The starting point was data from 58 randomised controlled trials (RCTs) of over 10,000 patients worldwide with chronic low back pain. First, the data relevant to the topic were filtered out of the original manuscripts and then evaluated in groups. When evaluating these data, the researchers examined on the one hand whether and to what extent standard forms of treatment and individualised treatment differ in terms of the result. “Individualised” means that there is some type of personal coaching, where therapists specifically target the potentials and requirements of each patient and decide together with them how their therapy should look.

The study concluded that individualised treatment for chronic back pain led to a significantly increased effect in comparison to standard exercise therapies. The success rate in pain relief was 38 percent higher than with standard treatment. “The higher effort required for individual treatment is worthwhile because patients benefit to an extent that is clinically important,” says lead author Dr Johannes Fleckenstein from the Institute of Sport Sciences at Goethe University Frankfurt.

However, the study went even further. The research team in Frankfurt compared a third group of treatment methods alongside the standard and individualised ones. In this group, individualised training sessions were combined with cognitive behavioural therapy (CBT). This procedure — a type of talk therapy — is based on the assumption that negative thoughts and behaviours surrounding pain tend to exacerbate it. Through CBT, pain patients learn to change the way they handle it. They stop being afraid to move or are taught tactics for coping with pain. This makes them realise that they are by no means helpless. But what does the psychotherapeutic support through CBT actually contribute to the success of the treatment? Analysis of the data revealed the following: When an individualised approach and CBT were combined, the success rate in terms of pain relief was an impressive 84 percent higher than with standard treatment. The combined therapy, also called multimodal therapy, thus led to the best result by far.

Fleckenstein sees in the study “an urgent appeal to public health policy” to promote combined therapies both in terms of patient care and remuneration. “Compared to other countries, such as the USA, we are in a relatively good position in Germany. For example, we issue less prescriptions for strong narcotic drugs such as opiates. But the number of unnecessary X-rays, which, by the way, can also contribute to pain chronicity, and inaccurate surgical indications is still very high.” This is also due, according to Fleckenstein, to economic incentives, that is, the relatively high remuneration for such interventions. The situation is different for organisations working in the area of pain therapy, he says. Although these are not unprofitable, they are not a cash cow for investors either. In his view, it is important here to improve the economic conditions. After all, pain therapy saves a lot of money in the long run as far as health economics are concerned, whereas tablets and operations rarely lead to medium and long-term pain relief.

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Materials provided by Goethe University Frankfurt. Note: Content may be edited for style and length.

It means spending hours in the field listening for calls. Or leaving a recording device in the field to catch what sounds are made — only to spend hours later listening to that audio. Then, repeating this process until there’s enough information to start making population estimates.

But a new model developed by researchers at the University of Georgia aims to streamline this process. By using artificial intelligence to analyze terabytes of recordings for quail calls, the process gives wildlife managers the ability to gather the data they need in a matter of minutes.

“The model is very accurate, picking up between 80% and 100% of all calls even in the noisiest recordings. So, you could take a recording, put it through our model and it will tell you how many quail calls that the recorder heard,” said James Martin, an associate professor at the UGA Warnell School of Forestry and Natural Resources who has been working on the project, in collaboration with the Georgia Department of Natural Resources, for about five years. “This new model allows you to analyze terabytes of data in seconds, and what that will allow us to do is scale up monitoring, so you can literally put hundreds of these devices out and cover a lot more area and do so with a lot less effort than in the past.”

The software represents about five years of work by Martin, postdoctoral researcher Victoria Nolan and numerous key contributors who have worked with a code writer to create the model. It’s also part of a larger shift taking place in the field of wildlife research, where computer algorithms are now assisting with work that once took humans thousands of hours to complete.

Increasingly, computers are getting smarter at, for example, identifying specific noises or certain traits in photos and sound recordings. For researchers such as Martin, it means hours once spent on tasks such as listening to audio or looking at game camera images can now be done by a computer, freeing up valuable time to focus on other aspects of a project.

The new tool can also be a valuable resource for state and federal agencies looking for information on their quail populations, but with limited funds to spend on any one project. “So, I think this is something states might jump on as far as replacing their current monitoring with acoustic recording devices,” added Martin.

The software’s success was recently documented by the Journal of Remote Sensing in Ecology and Conservation.

As the software gets more use and is exposed to sounds from new geographic areas, Martin said, it gets even “smarter.” As it is, quail offer several different kinds of calls. But when the software is exposed to a variety of sounds that aren’t quail, he said, it’s better able to distinguish the correct calls from the ambient noises of the grasses and trees around them.

