Stories are an important indicator of our vision of the future. In the case of artificial intelligence (AI), dominant stories are polarized between notions of threat and myopic solutionism. The central storytellers—big tech, popular media, and authors of science fiction—represent particular demographics and motivations. Many stories, and storytellers, are missing. This paper details the accounts of missing AI narratives by leading scholars from a range of disciplines interested in AI Futures. Participants focused on the gaps between dominant narratives and the untold stories of the capabilities, issues, and everyday realities of the technology. One participant proposed a “story crisis” in which these narratives compete to shape the public discourse on AI. Our findings indicate that dominant narratives distract and mislead public understandings and conceptions of AI. This suggests a need to pay closer attention to missing AI narratives. It is not simply about telling new stories, it is about listening to existing stories and asking what is wanted from AI. We call for realistic, nuanced, and inclusive stories, working with and for diverse voices, which consider (1) story-teller; (2) genre, and (3) communicative purpose. Such stories can then inspire the next generation of thinkers, technologists, and storytellers. Since the dawn of language, we have made sense of the world through stories, narrating our experiences so as to understand the past, present, and future Mead . It contNarratives about the potential of AI vary from works of science fiction and corporate marketing by big tech firms to subtler storytelling told by scholars and public intellectuals in popular academic books and documentaries e.g., Noble , CrawforTwo prominent voices in AI narratives research are Stephen Cave and Kanta Dihal , in which leading scholars from a range of domains including AI and Computing, Science and Technology Studies, Interactive Media, Literature, Education and AI Ethics describe common or dominant narratives, perceived by them to influence the public discourse on AI, before exploring missing or alternative narratives under the rubric of novel, nuanced and niche. To do this, we first discuss current AI narrative research. We then introduce our conceptual framework and empirical methods and present our findings about missing, nuanced narratives. As ‘dominant narratives’ are known, polarised, and established, participants use them as a foundation on which to build the future direction of AI discourse and development. We find that what is missing are narratives and stories about what people might want or hope for from AI in their everyday life; these niche stories are often suppressed because they are less sensational, and hence less valuable in capturing attention and advertising revenue. We argue that central to the promotion of missing narratives is a question about who ought to feature in them and who is telling them and why, e.g., the way power operates dictates the way stories develop and inspire. It seems critical to consider who controls the dominant narratives, and who stands to gain from these narratives (this may often be the same person — consider Zuckerberg or Musk). There is then the need to make it harder for those who benefit from the narratives to influence what the dominant narratives are  online interviews with academic leaders and scholars in AI and AI futures and related domains including literature, media and engineering. A full list of these disciplines can be found in previously published work, Table This paper reports on the findings from  the commonly told stories or dominant narratives in AI? (2) the extent to which these may or may not differ from reality; (3) who features in the narratives?; and (4) who is telling stories about AI? We also asked which narratives were missing or diminished. Appendix 1, provides a complete interview schedule with questions and themes for a semi-structured style interview conducted online in the summer of 2020.Deductive and inductively coded thematic analysis  attribute magical qualities. With the first, AI is often positioned as the answer to all the world's problems seen in its application to grand challenges . With the second, AI is a form of voodoo, rooted in fiction, appealing to our imagination and fantasies. On the one hand, this might help us make sense of what we want and on the other distract us from clarifying the role AI could take; one person’s utopia could well be someone else’s dystopia existentially spectacular, where AI will take over and surpass human intelligence and become super-intelligent, or (4) individually threatening, where AI is portrayed and understood as a risk to our livelihoods and safety. Positioned at either ends of a spectrum, these positive and negative narratives leave an intervening space that might be filled with more nuanced and novel stories. In turn, these alternative, and currently missing, narratives can provide a more realistic means to understand and anticipate a beneficial and equitably future.Experts suggest that negative stories about AI are either (3) I think narratives polarise and I think that’s unhelpful because for me the debate needs to happen in a way that allows us to acknowledge that in almost every instance that we’re going to be using these sorts of methods and technologies, there will be things that we conventionally think of as ethically good or bad, those things will be entangled still. [Social Science 10]Participants unanimously felt that such polarisation was unhelpful and in almost every case the result of hyped-up storytelling by the media, science fiction writers, or big tech companies who want to portray a particular view of a ‘good or bad use’ of technology, to attract (monetized) attention  and looked to say something deeper about AI Futures. Nuanced and novel stories are missing, they claim. We first explore what those dominant narratives are as described by participants before discussing missing narratives.You can talk a lot about different aspects of how an AGI might emerge and how this might cause negative outcomes and you can paint some very vivid pictures with thought experiments and... I don’t know if you’ve read Max Tegmark’s book or Nick Bostrom’s book but they have wonderful stories within that. So that makes it a very easy thing to talk about but just because it’s simple and easy doesn’t necessarily mean that that’s actually where the greatest risk lies. [Arts and Humanities 15]Analysis reveals antipathy towards ‘commonly told’ AI narratives, this includes a tendency towards a dismantling of 'grand narratives' of AI and the very term AI as a placeholder. In particular, participants expressed that dominant views of AI are polarised as a result of the portrayals propagated by the narratives we read, watch and consume via popular culture. Such views distract the public from understanding the current capabilities of the technology, which, while entertaining, may also be disproportionate and disruptive. For instance, superintelligence and existential risk narratives while important, may distract from the pressing issues of the day:Well, certainly I would say in Hollywood and science fiction the two main narratives are basically the Pinocchio story which is like Commander Data in Star Trek Next Generation, the mechanical thing that wants to become a real boy, wants to have human emotion, wants to become human and then there’s The Terminator scenario or somehow the AI uprising scenario. Both of those are much more about people than they are about AI, they’re not really rooted in reality because I think of artificial intelligence primarily as a set of tools for building software so in that sense it’s a different kind of software engineering and I think whenever you’re thinking about an AI system and a story, if you replace the word AI with the word software does it still make sense? [Science 03]Dominant narratives were for most, based not on the reality of the state of the technology, but on the speculative imagination of science fiction authors:Figure The following section describes some of the ways in which participants described these dominant narratives about AI. The findings provide a backdrop context from which participants navigate toward new narratives. The dominant narratives frame the other findings that follow. Future research might test out the prevalence or accuracy of the dominant narratives as perceived by a broader set of experts.I think the narrative of either the inventor in the garage narrative, or I think much more common now, the couple at multinationals who are using data, violating privacy, kind of generating surveillance capitalism and so on, all of that kind of rise of corporatism and the loss of the individual and the weakening of the state, dovetailed to the building of this very powerful technologies. [Social Science 10]First to emerge from the interviews was the extent to which participants felt that narratives about surveillance and privacy would be at the forefront of people’s minds when thinking about AI:Big Hack and the Social Network Dilemma, describing the Facebook and Cambridge Analytica scandal and asking “How can we all be anonymous and safe?” [Social Science 17].Several mentioned stories relating to the manipulation of data, such as those depicted on screen in the We obviously are seeing some of those stories, I think mostly about AI in policing and surveillance but also obviously AI in advertising and, you know, optimising the wrong thing. [Arts and Humanities 03]Most felt that the benefits of AI came at a cost. This was closely linked to concerns about loss of power, freedom and manipulation:What do we want from AI? The clearer question is who do we include in that ‘we’. Does it impact upon some groups more than others? [Arts and Humanities 04]In particular, everyday privacy was a concern when considering the effects on vulnerable or marginalised groups in society:For me, every use of AI is a subversive one, right, which is, and has to be one about fixing a broken system, so to speak, or replacing a broken system with something else. Narratives need to support that. [Arts and Humanities 09]Experts felt they had a role in ensuring that those inequalities were exposed and subverted. Stories about AI, ought to serve a broader social purpose to tackle these concerns.In a sense, these findings reflect their roles as experts as less is known about how the public relate concerns about privacy with AI. There were also comments that AI was subject to so much hype that it was often presented as the solution to all problems.Highly anthropomorphised machine in a robot body that is humanoid either as a servant or as a warrior killer robot kind of Terminator type story but these are the things that kind of serve us or live with us or trying to exterminate us but they are very much, kind of embodied, I think that’s a very common and very problematic narrative. [Arts and Humanities 12]This theme associated AI narratives with embodied, anthropomorphised robots. Associations made to Frankenstein’s monster, non-human, cyborgs were coupled with predominantly negative connotations, such as ‘disturbing’, ‘misleading’, and ‘killing’, were made more readily than connections to robots as ‘companions’, ‘carers’, and ‘affective systems’ unless they directly referred to healthcare robots:You know, this is sci-fi-inspired fear, so, I mean, perhaps, to some extent, legitimate about robots taking over. The Terminator scenario or somehow the AI uprising scenario. [Arts and Humanities 13]Again, and unsurprisingly, science fiction was mentioned as most influential in shaping public perception. The most commonly referred to example was The Terminator, followed by HAL in 2001: A Space Odyssey, and then more general references to ‘Hollywood’ and films like Ex-Machina, Alien, Wall-E and i-Robot.AI is sort of an alien type of being that becomes, you know, sometimes evil” [Social Science 02]. “Man versus machines” narratives were also dominant; often participants suggested that such stories tended to depict programmers making errors in the design of systems leading to bad outcomes, e.g. 2001: A Space Odyssey.The system put the outcome of the mission as more important than the lives of the crew, the human crew, and that’s an error of specification from a software standpoint. [Science 17]What characterised these narratives, participants felt, were mentions of evil AI, invisible programmers, or aliens:”  mean people refer to bureaucracies as machines themselves and that’s of great concern obviously from the point of view of maintaining moral responsibility for the actions of the system rather than blaming everything on, well, we can’t do it because our computer system won’t let us. [Science 12]IFurther, several commented that what was common across these science fiction narratives was that designers, programmers, and even the operators are often invisible. This heightens the human versus the machine narrative and maintains the narrative of moral responsibility:A lot of cis white men write science fiction, and that perspective tends to get engrained in the science fiction stories that are told, and so we get science fiction AI stories dealing specifically with these kind of fears, and I have seen it. [Science 06]Additionally, stories about embodied robots were generally seen as a cause for concern because of the racialised and gendered stereotypes. Films tend to over-sexualise female androids  is seen to threaten), or project a view of AI as white, suggestive of a lack of representation.Concerns about portrayals of robots in science fiction extended to a further consideration that the demographics of storytellers in AI as being largely elite, white and male — this is particularly the case in Silicon Valley and Big Tech. As such, participants regularly couple concerns about narratives issues of bias both in terms of stories told and the algorithms themselves.So, we are not cleaning the streets, we are not doing the garbage, we are not doing taxes, we are not selling things to people – all that stuff is AI. And, we