1,792 research outputs found

    Dissecting structural and biochemical features of DNA methyltransferase 1

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    DNA methylation is an epigenetic modification found in every branch of life. An essential enzyme for the maintenance of DNA methylation patterns in mammals is DNA methyltransferase 1 (DNMT1). Its recruitment is regulated through its large N-terminus, which contains six annotated domains. Although most of these have been assigned a function, we are still lacking a holistic understanding of the enzyme's spatio-temporal regulation. Interestingly, a large segment of the N-terminus is devoid of any known domain and appears to be disordered in its sequence. Over the past years, such disordered sequences have increasingly gained attention, due to their role in forming biomolecular condensates through liquid-liquid phase separation (LLPS). These liquid compartments offer specific environmental conditions distinct from the surrounding that can enhance protein recruitment and function. In this work, we explore a potential role for the intrinsically disordered domain (IDR) in the recruitment of DNMT1. Taking an evolutionary approach, we uncover that structural features of the region that are key for IDR function are highly conserved. Moreover, we find conserved biochemical signatures compatible with a role in LLPS. Using a reconstitution assay and an opto-genetic approach in cells, we for the first time show that the DNMT1 IDR is capable of undergoing LLPS in vitro and in vivo. In addition, we define a novel region of interest (ROI) of about 120 amino acids in the IDR that appears to have been inserted in the ancestor of eutherian mammals. Although the ROI has a distinct biochemical signature, we find no effect on the LLPS behavior of the IDR. Therefore, we discuss other potential roles of the ROI related to DNA methylation, for example, imprinting. Finally, we lay the foundation for investigating a biological function of the IDR and establish a system for screening DNMT1 mutant phenotypes in mouse embryonic stem cells. Swift depletion of the endogenous protein is enabled by degron-mediated degradation, while our optimized construct design and efficient derivation strategy ensure the robust expression of the large transgenes. In combination with different methods for DNA methylation read-out, this system can now be used to study the role of the IDR and ROI in maintaining the steady-state level of DNA methylation against mechanisms of passive and active demethylation, but also for studying phenotypes affecting the efficiency of DNMT1 recruitment in the future

    3D Innovations in Personalized Surgery

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    Current practice involves the use of 3D surgical planning and patient-specific solutions in multiple surgical areas of expertise. Patient-specific solutions have been endorsed for several years in numerous publications due to their associated benefits around accuracy, safety, and predictability of surgical outcome. The basis of 3D surgical planning is the use of high-quality medical images (e.g., CT, MRI, or PET-scans). The translation from 3D digital planning toward surgical applications was developed hand in hand with a rise in 3D printing applications of multiple biocompatible materials. These technical aspects of medical care require engineers’ or technical physicians’ expertise for optimal safe and effective implementation in daily clinical routines.The aim and scope of this Special Issue is high-tech solutions in personalized surgery, based on 3D technology and, more specifically, bone-related surgery. Full-papers or highly innovative technical notes or (systematic) reviews that relate to innovative personalized surgery are invited. This can include optimization of imaging for 3D VSP, optimization of 3D VSP workflow and its translation toward the surgical procedure, or optimization of personalized implants or devices in relation to bone surgery

    AI: Limits and Prospects of Artificial Intelligence

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    The emergence of artificial intelligence has triggered enthusiasm and promise of boundless opportunities as much as uncertainty about its limits. The contributions to this volume explore the limits of AI, describe the necessary conditions for its functionality, reveal its attendant technical and social problems, and present some existing and potential solutions. At the same time, the contributors highlight the societal and attending economic hopes and fears, utopias and dystopias that are associated with the current and future development of artificial intelligence

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    Optimisation for Optical Data Centre Switching and Networking with Artificial Intelligence

