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.

The post Can melting ice increase glacial hazards? appeared first on Sciworthy.

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.

The post Testing local weather knowledge in the Bolivian Altiplano appeared first on Sciworthy.

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.

The post What colors were prehistoric mammals? appeared first on Sciworthy.

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. 

The post Predicting microbial antibiotic resistance with AI appeared first on Sciworthy.

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!

The post The Sun’s twin star may have tilted the planets’ orbits appeared first on Sciworthy.

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.

The post Using seaweed to harvest valuable metals appeared first on Sciworthy.

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. 

The post Industrial waste solidified a UK beach in just 35 years appeared first on Sciworthy.

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.

The post Viruses help plants obtain nitrogen appeared first on Sciworthy.

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.

The post Cold shocks in a warming ocean affect large marine animals appeared first on Sciworthy.

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!

The post How many asteroids can we detect before they hit Earth? appeared first on Sciworthy.

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.

The post A parasite that numbs your pain appeared first on Sciworthy.

Nearly 170 billion tons of ice melt off the Greenland Ice Sheet every year, but scientists don’t fully understand the process by which this melting occurs. Researchers in the past suggested that liquid water forms at the glacier’s surface, where warm air and sunlight melt the ice. This meltwater travels down through cracks in the glacier and along the bedrock beneath it and flows out to sea. 

However, some scientists view this model as too simple because it assumes water only flows downward and ignores how temperature affects water movement. They argue that an oversimplified melting model is unreliable for predicting how future ice sheets will behave.

One group of researchers tested this by investigating how water movement across Greenland is controlled by lakes beneath the ice sheet, called subglacial lakes. They monitored a newly discovered subglacial lake using high-resolution satellite images taken from 2012 to 2019 and 3D surface maps generated from the images. They focused on ice sheet changes from July 22 to August 1, 2014, during a suspected subglacial lake drainage event. A drainage event is like a water balloon that’s too full, bursting from excess pressure. 

They examined images of the Greenland ice sheet taken from Landsat-8, 3D surface maps from the Polar Geospatial Center, and data from ICESat and ICESat-2 taken in the summer of 2014. Based on these data, the researchers identified a 2-square-kilometer (0.8-square-mile) region of ice rising 10 to 15 meters (about 30 to 50 feet), forming a dome on the ice sheet’s surface. They suggested that this dome formed when a large, meltwater-fed lake developed on the bedrock beneath the ice sheet, pushing the ice upward. 

They also saw that on July 22, 2014, the dome began to collapse. Over the next 10 days, it dropped by 85 meters (about 280 feet), forming a basin. Using the dome’s dimensions, the researchers estimated that 90 million cubic meters, or about 3 billion cubic feet, of water drained from the lake at an average rate of 100 cubic meters, or about 3500 cubic feet, per second over the 10 days. This is roughly equivalent to 36,000 Olympic swimming pools draining at a rate of 1 pool every 25 seconds! 

The researchers also found that during the collapse, 40-meter-high (130-foot) ice blocks were uprooted about 1 kilometer (0.6 mile) downstream from the basin, followed by 6 square kilometers (about 2 square miles) of smooth ice. The researchers hypothesized that these features formed when water burst through the ice, flowed along its surface, and then reentered the ice sheet.

Next, they used data from Landsat 5, Landsat 9, the National Snow and Ice Data Center, and the U.S. Geological Survey to demonstrate that the drainage event also affected the surrounding environment. When the water reentered the ice sheet, it flowed beneath the Harding Glacier downstream. This influx of fast-moving water reduced pressure at the glacier’s base, slowing its movement. Simultaneously, the increased drainage beneath the Harding Glacier caused 500 to 600 meters (about 1,600 to 2,000 feet) of ice to break off its end. 

In response to these changes, the researchers hypothesized that subglacial waters rose to the surface rather than sinking to the bedrock because the ice sheet was frozen to it. To test this, they simulated temperatures at the base of the ice sheet using computer-generated thermal models. They input a range of likely bedrock temperatures and ice thicknesses to simulate possible conditions under the ice sheet. They found that all the simulations predicted the glacier’s base would remain below -5°C (23°F). At such low temperatures, the ice would be frozen to the bedrock before subglacial water could drain out. Since water couldn’t flow under the glacier, it was forced to travel upward. 

