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Despite the tremendous efforts to improve river water quality, chemical contamination remains a significant issue. Besides well-known contaminants, in recent years, pollutants of industrial origin received increasing attention because of the huge knowledge gap regarding their occurrence, fate and environmental risks. Moreover, such pollutants often exhibit high concentration fluctuations over time, which makes them less predictable and measurable with classical short-time campaigns. This study provides insights into the different sources of chemical contamination of the Rhine River based on temporal high-frequency LC-HRMS monitoring data from a single location. A newly developed prioritization strategy selected nearly 3000 substances as potentially major contaminants. A novel classification analysis based on temporal behavior identified 53 % of these compounds (accounting for 62 % of the time-integrated intensity recorded in the dataset) as originating from irregular emission sources. Irregular emissions can originate from industrial production cycles. After delimiting other potential irregular sources, we have strong evidence indicating that a considerable share of the irregular emissions likely comes from industrial activities. This finding is supported by the structural elucidation of sixteen irregularly emitted substances, for which the industrial origin was successfully confirmed. Those compounds include 3-chloro-5-(trifluoromethyl)pyridine-2-carboxylic acid and 4-(dimethylamino)-2,2-diphenylpentanenitrile. In addition, 40 other compounds exhibited temporal emission patterns similar to the sixteen industrial compounds, which strongly suggests a common contamination source. Finally, 100 top-ranking compounds were selected for further structural elucidation and emission reduction measures. The computational approach outlined within this study can be effectively applied in other large river catchments to identify unknown contaminants stemming from industrial sources., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

The article discusses the integration of ocean carbonate chemistry data by the European Network of Excellence for Ocean Ecosystems Analysis (EUR-OCEANS) and the European Project on Ocean Acidification (EPOCA). It states that only few papers have provided details regarding carbonate chemistry and complementary observations on the impact of ocean acidification on marine organisms and its communities. It mentions the efforts of gathering data, transforming information, and making data available.

Global energy consumption is growing rapidly, with the frequency and intensity of extreme events constantly increasing, posing a long-term threat to power supply and consumption. Therefore, analyzing the spatiotemporal characteristics of electricity consumption and quantitatively assessing the impact of extreme events on electricity consumption are of great significance. Based on fine-grained electricity consumption data from Europe for the years 2019–2022, this paper employs a data mining perspective and four methods including Z-score, Isolation Forest, Local Outlier Factor, and Autoencoder to detect abnormal electricity consumption during extreme events. Additionally, it combines indicators such as elastic loss, vulnerability, and duration to measure the impact of extreme events on electricity consumption. It is found that low temperatures could lead to abrupt changes in electricity consumption, with Northern Europe being more significantly affected by low temperatures. The COVID-19 pandemic had the most significant impact on electricity consumption in Europe, with the middle part of Europe being the hardest hit during the first wave of the pandemic. Electricity anomalies during the pandemic period were related to national pandemic control policies and exhibited some lag. High temperatures persisted for a longer duration in the middle part of Europe. [ABSTRACT FROM AUTHOR]

In the field of life sciences there is a growing need for literature analysis tools that help scientists tackle information overload. Europe PubMed Central (Europe PMC), a partner of PubMed Central (PMC; National Library of Medicine, 2022), is an open access database of over 41 million life science publications and preprints, enriched with supporting data, reviews, protocols, and other relevant resources. Europe PMC is a trusted repository of choice for many life science funders (Europe PMC, 2022a), offering a suite of innovative search tools that allow users to search and evaluate the literature, including finding highly cited articles, preprints with community peer reviews, or papers referencing a proteomics dataset in the figure legend. In addition, Europe PMC utilizes text-mining to help researchers identify key terms and find data and evidence in the literature. First-time users often do not utilize the wealth of tools Europe PMC offers and can feel overwhelmed about how to perform the most effective search. This protocol, describing how to search and evaluate publications and preprints using Europe PMC, demonstrates how to carry out more efficient and effective literature searches using the tools provided by Europe PMC. This includes discovering the latest findings on a research topic, following research from a specific author, journal, or preprint server, exploring literature on a new method, expanding your reading list with relevant articles, as well as accessing and evaluating publications and preprints of interest. © 2023 EMBL-EBI. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Finding articles and preprints on a topic of interest Basic Protocol 2: Accessing an article Basic Protocol 3: Browsing the article Basic Protocol 4: Evaluating the article Basic Protocol 5: Refining search results Basic Protocol 6: Finding research by author Basic Protocol 7: Finding a specific article Basic Protocol 8: Finding information about a methodology Basic Protocol 9: Finding evidence of biological interactions, relations, and modifications Basic Protocol 10: Finding data behind a publication Basic Protocol 11: Expanding a reading list and building a bibliography Basic Protocol 12: Staying on top of the current literature., (© 2023 EMBL-EBI. Current Protocols published by Wiley Periodicals LLC.)

