Open research Europe最新文献

Background: Maritime activity is expanding globally, increasing the demand for robust port security systems capable of detecting illegal trafficking. Due to the growing sophistication of smuggling methods, law enforcement agencies require advanced surveillance and prevention technologies such as those developed in the SMAUG project. In this context, initiatives such as the SMAUG project aim to deliver integrated surveillance capabilities coordinated by a high-level deep reinforcement learning (DRL) decision-making system that operates on image-based environmental representations. Despite their effectiveness, DRL models are closed-boxes, complicating continuous model monitoring (CMM). Conventional drift detection captures shifts in input or output distributions yet often fails to explain underlying problems. Explainable AI (XAI) techniques can provide a complementary approach with insights into the agent's inner workings, enabling monitoring of the concept rather than just the data.

Methods: We propose FADMON, an XAI-driven concept drift detection method for image-based models. FADMON performs statistical drift tests on feature attributions to detect deviations in learned policies. We demonstrate how FADMON can enhance CMM with a three-stage model monitoring architecture that enables semi-supervised explainable model monitoring. We validate our approach with SMAUG's decision-making DRL model on a simulated maritime port surveillance environment under multiple unforeseen scenarios.

Results: FADMON consistently flags drift on all drifted scenarios with mean p-values of 0.000 with no variance trough 30 repetitions, with lower mean p-values (0.553±0.215) on non-drifted scenarios with respect to other established drift detection methodologies such as prior probability shift detection (0.65 ± 0.000), though well above the standard 0.05 threshold.

Conclusions: FADMON can add an explainability layer to the monitoring system while also supporting detection of changes in the underlying interpretation of the input data by the model, monitoring the concept rather than the data, while matching established drift detection methods metrics-wise.

Easy-to-use libraries such as scikit-learn have accelerated the adoption and application of machine learning (ML) workflows and data-driven methods. While many of the algorithms implemented in these libraries originated in specific scientific fields, they have gained in popularity in part because of their generalisability across multiple domains. Over the past two decades, researchers in the chemical and materials science community have put forward general-purpose machine learning methods. The deployment of these methods into workflows of other domains, however, is often burdensome due to the entanglement with domain-specific functionalities. We present the python library scikit-matter that targets domain-agnostic implementations of methods developed in the computational chemical and materials science community, following the scikit-learn API and coding guidelines to promote usability and interoperability with existing workflows.

Background: This study introduces a systematic coarse-graining approach to model poly(ε-caprolactone) (PCL) in its melt state. The primary goal is to provide a simple and adaptable method for creating computational models of biodegradable polymers, which can then be used to study materials with a wide range of molecular weights and compositions that are relevant to industry. This research addresses the growing need for sustainable materials across various industrial applications.

Methods: To study long polymer chains, the L-OPLS force field, an adapted version of the OPLS-AA force field, was used for atomistic simulations. The data from these simulations were first thoroughly checked against existing literature and theoretical predictions to ensure their validity. These validated atomistic configurations then became the foundation for developing the coarse-grained model.

Results: The research meticulously measured both the structural and dynamic properties of the PCL at the atomistic and coarse-grained levels. The findings show that the model is successful at accurately reproducing key characteristics across these different levels of resolution.

Conclusions: The methodology presented in this work aims to facilitate the development of computational studies that can help optimize the properties of PCL-based materials. By doing so, it has the potential to reduce the environmental and economic impact of developing new sustainable materials.

The eastern North European Plain is an important area for studying Quaternary climate change and archaeology; however, providing chronological constraints for deposits can be challenging. Amino acid geochronology (AAG) is a relative dating technique that has been useful in correlating isolated Quaternary deposits. The intra-crystalline protein decomposition (IcPD) approach to AAG using the opercula of Bithynia snails has previously been used to provide relative dating frameworks across northern and central Europe in areas where the integrated diagenetic temperature can be assumed to be similar. Here, the first aminostratigraphies for the eastern North European Plain are presented, incorporating deposits from at least the last ~1 Ma, which are used to assess the current age attributions to Middle and Late Pleistocene interglacials. These aminostratigraphies are then used to explore expected differences in the extent of IcPD due to differing temperature histories across the study area. Correlations of opercula to regional pollen assemblages representative of the Holsteinian, Eemian and Holocene are used to evaluate the temporal resolution achievable by IcPD within a given interglacial. This work has produced four new aminostratigraphies that can now be used as reference datasets for relative age estimation for the late Middle Pleistocene to the Holocene in the eastern North European Plain.