The researchers used these findings to develop a new conceptual model of how glacial meltwater moves. First, surface ice melts and flows into a subglacial lake. As meltwater accumulates, pressure builds at the base of the ice sheet, forming a dome on the surface. A drainage event causes the dome to collapse. As water moves downward through the glacier, it is blocked from reaching the base because the ice is frozen to the bedrock. The water then moves upward, bursts through the surface, travels across it, and eventually reenters the glacier before draining to the ocean.

The team concluded that water movement above, through, and below glaciers is interconnected. Destructive upward flow of meltwater, followed by downward flow beneath glaciers, can weaken ice sheet structure and alter downstream glacier dynamics. Their work highlights the need for more scientists to consider the processes driving glacial ice loss.

The post How do ice sheets melt? appeared first on Sciworthy.

Many volcano scientists spend their time monitoring volcanoes to predict if, or when, they’ll next erupt. The challenge for these scientists is to forecast the exact probability of an eruption and the moment it will occur. 

To address this problem, a team of researchers developed an early-detection system to determine when to issue alerts about volcanic eruptions. Their motivation was that previous alert systems weren’t accurate enough because they couldn’t detect underground volcanic processes that occur right before an eruption. 

From 2014 to 2023, the researchers conducted a series of tests to accurately detect temporary, low-frequency vibrations using an instrument called a seismometer. The researchers focused on these transient signals because they come from earthquakes and other ground phenomena, which scientists often rely on to predict volcanic eruptions. These phenomena include ground tilting caused by fracturing from the movement of magma and volcanic gases. The scientists named these signals the “Jerk” signals because they occur due to sudden movements. 

The team set up their experiment at Piton de la Fournaise, a volcano at La Réunion Island off the east coast of Madagascar. They discovered the Jerk signals by detecting and analyzing mysterious short-term signals from existing and experimental seismic data. During real-time experiments, they detected signals 8 kilometers (about 5 miles) from the volcano at the Rivière de l’Est seismic station. These signals resulted from changes in ground elevation as magma moved, and they preceded eruptions by minutes and sometimes hours. 

The researchers measured the Jerk signals in units of newton-meters per second, which conveys how fast physical changes occurred in or around the volcano. In addition to real-time experiments, the team checked the Jerk signals’ accuracy by analyzing the station’s past data to determine when these signals would have occurred before an eruption.

To avoid false alarms during their experiments, the researchers needed to distinguish between the signal amplitudes of ocean tides and the transient Jerk signals. So, they used computational software to predict the signals from ocean tides. This was necessary because the instruments that record ground phenomena are also sensitive to tidal movements.  

The Jerk system sent its first automatic alarm in June 2014, 1 hour before the first volcanic tremor, signalling that magma was at the Earth’s surface. The team recorded the last signal in their study on July 2, 2023, 40 minutes before the eruption, at a slow 1.5 newton-meters per second. From 2014 to 2023, other Jerk detections occurred between a few minutes and up to 8 hours before an eruption.

To further assess the strength of the Jerk signals, the team examined previous eruption records at Piton de la Fournaise. They found that for the 24 eruptions between 1998 and 2010, a Jerk alarm would have gone off 83% of the time. Of the 48 eruptions that occurred between 1998 and 2023, including the eruptions during their real-time tests, about 42 Jerk alarms would have occurred. However, they found that the Jerk signals were more accurate during their real-time analysis than they would have been for the eruptions 20 years earlier. 

Although the researchers successfully detected pre-eruption signals, they noted that the Jerk alarms occasionally arrived too late due to the data processing time, which takes at least 10 to 15 minutes. They reported 2 late alarms: one on June 11, 2019, and the other on February 10, 2020. Therefore, after 10 years of tracking the Jerk signals, the team evaluated their success rates at 22 successful alarms out of 24 eruptions, for a 92% positive rate. 

Before using the Jerk signals, many volcanologists struggled to predict the exact moment a volcano erupts. The team concluded that, in addition to the usual methods for detecting pre-eruption earthquakes, observatory scientists could now use Jerk signals to confidently notify authorities in advance of volcanic eruptions, so they can alert people living near volcanoes worldwide. 

The post A new method to detect volcanic eruptions appeared first on Sciworthy.

What if your daily coffee routine could help power the world around you? 

