V1674 Herculis is a classical nova hosted by a white dwarf and dwarf companion and is currently the fastest classical nova on record. While studying V1674Her with the VLBA, Montana Williams, a graduate student at New Mexico Tech who is leading the investigation into the VLBA properties of this nova, confirmed the unexpected: non-thermal emission coming from it. This data is important because it tells Williams and her collaborators a lot about what’s happening in the system. What the team has found is anything but the simple heat-induced explosions scientists previously expected from classical novae.

“Classical novae have historically been considered simple explosions, emitting mostly thermal energy,” said Williams. “However, based on recent observations with the Fermi Large Area Telescope, this simple model is not entirely correct. Instead, it seems they’re a bit more complicated. Using the VLBA, we were able to get a very detailed picture of one of the main complications, the non-thermal emission.”

Very long baseline interferometry (VLBI) detections of classical novae with dwarf companions like V1674Her are rare. They’re so rare, in fact, that this same type of detection, with resolved radio synchrotron components, has been reported just one other time to date. That’s partly because of the assumed nature of classical novae.

“VLBI detections of novae are only recently becoming possible because of improvements to VLBI techniques, most notably the sensitivity of the instruments and the increasing bandwidth or the amount of frequencies we can record at a given time,” said Williams. “Additionally, because of the previous theory of classical novae they weren’t thought to be ideal targets for VLBI studies. We now know this isn’t true because of multi-wavelength observations which indicate a more complex scenario.”

That rarity makes the team’s new observations an important step in understanding the hidden lives of classical novae and what ultimately leads to their explosive behavior.

“By studying images from the VLBA and comparing them to other observations from the Very Large Array (VLA), Fermi-LAT, NuSTAR, and NASA-Swift, we can determine what might be the cause of the emission and also make adjustments to the previous simple model,” said Williams. “Right now, we’re trying to determine if the non-thermal energy is coming from clumps of gas running into other clumped gas which produces shocks, or something else.”

Because Fermi-LAT and Nu-Star observations had already indicated that there might be non-thermal emission coming from V1674Her, that made the classical nova an ideal candidate for study because Williams and her collaborators are on a mission to either confirm or deny those types of findings. It was also more interesting, or cute, as Williams puts it, because of its hyper-fast evolution, and because, unlike supernovae, the host system isn’t destroyed during that evolution, but rather, remains almost completely intact and unchanged after the explosion. “Many astronomical sources don’t change much over the course of a year or even 100 years. But this nova got 10,000 times brighter in a single day, then faded back to its normal state in just about 100 days,” she said. “Because the host systems of classical novae remain intact they can be recurrent, which means we might see this one erupt, or cutely explode, again and again, giving us more opportunities to understand why and how it does.”

The National Radio Astronomy Observatory (NRAO) is a major facility of the National Science Foundation (NSF) operated under cooperative agreement by Associated Universities, Inc.

Now, a new Cornell University-led study measures methane and carbon dioxide emissions from 30 small lakes and ponds (one acre or less) in temperate areas of Europe and North America, revealing that the smallest and shallowest bodies of water exhibit the greatest variability over time.

The paper marks an important step toward calibrating climate models so they better predict emissions from inland waterbodies, and it points to the need to study small waterbodies more closely.

“This study helps understand both the drivers of greenhouse gas concentrations, and importantly, what makes some ponds more variable in their concentrations,” said Meredith Holgerson, assistant professor of ecology and evolutionary biology and senior author of the study, published in the journal Limnology and Oceanography.

“The paper points to patterns across a broad geographic range, such that we can actually get in and predict which waterbodies are going to vary and will be most variable, and it confirms that we need to go out and sample frequently,” said Nicholas Ray, a postdoctoral researcher in Holgerson’s lab and the paper’s first author.

Holgerson and colleagues have previously estimated that shallow lakes and ponds may contribute 5% of the global methane emissions to the atmosphere. But without accurate measurements across many water bodies, they said, the true number could be as little as half or as much as twice that percentage.

While some small lakes and ponds emit greenhouse gasses in consistent, predictable amounts, others are highly variable. Understanding these dynamics is important as carbon dioxide and methane act as greenhouse gases in the atmosphere, with methane being 25 times more potent at trapping heat than carbon dioxide.

Each body of water analyzed was sampled over the 2018 and 2019 summers at three times in three locations, including the deepest point and then two locations on opposite ends (but not too close to the shore).

“One key result we found was that the smaller the system is, in regard to surface area, the higher emissions are likely to be,” Ray said.

