7,994 research outputs found

    Aberrant phase separation and nucleolar dysfunction in rare genetic diseases

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    Thousands of genetic variants in protein-coding genes have been linked to disease. However, the functional impact of most variants is unknown as they occur within intrinsically disordered protein regions that have poorly defined functions1-3. Intrinsically disordered regions can mediate phase separation and the formation of biomolecular condensates, such as the nucleolus4,5. This suggests that mutations in disordered proteins may alter condensate properties and function6-8. Here we show that a subset of disease-associated variants in disordered regions alter phase separation, cause mispartitioning into the nucleolus and disrupt nucleolar function. We discover de novo frameshift variants in HMGB1 that cause brachyphalangy, polydactyly and tibial aplasia syndrome, a rare complex malformation syndrome. The frameshifts replace the intrinsically disordered acidic tail of HMGB1 with an arginine-rich basic tail. The mutant tail alters HMGB1 phase separation, enhances its partitioning into the nucleolus and causes nucleolar dysfunction. We built a catalogue of more than 200,000 variants in disordered carboxy-terminal tails and identified more than 600 frameshifts that create arginine-rich basic tails in transcription factors and other proteins. For 12 out of the 13 disease-associated variants tested, the mutation enhanced partitioning into the nucleolus, and several variants altered rRNA biogenesis. These data identify the cause of a rare complex syndrome and suggest that a large number of genetic variants may dysregulate nucleoli and other biomolecular condensates in humans.© 2023. The Author(s)

    The Globalization of Artificial Intelligence: African Imaginaries of Technoscientific Futures

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    Imaginaries of artificial intelligence (AI) have transcended geographies of the Global North and become increasingly entangled with narratives of economic growth, progress, and modernity in Africa. This raises several issues such as the entanglement of AI with global technoscientific capitalism and its impact on the dissemination of AI in Africa. The lack of African perspectives on the development of AI exacerbates concerns of raciality and inclusion in the scientific research, circulation, and adoption of AI. My argument in this dissertation is that innovation in AI, in both its sociotechnical imaginaries and political economies, excludes marginalized countries, nations and communities in ways that not only bar their participation in the reception of AI, but also as being part and parcel of its creation. Underpinned by decolonial thinking, and perspectives from science and technology studies and African studies, this dissertation looks at how AI is reconfiguring the debate about development and modernization in Africa and the implications for local sociotechnical practices of AI innovation and governance. I examined AI in international development and industry across Kenya, Ghana, and Nigeria, by tracing Canada’s AI4D Africa program and following AI start-ups at AfriLabs. I used multi-sited case studies and discourse analysis to examine the data collected from interviews, participant observations, and documents. In the empirical chapters, I first examine how local actors understand the notion of decolonizing AI and show that it has become a sociotechnical imaginary. I then investigate the political economy of AI in Africa and argue that despite Western efforts to integrate the African AI ecosystem globally, the AI epistemic communities in the continent continue to be excluded from dominant AI innovation spaces. Finally, I examine the emergence of a Pan-African AI imaginary and argue that AI governance can be understood as a state-building experiment in post-colonial Africa. The main issue at stake is that the lack of African perspectives in AI leads to negative impacts on innovation and limits the fair distribution of the benefits of AI across nations, countries, and communities, while at the same time excludes globally marginalized epistemic communities from the imagination and creation of AI

    Improving diagnostic procedures for epilepsy through automated recording and analysis of patients’ history

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    Transient loss of consciousness (TLOC) is a time-limited state of profound cognitive impairment characterised by amnesia, abnormal motor control, loss of responsiveness, a short duration and complete recovery. Most instances of TLOC are caused by one of three health conditions: epilepsy, functional (dissociative) seizures (FDS), or syncope. There is often a delay before the correct diagnosis is made and 10-20% of individuals initially receive an incorrect diagnosis. Clinical decision tools based on the endorsement of TLOC symptom lists have been limited to distinguishing between two causes of TLOC. The Initial Paroxysmal Event Profile (iPEP) has shown promise but was demonstrated to have greater accuracy in distinguishing between syncope and epilepsy or FDS than between epilepsy and FDS. The objective of this thesis was to investigate whether interactional, linguistic, and communicative differences in how people with epilepsy and people with FDS describe their experiences of TLOC can improve the predictive performance of the iPEP. An online web application was designed that collected information about TLOC symptoms and medical history from patients and witnesses using a binary questionnaire and verbal interaction with a virtual agent. We explored potential methods of automatically detecting these communicative differences, whether the differences were present during an interaction with a VA, to what extent these automatically detectable communicative differences improve the performance of the iPEP, and the acceptability of the application from the perspective of patients and witnesses. The two feature sets that were applied to previous doctor-patient interactions, features designed to measure formulation effort or detect semantic differences between the two groups, were able to predict the diagnosis with an accuracy of 71% and 81%, respectively. Individuals with epilepsy or FDS provided descriptions of TLOC to the VA that were qualitatively like those observed in previous research. Both feature sets were effective predictors of the diagnosis when applied to the web application recordings (85.7% and 85.7%). Overall, the accuracy of machine learning models trained for the threeway classification between epilepsy, FDS, and syncope using the iPEP responses from patients that were collected through the web application was worse than the performance observed in previous research (65.8% vs 78.3%), but the performance was increased by the inclusion of features extracted from the spoken descriptions on TLOC (85.5%). Finally, most participants who provided feedback reported that the online application was acceptable. These findings suggest that it is feasible to differentiate between people with epilepsy and people with FDS using an automated analysis of spoken seizure descriptions. Furthermore, incorporating these features into a clinical decision tool for TLOC can improve the predictive performance by improving the differential diagnosis between these two health conditions. Future research should use the feedback to improve the design of the application and increase perceived acceptability of the approach