European universities and public research organizations (PROs) contribute substantially to their countries' overall inventive activity, but are far from being exclusive owners of the related intellectual property. Business companies and, to a lesser extent, individuals appear to play a major, sometime dominant role. This special issue offers a selection of papers addressing issues of measurement, commercialization, and ownership of such academic patents in Europe. Measuring the extent of the phenomenon requires identification of the academic inventors, a data-mining operation that imposes technical as well as procedural challenges for social scientists. The heterogeneity of ownership models poses the question of whether ownership is related to the patents' quality and/or successful commercialization. Further questions concern the identity and business models of firms holding academic patents in their portfolios, and the economic and legal factors that explain a university's or PRO's choice of whether to maintain the intellectual property of its staff's inventions. The papers in this special issue discuss these topics at length and rely on data collected following a joint methodology, made available to readers for use and extension. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to analyze the current temporal and spatial trajectories in nanotechnology research in order to display the worldwide patterns of scientific domains across main geo-economic players. Current trends of nanotechnology have been growing in chemistry and medicine because of technological applications of new nanomaterials mainly in Chemical Engineering, Biochemistry, Genetics and Molecular Biology. In addition, the results show that the laboratories of Europe and North America have intensive scientific collaborations with foreign scholars in nanotechnology studies. This paper can provide vital findings to support new research and innovation policies in order to foster the development of these driving converging technologies for future patterns of economic growth. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) has resulted in approximately 5 million deaths around the world with unprecedented consequences in people's daily routines and in the global economy. Despite vast increases in time and money spent on COVID-19-related research, there is still limited information about the factors at the country level that affected COVID-19 transmission and fatality in EU. The paper focuses on the identification of these risk factors using a machine learning (ML) predictive pipeline and an associated explainability analysis. To achieve this, a hybrid dataset was created employing publicly available sources comprising heterogeneous parameters from the majority of EU countries, e.g., mobility measures, policy responses, vaccinations, and demographics/generic country-level parameters. Data pre-processing and data exploration techniques were initially applied to normalize the available data and decrease the feature dimensionality of the data problem considered. Then, a linear ε-Support Vector Machine (ε-SVM) model was employed to implement the regression task of predicting the number of deaths for each one of the three first pandemic waves (with mean square error of 0.027 for wave 1 and less than 0.02 for waves 2 and 3). Post hoc explainability analysis was finally applied to uncover the rationale behind the decision-making mechanisms of the ML pipeline and thus enhance our understanding with respect to the contribution of the selected country-level parameters to the prediction of COVID-19 deaths in EU. [ABSTRACT FROM AUTHOR]

The Single Europe Sky Air Traffic Management Research (SESAR) program develops and implements innovative technological and operational solutions to modernize European air traffic management and to eliminate the negative environmental impacts of aviation activity. This article presents our developments within the SESAR Solution "Safety Support Tools for Avoiding Runway Excursions". This SESAR Solution aims to mitigate the risk of runway excursion, to optimize airport operation management by decreasing the number of runway inspections, to make chemical treatment effective with respect to the environment, and to increase resilience, efficiency and safety in adverse weather situations. The proposed approach is based on the enhancement of runway surface condition awareness by integrating data from various sources. Dangerous windy conditions based on Lidar measurements are also discussed as another relevant factor in relation to runway excursions. The paper aims to explore four different data mining methods to obtain runway conditions from the available input data sources, examines their performance and discusses their pros and cons in comparison with a rule-based algorithm approach. The output of the SESAR Solution is developed in compliance with the new Global Reporting Format of the International Civil Aviation Organization for runway condition description to be valid from 2020. This standard is expected to provide concerned stakeholders with more precise information to enhance flight safety and environmental protection., Competing Interests: The authors declare no conflict of interest.

Background: In recent years, the development and application of emerging information technologies, such as artificial intelligence, cloud computing, Internet of Things, and wearable devices, has expanded the content of electronic health (e-health). Electronic health has become a research focus, but few studies have explored its knowledge structure from a global perspective., Methods: To detect the evolution track, knowledge base and research hotspots of e-health, we conducted a series of bibliometric analyses on the retrieved 3,085 papers from the Web of Science core database in 1992-2017. We used several bibliometric tools, such as HistCite, CiteSpace, NetDraw, and NEViewer, to describe the evolution process, time-and-space knowledge map, and hotspots in e-health., Results: The research results are as follows. (a) The number of publications has been obviously increasing after 2005 and according to the trend line it is expected to continue increase exponentially in the future. (b) Countries/regions conducting e-health research have close cooperative relationship, among which European countries have the closest cooperation. (c) Electronic health records, mobile health and health information technology are research hotspots in electronic health. Moreover, scholars also pay attention to the hot issues such as privacy, security, and quality improvement., Conclusions: Electronic health is a large and growing field with quite a number of research articles in medical journals. This study provides a comprehensive knowledge structure of electronic health for scholars in the healthcare informatics field, which can help them quickly grasp research hotspots and choose future research projects., (Copyright © 2019 Elsevier B.V. All rights reserved.)

