Aurelia Butler

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    in Heart

    Predictions for the 2020 Atlantic hurricane season just got worse

    Chalk up one more way 2020 could be an especially stressful year: The Atlantic hurricane season now threatens to be even more severe than preseason forecasts predicted, and may be one of the busiest on record.
    With as many as 25 named storms now expected — twice the average number — 2020 is shaping up to be an “extremely active” season with more frequent, longer and stronger storms, the National Oceanic and Atmospheric Administration warns. Wind patterns and warmer-than-normal seawater have conspired to prime the Atlantic Ocean for a particularly fitful year — although it is not yet clear whether climate change had a hand in creating such hurricane-friendly conditions. “Once the season ends, we’ll study it within the context of the overall climate record,” Gerry Bell, lead seasonal hurricane forecaster at NOAA’s Climate Prediction Center, said during an Aug. 6 news teleconference.
    The 2020 hurricane season is already off to a rapid start, with a record-high nine named storms by early August, including two hurricanes. The average season, which runs June through November, sees two named storms by this time of year.
    “We are now entering the peak months of the Atlantic hurricane season, August through October,” National Weather Service Director Louis Uccellini said in the news teleconference. “Given the activity we have seen so far this season, coupled with the ongoing challenges that communities face in light of COVID-19, now is the time to organize your family plan and make necessary preparations.”

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    Storms get names once they have sustained wind speeds of at least 63 kilometers per hour. In April, forecasters predicted there would be 18 named storms, with half reaching hurricane status (SN: 4/16/20). Now, NOAA anticipates that 2020 could deliver a total of 19 to 25 named storms. That would put this year in league with 2005, which boasted over two dozen named storms including Hurricane Katrina (SN: 8/23/15).
    Seven to 11 of this year’s named storms could become hurricanes, including three to six major hurricanes of Category 3 or higher, NOAA predicts. By contrast, the average season brings 12 named storms and six hurricanes, including three major ones.
    Given that heightened activity, NOAA projects that 2020 will have an Accumulated Cyclone Energy, or ACE, value between 140 to 230 percent the norm. That value accounts for both the duration and intensity of all a season’s named storms, and seasons that exceed 165 percent the average ACE value qualify as “extremely active.”
    Researchers at Colorado State University released a similar prediction on August 5. They foresee  24 named storms in total, 12 of which could be hurricanes, including five major ones. The probability of at least one major hurricane making landfall in the continental United States before the season is up is 74 percent — compared with the average seasonal likelihood of 52 percent, the Colorado State researchers say.
    It’s hard to know how many storms in total will make landfall. But “when we do have more activity, there is a [trend] of more storms coming towards major landmasses — coming towards the U.S., coming towards Central America, and the Caribbean, and even sometimes up towards Canada,” says meteorologist Matthew Rosencrans of NOAA’s Climate Prediction Center in College Park, Md.
    Two main climate patterns are setting the stage for an extremely intense hurricane season, says Jhordanne Jones, an atmospheric scientist at Colorado State in Fort Collins. Warmer-than-normal sea surface temperatures in the tropical Atlantic are poised to fuel stronger storms. What’s more, there are hints that La Niña may develop around the height of Atlantic hurricane season. La Niña, the flip side of El Niño, is a naturally occurring climate cycle that brings cooler waters to the tropical Pacific, changing wind patterns over that ocean (SN: 1/26/15). The effects of that disturbance in air circulation can be felt across the globe, suppressing winds over the Atlantic that might otherwise pull tropical storms apart. More

  • 75 Shares159 Views

    in Computers Math

    Updating Turing's model of pattern formation

    In 1952, Alan Turing published a study which described mathematically how systems composed of many living organisms can form rich and diverse arrays of orderly patterns. He proposed that this ‘self-organisation’ arises from instabilities in un-patterned systems, which can form as different species jostle for space and resources. So far, however, researchers have struggled to reproduce Turing patterns in laboratory conditions, raising serious doubts about its applicability. In a new study published in EPJ B, researchers led by Malbor Asllani at the University of Limerick, Ireland, have revisited Turing’s theory to prove mathematically how instabilities can occur through simple reactions, and in widely varied environmental conditions.

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    The team’s results could help biologists to better understand the origins of many ordered structures in nature, from spots and stripes on animal coats, to clusters of vegetation in arid environments. In Turing’s original model, he introduced two diffusing chemical species to different points on a closed ring of cells. As they diffused across adjacent cells, these species ‘competed’ with each other as they interacted; eventually organising to form patterns. This pattern formation depended on the fact that the symmetry during this process could be broken to different degrees, depending on the ratio between the diffusion speeds of each species; a mechanism now named the ‘Turing instability.’ However, a significant drawback of Turing’s mechanism was that it relied on the unrealistic assumption that many chemicals diffuse at different paces.
    Through their calculations, Asllani’s team showed that in sufficiently large rings of cells, where diffusion asymmetry causes both species to travel in the same direction, the instabilities which generate ordered patterns will always arise — even when competing chemicals diffuse at the same rate. Once formed, the patterns will either remain stationary, or propagate steadily around the ring as waves. The team’s result addresses one of Turing’s key concerns about his own theory, and is an important step forward in our understanding of the innate drive for living systems to organise themselves.

