team estimated that the Rubin Observatory could find 4% of all objects longer than 1 meter (3 feet) before they hit Earth. This percentage of discovery translates to 1 to 2 imminent impactors detected every year, at least double their current discovery rate.

The ability to more consistently find imminent impactors would allow researchers to study the smallest pieces of the solar system in detail and to draw general conclusions about the properties of objects that could potentially collide with Earth. This would also allow scientists to more accurately calculate the orbits of imminent impactors, determine where more meteorites land to recover them, and keep tabs on the extremely rare but more hazardous large impactors for planetary defense initiatives. So, humanity can learn more about the ancient solar system, and hopefully avoid going the way of the dinosaurs while doing so!

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Schistosoma mansoni is a parasitic worm that infects millions of people worldwide, entering the body through the skin. Unlike bacterial infections that often cause pain and inflammation, S. mansoni usually slips in unnoticed, rarely causing discomfort. Scientists have long wondered why our skin’s defenses don’t react more strongly to this invader.

Researchers from the University of Pennsylvania and Tulane University explored whether a specialized group of pain-sensing neurons, called TRPV1+, plays a role in detecting and fighting off S. mansoni during its invasion. TRPV1+ neurons are nerve cells in the skin that respond to heat, spicy foods, and certain bacteria and fungi. When activated, they can trigger pain and release molecules that alert the immune system. Previous researchers have shown that these neurons help defend against bacterial and fungal skin infections, but their role in fighting off parasites was unclear. 

To examine how S. mansoni infects its hosts, the team conducted experiments using groups of 4 to 8 mice, matched for age and sex. They repeated each experiment 2 to 3 times to ensure their data was reproducible. They anesthetized the mice, exposed their ears to a solution containing 100 to 150 live S. mansoni larvae, and covered the area with vaseline to keep the solution in place. After 20 to 30 minutes, they counted the number of larvae remaining in the mice’s ears to assess how many had successfully penetrated their skin. 

Next, the team measured pain sensitivity in the mice exposed to the parasite by placing them in chambers and applying an infrared heat source to their paws. They recorded how long it took each mouse to withdraw its paw as a sign of pain. They repeated this test 3 times per paw on each mouse, waited at least 5 minutes between each measurement, and calculated an average withdrawal time. They found that mice infected with S. mansoni were approximately half as sensitive to heat-induced pain as an uninfected control group, suggesting that the parasite could be suppressing the mice’s TRPV1+ neurons.

To investigate this further, the team isolated nerve cells that transmit sensory signals from the mice’s skin, known as dorsal root ganglia, and grew them in the lab. They stimulated these neurons by adding a spicy compound in chili peppers, known as capsaicin. Then they measured calcium influx, a sign the neurons were active, using a special dye that glows differently under a microscope depending on calcium levels. This allowed them to see how active the neurons were and how many molecules they released in response to infection and stimulation. They found that 68% of neurons from uninfected mice responded to capsaicin, but only 26% of neurons from infected mice did so, confirming that the parasite dampened their sensory neuron responses.

To test whether restoring the mice’s neuron activity could help their bodies resist parasite invasion, the researchers genetically engineered the mice to produce a light-sensitive protein in their TRPV1+ neurons, called channelrhodopsin. They “turned on” these neurons by stimulating them with blue light for 30 minutes every day for 5 days before infecting the mice with parasites. Six days later, they assessed the mice’s immune response by using a laser to count immune cells in samples of ear tissue. They also determined how far the parasites had spread by counting larvae in lung samples. They saw that mice with the artificially activated TRPV1+ neurons had nearly double the immune response, blocked roughly twice as many larvae at the skin, and had about 20% fewer parasites in their lungs.

Next, the researchers selectively destroyed TRPV1+ neurons in a new set of mice using a compound known as resiniferatoxin. Three weeks later, they confirmed the treatment had worked by testing the mice’s pain sensitivity using the same heat assay as before. After confirming the mice had lost their pain sensitivity, the team exposed the mice to the parasites. They found that the reinfected mice had 1% to 3% weaker skin immune responses and approximately 25% to 30% more parasites in their lungs than a control group, further confirming that TRPV1+ neurons play a protective role during early infection.

The team concluded that S. mansoni has evolved ways to silence pain-sensing neurons, like TRPV1+. Their discovery suggests that future scientists could boost skin immunity against parasites by targeting sensory neurons. For example, they could develop topical creams or therapies that activate these neurons to help our bodies fight off S. mansoni and similar parasitic invaders.

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