Cost-Effectiveness of Prevention for People at Risk for Dementia: A Scoping Review and Qualitative Synthesis

Dementia is from an economic perspective a main challenge for economies worldwide because of increasing costs. Since there is no cure in sight, prevention seems the most promising approach for reducing health care cost due to Dementia. On the contrary, approximately 40% of dementias is attributable to modifiable risk factors and first studies showed that multidomain interventions may be effective for preventing dementia. Considering the increasing economic burden, for many health administrations worldwide, cost-effectiveness plays a mayor role. This scoping review wants to bring evidence to the question if prevention for people at risk may be cost-effective. Therefore, the four databases Medline (via Pubmed), CINHAL (via EBSCO), Business Source Complete (via EBSCO), and the Health Economic Evaluation database (HEED) were used to conduct a scoping review using PICO and a systematic search string. 3,629 studies were identified and seven met all inclusion criteria. The included studies showed clear cost-effectiveness for most multidomain interventions. The gained QALYs at mean were 0.08 (SD=0.08) and the intervention average costs 472.20 EUR per Person (SD=74.06 EUR). The Incremental Cost-Effectiveness Ratios varied between -80,427.97 and 104,189.82 EUR per QALY. The three core results are (i) prevention programs focusing on people at risk may be cost-effective and cost-efficient, (ii) multimodal prevention reveal cost saving potential, when the people at risk are defined well, (iii prevention in middle-aged cohorts may be also cost-effective if life-style related risk factors are addressed.

Impact of COVID-19 research: a study on predicting influential scholarly documents using machine learning and a domain-independent knowledge graph

Multiple studies have investigated bibliometric features and uncategorized scholarly documents for the influential scholarly document prediction task. In this paper, we describe our work that attempts to go beyond bibliometric metadata to predict influential scholarly documents. Furthermore, this work also examines the influential scholarly document prediction task over categorized scholarly documents. We also introduce a new approach to enhance the document representation method with a domain-independent knowledge graph to find the influential scholarly document using categorized scholarly content. As the input collection, we use the WHO corpus with scholarly documents on the theme of COVID-19. This study examines different document representation methods for machine learning, including TF-IDF, BOW, and embedding-based language models (BERT). The TF-IDF document representation method works better than others. From various machine learning methods tested, logistic regression outperformed the other for scholarly document category classification, and the random forest algorithm obtained the best results for influential scholarly document prediction, with the help of a domain-independent knowledge graph, specifically DBpedia, to enhance the document representation method for predicting influential scholarly documents with categorical scholarly content. In this case, our study combines state-of-the-art machine learning methods with the BOW document representation method. We also enhance the BOW document representation with the direct type (RDF type) and unqualified relation from DBpedia. From this experiment, we did not find any impact of the enhanced document representation for the scholarly document category classification. We found an effect in the influential scholarly document prediction with categorical data.

The SciQA Scientific Question Answering Benchmark for Scholarly Knowledge

Knowledge graphs have gained increasing popularity in the last decade in science and technology. However, knowledge graphs are currently relatively simple to moderate semantic structures that are mainly a collection of factual statements. Question answering (QA) benchmarks and systems were so far mainly geared towards encyclopedic knowledge graphs such as DBpedia and Wikidata. We present SciQA a scientific QA benchmark for scholarly knowledge. The benchmark leverages the Open Research Knowledge Graph (ORKG) which includes almost 170,000 resources describing research contributions of almost 15,000 scholarly articles from 709 research fields. Following a bottom-up methodology, we first manually developed a set of 100 complex questions that can be answered using this knowledge graph. Furthermore, we devised eight question templates with which we automatically generated further 2465 questions, that can also be answered with the ORKG. The questions cover a range of research fields and question types and are translated into corresponding SPARQL queries over the ORKG. Based on two preliminary evaluations, we show that the resulting SciQA benchmark represents a challenging task for next-generation QA systems. This task is part of the open competitions at the 22nd International Semantic Web Conference 2023 as the Scholarly Question Answering over Linked Data (QALD) Challenge.

Creating and validating a scholarly knowledge graph using natural language processing and microtask crowdsourcing

Due to the growing number of scholarly publications, finding relevant articles becomes increasingly difficult. Scholarly knowledge graphs can be used to organize the scholarly knowledge presented within those publications and represent them in machine-readable formats. Natural language processing (NLP) provides scalable methods to automatically extract knowledge from articles and populate scholarly knowledge graphs. However, NLP extraction is generally not sufficiently accurate and, thus, fails to generate high granularity quality data. In this work, we present TinyGenius, a methodology to validate NLP-extracted scholarly knowledge statements using microtasks performed with crowdsourcing. TinyGenius is employed to populate a paper-centric knowledge graph, using five distinct NLP methods. We extend our previous work of the TinyGenius methodology in various ways. Specifically, we discuss the NLP tasks in more detail and include an explanation of the data model. Moreover, we present a user evaluation where participants validate the generated NLP statements. The results indicate that employing microtasks for statement validation is a promising approach despite the varying participant agreement for different microtasks.