Scientists are exploring ways to transform everyday waste, like spent coffee grounds, into materials for greener energy production. One promising method, called triboelectrification, uses electricity generated from motion. With this approach, materials that gain positive and negative charges are rubbed together to generate an electrical charge that can be harnessed by devices called triboelectric nanogenerators, or TENGs. TENGs are a greener alternative to fossil fuels because they generate electricity from everyday movement, such as pedestrian or vehicle motion. 

However, many negatively charged materials used in these devices are made from nonbiodegradable plastics, like PTFE, that persist as long-term waste in the environment. Over time, TENGs are also easily damaged by environmental conditions outside the laboratory. To create sustainable TENGs for real-world applications, scientists need to develop biodegradable and durable designs. 

To address this problem, researchers from Guangxi University in China developed a TENG that uses coffee grounds as its negatively charged material. Their design repurposes coffee waste to create a robust, low-cost device that can harvest energy.

To create the coffee-based material, the researchers mixed coffee grounds with a biodegradable glue-like plastic called polycaprolactone (PCL). They heated this mixture to 80°C (176°F) and molded it into cylindrical pellets. Then, they used a biodegradable plastic called polylactic acid (PLA) to 3D-print a fully enclosed box-like TENG containing vertical channels arranged like the parallel slots of a file organizer to house the pellets. 

Their TENG worked by shaking the coffee ground pellets within the channels, causing them to repeatedly rub against the PLA surfaces. Because coffee grounds strongly attract electrons, the pellets become negatively charged while the PLA surfaces become positively charged. This repeated pellet movement drove the electrons back and forth, generating electricity that was harvested through the TENG’s external circuit. 

To evaluate their TENG’s electrical performance, the researchers used an instrument that measures small amounts of electricity, called an electrometer. The electrometer measures electrical output based on how strongly the device pushes electrons through a circuit, called its voltage, how quickly electrons flow through it, called its current, and how many electrons flow through it, called its transferred charge.

The researchers verified that coffee ground pellets could serve as a viable TENG material by comparing their performance with commercial plastic pellets. The coffee ground pellet system produced a voltage of 0.6 Volts (V), a current of 18 nanoamperes (nA), and a charge transfer of 0.25 nanocoulombs (nC). This output was more than twice that of pure PCL pellets and over half that of nonbiodegradable PTFE. The researchers suggested that the negatively charged components of the coffee grounds altered the surface of the PCL to increase its charge generation and transfer. 

To optimize the TENG, the researchers experimented with different coffee grounds to PCL mass ratios, pellet lengths, and device vibrational frequencies. They found that their device achieved the best electrical performance at a 3:1 ratio of coffee grounds to PCL, with 8 millimeter long pellets, at a vibrational frequency of 2.6 Hertz. These conditions boosted the device’s electrical output to 4.7 V, 75 nA, and 1.3 nC, high enough to power tiny environmental sensors. 

The team also tested their device’s long-term durability by comparing its electrical performance before and after 6 months of storage. They observed only a 6% drop in voltage after storage, demonstrating that it could maintain stable long-term electrical performance for at least 6 months. 

Finally, to explore real-world applications, the researchers integrated 4 TENGs into a wind-energy harvesting system designed for remote island settings. The system used a wheel of rotating cups to capture wind energy and a sliding mechanism to convert it into repeated shaking of the TENGs. As the TENGs shook, the moving pellets inside generated enough electricity to power an LED light and a small weather-monitoring device. 

The researchers concluded that spent coffee grounds can function as biodegradable TENG materials while reducing dependence on fossil fuel-derived materials. By combining coffee waste with biodegradable plastics, the researchers created a TENG with good electrical performance, sustainability, and long-term stability. They suggested that with further development, the device could someday power road warning lights and provide sound-based alerts in remote areas.

The post Brewing sustainable power from used coffee grounds appeared first on Sciworthy.

Around 66 million years ago, an asteroid wiped out 75% of the species on Earth. This event caused the extinction of all dinosaurs except birds, referred to as non-avian dinosaurs. However, an open question in paleontology is whether the dinosaurs were already declining before the asteroid hit. Many scientists think that around 5 to 10 million years before the extinction event, dinosaurs became less biologically diverse across America, making them more prone to extinction. 