are sitting around painting and inventing new religions and looking pretty! Some participants referenced how the public likely associates AI with negative implications, ranging from job loss  to what a few referred to as human ‘enfeeblement’, e.g. where humans would become subservient and powerless to AI. Positive associations about increased efficiency for improved freedom, and solutionist narratives where AI is the answer to climate change and the COVID-19 global health crisis were also among more positive framings while job loss too also posed a potential positive future:What was also common was a tendency for our participants to hint at alternative narratives in their responses. These were either relative and nuanced, or missing altogether.So I do think those words “artificial intelligence” summon up the kind of Terminator and that kind of robotic humanoid robots. [Arts and Humanities 22]Another theme when asked about dominant narratives was that the public would probably see AI in relation to a kind of ‘uprising’ of robots associated with speculation in science fiction.I’m talking about things like robot vacuum cleaners being anthropomorphised, attributed to agencies, being seen as the first step towards superintelligence. [Arts and Humanities 04]Most felt these were not likely scenarios and that AI was “narrow” and at a lower-level, far from anything like superintelligence – a view where AI would exceed human intelligence:I almost find it very difficult to imagine a world, you know, where some, you know, super-intelligence would cohabitate peacefully and well with humans, especially one that’s created by humans to serve their best interest at first. I think that would be very complicated. I’m not very optimistic about that. [Social Science 02]Narratives about such scenarios were seen as desirable and led to many participants describing concerns about human-AI value alignment. Several felt that current dominant narratives reinforce imaginaries of a human versus machine construct:I think the question of human-level AI and human-plus-level AI is really more of a question for science fiction than anything we can realistically expect to reach. [Science 06]When asked about superintelligence, almost all participants described how such notions were more suited to the realms of fiction than reality:It’s going to be more like Google than like The Terminator. It would be really hard and really pointless to try and turn everything into embodied robots. [Social Science 15]A more realistic narrative of AI futures, one claimed, would be more true to what’s actually happening in Silicon Valley:I think all the stories in this space that’s dominated by Silicon Valley. The big problem is the tech industry in my view. The Olympians of AI. [Social Science 23]It was felt that what is happening in Silicon Valley and other Big Tech companies reflects some of the grander (and scarier) narratives found in science fiction:It’s dumb AI but in the hands of powerful people whose motivations are highly questionable because it’s basically me and myself making shitloads of money, it’s extremely problematic. [Science 08]The companies most commonly referred to were Amazon, Facebook, Google, Apple, Facebook and Microsoft—all seen to be the loudest voices and the most influential of the storytellers, whose communicative purpose to sell and market technology was seen only to manipulate and be driven by the profit motive:it really comes down to the role of technology in the economy. Are we selling technology to make money for ourselves and our bosses? If we are co-existing in a free market-ish economy, that is going to be heavily driven by taking as much from us as possible, for as little value as possible – that is the goal! [Arts and Humanities 16]The most common theme when discussing big tech inevitably relates to accountability and responsibility. Few participants said anything positive about such companies. With respect to stories, participants felt that such companies over-sold the capability of AI and that the profit motive was harmful to humanity:Elon Musk has got such a huge number of fans when he’s clearly a not that nice human being: Abolish Silicon Valley! [Science 08]The personalities involved in such companies e.g. Zuckerberg, Bezos, Musk, etc. were seen as incredibly powerful—typically white, privileged and male, representing a narrow social group with, largely, shared ideologies:What if our earliest AI stories had not been 2001 with Hal and Terminator, with Skynet, or if had had WALL-E as our first AI story, how different would that have been? Or, some of the Japanese AI characters who are generally not murderous? [Science 06]We find that all narratives are relative to audiences of different ages, geographical locations, and abilities. They are also relative to the domain in which they operate and the purpose they have. One participant talked about how the cinematic portrayal of AI has shaped generations of people’s perceptions of AI and mused about how dystopian science fiction of the 80 s/90 s, might have a different effect to the socially minded robot Wall-E cleaning up the mess humanity had created.You might think that everyone in the world feels like this about AI and robots, but this isn’t true. So, it is very much a Western European and descendants type preoccupation which goes back to some of the myths of Greek society, if you look at the ancient Greek Talos, which is a big metal challenging thing, has popped up all the way through in stories, all the way through the period because the technology is now better, the stories made seem superficially more plausible. ... People in Japan don’t feel like this incidentally, so this is just another version of the Frankenstein complex. [Arts and Humanities 04]The participant noted that this is not just generational, but is also cultural, describing how AI is seen more as a friend to those in Japan, for instance, compared to the West:I think the problem is not so much people asking do we want this as making them wonder about who is included in that ‘we’ because I think with most of the things that are being developed the question do I want this is answered with a resounding yes, the stakeholders in self-driving cars want self-driving cars, the problem is asking those who don’t have a say in the development or implementation those who will experience a radical change in their lives when these technologies become implemented, those are not included in the ‘we’ and that is where the question of do others actually also want this or is it just the five of us in a rented office in Silicon Valley? [Arts and Humanities 04]Responses to dominant narratives are shaped by personal experience, culture and age, for instance. Our participants stressed how AI narratives ought to speak to, and be shaped by, a range of individuals of different race, cultural background and ability:I’m waiting for the day for autonomous vehicles because I have very, very poor eyesight, I’m never going to be able to drive, and that has had a big impact on me. I have to say my partner hates the idea of autonomous vehicles, they’re very distrustful of computers and... I could never imagine them feeling comfortable in an autonomous vehicle, whereas I imagine I would really want to get one as soon as they’re available. [Arts and Humanities 18]For instance, for one participant who described a disability, the idea of a self-driving car was transformative. For them, stories about freedom through the advent of autonomous vehicles were appealing, whereas their partner, might have a different view:hope for and want from AI. So too are questions about who should feature in these stories. We envisage a domain in which niche stories—so often under-reported because they are less fantastical—flourish and grow; one that advocates for the inclusion of people not normally associated with AI stories.For participants, these binary positions were adopted by the public in response to notions of embodied, super intelligent AI depicted in science fiction and cinematic stories in everyday culture. Interviewees spoke in detail about the need for more nuanced stories that challenge those pervasive narratives. Missing are narratives and stories about what people Our participants felt that there is a need to focus on and gather niche, novel and nuanced stories and narratives about the impact of AI, grounded less in the spectacular and more in the everyday.Participant: AI is presented as a threat, both in the short-term for jobs and in the existential‘Humans are doomed!’ type setting. Both of these threads are very annoying ifyou work in the field.Researcher: Can you tell me a little bit why they annoy you?Participant: Because they are completely untrue, broadly. None of these systems can do anything like what most humans can do. In particular, they are not flexible, they don’t have wide scope, they can do specific tasks narrowly defined better than humans. substitute for humans – this is just nonsense! Absolute nonsense! [Science 07]While the experts disagreed about the relative likelihood of a super intelligence , you know, I would sometimes say that the negative stories over-emphasise the negative. [Social Science14].Yet the ‘everyday’ does not have to be boring Alternative narratives are the sum of the storyteller, genre and the communicate agenda or purpose – participants urged for the public to consider each component of the stories they are told about AI.I think it’s about using AI to do all the types of storytelling that is not mainstream. [Arts and Humanities 18]I mean, there are a lot of examples, like in medicine or in the justice system etc., but stick to the objective parts and don’t try to make emotional machines or something. [Social Science 11]Broadly participants expressed a preference for narratives that supported the idea of AI as augmenting, rather than replacing human intelligence, and was more closely aligned with moral behaviour, e.g., Narratives supporting truth, justice and fairness.I don’t think that I am the one who stands most to benefit because my life is already fairly privileged and convenient and I think a lot of the potential for AI is actually in making services and functions that, you know, we can pay people to do in affluent societies and extend those to places where that is just not possible at the moment. [Science 12]Participants urged that AI narratives ought to tell the tales of more realistic and accurate truths, relating to issues of social injustice and inequalities to shift perception and create positive change. This is in line with Coeckelbergh’s  theorisaAgainst this backdrop, the following themes emerged as important for narrative development. Figure Figure art and creativity. Though present in current narratives, almost all participants felt that it was there that AI really could be beneficial and transformative. For instance, many described the use of AI as a tool for leveraging human creativity in art, fiction or music.It changes what we can achieve, and it changes the music we will write. [Arts and Humanities 13]Looking at the most dominant areas for new narratives, over half referred to the benefits AI brought to I think there’s a certain mystery surrounding the act of creativity, but, you know, I, kind of, was able, as a human alone, acting alone without any AI, shall we say, was able to reach, you know, good endpoints, good songs, and we can talk about, you know, what it means to write something that’s ‘good’. But, yeah, I thought, “Oh, I can, kind of, try to, you know, reflect on and implement, in code, some of what I’m doing here. [Science 13]AI was broadly seen as a route to creativity with over half of the participants describing the need for more narratives about AI and creativity in their interviews.Using AI was seen to be fun and interesting, leading people in new directions. AI was seen as crucial to the creative industries where most of the participants felt AI was most beneficial e.g. digital technology, art and storytelling. However, many felt this creativity would permeate to other domains, such as science and education.I think the academic world is more than knowledge; it’s about having a theory and the capacity to come up with a vision of the world, a theory or feelings about the world, is actually inherently human. And so I don’t think AI is going to steal the job of an academic. [Arts and Humanities]A second area of opportunity for new narratives was science and education. Participants saw a role for AI in science, particularly with respect to university research and teaching: “we should be talking about the future of universities” [Social Science 11]. Such benefits in science and education should not be at the expense of human judgment. With respect to teaching and learning, a role for AI in becoming a personal tutor ‘transforming the role of the teacher’ was described and welcomed. Others felt AI would not pose a threat to the academic role:Participants imagined futures where AI plays a role in research, for example “astronomy, anything that requires the processing of vast amounts of data'' [Arts and Humanities 04]. Big data and AI were seen to be potentially transformative.I think the way AI is working now can be useful for repetitive tasks, dull work, and dangerous work. [Social Science 01]A third possibility was to build narratives about the everyday. Here mundane tasks and everyday decision making could become topics for stories.It will just create new jobs; people will want to maintain the robots. [Arts and Humanities 16]Specific suggestions included, information retrieval, logistics, robots doing mundane tasks, such as watering grass and controlling heating systems. By focusing on mundane tasks, we could counter the narrative of fear around job losses. Instead, AI would support human practices. Indeed, there was a sense from over a quarter of the participants that AI will create jobs, not replace them.The hardest thing is to get the administrators and the teachers to come over to a completely different…they’ll have got a new job, it’s a different job. [Arts and Humanities 16]An alternative