3,737 research outputs found

    Computational techniques to interpret the neural code underlying complex cognitive processes

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    Advances in large-scale neural recording technology have significantly improved the capacity to further elucidate the neural code underlying complex cognitive processes. This thesis aimed to investigate two research questions in rodent models. First, what is the role of the hippocampus in memory and specifically what is the underlying neural code that contributes to spatial memory and navigational decision-making. Second, how is social cognition represented in the medial prefrontal cortex at the level of individual neurons. To start, the thesis begins by investigating memory and social cognition in the context of healthy and diseased states that use non-invasive methods (i.e. fMRI and animal behavioural studies). The main body of the thesis then shifts to developing our fundamental understanding of the neural mechanisms underpinning these cognitive processes by applying computational techniques to ana lyse stable large-scale neural recordings. To achieve this, tailored calcium imaging and behaviour preprocessing computational pipelines were developed and optimised for use in social interaction and spatial navigation experimental analysis. In parallel, a review was conducted on methods for multivariate/neural population analysis. A comparison of multiple neural manifold learning (NML) algorithms identified that non linear algorithms such as UMAP are more adaptable across datasets of varying noise and behavioural complexity. Furthermore, the review visualises how NML can be applied to disease states in the brain and introduces the secondary analyses that can be used to enhance or characterise a neural manifold. Lastly, the preprocessing and analytical pipelines were combined to investigate the neural mechanisms in volved in social cognition and spatial memory. The social cognition study explored how neural firing in the medial Prefrontal cortex changed as a function of the social dominance paradigm, the "Tube Test". The univariate analysis identified an ensemble of behavioural-tuned neurons that fire preferentially during specific behaviours such as "pushing" or "retreating" for the animal’s own behaviour and/or the competitor’s behaviour. Furthermore, in dominant animals, the neural population exhibited greater average firing than that of subordinate animals. Next, to investigate spatial memory, a spatial recency task was used, where rats learnt to navigate towards one of three reward locations and then recall the rewarded location of the session. During the task, over 1000 neurons were recorded from the hippocampal CA1 region for five rats over multiple sessions. Multivariate analysis revealed that the sequence of neurons encoding an animal’s spatial position leading up to a rewarded location was also active in the decision period before the animal navigates to the rewarded location. The result posits that prospective replay of neural sequences in the hippocampal CA1 region could provide a mechanism by which decision-making is supported

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    Investigating ADI-PEG20 metabolic therapy to target improved anti-cancer immune responses and outcomes from chimeric antigen receptor T-cell therapy

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    Amino acid starvation with the asparagine degrading enzyme asparaginase has been a key component of therapy for Acute Lymphoblastic Leukaemia (ALL) since its introduction in the 1960s and 70s, and has contributed to the radical improvement of treatment outcomes during this time, especially in children. However, it is a high toxicity agent and is therefore reduced or omitted from treatment protocols used for some adults, where outcomes lag behind those of paediatric counterparts. Recently, an alternative amino acid starvation approach with arginine degradation has gained attention in solid cancer for tumours lacking the arginine synthesising enzyme Argininosuccinate synthetase 1 (ASS1), with a survival benefit demonstrated as part of multi-agent treatment of mesothelioma, along with a favourable safety profile. We therefore aimed to characterise the scope, functionality and potential of arginine starvation as a low-toxicity addition to ALL therapy, particularly for those patients or phases of treatment where asparaginase is not suitable. Through analysis of large scale transcriptome data we show that large portions of both adult and paediatric B-ALL express non-random, low-levels of ASS1 and this is associated with consistent alterations in a wider network of metabolism related genes. This finding theoretically supports the usage of arginine starvation as therapy for B-ALL, since deficient expression of ASS1 predicts impaired capacity for arginine synthesis and therefore vulnerability to its degradation. Using in vitro cell line models as well as mouse models of primary human B-ALL we then show that pegylated arginine deiminase (ADI-PEG20), the clinical grade arginine degrading enzyme that has reached phase 3 trials in solid tumour oncology, leads to cell cycle arrest, DNA damage and apoptosis with caspase cleavage in those tumours where baseline ASS1 expression is lowest. Furthermore, we show that the effect of ADI-PEG20 can be potentiated when combined with BH3 mimetic agents, along with an additive effect when combined with the standard of care drug dexamethasone. Finally, based on a hypothesised interaction between arginine starvation and the death receptor apoptosis pathway, which is known to be a key mediator of Chimeric Antigen Receptor (CAR)-T cell cytotoxicity, we show the results of an investigation into the potential of ADI-PEG20 to be used as a tumour-priming therapy prior to CAR-T. Using in vitro models, we delineate an exciting effect whereby pre-treatment of ALL blasts with ADI-PEG20 leads to improvement in CAR-T "cytokine efficiency", that we propose to have the potential to generate clinically significant benefits in terms of both treatment efficacy and toxicity management. Collectively, these data strongly support the further development of ADI-PEG20 for ASS1-low ALL, both as a component of pharmacological and immunotherapy treatment paradigms

