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Comparative diagnostic accuracy of photographic methods for detecting diabetic retinopathy: a systematic review and meta-analysis
No full textDiabetic retinopathy (DR) remains a leading cause of vision loss globally. Timely detection through photographic screening is key to prevention, yet considerable variability exists across imaging protocols. This systematic review and meta-analysis evaluates the diagnostic accuracy of various photographic strategies for DR detection to inform evidence-based screening practices. We systematically searched six databases and trial registries up to June 2023. Eligible studies included adults (≥18 years) with type 1 or 2 diabetes undergoing DR screening using fundus photography, compared against a reference standard (7-field ETDRS or dilated ophthalmoscopy). We performed bivariate meta-analyses for direct comparisons (e.g., mydriatic vs. non-mydriatic), and indirect armbased meta-analyses. Eighty studies (over 50,000 participants) were included. Direct comparisons including 8 studies (16 arms, 2967 participants), found that the sensitivity for detecting any DR was comparable between mydriatic and non-mydriatic imaging (90% vs. 89%), though specificity was lower without dilation (85% vs. 90%). Sensitivity improved with the number of fields (from 0.82 with 1-field to 0.98 with ≥4-fields), particularly in nonmydriatic settings. Results also showed an observed heterogeneity in accuracy of screening across settings showing the need for continuous auditing of screening performance. Photographic screening for DR was effective across multiple configurations. Two-field mydriatic protocols offer high diagnostic accuracy, while trained graders and portable devices can enhance scalability. Findings support the design of structured screening programs adapted to local resources and clinical priorities.<br/
From the lens of early-career researchers: bridging science, technology, arts, and humanities to tackle antimicrobial resistance
No full textCoping during the COVID-19 pandemic: insights from a qualitative study of organizational resilience in an English substance use support service
No full textIntroductionThe COVID-19 pandemic posed unprecedented challenges to health and care services, including substance use support, necessitating adaptations to maintain operational continuity. A lack of research exists into the factors that helped substance use services cope and maintain provision. This study aimed to elucidate the organizational resilience factors that assisted a substance use support service in sustaining operations and adapting during the COVID-19 pandemic.TheoryThe study draws on recent conceptual and theoretical developments in the study of organizational resilience, examining findings primarily through the lens of bounce-back and bounce-forward resilience, and Duchek’s three-stage framework.MethodParticipants were 36 staff members working for (n=28) or in partnership with (n=8) an organization delivering substance use support services across an area of Northern England. A multi-method qualitative approach, including digital timelines (n=16), in-depth interviews (n=17), and five focus groups (n=12), was employed. Timeline text was treated as qualitative text-based data. Interviews and focus groups were recorded and transcribed prior to coding. Data underwent Framework Analysis.ResultsSeven themes were identified: 1) pre-existing relationships and effective multiagency working; 2) prioritization of service delivery; 3) development and implementation of guidance and protocols; 4) autonomy, devolution, and deference to expertise; 5) effective communication, regular meetings, and coordinated decision-making; 6) allowing flexibility and creativity; and 7) development of new and innovative approaches to facilitate the pandemic response.DiscussionThis study identifies resilience factors that substance use and other support organizations should focus on in preparation for potential future crises, to minimise adverse impacts on vulnerable populations.<br/
The genome sequence of a fungus weevil, Platystomos albinus (C.Linnaeus, 1758)
No full textWe present a genome assembly from a female specimen of (fungus weevil; Arthropoda; Insecta; Coleoptera; Anthribidae). The assembly contains two haplotypes with total lengths of 555.31 megabases and 554.55 megabases. Haplotype 1 is scaffolded into 11 chromosomal pseudomolecules, including the X sex chromosome. Haplotype 2 was assembled to scaffold level. The mitochondrial genome has also been assembled, with a length of 17.09 kilobases
Advancing PEAR: development of a Bridge Benchmark Datasets for PBSHM research