This paper aims to bridge a gap in the literature by investigating a continuous audit case for anticorruption. The evolution of technology can offer valuable opportunities to integrate legality checks and business processes that are consistent with the growing call to fight corruption at the institutional level. Since the last decade, researchers have proposed conceptual frameworks demonstrating the visible advantages of continuous auditing and data mining, but significant difficulties still exist in practice. This process has been implemented in only a few cases, and the lack of empirical studies implies a need for additional research on this topic. To fill this gap, we adopt the framework proposed by Chan and Vasarhelyi (2011) and identify success factors for the implementation of continuous auditing. For the analysis, we use the research methodology of a single case study and focus on the Italian company Acciai Speciali Terni Spa (AST), the only steelwork company in Europe that has been ISO 37001 certified. This study demonstrates the practical impact of continuous auditing and data mining on strategic risk control by empirically testing the Chan and Vasarhelyi (2011) framework for the specific issue of anticorruption. The results show that effective continuous auditing is centered on an integrative and change management approach and that strategic vision, risk mapping, and a no‐corruption culture are among the most influential factors. The AST case demonstrates that technology is currently essential for supporting strategic risk control but only if it is integrated with consistent growth in organizational and managerial capabilities. [ABSTRACT FROM AUTHOR]

Autonomous Vehicles (AV) technology is emerging. Field tests on public roads have been on going in several states in the US as well as in Europe and Asia. During the US public road tests, crashes with AV involved happened, which becomes a concern to the public. Most previous studies on AV safety relied heavily on assessing drivers’ performance and behaviors in a simulation environment and developing automated driving system performance in a closed field environment. However, contributing factors and the mechanism of AV-related crashes have not been comprehensively and quantitatively investigated due to the lack of field AV crash data. By harnessing California’s Report of Traffic Collision Involving an Autonomous Vehicle Database, which includes the AV crash data from 2014 to 2018, this paper investigates by far the most current and complete AV crash database in the US using statistical modeling approaches that involve both ordinal logistic regression and CART classification tree. The quantitative analysis based on ordinal logistic regression and CART models has successfully explored the mechanism of AV-related crash, via both perspectives of crash severity and collision types. Particularly, the CART model reveals and visualize the hierarchical structure of the AV crash mechanism with knowledge of how these traffic, roadway, and environmental contributing factors can lead to crashes of various serveries and collision types. Statistical analysis results indicate that crash severity significantly increases if the AV is responsible for the crash. The highway is identified as the location where severe injuries are likely to happen. AV collision types are affected by whether the vehicle is on automated driving mode, whether the crashes involve pedestrians/cyclists, as well as the roadway environment. The method used in this research provides a proven approach to statistically analyze and understand AV safety issues. And this benefit is potential be even enhanced with an increasing sample size of AV-related crashes records in the future. The comprehensive knowledge obtained ultimately facilitates assessing and improving safety performance of automated vehicles. [ABSTRACT FROM AUTHOR]

This paper presents the methodology, programme logic and conceptual framework that drove the evaluation of the Fifth Phase of the WHO European Healthy Cities Network. Towards the end of the phase, 99 cities were designated progressively through the life of the phase (2009-14). The paper establishes the values, systems and aspirations that these cities sign up for, as foundations for the selection of methodology. We assert that a realist synthesis methodology, driven by a wide range of qualitative and quantitative methods, is the most appropriate perspective to address the wide geopolitical, demographic, population and health diversities of these cities. The paper outlines the rationale for a structured multiple case study approach, the deployment of a comprehensive questionnaire, data mining through existing databases including Eurostat and analysis of management information generation tools used throughout the period. Response rates were considered extremely high for this type of research. Non-response analyses are described, which show that data are representative for cities across the spectrum of diversity. This paper provides a foundation for further analysis on specific areas of interest presented in this supplement. [ABSTRACT FROM AUTHOR]