Cold thermopeaking-induced drift of nase Chondrostoma nasus larvae

Research on how intermittent water releases from hydropower plants affect the early life stages of fish has advanced in the last years, focusing not only on the direct impacts of rapid flow changes (hydropeaking), but also on the short-term fluctuations in water temperature (thermopeaking). Flow and thermal fluctuations caused by hydropeaking may affect fish movement patterns and migration at critical stages of a species' life cycle, e.g., by inducing passive downstream drift. Using two experimental outdoor channels, we investigated how nase (Chondrostoma nasus, Cypriniformes) larvae respond to a rapid drop in water temperature during hydropeaking (simulating a cold thermopeaking event), reaching on average 5.5 °C under peak flow (maximum discharge) conditions, in comparison with a hydropeaking treatment with a constant water temperature regime. Responses of fish larvae were analyzed during acclimation, up-ramping (increase in discharge), peak flow and down-ramping (decrease in discharge) phases. Fish drift increased during peak flow in the cold thermopeaking treatment compared to hydropeaking. Higher drift rates were also negatively associated with pronounced water temperature drops during peak flow conditions. In addition, the starting temperature of the experiment influenced drift during up-ramping. Overall, the results suggest that cold thermopeaking may increase drift in the early life stages of cypriniform fish compared with hydropeaking with stable water temperature. Hence, monitoring and active water temperature adjustments following hydropower releases should be adopted as strategies to mitigate power plant-related impacts on aquatic organisms.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s00027-023-00955-x.

Information extraction pipelines for knowledge graphs

In the last decade, a large number of knowledge graph (KG) completion approaches were proposed. Albeit effective, these efforts are disjoint, and their collective strengths and weaknesses in effective KG completion have not been studied in the literature. We extend Plumber, a framework that brings together the research community's disjoint efforts on KG completion. We include more components into the architecture of Plumber  to comprise 40 reusable components for various KG completion subtasks, such as coreference resolution, entity linking, and relation extraction. Using these components, Plumber dynamically generates suitable knowledge extraction pipelines and offers overall 432 distinct pipelines. We study the optimization problem of choosing optimal pipelines based on input sentences. To do so, we train a transformer-based classification model that extracts contextual embeddings from the input and finds an appropriate pipeline. We study the efficacy of Plumber for extracting the KG triples using standard datasets over three KGs: DBpedia, Wikidata, and Open Research Knowledge Graph. Our results demonstrate the effectiveness of Plumber in dynamically generating KG completion pipelines, outperforming all baselines agnostic of the underlying KG. Furthermore, we provide an analysis of collective failure cases, study the similarities and synergies among integrated components and discuss their limitations.

A Scholarly Knowledge Graph-Powered Dashboard: Implementation and User Evaluation

Scholarly knowledge graphs provide researchers with a novel modality of information retrieval, and their wider use in academia is beneficial for the digitalization of published works and the development of scholarly communication. To increase the acceptance of scholarly knowledge graphs, we present a dashboard, which visualizes the research contributions on an educational science topic in the frame of the Open Research Knowledge Graph (ORKG). As dashboards are created at the intersection of computer science, graphic design, and human-technology interaction, we used these three perspectives to develop a multi-relational visualization tool aimed at improving the user experience. According to preliminary results of the user evaluation survey, the dashboard was perceived as more appealing than the baseline ORKG-powered interface. Our findings can be used for the development of scholarly knowledge graph-powered dashboards in different domains, thus facilitating acceptance of these novel instruments by research communities and increasing versatility in scholarly communication.

SKG4EOSC - Scholarly Knowledge Graphs for EOSC: Establishing a backbone of knowledge graphs for FAIR Scholarly Information in EOSC

