Exemplary Data Research Articles

Robust Production, Crystallization, Structure Determination, and Analysis of [Fe-S] Proteins: Uncovering Control of Electron Shuttling and Gating in the Respiratory Metabolism of Molybdopterin Guanine Dinucleotide Enzymes.

[Fe-S] clusters are essential cofactors in all domains of life. They play many biological roles due to their unique abilities for electron transfer and conformational control. Yet, producing and analyzing Fe-S proteins can be difficult and even misleading if not done anaerobically. Due to unique redox properties of [Fe-S] clusters and their oxygen sensitivity, they pose multiple challenges and can lose enzymatic activity or cause their component proteins to be structurally disordered due to [Fe-S] cluster oxidation and loss in air. Here we highlight tested protocols and strategies enabling efficient and stable [Fe-S] protein production, purification, crystallization, X-ray diffraction data collection, and structure determination. From multiple high-resolution anaerobic crystal structures, we furthermore analyze exemplary data defining [Fe-S] clusters, substrate entry, and product exit for the functional oxidation states of type II molybdo-bis(molybdopterin guanine dinucleotide) (Mo-bisMGD) enzymes. Notably, these enzymes perform electron shuttling between quinone pools and specific substrates to catalyze respiratory metabolism. The identified structure-activity relationships for this enzyme class have broad implications germane to perchlorate environments on Earth and Mars extending to an alternative mechanism underlying metabolic origins for the evolution of the oxygen atmosphere. Integrated structural analyses of type II Mo-bisMGD enzymes unveil novel distinctive shared molecular mechanisms for dynamic control of substrate entry and product release gated by hydrophobic residues. Collective findings support a prototypic model for type II Mo-bisMGD enzymes including insights for a fundamental molecular mechanistic understanding of selectivity and regulation by a conformationally gated channel with general implications for [Fe-S] cluster respiratory enzymes.

Data discovery with DATS: exemplar adoptions and lessons learned.

The DAta Tag Suite (DATS) is a model supporting dataset description, indexing, and discovery. It is available as an annotated serialization with schema.org, a vocabulary used by major search engines, thus making the datasets discoverable on the web. DATS underlies DataMed, the National Institutes of Health Big Data to Knowledge Data Discovery Index prototype, which aims to provide a “PubMed for datasets.” The experience gained while indexing a heterogeneous range of >60 repositories in DataMed helped in evaluating DATS’s entities, attributes, and scope. In this work, 3 additional exemplary and diverse data sources were mapped to DATS by their representatives or experts, offering a deep scan of DATS fitness against a new set of existing data. The procedure, including feedback from users and implementers, resulted in DATS implementation guidelines and best practices, and identification of a path for evolving and optimizing the model. Finally, the work exposed additional needs when defining datasets for indexing, especially in the context of clinical and observational information.

Mixing laws and causality in high frequency induction log applications

High frequency electromagnetic technologies for subsurface formation evaluation provide high spatial resolution and new opportunities for petrophysical interpretation of data. Dispersion of rock properties and up-scaling from pore to reservoir scale (homogenization) represent the two most challenging problems. In electrodynamics of porous media, various mixing and dispersion laws are used to homogenize rock properties and describe their frequency behavior. Mixing laws and dispersion have a close link to the fundamental physical principle of causality and therefore cannot be introduced arbitrarily. For any mixing/dispersion law, we need to prove that causality holds. For testing causality, we use Titchmarsh’s theorem and, particularly, one of its modifications—Kramers-Kronig relations. Causality is discussed for Debye, Cole-Cole, Havriliak-Negami, and CRIM models. Dispersion is closely related to wave propagation. Evaluation of phase and group velocities shed new light on the physics of phase and amplitude measurements in lossy media. We evaluated various definitions of both velocities and their dependence on spatial spectra or, in other words, on the arrangement of transmitting and receiving elements. To illustrate theoretical findings, we use dielectric logging as an exemplary technology. Usually, in modern dielectric tools, amplitude and phase data are acquired, for various frequencies and sensor positions. The measured phase is discontinuous at high frequencies and requires detection of discontinuity as well as unwrapping. Remarkably, one can determine formation attenuation and loss angle based on multifrequency/multisensor amplitude data and transform them into dielectric permittivity, resistivity, and true continuous phase. Transformations of exemplary tool data used in this paper are suitable for a conceptual study and are specific for a uniform formation. We intentionally do not address the accuracy of measurements and propagation of errors in the inversion process, since they are tool- and processing-specific. Different tools require joint analysis of all available data and special noise reduction techniques associated with the structure of the acquisition system.