To investigate this hypothesis, scientists recently revisited fossils in a rock layer from the end of the dinosaur era that previous researchers had incorrectly dated. The bulk of the fossils from this period had previously been collected from a different section of rocks called the Hell Creek Formation in Montana. These researchers studied a rock layer in New Mexico that is exposed to the surface, known as the Naashoibito Member. It’s approximately 30 meters (about 100 feet) thick and formed from ancient river sediments. 

To determine when the Naashoibito Member formed, the scientists measured the decay of radioactive elements within its mineral grains over time, a technique called radiometric dating. The scientist reasoned that these mineral grains must have crystallized before being transported and incorporated into the Naashoibito Member, so their youngest ages should provide a maximum depositional age. 

They determined the ages of 1,046 crystals of the mineral feldspar from 2 dinosaur-bearing rock layers in the Naashoibito Member. Based on these ages, the researchers found that a sandstone within 5 meters (about 16 feet) of the lowest boundary of the Naashoibito Member must be younger than 66.9 million years old. They found that the other layer, which is around 3.5 meters (11 feet) from the base of the lowest boundary, must be younger than 66.4 million years old. These age ranges indicated that the sandstone contains dinosaur fossils formed 400,000 years before their extinction.

Next, the researchers measured the direction of magnetic minerals inside the rocks, using a practice called magnetostratigraphy. The direction of these minerals indicates where Earth’s magnetic field was pointing at that time. Scientists have shown that the Earth’s magnetic field has flipped multiple times over Earth’s history, so they can use these magnetic signals to create a timescale for when the sediments formed. 

The researchers wanted to determine whether the magnetic minerals were lined up in a direction like today, called normal polarity, or the opposite, called reverse polarity. The researchers found one rock layer with normal polarity at the base of the Naashoibito Member, and one with reverse polarity in the upper part of the Naashoibito Member. These magnetic field directions lined up with 2 known polarity intervals over a timeline that matched the feldspar dates. Together, these data demonstrated that the Naashoibito Member formed at the end of the dinosaur era, around the same time as the Hell Creek Formation.

Next, the researchers wanted to test whether non-avian dinosaurs maintained their diversity across different regions of Western North America during this time period. They compared fossil patterns from locations across North America using statistical grouping methods that determine how similar or different their environments were. They found that temperature, rather than geographical location alone, was the best predictor of diversity at the extinction event. 

The team concluded that a diverse population of Naashoibito dinosaurs lived at around the same time as the Hell Creek dinosaurs. Earlier scientists surmised that dinosaurs were already on the decline because they studied the colder northern regions, where diversity was lower. However, these results suggest that warmer conditions in the south still supported diverse dinosaur populations. Together, this evidence suggests that dinosaurs were still diverse and thriving 66 million years ago, and that their extinction was indeed a result of the asteroid impact.

The post Were dinosaurs doomed before the asteroid hit? appeared first on Sciworthy.

As our world is rapidly reshaped by climate change, glaciers have retreated and revealed new swaths of bare land. Throughout the coming decades to centuries, this rocky ground will become dappled with lichens and scrub, eventually giving rise to newborn forests in a process known as ecological succession. 

Ecologists have mapped out the stages of ecological succession for plant communities, observing which species are the first to colonize land and how these pioneer species promote secondary growth. Yet before any plants sink their roots into the soil, it is populated by thriving communities of single-celled microbes that prepare it for further habitation. Scientists study how these microbial communities arise to understand how healthy ecosystems develop.

Newly exposed land is nutrient-poor and subject to large temperature fluctuations, so the first species to settle there must overcome these challenges. Pioneer plant species are often habitat generalists, meaning they can thrive under a variety of environmental conditions. However, all plants convert water and sunlight into carbon and energy. Microbes can use many different energy sources, and often contain genes for multiple metabolisms in their genomes. Therefore, researchers wondered whether pioneer microbes could also be defined by their metabolic flexibility. 

A team of scientists based at Monash University in Australia tested this hypothesis by studying land left in the wake of 2 retreating glaciers: one on an island off the coast of Antarctica and one in the Swiss Alps. The researchers sampled soils along a path from the tip of the glacier outward, which had been exposed to the air for different amounts of time. By comparing the microbial communities within these soils, the researchers could track the different stages of ecological succession after glacial retreat.

The researchers extracted DNA from the soils and analyzed it using 2 sequencing methods. First, they sequenced a gene called 16S rRNA, which is like a microbial fingerprint for the different species present. They used this method to understand the diversity of these communities and track their species overlap, helping them to identify habitat generalists that thrived under different soil conditions. 