In the age of advanced information systems powering fast-paced knowledge economies that face global societal challenges, it is no longer adequate to express scholarly information - an essential resource for modern economies - primarily as article narratives in document form. Despite being a well-established tradition in scholarly communication, PDF-based text publishing is hindering scientific progress as it buries scholarly information into non-machine-readable formats. The key objective of SKG4EOSC is to improve science productivity through development and implementation of services for text and data conversion, and production, curation, and re-use of FAIR scholarly information. This will be achieved by (1) establishing the Open Research Knowledge Graph (ORKG, orkg.org), a service operated by the SKG4EOSC coordinator, as a Hub for access to FAIR scholarly information in the EOSC; (2) lifting to EOSC of numerous and heterogeneous domain-specific research infrastructures through the ORKG Hub’s harmonized access facilities; and (3) leverage the Hub to support cross-disciplinary research and policy decisions addressing societal challenges. SKG4EOSC will pilot the devised approaches and technologies in four research domains: biodiversity crisis, precision oncology, circular processes, and human cooperation. With the aim to improve machine-based scholarly information use, SKG4EOSC addresses an important current and future need of researchers. It extends the application of the FAIR data principles to scholarly communication practices, hence a more comprehensive coverage of the entire research lifecycle. Through explicit, machine actionable provenance links between FAIR scholarly information, primary data and contextual entities, it will substantially contribute to reproducibility, validation and trust in science. The resulting advanced machine support will catalyse new discoveries in basic research and solutions in key application areas.

Analysing the requirements for an Open Research Knowledge Graph: use cases, quality requirements, and construction strategies

Current science communication has a number of drawbacks and bottlenecks which have been subject of discussion lately: Among others, the rising number of published articles makes it nearly impossible to get a full overview of the state of the art in a certain field, or reproducibility is hampered by fixed-length, document-based publications which normally cannot cover all details of a research work. Recently, several initiatives have proposed knowledge graphs (KG) for organising scientific information as a solution to many of the current issues. The focus of these proposals is, however, usually restricted to very specific use cases. In this paper, we aim to transcend this limited perspective and present a comprehensive analysis of requirements for an Open Research Knowledge Graph (ORKG) by (a) collecting and reviewing daily core tasks of a scientist, (b) establishing their consequential requirements for a KG-based system, (c) identifying overlaps and specificities, and their coverage in current solutions. As a result, we map necessary and desirable requirements for successful KG-based science communication, derive implications, and outline possible solutions.

ORKG: Facilitating the Transfer of Research Results with the Open Research Knowledge Graph

This document is an edited version of the original funding proposal entitled 'ORKG: Facilitating the Transfer of Research Results with the Open Research Knowledge Graph' that was submitted to the European Research Council (ERC) Proof of Concept (PoC) Grant in September 2020 (https://erc.europa.eu/funding/proof-concept). The proposal was evaluated by five reviewers and has been placed after the evaluations on the reserve list. The main document of the original proposal did not contain an abstract.

Improving Access to Scientific Literature with Knowledge Graphs

The transfer of knowledge has not changed fundamentally for many hundreds of years: It is usually document-based-formerly printed on paper as a classic essay and nowadays as PDF. With around 2.5 million new research contributions every year, researchers drown in a flood of pseudo-digitized PDF publications. As a result research is seriously weakened. In this article, we argue for representing scholarly contributions in a structured and semantic way as a knowledge graph. The advantage is that information represented in a knowledge graph is readable by machines and humans. As an example, we give an overview on the Open Research Knowledge Graph (ORKG), a service implementing this approach. For creating the knowledge graph representation, we rely on a mixture of manual (crowd/expert sourcing) and (semi-)automated techniques. Only with such a combination of human and machine intelligence, we can achieve the required quality of the representation to allow for novel exploration and assistance services for researchers. As a result, a scholarly knowledge graph such as the ORKG can be used to give a condensed overview on the state-of-the-art addressing a particular research quest, for example as a tabular comparison of contributions according to various characteristics of the approaches. Further possible intuitive access interfaces to such scholarly knowledge graphs include domain-specific (chart) visualizations or answering of natural language questions.

Die TIB: Mehr als eine Bibliothek

Die TIB gestaltet aktiv den digitalen Wandel von Bibliotheken mit – in Form von Open Science, Open Access, Forschungsdaten und digitaler Langzeitarchivierung sowie bei der Entwicklung von Diensten an der Schnittstelle von analogen und digitalen Formaten. Sie baut ihre Rolle als deutsches Informationszentrum für die Digitalisierung von Wissenschaft und Technik in Zukunft weiter aus und setzt auf die Entwicklung neuer digitaler Angebote, die klassische bibliothekarische Dienste und innovative Forschungsergebnisse miteinander verzahnen. Als wissenschaftliche Infrastruktureinrichtung steht sie vor zwei großen Herausforderungen: Als Deutsche Zentrale Fachbibliothek für Technik und Naturwissenschaften bewegt sie sich als Akteurin im Zentrum des digitalen Wandels des wissenschaftlichen Arbeitens, des digitalen Publikationswesens und der Globalisierung von Information und Kommunikation. Gleichzeitig erbringt sie als von Bund und Ländern finanzierte Einrichtung verstärkt eigene Forschung.

