Simplifying knowledge creation and access for end-users on the SW

In this position paper, we argue that improved mechanisms for knowledge acquisition and access on the semantic web (SW) will be necessary before it will be adopted widely by end-users. In particular, we propose an investigation surrounding improved languages for knowledge exchange, better UI mechanisms for interaction, and potential help from user modeling to enable accurate, efficient, SW knowledge modeling for everyone

AtomsMasher: Personal Reactive Automation for the Web

The rise of "Web 2.0" has seen an explosion of web sites for the social sharing of personal information. To enable users to make valuable use of the rich yet fragmented sea of public, social, and personal information, data mashups emerged to provide a means for combining and filtering such information into coherent feeds and visualizations. In this paper we present AtomsMasher (AM), a new framework which extends data mashups into the realm of context-aware reactive behaviors. Reactive scripts in AM can be made to trigger automatically in response to changes in its world model derived from multiple web-based data feeds. By exposing a simple state-model abstraction and query language abstractions of data derived from heterogeneous web feeds through a simulation-based interactive script debugging environment, AM greatly simplifies the process of creating such automation in a way that is flexible, predictable, scalable and within the reach of everyday Web programmers

Evaluating CENTURY and Yasso soil carbon models for CO2 emissions and organic carbon stocks of boreal forest soil with Bayesian multi-model inference

We can curb climate change by improved management decisions for the most important terrestrial carbon pool, soil organic carbon stock (SOC). However, we need to be confident we can obtain the correct representation of the simultanous effect of the input of plant litter, soil temperature and water (which could be altered by climate or management) on the decomposition of soil organic matter. In this research, we used regression and Bayesian statistics for testing process-based models (Yasso07, Yasso15 and CENTURY) with soil heterotrophic respiration (Rh) and SOC, measured at four sites in Finland during 2015 and 2016. We extracted climate modifiers for calibration with Rh. The Rh values of Yasso07, Yasso15 and CENTURY models estimated with default parameterization correlated with measured monthly heterotrophic respiration. Despite a significant correlation, models on average underestimated measured soil respiration by 43%. After the Bayesian calibration, the fitted climate modifier of the Yasso07 model outperformed the Yasso15 and CENTURY models. The Yasso07 model had smaller residual mean square errors and temperature and water functions with fewer, thus more efficient, parameters than the other models. After calibration, there was a small overestimate of Rh by the models that used monotonic moisture functions and a small generic underestimate in autumn. The mismatch between measured and modelled Rh indicates that the Yasso and CENTURY models should be improved by adjusting climate modifiers of decomposition or by accounting for missing controls in, for example, microbial growth.Peer reviewe

Tankyrase Inhibition Attenuates Cardiac Dilatation and Dysfunction in Ischemic Heart Failure

Hyperactive poly(ADP-ribose) polymerases (PARP) promote ischemic heart failure (IHF) after myocardial infarction (MI). However, the role of tankyrases (TNKSs), members of the PARP family, in pathogenesis of IHF remains unknown. We investigated the expression and activation of TNKSs in myocardium of IHF patients and MI rats. We explored the cardioprotective effect of TNKS inhibition in an isoproterenol-induced zebrafish HF model. In IHF patients, we observed elevated TNKS2 and DICER and concomitant upregulation of miR-34a-5p and miR-21-5p in non-infarcted myocardium. In a rat MI model, we found augmented TNKS2 and DICER in the border and infarct areas at the early stage of post-MI. We also observed consistently increased TNKS1 in the border and infarct areas and destabilized AXIN in the infarct area from 4 weeks onward, which in turn triggered Wnt/β-catenin signaling. In an isoproterenol-induced HF zebrafish model, inhibition of TNKS activity with XAV939, a TNKSs-specific inhibitor, protected against ventricular dilatation and cardiac dysfunction and abrogated overactivation of Wnt/β-catenin signaling and dysregulation of miR-34a-5p induced by isoproterenol. Our study unravels a potential role of TNKSs in the pathogenesis of IHF by regulating Wnt/β-catenin signaling and possibly modulating miRNAs and highlights the pharmacotherapeutic potential of TNKS inhibition for prevention of IHF

Inferring Presence in a Context-Aware Instant Messaging System

Abstract. The increasing volume of digital communication is raising new challenges in the management of the information flow. We discuss the usage of context to infer presence information automatically for instant messaging applications. This results in easy-to-use applications and more reliable presence information. We suggest a new model, context relation, for representing the contexts that are relevant for inferring presence. The key idea is to represent both the communication initiator’s and the receiver’s contexts. The model allows sophisticated control over presence information. We describe a fully functional prototype utilizing context relations.

AtomsMasher: Personalised Context-Sensitive Automation for the Web

This paper introduces AtomsMasher, an environment for creating reactive scripts that can draw upon widely heterogeneous information to automate common information-intensive tasks. AtomsMasher is enabled by the wealth of user-contributed personal, social and contextual information that has arisen from Web2.0 social networking content sharing and micro-blogging sites. Starting with existing web mashup tools and end-user automation, we describe new challenges in achieving reactive behaviours: deriving a consistent representation that can be used to predictably drive discrete action from a multitude of noisy, incomplete and inconsistent data sources. Our solution employs a mix of automatic and user-assisted approaches to build a common internal representation in RDF, which is used to provide a simplified programming model that lets Web2.0 programmers succinctly specify behaviours in terms of high level relationships between entities and their current contextual state. We highlight the advantages and limitations of this architecture, and conclude with ongoing work towards making the system more predictable and understandable, and accessible to non-programmers