For carbon dioxide, samples were consistent in all parts of the waterbody, which revealed that researchers likely only needed to collect a sample from one location to get an accurate prediction of the whole body of water. Methane, on the other hand, required samples from multiple locations to get an accurate measure. Also, for methane, shallower systems were more variable, suggesting stratification of the water column in deeper water may prevent gases from rising to the surface.

For carbon dioxide, the amount of plant life in the water played a large role in variability over time. For methane, variability was more driven by the water depth and likely associated with stratification in the water column.

Among other uses, the study sets the groundwork for informing a New York state climate mitigation strategy to build more ponds to help farmers better handle droughts.

“We’re working to identify how ponds can be built, or if there are simple management strategies people can employ, to minimize emissions,” Ray said.

This easy-to-make sponge-jamming device can help stiff robots handle delicate items carefully by mimicking the nuanced touch, or variable stiffness, of a human.

Robots can skip, jump and do somersaults, but they’re too rigid to hold an egg easily. Variable-stiffness devices are potential solutions for contact compliance on hard robots to reduce damage, or for improving the load capacity of soft robots.

This study, published at the IEEE International Conference on Robotics and Automation (ICRA) 2023, shows that variable stiffness can be achieved by a silicone sponge.

Lead author Tianqi Yue from Bristol’s Department of Engineering Mathematics explained: “Stiffness, also known as softness, is important in contact scenarios.

“Robotic arms are too rigid so they cannot make such a soft human-like grasp on delicate objects, for example, an egg.

“What makes humans different from robotic arms is that we have soft tissues enclosing rigid bones, which act as a natural mitigating mechanism.

“In this paper, we managed to develop a soft device with variable stiffness, to be mounted on the end robotic arm for making the robot-object contact safe.”

Silicone sponge is a cheap and easy-to-fabricate material. It is a porous elastomer just like the cleaning sponge used in everyday tasks.

By squeezing the sponge, the sponge stiffens which is why it can be transformed into a variable-stiffness device.

This device could be used in industrial robots in scenarios including gripping jellies, eggs and other fragile substances. It can also be used in service robots to make human-robot interaction safer.

Mr Yue added: “We managed to use a sponge to make a cheap and nimble but effective device that can help robots achieve soft contact with objects. The great potential comes from its low cost and light weight.

“We believe this silicone-sponge based variable-stiffness device will provide a novel solution in industry and healthcare, for example, tunable-stiffness requirement on robotic polishing and ultrasound imaging.”

The team will now look at making the device achieve variable stiffness in multiple directions, including rotation.

To bring relief to this and other problems, University of Waterloo researchers have created a new type of wound dressing material using advanced polymers. This new dressing could enhance the healing process for burn patients and have potential applications for drug delivery in cancer treatment as well as in the cosmetic industry.

“To treat burn victims, we can customize the shape using a 3D printer, secondly, the material has fine-tuned surface adhesion, which is a key feature,” said Dr. Boxin Zhao, a professor in Waterloo’s Department of Chemical Engineering, whose team has made significant strides in developing intelligent hydrogel materials for use as a reusable wound dressing. “The material can easily adhere to the skin and be taken off. It’s a very delicate balance within the material to make the adhesion work.”

In developing the dressing, the researchers conducted a 3D scan of the patient’s face and body parts to customize it to an individual’s needs. This enables the dressing to make good contact with surfaces like noses and fingers, making it ideal for creating personalized wound dressings for burn patients.

The material also has applications for cancer treatment. In traditional chemotherapy treatment, a patient may need to be in a clinic for hours, which can be tiring and uncomfortable. This dressing can provide a constant drug release outside the clinic setting, alleviating some of the challenges associated with traditional methods.

The material used to create these smart dressings includes a biopolymer derived from seaweed, a thermally responsive polymer, and cellulose nanocrystals. The dressing’s thermal responsiveness allows it to warm on the skin and gently lower to room temperature. Additionally, when chilled in the fridge, the dressing expands but shrinks to a smaller size at body temperature, making it easier and less painful to remove. Also, the dressing is designed to provide time-release medication, allowing for longer-lasting pain relief.

“We also envision applications in the beauty and cosmetic industry,” said Zhao, Waterloo’s Endowed Chair in Nanotechnology. “Cosmetologists can utilize 3D scanning technology to analyze their clients’ facial features and customize hydrogel masks infused with specific facial and skin regimen products. Additionally, this innovative approach can benefit plastic surgeons.”

This research is proof of concept for Zhao’s Surface Science and Bio- nanomaterials Laboratory Group. The next step for Zhao’s research group is to continue improving the material’s properties to make it healthier and commercially viable.

A study highlighting the team’s progress was recently published in the Journal of Colloids and Interface Science.