A comprehensive quality assessment framework for scientific events

Systematic assessment of scientific events has become increasingly important for research communities. A range of metrics (e.g., citations, h-index) have been developed by different research communities to make such assessments effectual. However, most of the metrics for assessing the quality of less formal publication venues and events have not yet deeply investigated. It is also rather challenging to develop respective metrics because each research community has its own formal and informal rules of communication and quality standards. In this article, we develop a comprehensive framework of assessment metrics for evaluating scientific events and involved stakeholders. The resulting quality metrics are determined with respect to three general categories—events, persons, and bibliometrics. Our assessment methodology is empirically applied to several series of computer science events, such as conferences and workshops, using publicly available data for determining quality metrics. We show that the metrics’ values coincide with the intuitive agreement of the community on its “top conferences”. Our results demonstrate that highly-ranked events share similar profiles, including the provision of outstanding reviews, visiting diverse locations, having reputed people involved, and renowned sponsors.

Evaluation of Mass Sensitive Micro-Array biosensors for their feasibility in multiplex detection of low molecular weight toxins using mycotoxins as model compounds

Mycotoxins produced by Fusarium species including trichothecenes, zearalenone and fumonisins, can co-contaminate food and feed throughout the supply chain, including cereal grains and animal feeds. There is an increasing demand to enhance global food security by improving the monitoring of mycotoxins throughout our food supply chain. For time and cost-efficient analysis, rapid tests capable of detecting multiple toxins from a single sample are ideal. Considering these current trends in mycotoxin testing, this project examined the feasibility of using both a portable and non-portable mass-based biosensor for multiplex mycotoxin detection. The biosensor was a mass sensitive microarray (MSMA) which consisted of 4 × 16 miniaturized mass sensitive transducer pixels based on solidly mounted resonator (SMR) technology. Functionalisation of individual pixels on the sensor surface using nano-spotting technology for the simultaneous and semi-quantitative detection of three regulated mycotoxins: T2-toxin (T2) zearalenone (ZEN), and fumonisin B1 (FumB1) was examined. With the integration of portable and non-portable microfluidic devices for antibody and standard sample injections, competitive inhibition assays were developed followed by singleplex and multiplex calibration curves. The characteristics and performance of the MSMA were evaluated including sensitivity which was determined as the concentration causing 50% inhibition. Sensitivity of singleplex assays using the portable microfluidic device (PMD) were 1.3 ng/ml, 2.0 ng/ml and 6.8 ng/ml for T2, FumB1 and ZEN, respectively. Sensitivity of the multiplex assay again using the PMD was 6.1 ng/ml, 3.6 ng/ml and 2.4 ng/ml for T2, FumB1 and ZEN, respectively. The PMD was an easy to use and highly sensitive screening tool which has been demonstrated for the multiplex detection of three regulated mycotoxins. Analysis was in real time and results were fully digital. Since detection of analytes was by mass it was both a label-free and cost-efficient method proposed method of analysis for mycotoxins.

Scholarly event characteristics in four fields of science: a metrics-based analysis

One of the key channels of scholarly knowledge exchange are scholarly events such as conferences, workshops, symposiums, etc.; such events are especially important and popular in Computer Science, Engineering, and Natural Sciences. However, scholars encounter problems in finding relevant information about upcoming events and statistics on their historic evolution. In order to obtain a better understanding of scholarly event characteristics in four fields of science, we analyzed the metadata of scholarly events of four major fields of science, namely Computer Science, Physics, Engineering, and Mathematics using Scholarly Events Quality Assessment suite, a suite of ten metrics. In particular, we analyzed renowned scholarly events belonging to five sub-fields within Computer Science, namely World Wide Web, Computer Vision, Software Engineering, Data Management, as well as Security and Privacy. This analysis is based on a systematic approach using descriptive statistics as well as exploratory data analysis. The findings are on the one hand interesting to observe the general evolution and success factors of scholarly events; on the other hand, they allow (prospective) event organizers, publishers, and committee members to assess the progress of their event over time and compare it to other events in the same field; and finally, they help researchers to make more informed decisions when selecting suitable venues for presenting their work. Based on these findings, a set of recommendations has been concluded to different stakeholders, involving event organizers, potential authors, proceedings publishers, and sponsors. Our comprehensive dataset of scholarly events of the aforementioned fields is openly available in a semantic format and maintained collaboratively at OpenResearch.org.