Introduction: This paper is in response to criticism of our article 'The human sex odds at birth after the atmospheric atomic bomb tests, after Chernobyl, and in the vicinity of nuclear facilities' published in Environ Sci Pollut Res 18(5):697-707, . Methods: Our findings and methods concerning the disturbed human sex odds at birth have been criticized in this journal for being artifacts of data mining, that the concept of statistical significance was misunderstood, and that confounding factors have not been accounted for. Here, we show that this criticism has no basis. We applied well-established statistical methods to large official data sets, and confounding is less important at the level of secular sex odds trends in aggregated annual figures from countries or continents. Results and conclusions: Moreover, our results are strengthened by recent findings concerning increased infant death sex odds in Germany and increased Down syndrome prevalence at birth across Europe after Chernobyl. Prompted by our studies, an official investigation in Lower Saxony, Germany, by the 'Niedersächsisches Landesgesundheitsamt (NLGA)' confirmed our observation of severely escalated sex odds within 40 km distance from the nuclear storage site in Gorleben, Germany. [ABSTRACT FROM AUTHOR]

Translational medicine is becoming fundamental in promoting information flow between basic research and clinical practice, and in improving patients' health. The need for efficient systems to process and share information from multiple sources--including distinct areas of medicine--is a pressing need in biomedical research. Nevertheless, healthcare information systems are fragmented over different databases, medical institutions, and geographical locations. There are already several approaches to tackle this problem based on centralized or distributed solutions. Nonetheless, mainly due to privacy reasons, these models only work for specific silos. In this paper, we present a new ecosystem for connecting database owners and researchers. Our approach consists of gathering, via a common fingerprint, an extensive characterization of dispersed databases. This fingerprint typically contains high-level aggregated information addressing questions at a population level and allowing, for instance, quick identification of databases with data that may help to answer a specific research question. This work is being conducted in the context of EMIF (European Medical Information Framework), an IMI (The Innovative Medicines Initiative) joint undertaking funded project, wherein the Catalogue is being used to collect data from cohorts and Electronic Health Record systems of several European countries.

Background: Over the past 30 years, the international conference on Artificial Intelligence in MEdicine (AIME) has been organized at different venues across Europe every 2 years, establishing a forum for scientific exchange and creating an active research community. The Artificial Intelligence in Medicine journal has published theme issues with extended versions of selected AIME papers since 1998.Objectives: To review the history of AIME conferences, investigate its impact on the wider research field, and identify challenges for its future.Methods: We analyzed a total of 122 session titles to create a taxonomy of research themes and topics. We classified all 734 AIME conference papers published between 1985 and 2013 with this taxonomy. We also analyzed the citations to these conference papers and to 55 special issue papers.Results: We identified 30 research topics across 12 themes. AIME was dominated by knowledge engineering research in its first decade, while machine learning and data mining prevailed thereafter. Together these two themes have contributed about 51% of all papers. There have been eight AIME papers that were cited at least 10 times per year since their publication.Conclusions: There has been a major shift from knowledge-based to data-driven methods while the interest for other research themes such as uncertainty management, image and signal processing, and natural language processing has been stable since the early 1990s. AIME papers relating to guidelines and protocols are among the most highly cited. [ABSTRACT FROM AUTHOR]

Different programs of The European Science Foundation (ESF) have contributed significantly to connect researchers in Europe and beyond through several initiatives. This support was particularly relevant for the development of the areas related with extracting information from papers (text-mining) because it supported the field in its early phases long before it was recognized by the community. We review the historical development of text mining research and how it was introduced in bioinformatics. Specific applications in (functional) genomics are described like it's integration in genome annotation pipelines and the support to the analysis of high-throughput genomics experimental data, and we highlight the activities of evaluation of methods and benchmarking for which the ESF programme support was instrumental., (Copyright © 2013 Elsevier B.V. All rights reserved.)

Many people suffering from chronic headaches use headache diaries to help track trends in their episodes. Studies have shown that electronic headache diaries are more effective than paper diaries for both patients and providers. We de-signed and implemented a full-size and mobile Web-based headache tracker and a database management system to store the data from the websites. The fullsize website allows users to retrieve their data by viewing or downloading a table of the raw values they entered, or they can see a graphical visualization linking the severity of their headaches to any of several variables. Through this system's capacity to illustrate trends and track contributing factors for headaches, users, researchers and health service providers may gain better understanding of and control over chronic headaches.

The paper describes the Eurogene portal for sharing and reusing multilingual multimedia educational resources in human genetics. The content is annotated using concepts of two ontologies and a topic hierarchy. The ontology annotation is used to guide search and for calculating semantically similar content. Educational resources can be aggregated into learning packages. The system is in routine use since 2009.

