9,887 research outputs found

    Modular lifelong machine learning

    Get PDF
    Deep learning has drastically improved the state-of-the-art in many important fields, including computer vision and natural language processing (LeCun et al., 2015). However, it is expensive to train a deep neural network on a machine learning problem. The overall training cost further increases when one wants to solve additional problems. Lifelong machine learning (LML) develops algorithms that aim to efficiently learn to solve a sequence of problems, which become available one at a time. New problems are solved with less resources by transferring previously learned knowledge. At the same time, an LML algorithm needs to retain good performance on all encountered problems, thus avoiding catastrophic forgetting. Current approaches do not possess all the desired properties of an LML algorithm. First, they primarily focus on preventing catastrophic forgetting (Diaz-Rodriguez et al., 2018; Delange et al., 2021). As a result, they neglect some knowledge transfer properties. Furthermore, they assume that all problems in a sequence share the same input space. Finally, scaling these methods to a large sequence of problems remains a challenge. Modular approaches to deep learning decompose a deep neural network into sub-networks, referred to as modules. Each module can then be trained to perform an atomic transformation, specialised in processing a distinct subset of inputs. This modular approach to storing knowledge makes it easy to only reuse the subset of modules which are useful for the task at hand. This thesis introduces a line of research which demonstrates the merits of a modular approach to lifelong machine learning, and its ability to address the aforementioned shortcomings of other methods. Compared to previous work, we show that a modular approach can be used to achieve more LML properties than previously demonstrated. Furthermore, we develop tools which allow modular LML algorithms to scale in order to retain said properties on longer sequences of problems. First, we introduce HOUDINI, a neurosymbolic framework for modular LML. HOUDINI represents modular deep neural networks as functional programs and accumulates a library of pre-trained modules over a sequence of problems. Given a new problem, we use program synthesis to select a suitable neural architecture, as well as a high-performing combination of pre-trained and new modules. We show that our approach has most of the properties desired from an LML algorithm. Notably, it can perform forward transfer, avoid negative transfer and prevent catastrophic forgetting, even across problems with disparate input domains and problems which require different neural architectures. Second, we produce a modular LML algorithm which retains the properties of HOUDINI but can also scale to longer sequences of problems. To this end, we fix the choice of a neural architecture and introduce a probabilistic search framework, PICLE, for searching through different module combinations. To apply PICLE, we introduce two probabilistic models over neural modules which allows us to efficiently identify promising module combinations. Third, we phrase the search over module combinations in modular LML as black-box optimisation, which allows one to make use of methods from the setting of hyperparameter optimisation (HPO). We then develop a new HPO method which marries a multi-fidelity approach with model-based optimisation. We demonstrate that this leads to improvement in anytime performance in the HPO setting and discuss how this can in turn be used to augment modular LML methods. Overall, this thesis identifies a number of important LML properties, which have not all been attained in past methods, and presents an LML algorithm which can achieve all of them, apart from backward transfer

    An evaluation of non-pharmacological, non-invasive complementary interventions for reducing Parkinson's disease symptom severity and rate of disease progression

    Full text link
    Parkinson’s Disease (PD) is the second most common neurodegenerative disorder and has a rapidly increasing prevalence. It is characterized by motor deficits, primarily resting tremor, rigidity, postural instability, and bradykinesia, associated with the progressive loss of dopaminergic neurons and formation of Lewy bodies. Current pharmacological treatments address mainly the primary motor symptoms of the disease and do not provide protection against further neurodegeneration. Therefore, complementary interventions are examined for their potential role in reducing symptoms, both motor and non-motor, and rate of PD progression. The Mediterranean, ketogenic, and MIND diets are promising interventions that simulate fasting states, thereby inducing adaptive and protective cellular stress responses. The large quantities of foods high in antioxidants, anti-inflammatory effects, and healthy fats recommended by these diet plans may combat PD pathology, particularly neuroinflammation, oxidative stress, and mitochondrial dysfunction. Ketogenic diets, in addition, provide more efficient brain energy sources, in the form of ketone bodies, that may further curb effects of mitochondrial dysfunction. Fats, omega-3 fatty acids in particular, provide significant, clinically relevant neuroprotection from the disease and supplementation is recommended. PD patients, on average, have insufficient serum levels of certain vitamins which may contribute to PD progression. When supplemented in large amounts, these vitamins may have the opposite effect. Certain foods, such as dairy products, red meats, and highly processed foods, are associated with increased risk of PD and may be considered neurodegenerative. Sodas, especially diet sodas, are significantly correlated with more rapid disease progression and increased symptom severity. Physical activity is highly recommended for PD patients for its motor and non-motor benefits and neuroprotective roles. Among the most effective forms of PA are suggested to be aerobic exercise and progressive training programs. Consistent exercise is advised for consistent cognitive benefits and alleviation of other symptoms. The potential benefits of cognitive training for individuals with PD remain to be seen. Further research in all areas is needed to elucidate the most effective complementary interventions in combating PD