Regional conduction velocity calculation from clinical multichannel electrograms in human atria

BackgroundDuring atrial fibrillation, heterogeneities and anisotropies result in a chaotic propagation of the depolarization wavefront. The electrophysiological parameter called conduction velocity (CV) influences the propagation pattern over the atrium. We present a method that determines the regional CV for deformed catheter shapes, which result due to the catheter movement and changing wall contact. MethodsThe algorithm selects stable catheter positions, finds the local activation times (LAT), considers the wall contact and calculates all CV estimates within the area covered by the catheter. The method is evaluated with simulated data and then applied to four clinical data sets. Both sinus rhythm activity as well as depolarization wavefronts initiated by stimulation are analyzed. The regional CV is compared with the fractionation duration (FD) and peak-to-peak (P2P) voltages. A speed of 0.5 m/s was defined to create the simulated LAT. ResultsAfter analyzing the simulated LAT with clinical catheter spatial coordinates, the median CV of 0.5 m/s with an interquartile range of 0.22 and exact CV direction vectors were obtained. For clinical cases, the CV magnitude range of 0.08 m/s to 1.0 m/s was obtained. The P2P amplitude of 0.7 mV to 3.7 mV and the mean FD from 40.79ms to 48.66ms was obtained. The correlation of 0.86 was observed between CV and P2P amplitude, and 0.62 between CV and FD. ConclusionIn this paper, a method is presented and validated which calculates the CV for the deformed catheter and changing wall contact. In an exemplary clinical data set correlation between regional CV with FD and the P2P voltage was observed.

Household electricity consumers’ incentive to choose dynamic pricing under different taxation schemes

Dynamic pricing of retail electricity, as opposed to the widely applied average pricing, has often been proposed to enhance economic efficiency through demand response. The development of variable production from renewable energies and expectations about the installation of heat pumps and electric vehicles has now reinforced interest in flexible demand and dynamic pricing. With a roll‐out of smart metering one important technical hurdle is going to be cleared, and dynamic retail pricing may soon become an eligible option for many households. We quantify the potential incentives to adopt new pricing schemes using exemplary Danish data. Until now, limited activity of household consumers on retail markets indicates that switching supplier or contract is perceived costly. We apply the concept of switching costs to explain this hesitant behavior, and use it to estimate a threshold level based on recent observations in the Danish market. We calculate potential savings from dynamic pricing and show how the choice of electricity taxation technique may hamper or enhance potential benefits. In the light of switching costs, our results suggest that the combination of smart meter roll‐out and dynamic pricing offerings might be insufficient to convince the majority of households to switch contracts and become active in response to prices, unless they hold a substantial flexibility potential. Dynamic taxation, even if applied to parts of the levies, could contribute significantly to inducing flexible consumption. WIREs Energy Environ 2018, 7:e270. doi: 10.1002/wene270.This article is categorized under: Energy Infrastructure > Economics and Policy Energy Systems Economics > Economics and Policy Energy Policy and Planning > Economics and Policy

Semi-analytical Karhunen–Loeve representation of irregular waves based on the prolate spheroidal wave functions

A new semi-analytical approach is presented to solving the matrix eigenvalue problem or the integral equation in Karhunen–Loeve (K–L) representation of random data such as irregular ocean waves. Instead of direct numerical approach to this matrix eigenvalue problem, which may suffer from the computational inaccuracy for big data, a pair of integral and differential equations are considered, which are related to the so-called prolate spheroidal wave functions (PSWF). First, the PSWF is expressed as a summation of a small number of the analytical Legendre functions. After substituting them into the PSWF differential equation, a much smaller size matrix eigenvalue problem is obtained than the direct numerical K–L matrix eigenvalue problem. By solving this with a minimal numerical effort, the PSWF and the associated eigenvalue of the PSWF differential equation are obtained. Then, the eigenvalue of the PSWF integral equation is analytically expressed by the functional values of the PSWF and the eigenvalues obtained in the PSWF differential equation. Finally, the analytically expressed PSWFs and the eigenvalues in the PWSF integral equation are used to form the kernel matrix in the K–L integral equation for the representation of exemplary wave data such as ordinary irregular waves. It is found that, with the same accuracy, the required memory size of the present method is smaller than that of the direct numerical K–L representation and the computation time of the present method is shorter than that of the semi-analytical method based on the sinusoidal functions.