To understand the metabolic flexibility of these microbes, the researchers used a second method, known as metagenomics, which sequences all of the DNA in a sample rather than just a single gene. They used these data to reconstruct the entire genomes of microbes present in the soils, providing information on what kinds of metabolic activities they were capable of. The team also measured chemicals like ammonium and sulfide in the soils, as well as atmospheric gases like methane and carbon monoxide, to assess how the microbes used these compounds for growth.

The researchers found microbes inhabiting even the youngest soils, demonstrating the speed at which life takes hold in new environments. The abundance of microbes increased by approximately 8-fold in older soils, alongside a similar rise in species diversity, indicating that complex communities persisted over time. They also found the metabolic capabilities of microbes inhabiting Antarctic and Swiss glacial soils to be remarkably similar, suggesting common selective pressures drove the establishment of these new ecosystems.

They were surprised to discover that the most abundant microbes in younger soils were habitat specialists that were rare in older soils. These pioneer microbes, while still metabolically flexible, had adapted to utilize meager energy sources like atmospheric trace gases, including hydrogen, methane, and carbon monoxide. Many of these microbes could also derive energy from chemicals that dissolved out of rocks, like inorganic sulfur compounds. The researchers proposed that pioneer microbes could quickly take advantage of newly opened ecological niches, like soils exposed during a glacier’s retreat, because of their efficiency at using scant resources. 

They found that habitat generalists, on the other hand, tended to dominate older soils.  This result suggested that the habitat specialists were eventually outcompeted by the slow and steady growth of the habitat generalists in a real-life turtle-and-hare race.

The team concluded that using multiple growth strategies helps microbes adapt to new environments. However, they acknowledged that ecological succession could occur differently in other landscapes, such as in the aftermath of volcanic eruptions, meteorite impacts, or forest fires. They suggested that future researchers work to uncover how microbial communities lay the groundwork across these diverse ecosystems. 

The post When glaciers retreat, microbes take over appeared first on Sciworthy.

In 2011, miles of rock shifted deep underwater in the Japan Trench, causing an earthquake that deformed the seafloor and caused a devastating tsunami. Off the Eastern Coast of the Tōhoku region in Japan, a huge piece of the Earth’s crust carrying the Pacific Ocean, known as a tectonic plate, is forcing itself beneath Japan and into the Earth’s interior. Researchers think rock from this plate pushed against rock far beneath Japan, building pressure until it became too great. The lower plate slipped, spreading energy from the slip site and causing the earthquake. 

Plate slip can cause 2 types of outcomes. The first occurs kilometers deep in the Earth’s crust, causing earthquakes too small to trigger tsunamis. The second type causes extreme slip events that begin deep in the crust, like the 2011 event that extended into the Japan Trench and warped the seafloor. The moving seafloor displaces seawater, causing a tsunami. These events are referred to as slip-to-the-trench style earthquakes.

Researchers think this phenomenon is not unique to 2011, as Japan has a long history of tsunami-causing earthquakes. Charlotte Pizer and colleagues investigated sediment from the Japan Trench to unearth the geologic history of slip-to-the-trench style earthquakes.

In between earthquakes, Japanese islands continuously shed sediment that fills the trench, forming horizontal layers. Slip-to-the-trench earthquakes bend and fold these layers. The team hypothesized that if they found these folds in the Japan Trench and dated them, they could catalogue the record of slip-to-the-trench style earthquakes in the area.

Earthquakes like these occur repeatedly, meaning that younger ones can erase folds caused by older ones. To account for this overprinting, the researchers chose a study site within the Japan Trench about 100 kilometers (60 miles) north of where the most severe earthquakes occur. This site is only mildly deformed by slip-to-the-trench earthquakes since it’s so far from their source, making it easier for the team to identify individual events. 

Pizer and her group pieced together data collected from this site by previous researchers to reconstruct Japan’s earthquake history. They compiled 3 main types of data. Two of these were images of sediment layers in the trench constructed from reflections of vibrations sent into the ground, called seismic profiles. 

The first seismic profile captured the full trench, over 1 kilometer deep (about 0.6 miles), at 5-meter (16-feet) resolution. Using this method, any layers smaller than 5 meters thick won’t appear in the image. The second seismic profile captured only the top 40 meters (130 feet) of sediment, but could detect layers as thin as 10 centimeters (4 inches). 

