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    Activation of LXRs alleviates neuropathic pain-induced cognitive dysfunction by modulation 2 of microglia polarization and synaptic plasticity via PI3K/AKT pathway.

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    Background Cognitive dysfunction is one of the most common comorbidities in patients with chronic pain. It has been shown that activation of Liver X receptors (LXRs) plays a potential role in improving cognitive disorders in multiple central nervous diseases by modulating neuroinflammation and synaptic plasticity. In this study, we mainly investigated whether LXRs could reverse cognitive deficits induced by neuropathic pain. Methods The spared nerve injury (SNI) model was established to explore the roles of LXRs in neuropathic pain induced-cognitive dysfunction. Pharmacological activation of LXRs by T0901317 or inhibition by GSK2033 was applied. In addition, the phosphatidylinositol 3-kinase (PI3K) inhibitor LY294002 was administered to examine the downstream mechanism of LXRs. Changes in neuroinflammation, microglia polarization, and synaptic plasticity were assessed using biochemical technologies. Results We found that SNI induced mechanical allodynia and novel object recognition dysfunction in mice, accompanied by the reduction in expression levels of LXRβ, synaptic proteins, and the PI3K/AKT pathway in the hippocampus. Microglia were activated in the hippocampus after SNI, with an increase in M1 phenotype and decrease in M2 phenotype, as well as upregulation of pro-inflammatory cytokines. Activation of LXRs with T0901317 significantly ameliorated SNI-induced cognitive dysfunction including anxiety, learning and memory. Neuroinflammation and microglia M1-polarization also induced by SNI were reversed after using T0901317. Moreover, T0901317 upregulated expression levels of synaptic proteins and phosphorylation of PI3K and AKT. However, administration of the LXRs inhibitor GSK2033 or PI3K inhibitor LY294002 abolished all the protective effects of T0901317 on cognitive dysfunction in SNI mice. Conclusion Our data indicate that LXRs activation alleviated neuropathic pain-induced cognitive dysfunction by modulating microglia polarization, neuroinflammation, and synaptic plasticity via the PI3K/AKT signaling pathway, and thus, LXRs may be identified as potential new targets for pain-related cognitive deficits. Keywords Liver X receptors; Neuropathic pain; Microglia polarization; Cognitive dysfunction; Neuroinflammatio

    Real-time operation of municipal anaerobic digestion using an ensemble data mining framework

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    This study presents a novel approach for real-time operation of anaerobic digestion using an ensemble decision-making framework composed of weak learner data mining models. The framework utilises simple but practical features such as waste composition, added water and feeding volume to predict biogas yield and to generate an optimised weekly operation pattern to maximise biogas production and minimise operational costs. The effectiveness of this framework is validated through a real-world case study conducted in the UK. Comparative analysis with benchmark models demonstrates a significant improvement in prediction accuracy, increasing from the range of 50–80% with benchmark models to 91% with the proposed framework. The results also show the efficacy of the weekly operation pattern, which leads to a substantial 78% increase in biogas generation during the testing period. Moreover, the pattern contributes to a reduction of 71% in total days required for feeding and 30% in total days required for pre-feeding

    Book review: “A Research Agenda for Urban Tourism” edited by Jan van der Borg, Edward Elgar Publishing, 2022

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    “A Research Agenda for Urban Tourism” is an edited collection that brings together a number of scholars in urban tourism, including well-known names in the field and younger researchers who look at urban tourism from different perspectives. This book is part of the Elgar Research Agendas series, aimed at exploring certain subjects and outlining the future of research in those fields, in this case urban tourism

    Active Learning for Left Ventricle Segmentation in Echocardiography

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    Background and Objective: Training deep learning models for medical image segmentation requires large annotated datasets, which can be expensive and time-consuming to create. Active learning is a promising approach to reduce this burden by strategically selecting the most informative samples for segmentation. This study investigates the use of active learning for efficient left ventricle segmentation in echocardiography with sparse expert annotations. Methods: We adapt and evaluate various sampling techniques, demonstrating their effectiveness in judiciously selecting samples for segmentation. Additionally, we introduce a novel strategy, Optimised Representativeness Sampling, which combines feature-based outliers with the most representative samples to enhance annotation efficiency. Results: Our findings demonstrate a substantial reduction in annotation costs, achieving a remarkable 99% upper bound performance while utilizing only 20% of the labelled data. This equates to a reduction of 1680 images needing annotation within our dataset. When applied to a publicly available dataset, our approach yielded a remarkable 70% reduction in required annotation efforts, representing a significant advancement compared to baseline active learning strategies, which achieved only a 50% reduction. Our experiments highlight the nuanced performance of diverse sampling strategies across datasets within the same domain. Conclusions: The study provides a cost-effective approach to tackle the challenges of limited expert annotations in echocardiography. By introducing a distinct dataset, made publicly available for research purposes, our work contributes to the field’s understanding of efficient annotation strategies in medical image segmentation

    Automated mitral inflow Doppler peak velocity measurement using deep learning

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    Doppler echocardiography is a widely utilised non-invasive imaging modality for assessing the functionality of heart valves, including the mitral valve. Manual assessments of Doppler traces by clinicians introduce variability, prompting the need for automated solutions. This study introduces an innovative deep learning model for automated detection of peak velocity measurements from mitral inflow Doppler images, independent from Electrocardiogram information. A dataset of Doppler images annotated by multiple expert cardiologists was established, serving as a robust benchmark. The model leverages heatmap regression networks, achieving 96% detection accuracy. The model discrepancy with the expert consensus falls comfortably within the range of inter- and intra-observer variability in measuring Doppler peak velocities. The dataset and models are open-source, fostering further research and clinical application