Combining task‐related activation and connectivity analysis of fMRI data reveals complex modulation of brain networks

Task-related effects in functional magnetic resonance imaging (fMRI) data are usually analyzed with local activation approaches or integrative connectivity approaches, for example, by psychophysiological interaction (PPI) analysis. While both approaches are often applied to the same data set, a systematic combination of the results with a whole-brain (WB) perspective is rarely conducted and the relationship between task-dependent activation and connectivity effects is relatively unexplored. Here, we combined brain activation and graph theoretical analysis of WB-PPI results in an exemplary episodic memory data set of N = 136 healthy human participants and found regions with congruent as well as incongruent activation and connectivity changes between task and control conditions. A comparison with large-scale resting state networks showed that in congruent as well as incongruent regions task-positively modulated connections were mainly between-network connections, especially with the default mode network, while task-negatively modulated connections were mainly found within resting state networks. Over all regions, the strength of absolute activation effects was associated with the tendency to exhibit task-positive connectivity changes, mainly driven by a strong relationship in negatively activated regions. These results demonstrate that task demands lead to a complex modulation of brain networks and provide evidence that task-evoked activation and connectivity effects reflect separable and complementary information on the macroscale brain level assessed by fMRI. Hum Brain Mapp 38:5726-5739, 2017. © 2017 Wiley Periodicals, Inc.

Non-Gaussian Assessment of the Benefits from Improved Control

Control quality significantly contributes to the process technical and financial performance. Productivity, environmental issues and energy management push systems towards their technological constraints calling for better regulation closer to process limitations. Any control improvement initiative should be predated with the estimation of the potential benefits associated with control rehabilitation project. The assessment is based on the appropriate measures. Classical methods are based on Gaussian approach. However, investigation of industrial data frequently is not compliant with normal assumption on control signals. This paper proposes approach using non-Gaussian probabilistic distributions like Cauchy, Laplace and Lévy. The methodology is illustrated on the exemplary industrial data.

Diagnostic information system dynamics in the evaluation of machine learning algorithms for the supervision of energy efficiency of district heating-supplied buildings

Modern ways of exploring the diagnostic knowledge provided by data mining and machine learningraise some concern about the ways of evaluating the quality of output knowledge, usually represented by information systems. Especially in district heating, the stationarity of efficiency models, and thus the relevance of diagnostic classification system, cannot be ensured due to the impact of social, economic or technological changes, which are hard to identify or predict. Therefore, data mining and machine learning have become an attractive strategy for automatically and continuously absorbing such dynamics.This paper presents a new method of evaluation and comparison of diagnostic information systems gathered algorithmically in district heating efficiency supervision based on exploring the evolution of information system and analyzing its dynamic features.The process of data mining and knowledge discovery was applied to the data acquired from district heating substations’ energy meters to provide the automated discovery of diagnostic knowledge base necessary for the efficiency supervision of district heating-supplied buildings. The implemented algorithm consists of several steps of processing the billing data, including preparation, segmentation, aggregation and knowledge discovery stage, where classes of abstract modelsrepresenting energy efficiency constitute an information system representing diagnostic knowledge about the energy efficiency of buildings favorably operatingunder similar climate conditions and supplied from the same district heating network.The authors analyzed the evolution of a series of information systems originating from the same knowledge discovery algorithm applied to a sequence of energy consumption-related data. Specifically, the rough sets theory was applied to describe the knowledge base and measure the uncertainty of machine learning predictions of currentclassification based on a past knowledge base. Fluctuations of diagnostic class membership were identified and provided for the differentiationbetween returning and novel fault detections, thus introducing the qualities of information system uncertainty and its sustainability. Theusability of the new method was demonstrated in the comparison of results for exemplary data mining algorithms implemented on real data from over one thousand buildings.

