Between 2001 and 2011, various teams of astronomers studied the mysterious massive star, located in the Kinman Dwarf galaxy, and their observations indicated it was in a late stage of its evolution. Allan and his collaborators in Ireland, Chile and the US wanted to find out more about how very massive stars end their lives, and the object in the Kinman Dwarf seemed like the perfect target. But when they pointed ESO’s VLT to the distant galaxy in 2019, they could no longer find the telltale signatures of the star. “Instead, we were surprised to find out that the star had disappeared!” says Allan, who led a study of the star published today in Monthly Notices of the Royal Astronomical Society.

Located some 75 million light-years away in the constellation of Aquarius, the Kinman Dwarf galaxy is too far away for astronomers to see its individual stars, but they can detect the signatures of some of them. From 2001 to 2011, the light from the galaxy consistently showed evidence that it hosted a ‘luminous blue variable’ star some 2.5 million times brighter than the Sun. Stars of this type are unstable, showing occasional dramatic shifts in their spectra and brightness. Even with those shifts, luminous blue variables leave specific traces scientists can identify, but they were absent from the data the team collected in 2019, leaving them to wonder what had happened to the star. “It would be highly unusual for such a massive star to disappear without producing a bright supernova explosion,” says Allan.

The group first turned the ESPRESSO instrument toward the star in August 2019, using the VLT’s four 8-metre telescopes simultaneously. But they were unable to find the signs that previously pointed to the presence of the luminous star. A few months later, the group tried the X-shooter instrument, also on ESO’s VLT, and again found no traces of the star.

“We may have detected one of the most massive stars of the local Universe going gently into the night,” says team-member Jose Groh, also of Trinity College Dublin. “Our discovery would not have been made without using the powerful ESO 8-metre telescopes, their unique instrumentation, and the prompt access to those capabilities following the recent agreement of Ireland to join ESO.” Ireland became an ESO member state in September 2018.

The team then turned to older data collected using X-shooter and the UVES instrument on ESO’s VLT, located in the Chilean Atacama Desert, and telescopes elsewhere.”The ESO Science Archive Facility enabled us to find and use data of the same object obtained in 2002 and 2009,” says Andrea Mehner, a staff astronomer at ESO in Chile who participated in the study. “The comparison of the 2002 high-resolution UVES spectra with our observations obtained in 2019 with ESO’s newest high-resolution spectrograph ESPRESSO was especially revealing, from both an astronomical and an instrumentation point of view.”

The old data indicated that the star in the Kinman Dwarf could have been undergoing a strong outburst period that likely ended sometime after 2011. Luminous blue variable stars such as this one are prone to experiencing giant outbursts over the course of their life, causing the stars’ rate of mass loss to spike and their luminosity to increase dramatically.

Based on their observations and models, the astronomers have suggested two explanations for the star’s disappearance and lack of a supernova, related to this possible outburst. The outburst may have resulted in the luminous blue variable being transformed into a less luminous star, which could also be partly hidden by dust. Alternatively, the team says the star may have collapsed into a black hole, without producing a supernova explosion. This would be a rare event: our current understanding of how massive stars die points to most of them ending their lives in a supernova.

Future studies are needed to confirm what fate befell this star. Planned to begin operations in 2025, ESO’s Extremely Large Telescope (ELT) will be capable of resolving stars in distant galaxies such as the Kinman Dwarf, helping to solve cosmic mysteries such as this one.

Story Source:

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

Implants, such as pacemakers or insulin pumps, are a regular part of modern medicine. However, it is not uncommon for complications to arise after implantation. The immune system identifies the implant as a foreign body and attempts to remove it. “This is actually a completely natural and useful reaction by the immune system,” says Professor Thomas Groth, a biophysicist at MLU. It helps to heal wounds and kills harmful pathogens. If this reaction does not subside on its own after a few weeks, it can lead to chronic inflammation and more serious complications. “The immune system attracts various cells that try to isolate or remove the foreign entity. These include macrophages, a type of phagocyte, and other types of white blood cells and connective tissue cells,” explains Groth. Implants can become encapsulated by connective tissue, which can be very painful for those affected. In addition, the implant is no longer able to function properly. Drugs that suppress the immune response in a systemic manner are often used to treat chronic inflammation, but may have undesired side effects.