It is well-known that the dopamine system is implicated in signaling reward-related information as well as in actions that generate rewarding outcomes. This can be investigated using either Pavlovian and operant conditioning experiments. Pavlovian conditioning describes how your brain makes an association between two situations or stimuli that previously seemed unrelated. A famous example is Pavlov’s experiment, where a dog heard a sound before receiving food. After several such pairings of the sound with food delivery, the sound alone began to cause the dog to salivate. Operant conditioning, or instrumental learning, differs from this in that the behavior of an individual is important to earn a food reward. Meaning that the individual after hearing a sound, has to perform a so-called operant action to receive the reward. In animal experiments, such a operant response is often the pressing of a lever.

Dopamine measurements in nucleus accumbens

In the final PhD paper of Jessica Goedhoop in collaboration with Tara Arbab and Ingo Willuhn from the Netherlands Institute for Neuroscience, they take a closer look at the role of dopamine signaling in learning and motivation. The team directly compared the two conditioning paradigms: male rats underwent either Pavlovian or operant conditioning while dopamine release was measured in the nucleus accumbens, a brain region central for processing this information. During the experiments a cue light was illuminated for a duration of 5 seconds. For the Pavlovian group, a food pellet was delivered into the reward magazine directly after the cue light turned off. For the operant conditioning group, turning off the cue light was followed by extension of the lever below the cue light into the operant box. The lever was retracted after one lever press, which immediately resulted in the delivery of one food pellet reward into the food magazine. If there was no lever press within 5 seconds after lever extension, the lever was retracted and no reward was delivered.

Sustained dopamine release in operant conditioning

Rats in both groups released the same quantity of dopamine at the onset of the reward-predictive cue. However, only the operant-conditioning group showed a subsequent, sustained plateau in dopamine concentration throughout the entire 5-second cue presentation (throughout cue presentation and before lever press). This dopamine sustainment was observed reliably and consistently throughout systematic manipulation of experimental parameters and behavioral training. Therefore, the researchers believe that sustained dopamine levels may be an intermediate between learning and action, conceptually related to the motivation to generate a reward-achieving action.

Ingo Willuhn: ‘There have been a lot of studies on dopamine. We have a decent idea of when dopamine is released in the brain, but there is still lots of discussion on what the precise variables are that determine such dopamine signaling. Essentially discussion on what dopamine “means.” To investigate this, scientists usually perform either Pavlovian or operant conditioning experiments. But they test slightly different things. Both have to do with learning an association between a neutral stimulus and a reward. But operant conditioning requires the motivation to perform an action in addition to that (to earn the reward). Therefore, we compared the two types of conditioning in the same experiment.’

Adding a piece to the puzzle

‘Our results bring together the two camps of scientists that often battle with each other: one says that dopamine is a so-called reward-prediction error signal, meaning that dopamine is released when something better than expected happens, and is suppressed when something worse than expected happens. It is a learning (or teaching) signal. The other camp says that this is not true. They say that dopamine has something to do with motivation. Increased dopamine release will invigorate the subjects and they work harder to get the reward. There have been a few attempts in the past to bring these two camps together, but there is still need for more knowledge on the subject.’

‘What we saw in our study is that only in the operant-learning task dopamine levels stayed high. It seems that the motivation is encoded in this plateau. Reward prediction is the initial dopamine peak, but how much the signal stays up, reflects motivation. Thus, our paper suggests that there is a possibility that dopamine is involved in both, learning and motivation. The next steps will be to get more details out of this. We need to replicate the experiments and make them more sophisticated. The more sophisticated you make it, the more precise our predictions have to be. We are going to build on it and see whether it still holds up.’

Implications

‘Dopamine is not only involved in everyday life but also in disorders such as addiction, Parkinson’s disease, and schizophrenia. Because of the two camps existing, there is disagreement about what happens exactly. For example, some researchers say that when addicts take drugs dopamine release increases and as a consequence all the environmental cues become more meaningful. Addicts learn that these cues are associated with the drug and they take more and more drug, because they are constantly reminded of the drug everywhere. In this view, addiction is misguided learning. Other researchers would say that motivation to take the drug intensifies with more frequent drug intake, because the drug elevates dopamine release. This study indicates that it may be both. Depending on the precise timing, both systems could be the driver, and both could be involved.’

‘This is also relevant for the clinic. Prescribed drugs can influence both learning and motivation systems at the same time: and then it can get messy. If you give schizophrenic patients classic antipsychotic medication, they become slow and cannot act much because their motivation system is down. Parkinson’s patients take pro-dopamine drugs essentially because they lost their dopamine, but some patients start to gamble because their dopamine system is on overdrive suddenly. We cannot influence learning and motivation components separately. As soon as you give a drug it is going to hit all of it, so it is good to keep that in mind.’