A stochastic volatility factor model of heston type. Statistical properties and estimation

In this paper we study the properties of a new multidimensional continuous-time stochastic covariance process, the Stochastic Volatility Factor model. Two helpful conditional characteristic functions, one needed for estimation and the other used in the pricing of financial derivatives are provided, together with the properties of the instantaneous dependence structure. Conditions for stationarity, ergodicity and mixing properties of the increments are studied. The estimation of the model is performed using the Continuum-Generalized Method of Moments (CGMM). A simulation exercise is included showing parameters are recovered, confirming identifiability. The model is also calibrated to exemplary financial data.

RivaMap: An automated river analysis and mapping engine

Rivers are essential to the Earth's water cycle and deeply impact many human societies and ecosystems, yet they are currently monitored poorly at the global scale. In-situ gauging stations are distributed sparsely and heterogeneously and do not cover much of the world, whereas remotely sensed images are spatially and temporally dense and available globally. Remotely sensed multispectral images, such as the ones acquired by Landsat missions, are available to enable the analysis and surveying of rivers using suitable algorithms. However, existing algorithms are limited in ways that restrict the coverage of the produced results and that prevent the automated analysis of river networks at large scales over short periods of time. Ideally, river maps should be as “live” as possible, e.g., computed quickly and continuously as new Earth imaging data becomes available. Towards advancing progress on this problem, we describe an automated river analysis and mapping engine, RivaMap, that enables the computation of large-scale hydrography data sets from remotely sensed data in a short period of time. RivaMap facilitates water resource management by providing tools to delineate rivers and to estimate their width. As a practical application of RivaMap, we present a continental-scale centerline and width data set of North American rivers, that is automatically computed on Landsat data. We validate our mapping engine by comparing the RivaMap-generated data to a similar data set, NARWidth, and also to in-situ measurements. Our experimental results show that RivaMap is able to efficiently and accurately extract rivers from remotely sensed images at large scales. The outcomes of this research, the software, and the computed exemplary data set are publicly available.

A Landscape Archaeological Approach to Link Human Activities to Past Landscape Change in the Built-up Area of the Late Bronze Age Enclosure Corneşti-Iarcuri, Western Romania

This study exemplifies the theoretical and methodological process of integration of disciplinary results, the joint development of new hypotheses and its interdisciplinary interpretation in the framework of landscape archaeological research. A conceptual model is introduced to visualize the integration process. The findings of two recently published studies and the archaeological state of the art regarding the largest known prehistoric enclosure in Europe – Cornesti-Iarcuri – are used as exemplary data to demonstrate the applicability of the conceptual model. The presented discussion shows how integration of disciplinary findings leads to a more holistic and more rigorous interpretation and opens the opportunity to jointly develop new hypotheses that can be integrated subsequently.

BBCAnalyzer: a visual approach to facilitate variant calling

BackgroundDeriving valid variant calling results from raw next-generation sequencing data is a particularly challenging task, especially with respect to clinical diagnostics and personalized medicine. However, when using classic variant calling software, the user usually obtains nothing more than a list of variants that pass the corresponding caller’s internal filters. Any expected mutations (e.g. hotspot mutations), that have not been called by the software, need to be investigated manually.ResultsBBCAnalyzer (Bases By CIGAR Analyzer) provides a novel visual approach to facilitate this step of time-consuming, manual inspection of common mutation sites. BBCAnalyzer is able to visualize base counts at predefined positions or regions in any sequence alignment data that are available as BAM files. Thereby, the tool provides a straightforward solution for evaluating any list of expected mutations like hotspot mutations, or even whole regions of interest. In addition to an ordinary textual report, BBCAnalyzer reports highly customizable plots. Information on the counted number of bases, the reference bases, known mutations or polymorphisms, called mutations and base qualities is summarized in a single plot. By uniting this information in a graphical way, the user may easily decide on a variant being present or not – completely independent of any internal filters or frequency thresholds.ConclusionsBBCAnalyzer provides a unique, novel approach to facilitate variant calling where classical tools frequently fail to call. The R package is freely available at http://bioconductor.org. The local web application is available at Additional file 2. A documentation of the R package (Additional file 1) as well as the web application (Additional file 2) with detailed descriptions, examples of all input- and output elements, exemplary code as well as exemplary data are included. A video demonstrates the exemplary usage of the local web application (Additional file 3).D8dxzuKLnhVEJz8ATyJuw2Additional file 3: Supplement_3. Video demonstrating the exemplary usage of the web application “BBCAnalyzer”. (MP4 11571 kb)