DESERT: A Continuous SPARQL Query Engine for On-Demand Query Answering

The Internet of Things (IoT) has been rapidly adopted in many domains ranging from household appliances e.g. ventilation, lighting, and heating, to industrial manufacturing and transport networks. Despite the, enormous benefits of optimization, monitoring, and maintenance rendered by IoT devices, an ample amount of data is generated continuously. Semantically describing IoT generated data using ontologies enables a precise interpretation of this data. However, ontology-based descriptions tremendously increase the size of IoT data and in presence of repeated sensor measurements, a large amount of the data are duplicates that do not contribute to new insights during query processing or IoT data analytics. In order to ensure that only required ontology-based descriptions are generated, we devise a knowledge-driven approach named DESERT that is able to on-[Formula: see text]emand factoriz[Formula: see text] and [Formula: see text]emantically [Formula: see text]nrich st[Formula: see text]eam da[Formula: see text]a. DESERT resorts to a knowledge graph to describe IoT stream data; it utilizes only the data that is required to answer an input continuous SPARQL query and applies a novel method of data factorization to reduce duplicated measurements in the knowledge graph. The performance of DESERT is empirically studied on a collection of continuous SPARQL queries from SRBench, a benchmark of IoT stream data and continuous SPARQL queries. Furthermore, data streams with various combinations of uniform and varying data stream speeds and streaming window size dimensions are considered in the study. Experimental results suggest that DESERT is capable of speeding up continuous query processing while creates knowledge graphs that include no replications.

Regarding loads after spinal fusion, every level should be seen separately: a musculoskeletal analysis

INTRODUCTION

The number of spinal fusion surgeries is steadily increasing and biomechanical consequences are still in debate. The aim of this study is to provide biomechanical insights into the sagittal balance of the spine and to compare spinal load before and after spinal fusion.

METHOD

The joint reaction forces of 52 patients were analyzed in proximo-distal and antero-posterior direction from the levels T12-L1 to L5-S1 using musculoskeletal simulations.

RESULTS

In 104 simulations, pre-surgical forces were equal to post-surgical. The levels L4-L5 and T12-L1, however, showed increased spinal forces compression forces with higher sagittal displacement. Improved restauration of sagittal balance was accompanied by lower spinal load. AP shear stress, interestingly decreased with sagittal imbalance.

CONCLUSION

Imbalanced spines have a risk of increased compression forces at Th12-L1. L4-L5 always has increased spinal loads. These slides can be retrieved under Electronic Supplementary Material.

Experience

Public administrations are continuously publishing open data, increasing the amount of government open data over time. The published data includes budgets and spending as part of fiscal data; publishing these data is an important part of transparent and accountable governance. However, open fiscal data should also meet open data publication guidelines. When requirements in data guidelines are not met, effective data analysis over published datasets cannot be performed effectively. In this article, we present Open Fiscal Data Publication (OFDP), a framework to assess the quality of open fiscal datasets. We also present an extensive open fiscal data assessment and common data quality issues found; additionally, open fiscal data publishing guidelines are presented. We studied and surveyed main quality factors for open fiscal datasets. Moreover, the collected quality factors have been scored according to the results of a questionnaire to score quality factors within the OFDP assessment framework. We gather and comprehensively analyze a representative set of 77 fiscal datasets from several public administrations across different regions at different levels (e.g., supranational, national, municipality). We characterize quality issues commonly arising in these datasets. Our assessment shows that there are many quality factors in fiscal data publication that still need to be taken care of so that the data can be analyzed effectively. Our proposed guidelines allow for publishing open fiscal data where these quality issues are avoided.

Git4Voc: Collaborative Vocabulary Development Based on Git

Collaborative vocabulary development in the context of data integration is the process of finding consensus between experts with different backgrounds, system understanding and domain knowledge. The complexity of this process increases with the number of people involved, the variety of the systems to be integrated and the dynamics of their domain. In this paper, we advocate that the usage of a powerful version control system is one of the keys to address this problem. Driven by this idea and the success of the version control system Git in the context of software development, we investigate the applicability of Git for collaborative vocabulary development. Even though vocabulary development and software development have much more similarities than differences, there are still important challenges. These need to be considered in the development of a successful versioning and collaboration system for vocabulary development. Therefore, this paper starts by presenting the challenges we are faced with during the collaborative creation of vocabularies and discusses its distinction to software development. Drawing from these findings, we present Git4Voc which comprises guidelines on how Git can be adopted to vocabulary development. Finally, we demonstrate how Git hooks can be implemented to go beyond the plain functionality of Git by realizing vocabulary-specific features like syntactic validation and semantic diffs.

Importance of non-affine viscoelastic response in disordered fibre networks

Disordered fibre networks are ubiquitous in nature and have a wide range of industrial applications as novel biomaterials. Predicting their viscoelastic response is straightforward for affine deformations that are uniform over all length scales, but when affinity fails, as has been observed experimentally, modelling becomes challenging. Here we present a numerical methodology, related to an existing framework for amorphous packings, to predict the steady-state viscoelastic spectra and degree of affinity for disordered fibre networks driven at arbitrary frequencies. Applying this method to a peptide gel model reveals a monotonic increase of the shear modulus as the soft, non-affine normal modes are successively suppressed as the driving frequency increases. In addition to being dominated by fibril bending, these low frequency network modes are also shown to be delocalised. The presented methodology provides insights into the importance of non-affinity in the viscoelastic response of peptide gels, and is easily extendible to all types of fibre networks.