    Developing a Data-Driven AI Model to Enhance Energy Efficiency in UK Residential Buildings

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    Residential buildings contribute to 30% of the UK’s total final energy consumption. However, with less than one percent of its housing stock being replaced annually, retrofitting existing homes has significant importance in meeting energy efficiency targets. Consequently, many physics-based and data-driven models and tools have been developed to analyse the effects of retrofit strategies from various points of view. This paper aims to develop a data-driven AI model that predicts buildings’ energy performance based on their features under various retrofit scenarios. In this context, four different machine learning models were developed based on the Energy Performance Certificate (EPC) dataset for residential buildings and Standard Assessment Procedure (SAP) guidelines in the UK. Additionally, an interface was designed that enables users to analyse the effect of different retrofit strategies on a building’s energy performance using the developed AI models. The results of this study revealed the artificial neural network as the most accurate predictive model, with a coefficient of determination (R^2) of 0.82 and a mean percentage error of 11.9 percent. However, some conceptual irregularities were observed across all the models when dealing with different retrofit scenarios. In summary, such tools can be further improved to offer a potential alternative or support to physics-based models, enhancing the efficiency of retrofitting processes in buildings. Keywords: machine learning, energy performance certificate, building energy consumptio

    Challenges Encountered by Healthcare Professionals as Frontline Fighters during the COVID-19 Pandemic in Bangladesh: A Qualitative Study

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    Throughout the pandemic, healthcare professionals (HCPs) around the world encountered numerous challenges. This study was conducted in the middle of the pandemic, from June to November 2021, and explored the multiple issues that HCPs faced in Dhaka, Bangladesh. Thirty doctors and nurses, covering a wide range of workplaces and experiences, were interviewed. A qualitative investigation was performed to assess the influence that diverse organizational, familial, social, and religious factors had on their commitment to fulfil their professional duties. Thematic content analysis was performed on the findings. The results emphasize the physical and mental health problems of HCPs, the vital role of organizations in addressing the wellbeing of HCPs, and the necessity of providing training for them, along with workloads and PPE-related problems. It also explores the roles of families, the influence of society, and the impact of religious beliefs on their commitment during the pandemic

    Bridge Monitoring Strategies for Sustainable Development with Microwave Radar Interferometry

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    The potential of a coherent microwave radar for infrastructure health monitoring has been investigated over the past decade. Microwave radar measuring based on interferometry processing is a non-invasive technique that can measure the line-of-sight (LOS) displacements of large infrastructure with sub-millimeter precision and provide the corresponding frequency spectrum. It has the capability to estimate infrastructure vibration simultaneously and remotely with high accuracy and repeatability, which serves the long-term serviceability of bridge structures within the context of the long-term sustainability of civil engineering infrastructure management. In this paper, we present three types of microwave radar systems employed to monitor the displacement of bridges in Japan and Italy. A technique that fuses polarimetric analysis and the interferometry technique for bridge monitoring is proposed. Monitoring results achieved with full polarimetric real aperture radar (RAR), step-frequency continuous-wave (SFCW)-based linear synthetic aperture, and multi-input multi-output (MIMO) array sensors are also presented. The results reveal bridge dynamic responses under different loading conditions, including wind, vehicular traffic, and passing trains, and show that microwave sensor interferometry can be utilized to monitor the dynamics of bridge structures with unprecedented spatial and temporal resolution. This paper demonstrates that microwave sensor interferometry with efficient, cost-effective, and non-destructive properties is a serious contender to employment as a sustainable infrastructure monitoring technology serving the sustainable development agenda

    Factors Affecting Healthy Aging and Its Interconnected Pathways

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    The process of aging is influenced by factors across different aspects of life including biology, lifestyle, and the surrounding environment. As the global population continues to age it is crucial to understand the complexities involved in maintaining the health and quality of life of adults. This article offers an overview of the dimensions of aging by examining the key factors and demonstrating how they are interconnected in shaping the aging process. Biological factors play a role at molecular and systemic levels. Lifestyle choices also have an impact with regular physical activity, a balanced diet, sufficient sleep, and cognitive engagement all contributing to overall well-being. Social support networks, community resources and access to healthcare services are environmental factors that also influence healthy aging trajectories. In addition, socioeconomic circumstances contribute to disparities in aging outcomes with individuals from poorer backgrounds often facing greater challenges in accessing resources and healthcare services. Genetic predisposition can play a role in determining how well we are with certain genes and molecular pathways associated with age related diseases that can affect longevity. In order to comprehensively explore the literature on aging, this study employed the methodological approach of a scoping review to identify topics and evidence types, and also a rapid review to systematically map current knowledge. This combination provides a focus on qualitative summaries rather than exhaustive analysis and enabled a systematic search for relevant papers while ensuring rigorous screening processes for categorizing and synthesizing the findings. It involved searching the PubMed database by scanning titles and abstracts for relevance and then organizing information based on the dominant themes. A qualitative analysis of the evidence was then carried out, related to the concept of healthy aging, while also identifying gaps in the research. The result is an overview of the evidence surrounding aging and areas that require investigation. To summarize, this innovative approach using scoping and rapid review methodologies enabled a systematic mapping of current knowledge about aging. By examining these factors, and understanding their interconnectedness, approaches can be developed that will be effective in helping to promote healthy aging and thereby enhance the quality of life for older adults. This article aims to provide insights into the influencers of the aging process while also highlighting potential avenues for future research and intervention

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