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Heterotrophic bacteria in the ocean initiate biopolymer degradation using extracellular enzymes that yield low molecular weight hydrolysis products in the environment, or by using a selfish uptake mechanism that retains the hydrolysate for the enzyme‐producing cell. The mechanism used affects the availability of hydrolysis products to other bacteria, and thus also potentially the composition and activity of the community. In marine systems, these two mechanisms of substrate processing have been studied in the water column, but to date, have not been investigated in sediments. In surface sediments from an Arctic fjord of Svalbard, we investigated mechanisms of biopolymer hydrolysis using four polysaccharides and mucin, a glycoprotein. Extracellular hydrolysis of all biopolymers was rapid. Moreover, rapid degradation of mucin suggests that it may be a key substrate for benthic microbes. Although selfish uptake is common in ocean waters, only a small fraction (0.5%–2%) of microbes adhering to sediments used this mechanism. Selfish uptake was carried out primarily by Planctomycetota and Verrucomicrobiota. The overall dominance of extracellular hydrolysis in sediments, however, suggests that the bulk of biopolymer processing is carried out by a benthic community relying on the sharing of enzymatic capabilities and scavenging of public goods. [ABSTRACT FROM AUTHOR]

Networks are abstract and ubiquitous data structures, defined as a set of data points and relationships between them. Network visualization provides meaningful representations of these data, supporting researchers in understanding the connections, gathering insights, and detecting and identifying unexpected patterns. Research in this field is focusing on increasingly challenging problems, such as visualizing dynamic, complex, multivariate, and geospatial networked data. This ever‐growing, and widely varied, body of research led to several surveys being published, each covering one or more disciplines of network visualization. Despite this effort, the variety and complexity of this research represents an obstacle when surveying the domain and building a comprehensive overview of the literature. Furthermore, there exists a lack of clarification and uniformity between the terminology used in each of the surveys, which requires further effort when mapping and categorizing the plethora of different visualization techniques and approaches. In this paper, we aim at providing researchers and practitioners alike with a "roadmap" detailing the current research trends in the field of network visualization. We design our contribution as a meta‐survey where we discuss, summarize, and categorize recent surveys and task taxonomies published in the context of network visualization. We identify more and less saturated disciplines of research and consolidate the terminology used in the surveyed literature. We also survey the available task taxonomies, providing a comprehensive analysis of their varying support to each network visualization discipline and by establishing and discussing a classification for the individual tasks. With this combined analysis of surveys and task taxonomies, we provide an overarching structure of the field, from which we extrapolate the current state of research and promising directions for future work. [ABSTRACT FROM AUTHOR]

Computational models, such as simulations, are central to a wide range of fields in science and industry. Those models take input parameters and produce some output. To fully exploit their utility, relations between parameters and outputs must be understood. These include, for example, which parameter setting produces the best result (optimization) or which ranges of parameter settings produce a wide variety of results (sensitivity). Such tasks are often difficult to achieve for various reasons, for example, the size of the parameter space, and supported with visual analytics. In this paper, we survey visual parameter space exploration (VPSE) systems involving spatial and temporal data. We focus on interactive visualizations and user interfaces. Through thematic analysis of the surveyed papers, we identify common workflow steps and approaches to support them. We also identify topics for future work that will help enable VPSE on a greater variety of computational models. [ABSTRACT FROM AUTHOR]

Modelling the dynamics of a growing financial environment is a complex task that requires domain knowledge, expertise and access to heterogeneous information types. Such information can stem from several sources at different scales, complicating the task of forming a holistic impression of the financial landscape, especially in terms of the economical relationships between firms. Bringing this scattered information into a common context is, therefore, an essential step in the process of obtaining meaningful insights about the state of an economy. In this paper, we present Sabrina 2.0, a Visual Analytics (VA) approach for exploring financial data across different scales, from individual firms up to nation‐wide aggregate data. Our solution is coupled with a pipeline for the generation of firm‐to‐firm financial transaction networks, fusing information about individual firms with sector‐to‐sector transaction data and domain knowledge on macroscopic aspects of the economy. Each network can be created to have multiple instances to compare different scenarios. We collaborated with experts from finance and economy during the development of our VA solution, and evaluated our approach with seven domain experts across industry and academia through a qualitative insight‐based evaluation. The analysis shows how Sabrina 2.0 enables the generation of insights, and how the incorporation of transaction models assists users in their exploration of a national economy. [ABSTRACT FROM AUTHOR]