The EU-ADR project aims to exploit different European electronic healthcare records (EHR) databases for drug safety signal detection. In this paper we report the preliminary results concerning the comparison of signal detection between EU-ADR network and two spontaneous reporting databases, the Food and Drug Administration and World Health Organization databases. EU-ADR data sources consist of eight databases in four countries (Denmark, Italy, Netherlands, and United Kingdom) that are virtually linked through distributed data network. A custom-built software (Jerboa©) elaborates harmonized input data that are produced locally and generates aggregated data which are then stored in a central repository. Those data are subsequently analyzed through different statistics (i.e. Longitudinal Gamma Poisson Shrinker). As potential signals, all the drugs that are associated to six events of interest (bullous eruptions - BE, acute renal failure - ARF, acute myocardial infarction - AMI, anaphylactic shock - AS, rhabdomyolysis - RHABD, and upper gastrointestinal bleeding - UGIB) have been detected via different data mining techniques in the two systems. Subsequently a comparison concerning the number of drugs that could be investigated and the potential signals detected for each event in the spontaneous reporting systems (SRSs) and EU-ADR network was made. SRSs could explore, as potential signals, a larger number of drugs for the six events, in comparison to EU-ADR (range: 630-3,393 vs. 87-856), particularly for those events commonly thought to be potentially drug-induced (i.e. BE: 3,393 vs. 228). The highest proportion of signals detected in SRSs was found for BE, ARF and AS, while for ARF, and UGIB in EU-ADR. In conclusion, it seems that EU-ADR longitudinal database network may complement traditional spontaneous reporting system for signal detection, especially for those adverse events that are frequent in general population and are not commonly thought to be drug-induced. The methodology for signal detection in EU-ADR is still under development and testing phase.

This paper suggests a solution to acquire medical data from hospitals located in a region (addressing especially the DKMT Euroregion), and then perform data mining. The medical data from the hospital databases are exported in XML format, according to HL7 CDA standard. Afterwards, they are automatically centralized on a server in a database using web services calls. The data will be analyzed with the data mining tool WEKA. Data of interest are converted into ARFF format and loaded into WEKA. The next stage consists in preprocessing and analyzing the data based on the algorithms provided by WEKA, having as a goal several relevant medical conclusions. WEKA application interface has been improved to facilitate the process of performing predictions.

Unlabelled: The EU-ADR project aims to exploit different European electronic healthcare records (EHR) databases for drug safety signal detection. In this paper we describe the project framework and the preliminary results., Methods: As first step we created a ranked list of the events that are deemed to be important in pharmacovigilance as mining on all possible events was considered to unduly increase the number of spurious signals. All the drugs that are potentially associated to these events will be detected via data mining techniques. Data sources are eight 8 databases in four countries (Denmark, Italy, the Netherlands, and the United Kingdom) that are virtually linked through harmonisation of input data followed by local elaboration of input data through custom-built software (Jerboa). All the identified drug-event associations (signals) will be thereafter biologically substantiated and epidemiologically validated. To date, only Upper gastrointestinal bleeding (UGIB) event has been used to test the ability of the system in signal detection., Results: An initial ranked list comprising 23 adverse events was identified. The top-ranking events were: cutaneous bullous eruptions, acute renal failure, acute myocardial infarction, anaphylactic shock, and rhabdomyolysis. Regarding the UGIB test, a total of 48,016 first-ever episodes were identified. The age-standardized incidence rates of UGIB varied between 40-100/100,000 person-years depending on country and type of healthcare database. A statistically significant association between use of NSAIDs and UGIB was detected in all of the databases., Conclusion: a dynamic ranked list of 23 adverse drug events judged as important in pharmacovigilance was created to permit focused data mining. Preliminary results on the UGIB event detection demonstrate the feasibility of harmonizing various health care databases in different European countries through a distributed network approach.

The Europe Media Monitor (EMM) family of applications is a set of multilingual tools that gather, cluster and classify news in currently fifty languages and that extract named entities and quotations (reported speech) from twenty languages. In this paper, we describe the recent effort of adding the African Bantu language Swahili to EMM. EMM is designed in an entirely modular way, allowing plugging in a new language by providing the language-specific resources for that language. We thus describe the type of language-specific resources needed, the effort involved, and ways of boot-strapping the generation of these resources in order to keep the effort of adding a new language to a minimum. The text analysis applications pursued in our efforts include clustering, classification, recognition and disambiguation of named entities (persons, organisations and locations), recognition and normalisation of date expressions, as well as the identification of reported speech quotations by and about people. [ABSTRACT FROM AUTHOR]

lthough business aviation has been popular in the USA, Europe, and South America, however, top economies in East Asia, including Japan, Korea, and Taiwan, have been more conservative and lag behind in the development of business aviation. In this paper, we hope to discover possible trends and needs of business aviation for supporting the government to make decision in anticipation of eventual deregulation in the near future. We adopt knowledge-discovery tools based on rough set to analyze the potential for business aviation through an empirical study. Although our empirical study uses data from Taiwan, we are optimistic that our proposed method can be similarly applied in other countries to help governments there make decisions about a deregulated market in the future. [ABSTRACT FROM AUTHOR]