twist on this may come from the application of AI to particular domains. For instance, one participant suggested that AI will shift the role of teaching and in so doing create a new and exciting job:It follows that stories that detail and discuss the impacts on individuals and their adaptation to change could be a useful line. Rather than stoking fear, they could be accepting of change while championing human flexibility and growth.relationships and community: in bringing people together. As one such example, participants talked about a lack of stories which promote positive civic life:… possibilities for digital technologies or social technical processes to contribute positively and meaningfully to civic life… possibilities for new and emerging technologies to support social justice. [Social Science 19]Participants felt that stories might promote the role of AI in developing “help with place making, and processes that brought together institutions and communities, to redesign and transform public services.” [Social Science 19] Participants described that while stories regularly refer to AI in relation to community there are often downsides:Often, you know, perhaps every day think wow, that’s amazing, that particular connection on the Twitter feed or the way that the hashtags work, that is creating possibilities of community at the very same time as its creating possibilities for trolling and for racism and for abuse of others. So I guess, you know, it’s not that it’s unwelcome in my house, it’s very welcome but it’s just that all the time those things are happening simultaneously. [Social Science 10]AI was seen as something which could help cultural identities; Some experts mentioned how AI could be used to strengthen relationships, build community, to ‘connect people’, ‘help us build better relationships’, using robots to support isolated individuals (e.g. care robots), and social networks. Another example that occurred frequently was dating.My kids are teenagers and they don’t even need their dad, never mind AI, they need me to talk to about their shit, and there’s no replacement, not even a human replacement. You know, very, very kind of particular human, yeah, activities that are very specific, so... I mean, I know there are sort of AI kind of experiments with mental health support and stuff but I just can’t see that working here. [Social Science 14]Crucially, it was also explicit what AI should not do in this space: that it should not replace relationships between humans. This comes to the fore as one participant brings up the issue of mental health support:environment. For example, in agriculture and food stability, climate change and ecology:They’re doing a lot of good work in factories and increasingly in farms and in supply chain management and in postal processing and these are not places where either storytellers or lay people are busy very often so I mean there is a general lack of awareness about what happens in terms of how everything gets into our home and how civilisation functions and most of what AI does it does it in those spaces and so I think those are actually fairly I mean in some sense they are boring stories but in another sense it’s the way AI gets woven into how our civilisation is run these are just completely missing narratives. [Science 12]A connected issue is the relationship to the I think meat, dairy, meat and dairy are such a huge problem on a global scale and I think the role AI plays in finding healthy t or convincing plant-based meat alternatives and dairy alternatives. [Social Science 01]asteMany talked about uses of AI which could be seen to support the action against climate change. For example, one talked about how AI is helping to design plant-based products instead of animal-based products:I think healthcare probably is the area for me where you know, particularly combined with something like quantum, the benefits of it in terms of their visions, are huge. [Social Science 21]Participants felt that to counter pessimistic and dystopian views of AI, health was an area where AI could be seen to help.I mean the obvious answer is in the medical field, but that’s hard to say because actually I think there are very few tangible applications that are specifically aiming at the common good and that are not connected with economic purposes. [Science 01]Almost all mentioned how AI can transform health particularly in the areas of care for the elderly, applications in psychology and infectious diseases. Other suggestions for new narratives related to the need to move the conversation away from the uncanny valley and robots in care, towards embracing possibilities to reduce patient loneliness, isolation, and pressure on public services. At the mundane level, stories could orient to the everyday wellbeing aspects of our lives, such as through the small things, like Fitbits and using timers to remind us to stand and stretch. Other examples of new narratives could include conversations about the role of human intelligence in the health space, such as using AI-enabled X-rays to find tumours.Participants noted that of course, narratives that look to answer the biggest opportunities in health and social care, require thoughtful reflection to educate about the potential impacts of their implementation, and the underlying, but rarely publicly aired, business models.social justice, mitigating against societal biases through a process of bringing marginalised communities into the process of technology design (design justice) and empowerment.I think the question of design justice is a question for AI. What does it mean to proceed from a position of justice in that space? [Social Science 20]Further, participants also talked about the possibilities for new and emerging technologies to support Who do these stories speak to, and what are the concerns that people live within their day-to-day lives? For it to be beneficial, people need to be put in the conditions to be able to benefit from it. And that requires that sort of social capabilities. [Science 02]Importantly, participants felt that inclusivity and equality was paramount in telling stories about AI for justice:That’s often what the narrative might be around object detection, facial recognition and so on, that you might well have a large percentage of people coming back and saying actually, yes, I would be willing to have that happen but in practice, what will happen is that those communities already at greatest risk of being stopped and searched by the police or being targeted as high risk will find that intensified through the use of the technology. [Social Science 10]The issue of algorithmic injustice was regularly referred to by all participants, focusing mainly around particular areas. All felt a deeper focus on narratives of social injustice could be used to build momentum in the movement toward a more just AI.“put a magnifying glass on our own behaviour.” [Social Science 11] Participants couched a way of weaving AI into how civilisation is run in terms of mobilising AI to reflect human virtues, cognisant that the technology is never neutral. These virtues could underpin stories of culture, social, justice, and freedom. For instance, one participant felt that from this position, the technology will mirror ‘good’ human virtues:If you talk to young people and you ask them what the future is going to be like, very few of them have stories of abundance, stories of leisure, stories of significant new freedom, significant new protections, new social contracts, all of these are very… I mean we have had periods in the past where these were highly imaginable and they made change happen, it wasn’t just them, right, I’m not blind to the way power operates but they are meaningful and I think we start there and the technology kind of will… technology will follow from them. [Arts and Humanities 14]In almost every instance, participants called for a need for stories which explained more about bias. As one participant put it, to spiritual growth and taking more time to tune into inner experience.It would be nice to be able to turn up the zen setting on my phone, without the phone even noticing that my heart rate is especially up. [Arts and Humanities 16]A further area to explore might be AI and its relationship with spirituality. Participants described how there were missing AI narratives about the role AI can play with respect to Those things from the core of our… you know, I mean it can be spiritual growth and it can be community growth and it can be creative growth, like all of those things you can imagine a world of much more significant abundance if you give people back time and if you give people back connections and if you give people back kind of you cut back on the amount of work they do. [Arts and Humanities 12]There was a sense that these notions were in conflict with the notion of personalisation which AI thrives on. In that it presents in ways, a false reality which could be seen to distract from the harsh realities of the world. In some senses, ‘tuning out’ noise from the world was seen as worth it, for a lot of participants. Notably, these responses might have been shaped by the context of the pandemic at the time of interviewing where there was increased focus on digital interaction.I think what should be reflected is the clash between the development of AI and economic trajectories. [Science 01]Finally, participants talked about the need for more nuanced approaches to narratives around economics and policy. Here, a more nuanced and truthful approach to storytelling was desired, acknowledging tensions between economics and wellbeing. Participants discussed how narratives ought to focus on nuanced explorations of growth, responsible innovation, austerity, Universal Basic Income, public services, the clash between AI and economic trajectories, economics vs wellbeing, the problems with solutionist narratives that pose that AI can solve intractable and impossible-to-frame political and social problems.it all has to change, because you can't have both”. [Social Science 20].Perhaps exacerbated by the pandemic, many participants reported the tension between economic measures such as GDP and more important and difficult-to-measure considerations such as people’s wellbeing. These tensions, they suggest, extend to the economy between developing responsible technologies and what we understand to be an economic imperative: “We need more AI Futures to try and understand all of this mess because if your futures like superintelligence capture a lot of our attention but there are other ones and we need to be paying more attention to them. [Arts and Humanities 17]From these accounts it was clear that these domains far extend the dichotomous views prevailing in the commonly told stories about AI. As one participant suggested, we need to move past the spectacular to focus on more pressing issues.Widely retold dystopian stories portray AI as a risk to our livelihoods, safety, jobs and very existence. Whilst noting the inherent value of narratives of science fiction for their entertainment and speculative value, the issue for many relates to what happens when those narratives influence those in power and they are given credence beyond a role as entertainment, and when those with capital prefer to fund sensationalist stories and not stories about humans in an AI-enriched future. Explicit in our findings is the presence of established and well-known dominant narratives, often emphasising or embellishing one aspect of a story. For instance, the participants talk about a preoccupation with solutionism or AI for optimisation — but often with a view to optimizing ‘the wrong thing’. This trend continued with other narratives, including a tendency for dominant stories to focus on the negative connotations of robotics, for instance — e.g., job loss, or even fear of enfeeblement, at the expense of making more of their role in care and health. Interestingly, when the COVID-19 crisis hit, suddenly robots (who could not catch a virus) were welcomed more readily into our care environments, than prior to the pandemic. Other dominant narratives about superintelligence were largely dismissed by as an esoteric preoccupation of a privileged few closer to the realms of science fiction than the current state of technology.This disconnect led many to describe concerns over ethics and responsibility in AI. Most concerns about dominant narratives were suggestive of a need to better interrogate and reflect on the design and development of AI. Indeed, despite momentum in the space of AI ethics Jobin , most paSuch is the shifting and contingent nature of AI narratives – vis a vis the ‘why’, the ‘who’ and the ‘how’ Bory . If narr3To do this, reflection on the relative nature and trends in storytelling; e.g., relative to groups of people, Yee , place , time annot be applied as well as where it should be. In a recent report, UKRI  the shifting nature of what we mean by the term artificial intelligence and its capabilities is in flux and ‘that too is part of the story’.Narratives that probe nuanced answers to these questions can help us get closer to broadly acceptable answers. Attention needs to be paid to the development of nuanced AI narratives that continue 'story-listening' beyond 'expert' voices toward collective, public participation, engage with the everyday and recognise the relative nature of narratives extending beyond the Anglophone West. Narratives are also relative to the domain of use they are describing.There is a need to encourage and foster diverse storytellers, mindful of the way power operates. These narratives can take many forms, including forms that do not yet exist. Educating and empowering people to tell and deeply listen to compelling stories will promote the best possible futures for AI. To do that there has to be an opening and broadening of stories and storytellers that might shape public understanding. Further research must ask how we can begin to reshape the dynamics of public storytelling around AI to understand whether or not there really is an “AI story crisis”.