    Ausubel's meaningful learning re-visited

    Get PDF
    This review provides a critique of David Ausubel’s theory of meaningful learning and the use of advance organizers in teaching. It takes into account the developments in cognition and neuroscience which have taken place in the 50 or so years since he advanced his ideas, developments which challenge our understanding of cognitive structure and the recall of prior learning. These include (i) how effective questioning to ascertain previous knowledge necessitates in-depth Socratic dialogue; (ii) how many findings in cognition and neuroscience indicate that memory may be non-representational, thereby affecting our interpretation of student recollections; (iii) the now recognised dynamism of memory; (iv) usefully regarding concepts as abilities or simulators and skills; (v) acknowledging conscious and unconscious memory and imagery; (vi) how conceptual change involves conceptual coexistence and revision; (vii) noting linguistic and neural pathways as a result of experience and neural selection; and (viii) recommending that wider concepts of scaffolding should be adopted, particularly given the increasing focus on collaborative learning in a technological world

    Complement mediated synapse elimination in schizophrenia

    Get PDF
    Schizophrenia (SCZ) is a devastating psychiatric disorder with a typically age of onset in late adolescence. The heritability is estimated to be in between 60-80% and large-scale genome-wide studies have revealed a prominent polygenic component to SCZ risk and identified more than three-hundred common risk variants. Despite a better understanding of which genetic risk variants that increases SCZ risk, it has been challenging to map out the pathophysiology of the disorder. This has stalled the development of target drugs and current treatment options display moderate efficacy and are prone to produce side-effects. SCZ is generally considered a neurodevelopmental disorder and it was proposed more than forty years ago that physiological removal of less active synapses in adolescence, i.e., synaptic pruning, is increased in SCZ and hereby causes the core symptoms of the disorder. This theory has then been supported by post-mortem brain tissue and imaging studies displaying decreased synapse density in SCZ. More recently, it was then shown that the most strongly associated risk loci can largely be explained by copy numbers of a gene coding for the complement factor 4A (C4A). As microglia prune synapses with the help of complement signalling, we therefore decided to use a recently developed human 2D in vitro assay to assess microglial uptake of synaptic structures in models based on cells from individuals with SCZ and healthy controls (study I). We observed excessive uptake of synaptic structures in SCZ models and by mixing synapses from healthy controls with microglia from SCZ patients, and vice versa, we showed the contribution of microglial and neuronal factors contributing to this excessive uptake of synaptic structures. We then developed an in vitro assay to study neuronal complement deposition dependent on copy numbers of C4A in the neuronal lines. Complement 3 (C3) deposition increased by C4A copy numbers but was independent of C4B copy numbers (also unrelated to SCZ risk). Similar C4A copy numbers correlated with the extent of microglial uptake of synapses. Microglial uptake of synaptic structures could also be inhibited by the tetracycline minocycline that also decreased risk of developing SCZ in an electronic health record cohort. In study II, we cerebrospinal fluid (CSF) from first-episode psychosis patients to measure protein levels of C4A. In two independent cohorts, we observed elevated C4A levels (although not C4B levels) in first-episode patients that later were to develop SCZ and could show correlations with markers of synapse density. However, elevated C4A levels could not fully be explained by more copy numbers of C4A in individuals with SCZ and in vitro experiments revealed that SCZ-associated cytokines can induce C4A mRNA expression while also correlating with C4A in patient-derived CSF. In study III, we set-up a 3D brain organoid models to more fully comprehensively capture processes in the developing human brain and then also included innately developing microglia. We display synaptic pruning within these models and use single cell RNA sequencing to validate them. In conclusion, this thesis uses patient-derived cellular modelling to uncover a disease mechanism in SCZ that link genetic risk variants with bona fide protein changes in living patients