    An American Knightmare: Joker, Fandom, and Malicious Movie Meaning-Making

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    This monograph concerns the long-standing communication problem of how individuals can identify and resist the influence of unethical public speakers. Scholarship on the issue of what Socrates & Plato called the “Evil Lover” – i.e., the ill-intended rhetor – began with the Greek philosophers, but has carried into [post]Modern anxieties. For instance, the study of Nazi propaganda machines, and the rhetoric of Hitler himself, rejuvenated interest in the study of speech and communication in the U.S. and Europe. Whereas unscrupulous sophists used lectures and legal forums, and Hitler used a microphone, contemporary Evil Lovers primarily draw on new, internet-related tools to share their malicious influence. These new tools of influence are both more far-reaching and more subtle than the traditional practices of listening to a designated speaker appearing at an overtly political event. Rhetorician Ashley Hinck has recently noted the ways that popular culture – communication about texts which are commonly accessible and shared – are now significant sites through which citizens learn moral and political values. Accordingly, the talk of internet influencers who interpret popular texts for other fans has the potential to constitute strong persuasive power regarding ethics and civic responsibility. The present work identifies and responds to a particular case example of popular culture text that has been recently, and frequently, leveraged in moral and civic discourses: Todd Phillips’ Joker. Specifically, this study takes a hermeneutic approach to understanding responses, especially those explicitly invoking political ideology, to Joker as a method of examining civic meaning-making. A special emphasis is placed on the online film criticisms of Joker from white nationalist movie fans, who clearly exemplify ways that media responses can be leveraged by unethical speakers (i.e., Evil Lovers) and subtly diffused. The study conveys that these racist movie fans can embed values related to “trolling,” incelism, and xenophobia into otherwise seemingly innocuous talk about film. While the sharing of such speech does not immediately mean its positive reception, this kind of communication yet constitutes a new and understudied attack on democratic values such as justice and equity. The case of white nationalist movie fan film criticism therefore reflects a particular brand of communicative strategy for contemporary Evil Lovers in communicating unethical messages under the covert guise of mundane movie talk

    InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation

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    This paper introduces InternVid, a large-scale video-centric multimodal dataset that enables learning powerful and transferable video-text representations for multimodal understanding and generation. The InternVid dataset contains over 7 million videos lasting nearly 760K hours, yielding 234M video clips accompanied by detailed descriptions of total 4.1B words. Our core contribution is to develop a scalable approach to autonomously build a high-quality video-text dataset with large language models (LLM), thereby showcasing its efficacy in learning video-language representation at scale. Specifically, we utilize a multi-scale approach to generate video-related descriptions. Furthermore, we introduce ViCLIP, a video-text representation learning model based on ViT-L. Learned on InternVid via contrastive learning, this model demonstrates leading zero-shot action recognition and competitive video retrieval performance. Beyond basic video understanding tasks like recognition and retrieval, our dataset and model have broad applications. They are particularly beneficial for generating interleaved video-text data for learning a video-centric dialogue system, advancing video-to-text and text-to-video generation research. These proposed resources provide a tool for researchers and practitioners interested in multimodal video understanding and generation.Comment: Data and Code: https://github.com/OpenGVLab/InternVideo/tree/main/Data/InternVi

    Facilitating prosociality through technology: Design to promote digital volunteerism

