from the world's big
Nom-nom or dinner call? Silverbacks sing as they eat.
Dominant wild silverbacks wax musical with their mouths full.
- Recent recordings of gorillas singing as they eat add the species to a lengthening list of musical eaters in the animal kingdom.
- Two types of songs have been recorded: a hum, and, well, gorilla improv.
- It's suspected that spoken language may begin with songs.
Sing for your supper, and you'll get breakfast
Songbirds always eat — Moss Hart, The Boys from Syracuse, 1938
Gorillas, too, apparently. Primatologist Eva Luef of the Max Planck Institute for Ornithology in Seewiesen, Germany recently observed — and recorded — two wild western lowland gorillas in the Republic of Congo singing as they ate. The late primatologist Dian Fossey previously described the phenomenon as "belch vocalizations," which sounds about right, especially after our recent Thanksgiving. The new research, however, for the first time ties it to specific behaviors. Luef's finding are published in PLOS ONE.
Leuf actually caught two type of vocalizations in the dominant silverback blackback adolescent males she observed. It seems that in the wild, they're the only community members with singing rights. In wild primate cultures with less rigid hierarchies, notably chimps and bonobos, everybody gets to chime in at mealtime.
Though in captivity things are a bit different, Ali Vella-Irving of the Toronto Zoo is hardly surprised by Leuf's discovery, she tells New Scientist. She hears this kind of singing all the time. "Each gorilla has its own voice: you can really tell who's singing. And if it's their favorite food, they sing louder."
This jibes with what Luef found: The gorillas seem to be inspired only by their preferred food. She found that "aquatic vegetation or seeds elicited a lot of food calls. And… they never called when they were eating insects like termites or ants." Because of course.
One of Leuf's silverbacks emitted a low-frequency hum as he ate. The scientist hypothesizes two possible meanings for the hum. First, it sounds like a noise of satisfaction. Secondly, says Leuf, since "He's the one making the collective decisions for the group. We think he uses this vocalization to inform the others 'OK, now we're eating.'" Others have suggested it may mean, "Go away, I'm eating here."
Singing the praises of a meal
Another silverback was more improvisatory, singing higher in pitch, and continually giving voice to a series of melodies that didn't repeat. The suspicion is that this ever-evolving ditty was simply a happy tune accompanying happy eating.
The variety of songs and the individuals allowed to sing them among different species provides further insight into the way language incorporating visual signals and sounds may evolve. Earlier research suggests that songs may represent a precursor of our spoken language.These variations also provide clues into each species' social structures, as psychologist Zanna Clay explains: "We think food calls are a very social signal; it's about coordinating feeding events with others. So in gorillas you get the dominant male producing the calls, because he has to keep hold of all the females in his group."
If music is a universal language as is often claimed, does this finding suggest "nom-nom" is a universal song?
What would it be like to experience the 4th dimension?
Physicists have understood at least theoretically, that there may be higher dimensions, besides our normal three. The first clue came in 1905 when Einstein developed his theory of special relativity. Of course, by dimensions we’re talking about length, width, and height. Generally speaking, when we talk about a fourth dimension, it’s considered space-time. But here, physicists mean a spatial dimension beyond the normal three, not a parallel universe, as such dimensions are mistaken for in popular sci-fi shows.
Duke University researchers might have solved a half-century old problem.
- Duke University researchers created a hydrogel that appears to be as strong and flexible as human cartilage.
- The blend of three polymers provides enough flexibility and durability to mimic the knee.
- The next step is to test this hydrogel in sheep; human use can take at least three years.
Duke researchers have developed the first gel-based synthetic cartilage with the strength of the real thing. A quarter-sized disc of the material can withstand the weight of a 100-pound kettlebell without tearing or losing its shape.