Temporal networks can naturally model real‐world complex phenomena such as contact networks, information dissemination and physical proximity. However, nodes and edges bear real‐time coordinates, making it difficult to organize them into discrete timeslices, without a loss of temporal information due to projection. Event‐based dynamic graph drawing rejects the notion of a timeslice and allows each node and edge to retain its own real‐valued time coordinate. While existing work has demonstrated clear advantages for this approach, they come at a running time cost. We investigate the problem of accelerating event‐based layout to make it more competitive with existing layout techniques. In this paper, we describe the design, implementation and experimental evaluation of MultiDynNoS, the first multi‐level event‐based graph layout algorithm. We consider three operators for coarsening and placement, inspired by Walshaw, GRIP and FM3, which we couple with an event‐based graph drawing algorithm. We also propose two extensions to the core algorithm: AutoTau and Bend Transfer. We perform two experiments: first, we compare MultiDynNoS variants to existing state‐of‐the‐art dynamic graph layout approaches; second, we investigate the impact of each of the proposed algorithm extensions. MultiDynNoS proves to be competitive with existing approaches, and the proposed extensions achieve their design goals and contribute in opening new research directions. [ABSTRACT FROM AUTHOR]

Guidance has been proposed as a conceptual framework to understand how mixed‐initiative visual analytics approaches can actively support users as they solve analytical tasks. While user tasks received a fair share of attention, it is still not completely clear how they could be supported with guidance and how such support could influence the progress of the task itself. Our observation is that there is a research gap in understanding the effect of guidance on the analytical discourse, in particular, for the knowledge generation in mixed‐initiative approaches. As a consequence, guidance in a visual analytics environment is usually indistinguishable from common visualization features, making user responses challenging to predict and measure. To address these issues, we take a system perspective to propose the notion of guidance tasks and we present it as a typology closely aligned to established user task typologies. We derived the proposed typology directly from a model of guidance in the knowledge generation process and illustrate its implications for guidance design. By discussing three case studies, we show how our typology can be applied to analyze existing guidance systems. We argue that without a clear consideration of the system perspective, the analysis of tasks in mixed‐initiative approaches is incomplete. Finally, by analyzing matchings of user and guidance tasks, we describe how guidance tasks could either help the user conclude the analysis or change its course. [ABSTRACT FROM AUTHOR]

Analysis of spatial multivariate data, i.e., measurements at irregularly‐spaced locations, is a challenging topic in visualization and statistics alike. Such data are inteGral to many domains, e.g., indicators of valuable minerals are measured for mine prospecting. Popular analysis methods, like PCA, often by design do not account for the spatial nature of the data. Thus they, together with their spatial variants, must be employed very carefully. Clearly, it is preferable to use methods that were specifically designed for such data, like spatial blind source separation (SBSS). However, SBSS requires two tuning parameters, which are themselves complex spatial objects. Setting these parameters involves navigating two large and interdependent parameter spaces, while also taking into account prior knowledge of the physical reality represented by the data. To support analysts in this process, we developed a visual analytics prototype. We evaluated it with experts in visualization, SBSS, and geochemistry. Our evaluations show that our interactive prototype allows to define complex and realistic parameter settings efficiently, which was so far impractical. Settings identified by a non‐expert led to remarkable and surprising insights for a domain expert. Therefore, this paper presents important first steps to enable the use of a promising analysis method for spatial multivariate data. [ABSTRACT FROM AUTHOR]

Magnetic resonance techniques at nuclei and electrons are applied to characterize the electronic structure and collective magnetic excitations throughout the magnetic phase diagrams of the lacunar spinels GaV4S8 and GaV4Se8 showing cycloidal, Néel‐type skyrmion lattice and ferromagnetically polarized phases. 71Ga nuclear magnetic resonance (NMR) provides a local probe of the rhombohedral distortion and the resulting uniaxial magnetic anisotropy via the detection of electric field gradients (EFGs) and hyperfine coupling at the gallium sites of these lacunar spinels. Broadband electron spin resonance (ESR) allows identifying clockwise, counterclockwise, and breathing modes of the skyrmion‐lattice phase supported by theoretical simulations. [ABSTRACT FROM AUTHOR]