Amyloid Fibril Solubility

It is well established that amyloid fibril solubility is protein specific, but how solubility depends on the interactions between the fibril building blocks is not clear. Here we use a simple protein model and perform Monte Carlo simulations to directly measure the solubility of amyloid fibrils as a function of the interaction between the fibril building blocks. Our simulations confirms that the fibril solubility depends on the fibril thickness and that the relationship between the interactions and the solubility can be described by a simple analytical formula. The results presented in this study reveal general rules how side-chain-side-chain interactions, backbone hydrogen bonding, and temperature affect amyloid fibril solubility, which might prove to be a powerful tool to design protein fibrils with desired solubility and aggregation properties in general.

Universality in the morphology and mechanics of coarsening amyloid fibril networks

Peptide hydrogels have important applications as biomaterials and in nanotechnology, but utilization often depends on their mechanical properties for which we currently have no predictive capability. Here we use a peptide model to simulate the formation of percolating amyloid fibril networks and couple these to the elastic network theory to determine their mechanical properties. We find that the time variation of network length scales can be collapsed onto master curves by using a time scaling function that depends on the peptide interaction anisotropy. The same scaling applies to network mechanics, revealing a nonmonotonic dependence of the shear modulus with time. Our structure-function relationship between the peptide building blocks, network morphology, and network mechanical properties can aid in the design of amyloid fibril networks with tailored mechanical properties.

Development of the nano-dust analyzer (NDA) for detection and compositional analysis of nanometer-size dust particles originating in the inner heliosphere

A linear time-of-flight mass spectrometer is developed for the detection and chemical analysis of nanometer-sized particles originating near the Sun. Nano-dust particles are thought to be produced by mutual collisions between interplanetary dust particles slowly spiraling toward the Sun and are accelerated outward to high velocities by interaction with the solar wind plasma. The WAVES instruments on the two STEREO spacecraft reported the detection, strong temporal variation, and potentially high flux of these particles. Here we report on the optimization and the results from the detailed characterization of the instrument's performance using submicrometer sized dust particles accelerated to 8-60 km/s. The Nano Dust Analyzer (NDA) concept is derived from previously developed detectors. It has a 200 cm(2) effective target area and a mass resolution of approximately m/Δm = 50. The NDA instrument is designed to reliably detect and analyze nanometer-sized dust particles while being pointed close to the Sun's direction, from where they are expected to arrive. Measurements by such an instrument will determine the size-dependent flux of the nano-dust particles and its variations, it will characterize the composition of the nano-dust and, ultimately, it may determine their source. The flight version of the NDA instrument is estimated to be <5 kg and requires <10 W for operation.

Ubiquitous Semantic Applications

Recently practical approaches for development of ubiquitous semantic applications have made quite some progress. In particular in the area of the ubiquitous access to the semantic data the authors recently observed a large number of approaches, systems and applications being described in the literature. With this survey the authors aim to provide an overview on the rapidly emerging field of Ubiquitous Semantic Applications (UbiSA). The authors conducted a systematic literature review comprising a thorough analysis of 48 primary studies out of 172 initially retrieved papers. The authors obtained a comprehensive set of quality attributes for UbiSA together with corresponding application features suggested for their realization. The quality attributes include aspects such as mobility, usability, heterogeneity, collaboration, customizability and evolvability. The primary studies were surveyed in the light of these quality attributes and the authors performed a thorough analysis of five ubiquitous semantic applications, six frameworks for UbiSA, three UbiSA specific ontologies, five ubiquitous semantic systems and nine general approaches. The proposed quality attributes facilitate the evaluation of existing approaches and the development of novel, more effective and intuitive UbiSA.

[A training program for dementia trainers: does this program have practical relevance?]

BACKGROUND

Specific curricula for professionals working in various settings with persons with dementia have been developed and implemented into practice. In this study, the practical relevance of a teaching program for the M.A.S (Morbus Alzheimer syndrome) dementia trainer was evaluated. The curriculum was developed in 2002 within a scientific project. The goal was that care professionals and noncare professionals learn how to support and train persons with dementia and their caregivers. The task of the trainer is to support the functional and emotional resources of the person with dementia employing stage-specific training according to principles of the theory of retrogenesis. Trainers are also able to support family caregivers in their difficult day-to-day care for the person with dementia. With this training and support program, persons with dementia can train their residual capacities and develop a life perspective which enables them to cope with the long duration of Alzheimer's disease.