The final type of data they used came from a 36-meter (120-foot) long cylinder of sediment extracted from the bottom of the trench, called a sediment core. Researchers in the past tied layers within this sediment core to historic earthquake events. They showed that it recorded 2 specific earthquakes in the area, the Kyotoku earthquake of 1454 CE, and the Jogan earthquake of 869 CE, both of which likely caused tsunamis. 

Pizer’s team could observe the depth of the Kyotoku and Jogan earthquake layers taken from the sediment core in their high-resolution seismic profile of the trench. In these high-resolution data, they noticed that deformation occurred in the sediment up to the layer marking the Jogan earthquake, but not above it, meaning deformation of the trench happened during this 869 CE event. 

The high-resolution data were only able to image sediments from recent earthquakes since 869 CE, but the low-resolution profile, which imaged the full trench, was able to capture the deeper, older record. When they looked deeper into the trench sediment, the researchers noticed that deformation from the 869 CE event had spread from where the tectonic plate is colliding with Japan. They saw at least 6 more similarly deformed layers, implying each was its own slip-to-the-trench event, although the dates of these events are unknown.

Pizer and colleagues concluded that slip-to-the-trench earthquakes are long-standing, common events in the Japan Trench. They hope future scientists will continue to research this site to better assess tsunami risks in Japan.

The post The history of tsunami-causing earthquakes in Japan appeared first on Sciworthy.

Electric vehicles and other clean energy devices rely on batteries that use a metal called lithium. The expanded use of lithium has made lithium mining a rapidly growing industry. Lithium is most often found in volcanic rocks called pegmatites. Researchers have studied how much carbon and water pegmatite mining uses, but they don’t know how it affects local water quality. 

Pegmatite mining releases not only lithium but also other metals commonly found in pegmatite, such as rubidium and cesium. These trace metals are under-researched in terms of their environmental effects and possible human toxicity, though the EPA has linked high levels of lithium to adverse health effects in kidney function, neurological development, and thyroid function. As such, there’s currently no established safe level for lithium in drinking water, but the U.S. Geological Survey suggests a preliminary safety level of 10 micrograms per liter (μg/L). 

Researchers at Duke University recently tested how much pegmatite mining increased lithium levels in local waters, and how long those effects lingered after mining processes ended. To do so, they measured metal concentrations in waters downstream from 2 lithium mines and adjacent processing plants. They hypothesized that mining activities would increase interactions between rocks and the surrounding surface water and groundwater, impacting their lithium concentrations.

The researchers collected 99 water samples from surface streams and 93 samples from groundwater wells surrounding the Kings Mountain and Hallman Beam lithium mines, in a zone of about 40 kilometers (25 miles) along the border of South Carolina and North Carolina. The researchers took 51 surface water samples upstream of the mines to evaluate natural metal levels, and compared these with 48 downstream samples showing the mining-impacted metal levels. 

The researchers measured lithium and other trace elements, like rubidium, cesium, arsenic, and strontium, in the waters using an instrument called an inductively coupled plasma mass spectrometer. They found that lithium concentrations in the surface waters increased from a background of 0.2 to 79 μg/L to concentrations of 785 to 1,249 μg/L within 10 kilometers (6.2 miles) of the mines and processing facilities. The groundwater lithium levels were as high as 4,500 to 47,000 μg/L in the wells at the mines, but were not affected at downstream wells where lithium levels ranged from 0.5 to 890 μg/L. 

They suggested that lithium in the groundwater downstream from the mines came from natural interactions of the pegmatite rocks with the water, rather than from the mines themselves. This natural process was confirmed when the researchers noticed that lithium levels in the groundwater rose after rainfall, which caused more water-rock interaction.

The researchers also measured ions like calcium, sulfate, and chloride in the waters using a tool that can analyze compound molecules, called an ion chromatograph. They found that calcium and sulfate concentrations increased in surface waters within 10 kilometers (6.2 miles) downstream of the processing facilities. These downstream waters had 50 to 120 milligrams per liter (mg/L) of calcium and 100 to 300 mg/L of sulfate, compared to 5 to 20 mg/L of calcium and 3 to 10 mg/L of sulfate in the background surface waters. These ions are also byproducts of pegmatite processing, produced from a waste product made up of calcium sulfate, called gypsum. 