Trust‐ability, reputation, security and quality are the main concerns for public and private financial institutions. To detect fraudulent behaviour, several techniques are applied pursuing different goals. For well‐defined problems, analytical methods are applicable to examine the history of customer transactions. However, fraudulent behaviour is constantly changing, which results in ill‐defined problems. Furthermore, analysing the behaviour of individual customers is not sufficient to detect more complex structures such as networks of fraudulent actors. We propose NEVA (Network dEtection with Visual Analytics), a Visual Analytics exploration environment to support the analysis of customer networks in order to reduce false‐negative and false‐positive alarms of frauds. Multiple coordinated views allow for exploring complex relations and dependencies of the data. A guidance‐enriched component for network pattern generation, detection and filtering support exploring and analysing the relationships of nodes on different levels of complexity. In six expert interviews, we illustrate the applicability and usability of NEVA. [ABSTRACT FROM AUTHOR]

Guidance is an emerging topic in the field of visual analytics. Guidance can support users in pursuing their analytical goals more efficiently and help in making the analysis successful. However, it is not clear how guidance approaches should be designed and what specific factors should be considered for effective support. In this paper, we approach this problem from the perspective of guidance designers. We present a framework comprising requirements and a set of specific phases designers should go through when designing guidance for visual analytics. We relate this process with a set of quality criteria we aim to support with our framework, that are necessary for obtaining a suitable and effective guidance solution. To demonstrate the practical usability of our methodology, we apply our framework to the design of guidance in three analysis scenarios and a design walk‐through session. Moreover, we list the emerging challenges and report how the framework can be used to design guidance solutions that mitigate these issues. [ABSTRACT FROM AUTHOR]

Visual data analysis can be envisioned as a collaboration of the user and the computational system with the aim of completing a given task. Pursuing an effective system‐user integration, in which the system actively helps the user to reach his/her analysis goal has been focus of visualization research for quite some time. However, this problem is still largely unsolved. As a result, users might be overwhelmed by powerful but complex visual analysis systems which also limits their ability to produce insightful results. In this context, guidance is a promising step towards enabling an effective mixed‐initiative collaboration to promote the visual analysis. However, the way how guidance should be put into practice is still to be unravelled. Thus, we conducted a comprehensive literature research and provide an overview of how guidance is tackled by different approaches in visual analysis systems. We distinguish between guidance that is provided by the system to support the user, and guidance that is provided by the user to support the system. By identifying open problems, we highlight promising research directions and point to missing factors that are needed to enable the envisioned human‐computer collaboration, and thus, promote a more effective visual data analysis. [ABSTRACT FROM AUTHOR]

The Curriculum Vitae (CV, also referred to as "résumé") is an established representation of a person's academic and professional history. A typical CV is comprised of multiple sections associated with spatio‐temporal, nominal, hierarchical, and ordinal data. The main task of a recruiter is, given a job application with specific requirements, to compare and assess CVs in order to build a short list of promising candidates to interview. Commonly, this is done by viewing CVs in a side‐by‐side fashion. This becomes challenging when comparing more than two CVs, because the reader is required to switch attention between them. Furthermore, there is no guarantee that the CVs are structured similarly, thus making the overview cluttered and significantly slowing down the comparison process. In order to address these challenges, in this paper we propose "CV3", an interactive exploration environment offering users a new way to explore, assess, and compare multiple CVs, to suggest suitable candidates for specific job requirements. We validate our system by means of domain expert feedback whose results highlight both the efficacy of our approach and its limitations. We learned that CV3 eases the overall burden of recruiters thereby assisting them in the selection process. [ABSTRACT FROM AUTHOR]