N‐ethylmaleimide sensitive factor (NSF) and SNAPs to recycle the SNAREs, whereas Munc18‐1 and Munc13s organize SNARE complex assembly in an NSF‐SNAP‐resistant manner. Synaptotagmin‐1 acts as the Ca2+ sensor that triggers exocytosis in a tight interplay with the SNAREs and complexins. Here, we review technical aspects associated with investigation of protein interactions underlying these steps, which is hindered because the release machinery is assembled between two membranes and is highly dynamic. Moreover, weak interactions, which are difficult to characterize, play key roles in neurotransmitter release, for instance by lowering energy barriers that need to be overcome in this highly regulated process. We illustrate the crucial role that structural biology has played in uncovering mechanisms underlying neurotransmitter release, but also discuss the importance of considering the limitations of the techniques used, including lessons learned from research in our lab and others. In particular, we emphasize: (a) the promiscuity of some protein sequences, including membrane‐binding regions that can mediate irrelevant interactions with proteins in the absence of their native targets; (b) the need to ensure that weak interactions observed in crystal structures are biologically relevant; and (c) the limitations of isothermal titration calorimetry to analyze weak interactions. Finally, we stress that even studies that required re‐interpretation often helped to move the field forward by improving our understanding of the system and providing testable hypotheses.The mechanism of neurotransmitter release has been extensively characterized, showing that vesicle fusion is mediated by the SNARE complex formed by syntaxin‐1, SNAP‐25 and synaptobrevin. This complex is disassembled by  2+‐triggered synaptic vesicle fusion.The variety of synaptotagmin‐1‐SNARE interactions that have been described illustrates the challenges involved in determining the protein interactions that control the different steps leading to Ca This process involves tethering of synaptic vesicles at specialized areas of the presynaptic plasma membrane called active zones, a priming step(s) that leaves the vesicles ready for release, and fast (< 1 ms) vesicle fusion upon Ca2+ influx into a presynaptic terminal  from membrane‐anchored SNARE complexes [2B domain as well as two residues from an acidic patch of the C‐terminus of SNAP‐25 , which explains the co‐elution of C2AB, complexin‐1 and the slightly truncated SNARE complex in gel filtration. And fourth, the proximity of the SNAP‐25 acidic sequence containing D186 and D193 to the C‐terminus  performed on supported bilayers [2AB from membrane‐anchored SNARE complex did not involve another acidic sequence of SNAP‐25 including D51, E52 and E55, which is further from the C‐terminus [2B domain [To make matters even more confusing, gel filtration experiments showed that Cpx1(26–83) or full‐length complexin‐1 can bind simultaneously with C complex , even thn assays . Both se complex . Second, complex  and the gregates . Third, nus Fig. , which ibilayers . Indeed,terminus   or a control K354E,R388E mutation, were consistent with this dynamic structure and correlated with the effects of these four mutations on neurotransmitter release, whereas these effects did not correlate with those induced by the mutations on Ca2+‐independent binding of C2AB to PIP2‐containing liposomes [Characterizing the structural basis of the underlying binding modes was hindered not only by binding heterogeneity but also because the combination of binding modes led to a strong tendency of Syt1‐SNARE complexes to precipitate in the presence of Caractions . Measureiposomes .2B‐SNARE binding mode defined by NMR spectroscopy in solution. However, as explained above, subsequent systematic studies of Syt1‐SNARE and Syt1‐lipid interactions using nanodiscs showed that the electrophysiological data could also be explained by the effects of the mutations on Ca2+‐dependent binding of Syt1 to PIP2‐containing membranes, which is much tighter than SNARE complex binding [2B‐SNARE complex binding mode defined by the NMR studies is not physiologically relevant and arose because of the absence of the native target of the polybasic region (PIP2‐containing membranes) in the experiments, providing a dramatic illustration that results may need to be re‐interpreted even after a clear correlation between biochemical and physiological data was established with four mutations.These observations strongly supported the physiological relevance of the dynamic C binding . These a2AB was covalently linked to different SNAREs using flexible sequences of different lengths and one such chimeric construct led to a crystal structure revealing a binding mode between the Syt1 C2B domain and the SNARE complex [2B domain β‐sandwich opposite to the polybasic region  bind to the acidic sequences of SNAP‐25 and syntaxin‐1. The physiological importance of this interface has been demonstrated by the strong disruption of Ca2+‐triggered release caused by the R398Q,R399Q mutation in region II [A major breakthrough to understand Syt1‐SNARE coupling came from an ingenious approach where Syt1 C complex  that was complex . In the ion Fig. . This inegion II  as well egion II . Moreoveegion II . Intriguegion II , suggest2B domain disrupts binding to the SNARE complex through the primary interface but the E295A,Y338W mutation actually enhances this interaction, likely because the tryptophan introduced by the mutation establishes more extensive contacts with the SNAREs than the native tyrosine. The FRET assays revealed that binding of Syt1 C2AB to nanodisc‐anchored SNARE complexes involved at least two types of interactions mediated by the primary interface or the polybasic region, both in the absence and presence of Ca2+, but the results depended on the conditions of the experiments. Importantly, when PIP2 was included in the membranes and the buffer contained physiological ionic strength and ATP concentrations, the affinity of C2AB for nanodiscs and SNARE‐complex nanodiscs was similar in the presence of Ca2+. These findings indicated that C2AB does not bind to the SNARE complex under these conditions [2AB still bound to nanodisc‐anchored SNARE complex in the absence of Ca2+ even when ATP was present, also consistent with previous data [2+‐independent interaction was dominated by the primary interface. These results can be rationalized from the realization that, in the absence of Ca2+, inclusion of PIP2 in the nanodiscs should lead to interactions of PIP2 with the C2B domain polybasic region, hindering interactions of this region with the SNAREs and thus favoring binding of the C2B domain to the SNARE complex through the primary interface. However, this binding mode is incompatible with the very tight, Ca2+‐dependent interaction of C2AB with a PIP2‐containing membrane because the approximately perpendicular orientation of the C2B domain with respect to the membrane caused by this interaction [These results were explained by systematic analyses of Syt1‐SNARE complex interactions in solution by NMR spectroscopy and on nanodiscs containing or lacking anchored SNARE complexes by fluorescence resonance energy transfer (FRET) . The NMRnditions , in agrenditions . Howeverous data , and bineraction   Ca2+ binding to Syt1 induces a different orientation of the C2B domain with respect to the membrane , but caain Fig.  that areain Fig. . In thisulations . Altogetane Fig. , dissociane Fig. . The mecembranes , 116, inembranes , 136 andng loops .2B domain disrupt release [2+‐induced dissociation of Syt1 from the SNAREs, but preserves the ability of Syt1 to inhibit spontaneous release. This model also explains the observation that complexins are required for the dominant negative effect caused by overexpression of Syt1 bearing mutations that abolish Ca2+ binding to the C2B domain [2+ binding to the Syt1 C2B domain.In this model, the R398Q,R399Q and quintuple mutations in the C release , 115 bec release  because B domain . MoreoveB domain , support2B domain binds to the groove between the synaptobrevin and syntaxin‐1 SNARE motifs of the SNARE complex, next to the complexin‐1 central helix, which is called the tripartite interface [2B domain disrupted binding to the SNARE‐complexin‐1 complex, as assessed by ITC, and neurotransmitter release in neurons [2B domain purified by our protocol [2B binding to complexin‐1‐SNARE complex through the tripartite interface in solution by ITC or by a very sensitive NMR method involving paramagnetic broadening effects . These results again illustrate the limitations of ITC to unambiguously demonstrate weak protein interactions in solution. The available data do not rule out the possibility that binding of Syt1 to the SNARE complex through the tripartite interface is biologically relevant, as it is plausible that this interaction is very weak and is strengthened by cooperativity with other interactions at the synapse. However, such relevance will need to be investigated with further research using methods to analyze protein interactions on membranes and establishing clear correlations between binding and function with multiple mutations.A crystal structure of a Syt1‐SNARE‐complexin‐1 complex revealed yet another Syt1‐SNARE binding mode whereby an α‐helix of the Cnterface   [abc domain [abc domain and the SNARE four‐helix bundle [Munc18‐1 is a member of a family of soluble proteins that are collectively known as Sec1/Munc18 (SM) proteins and play crucial roles in all forms of intracellular membrane traffic that depend on SNAREs . This crthe RRP) . Hence, the RRP) . Munc18‐the RRP)  and thisthe RRP) . This cothe RRP)   , but Secthe RRP) . Moreovec domain . Sed5 anc domain , 155. Thx bundle , 69. Thex bundle .It now appears that most SM proteins bind to their cognate SNARE complexes through similar modes, whereas the interaction of Munc18‐1 with closed syntaxin‐1 emerged as a specialization for the exquisite regulation of neurotransmitter release . The rolin vitro and in functional assays in C. elegans. Moreover, a systematic study using optical tweezers yielded overwhelming evidence that Munc18‐1 acts as a template for SNARE complex assembly through its interactions with synaptobrevin and syntaxin‐1, and, interestingly, showed that the syntaxin‐1 N‐terminal region plays a critical role in the assembly mechanism beyond its participation in forming the closed conformation [Munc18‐1 was also found to bind weakly to synaptobrevin, but binding appeared to be mediated by the juxtamembrane region linking the SNARE motif and TM region of synaptobrevin, which is highly promiscuous because it contains multiple basic residues and three aromatic residues . Neverthormation .abc domain of syntaxin remain in contact with analogous surfaces of Munc18‐1 as in the Munc18‐1‐syntaxin‐1 complex. Interestingly, the N‐termini of the two SNARE motifs form a small four‐helix bundle with helices formed by the linker between the Habc domain and the SNARE motif, but the extent of contact between the SNARE motif and the Habc domain is different in the two cryo‐EM structures, and substantially lower than in closed syntaxin‐1 bound to Munc18‐1  and