    Anuário científico da Escola Superior de Tecnologia da Saúde de Lisboa - 2021

    Get PDF
    É com grande prazer que apresentamos a mais recente edição (a 11.ª) do Anuário Científico da Escola Superior de Tecnologia da Saúde de Lisboa. Como instituição de ensino superior, temos o compromisso de promover e incentivar a pesquisa científica em todas as áreas do conhecimento que contemplam a nossa missão. Esta publicação tem como objetivo divulgar toda a produção científica desenvolvida pelos Professores, Investigadores, Estudantes e Pessoal não Docente da ESTeSL durante 2021. Este Anuário é, assim, o reflexo do trabalho árduo e dedicado da nossa comunidade, que se empenhou na produção de conteúdo científico de elevada qualidade e partilhada com a Sociedade na forma de livros, capítulos de livros, artigos publicados em revistas nacionais e internacionais, resumos de comunicações orais e pósteres, bem como resultado dos trabalhos de 1º e 2º ciclo. Com isto, o conteúdo desta publicação abrange uma ampla variedade de tópicos, desde temas mais fundamentais até estudos de aplicação prática em contextos específicos de Saúde, refletindo desta forma a pluralidade e diversidade de áreas que definem, e tornam única, a ESTeSL. Acreditamos que a investigação e pesquisa científica é um eixo fundamental para o desenvolvimento da sociedade e é por isso que incentivamos os nossos estudantes a envolverem-se em atividades de pesquisa e prática baseada na evidência desde o início dos seus estudos na ESTeSL. Esta publicação é um exemplo do sucesso desses esforços, sendo a maior de sempre, o que faz com que estejamos muito orgulhosos em partilhar os resultados e descobertas dos nossos investigadores com a comunidade científica e o público em geral. Esperamos que este Anuário inspire e motive outros estudantes, profissionais de saúde, professores e outros colaboradores a continuarem a explorar novas ideias e contribuir para o avanço da ciência e da tecnologia no corpo de conhecimento próprio das áreas que compõe a ESTeSL. Agradecemos a todos os envolvidos na produção deste anuário e desejamos uma leitura inspiradora e agradável.info:eu-repo/semantics/publishedVersio

    Neuroanatomical and gene expression features of the rabbit accessory olfactory system. Implications of pheromone communication in reproductive behaviour and animal physiology

    Get PDF
    Mainly driven by the vomeronasal system (VNS), pheromone communication is involved in many species-specific fundamental innate socio-sexual behaviors such as mating and fighting, which are essential for animal reproduction and survival. Rabbits are a unique model for studying chemocommunication due to the discovery of the rabbit mammary pheromone, but paradoxically there has been a lack of knowledge regarding its VNS pathway. In this work, we aim at filling this gap by approaching the system from an integrative point of view, providing extensive anatomical and genomic data of the rabbit VNS, as well as pheromone-mediated reproductive and behavioural studies. Our results build strong foundation for further translational studies which aim at implementing the use of pheromones to improve animal production and welfare

    Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review

    Full text link
    In this paper, a critical bibliometric analysis study is conducted, coupled with an extensive literature survey on recent developments and associated applications in machine learning research with a perspective on Africa. The presented bibliometric analysis study consists of 2761 machine learning-related documents, of which 98% were articles with at least 482 citations published in 903 journals during the past 30 years. Furthermore, the collated documents were retrieved from the Science Citation Index EXPANDED, comprising research publications from 54 African countries between 1993 and 2021. The bibliometric study shows the visualization of the current landscape and future trends in machine learning research and its application to facilitate future collaborative research and knowledge exchange among authors from different research institutions scattered across the African continent

    A Decision Support System for Economic Viability and Environmental Impact Assessment of Vertical Farms