Abstract: To complement the currently existing definitions and conceptual frameworks of visual analytics, which focus mainly on activities performed by analysts and types of techniques they use, we attempt to define the expected results of these activities. We argue that the main goal of doing visual analytics is to build a mental and/or formal model of a certain piece of reality reflected in data. The purpose of the model may be to understand, to forecast or to control this piece of reality. Based on this model‐building perspective, we propose a detailed conceptual framework in which the visual analytics process is considered as a goal‐oriented workflow producing a model as a result. We demonstrate how this framework can be used for performing an analytical survey of the visual analytics research field and identifying the directions and areas where further research is needed. [ABSTRACT FROM AUTHOR]

Abstract: The biological carbon pump is largely driven by the formation and sinking of marine snow. Because of their high organic matter content, marine snow aggregates are hotspots for microbial activity, and microbial organic matter degradation plays an important role in the attenuation of carbon fluxes to the deep sea. Our inability to examine and characterize microscale distributions of compounds making up the aggregate matrix, and of possible niches inside marine snow, has hindered our understanding of the basic processes governing marine carbon export and sequestration. To address this issue, we have adapted soft‐embedding and sectioning to study the spatial structure and components of marine aggregates at high resolution. Soft‐embedding enables rapid quantitative sampling of undisturbed marine aggregates from the water column and from sediment traps, followed by spatially resolved staining and characterization of substrates of the aggregate matrix and the microorganisms attached to it. Particular strengths of the method include in situ embedding in sediment traps and successful fluorescence in situ hybridization (FISH)‐probe labeling, supporting studies of microbial diversity and ecology. The high spatial resolution achieved by thin‐sectioning of soft‐embedded aggregates offers the possibility for improved understanding of the composition and structure of marine snow, which directly influence settling velocity, microbial colonization and diversity, degradation rates, and carbon content. Our method will help to elucidate the small‐scale processes underlying large‐scale carbon cycling in the marine environment, which is especially relevant in the context of rising anthropogenic CO2 emissions and global change. [ABSTRACT FROM AUTHOR]

Implant failures are primarily related to bacterial infections and inflammation. Nanocoating of implant devices with organic molecules is a method used for improving their integration into host tissues and limiting inflammation. Bioengineered plant-derived rhamnogalacturonan-Is (RG-Is) from pectins improve tissue regeneration and exhibit anti-inflammatory properties. Therefore, the aim of this study is to evaluate the in vitro effect of RG-I nanocoating on human gingival primary fibroblast (HGF) activity and proinflammatory response following Porphyromonas gingivalis ( P. gingivalis) infection. Infected HGFs were incubated on tissue culture polystyrene (TCPS) plates coated with unmodified RG-I isolated from potato pectin (PU) and dearabinanated RG-I (PA). HGF morphology, proliferation, metabolic activity, and expression of genes responsible for extracellular matrix (ECM) turnover and proinflammatory response were examined. Following the P. gingivalis infection, PU and PA significantly promoted HGF proliferation and metabolic activity. Moreover, gene expression levels of IL1B, IL8, TNFA, and MMP2 decreased in the infected cells cultured on PU and PA, whereas the expression of COL1A1, FN1, and FGFR1 was upregulated. The results indicate that RG-Is are promising candidates for nanocoating of an implant surface, can reduce inflammation, and enhance implant integration, particularly in medically compromised patients with chronic inflammatory diseases such as periodontitis and rheumatoid arthritis. © 2017 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 105A: 3475-3481, 2017. [ABSTRACT FROM AUTHOR]

The cycle plot is an established and effective visualization technique for identifying and comprehending patterns in periodic time series, like trends and seasonal cycles. It also allows to visually identify and contextualize extreme values and outliers from a different perspective. Unfortunately, it is limited to univariate data. For multivariate time series, patterns that exist across several dimensions are much harder or impossible to explore. We propose a modified cycle plot using a distance-based abstraction (Mahalanobis distance) to reduce multiple dimensions to one overview dimension and retain a representation similar to the original. Utilizing this distance-based cycle plot in an interactive exploration environment, we enhance the Visual Analytics capacity of cycle plots for multivariate outlier detection. To enable interactive exploration and interpretation of outliers, we employ coordinated multiple views that juxtapose a distance-based cycle plot with Cleveland's original cycle plots of the underlying dimensions. With our approach it is possible to judge the outlyingness regarding the seasonal cycle in multivariate periodic time series. [ABSTRACT FROM AUTHOR]