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    Materials provided by Springer. Note: Content may be edited for style and length.

    Journal Reference:
    Malbor Asllani, Timoteo Carletti, Duccio Fanelli, Philip K. Maini. A universal route to pattern formation in multicellular systems. The European Physical Journal B, 2020; 93 (7) DOI: 10.1140/epjb/e2020-10206-3

    Cite This Page:

    Springer. “Updating Turing’s model of pattern formation.” ScienceDaily. ScienceDaily, 7 August 2020. .
    Springer. (2020, August 7). Updating Turing’s model of pattern formation. ScienceDaily. Retrieved August 7, 2020 from www.sciencedaily.com/releases/2020/08/200807111942.htm
    Springer. “Updating Turing’s model of pattern formation.” ScienceDaily. www.sciencedaily.com/releases/2020/08/200807111942.htm (accessed August 7, 2020). More

  • 150 Shares169 Views

    in Computers Math

    Machine learning research may help find new tungsten deposits in SW England

    Geologists have developed a machine learning technique that highlights the potential for further deposits of the critical metal tungsten in SW England.
    Tungsten is an essential component of high-performance steels but global production is strongly influenced by China and western countries are keen to develop alternative sources.
    The work, published in the leading journal Geoscience Frontiers, has been led by Dr Chris Yeomans, from the Camborne School of Mines, and involved geoscientists from the University of Nottingham, Geological Survey of Finland (GTK) and the British Geological Survey.
    The research applies machine learning to multiple existing datasets to examine the geological factors that have resulted in known tungsten deposits in SW England.
    These findings are then applied across the wider region to predict areas where tungsten mineralisation is more likely and might have previously been overlooked. The same methodology could be applied to help in the exploration for other metals around the world.
    Dr Yeomans, a Postdoctoral Research Fellow at the Camborne School of Mines, based at the University of Exeter’s Penryn Campus in Cornwall said: “We’re really pleased with the methodology developed and the results of this study.

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    “SW England is already the focus of UK mineral exploration for tungsten but we wanted to demonstrate that new machine learning approaches may provide additional insights and highlight areas that might otherwise be overlooked.”
    SW England hosts the fourth biggest tungsten deposit in the world (Hemerdon, near Plympton), that resulted in the UK being the sixth biggest global tungsten producer in 2017; the mine is currently being re-developed by Tungsten West Limited.
    The Redmoor tin-tungsten project, being developed by Cornwall Resources Limited, has also been identified as being a potentially globally significant mineral deposit.
    The new study suggests that there may be a wider potential for tungsten deposits and has attracted praise from those currently involved in the development of tungsten resources in SW England.
    James McFarlane, from Tungsten West, said: “Tungsten has only been of economic interest in the last 100 years or so, during which exploration efforts for this critical metal have generally been short-lived.
    “As such is very encouraging to see work that aims to holistically combine the available data to develop a tungsten prospectivity model in an area that has world-class potential.”
    Brett Grist, from Cornwall Resources added: “Our own work has shown that applying modern techniques can reveal world-class deposits in this historic and globally-significant mining district.
    “Dr Yeomans’ assertion, that the likelihood of new discoveries of tungsten mineralisation may be enhanced by a high-resolution gravity survey, is something in which we see great potential.
    “Indeed, such a programme could stimulate the new discovery of economically significant deposits of a suite of critical metals, here in the southwest of the UK, for years to come.”