A Microfluidic Flow Chamber Model for Platelet Transfusion and Hemostasis Measures Platelet Deposition and Fibrin Formation in Real-time

Microfluidic models of hemostasis assess platelet function under conditions of hydrodynamic shear, but in the presence of anticoagulants, this analysis is restricted to platelet deposition only. The intricate relationship between Ca2+-dependent coagulation and platelet function requires careful and controlled recalcification of blood prior to analysis. Our setup uses a Y-shaped mixing channel, which supplies concentrated Ca2+/Mg2+ buffer to flowing blood just prior to perfusion, enabling rapid recalcification without sample stasis. A ten-fold difference in flow velocity between both reservoirs minimizes dilution. The recalcified blood is then perfused in a collagen-coated analysis chamber, and differential labeling permits real-time imaging of both platelet and fibrin deposition using fluorescence video microscopy. The system uses only commercially available tools, increasing the chances of standardization. Reconstitution of thrombocytopenic blood with platelets from banked concentrates furthermore models platelet transfusion, proving its use in this research domain. Exemplary data demonstrated that coagulation onset and fibrin deposition were linearly dependent on the platelet concentration, confirming the relationship between primary and secondary hemostasis in our model. In a timeframe of 16 perfusion min, contact activation did not take place, despite recalcification to normal Ca2+ and Mg2+ levels. When coagulation factor XIIa was inhibited by corn trypsin inhibitor, this time frame was even longer, indicating a considerable dynamic range in which the changes in the procoagulant nature of the platelets can be assessed. Co-immobilization of tissue factor with collagen significantly reduced the time to onset of coagulation, but not its rate. The option to study the tissue factor and/or the contact pathway increases the versatility and utility of the assay.

A Microfluidic Flow Chamber Model for Platelet Transfusion and Hemostasis Measures Platelet Deposition and Fibrin Formation in Real-time.

Microfluidic models of hemostasis assess platelet function under conditions of hydrodynamic shear, but in the presence of anticoagulants, this analysis is restricted to platelet deposition only. The intricate relationship between Ca2+-dependent coagulation and platelet function requires careful and controlled recalcification of blood prior to analysis. Our setup uses a Y-shaped mixing channel, which supplies concentrated Ca2+/Mg2+ buffer to flowing blood just prior to perfusion, enabling rapid recalcification without sample stasis. A ten-fold difference in flow velocity between both reservoirs minimizes dilution. The recalcified blood is then perfused in a collagen-coated analysis chamber, and differential labeling permits real-time imaging of both platelet and fibrin deposition using fluorescence video microscopy. The system uses only commercially available tools, increasing the chances of standardization. Reconstitution of thrombocytopenic blood with platelets from banked concentrates furthermore models platelet transfusion, proving its use in this research domain. Exemplary data demonstrated that coagulation onset and fibrin deposition were linearly dependent on the platelet concentration, confirming the relationship between primary and secondary hemostasis in our model. In a timeframe of 16 perfusion min, contact activation did not take place, despite recalcification to normal Ca2+ and Mg2+ levels. When coagulation factor XIIa was inhibited by corn trypsin inhibitor, this time frame was even longer, indicating a considerable dynamic range in which the changes in the procoagulant nature of the platelets can be assessed. Co-immobilization of tissue factor with collagen significantly reduced the time to onset of coagulation, but not its rate. The option to study the tissue factor and/or the contact pathway increases the versatility and utility of the assay.

Stochastic Forecasting of Vehicle Dynamics Using Sequential Monte Carlo Simulation

Predictive control is a popular approach for further improving the efficiency and performance of vehicular systems enabling intelligent systems behavior appropriate to the driving situation. To calculate such control strategies, the future vehicle dynamics or subsequent states have to be predicted. We introduce a stochastic framework based on an explanatory model and stochastic processes to predict future vehicle dynamics with road network data. The distributions of the future states are approximated using sequential Monte Carlo simulation. The proposed approach enables stochastic forecasts incorporating uncertain driver behavior and available road data. Parameter inference is shown for exemplary real-drive test data, and predictive performance is evaluated using commonly used reference models. The results show that the explanatory model provides more specific information than time-series models do, still considering the uncertainty in the driver's behavior or the situation. The framework can be applied with predictive control algorithms enabling intelligent control of vehicular systems. Furthermore, the framework or parts of it may be usable for other applications like predicting behavior of traffic participants or general characterization of driver behavior.