Over time, the software will grow more discerning.

“So that’s why you have to keep giving it training data, and when you move geographies, you encounter new sounds that you didn’t train the model for,” he added. “It’s always about adaption.”

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Materials provided by University of Georgia. Original written by Kristen Morales. Note: Content may be edited for style and length.

“This work expands the way we can look at brain activity during sleep,” said Prerau. “By moving beyond traditional notions that break up the complex continuum of sleep into specific categories and waveform classes, we can reveal new types of signals and dynamics that may be important for understanding brain health and disease.”

Scientists typically study brain activity during sleep using the electroencephalogram, or EEG, which measures brainwaves at the scalp. Starting in the mid 1930s, the sleep EEG was first studied by looking at the traces of brainwaves drawn on a paper tape by a machine. Many important features of sleep are still based on what people almost a century ago could most easily observe in the complex waveform traces. Even the latest machine learning and signal processing algorithms for detecting sleep waveforms are judged against their ability to recreate human observation. In this study, the researchers asked: What can we learn if we expand our notion of sleep brainwaves beyond what was historically easy to identify by eye?

One particularly important set of sleep brainwave events are called sleep spindles. These spindles are short oscillation waveforms, usually lasting less than 1-2 seconds, that are linked to our ability to convert short-term memories to long-term memories. Changes in spindle activity have been linked with numerous disorders such as schizophrenia, autism, and Alzheimer’s disease, as well as with natural aging. In this study, rather than looking for spindle activity according to the historical definition, the team developed a new approach to automatically extract tens of thousands of short spindle-like waveform events from the EEG data throughout the entire night. Next, instead of looking at the waveforms in terms of fixed sleep stages (i.e., Wake, REM, and non-REM stages 1-3) as standard sleep studies do, they characterized the full continuum of gradual changes that occur in the brain during sleep. Using all these data, the team created graphical representations called slow oscillation power and phase histograms, which provide a powerful visualization of the activity of all the waveforms as a function of continuous sleep depth and synchronized activity in the cortex. “This further demonstrates the richness of the information that traditional, manual scoring leaves on the table,” said co-author Shaun Purcell, PhD, of the Department of Psychiatry at the Brigham.

When the team looked at a group of healthy participants, each with two nights of sleep recordings, the patterns observed appeared to be almost like fingerprints — highly specific to each person with strong consistency across nights. These results suggest new ways in which brain activity differs from person to person, even within groups of healthy people selected as control groups. The researchers then compared the activity between the healthy subjects and a population of people with schizophrenia, a disorder that reduces spindle activity. Using their approach, the team not only saw the known differences in participants with schizophrenia, but also found differences in other spindle-like waveforms occurring at other frequencies in the brain. This suggests new potential EEG biomarkers of schizophrenia that could be useful in better understanding the mechanisms of the disorder and in the development of targeted treatments.

“This approach is really exciting,” said co-author Dara Manoach, PhD, of the Department of Psychiatry at Massachusetts General Hospital. “We look forward to seeing how we can enhance our understanding, not only of schizophrenia, but also of other neurodevelopmental disorders characterized by differences in sleep, such as autism and pediatric epilepsy.”

“We are just starting to understand the scope of neurodiversity that exists within the general population,” said Prerau. “If we can more accurately characterize the individual differences observed in both neurological health and disease, we can work towards improved diagnostics and treatments.”

Co-authors were Preetish Rath, Tom Possidente, Mingjian He, Shaun Purcell, Dara Manoach, and Robert Stickgold. Michael Prerau was the senior author.

This work was supported by the National Institute of Neurological Disorders and Stroke (R01NS096177) and the National Institute of Aging (R01AG054081 (MJP).

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Materials provided by Brigham and Women’s Hospital. Note: Content may be edited for style and length.

A new study from University of Utah distinguished professor Thure Cerling and colleagues, published in Proceedings of the National Academy of Sciences, uses carbon isotope science to show that the marked tusks were more than 30 years old and somehow had found their way from the guarded government stockpile into the hands of illegal ivory traders. The results suggest that governments that maintain ivory stockpiles may want to take a closer look at their inventory.

Ivory’s isotope signatures

Cerling is a pioneer in the use of isotopes to answer questions about physical and biological processes. “Isotopes” of a given element refer to atoms of the element that vary in their number of neutrons, and thus vary oh-so-slightly in mass. A carbon-14 isotope has one more neutron than carbon-13, for example.