phorbol ester‐dependent augmentation of release [2B domain that binds Ca2+ and PIP2 and also mediates short‐term Ca2+‐dependent plasticity [2C domain that binds weakly to membranes in a Ca2+‐independent manner [2A domain and CaMb‐binding region is variable in the Munc13 family, whereas the C‐terminal region spanning the C1, C2B, MUN and C2C domains (Munc13C) is conserved in all Munc13s and is sufficient to rescue neurotransmitter release in Munc13‐1/2 double KO mice [Munc13‐1 is the most abundant Munc13 isoform in mammalian brain and has a large (200 kDa) multidomain architecture that includes Fig. : (a) an  to RIMs , 169;  a Masticity , 25, 174t manner . The N‐t KO mice . Key ste KO mice . Howeverabc domain and the SNARE motif (R151 and I155) were critical for such stimulation, and the physiological relevance of this observation was shown by the substantial impairment in synaptic vesicle priming caused by R151A or I155A mutations in syntaxin‐1 [1H‐15N HSQC spectra acquired at 20 μm concentration, and evidence for such binding could only be obtained by NMR spectroscopy using paramagnetic broadening effects [Analyses by NMR spectroscopy showed that the Munc13‐1 MUN domain binds weakly to Munc18‐1, to the closed syntaxin‐1/Munc18‐1 complex, to the SNARE complex, and to the syntaxin‐1 SNARE motif, and the latter interaction was proposed to be key for the activity of the MUN domain in opening syntaxin‐1 . Howeverntaxin‐1 . Single  effects . These rAnother mutagenesis screen identified two residues of the Munc13‐1 MUN domain (N1128 and F1131) that are also important for stimulation of SNARE complex formation starting from the closed syntaxin‐1/Munc18‐1 complex , perhaps1, C2B and MUN domains (C1C2BMUN), revealing a banana‐shaped architecture in which the C1 and C2B domains emerge on one end of the highly elongated MUN domain and the C2C domain (not present in the fragment) is expected to emerge at the other end [1 and C2B domains bind lipids from the plasma membrane  and the C2C domain might also bind to membranes, this architecture and liposome clustering assays led to the proposal that the conserved C‐terminal region of Munc13‐1 bridges the synaptic vesicle and plasma membranes [2C domains showed that the C2C domain binds weakly to membranes, and the Munc13C fragment spanning the C1, C2B, MUN and C2C domains of Munc13‐1 was shown to bridge liposomes by cryo‐ET [2C domain strongly disrupted the liposome bridging activity of Munc13C, the activation of liposome fusion by Munc13C in vitro and the ability of Munc13‐1 to rescue neurotransmitter release in Munc13‐1/2 double KO neurons, demonstrating the critical functional importance of the membrane‐membrane bridging activity [1C2BMUN also suggested that Munc13‐1 can bind to the plasma membrane in two orientations: (a) an almost perpendicular orientation resulting from binding through a polybasic face formed by the C1 and C2B domains, which could allow initiation of SNARE complex formation but would hinder full SNARE complex assembly; and (b) a much more slanted orientation favored by Ca2+ binding to the C2B domain and DAG binding to the C1 domain, which would bring the two membranes into proximity and favor full SNARE complex zippering [A crystal structure was determined for an isolated Munc13‐1 fragment spanning its Cther end . Since tembranes , 176  [2A domain was used to guide crystallization efforts and eventually led to the crystal structure of the homodimer  , the quamer Fig. . The strerodimer . Surprisule Fig.  even tho2+‐induced membrane fusion to enable the many wonders of the human brain.The research summarized above has led to enormous advances in elucidating the molecular mechanisms underlying neurotransmitter release. At the same time, this research has provided and is providing lessons of persistence and innovation from laboratories around the world to investigate a biological process of the utmost interest, integrating approaches from very diverse disciplines. These efforts have led to seminal contributions in many cases and sometimes to studies that needed to be re‐interpreted. However, even the latter studies often moved the field forward because they generated hypotheses that could be tested and/or yielded insights into interactions that needed to be characterized to understand the behaviors of the corresponding proteins, even if the interactions are not biologically relevant. Both the knowledge that has been gained on the mechanisms of action of the release machinery and the technical lessons that have been learned, often more instructive in cases of failure than in success, will be invaluable to make further progress in this field and address fundamental questions that remain, particularly regarding how the SNAREs and Syt1 trigger fast CaThe authors declare no conflict of interest.


Ice-binding proteins (IBPs) are a group of ecologically and biotechnologically relevant enzymes produced by psychrophilic organisms. Although putative IBPs containing the domain of unknown function (DUF) 3494 have been identified in many taxa of polar microbes, our knowledge of their genetic and structural diversity in natural microbial communities is limited. Here, we used samples from sea ice and sea water collected in the central Arctic Ocean as part of the MOSAiC expedition for metagenome sequencing and the subsequent analyses of metagenome-assembled genomes (MAGs). By linking structurally diverse IBPs to particular environments and potential functions, we reveal that IBP sequences are enriched in interior ice, have diverse genomic contexts and cluster taxonomically. Their diverse protein structures may be a consequence of domain shuffling, leading to variable combinations of protein domains in IBPs and probably reflecting the functional versatility required to thrive in the extreme and variable environment of the central Arctic Ocean. Ice-binding proteins (IBPs) are a large group of cold-active enzymes found across all three domains of life, but little is known about their diversity in natural environments. Depending on their concentration, IBPs function in one of two dominant modes: thermal hysteresis (TH) or ice-recrystallisation inhibition (IRI) . TH refeShewanella frigidimarina and Marinomonas primoryensis contain bacterial immunoglobulin-like repeats which act as a tether between the cell membrane and the IBP, permitting an ice adhesion function [A number of bacterial IBPs have been functionally characterised, revealing varied potential environmental roles related to their structures. The Pfam library reports over 4000 IBP sequences from over 3000 taxa, the majority of which are prokaryotic . Among tfunction ,12. Ig-lfunction . In addifunction  and intrfunction .The diversity of IBPs may be connected to their environmental, taxonomic and specific genomic contexts. Prokaryotic IBPs are found in various frozen environments, including glacier cryoconites , subglacHere, we used metagenome-informed genomics to explore the genetic diversity and predicted structural diversity of prokaryotic DUF3494 IBPs from the central Arctic Ocean. During leg 2 of the MOSAiC expedition, 15 metagenomic samples were collected spanning the bathypelagic, mesopelagic, epipelagic, sea–ice interface and interior ice layers . Unlike Fifteen metagenome samples were collected during leg 2 of the MOSAiC expedition (collection dates between 13 January 2019 and 7 February 2020), during the Arctic winter . These sThe DNA was extracted at the Alfred Wegener Institute, using the Qiagen PowerWater DNA kit, following a slightly modified version of the QIAGEN DNeasy Power Water SOP v1  . SamplesFor the regular protocol, the DNA was sheared to 300 bp using the Covaris LE220-Plus and size selected with SPRI using TotalPure NGS beads . The fragments were treated with end-repair, A-tailing and the ligation of Illumina compatible adapters  using the KAPA-HyperPrep kit . The prepared libraries were quantified using KAPA Biosystems’ next-generation sequencing library qPCR kit and run on a Roche LightCycler 480 real-time PCR instrument. The sequencing of the flowcell was performed with the Illumina NovaSeq sequencer using NovaSeq XP V1.5 reagent kits, S4 flowcell, following a 2 × 151 indexed run recipe. For the low input protocol (10 ng of DNA), the procedure was the same, except that the sample was enriched using 5 cycles of PCR.The sequence quality control, assembly, annotation and binning were all performed using the IMG/M metagenome annotation pipeline (v.5.0.23) . BrieflyWe compared the prokaryotic community compositions of the total assembly, the MAGs, and their respective IBP-producing communities across sites. We used the R packages phyloseq (v1.40.0) and ggplot2 (v3.4.0) ,48 to plWe then explored which bacterial orders encoded IBPs with diverse gene architectures—defined as containing >1 domain in the IBP or containing a signal peptide and/or transmembrane domain(s).The domain architectures for modelling were identified by the presence of multiple protein families (Pfams) within the same gene. We selected the five most environmentally abundant  domain architectures in the total dataset for modelling. Representative IBPs for each domain architecture were further selected on the basis of their environmental abundance. The structures were modelled using AlphaFold (v2.1.1) , with thThe domain architecture of the genes surrounding the IBPs in MAGs was determined by querying the genes found the closest, upstream or downstream, to the IBP genes, and recording their relative locations and which protein families were present. We queried which domains and domain architectures were the most abundant within these upstream and downstream genes. Genomic context and domain architecture figures were produced using Inkscape v1.2.1. As above, broader functional characterisations were obtained using InterPro .Polarella glacialis [To determine how the phylogenetic relationships between IBPs varied depending on the domain architecture, environment and taxonomic assignments, we produced gene trees of the most environmentally abundant gene architectures, as well as gene trees of IBPs across all domain architectures. The alignments of the amino acid sequences of HMMER hits to the DUF3494 domain were produced using muscle (v2.0.4) , and lowlacialis   and Rhodobacterales (13.3%). The most common archaeal orders were Nitrosopumilales (0.88%) and Candidatus Poseidoniales (0.46%). We also retrieved 750 total medium and high quality MAGs from these samples  were found was analysed separately. In all, 85.74% of IBPs could be assigned order-level taxonomy. These IBP-encoding communities were composed of 60 bacterial orders and 5 archaeal orders. The most common bacterial orders were Flavobacteriales  (Bacteroidetes) and Alteromonadales  (Gammaproteobacteria) . The mosp = 0.001, R2 = 0.651; MAGs (genus level): F = 4.81, p = 0.002, R2 = 0.74) , followed by the sea–ice interface (8/79 MAGs), with three and one MAGs found in the epipelagic and meso/bathypelagic environments, respectively. The order level composition of the IBP-encoding communities varied among different sites  .Diverse domain architectures were predicted from the genomic sequences of the IBPs. A total of 116 