Diskursrealisationen in Online-Foren

Abstract This paper deals with the question in how far pragmatic and discourse conditions affect the communicative construction of collective knowledge. Research in Discourse Semantics predominantly deals with corpora consisting of mass media texts. In doing so, knowledge that becomes manifest in these texts can be identified and substantially described. However, to what extend this knowledge is also used as a pragmatic-interactive resource in other communicative contexts and in social media communication cannot be explored. This paper aims at reducing this research desideratum by analyzing a different type of data (realisations in internet forums), using the example of internet forums dealing with the topic of OSTDEUTSCH (East German). Following discourse pragmatic procedures exemplary data will be discussed in order to reveal which pragmatic conditions are essential and for what interactive reasons discourse knowledge is used as a communicative resource by the participants in the forum. There will be a focus on the essential condition of interactionality and on interferences between realizations of discourse in mass media and in internet forums.

Efficient Application of De Novo RNA Assemblers for Proteomics Informed by Transcriptomics

RNA sequencing is a powerful method to build reference transcriptome assemblies and eventually sample-specific protein databases for mass-spectrometry-based analyses. This novel proteomics informed by transcriptomics (PIT) workflow improves proteome characterization of dynamic and especially nonmodel organism proteomes and moreover helps to identify novel gene products. With increasing popularity of such proteogenomics applications a growing number of researchers demand qualitative but resource-friendly and easy to use analysis strategies. Most PIT applications so far rely on the initially introduced Trinity de novo assembly tool. To aid potential users to start off with PIT, we compared main performance criteria of Trinity and other alternative RNA assembly tools known from the transcriptomics field including Oases, SOAPdenovo-Trans, and Trans-ABySS. Using exemplary data sets and software-specific default parameters, Trinity and alternative assemblers produced comparable and high-quality reference data for vertebrate transcriptomes/proteomes of varying complexity. However, Trinity required large computational resources and time. We found that alternative de novo assemblers, in particular, SOAPdenovo-Trans but also Oases and Trans-ABySS, rapidly produced protein databases with far lower computational requirements. By making choice among various RNA assembly tools, proteomics researchers new to transcriptome assembly and with future projects with high sample numbers can benefit from alternative approaches to efficiently apply PIT.

Die Erforschung der Luxemburger Wortbildung als Aufgabe. Eine Projektvorstellung

Research on Luxembourgish word formation – both from a historical and a synchronic perspective – is still at its very beginning. Its most challenging aspect is the complex multilingual situation in Luxembourg which arose due to the country’s intricate history. The research project “WBLUX” takes this unique situation into account; It has been designed as a multi-stage corpus-based tandem project as a cooperation between German and Luxembourgish studies. This paper commences by introducing the project design and the corpus concept, followed by a presentation of some exemplary corpus data.

On optimization of absorption–dispersion spectra

A modified approach to the analysis of spectra of the complex electric permittivity of liquids in the Infrared region is presented. These spectra are derived from experimental spectra of the complex refractive index. Subsequently they are used to determine important secondary quantities, e.g. spectra of complex molecular polarizabilities and an integral property – the molar vibrational polarization. The accuracy of these quantities depends essentially on the accuracy of both components of the complex electric permittivity spectrum.In the proposed procedure, the spectra of the complex electric permittivity are approximated using the Classical Damped Harmonic Oscillator (CDHO) model for the description of individual bandshapes. The CDHO model defines both the real and imaginary part of the complex permittivity. The fitting procedure includes a simultaneous optimization of both the real and imaginary parts of the complex permittivity spectrum. A comparison of absorption-only curve fitting and the novel absorption-dispersion double curve fitting is presented; advantages of the new approach in accuracy, reliability and convergence time are pointed out.Due to the complexity of the problem, the choice was restricted to non-gradient methods of optimization. The performance of several gradientless algorithms was tested. Among numerous procedures the Powell General Least Squares Method Without Derivatives was found to be the most efficient. The reliability of obtained results of the band separatiovn process was tested on several simulated spectra of increasing complexity.The applicability of the developed approach to the analysis of exemplary experimental data was evaluated and discussed.