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    Cloud and cluster computing platforms have become standard across almost every domain of business, and their scale quickly approaches O(106)\mathbf{O}(10^6) servers in a single warehouse. However, the tier-based opto-electronically packet switched network infrastructure that is standard across these systems gives way to several scalability bottlenecks including resource fragmentation and high energy requirements. Experimental results show that optical circuit switched networks pose a promising alternative that could avoid these. However, optimality challenges are encountered at realistic commercial scales. Where exhaustive optimisation techniques are not applicable for problems at the scale of Cloud-scale computer networks, and expert-designed heuristics are performance-limited and typically biased in their design, artificial intelligence can discover more scalable and better performing optimisation strategies. This thesis demonstrates these benefits through experimental and theoretical work spanning all of component, system and commercial optimisation problems which stand in the way of practical Cloud-scale computer network systems. Firstly, optical components are optimised to gate in 500ps\approx 500 ps and are demonstrated in a proof-of-concept switching architecture for optical data centres with better wavelength and component scalability than previous demonstrations. Secondly, network-aware resource allocation schemes for optically composable data centres are learnt end-to-end with deep reinforcement learning and graph neural networks, where 3×3\times less networking resources are required to achieve the same resource efficiency compared to conventional methods. Finally, a deep reinforcement learning based method for optimising PID-control parameters is presented which generates tailored parameters for unseen devices in O(103)s\mathbf{O}(10^{-3}) s. This method is demonstrated on a market leading optical switching product based on piezoelectric actuation, where switching speed is improved >20%>20\% with no compromise to optical loss and the manufacturing yield of actuators is improved. This method was licensed to and integrated within the manufacturing pipeline of this company. As such, crucial public and private infrastructure utilising these products will benefit from this work

    Duality, Derivative-Based Training Methods and Hyperparameter Optimization for Support Vector Machines

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    In this thesis we consider the application of Fenchel's duality theory and gradient-based methods for the training and hyperparameter optimization of Support Vector Machines. We show that the dualization of convex training problems is possible theoretically in a rather general formulation. For training problems following a special structure (for instance, standard training problems) we find that the resulting optimality conditions can be interpreted concretely. This approach immediately leads to the well-known notion of support vectors and a formulation of the Representer Theorem. The proposed theory is applied to several examples such that dual formulations of training problems and associated optimality conditions can be derived straightforwardly. Furthermore, we consider different formulations of the primal training problem which are equivalent under certain conditions. We also argue that the relation of the corresponding solutions to the solution of the dual training problem is not always intuitive. Based on the previous findings, we consider the application of customized optimization methods to the primal and dual training problems. A particular realization of Newton's method is derived which could be used to solve the primal training problem accurately. Moreover, we introduce a general convergence framework covering different types of decomposition methods for the solution of the dual training problem. In doing so, we are able to generalize well-known convergence results for the SMO method. Additionally, a discussion of the complexity of the SMO method and a motivation for a shrinking strategy reducing the computational effort is provided. In a last theoretical part, we consider the problem of hyperparameter optimization. We argue that this problem can be handled efficiently by means of gradient-based methods if the training problems are formulated appropriately. Finally, we evaluate the theoretical results concerning the training and hyperparameter optimization approaches practically by means of several example training problems

    Understanding comparative questions and retrieving argumentative answers

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    Making decisions is an integral part of everyday life, yet it can be a difficult and complex process. While peoples’ wants and needs are unlimited, resources are often scarce, making it necessary to research the possible alternatives and weigh the pros and cons before making a decision. Nowadays, the Internet has become the main source of information when it comes to comparing alternatives, making search engines the primary means for collecting new information. However, relying only on term matching is not sufficient to adequately address requests for comparisons. Therefore, search systems should go beyond this approach to effectively address comparative information needs. In this dissertation, I explore from different perspectives how search systems can respond to comparative questions. First, I examine approaches to identifying comparative questions and study their underlying information needs. Second, I investigate a methodology to identify important constituents of comparative questions like the to-be-compared options and to detect the stance of answers towards these comparison options. Then, I address ambiguous comparative search queries by studying an interactive clarification search interface. And finally, addressing answering comparative questions, I investigate retrieval approaches that consider not only the topical relevance of potential answers but also account for the presence of arguments towards the comparison options mentioned in the questions. By addressing these facets, I aim to provide a comprehensive understanding of how to effectively satisfy the information needs of searchers seeking to compare different alternatives