unique domain architectures were found in 3869 prokaryotic IBPs spanning 65 identified orders. These diverse architectures included a total of 46 protein families. Single domain IBPs were by far the most abundant in the environment  and the most prevalent across the samples , followed by double domain IBPs  . Triple Some differences in the structure of the DUF3494 domain were observed. All modelled proteins contained the discontinuous right-handed β-solenoid with three flat faces and a braced α helix. However, the length of the β-solenoid varied, with longer solenoids containing 14 coils found in some of the most environmentally abundant IBPs e–g.In the natural environment, IBPs containing a protein family classified as immunoglobulin-like make up a large proportion of the environmental relative abundance , but they were not as prevalent across samples, accounting for only 3.57% of all of the IBPs found. They were, therefore, likely found in highly abundant individual IBPs rather than in a large number of distinct IBPs with the same architectures. Of these proteins, 15.22% contained a transmembrane domain, 45.65% contained a signal peptide and 7.25% contained both. In all, 84.06% of these IBPs came from interior ice, 14.49% from the sea–ice interface and 1.45% from the epipelagic zone.The most prevalent domain architectures across the samples consisted of protein families whose role involves cell adhesion and exopolysaccharides. IBPs with these domain architectures had an abundance of 411.20 RPKM , constituting 4.88% of all IBPs found. This is reflected in certain architectures with large numbers of repeated domains. The most striking examples of them among our samples are double domain IBPs with up to 26 C-terminal thrombospondin type-3 repeats, and single and double domain IBPs containing up to 7 C- or N-terminal bacterial immunoglobulin-like (BIg) domains. A total of 33.93% of IBPs with an adhesion function contained a TMD, while 48.68% contained a signal peptide and 32.8% contained both. As for their origins, 84.66% of these IBPs came from interior ice, 12.70% from the sea–ice interface and 2.65% from the epipelagic zone.Other broadly abundant functions or protein families found in these domain architectures include protein families with no known or suggested functions , calcium binding proteins  and trafficking/secretion-related proteins .In the MAGs, the most abundant domain architectures were single domain , double domain , single domain with a DUF4842 , triple domain  and single domain with a PEP C-term motif .MAGs were used to explore the protein families present in the genes flanking IBP genes. Forty-six of the seventy-nine MAGs contained >1 IBP. The highest number of IBPs in a single MAG was nine e.g., b. In MAGOne hundred and three unique domain architectures were found downstream of IBPs . The mosThe most frequent individual domains found downstream of IBPs, rather than whole architectures, were generally characterised by IBPs and repeats. They are as follows: IBPs , FG-GAP repeats , response regulator receiver domains , DNA binding domains  and bacterial transferase hexapeptides .One hundred and seven unique domain architectures were found upstream of IBPs . The mosThe most frequent individual domains found upstream of IBPs were IBPs , a metal-binding motif , a β-propellor repeat , a WG repeat motif  and an aminotransferase .The sequences of the DUF3494 domain(s) of IBPs with the most abundant domain architectures were compared . A numbeIn all, 2886 single domain IBPs were found. Of these, just 39.92% contained a signal peptide, and 90.6% contained no transmembrane domain, while 8.34% contained one TMD, 0.90% contained two TMDs and 0.07% contained three TMDs. A total of 2.94% contained both an SP and at least one TMD. Of the 2886 total IBPs, 2501 could be classified to the order level. Among them, the five most abundant orders encoding this domain architecture were Flavobacteriales (1130 IBPs), Alteromonadales (714 IBPs), Burkholderiales (112 IBPs), Oceanospirillales (71 IBPs) and Acidimicrobiales (47 IBPs). As for their origin, 78.55% of these IBPs came from interior ice, 19.82% from the sea–ice interface, 1.42% from the epipelagic zone and 0.021% from the meso/bathypelagic zones.A total of 455 double domain IBPs were found a, 57.80%In all, 86 single domain IBPs contained a DUF4842 (pfam16130) the function of which is unknown, but which contains a β-barrel immunoglobulin fold b. Of thaA total of 92 single domain IBPs contained a PEP C-term motif (pfam07589) that is exopolysaccharide-related c. Among In all, 23 double domain IBPs contained a DUF11 (pfam01345), whose function is unknown but is thought to be cell-wall related. Of them, 73.91% contained a signal peptide, implying that the majority were secreted. Only 8.70% contained a transmembrane domain, and none contained both an SP and a TMD; 17.39% contained neither. Of the 22 IBPs with this domain architecture which could be classified to the order level, 20 were found within the Flavobacteriales and 2 were found within the Saprospirales. A total of 82.61% of these IBPs came from interior ice, and 17.39% from the sea–ice interface.Seventeen triple domain IBPs were found d. Of theA large amount of structural diversity was distributed across the tree of 3869 IBP sequences . In the The most abundant orders in which signal-peptide-containing IBPs were found were Flavobacteriales (49.59%), Alteromonadales (29.42%), Oceanospirillales (3.14%), Burkholderiales (2.69%) and Cytophagales (1.72%). For IBPs without signal peptides, the most abundant orders were Flavobacteriales (47.94%), Alteromonadales (25.98%), Burkholderiales (4.45%), Oceanospirillales (2.57%), and Micrococales (2.14%).IBPs with TMDs were mostly found in the order Alteromonadales (58.58%), followed by Flavobacteriales (16.18%), Oceanospirillales (3.88%), Thiotrichales (2.27%) and Acidimicrobiales (1.94%). Those without TMDs were mostly found in Flavobacteriales (51.81%), followed by Alteromonadales (24.09%), Burkholderiales (3.94%), Oceanospirillales (2.69%) and Micrococcales (1.59%).A total of 25.41% of IBPs had a diverse domain architecture, defined as a protein with more than one domain (IBP or other protein family) . Of thesOur findings suggest that the structural diversity of prokaryotic IBPs is associated with their taxonomy. By surveying the complement of ice-binding proteins encoded by prokaryotic sea ice and marine communities during an Arctic winter, we compared ecological and individual-scale observations. We queried environmentally abundant IBP domain architectures, linking these to broader functions as well as to genomic context and taxonomy. The IBPs were encoded by a diverse subset of communities and MAGs. IBPs containing immunoglobulin-like domains and domains involved in cell adhesion were abundant. The genomic context of the IBPs was dominated by other IBPs. The taxonomic clustering of the IBPs was sometimes also reflected in the variable presence of signal peptides and transmembrane domains. Together, these results provide new insight into the previously underexplored natural diversity of prokaryotic IBPs in the central Arctic Ocean. Furthermore, these results highlight the value of MAGs as a complement to whole metagenomes , especiaIg-like domains and cell adhesion-related domains were the most abundant non-ice binding domains found in the IBPs. The presence of bacterial immunoglobulin (BIg) domains in IBPs has previously been attributed to an adhesin function. These domains act as a flexible tether between the IBP and the cell ,12. The Single (sd) and double (dd) domain IBPs were by far the most abundant protein domain architectures; however, less than half of the sdIBPs contained a signal peptide. Although there are other pathways for secretion that are not SP-mediated , this suIn the MAGs, IBPs were most frequently flanked by other IBPs. Gene synteny can be used as a method of inferring the biological function of proteins ,68,69. GIBPs clustered taxonomically when comparing abundant domain architectures . There wIBPs also clustered taxonomically when all sequences, not just those with abundant domain architectures, were compared , with IB


Unsupervised domain adaptation (UDA) is an emerging technique that enables the transfer of domain knowledge learned from a labeled source domain to unlabeled target domains, providing a way of coping with the difficulty of labeling in new domains. The majority of prior work has relied on both source and target domain data for adaptation. However, because of privacy concerns about potential leaks in sensitive information contained in patient data, it is often challenging to share the data and labels in the source domain and trained model parameters in cross-center collaborations. To address this issue, we propose a practical framework for UDA with a black-box segmentation model trained in the source domain only, without relying on source data or a white-box source model in which the network parameters are accessible. In particular, we propose a knowledge distillation scheme to gradually learn target-specific representations. Additionally, we regularize the confidence of the labels in the target domain via unsupervised entropy minimization, leading to performance gain over UDA without entropy minimization. We extensively validated our framework on a few datasets and deep learning backbones, demonstrating the potential for our framework to be applied in challenging yet realistic clinical settings. Semantic segmentation provides the pixel-wise annotation of lesions or anatomical structures and has been an important prerequisite for early diagnosis and treatment planning  and thus invariant across domains. This assumption, however, is deemed unrealistic in many clinical settings. For example, tumors with different grades are likely to exhibit different data distributions, due to varying degrees of tumor severity and growth patterns  differs from a testing distribution . This is because the majority of deep learning architectures assume that the source and target data distributions are independent and identically distributed  has been developed   and a target domain pt, where x indicates the to be segmented image and y is its corresponding label of the segmentation map. In the setting of black-box UDA segmentation, we have a segmentation network fs trained with a labeled source domain set i.i.d. from ps, where fs is fixed and accessed only through a nontransparent API during the adaptation stage. At the adaptation stage, we only have access to a black-box fs and an unlabeled target domain set i.i.d. from the marginal distribution pt(x), to train a target domain network ft to achieve a good segmentation performance in the target domain. It is noteworthy that the backbones of fs and ft do not need to be the same. The network structure details may also not be available at the adaptation stage.Considering the potential distribution shift between two domains, we assume that there are a source domain In this work, we propose a practical solution to black-box UDA for segmentation with a noise-aware knowledge distillation scheme using pseudo labels with exponential mixup decay (EMD). The