Thomas Groth’s team was looking for a simple way to modify the immune system’s response to an implant in advance. “This is kind of tricky, because we obviously do not want to completely turn off the immune system as its processes are vital for healing wounds and killing pathogens. So, in fact we only wanted to modulate it,” says the researcher. To do this, his team developed a new coating for implants that contains anti-inflammatory substances. For their new study, the team used two substances that are already known to have an anti-inflammatory effect: heparin and hyaluronic acid.

In the laboratory, the scientists treated a surface with the two substances by applying a layer that was only a few nanometres thick. “The layer is so thin that it does not affect how the implant functions. However, it must contain enough active substance to control the reaction of the immune system until the inflammatory reaction has subsided,” adds Groth. In cell experiments, the researchers observed how the two substances were absorbed by the macrophages, thereby reducing inflammation in the cell cultures. The untreated cells showed clear signs of a pronounced inflammatory reaction. This is because the active substances inside the macrophages interfere with a specific signalling pathway that is crucial for the immune response and cell death. “Both heparin and hyaluronic acid prevent the release of certain pro-inflammatory messenger substances. Heparin is even more effective because it can be absorbed by macrophage cells,” Groth concludes.

So far, the researchers have only tested the method on model surfaces and in cell cultures. Further studies on real implants and in model organisms are to follow.

Story Source:

Materials provided by Martin-Luther-Universität Halle-Wittenberg. Note: Content may be edited for style and length.

Specifically, the method takes nonpolite directives or requests — those that use either impolite or neutral language — and restructures them or adds words to make them more well-mannered. “Send me the data,” for instance, might become “Could you please send me the data?”

The researchers will present their study on politeness transfer at the Association for Computational Linguistics annual meeting, which will be held virtually beginning July 5.

The idea of transferring a style or sentiment from one communication to another — turning negative statements positive, for instance — is something language technologists have been doing for some time. Shrimai Prabhumoye, a Ph.D. student in CMU’s Language Technologies Institute (LTI), said performing politeness transfer has long been a goal.

“It is extremely relevant for some applications, such as if you want to make your emails or chatbot sound more polite or if you’re writing a blog,” she said. “But we could never find the right data to perform this task.”

She and LTI master’s students Aman Madaan, Amrith Setlur and Tanmay Parekh solved that problem by generating a dataset of 1.39 million sentences labeled for politeness, which they used for their experiments.

The source of these sentences might seem surprising. They were derived from emails exchanged by employees of Enron, a Texas-based energy company that, until its demise in 2001, was better known for corporate fraud and corruption than for social niceties. But half a million corporate emails became public as a result of lawsuits surrounding Enron’s fraud scandal and subsequently have been used as a dataset for a variety of research projects.

But even with a dataset, the researchers were challenged simply to define politeness.

“It’s not just about using words such as ‘please’ and ‘thank you,'” Prabhumoye said. Sometimes, it means making language a bit less direct, so that instead of saying “you should do X,” the sentence becomes something like “let us do X.”

And politeness varies from one culture to the next. It’s common for native North Americans to use “please” in requests to close friends, but in Arab culture it would be considered awkward, if not rude. For their study, the CMU researchers restricted their work to speakers of North American English in a formal setting.

The politeness dataset was analyzed to determine the frequency and distribution of words in the polite and nonpolite sentences. Then the team developed a “tag and generate” pipeline to perform politeness transfers. First, impolite or nonpolite words or phrases are tagged and then a text generator replaces each tagged item. The system takes care not to change the meaning of the sentence.

“It’s not just about cleaning up swear words,” Prabhumoye said of the process. Initially, the system had a tendency to simply add words to sentences, such as “please” or “sorry.” If “Please help me” was considered polite, the system considered “Please please please help me” even more polite.

But over time the scoring system became more realistic and the changes became subtler. First person singular pronouns, such as I, me and mine, were replaced by first person plural pronouns, such as we, us and our. And rather than position “please” at the beginning of the sentence, the system learned to insert it within the sentence: “Could you please send me the file?”

Prabhumoye said the researchers have released their labeled dataset for use by other researchers, hoping to encourage them to further study politeness.

###

In addition to the students, the study’s co-authors included several professors from the LTI and the Machine Learning Department — Barnabas Poczos, Graham Neubig, Yiming Yang, Ruslan Salakhutdinov and Alan Black. The Air Force Research Laboratory, Office of Naval Research, National Science Foundation, Apple and NVIDIA supported this research.