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    Volunteerism covers many activities involving no financial rewards for volunteers but which contribute to the common good. There is existing work in designing technology for volunteerism in HumanComputer Interaction (HCI) and related disciplines that focuses on motivation to improve performance, but it does not account for volunteer wellbeing. Here, I investigate digital volunteerism in three case studies with a focus on volunteer motivation, engagement, and wellbeing. My research involved volunteers and others in the volunteering context to generate recommendations for a volunteer-centric design for digital volunteerism. The thesis has three aims: 1. To investigate motivational aspects critical for enhancing digital volunteers’ experiences 2. To identify digital platform attributes linked to volunteer wellbeing 3. To create guidelines for effectively supporting volunteer engagement in digital volunteering platforms In the first case study I investigate the design of a chat widget for volunteers working in an organisation with a view to develop a design that improves their workflow and wellbeing. The second case study investigates the needs, motivations, and wellbeing of volunteers who help medical students improve their medical communication skills. An initial mixed-methods study was followed by an experiment comparing two design strategies to improve volunteer relatedness; an important indicator of wellbeing. The third case study looks into volunteer needs, experiences, motivations, and wellbeing with a focus on volunteer identity and meaning-making on a science-based research platform. I then analyse my findings from these case studies using the lens of care ethics to derive critical insights for design. The key contributions of this thesis are design strategies and critical insights, and a volunteer-centric design framework to enhance the motivation, wellbeing and engagement of digital volunteers

    AQ-GT: a Temporally Aligned and Quantized GRU-Transformer for Co-Speech Gesture Synthesis

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    The generation of realistic and contextually relevant co-speech gestures is a challenging yet increasingly important task in the creation of multimodal artificial agents. Prior methods focused on learning a direct correspondence between co-speech gesture representations and produced motions, which created seemingly natural but often unconvincing gestures during human assessment. We present an approach to pre-train partial gesture sequences using a generative adversarial network with a quantization pipeline. The resulting codebook vectors serve as both input and output in our framework, forming the basis for the generation and reconstruction of gestures. By learning the mapping of a latent space representation as opposed to directly mapping it to a vector representation, this framework facilitates the generation of highly realistic and expressive gestures that closely replicate human movement and behavior, while simultaneously avoiding artifacts in the generation process. We evaluate our approach by comparing it with established methods for generating co-speech gestures as well as with existing datasets of human behavior. We also perform an ablation study to assess our findings. The results show that our approach outperforms the current state of the art by a clear margin and is partially indistinguishable from human gesturing. We make our data pipeline and the generation framework publicly available

    Describing Faces for Identification: Getting the Message, But Not The Picture

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    Although humans rely on faces and language for social communication, the role of language in communicating about faces is poorly understood. Describing faces and identifying faces from verbal descriptions are important tasks in social and criminal justice settings. Prior research indicates that people have difficulty relaying face identity to others via verbal description, however little is known about the process, correlates, or content of communication about faces (hereafter ‘face communication’). In Chapter Two, I investigated face communication accuracy and its relationship with an individual’s perceptual face skill. I also examined the efficacy of a brief training intervention for improving face description ability. I found that individuals could complete face communication tasks with above chance levels of accuracy, in both interactive and non-interactive conditions, and that abilities in describing faces and using face descriptions for identification were related to an individual’s perceptual face skill. However, training was not effective for improving face description ability. In Chapter Three, I investigated qualitative attributes of face descriptions. I found no evidence of qualitative differences in face descriptions as a function of the describer’s perceptual skill with faces, the identification utility of descriptions, or the describer’s familiarity with the face. In Chapters Two and Three, the reliability of measures may have limited the ability to detect relationships between face communication accuracy and potential correlates of performance. Consequently, in Chapter Four, I examined face communication accuracy when using constrained face descriptions, derived using a rating scale, and the relationship between the identification utility of such descriptions and their reliability (test-retest and multi-rater). I found that constrained face descriptions were less useful for identification than free descriptions and the reliability of a description was unrelated to its identification utility. Together, findings in this thesis indicate that face communication is very challenging – both for individuals undertaking the task, and for researchers seeking to measure performance reliably. Given the mechanisms contributing to variance in face communication accuracy remain largely elusive, legal stakeholders would be wise to use caution when relying on evidence involving face description

    Modeling biological face recognition with deep convolutional neural networks

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    Deep convolutional neural networks (DCNNs) have become the state-of-the-art computational models of biological object recognition. Their remarkable success has helped vision science break new ground and recent efforts have started to transfer this achievement to research on biological face recognition. In this regard, face detection can be investigated by comparing face-selective biological neurons and brain areas to artificial neurons and model layers. Similarly, face identification can be examined by comparing in vivo and in silico multidimensional "face spaces". In this review, we summarize the first studies that use DCNNs to model biological face recognition. On the basis of a broad spectrum of behavioral and computational evidence, we conclude that DCNNs are useful models that closely resemble the general hierarchical organization of face recognition in the ventral visual pathway and the core face network. In two exemplary spotlights, we emphasize the unique scientific contributions of these models. First, studies on face detection in DCNNs indicate that elementary face selectivity emerges automatically through feedforward processing even in the absence of visual experience. Second, studies on face identification in DCNNs suggest that identity-specific experience and generative mechanisms facilitate this particular challenge. Taken together, as this novel modeling approach enables close control of predisposition (i.e., architecture) and experience (i.e., training data), it may be suited to inform long-standing debates on the substrates of biological face recognition.Comment: 41 pages, 2 figures, 1 tabl
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