Photo: Feichen Yang.<p>That's the word from a team in the Department of Chemistry and Department of Mechanical Engineering and Materials Science at Duke University. Their <a href="https://onlinelibrary.wiley.com/doi/abs/10.1002/adfm.202003451" target="_blank">new paper</a>, published in the journal,<em> Advanced Functional Materials</em>, details this exciting evolution of this frustrating joint.<br></p><p>Researchers have sought materials strong and versatile enough to repair a knee since at least the seventies. This new hydrogel, comprised of three polymers, might be it. When two of the polymers are stretched, a third keeps the entire structure intact. When pulled 100,000 times, the cartilage held up as well as materials used in bone implants. The team also rubbed the hydrogel against natural cartilage a million times and found it to be as wear-resistant as the real thing. </p><p>The hydrogel has the appearance of Jell-O and is comprised of 60 percent water. Co-author, Feichen Yang, <a href="https://today.duke.edu/2020/06/lab-first-cartilage-mimicking-gel-strong-enough-knees" target="_blank">says</a> this network of polymers is particularly durable: "Only this combination of all three components is both flexible and stiff and therefore strong." </p><p> As with any new material, a lot of testing must be conducted. They don't foresee this hydrogel being implanted into human bodies for at least three years. The next step is to test it out in sheep. </p><p>Still, this is an exciting step forward in the rehabilitation of one of our trickiest joints. Given the potential reward, the wait is worth it. </p><p><span></span>--</p><p><em>Stay in touch with Derek on <a href="http://www.twitter.com/derekberes" target="_blank">Twitter</a>, <a href="https://www.facebook.com/DerekBeresdotcom" target="_blank">Facebook</a> and <a href="https://derekberes.substack.com/" target="_blank">Substack</a>. His next book is</em> "<em>Hero's Dose: The Case For Psychedelics in Ritual and Therapy."</em></p>
An algorithm may allow doctors to assess PTSD candidates for early intervention after traumatic ER visits.
- 10-15% of people visiting emergency rooms eventually develop symptoms of long-lasting PTSD.
- Early treatment is available but there's been no way to tell who needs it.
- Using clinical data already being collected, machine learning can identify who's at risk.
The psychological scars a traumatic experience can leave behind may have a more profound effect on a person than the original traumatic experience. Long after an acute emergency is resolved, victims of post-traumatic stress disorder (PTSD) continue to suffer its consequences.
In the U.S. some 30 million patients are annually treated in emergency departments (EDs) for a range of traumatic injuries. Add to that urgent admissions to the ED with the onset of COVID-19 symptoms. Health experts predict that some 10 percent to 15 percent of these people will develop long-lasting PTSD within a year of the initial incident. While there are interventions that can help individuals avoid PTSD, there's been no reliable way to identify those most likely to need it.
That may now have changed. A multi-disciplinary team of researchers has developed a method for predicting who is most likely to develop PTSD after a traumatic emergency-room experience. Their study is published in the journal Nature Medicine.
70 data points and machine learning
Image source: Creators Collective/Unsplash
Study lead author Katharina Schultebraucks of Columbia University's Department Vagelos College of Physicians and Surgeons says:
"For many trauma patients, the ED visit is often their sole contact with the health care system. The time immediately after a traumatic injury is a critical window for identifying people at risk for PTSD and arranging appropriate follow-up treatment. The earlier we can treat those at risk, the better the likely outcomes."
The new PTSD test uses machine learning and 70 clinical data points plus a clinical stress-level assessment to develop a PTSD score for an individual that identifies their risk of acquiring the condition.
Among the 70 data points are stress hormone levels, inflammatory signals, high blood pressure, and an anxiety-level assessment. Says Schultebraucks, "We selected measures that are routinely collected in the ED and logged in the electronic medical record, plus answers to a few short questions about the psychological stress response. The idea was to create a tool that would be universally available and would add little burden to ED personnel."
Researchers used data from adult trauma survivors in Atlanta, Georgia (377 individuals) and New York City (221 individuals) to test their system.
Of this cohort, 90 percent of those predicted to be at high risk developed long-lasting PTSD symptoms within a year of the initial traumatic event — just 5 percent of people who never developed PTSD symptoms had been erroneously identified as being at risk.
On the other side of the coin, 29 percent of individuals were 'false negatives," tagged by the algorithm as not being at risk of PTSD, but then developing symptoms.
Image source: Külli Kittus/Unsplash
Schultebraucks looks forward to more testing as the researchers continue to refine their algorithm and to instill confidence in the approach among ED clinicians: "Because previous models for predicting PTSD risk have not been validated in independent samples like our model, they haven't been adopted in clinical practice." She expects that, "Testing and validation of our model in larger samples will be necessary for the algorithm to be ready-to-use in the general population."
"Currently only 7% of level-1 trauma centers routinely screen for PTSD," notes Schultebraucks. "We hope that the algorithm will provide ED clinicians with a rapid, automatic readout that they could use for discharge planning and the prevention of PTSD." She envisions the algorithm being implemented in the future as a feature of electronic medical records.
The researchers also plan to test their algorithm at predicting PTSD in people whose traumatic experiences come in the form of health events such as heart attacks and strokes, as opposed to visits to the emergency department.