No full textPopulation-Based Structural Health Monitoring (PBSHM) is an emerging field in Structural Health Monitoring that leverages data from multiple structures to enhance the assessment of individual structures. Unlike traditional SHM, which generally relies on data from a single structure, PBSHM utilises collective knowledge from a population to facilitate increasing the knowledge on an individual structure. Transfer learning enables the inference from a source structure to a target structure within the population. One of the limitations of this method is that a lot of transfer-learning methods require data models that are trained using substantial amounts of high-quality data which can be difficult to obtain. To support PBSHM research, the concept of the Population-based SHM Engineered Asset Resource (PEAR) has been introduced. PEAR is conceptualised as a benchmark dataset containing semi-realistic structures and associated data intended to drive the development and validation of PBSHM methodologies. This work advances the PEAR prototype by developing complete populations for two types of bridges, along with their associated data. The pipelines for generating these populations are presented, detailing how they produce structural data and PBSHM-specific models. Additionally, a simple analysis of the generated populations is conducted, demonstrating their utility in PBSHM research and showcasing the potential of PEAR as a resource for current and future PBSHM research.<br/
Polymer mediated control and migration effects in spin-crossover-polymer hybrids towards tunable thermal sensing applications
No full textTailoring the spin crossover (SCO) effect in molecular materials remains a fundamental challenge, driven by the need to control critical parameters, such as the spin transition temperature (T1/2), hysteresis width, cooperativity, and switching kinetics for applications in sensing, memory, and actuation devices. SCO behavior is highly sensitive to small changes in the structure or crystal structure of the surrounding environment. In this context, achieving predictable and reproducible control remains elusive. Embedding SCO complexes into polymer matrices offers a more versatile and processable approach, but understanding how matrix–guest interactions affect spin-state behavior is still limited. In this study, we investigate a polymer-mediated strategy to tune SCO properties by incorporating the well-characterized Fe(II) complex [Fe(1,10-phenanthroline)2(NCS)2] into three polymers with distinct structural features: polylactic acid (PLA), polystyrene (PS), and polysulfone (PSF). In terms of potential electrostatic interaction between the complex and the polymeric matrixes, the polymers offer distinct features. Either there does not seem to be any specific interaction (PLA case) or, rather, there is π-π stacking between the aromatic rings of the SCO complex, and the corresponding ones present either in the backbone or in the side chain of the polymer (PSF and PS, respectively). The latter can potentially influence spin-state energetics and dynamics. Importantly, we also reveal and quantify the migration behavior of SCO particles within different polymer matrices, an aspect that has not been previously examined in SCO–polymer systems. Using magnetic susceptibility, spectroscopic, diffraction, and migration studies, we show that the polymer environment, PLA as well, actively modulates the SCO response. PSF yields lower T1/2, slower switching kinetics, and enhanced retention of the complex, indicative of strong matrix confinement and interaction. In contrast, PLA and PS composites exhibit sharper transitions and higher migration, suggesting weaker interactions and greater mobility. In addition, the semi-crystalline nature of PLA seems to induce the extension of the hysteresis width. These results highlight both the challenge and the opportunity in SCO polymer composites to tune SCO behavior, offering a scalable route toward functional hybrid materials for thermal sensing and responsive devices
Joint EIFAAC/ICES/GFCM Working Group on Eels (WGEEL)
No full textThe Joint EIFAAC/ICES/GFCM Working Group on Eel (WGEEL) conducts the annual stock assessment for European eel (<i>Anguilla anguilla</i>) and reports on new emerging threats and opportunities. WGEEL provides the scientific basis for the ICES advice on fishing opportunities and conservation aspects for the European eel and further addresses requests from EIFAAC and GFCM.This year, 2025, WGEEL assessed the state of the European eel population and its fisheries, reviewed the implementation of the WKFEA (Workshop on the Future of Eel Advice) roadmap, examined available recruitment data from coastal and marine habitats, reported on new scientific knowledge including developments in the Mediterranean region, agreed on the contents of an information sheet destined to inform stakeholders on the work of WGEEL, and provided information on data needs to be included in EU regional workplans.After high levels in the late 1970s, European eel recruitment declined dramatically in the 1980s and remains low. Compared to 1960–1979, the recruitment in the “North Sea” index series was 1.3% with a confidence interval (C.I.) of [0.6-3.1%] (final) in 2024 and was 0.7% [0.4-1.1%] (provisional) in 2025. The “Elsewhere Europe” index was 