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  • 88 Shares99 Views

    in Computers Math

    Smartwatch tracks medication levels to personalize treatments

    Engineers at the UCLA Samueli School of Engineering and their colleagues at Stanford School of Medicine have demonstrated that drug levels inside the body can be tracked in real time using a custom smartwatch that analyzes the chemicals found in sweat. This wearable technology could be incorporated into a more personalized approach to medicine — where an ideal drug and dosages can be tailored to an individual.
    A study detailing the research was published in Proceedings of the National Academy of Sciences.
    In general, medications are prescribed with a ‘one-size-fits-all’ approach — drugs are designed and prescribed based on statistical averages of their effectiveness. There are guidelines for factors such as patients’ weight and age. But in addition to these basic differentiators, our body chemistry constantly changes — depending on what we eat and how much we’ve exercised. And on top of these dynamic factors, every individual’s genetic makeup is unique and hence responses to medications can vary. This affects how fast drugs are absorbed, take effect and get eliminated from an individual.
    According to the researchers, current efforts to personalize the drug dosage rely heavily on repeated blood draws at the hospital. The samples are then sent out to be analyzed in central labs. These solutions are inconvenient, time-consuming, invasive and expensive. That is why they are only performed on a small subset of patients and on rare occasions.
    “We wanted to create a wearable technology that can track the profile of medication inside the body continuously and non-invasively,” said study leader Sam Emaminejad, an assistant professor of electrical and computer engineering at UCLA. “This way, we can tailor the optimal dosage and timing of the intake for each individual. And using this personalization approach, we can improve the efficacy of the therapeutic treatments.”
    Because of their small molecular sizes, many different kinds of drugs end up in sweat, where their concentrations closely reflect the drugs’ circulating levels. That’s why the researchers created a smartwatch, equipped with a sensor that analyzes the sampled tiny droplets of sweat.
    The team’s experiment tracked the effect of acetaminophen, a common over-the-counter pain medication, on individuals over the period of a few hours. First, the researchers stimulated sweat glands on the wrist by applying a small electric current, the same technique that Emaminejad’s research group demonstrated in previous wearable technologies.
    This allowed the researchers to detect changes in body chemistry, without needing subjects to work up a sweat by exercising. As different drugs each have their own unique electrochemical signature, the sensor can be designed to look for the level of a particular medication at any given time.
    “This technology is a game-changer and a significant step forward for realizing personalized medicine,” said study co-author Ronald W. Davis, a professor of biochemistry and genetics at Stanford Medical School. “Emerging pharmacogenomic solutions, which allow us to select drugs based on the genetic makeup of individuals, have already shown to be useful in improving the efficacy of treatments. So, in combination with our wearable solution, which helps us to optimize the drug dosages for each individual, we can now truly personalize our approaches to pharmacotherapy.”
    What makes this study significant is the ability to accurately detect a drug’s unique electrochemical signal, against the backdrop of signals from many other molecules that may be circulating in the body and in higher concentrations than the drug, said the study’s lead author Shuyu Lin, a UCLA doctoral student and member of Emaminejad’s Interconnected and Integrated Bioelectronics Lab (I²BL). Emaminejad added that the technology could be adapted to monitor medication adherence and drug abuse.
    “This could be particularly important for individuals with mental health issues, where doctors prescribe them prolonged pharmacotherapy treatments,” he said. ” The patients could benefit from such easy-to-use, noninvasive monitoring tools, while doctors could see how the medication is doing in the patient.” More

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    in Heart

    Emissions dropped during the COVID-19 pandemic. The climate impact won’t last

    To curb the spread of COVID-19, much of the globe hunkered down. That inactivity helped slow the spread of the virus and, as a side effect, kept some climate-warming gases out of the air.
    New estimates based on people’s movements suggest that global greenhouse gas emissions fell roughly 10 to 30 percent, on average, during April 2020 as people and businesses reduced activity. But those massive drops, even in a scenario in which the pandemic lasts through 2021, won’t have much of a lasting effect on climate change, unless countries incorporate “green” policy measures in their economic recovery packages, researchers report August 7 in Nature Climate Change.
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    “The fall in emissions we experienced during COVID-19 is temporary, and therefore it will do nothing to slow down climate change,” says Corinne Le Quéré, a climate scientist at the University of East Anglia in Norwich, England. But how governments respond could be “a turning point if they focus on a green recovery, helping to avoid severe impacts from climate change.” 
    Carbon dioxide lingers in the atmosphere for a long time, making month-to-month changes in CO2 levels difficult to measure as they happen. Instead, the researchers looked at what drives some of those emissions — people’s movements. Using anonymized cell phone mobility data released by Google and Apple, Le Quéré and colleagues tracked changes in energy-consuming activities, like driving or shopping, to estimate changes in 10 greenhouse gases and air pollutants. 

    “Mobility data have big advantages” for estimating short-term changes in emissions, says Jenny Stavrakou, a climate scientist at the Royal Belgian Institute for Space Aeronomy in Brussels who wasn’t involved in the study. Since those data are continuously updated, they can reveal daily changes in transportation emissions caused by lockdowns, she says. “It’s an innovative approach.”
    Google’s mobility data revealed that 4 billion people reduced their travel by more than 50 percent in April alone. By adding more traditional emissions estimates to fill in gaps (SN: 5/19/20), the researchers analyzed emissions trends across 123 countries from February to June. The researchers found that the peak drop occurred in April, when globally averaged CO2 emissions and nitrogen oxides fell by roughly 30 percent from baseline, mostly due to reduced driving.
    Fewer greenhouse gases should result in some cooling of the atmosphere, but the researchers found that effect will be largely offset by the roughly 20 percent fall in sulfur aerosols in April. These industrial emissions reflect sunlight and thus have a cooling effect. With fewer shading aerosols, more of the sun’s energy can heat the atmosphere, causing warming. On the whole, the stark drop in emissions in April alone will cool the globe a mere 0.01 degrees Celsius over the next five years, the study finds.
    In the long-term, the massive, but temporary, shifts in behavior caused by COVID-19 won’t change our current warming trajectory. But large-scale economic recovery plans offer an opportunity to enact climate-friendly policies, such as invest in low-carbon technologies, that could avert the worst warming (SN: 11/26/19). That could help reach a goal of cutting total global greenhouse gas emissions by 52 percent by 2050, limiting warming to 1.5 degrees Celsius above preindustrial levels through 2050, the researchers say.

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