framework is shown in A good source model is a basis for the target domain adaptation performance. The UDA is motivated by the following theorem Kouw, :Theorem 1 For a hypothesis hh in the source and target domains, respectively, and d[·] measures the divergence of the marginal distributions of x between two domains  − pt(y|x)|, Ex~pt|ps(y|x) − pt(y|x)|] is usually a small value and does not affect the performance. A small where For the supervision of training, a cross-entropy (CE) loss is usually used for optimization. Specifically, the pixel-wise CE loss can be formulated as:H0 and W0 are the height and width of an image, n indexes the pixel, and i indexes class labels. We note that other loss functions can also be used, e.g., dice loss, IoU loss, and boundary loss  divergence between their pixel-wise softmax histogram distributions. In the conventional knowledge distillation, we assume that there is no domain shift, and the predictions of the teacher model can be reliable and simply be used as ground truth. However, due to the domain shift, the prediction of fs in the target domain can be noisy. Simply using it as ground truth cannot outperform the source models, which is not expected in the UDA setting.Following knowledge distillation  and ft(xt), and adjust their ratio for the pseudo label Accordingly, we resort to a self-training scheme n and ft(xt)n are the histogram distributions of the softmax output of the n-th pixel of the predictions fs(xt) and ft(xt), respectively. λ is the target adaptation momentum parameter with the exponential decay with respect to iteration I. λ0 is the initial weight of fs(xt), which is empirically set to 1. Therefore, along with the increase in iteration I, we have smaller λ, which adjusts the contribution of the source model prediction to be large at the start of the training and to be smaller at the later training epochs. The loss knowledge distillation with the EMD pseudo label can be formulated as:where H0 and W0 are the height and width of the image. We note that the KL divergence is a measure of how a probability distribution, e.g., the histogram distribution of ft(xt)n, is different from a reference probability distribution, e.g., the histogram distribution of ft gradually represents the target data.where In addition to the supervision signal provided by the source domain black-box model, we opt to explore unsupervised learning protocols for unlabeled target domain data. Unsupervised learning has a long history, and there are a number of possible solutions for segmentation. Among them, entropy minimization n close to a one-hot distribution, i.e., confident prediction. The unsupervised learning is combined collaboratively with the black-box source model supervision to update the target model.Minimizing In summary, our training objective can be formulated aswhere α is used to balance between the knowledge distillation with the EMD pseudo label and the entropy minimization scheme. Since the entropy minimization may lead to a trivial solution in that the prediction of any unlabeled target samples is the same one-hot prediction  and Hausdorff distance (HD) metrics  injected into the blood stream of a patient can pass to the brain parenchyma, appearing as bright regions in the post-contrast T1-weighted MR images. Recent literature shows that this information is also encoded in non-contrast MRI images  to some extent  and cross-modality  adaptation tasks, demonstrated that our proposed BBUDA outperformed the source model, by a large margin, and importantly, the DSC and HD metrics of our framework were comparable to those of the white-box UDA approaches. In this work, while we only investigated brain tumor segmentation under the cross-subtype or cross-modality settings, the model could be broadly applicable to any segmentation UDA tasks using different modalities. In addition, more advanced knowledge distillation and unsupervised learning methods could be easily added to further augment performance.The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the first author or corresponding author.Ethical review and approval were not required for the study on human participants in accordance with the local legislation and institutional requirements. This research study was conducted retrospectively using human subject data made available in open access by BraTS18.XL was involved in the conceptualization, implementation, programming, and manuscript writing. CY conceived the project and was involved in writing the manuscript. FX conceived the project and was involved in writing the manuscript. C-CK conceived and supervised the project. GEF conceived and supervised the project. J-WK conceived the project and was involved in writing the manuscript. JW conceived and supervised the project and was involved in the conceptualization and writing the manuscript. All authors read and approved the final manuscript.This work was partially supported by NIH R01DC018511 and P41EB022544.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The high temperature constitutive equation was: −1.Hot deformation behaviors of an antibacterial 50Cr15MoVCu tool steel were studied. The flow stress curves presented three typical characteristics: (i) a single peak dynamic recrystallization curve, (ii) a monotone incremental work-hardening curve, and (iii) the equilibrium dynamic recovery curve. The flow stress increased with the increase of the deformation rate at each deformation temperature and decreased with the increase of the deformation temperature at the same deformation rate. The thermal activation energy and material constants were Q of 461.6574 kJ/mol, A of 3.42 × 10 E. coli up to 99.9%. Ji et al. [E. coli up to 99.9%. The antibacterial mechanism of the above various Cu-containing alloys was attributed to the release of Cu ions from the corresponding alloys [50Cr15MoV martensitic stainless steel has been widely used as kitchen knives due to its high strength, hardness, and good corrosion resistance ,3,4. Cu g alloys . Based oHowever, Cu needs to be controlled in the traditional alloy design and manufacturing process, because it can cause the thermal embrittlement of the alloy . Under tA previous study showed that the cracking failure phenomenon of a kitchen knife made by 5Cr15MoV steel was mainly due to the organizational stress caused by carbide segregation in the steel. The overhigh hardness and substandard chemical composition were the secondary causes of cracking . AnotherThe chemical composition of the 50Cr15MoVCu steel in this work is shown in −1, respectively. In order to ensure a uniform deformation and minimize the frictional effect, a tantalum foil with a thickness of 0.05 mm was introduced between the indenter and transverse surface of the specimen. Pure nickel and graphite were applied between the tantalum sheet and the specimen to prevent melting and improve the conductivity of the specimen during deformation. All the specimens were homogenized in Ar gas at 1200 °C for 5 min to eliminate the effect of the precipitations formed during solidification and forging on the deformation and to ensure the microstructure consistency of each group of specimens. The homogenized specimens were cooled at 10 °C/s to the designed deformation temperatures for 1 min and were compressed using the designed strain rate at a true strain of 0.9 (~60% height reduction), followed by water quenching to room temperature immediately to preserve the as-deformed microstructure. The procedures are schematically illustrated in The steel was melted in a vacuum induction-melting furnace and then forged into a bar with diameter of 22 mm at 1200 °C. The specimens for the compression test were cut from the center of the bar and machined into dimensions of 8 mm in diameter and 12 mm in gauge length. The compression tests were conducted on a Gleeble-3800 thermal-mechanical simulator to study the deformation characteristics of the experimental steel, in which temperatures at 900, 950, 1000, 1050, and 1100 °C we chosen, and the strain rates were chosen as 0.001, 0.01, 0.1, 1, and 10 s2O (10 mL) + HCl (4 mL) + HNO3 (4 mL). Then, the microstructures of the middle shadow area were observed by an optical microscope .The high-temperature constitutive equation was constructed based on the relationship between the stress, strain, strain rate, and temperature that was fitted by the data of the critical value at which dynamic recrystallization occurred. The critical value was deduced from the thermal deformation results using a differential function. Compressed specimens were cut at ~1/3 of the diameter from the edge along the direction of the hot compression to ensure the accuracy and stability of the thermal deformation, as schematically illustrated in −1 had a peak. The stress increased rapidly with the increase of the strain at the initial stage of deformation, reached a peak, and then decreased to a stable state. This clearly confirmed the occurrence of dynamic recrystallization (DRX). Flow curves at a high temperature (higher than 900 °C) and a strain rate of 1 s−1 had a monotonous increase. The stress increased rapidly with the increase of the strain at the initial stage of deformation and then increased slowly after reaching a certain critical value at which dynamic recovery (DRV) occurred. The increase in the stress could be attributed to the dominant effect of the work-hardening mechanism over the softening mechanism taking place, such as DRX and DRV [−1 firstly increased rapidly and then tended to be gentle and flat. There was no obvious peak stress on the curves, which should be attributed to the equilibrium between work hardening and DRV [All flow stress curves showed three typical characteristics, as reported : (i) a s and DRV . Flow cu and DRV ,14. −1, the movement capacity of the dislocation increased, the number of recrystallization nucleation increased, and accordingly, a typical DRX curve was presented.Moreover, the flow stress and the deformation temperature presented a negative correlation, i.e., the lower flow stress corresponded to the higher deformed temperature at the same deformation rate, as shown in −1−1); and Q is the deformation activation energy. The above three constitutive equations can be used for thermal deformation under different conditions. Especially, Equation (3) is a hyperbolic sine function that can be used for all stress conditions. Moreover, the Z-parameter method proposed by Zener and Hollomon [Reasonable hot processing technology can significantly reduce the porosity, shrinkage, segregation, and cracks, which often happen in the processing of metals. A constitutive equation that describes the relationship among the temperature, deformation rate, and flow stress can be used to predict the stress–strain behavior in hot processing. To date, the constitutive equation obtained from the hyperbolic sinusoidal formula modified by Sellar and Tegart  has beenHollomon  can reflEquation (3) is substituted into Equation (4), and the following expression is obtained: The peak stress–strain values under different conditions are recorded, and the constitutive equation is obtained by solving each of its parameters. The specific calculation process