Hirudo verbana Carena, 1820, commonly known as the southern medicinal leech, is one of several European medicinal leeches, whose full diversity has just recently started to be uncovered. Historically, it has been widely used as a medicinal leech and for centuries it was treated erroneously under the specific name of Hirudo medicinalis L. 1758. Recent molecular and taxonomic analyses have revealed subspecific diversity within the morphospecies H. verbana. Hirudo verbana is a blood-feeding species sucking blood from amphibians, fish, and mammals. It occupies freshwater habitats, typically shallow ponds and lakes. Studies show that this leech species has a "naturally limited microbiome", suggesting it may serve as a powerful model system for the study of gut microbiota. We expect this chromosome-level assembly of H. verbana to serve as a high-quality genomic resource for this most famous leech genus and to serve as a foundation to the study of the diversification and biodiversity of European medicinal leeches, as well as their gut-associated symbionts. The genome of H. verbana was assembled into two haplotypes through a phased assembly approach; however, only the primary haplotype was designated as the reference genome for annotation and downstream analyses. The entirety of the primary haplotype was assembled into 14 contiguous chromosomal pseudomolecules, including the mitogenome. This chromosome-level assembly encompasses 0.18 Gb, composed of 277 contigs and 27 scaffolds, with contig and scaffold N50 values of 1.3 Mb and 13.4 Mb, respectively.

Background: The European Chemicals Agency (ECHA) has been established to act as an independent body in the context of the implementation of the Regulation on the registration, evaluation, authorisation and restriction of chemicals (REACH) (Regulation (EC) 1907/2006). Quantitative exposure estimates are required for all exposure scenarios where hazardous emissions occur using exposure measurements or exposure models. REACH regulation specifies that exposure models need to be appropriate and quantitative. Here, we evaluated the criteria for regulatory exposure models by ECHA.

Methods: The evaluation was performed by asking ECHA the criteria for exposure models.

Results: ECHA does not specify any quality criteria for regulatory exposure models or have transparency requirements. Without quality criteria and transparency, there cannot be quality assurance or control. Thus, an appropriate model cannot be defined. ECHA does not recognize the quantitative term even though the fundamental requirement for quantitative exposure assessment is quantitative uncertainty assessment.

Conclusions: As a result of these shortcomings, ECHA R.14 Guidance for occupational exposure assessment allows the use of non-physical models containing qualitative parameters based on non-accessible calibration databases and statistical evaluations. Because of the lack of transparency, non-physical model construct, and subjective input parameters, model results cannot be associated with real-world operational conditions, and quantitative uncertainty assessment is not feasible. This makes the models qualitative by definition and is not applicable to regulatory exposure modelling. This raises questions about whether ECHA has followed its regulatory mandates in implementing the REACH legislation.

Background: Previous studies on the internal democracy of new digital parties in Western Europe suggest a plebiscitary tendency, but most focus on a limited number of cases. This paper aims to empirically analyze the intra-party democracy of electorally successful new parties in Western Europe and identify the main factors that may influence it.

Methods: Drawing on data from the second round of the Political Parties Database (PPDB) and the first wave of the Populism and Political Parties Expert Survey (POPPA), this study covers more than 100 parties across 13 countries. Adopting a generational approach, we define a cohort of "crisis parties"-founded between the economic crisis and the pandemic-and examine their internal democracy in comparison to older parties, using Von dem Berge and Poguntke's IPD model and Böhmelt et al.,'s (2022) framework, with ideology, digitalization, and populism treated as explanatory variables.

Results: Our findings show that being a crisis party-even a highly digitalized one on the left-does not entail more plebiscitary forms of intra-party democracy.

Conclusions: Digitalization emerges as the most consistent predictor shaping intra-party democracy, while the cohort effect matters only insofar as crisis parties are more populist than older parties, which ultimately reduces their internal democracy.