MATERIALS AND METHODS

The curriculum for the training methodology is based in the theory of retrogenesis. The 1-year training course is held in the form of modules and includes the following topics: (1) stages of dementia and medical aspects, (2) communication with persons with dementia, (3) stage-specific retrogenic training, (4) physical training for the elderly and persons with dementia, (5) coaching family caregivers through the long disease duration, and (6) care issues for persons without education in care. M.A.S trainers were questioned after they had concluded the teaching program successfully and had the chance to apply the content of the teaching program in their practical work. A short questionnaire was sent via e-mail or a telephone interview was performed.

RESULTS

A total of 279 trainers graduated and were certified. Of these, 140 persons (53.6% of the population) could be questioned after an average of 2.69 years after completion of the course: 93.6% of trainees were still using the principles of the teaching course successfully; of these, 56% were working in the function of a trainer full time and 44% used the principles within their work environment (mainly in the nursing home environment).

CONCLUSION

The study found that the majority of questioned trainers are still using the principles taught in the course successfully with persons with dementia living at home and the content was found to be relevant for practice. The content of the teaching course, applying principles of retrogenesis, which was originally designed for persons with dementia living at home, can also be successfully applied in the nursing home environment. Increasing interest has been shown by institutions employing professionals whose task it is to keep persons with dementia active and interested as well as physically functioning at their best possible level. As a consequence, persons with dementia perceive higher quality of life and exhibit fewer behavior problems which complicate care. More research is needed to accumulate evidence and to support these findings.

From Overview to Facets and Pivoting for Interactive Exploration of Semantic Web Data

The proliferation of Linked Open Data on the Web has increased the amount of data available for analysis and reuse. However, casual users find it difficult to explore and use Semantic Web Data due to the prevalence of specialised browsers that require complex queries to be formed and intimate knowledge on the structure of datasets. The authors address this problem in the Rhizomer tool by applying the data analysis mantra of overview, zoom and filter. These interaction patterns are implemented using information architecture components users are already familiar with but that are automatically generated from data and ontologies. This approach makes it possible to obtain an overview of the dataset being explored using techniques, such as navigation menus, treemaps or sitemaps, which are usually not available in text-based semantic web browsers. From there, users can interactively explore the data using facets. Moreover, facets also feature a pivoting operation, motivated during tests with lay users, that removes the main constraint of most faceted browsers, i.e. the inability to combine filters for differently faceted views to build complex queries.

Dust trajectory sensor: accuracy and data analysis

The Dust Trajectory Sensor (DTS) instrument is developed for the measurement of the velocity vector of cosmic dust particles. The trajectory information is imperative in determining the particles' origin and distinguishing dust particles from different sources. The velocity vector also reveals information on the history of interaction between the charged dust particle and the magnetospheric or interplanetary space environment. The DTS operational principle is based on measuring the induced charge from the dust on an array of wire electrodes. In recent work, the DTS geometry has been optimized [S. Auer, E. Grün, S. Kempf, R. Srama, A. Srowig, Z. Sternovsky, and V Tschernjawski, Rev. Sci. Instrum. 79, 084501 (2008)] and a method of triggering was developed [S. Auer, G. Lawrence, E. Grün, H. Henkel, S. Kempf, R. Srama, and Z. Sternovsky, Nucl. Instrum. Methods Phys. Res. A 622, 74 (2010)]. This article presents the method of analyzing the DTS data and results from a parametric study on the accuracy of the measurements. A laboratory version of the DTS has been constructed and tested with particles in the velocity range of 2-5 km/s using the Heidelberg dust accelerator facility. Both the numerical study and the analyzed experimental data show that the accuracy of the DTS instrument is better than about 1% in velocity and 1° in direction.

Nursing home structure and association with agitation and use of psychotropic drugs in nursing home residents in three countries: Norway, Austria and England

BACKGROUND

Understanding the underlying mechanisms and risk factors leading to agitation is crucial to reduce the severity of agitation and increase quality of life. International comparative studies offer special advantages in elucidating environmental risk factors by providing a wider diversity of environmental exposures such as nursing home structures, health care systems and genetic diversity.

METHODS

Baseline data for three different intervention studies in Austria (n = 38), England (n = 302) and Norway (n = 163) were combined posthoc. Patients were grouped according to their dementia severity using the global deterioration scale (GDS), functional assessment staging (FAST) and clinical dementia rating (CDR) scales. For the measurement of agitation, the Cohen-Mansfield Agitation Inventory (CMAI) was used. Data analysis was performed using one-way ANOVA, multivariate and linear regression analysis.