The team explained that active mining has been discontinued at this site for approximately 3 decades, meaning that the lithium concentrations they measured reflect the long-term release of lithium from inactive mines and mining waste. Drawing from historical data, they estimated that while the mines were active, they likely released 10 to 30 times the amount of trace metals they measured at the same location today. 

The researchers concluded that hard-rock processing impacts downstream metal and ion concentrations more than the mining itself. As lithium mining operations expand, the researchers suggested that future researchers examine lithium toxicity as well as the potential effects of its co-occurring metals, like rubidium and cesium. They also emphasized the need to find solutions for removing the trace metals and dissolved gypsum from water systems, or preventing them from leaking into the water systems to begin with.

The post Lithium mining impacts local waters appeared first on Sciworthy.

Brown rot is a common fungal disease in the Southeastern United States that affects stone fruits like peaches, cherries, and plums. Monilinia fructicola is a tan, fuzzy fungus that grows on tree flowers, twigs, and fruit, and is the most common cause of brown rot. Once brown rot infects a tree, the damage is irreversible, causing significant crop losses in commercial orchards. Farmers try to control the spread of M. fructicola by applying chemical sprays, called fungicides, to fruit trees in the spring.

In M. fructicola, a biosynthesis gene named MfCYP51 encodes a major protein critical to the fungi’s survival. Certain fungicides, called demethylation inhibitor (DMI) fungicides, stop M. fructicola from growing by targeting this protein. Over the last 20 years, DMI fungicides have become less effective against brown rot as M. fructicola becomes more resistant. 

Living microorganisms and their metabolites can control fungal diseases as an alternative to chemical fungicides, known as biofungicides. Many bacteria produce compounds like hydrogen cyanide and pyrrolnitrin that damage fungal cells. However, scientists have not found a biofungicide suitable for commercial production.​ A group of researchers at Clemson University in the USA and Huazhong Agricultural University in China set out to test whether the soil bacteria Pseudomonas chlororaphis and Bacillus subtilis could be effective biofungicides against brown rot. 

The researchers performed an incubation experiment to analyze how the fungal cells make their critical protein from the MfCYP51 gene, a process called gene expression, under different fungicide treatments. They used 3 M. fructicola strains sensitive to traditional DMI fungicides and 3 resistant strains. They tested these with 5 different fungicide treatments: a DMI fungicide, a mixture of P. chlororaphis metabolites, a mixture of living B. subtilis cells, a DMI fungicide + P. chlororaphis metabolites, and a DMI fungicide +  B. subtilis cells. They also set up a control treatment with sterile water instead of fungicide.  

After 6 hours, the researchers extracted the molecule that measures gene expression, called RNA, from the fungi. They found that both the P. chlororaphis treatment and the DMI fungicide + P. chlororaphis treatment substantially reduced MfCYP51 expression in both the sensitive and resistant isolates compared to the controls. In contrast, the DMI fungicide, B. subtilis, and DMI fungicide + B. subtilis treatments increased MfCYP51 expression in the resistant isolates. The researchers concluded that P. chlororaphis has a unique ability to reduce gene expression, unlike other biofungicides.

​To further investigate how the biofungicide treatments work, the team tested whether P. chlororaphis and B. subtilis produced the anti-fungal metabolite pyrrolnitrin. They added 1 milliliter of each treatment to a machine that separates and identifies liquid compounds using high pressure, called a high-performance liquid chromatograph. They found that the P. chlororaphis treatment contained pyrrolnitrin, so they hypothesized that it was responsible for reducing gene expression.

Additionally, the researchers tested their 5 treatments on brown rot in fruit. For each treatment, they took 10 Gala apples, washed and sterilized their surfaces, and sprayed them with the fungicides. After 24 hours, they poked holes in the apples with toothpicks and added 20 microliters of liquid containing M. fructicola cells to the poked holes. They placed the apples in a humid chamber for 5 days and measured the number and size of brown rot spots that developed on their surfaces. 

Despite decreasing MfCYP51 expression, ​the researchers found that P. chlororaphis treatment alone didn’t reduce brown rot disease compared to the control, but the DMI fungicide and B. subtilis treatments did. The mixed treatments of DMI fungicide + P. chlororaphis and DMI fungicide + B. subtilis both reduced brown rot disease compared to the control, but only the DMI fungicide + P. chlororaphis treatment prevented it. 