The output from Regional Climate Models (RCMs) can be difficult for non-specialists to handle, especially in cases where metadata describing coordinate systems is incomplete or absent. Standard geospatial analysis tools expect coordinate reference systems to be encoded inside file metadata. In addition to different metadata conventions, RCMs that are run over limited domains in the Arctic and Antarctic frequently have rotated longitude and latitude grids that add additional complexity compared to geographic datasets. In this article, we describe two post-processing methods that make RCM outputs easier to use for applications in the climate and related sciences. We demonstrate two different approaches that allow output from RCMs to be 1) read on the correct grid without interpolating or reprojecting the dataset, or 2) resampled onto a regular grid that includes geographic coordinates. These two approaches use the widely available and free software tools Python and Climate Data Operators (CDO). These transformations make outputs simple to use in Geographic Information Systems (GIS) and allow the full use of Python libraries, such as xarray, for plotting and analysis.

Metabolism disrupting chemicals (MDCs) elicit negative effects on metabolically active organs such as the liver and the pancreas, altering normal metabolic processes. Chemicals that are known, or suspected MDCs include compounds found in everyday consumer products and food, making low-dose, continuous exposure inevitable for humans. Through the discovery of chemically induced metabolic disruption, a concern has surfaced whether and how MDCs impact human health and the development of metabolic diseases. This has accelerated research around the topic, and it has been found that exposure to MDCs is linked to increased incidence of metabolic diseases including obesity and liver steatosis. Effective regulatory action is hindered by the lack of accurate methods to identify MDCs. The NEMESIS project addresses this regulatory gap by investigating the mechanisms through which MDCs cause metabolic disruption. The project aims at identifying novel biomarkers of exposure and link exposure to disease outcomes. As chemical toxicity testing is rapidly moving towards new approach methodologies (NAMs), NEMESIS promotes non-animal methodologies by employing state-of-the-art in vitro methods, epidemiological data, systems biology approaches, and seeks to replace mammalian in vivo experiments with alternative models. By understanding mechanisms of MDC-induced metabolic health effects, and through the development of reliable effect biomarkers and testing strategies, the NEMESIS project aims to facilitate more effective regulatory measures to improve and protect the health and well-being of EU citizens. The project is particularly focused on maximizing its impact through effective dissemination and communication efforts, to ensure that the project's message and results reach a broad audience and are tailored to different population groups. These actions will improve the risk assessment of MDCs and ensure that the EU citizens are informed and protected from the harmful effects of MDCs and can adapt their consumer patterns and behaviors to prevent exposure.

The fragmentation and decentralization of medical data, including radiological imaging, continue to challenge large-scale observational research across Europe. Artificial Intelligence (AI) applied to big datasets is transforming diagnosis and treatments towards precision medicine across many diseases, yet the lack of findable, accessible, and interoperable datasets still limits model development, validation, and final clinical translation. The European Federation for Cancer Images (EUCAIM) project was launched in 2023 to address these challenges by establishing a secure centralized and federated infrastructure for the secondary use of large-scale oncological imaging and related clinical data. By consolidating fragmented datasets, EUCAIM lays the groundwork for harmonized data governance and trusted cross-border sharing. Implementing a robust documentation framework is essential to ensure regulatory compliance, safeguard data integrity, and support secure data flows across institutional and national boundaries, fully aligned with European regulations and ethical standards. EUCAIM builds on the AI for Health Imaging (AI4HI) initiative (Predictive In-silico Multiscale Analytics to support cancer personalized diagnosis and prognosis, empowered by imaging biomarkers - PRIMAGE, Accelerating the lab to market transition of AI tools for cancer management - CHAIMELEON, Novel pan-European imaging platform for artificial intelligence advances in oncology - EuCanImage, An AI Platform integrating imaging data and models, supporting precision care through prostate cancer's continuum - ProCancer-I, A multimodal AI-based toolbox and an interoperable health imaging repository for the empowerment of imaging analysis related to the diagnosis, prediction and follow-up of cancer - INCISIVE and integrates over 94 partners and more than 180 stakeholders spanning medical imaging, high performance computing, data standardization, innovation, and legal compliance. This large collaborative ecosystem reinforces EUCAIM's role as a reference for General Data Protection Regulation (GDPR) and European Health Data Space Regulation (EHDSR) adherence. This publication presents the real-world experience of integrating imaging and clinical data from a reference university hospital into the EUCAIM infrastructure. It outlines the procedural, ethical, and legal challenges encountered, and details the strategies implemented to ensure compliance with data protection regulations, including privacy, security, and ethical standards. These insights offer a practical framework for future large-scale oncological imaging datasets harmonization and AI development, contributing to scalable, reproducible, and legally compliant research that strengthens Europe's capacity for trustworthy AI-driven oncology solutions.