Sustainable development as a concept that aims to enhance the quality of life without affecting environment has been in focus in European policies since the last decade of 20th century. The objectives of EU Sustainable Development Strategy (EU SDS) are grouped into ten thematic areas, where almost each theme has at least one headline and several operational sustainable development indicators (SDIs). A large number of SDIs is needed to evaluate all EU SDS goals, which imposes the use of multivariate data mining techniques. This paper presents an empirical study carried out to assess the sustainability performance of European countries using the differential multi criteria analysis (DMCA) technique. The PROMETHEE (Preference Ranking Organization MeTHod for Enrichment Evaluations) was applied on 38 headline and operational SDIs defined under the EU SDS. This DMCA was applied to 30 European countries over a 10-year period (2004–2014) with the aim to determine the theme specific, as well as, overall sustainability progress. The DMCA reveals that the majority of European countries have made progress in sustainability in the studied period, where Czech Republic, Germany, Hungary and Sweden, have enhanced their sustainability performance concerning all themes. There are only two countries, namely Greece and Ireland, which have not made overall progress in this period. Also, above the average progress has been made concerning social inclusion, sustainable transport, and climate change and energy, while in all other themes additional efforts should be made in order to ensure sustainable performance progress in future years. Although the progress in reducing uneven development between EU members has been made, after 2009 a positive trend can be observed only in a limited number of SDS themes. [ABSTRACT FROM AUTHOR]

Digitization of medicine requires systematic handling of the increasing amount of health data to improve medical diagnosis. In this context, the integration of the versatile diagnostic information, e.g., from anamnesis, imaging, histopathology, and clinical chemistry, and its comprehensive analysis by artificial intelligence (AI)-based tools is expected to improve diagnostic precision and the therapeutic conduct. However, the complex medical environment poses a major obstacle to the translation of integrated diagnostics into clinical research and routine. There is a high need to address aspects like data privacy, data integration, interoperability standards, appropriate IT infrastructure, and education of staff. Besides this, a plethora of technical, political, and ethical challenges exists. This is complicated by the high diversity of approaches across Europe. Thus, we here provide insights into current international activities on the way to digital comprehensive diagnostics. This includes a technical view on challenges and solutions for comprehensive diagnostics in terms of data integration and analysis. Current data communications standards and common IT solutions that are in place in hospitals are reported. Furthermore, the international hospital digitalization scoring and the European funding situation were analyzed. In addition, the regional activities in radiomics and the related publication trends are discussed. Our findings show that prerequisites for comprehensive diagnostics have not yet been sufficiently established throughout Europe. The manifold activities are characterized by a heterogeneous digitization progress and they are driven by national efforts. This emphasizes the importance of clear governance, concerted investments, and cooperation at various levels in the health systems.Key Points• Europe is characterized by heterogeneity in its digitization progress with predominantly national efforts. Infrastructural prerequisites for comprehensive diagnostics are not given and not sufficiently funded throughout Europe, which is particularly true for data integration.• The clinical establishment of comprehensive diagnostics demands for a clear governance, significant investments, and cooperation at various levels in the healthcare systems.• While comprehensive diagnostics is on its way, concerted efforts should be taken in Europe to get consensus concerning interoperability and standards, security, and privacy as well as ethical and legal concerns. [ABSTRACT FROM AUTHOR]

Background: Developed countries have a high prevalence of vitamin D deficiency. In previous studies, 25(OH)D was predominantly measured by immunoassays. The present study assessed serum 25(OH)D in a very large Southern European outpatient cohort by liquid chromatography tandem mass spectrometry (LC–MS/MS). Materials and methods: 74,235 serum 25(OH)D results generated under routine conditions between 2015 and 2016 were extracted from the laboratory information system of the Department of Clinical Pathology at Bolzano Hospital (Italy). In 3801 cases, parathyroid hormone (PTH) was requested in parallel. Serum 25(OH)D was measured by a NIST-972 aligned commercial LC–MS/MS method. The distribution of serum 25(OH)D concentrations in males and females of different age groups, the prevalence of 25(OH)D2 and seasonal variability were studied. Results: The average 25(OH)D concentration in the entire cohort was 68.6 nmol/L (7.5–1880 nmol/L). Females had a 7 nmol/L higher average 25(OH)D concentration than males, which increased significantly with age. 37.9 and 28.3% of males and females, respectively, had a deficient 25(OH)D concentration of < 50 nmol/L. 620 samples (0.84%) had measureable amounts of 25(OH)D2. In samples with a normal PTH, 25(OH)D was 11 nmol/L higher than in the entire cohort. Seasonal variation ranged between 20 and 30% and was most pronounced in young individuals. 25(OH)D2 remained constant throughout the year. Conclusion: Average serum 25(OH)D in South Tyrol is higher than in other parts of Europe. 25(OH)D and PTH show a continuous inverse relationship. Seasonal variation of serum 25(OH)D is an important aspect in young and middle-aged adults, but becomes less relevant in elderly subjects. 25(OH)D2 is of minor practical importance in South Tyrol. [ABSTRACT FROM AUTHOR]

The article reports on a proposal by the European Commission which would provide researchers in the European Union greater freedom to study text and data from copyright-protected papers and mentions the League of European Research Universities questioned a provision that could make data mining more difficult.

