152653 research outputs found
Sort by
The Paradox of Sustainable Urban Development Demonstrated in the Dominance of Informal Housing in Developing Countries
No full textDetermining the molecular basis for Arsenic-induced degradation of Promyelocytic Leukaemia protein (PML)
No full textAccelerating Isotope Dilution LC-MS-based Desmosine Quantification for Estimating Elastin Turnover
No full textAims: Circulating total desmosine, representing endogenous systemic elastin degradation activity, is an emerging biomarker for mortality risk in several diseases and aging. However, the existing analytical method takes more than 23 hours to complete, limiting its potential applications. The objective of this study was to shorten the turnover time of a stable isotope dilution liquid chromatogram mass spectrometry-based desmosine assay. Materials & Methods: Plasma samples were analysed using acid hydrolysis followed by solid-phase extraction and LC-MS. Two approaches to reduce assay time were tested: microwave-assisted acid hydrolysis and direct injection following solid-phase extraction. Results: The combination of acid hydrolysis at 180°C for 8 minutes and a low-volume elution design for solid-phase extraction reduced the overall assay time to ~30 minutes. The assay was validated with intra-day precision and accuracy ranging from 4% to 14%, and -7% to 9%, respectively, while inter-day precision and accuracy were 0% to 9% and 1% to 3%, respectively. The assay was tested in a cohort of patients with acute aortic dissection and control subjects, where desmosine concentrations were approximately three-fold higher in patients. Conclusions: These results demonstrated that rapid desmosine analysis can be achieved with the use of both microwave-assisted hydrolysis and streamlined solid-phase extraction
A Pedagogy of Convivência Exploring the Potência of Individual and Collective Subjectivity in Brazilian Peripheries
No full textThis chapter brings together a set of concepts that have been elaborated over more than two decades as part of a process of political-intellectual engagement with peripheral territories and people in Brazil. These reflections take place, mostly, from the Metropolitan Region of Rio de Janeiro, where several studies and actions took place since the creation of the NGOs Observatório de Favelas, in 2001, and, later, UNIPeriferias (also known as Institute Maria and João Aleixo), in 2017. Both organizations, in which the authors of this chapter have influential roles, propose alternative projects to address social justice and human dignity based on the perspective of favelas and other peripheral territories. Such a vision was underpinned by the recognition and valorization of social, aesthetic, cultural, and epistemological references elaborated from peripheries. This involved a strong emphasis on the creation of mechanisms to promote the intellectual production and influence of peripheral groups in public arenas and policymaking. In the making of this intellectual and political process, three core concepts emerged: peripheries, potência, and convivência. The chapter will articulate these concepts to provide our interpretation of subjectivities at the margins. We acknowledge the need to contextualize these concepts and provide some cultural translations for an international audience. We also acknowledge the relevance for such concepts to be resignified beyond the context in which they were produced, by promoting an international dialogue with scholars interested in the peripheries and the margins of urban sociocultural and political landscapes
Human Rights in the Context of Climate Change:Emerging investment-related responsibilities in law and policy
No full textThis chapter examines the implications of human rights standards for investment activity in the context of climate change. It explores three key domains: the responsibility of businesses and investors for the human rights impacts of climate change, the role of climate change laws in regulating business and investment activities, and the ‘greening’ of human rights norms. These areas intersect to shape the responsibilities of investors in climate change mitigation. The chapter maps the evolution of these norms and their interaction, highlighting how they inform investment-related responsibilities, and delves into the implications of these evolving standards for investors. By examining these dimensions, the chapter provides insights into the emerging duties of investors within the framework of human rights and climate change
Personalized palliative care for older adults with cancer:A call for action on oncogeriatric palliative care
No full textSolarSynthNet (SSN):A deep learning framework for binary and multiclass classification of damaged or obstructed solar panels using images
No full textThe rapid rise in solar photovoltaic (PV) installations globally drives demand for tools to optimize system operations and maintenance. Machine learning models can identify and classify faults (such as cracks or physical damage) or obstructions (such as dust or snow) that reduce performance. However, existing models lack the scalability, accuracy, and generalizability required to handle large, noisy, and diverse datasets of real-world solar panel images. This paper presents SolarSynthNet (SSN), a novel deep learning framework for classifying faulty or obstructed solar panels. SSN is built upon a base feature extraction block supported by an Enhanced Feature Mixing Block and Contextual Focus module. These improve feature extraction by combining information from multiple layers to capture complex patterns, and increase classification accuracy by prioritizing the most relevant regions to focus on discriminative features. Experiments across multiple datasets demonstrate SSN's accuracy, correctly identifying 91.48 % of solar panels in 6-class, 95.83 % in 3-class, and 97.42 % in binary classification. SSN outperforms all existing models in both binary and 6-class tasks. It offers strong potential for optimizing operational performance and maintenance strategies in real-world solar energy systems, thus improving project economics. Future work will improve sample balancing, image quality, and expand SSN to real-time applications.</p
Intrauterine devices and gynaecological malignancies - an umbrella review of systematic reviews and meta-analyses
No full textBackground and aims: The intrauterine device (IUD) is globally recognised as a safe, cost-effective, and reliable contraceptive. This umbrella review and meta-analysis synthesises current evidence regarding the association between IUD usage and incidence of gynaecological and breast malignancies. Materials and methods: A comprehensive search of PubMed, Cochrane Library, and Ovid databases was conducted for systematic reviews and meta-analyses examining any type of IUD in relation to gynaecological and breast cancers. The screening and data extraction processes adhered to PRISMA guidelines, and random-effects meta-analysis was employed for data synthesis. Results: 323 titles and abstracts were screened, leading to the review of 41 full texts and the inclusion of 17 articles. Analyses of these articles and of their 32 primary sources indicated a decreased risk of cervical, endometrial and ovarian cancer for ever-users of IUD (respective OR: 0.63 [95% CI 0.48–0.82], 0.41 [0.31–0.54], and 0.71 [0.59–0.86]; all p < 0.001). Our analysis did not yield any statistically significant association between IUD use and breast cancer risk (OR 1.00 [0.70–1.41], p = 0.99). Conclusion: IUD use is associated with a reduced risk of ovarian, endometrial, and cervical cancers, while no link was found with breast cancer. These findings can inform patient counselling on the benefits and risks of IUD use.</p
Artificial Intelligence Transformations in Geotechnics:Progress, Challenges and Future Enablers
Get PDFOur reliance on the underground space to deliver critical civil engineering infrastructure is growing: to accommodate utility and transport infrastructure in urban environments, to provide innovative housing and commercial solutions, and to support proliferating renewable energy infrastructure, particularly offshore. Artificial intelligence (AI) is arguably the most promising enabler to transform geotechnical engineering by extracting knowledge from data to achieve step-change increases in efficiency, sustainability, reliability and safety. This paper seeks to develop a shared understanding of the state of the art of AI in geotechnics and to explore future developments. By way of example, specific popular use cases in geotechnics are considered to highlight current progress in AI applications including intelligent site investigation, predictive modelling for soil behaviour, and optimisation of design and construction processes. The paper then addresses key research challenges, such as data scarcity and interpretability, and discusses the opportunities that lie ahead in the integration of AI with geotechnical engineering. Finally, priority technological enablers are identified for future transformations
Data sources:
Access Repository Dashboard
Do you manage University of Dundee Online Publications? Access insider analytics, issue reports and manage access to outputs from your repository in the CORE Repository Dashboard!