Sets comprise a generic data model that has been used in a variety of data analysis problems. Such problems involve analysing and visualizing set relations between multiple sets defined over the same collection of elements. However, visualizing sets is a non-trivial problem due to the large number of possible relations between them. We provide a systematic overview of state-of-the-art techniques for visualizing different kinds of set relations. We classify these techniques into six main categories according to the visual representations they use and the tasks they support. We compare the categories to provide guidance for choosing an appropriate technique for a given problem. Finally, we identify challenges in this area that need further research and propose possible directions to address these challenges. Further resources on set visualization are available at . [ABSTRACT FROM AUTHOR]

Phase transitions of finite systems of charged microspheres trapped in a gaseous discharge plasma in twodimensional and three-dimensional arrangements are investigated. Novel experimental techniques to drive phase transitions in these dust clusters as well as advanced theoretical concepts to quantitatively characterize these transitions are presented (© 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [ABSTRACT FROM AUTHOR]

Non-optimal pH, dissolved oxygen concentration, the presence of toxic substances, or the influence of grazers are known to cause disturbances in nitrification. Because activated sludge is a mixture of different organisms, bacteria, and higher organisms, the stability of processes such as carbon removal, nitrification, denitrification, and dephosphatation depends on a range of interactions. These interactions occur both between and within trophic levels. Understanding of the ecology of microorganisms involved in bioprocesses is essential for effective control of startup and operation of a particular process. The aim of the study was to gain further insight into the dynamics of nitrifiers in activated sludge at various sludge ages while treating higher concentrations of ammonium. The results confirmed the importance of Nitrosococcus mobilis and Nitrobacter sp. as the dominant nitrifiers responsible for nitritation and nitratation, respectively, in the presence of unlimited ammonium. The size of the dominant bacteria colony was larger compared to the other species present and reached 25 μm. Problems with nitrification occurred in all high-ammonium loaded reactors. The dynamics of nitrifier population was monitored by oxygen uptake rate (OUR) using a test enabling the OUR measurement separately for ammonium-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB). The results reveal the hypersensitivity of nitrifiers to the substrate and products of incomplete nitrification. [ABSTRACT FROM AUTHOR]

Rationale, aims and objectives It is mandatory for the design of an efficient software product to know the different groups of users of a software tool, the tasks the users want to perform with it, and the information that is required for it. Our goal is to establish a comprehensive information source for the development of a consistent software environment supporting all tasks emerging from the creation to the execution of a computerized clinical practice guideline (CPG) for different user groups. Methods We conducted a comprehensive literature review to investigate the different user groups of a computerized CPG as well as their specific information needs. Results We provide a complete catalogue of every single aspect that may be related to information needs of any party concerned. In particular, we give detailed information on the tasks of guideline modellers on the one hand, and clinical information needs (i.e. information needs of physicians, nurses, nurse practitioners and patients) on the other hand. Conclusion By providing categorized information from several studies and publications, we establish an exhaustive information basis for the design of a useful software tool facilitating the formalization and the execution of a CPG. [ABSTRACT FROM AUTHOR]

Line plots are very well suited for visually representing time-series. However, several difficulties arise when multivariate heterogeneous time-series data is displayed and compared visually. Especially, if the developments and trends of time-series of different units or value ranges need to be compared, a straightforward overlay could be visually misleading. To mitigate this, visualization pioneer Jacques Bertin presented a method called indexing that transforms data into comparable units for visual representation. In this paper, we want to provide empirical evidence for this method and present a comparative study of the three visual comparison methods linear scale with juxtaposition, log scale with superimposition and indexing. Although for task completion times, indexing only shows slight advantages, the results support the assumption that the indexing method enables the user to perform comparison tasks with a significantly lower error rate. Furthermore, a post-test questionnaire showed that the majority of the participants favour the indexing method over the two other comparison methods. [ABSTRACT FROM AUTHOR]