The co-occurrence of plant species is a fundamental aspect of plant ecology that contributes to understanding ecological processes, including the establishment of ecological communities and its applications in biological conservation. A priori algorithms can be used to measure the co-occurrence of species in a spatial distribution given by coordinates. We used 17 species of the genus Brachypodium, downloaded from the Global Biodiversity Information Facility data repository or obtained from bibliographical sources, to test an algorithm with the spatial points process technique used by Silva et al. (2016), generating association rules for co-occurrence analysis. Brachypodium spp. has emerged as an effective model for monocot species, growing in different environments, latitudes, and elevations; thereby, representing a wide range of biotic and abiotic conditions that may be associated with adaptive natural genetic variation. We created seven datasets of two, three, four, six, seven, 15, and 17 species in order to test the algorithm with four different distances (1, 5, 10, and 20 km). Several measurements (support, confidence, lift, Chi-square, and p-value) were used to evaluate the quality of the results generated by the algorithm. No negative association rules were created in the datasets, while 95 positive co-occurrences rules were found for datasets with six, seven, 15, and 17 species. Using 20 km in the dataset with 17 species, we found 16 positive co-occurrences involving five species, suggesting that these species are coexisting. These findings are corroborated by the results obtained in the dataset with 15 species, where two species with broad range distributions present in the previous dataset are eliminated, obtaining seven positive co-occurrences. We found that B. sylvaticum has co-occurrence relations with several species, such as B. pinnatum, B. rupestre, B. retusum, and B. phoenicoides, due to its wide distribution in Europe, Asia, and north of Africa. We demonstrate the utility of the algorithm implemented for the analysis of co-occurrence of 17 species of the genus Brachypodium, agreeing with distributions existing in nature. Data mining has been applied in the field of biological sciences, where a great amount of complex and noisy data of unseen proportion has been generated in recent years. Particularly, ecological data analysis represents an opportunity to explore and comprehend biological systems with data mining and bioinformatics tools. [ABSTRACT FROM AUTHOR]

Background and Objective: Spontaneous reporting systems (SRSs) remain the cornerstone of post-marketing drug safety surveillance despite their well-known limitations. Judicious use of other available data sources is essential to enable better detection, strengthening and validation of signals. In this study, we investigated the potential of electronic healthcare records (EHRs) to be used alongside an SRS as an independent system, with the aim of improving signal detection.Methods: A signal detection strategy, focused on a limited set of adverse events deemed important in pharmacovigilance, was performed retrospectively in two data sources-(1) the Exploring and Understanding Adverse Drug Reactions (EU-ADR) database network and (2) the EudraVigilance database-using data between 2000 and 2010. Five events were considered for analysis: (1) acute myocardial infarction (AMI); (2) bullous eruption; (3) hip fracture; (4) acute pancreatitis; and (5) upper gastrointestinal bleeding (UGIB). Potential signals identified in each system were verified using the current published literature. The complementarity of the two systems to detect signals was expressed as the percentage of the unilaterally identified signals out of the total number of confirmed signals. As a proxy for the associated costs, the number of signals that needed to be reviewed to detect one true signal (number needed to detect [NND]) was calculated. The relationship between the background frequency of the events and the capability of each system to detect signals was also investigated.Results: The contribution of each system to signal detection appeared to be correlated with the background incidence of the events, being directly proportional to the incidence in EU-ADR and inversely proportional in EudraVigilance. EudraVigilance was particularly valuable in identifying bullous eruption and acute pancreatitis (71 and 42 % of signals were correctly identified from the total pool of known associations, respectively), while EU-ADR was most useful in identifying hip fractures (60 %). Both systems contributed reasonably well to identification of signals related to UGIB (45 % in EudraVigilance, 40 % in EU-ADR) but only fairly for signals related to AMI (25 % in EU-ADR, 20 % in EudraVigilance). The costs associated with detection of signals were variable across events; however, it was often more costly to detect safety signals in EU-ADR than in EudraVigilance (median NNDs: 7 versus 5).Conclusion: An EHR-based system may have additional value for signal detection, alongside already established systems, especially in the presence of adverse events with a high background incidence. While the SRS appeared to be more cost effective overall, for some events the costs associated with signal detection in the EHR might be justifiable. [ABSTRACT FROM AUTHOR]