    Enabling neighbour-labelling: using synthetic biology to explore how cells influence their neighbours

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    Cell-cell interactions are central to development, but exploring how a change in any given cell relates to changes in the neighbour of that cell can be technically challenging. Here, we review recent developments in synthetic biology and image analysis that are helping overcome this problem. We highlight the opportunities presented by these advances and discuss opportunities and limitations in applying them to developmental model systems

    Exploiting molecular vulnerabilities in genetically defined lung cancer models

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    Lung cancer is the leading cause of cancer-related death worldwide, with approximately 1.8 million deaths in 2020. Based on histology, lung cancer is divided into non-small cell lung cancer (NSCLC) (85 %) and small cell lung cancer (SCLC) (15 %). The most common types of NSCLC are lung squamous cell carcinoma (LUSC), large-cell carcinoma (LCC), and lung adenocarcinoma (LUAD). LUAD, the largest subgroup of NSCLC, is characterized by genomic alterations in oncogenic driver genes such as KRAS or EGFR. Mutations in the kinase domain of EGFR result in aberrant signaling activation and subsequent cancer development. Tyrosine kinase inhibitors (TKIs) selectively target and inhibit mutant kinases, thereby killing oncogene-addicted cancer cells. The introduction of TKIs into clinical practice shifted NSCLC treatment from cytotoxic chemotherapy towards precision medicine, improving both survival and the quality of life during therapy. Patients with canonical EGFR mutations like the point-mutation L858R or exon 19 deletions mutations, which account for the majority of EGFR mutations, respond well to EGFR targeted TKIs. However, rare mutations like insertions in exon 20 insertions still represent challenging drug targets. C-helix–4-loop insertion mutations in exon 20 push the C-helix into the active, inward position without altering the binding site for TKIs. This leaves the binding site for TKIs in kinases with exon 20ins mutations highly similar to wild type (WT) EGFR. Thus, the challenge in the development of exon 20 inhibitors is the design of wild type sparing small molecules. Here, we analyzed a novel small molecule EGFR inhibitor (LDC0496) targeting an emerging cleft in exon 20-mutated EGFR to achieve selectivity over the wild type. In contrast to classical EGFR TKIs, LDC0496 reduces the cellular viability of EGFR exon 20 mutated cells but spares wild type EGFR. Targeted therapy inevitably results in the development of on- or off-target resistance. Drug induced resistance mutations require the constant development of novel drugs targeting the diverse landscape of resistance mechanisms. We detected BRAF mutations in EGFR-driven lung cancer patients as a resistance mechanism to EGFR inhibitors. Notably, we also detected co-occurrence of EGFR and BRAF mutations before treatment start. Combination treatment of EGFR and mitogen-activated protein kinase kinase (MEK) inhibition displayed activity in BRAF- and EGFR-mutated xenograft studies, therefore providing a treatment strategy to overcome BRAF mutation as a resistance mechanism. Compared to NSCLC, SCLC lacks druggable targets and the initial chemosensitive state rapidly turns into a chemoresistance state. SCLC is genetically defined by a biallelic loss of tumor suppressors RB1 and TP53 and alterations of MYC family members. The transcription factor MYC is a challenging target that cannot be directly targeted. Therefore, alternative strategies are needed, for example targeting its co-factors, such as the MYC-interacting zinc finger protein 1 (MIZ1). To study the complex interplay of Myc–Miz1 in SCLC, we developed a novel mouse model with a truncated Miz1, which is unable to stably bind chromatin (RPMM: Rb1fl/flTrp53fl/flMycLSL/LSLMIZ1∆POZfl/fl). Compared to Miz1 wild type the characterization of the novel mouse model revealed tumor-onset, localization, size and immune infiltration to be unaffected by the ablation of the Miz1-POZ domain, but mice with Miz1-∆POZ live longer, exhibit an increased number of apoptotic cells and are more sensitive towards chemotherapy. We found that truncated Miz1 alter SCLC tumorigenesis towards a less aggressive phenotype and prolongs the chemosensitive state. Our study highlights alternative strategies to define novel vulnerabilities and options to overcome chemoresistance