RESULTS

CMAI scores were available for 503 subjects with dementia. There were significant differences between the nursing home residents in the three countries regarding age, gender and dementia severity (all p values < 0.001). In the multivariate analyses, the level of agitation differed with higher mean scores in the Austrian (mean (SD) score 51.9(21.8)) compared to UK (43.3(16.1)) and Norwegian (41.6(13.2)) nursing homes (p = 0.002). Similarly, the use of psychotropic drugs differed significantly, with a higher proportion of neuroleptics in UK (48%, p < 0.001) and Austrian (52.6%; p = 0.001) compared to Norwegian (19%) nursing homes.

CONCLUSION

We found differences in agitation and antipsychotic drug use which are likely related to structural and cultural differences in nursing homes in three European countries. These findings suggest that structural changes can improve quality of care and quality of life for nursing home residents.

Structure-function studies of a Melanocarpus albomyces laccase suggest a pathway for oxidation of phenolic compounds

Melanocarpus albomyces laccase crystals were soaked with 2,6-dimethoxyphenol, a common laccase substrate. Three complex structures from different soaking times were solved. Crystal structures revealed the binding of the original substrate and adducts formed by enzymatic oxidation of the substrate. The dimeric oxidation products were identified by mass spectrometry. In the crystals, a 2,6-dimethoxy-p-benzoquinone and a C-O dimer were observed, whereas a C-C dimer was the main product identified by mass spectrometry. Crystal structures demonstrated that the substrate and/or its oxidation products were bound in the pocket formed by residues Ala191, Pro192, Glu235, Leu363, Phe371, Trp373, Phe427, Leu429, Trp507 and His508. Substrate and adducts were hydrogen-bonded to His508, one of the ligands of type 1 copper. Therefore, this surface-exposed histidine most likely has a role in electron transfer by laccases. Based on our mutagenesis studies, the carboxylic acid residue Glu235 at the bottom of the binding site pocket is also crucial in the oxidation of phenolics. Glu235 may be responsible for the abstraction of a proton from the OH group of the substrate and His508 may extract an electron. In addition, crystal structures revealed a secondary binding site formed through weak dimerization in M. albomyces laccase molecules. This binding site most likely exists only in crystals, when the Phe427 residues are packed against each other.

Large area mass analyzer instrument for the chemical analysis of interstellar dust particles

A new instrument to analyze the chemical composition of dust particles in situ in space has been developed. The large target area ( approximately 0.2 m(2)) makes this instrument well suited for detecting a statistically significant number of interstellar dust grains or other dust particles with a low flux. The device is a reflectron-type time-of-flight mass spectrometer that uses only flat electrodes for the generation of the parabolic potential. The instrument analyzes the ions from the impact generated plasma due to hypervelocity dust impacts onto a solid target surface. The SIMION ion optics software package is used to investigate different potential field configurations and optimize the mass resolution and focusing of the ions. The cylindrically symmetric instrument operates with six ring electrodes and six annular electrodes biased to different potentials to create the potential distribution of the reflectron. The laboratory model of the instrument has been fabricated and tested. Hypervelocity dust impacts are simulated by laser ablation using a frequency doubled Nd:YAG laser with approximately 8 ns pulse length. The experimental data show typical mass resolution m/Deltam approximately 200.

Breakdown of classical nucleation theory near isostructural phase transitions

We report simulations of crystal nucleation in binary mixtures of hard spherical colloids with a size ratio of 1:10. The stable crystal phase of this system can be either dense or expanded. We find that, in the vicinity of the solid-solid critical point where the crystallites are highly compressible, small crystal nuclei are less dense than large nuclei. This phenomenon cannot be accounted for by either classical nucleation theory or by the Gibbsian droplet model. We argue that the observed behavior is due to the surface stress of the crystal nuclei. The observed effect highlights a general deficiency of the most frequently used thermodynamic theories for crystal nucleation. Surface stress should lead to an experimentally observable expansion of crystal nuclei of colloids with short-ranged attraction and of globular proteins.

Onset of heterogeneous crystal nucleation in colloidal suspensions

The addition of small 'seed' particles to a supersaturated solution can greatly increase the rate at which crystals nucleate. This process is understood, at least qualitatively, when the seed has the same structure as the crystal that it spawns. However, the microscopic mechanism of seeding by a 'foreign' substance is not well understood. Here we report numerical simulations of colloidal crystallization seeded by foreign objects. We perform Monte Carlo simulations to study how smooth spherical seeds of various sizes affect crystallization in a suspension of hard colloidal particles. We compute the free-energy barrier associated with crystal nucleation. A low barrier implies that nucleation is easy. We find that to be effective crystallization promoters, the seed particles need to exceed a well-defined minimum size. Just above this size, seed particles act as crystallization 'catalysts' as newly formed crystallites detach from the seed. In contrast, larger seed particles remain covered by the crystallites that they spawn. This phenomenon should be experimentally observable and can have important consequences for the control of the resulting crystal size distribution.