In 2012, the author and colleagues surveyed eight publishers that had been involved with the Publishing and the Ecology of European Research (PEER) project to learn about the state of hybrid journal publishing. At the same time, one of the key questions asked to a panel of librarians at the International Association of Scientific, Technical & Medical Publishers May 2012 Meeting was what role librarians would play if scholarly publishing shortly went open access (OA) across the board? From the survey of the market, and the rapid OA developments in the UK and EU that include hybrid OA, a picture has begun to emerge of what roles librarians can play with regard to supporting hybrid OA publishing at their institutions. This article focuses on developing new partnerships within a given institution, looks at new budgetary models and the tracking of local scholarship creation. Current pertinent standards are highlighted. [ABSTRACT FROM AUTHOR]

The terms of digital economy and e-readiness are introduced and presented in the present area of evolution of economy stage within Europe and not only. The main aspects treated in the European University Institute ranking for e-readiness are briefly covered. An open source convertor for excel to arff file format is presented and used in the purpose of input transformation for using Weka Data Mining, an open source tool. The framework of ExceltoArff tool is demonstrated in a practical use, the one of 70 countries registrations of EUI Ranking. [ABSTRACT FROM AUTHOR]

In the context of OpenStreetMap (OSM), spatial data quality, in particular completeness, is an essential aspect of its fitness for use in specific applications, such as planning tasks. To mitigate the effect of completeness errors in OSM, this study proposes a methodological framework for predicting by means of OSM urban areas in Europe that are currently not mapped or only partially mapped. For this purpose, a machine learning approach consisting of artificial neural networks and genetic algorithms is applied. Under the premise of existing OSM data, the model estimates missing urban areas with an overall squared correlation coefficient (R 2) of 0.589. Interregional comparisons of European regions confirm spatial heterogeneity in the model performance, whereas the R 2 ranges from 0.129 up to 0.789. These results show that the delineation of urban areas by means of the presented methodology depends strongly on location. [ABSTRACT FROM AUTHOR]

In cold war times, the 'West' espoused liberal democracy and freedom from surveillance and control. It is thus ironic that with the cold war a distant memory — though it only ended less than twenty years ago — the EU and its member states are set on a path which will, in just a few years time, turn it into the most surveilled, monitored region in the world. The wider context for all this is increased state racism (both at the national and EU levels), combined with the emergence of the 'policing state', engendered by a political and governmental authoritarianism that legitimises itself through the trappings of representative democracy. [ABSTRACT FROM AUTHOR]

The Global Pollen Database is an example of a successful data synthesis effort that has uses for biogeographical and climate change studies. Results are of interest in many fields of physical geography. Continental-scale maps of past conditions have been used in data-model comparison studies. Time series, developed by averaging quantitative reconstructions from many sites, have indicated that millennial-scale climate variability has affected the vegetation of Europe and North America during the Holocene. Major transitions in the vegetation of Europe and North America occurred at the same time, suggesting the overriding climate effect on the vegetation of both continents. The database can also be used to test biogeographical hypotheses, as several examples illustrate, without the need for collecting new data. Hundreds of studies over the past 50 years show that pollen analysis is more precise than frequently acknowledged: vegetation responds rapidly to climate variations, changes in vegetation are spatially coherent and the taxonomic resolution available in the database is greater than frequently acknowledged. The availability of a public, freely available database enables different analyses to be performed on the same data, thereby ensuring that results are not dependent on methodology. [ABSTRACT FROM AUTHOR]

Charters are of crucial importance as a source when studying the history of the Middle Ages and the early modern era. There are a number of projects aiming at making charters available in digital format. All of these projects, as far as they use XML as the technological basis for their work, were represented at a conference in April 2004. It was found that, despite some structural differences in the individual approaches, there is considerable potential for integration as regards the individual charter as a legally relevant text. This potential can be tapped by using common standards for the tagging/ encoding of charters. This article presents a proposal for such a standard. The proposed standard follows the existing TEI standard, but also contains a number of significant enhancements. The article also looks at the problems caused by overlapping structures and presents an architecture for a search engine that could help to bring together charters from all over Europe. [ABSTRACT FROM AUTHOR]

The article offers news briefs related to European Science Editing as of May 2012. It says that Elsevier, a publisher company, has purchased Mendeley, which is a sharing and reference management service. It mentions the launch of Scientific Data, an online open-access journal, by Nature Publishing Group. It adds that a new text and data mining guide from the Publishing Research Consortium (PRC) explains how publishers can support mining by making their content more machine-readable.