is described as follows: (i) Equation (5) is transformed into a linear function; (ii) the peak stress, peak strain, and stress value under a specific strain are substituted into the linear function that has different parameters after deformation, and the slope and intercept of the curve after the fitting are recorded; and (iii) the obtained slope and intercept are substituted to obtain the final constitutive equation. Equations (1) and (2) are taken as the natural logarithms, and the following formulas are obtained: −1. As shown in Equation (3) is taken as the natural logarithm, and the following formula is obtained: 17 by fitting As shown in m), the fluctuation of Q is strongly influenced by the solid solution strengthening of alloying elements, the occurrence of dynamic recrystallization, the induced precipitation of a precipitated phase, and phase transformation. In this work, the Q value of the 50Cr15MoVCu tool steel is 461.6574 kJ/mol, much higher than the self-diffusion activation energy of γ-Fe , and acThe thermal processing maps can be applied to optimize the hot workability and control the microstructure in a wide temperature and strain rate regimes for hot working. It is of great significance to build a thermal processing map that can intuitively evaluate the thermal processing properties of metals and reflect the rationality of the thermal deformation parameters. At present, the main model of the thermal processing map is the dynamic material model (DMM), and the obtained thermal processing map includes a power dissipation diagram and rheological instability diagram .According to the DMM, when the deformation temperature is constant, the relationship between the flow stress and strain rate of the experimental steel in the hot compression process is described by : (10)σ=KEquation (10) is a basic dissipation relation. According to the dissipative structure theory, the total energy P given externally can be divided into two parts during thermal deformation: (i) the energy is lost in the form of heat during deformation, identified as G, and (ii) the energy is consumed for the internal microstructure evolution, identified as J. The relationship among P, J, and G is expressed as follows :(11)G=∫0When the deformation temperature and strain rate are constant, J can be obtained by combining Equations (10) and (13): Based on Equations (13) and (14), the relative values of G and J can be determined by m as follows:When m ranges from 0 to 1, the metal is in a dissipative state. When m is 1, half the total input energy is used in the microstructural change, which is an ideal state, and J reaches the peak value: The energy dissipation efficiency factor, ƞ, which is the dimensionless parameter value and is derived from the ratio of Equations (14)–(16) is defined as the ratio of the energy consumed by microstructural evolution in the thermal processing process to the energy in the ideal state . That isTherefore, ƞ is closely related to m, which depends on the deformation temperature, strain rate, and deformation amount. The value of ƞ under different conditions can be calculated by substituting the m value, and thus, the energy dissipation diagram can be obtained. The solution of the m value is described as follows. During thermal deformation, the relationship between ln σ and ln In which a, b, c, and d can be obtained by fitting the data at 900, 950, 1000, 1050, and 1100 °C, respectively. Therefore, the m value under different temperatures and strain rates can be obtained by combining Equations (12) and (16):Based on the extreme value principle of irreversible thermodynamics, Ziegler H et al. proposed the continuous instability judgment of the dynamic material model , which cWhen ξ is less than 0, the material is in the instability zone of thermal processing. When ξ is more than 0, the material is in the safety zone of thermal processing, and finally, the thermal processing diagrams are obtained. −1. When the deformation temperature is above 1000 °C and the deformation rate is 0.1–10 s−1, there are some defect areas, indicating that the experimental steel is sensitive to high temperatures and high strain rates. When the deformation temperature ranges from 900 °C to 950 °C and the deformation rate ranges from 0.1 to 10 s−1, there are also instability zones at this low deformation temperature, indicating that the deformation rate has a greater impact on the instability zone under this strain condition. Finally, by observing and analyzing −1 can ensure the stability, low deformation resistance, and the highest energy dissipation efficiency. However, the strain rate in an actual production is about 1 s−1, considering the time benefit. Even so, according to the maps above, the temperature can be controlled at 985–1000 °C to prevent instability at this deformation rate. −1 to 10 s−1. As shown in −1. In this case, the number of recrystallization nucleation increases, and the deformation time is short. Therefore, the grains cannot grow up fully, and there is fine and equiaxed grains in the microstructure. −1, the average particle size of the original austenite grain is about 200 μm. According to GB/T 4237-2015, there are no requirements for the grain size of the 50Cr15MoVCu steel plate. Since there are many subsequent processes, such as aging and annealing, the grain size will be greatly improved.It is necessary to add that, at a deformation temperature of 1050 °C and deformation rate of 0.001 s−1. At the deformation rate of 0.1 s−1 and the deformation temperature of 900 °C, the obtained microstructure is elongated, as shown in The flow stress curves have three typical characteristics: (i) a single peak dynamic recrystallization curve, (ii) a monotone incremental work-hardening curve, and (iii) the equilibrium dynamic recovery curve.The flow stress increased with the increase of the deformation rate at each deformation temperature and decreased with the increase of the deformation temperature at the same deformation rate.The high temperature constitutive equation is:4.−1 can ensure the highest energy dissipation efficiency without an instability phenomenon.Processing parameters with 1050 °C and a strain rate of 0.001 s


The linear ubiquitin chain assembly complex synthesises linear Ub chains which constitute a binding and activation platform for components of the TNF signalling pathway. One of the components of LUBAC is the ubiquitin ligase HOIL-1 which has been shown to generate oxyester linkages on several proteins and on linear polysaccharides. We show that HOIL-1 activity requires linear tetra-Ub binding which enables HOIL-1 to mono-ubiquitylate linear Ub chains and polysaccharides. Furthermore, we describe the crystal structure of a C-terminal tandem domain construct of HOIL-1 comprising the IBR and RING2 domains. Interestingly, the structure reveals a unique bi-nuclear Zn-cluster which substitutes the second zinc finger of the canonical RING2 fold. We identify the C-terminal histidine of this bi-nuclear Zn-cluster as the catalytic base required for the ubiquitylation activity of HOIL-1. Our study suggests that the unique zinc-coordinating architecture of RING2 provides a binding platform for ubiquitylation targets. The activated ubiquitin is then moved in a transthiolation reaction from the E2 onto a conserved cysteine located in the first zinc-coordinating loop of the RING2 domain to form a thioester intermediate between ubiquitin and the RBR ligase. Substrate binding near the Ub-thioester intermediate allows an aminolysis reaction to occur which attaches Ub to the target protein. Linear Ub chain synthesis by LUBAC is carried out by the ligase component HOIP which binds to an acceptor Ub molecule in the vicinity of the Ub-thioester at the RING2 domain that leads to formation of a peptide bond between the C-terminus of the donor Ub and the N-terminus of the acceptor Ub  for visualization of Coomassie Brilliant blue, Lumitein or Cy5.ITC measurements were performed using PEAQ-ITC micro calorimeter . All samples were dialyzed into buffer containing 50 mM HEPES pH 7.5, 150 mM NaCl and 1 mM TCEP. Titrations were performed at 20°C with 50 µM of HOIL-1 loaded into the cell and 500 µM linear tetra Ub into the syringe.2+-SAD phasing. Heavy-atom search, density modification and initial model building was performed using Phenix AutoSol  PEG 8000. Crystals were flash-frozen in the reservoir solution containing 30% glycerol. Crystals of HOIL-1 (366–510) diffracted to 2.44 Å. A data set was collected on beamline IO2 at the Diamond Light Source  and processed using XDS . The str AutoSol . The mod AutoSol . The ste AutoSol . Data co AutoSol . The atoin vitro reconstitution system which consists of purified components of the ubiquitylation cascade. Ubiquitylation of maltoheptaose is observed after 10 min in the presence of tetra-Ub  of the RING2 domain . A sequeR domain . K457 ofR domain . This stR domain , questioactivity .via a unique structural feature not observed in any other RBR ligase. Our structural and biochemical analysis identifies the C-terminal H510 as the catalytic base in HOIL-1. The residue is placed next to the catalytic cysteine through a stretch of 37 residues which fold into a binuclear zinc-cluster. We hypothesize that the extended loop structure of the C-terminal zinc-cluster creates a substrate binding platform for HOIL-1. Specific substrate binding domains have not been identified for any RBR ligase. Based on structural studies on other RBR members such as HOIP, RNF216, and HHARI it becomes increasingly evident that the C-terminal half of RING2 next to the first zinc finger generally determines the substrate specificity in RBR ligases (via interaction of the RING2 domain with its RING0 domain, while HHARI employs the C-terminal Ariadne domain to shield the catalytic cysteine in RING2 (HOIL-1 is a member of the RBR E3 ligase family with the ability to ubiquitylate its targets by linking a hydroxy group of the substrate to the C-terminus of Ub. The recent discovery that the resulting ester bonds cannot only be created on proteins but also on carbohydrates, opens new possibilities for the ubiquitin system beyond its definition as a posttranslational modification. Our study sheds light on the structural requirements for the ligase activity of HOIL-1 and uncovers a notable variation of the ubiquitin transfer activity in RBR ligases. Several studies have demonstrated that RBR ligases utilize a histidine as a general base for catalysis, which is located in the first zinc coordinating loop of RING2 next to the catalytic cysteine  Cotton . HOIL-1  ligases  (Figure  ligases . The dis ligases  (Nitschk ligases . The ove ligases  (Lechten ligases . Since tin RING2  (Duda etin RING2 . The strin RING2  (Kelsallin RING2 . It is tIn summary, we have uncovered that HOIL-1 adopts a unique structure in form of a binuclear zinc-cluster which is required for its catalytic function. To further dissect the mechanism of HOIL-1 catalysis it will be essential to obtain high-resolution structures of HOIL-1 in its active conformation bound to its substrates. Future studies need to provide an understanding how HOIL-1 is able to ubiquitylate carbohydrates in molecular detail.