    Get PDF
    Vertical farming (VF) is the practice of growing crops or animals using the vertical dimension via multi-tier racks or vertically inclined surfaces. In this thesis, I focus on the emerging industry of plant-specific VF. Vertical plant farming (VPF) is a promising and relatively novel practice that can be conducted in buildings with environmental control and artificial lighting. However, the nascent sector has experienced challenges in economic viability, standardisation, and environmental sustainability. Practitioners and academics call for a comprehensive financial analysis of VPF, but efforts are stifled by a lack of valid and available data. A review of economic estimation and horticultural software identifies a need for a decision support system (DSS) that facilitates risk-empowered business planning for vertical farmers. This thesis proposes an open-source DSS framework to evaluate business sustainability through financial risk and environmental impact assessments. Data from the literature, alongside lessons learned from industry practitioners, would be centralised in the proposed DSS using imprecise data techniques. These techniques have been applied in engineering but are seldom used in financial forecasting. This could benefit complex sectors which only have scarce data to predict business viability. To begin the execution of the DSS framework, VPF practitioners were interviewed using a mixed-methods approach. Learnings from over 19 shuttered and operational VPF projects provide insights into the barriers inhibiting scalability and identifying risks to form a risk taxonomy. Labour was the most commonly reported top challenge. Therefore, research was conducted to explore lean principles to improve productivity. A probabilistic model representing a spectrum of variables and their associated uncertainty was built according to the DSS framework to evaluate the financial risk for VF projects. This enabled flexible computation without precise production or financial data to improve economic estimation accuracy. The model assessed two VPF cases (one in the UK and another in Japan), demonstrating the first risk and uncertainty quantification of VPF business models in the literature. The results highlighted measures to improve economic viability and the viability of the UK and Japan case. The environmental impact assessment model was developed, allowing VPF operators to evaluate their carbon footprint compared to traditional agriculture using life-cycle assessment. I explore strategies for net-zero carbon production through sensitivity analysis. Renewable energies, especially solar, geothermal, and tidal power, show promise for reducing the carbon emissions of indoor VPF. Results show that renewably-powered VPF can reduce carbon emissions compared to field-based agriculture when considering the land-use change. The drivers for DSS adoption have been researched, showing a pathway of compliance and design thinking to overcome the ‘problem of implementation’ and enable commercialisation. Further work is suggested to standardise VF equipment, collect benchmarking data, and characterise risks. This work will reduce risk and uncertainty and accelerate the sector’s emergence

    In vitro investigation of the effect of disulfiram on hypoxia induced NFκB, epithelial to mesenchymal transition and cancer stem cells in glioblastoma cell lines

    Get PDF
    A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.Glioblastoma multiforme (GBM) is one of the most aggressive and lethal cancers with a poor prognosis. Advances in the treatment of GBM are limited due to several resistance mechanisms and limited drug delivery into the central nervous system (CNS) compartment by the blood-brain barrier (BBB) and by actions of the normal brain to counteract tumour-targeting medications. Hypoxia is common in malignant brain tumours such as GBM and plays a significant role in tumour pathobiology. It is widely accepted that hypoxia is a major driver of GBM malignancy. Although it has been confirmed that hypoxia induces GBM stem-like-cells (GSCs), which are highly invasive and resistant to all chemotherapeutic agents, the detailed molecular pathways linking hypoxia, GSC traits and chemoresistance remain obscure. Evidence shows that hypoxia induces cancer stem cell phenotypes via epithelial-to-mesenchymal transition (EMT), promoting therapeutic resistance in most cancers, including GBM. This study demonstrated that spheroid cultured GBM cells consist of a large population of hypoxic cells with CSC and EMT characteristics. GSCs are chemo-resistant and displayed increased levels of HIFs and NFκB activity. Similarly, the hypoxia cultured GBM cells manifested GSC traits, chemoresistance and invasiveness. These results suggest that hypoxia is responsible for GBM stemness, chemoresistance and invasiveness. GBM cells transfected with nuclear factor kappa B-p65 (NFκB-p65) subunit exhibited CSC and EMT markers indicating the essential role of NFκB in maintaining GSC phenotypes. The study also highlighted the significance of NFκB in driving chemoresistance, invasiveness, and the potential role of NFκB as the central regulator of hypoxia-induced stemness in GBM cells. GSC population has the ability of self-renewal, cancer initiation and development of secondary heterogeneous cancer. The very poor prognosis of GBM could largely be attributed to the existence of GSCs, which promote tumour propagation, maintenance, radio- and chemoresistance and local infiltration. In this study, we used Disulfiram (DS), a drug used for more than 65 years in alcoholism clinics, in combination with copper (Cu) to target the NFκB pathway, reverse chemoresistance and block invasion in GSCs. The obtained results showed that DS/Cu is highly cytotoxic to GBM cells and completely eradicated the resistant CSC population at low dose levels in vitro. DS/Cu inhibited the migration and invasion of hypoxia-induced CSC and EMT like GBM cells at low nanomolar concentrations. DS is an FDA approved drug with low toxicity to normal tissues and can pass through the BBB. Further research may lead to the quick translation of DS into cancer clinics and provide new therapeutic options to improve treatment outcomes in GBM patients
    • …
    corecore