    SUTMS - Unified Threat Management Framework for Home Networks

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    Home networks were initially designed for web browsing and non-business critical applications. As infrastructure improved, internet broadband costs decreased, and home internet usage transferred to e-commerce and business-critical applications. Today’s home computers host personnel identifiable information and financial data and act as a bridge to corporate networks via remote access technologies like VPN. The expansion of remote work and the transition to cloud computing have broadened the attack surface for potential threats. Home networks have become the extension of critical networks and services, hackers can get access to corporate data by compromising devices attacked to broad- band routers. All these challenges depict the importance of home-based Unified Threat Management (UTM) systems. There is a need of unified threat management framework that is developed specifically for home and small networks to address emerging security challenges. In this research, the proposed Smart Unified Threat Management (SUTMS) framework serves as a comprehensive solution for implementing home network security, incorporating firewall, anti-bot, intrusion detection, and anomaly detection engines into a unified system. SUTMS is able to provide 99.99% accuracy with 56.83% memory improvements. IPS stands out as the most resource-intensive UTM service, SUTMS successfully reduces the performance overhead of IDS by integrating it with the flow detection mod- ule. The artifact employs flow analysis to identify network anomalies and categorizes encrypted traffic according to its abnormalities. SUTMS can be scaled by introducing optional functions, i.e., routing and smart logging (utilizing Apriori algorithms). The research also tackles one of the limitations identified by SUTMS through the introduction of a second artifact called Secure Centralized Management System (SCMS). SCMS is a lightweight asset management platform with built-in security intelligence that can seamlessly integrate with a cloud for real-time updates

    Synthetic Aperture Radar (SAR) Meets Deep Learning

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    This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

    Automatic Generation of Personalized Recommendations in eCoaching

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    Denne avhandlingen omhandler eCoaching for personlig livsstilsstøtte i sanntid ved bruk av informasjons- og kommunikasjonsteknologi. Utfordringen er å designe, utvikle og teknisk evaluere en prototyp av en intelligent eCoach som automatisk genererer personlige og evidensbaserte anbefalinger til en bedre livsstil. Den utviklede løsningen er fokusert på forbedring av fysisk aktivitet. Prototypen bruker bærbare medisinske aktivitetssensorer. De innsamlede data blir semantisk representert og kunstig intelligente algoritmer genererer automatisk meningsfulle, personlige og kontekstbaserte anbefalinger for mindre stillesittende tid. Oppgaven bruker den veletablerte designvitenskapelige forskningsmetodikken for å utvikle teoretiske grunnlag og praktiske implementeringer. Samlet sett fokuserer denne forskningen på teknologisk verifisering snarere enn klinisk evaluering.publishedVersio

    Development of an R package to learn supervised classification techniques

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    This TFG aims to develop a custom R package for teaching supervised classification algorithms, starting with the identification of requirements, including algorithms, data structures, and libraries. A strong theoretical foundation is essential for effective package design. Documentation will explain each function’s purpose, accompanied by necessary paperwork. The package will include R scripts and data files in organized directories, complemented by a user manual for easy installation and usage, even for beginners. Built entirely from scratch without external dependencies, it’s optimized for accuracy and performance. In conclusion, this TFG provides a roadmap for creating an R package to teach supervised classification algorithms, benefiting researchers and practitioners dealing with real-world challenges.Grado en Ingeniería Informátic
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