Models for the structured delivery of care rely on organizational attributes of practice teams. The Survey of Organizational Attributes for Primary Care (SOAPC) is known to be a valid instrument to measure this aspect in the primary care setting. The aim of this study was to determine the validity of a translated and culturally adapted German version of the SOAPC. The SOAPC was translated and culturally adapted according to established standards. The external validity of the German SOAPC was assessed using the German version of the Warr-Cook-Wall scale. A total of 200 practices randomly selected from a conference database were asked to participate in the validation study. Practice, clinicians and staff characteristics were determined via short-form questionnaires. We used standardized statistical procedures to reveal the psychometric properties of the SOAPC. A total of 54 practice teams participated by returning 297 completed questionnaires (297/425, response rate 69.8%). All four domains of the SOAPC (communication, decision making, stress/chaos, history of change) could be approved by factor analysis. Internal consistency is underlined by a Cronbach's alpha of 0.70 or higher in all categories. We show strong correlation with the Warr-Cook-Wall scale in all corresponding categories indexing high external validity. The German SOAPC is a reliable and valid instrument for the assessment of organizational attributes of practice teams as the providers of quality of care. Moreover, the tool makes it possible to map the state of implementation of quality management and practice organization. The availability of the German SOAPC encourages further research on this topic in German-speaking countries. [ABSTRACT FROM AUTHOR]

The article investigates anisotropic pore closure due to non-thermal microwave effects in ceramic sintering through microwave heating. The disadvantages of conventional ceramic sintering is discussed. The samples where characterized after sintering through the Archimedes Principle. Scanning electron microscopy revealed the correlation between pore orientation and linear polarized microwave fields.

In thin walled structures a smooth reduction of the thickness results in a decreasing propagation speed and an increasing amplitude of the bending wave. Acoustic black holes (ABH) make use of this modulation to enhance the energy dissipation in the vicinity of the ABH. Modifying a thin walled structure with ABH can significantly influence the energy distribution in the structure. For large systems those investigations become computationally expensive. This contribution investigates the influence of the geometric parameter that defines the design of an ABH by using Model Order Reduction (MOR). Several numerical models are evaluated to assess the dependency between the geometric design parameters of the thickness variation and the energy distribution in the structure, the computational time and the quality of the approximation are compared with the solution obtained using the corresponding full‐scale FE model. [ABSTRACT FROM AUTHOR]

The demand for lightweight structures in aerospace and automotive is on the rise and so are the requirements regarding the vibro‐acoustic behavior. Materials with a high stiffness‐to‐mass ratio are often used in this field, but these materials typically have an unfavorable vibrational behavior regarding structure‐borne noise. Acoustic metamaterials offer a solution for improving the vibro‐acoustic performance of the structure, while the stiffness‐to‐mass ratio remains nearly constant. Such a metamaterial consists of periodic patterns of microstructures placed on the host structure. They modify the vibrational behavior of the host by adding local resonances and thus minimize vibrations at specific frequencies. Distinct resonances can have a major contribution to the acoustic behavior of a structure, so it is of interest to tailor microstructures, that generate stop bands at certain frequencies. This can be computationally expensive, so efficient numerical models are required for the design of acoustic metamaterials. Initial investigations show, that a parametric model order reduction (PMOR) method based on the Loewner framework can generate such models. [ABSTRACT FROM AUTHOR]

Thickness variations can modulate the propagation of bending waves in thin‐walled structures. A smooth reduction of the thickness results in a decreasing propagation speed and an increasing amplitude of the bending wave. Acoustic black holes (ABH) make use of this modulation to enhance the energy dissipation in the vicinity of this thickness diminution. Using the concept of ABH, no additional mass is needed to reduce structure‐borne noise. Therefore, this concept is suitable for reducing the vibrational energy in lightweight structures, which is vital in aerospace and automotive applications, among others. Locally modifying the thickness of a structure can significantly influence the global energy distribution in the structure. This contribution investigates the influence of different local thickness profiles on the global energy distribution. The results deliver insights for the design of such thickness variations in thin‐walled structures to improve their vibrational behavior. Several numerical models are evaluated to assess the dependency between the geometric design parameters of the thickness variation and the energy distribution in the structure. [ABSTRACT FROM AUTHOR]