​The researchers concluded that although biofungicides may not be fully effective on their own, commercializing them could allow farmers to use less DMI fungicide. They explained that scaling back the use of DMI fungicides would slow M. fructicola resistance. Finally, they proposed that future researchers test biofungicide mixtures containing pyrrolnitrin in the field to observe real-world effects on stone fruit trees.

The post Using bacteria to fight fungal brown rot appeared first on Sciworthy.

Earth has many unique features for a planet, such as a magnetic field, a large moon, and plate tectonics. It’s also the only planet we know of that harbors life. These facts form the basis of the Rare Earth hypothesis, which posits that we haven’t found aliens because other planets in the Galaxy probably don’t have all the right conditions for life. 

Another characteristic of Earth is that about 30% of its surface is land and about 70% is ocean. Recently, Columbia University Assistant Professor David Kipping investigated whether the proportion of Earth’s surface covered by dry land versus ocean, or its land fraction, is another reason Earth is habitable not only for simple single-celled organisms, but also for intelligent species like humans. 

To test this hypothesis, Kipping created 4 statistical models of planets with different land fractions that intelligent aliens could potentially evolve on. First, he created an equation to describe the likelihood that a planet in its star’s habitable zone has a particular land fraction, known as a probability distribution. Kipping weighted this probability distribution toward the extreme ends, making it more likely that a planet would be covered by a single huge landmass or a single vast ocean than by a mix of both, as on Earth. 

Kipping then incorporated this land fraction probability distribution into his statistical models to calculate the probability that a random planet will have that land fraction and host intelligent life. The 4 scenarios Kipping tested were: 1) that intelligent life is more likely to emerge on land-dominated planets, 2) that it’s more likely to emerge on ocean-dominated planets, 3) that it’s more likely to emerge on planets with roughly equal amounts of land and ocean, and 4) that its emergence is independent of a planet’s land fraction. 

As a first step in determining the kinds of planets intelligent aliens would tend to emerge on, Kipping used each model to predict the probability that intelligent life would emerge on a planet with the same land fraction as Earth. He then compared these probabilities by calculating the ratios between each value. Because Earth is the only known planet with intelligent life, a model that predicted a greater probability for humanity’s existence on Earth would be more likely to reflect reality.

Kipping considered it strong evidence that a given model was more realistic than another if the ratio between 2 of them was greater than 10, meaning one model was 10 times more likely to predict the existence of Earth and humanity. Kipping found that no comparison of any 2 models passed this threshold. However, the models assuming that intelligent life prefers ocean-dominated planets or planets with a land-ocean balance were 2.5 and 3 times more likely to predict the existence of humanity than the model assuming that intelligent life prefers land-dominated planets. Additionally, the model assuming that intelligent life prefers a land-ocean balance was always more likely to predict humanity than any other model, though marginally. 

Kipping also addressed whether finding more planets with intelligent life would affect which model was deemed most realistic, for example, if scientists discovered conclusive evidence of life on Mars in its distant past. Here, Kipping identified 2 complications. First, it’s uncertain how much of Mars’s surface was once covered by water – some estimate it had a land fraction as high as 81%, while others estimate it was as low as 25%. Second, proving that Mars once had life would not prove it once had intelligent life.

Regardless, Kipping reran the models assuming that ancient Mars had a land fraction comparable to Earth’s. Adding this second data point produced ratios similar to those in the earlier Earth-only calculations, meaning it still didn’t make any single model 10 times more likely to predict the existence of humans and Martians, respectively. 

Kipping then took the 10-times threshold and reversed the calculations to find what conditions would exceed it. In doing so, he calculated that astronomers would need to find 14 other planets with intelligent life and known land fractions to robustly determine whether intelligent life is more likely to occur on desert planets, ocean planets, balanced planets, or without bias.

Kipping concluded that he can’t yet definitively state whether there is something special about Earth’s land fraction when it comes to producing intelligent species. However, Earth’s existence would suggest that intelligent life is unlikely to favor extreme desert planets, so the Milky Way probably isn’t filled with Tatooines and Jakkus. And while his analysis doesn’t debunk the Rare Earth hypothesis, it does undermine the argument that Earth’s ocean size explains why Earth is rare. 

The post Is Earth’s water why intelligent life evolved here? appeared first on Sciworthy.