Some isotopes are stable and some are unstable. Unstable isotopes decay into other isotopes or elements through radioactive decay. Since the rate of decay is known for unstable isotopes, we can use the amounts present in a sample to determine ages. That’s how carbon dating works — it uses the rate of decay of unstable carbon-14 to determine the age of organic matter.

Around a decade ago, Cerling attended a presentation at the U by Sam Wasser of the University of Washington, who was studying the genetics of wildlife and using those tools to investigate the date and place of wildlife poaching. Cerling, recognizing that his expertise in isotope science might be able to add useful information, began an ongoing collaboration with Wasser.

In 2016, Cerling, Wasser and colleagues published a study that addressed a key question in the ivory trade: how old is the ivory seized by governments? Some traders have claimed their ivory is old, taken before 1976, and thus exempt from sales bans. And with the average size of ivory seizures more than 2.5 tons, researchers, governments and conservationists wonder how much of the ivory is recent and how much is coming from criminal stockpiles — or is stolen from one of several ivory stockpiles held by the governments of some countries in Africa.

“Governments keep their stockpiles for multiple reasons,” Wasser says. “They hope to sell the ivory for revenue, sometimes to support conservation efforts. However, they can only sell ivory from elephants that died of natural causes or were culled because they were problem animals. They can’t sell seized ivory because they don’t know it came from the country.”

With the combination of Cerling’s isotope data and Wasser’s genetic data, the 2016 study found that more than 90% of seized ivory was from elephants that had been killed less than three years before. It was a sobering result, showing active and well-developed poaching and export networks. The study seemed to show that little ivory from government stockpiles had ended up on the black market.

Marked tusks

But the 2019 seizure of ivory in Uganda showed something concerning. Some of the tusks sported markings that looked suspiciously like the markings that CITES, the Convention on International Trade in Endangered Species of Wild Fauna and Flora, uses to inventory stockpiled ivory.

Due to the markings seen on some samples of the ivory,” Cerling says, “it was thought that quite a few samples in this shipment could be related to material held in a government stockpile in Burundi. We were asked to date samples from this, and three other recent ivory seizures, to see if some samples could possibly be from older stockpiles.”

To determine the ivory’s age, the researchers collected small samples from the tusks and analyzed them for the amount of carbon-14 isotopes in each sample. They were looking specifically for the amount of “bomb carbon” in the tusks. Between 1945 and 1963, nuclear weapons testing doubled the amount of carbon-14 in the atmosphere, so anything living that’s consumed carbon since then — including you — has a measurable carbon-14 signature. The amount of carbon-14 in a sample of ivory that hasn’t yet radioactively decayed can tell scientists when the ivory stopped growing, or when the elephant died.

The method takes some calibration, using samples from organisms living in the same area. Some of the samples came from schoolchildren in Kenya, through a program called “Kids and Goats for Elephants.” Because most families in rural Kenya keep goats the program, run by Cerling and Paula Kahumbu of WildlifeDirect engages children in collecting hair samples from goats for isotopic analysis. The isotope data is useful for many applications, including fighting elephant poaching and, in this case, calibrating the bomb carbon decay rate for more accurate dating of ivory.

A consequential result

The researchers analyzed ivory from four seizures in Angola, Hong Kong, Singapore and Uganda. Genetic data ensured that they weren’t sampling two tusks from the same individual. The results of analysis from the Angola, Hong Kong and Singapore seizures were as expected — the samples were mostly around three years after the death of the elephant, with no tusks having been taken more than 10 years previous.

But the Uganda seizure, with the inventory markings on the tusks, showed something very different. Nine of the 11 tusks tested had been taken more than 30 years before, with the dates of death ranging between 1985 and 1988. Those dates are consistent with the age of ivory in the stockpile of the government of Burundi, which was inventoried and stored in sealed containers in 1989.

“My suspicions were affirmed,” Wasser says. “The bigger surprise was how near to 1989 the elephants were killed.” At the time Burundi assembled its stockpile, a condition of joining CITES, which assists governments in managing ivory reserves, was that the ivory to be stockpiled was old. The results suggest that that wasn’t the case, Wasser says, which would have violated conditions for Burundi to join CITES.

“The hope is that CITES will request the stockpile to be re-inventoried,” Wasser says, “including aging randomly selected tusks and secure the remaining stocks.”