7.2% [4.5-11.5%] (final) in 2024 and (provisional) 12.1% [7.4-19.5%] (provisional) in 2025. The yellow eel recruitment index was 14.3% [5.9-34.3%] in 2024.The trend of reported commercial landings shows a long-term continuing decline, from a level of around 10 000 t in the 1960s to remaining above 2 000 t (glass eel + yellow eel + silver eel) during the past decade. The commercial glass eel fishery was 57.7 t in 2024 and 56.7 t in 2025. Reported landings from yellow and silver eel commercial fisheries (Y, S, YS) totalled 2 391 t in 2023 and 1 855 t in 2024 (provisional data). Reported recreational landings for yellow and silver eel combined was 76 t in 2024 (20 countries reporting) and no countries reported so far in 2025.Other tasks carried out during the meeting consisted of defining and describing issues for the upcoming benchmark and clarifying the process of integrating the WGEEL database into the ICES Data Screening Utility (DATSU).The Mediterranean countries forming the southern limit of the eel's range, is still significantly underrepresented in terms of data compared to the rest of the species distribution. WGEEL notes recent initiatives—such as Croatia's national monitoring project (2023-2026), the GFCM's MedSea4Fish (2023-2025), Lebanon's initiation of an eel monitoring program, and WWF-Türkiye’s restoration of Lake Bafa and the Büyük Menderes Delta.Hydropower remains a key source of mortality; current research focuses on effective screening and fish passage solutions. Regarding health and non-native pressures, the parasite <i>Anguillicola crassus</i> is established in Türkiye, Greece, and Croatia, while AngHV-1 persists in the Adriatic. However, a coordinated European eel health monitoring programme is still lacking. Illegal trade enforcement intensified through EUROPOL’s Operation LAKE VII (2022/23), seizing ~24 million glass eels. New frameworks propose safe reintroduction protocols to turn enforcement gains into conservation outcomes. Overall, progress in data collection and regional cooperation is evident, but sustaining recovery of the critically endangered European eel requires coordinated, cross-border strategies that integrate science, management, and habitat restoration
For and against a united Ireland by Fintan O'Toole and Sam McBride. Arguing to end the apathy of the undecideds
No full textClumped isotopes of methane trace bioenergetics in the environment
No full textMethane is a major greenhouse gas and a key component of global biogeochemical cycles. Microbial methane often deviates from isotope and isotopolog equilibrium in surface environments but approaches equilibrium in deep subsurface sediments. The origin of this near-equilibrium isotopic signature in methane, whether directly produced by methanogens or achieved through anaerobic oxidation of methane (AOM), remains uncertain. Here, we show that, in the absence of AOM, microbial methane produced from deep-sea sediments exhibits isotopolog compositions approaching thermodynamic equilibrium due to energy limitation. In contrast, microbial methane from salt marsh and thermokarst lakes exhibits significant hydrogen and clumped isotopic disequilibrium due to high free-energy availability. We propose that clumped isotopologs of methane provide a proxy for characterizing the bioenergetics of environments for methane production. Together, these observations demonstrate methane clumped isotopes as a powerful tool to better understand the relation between methane metabolisms and the energy landscape in natural environments
Near-field beam focusing for extremely large-scale IRS-aided communication systems
No full textAn extremely large-scale intelligent reflecting surface (XL-IRS) aided communication system is studied. Although XL-IRS can effectively combat the double path-loss attenuation, the large aperture size introduces significant near-field effects. The complex near-field propagation and large number of XL-IRS elements can lead to optimality and complexity challenges in beam focusing design. Focusing on a spectral efficiency (SE) maximization problem, two unsupervised learning based algorithms are conceived for the joint optimization of base station and XL-IRS beam focusing, which operate without pre-training and exhibit strong robustness. Specifically, a dense-connected dilated autoencoder meta learning (DDAML) algorithm is proposed to achieve high SE by utilizing dense connections, while reasonably designing an autoencoder to reduce computational complexity. Furthermore, considering a need for low execution time in practical applications, a convolutional dilated autoencoder meta learning (CDAML) algorithm is also proposed to further reduce computational complexity. Simulation results show that the proposed DDAML algorithm achieves the highest SE, while the proposed CDAML algorithm significantly reduces computational complexity at the cost of limited SE loss. Moreover, the two proposed algorithms also demonstrate remarkable robustness in XL-IRS-aided near-field communications