Computers Math

Materials science researchers have demonstrated the first electrically injected laser made with germanium tin. Used as a semiconducting material for circuits on electronic devices, the diode laser could improve micro-processing speed and efficiency at much lower costs. More

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.

advertisement

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.

make a difference: sponsored opportunity

Story Source:
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

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.

advertisement

“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.”

Story Source:
Materials provided by University of Exeter. Note: Content may be edited for style and length. More

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

A new study found that 77% of low- to moderate-income American households fall below the asset poverty threshold, meaning that if their income were cut off they would not have the financial assets to maintain at least poverty-level status for three months. More

Researchers have developed a new type of laser beam that doesn’t follow long-held principles about how light refracts and travels. The findings could have huge implications for optical communication and laser technologies. More

For a population that herds livestock across wide stretches of wild savanna, mobile phones are a boon to their economy and life. But few studies have investigated how this new technology is impacting the lives of women in Maasai communities, which are traditionally patriarchal. In family units where men exert significant control, often over multiple wives, it is important to understand how phones have impacted gender dynamics. More

A team of researchers have developed an algorithm through machine learning that helps predict sites of DNA methylation – a process that can change the activity of DNA without changing its overall structure – and could identify disease-causing mechanisms that would otherwise be missed by conventional screening methods. More