    Statistical Anomaly Discovery Through Visualization

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    Developing a deep understanding of data is a crucial part of decision-making processes. It often takes substantial time and effort to develop a solid understanding to make well-informed decisions. Data analysts often perform statistical analyses through visualization to develop such understanding. However, applicable insight can be difficult due to biases and anomalies in data. An often overlooked phenomenon is mix effects, in which subgroups of data exhibit patterns opposite to the data as a whole. This phenomenon is widespread and often leads inexperienced analysts to draw contradictory conclusions. Discovering such anomalies in data becomes challenging as data continue to grow in volume, dimensionality, and cardinality. Effectively designed data visualizations empower data analysts to reveal and understand patterns in data for studying such paradoxical anomalies. This research explores several approaches for combining statistical analysis and visualization to discover and examine anomalies in multidimensional data. It starts with an automatic anomaly detection method based on correlation comparison and experiments to determine the running time and complexity of the algorithm. Subsequently, the research investigates the design, development, and implementation of a series of visualization techniques to fulfill the needs of analysis through a variety of statistical methods. We create an interactive visual analysis system, Wiggum, for revealing various forms of mix effects. A user study to evaluate Wiggum strengthens understanding of the factors that contribute to the comprehension of statistical concepts. Furthermore, a conceptual model, visual correspondence, is presented to study how users can determine the identity of items between visual representations by interpreting the relationships between their respective visual encodings. It is practical to build visualizations with highly linked views informed by visual correspondence theory. We present a hybrid tree visualization technique, PatternTree, which applies the visual correspondence theory. PatternTree supports users to more readily discover statistical anomalies and explore their relationships. Overall, this dissertation contributes a merging of new visualization theory and designs for analysis of statistical anomalies, thereby leading the way to the creation of effective visualizations for statistical analysis

    Neuroinflammatory and protein responses to TBI

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    Background: Traumatic brain injury (TBI) is a leading cause of death and disability world-wide, affecting approximately 69 million individuals each year. It is also recognised as one of the major, modifiable risk factors in the development of neurodegenerative disease in later life, with exposure to moderate to severe single TBI and repetitive, mild TBI both recognised as contributing factors in the development of dementia. However, the neuropathological mechanisms contributing to late neurodegeneration remain uncertain. The complex polypathology emerging from the initial biomechanical injury mirror other neurodegenerative disease and include abnormal aggregation of proteins such as tau and Aβ and neuroinflammation, which will be explored in this thesis. Methodology: Utilising material from the Glasgow TBI archive, in cohorts of patients aged ≤60 exposed to acute and long-term survival following moderate to severe, single TBI and appropriately age-matched controls, the role of the cellular adaptive immune response to injury was assessed through immunocytochemistry for T- and B-lymphocyte populations. These cohorts as well as tissue from patients exposed to repetitive mild TBI (rTBI) and appropriately matched controls were studied to characterise the differential astroglial response to injury across injury subgroups through measurement of GFAP, AQP4 and NQO1 immunoreactivity. A cohort of patients aged ≥60 years who survived acutely following moderate to severe, single TBI compared to age-matched, uninjured controls with no history of neurodegenerative disease, were examined to evaluate the effect of age and TBI using modifications of clinically recognised, standardised, semi-quantitative scoring systems of tau and Aβ immunoreactivity. Lastly, a protocol for the assessment of our archival, FFPE tissue was devised to allow comparison of proteomes of cohorts of patients exposed to mild rTBI, AD patients and appropriately matched controls, using liquid-chromatography mass-spectrometry. Results: There is no histological evidence of a significant cellular response from the adaptive immune system following exposure to moderate to severe, single TBI in patients surviving acutely or long-term after injury, with no increase in T- or B-lymphocytes. There was, unexpectedly, a decrease in T-lymphocytes in long-term survivors of TBI. Contrastingly, there was an increase in reactive astrogliosis following exposure to TBI; demonstrated by an increase in AQP4-immunoractive astrocytes in acutely surviving patients and increase in GFAP expression in long-term surviving patients and an increase in NQO1 expression in rTBI patients compared to age-matched uninjured controls. There was also an increase in both AQP4 and GFAP expression in elderly uninjured controls compared to younger uninjured controls. Examining the expression of Aβ and tau in elderly patients exposed to single, moderate to severe TBI showed an increase in neuritic Aβ plaque and in the regional distribution of all plaque compared to uninjured, elderly controls, but no increase in expression or distribution of NFTs. Finally, a protocol for processing archival FFPE resulted in identification of 267 proteins from across rTBI, AD and uninjured, non-NND controls with significantly differential expression in 84 of them. Conclusions: Together, these findings increase understanding of the neuropathological changes occurring following moderate to severe, single TBI and repetitive TBI. These data demonstrate that there is an astrogliotic but not adaptive cellular response after moderate to severe single TBI whilst also showing that age is correlated with an increase in reactive astrogliosis for several markers. Age was also examined in the examination of proteinopathic changes following acute survival after moderate to severe injury and changes suggest that TBI may occur as a result of increased amyloid pathology as neuritic plaque was observed > 2 weeks after injury. Lastly, that archival FFPE samples were successfully processed to allow identification of proteins from across a range of cohorts, revealing differences in protein expression that underpin the neuropathological changes which contribute to the long-term, post-TBI neurodegenerative process
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