TLR1-induced chemokine production is critical for mucosal immunity against Yersinia enterocolitica.

Our gastrointestinal tract is a portal of entry for a number of bacteria and viruses. Thus, this tissue must develop ways to induce antigen-specific T cell and antibody responses quickly. Intestinal epithelial cells are a central player in barrier function and also in communicating signals from invading pathogens to the underlying immune tissue. Here we demonstrate that activation of Toll-like receptor 1 (TLR1) in the epithelium leads to the upregulation of the chemokine CCL20 during oral infection with Yersinia enterocolitica. Further, both neutralization of CCL20 using polyclonal antibody treatment and deletion of TLR1 resulted in a defect in CCR6+ dendritic cells (DCs), which produce innate cytokines that help to induce anti-Yersinia-specific T helper 17 (TH17) cells and IgA production. These data demonstrate a novel role for TLR1 signaling in the intestinal epithelium and demonstrate that together TLR1 and CCL20 are critical mediators of TH17 immunity through the activation and recruitment of DCs

Studies on shark liver oil and its residue

Seasonal variation in oil and moisture contents and vitamin A potency of oil in livers from different species of sharks landed at Veraval coast were studied. Values of moisture, protein, ash and vitamins in defatted liver residue were determined

Erbium-doped fiber amplifier elements for structural analysis sensors

The use of erbium-doped fiber amplifiers (EDFA's) in optical fiber sensor systems for structural analysis is described. EDFA's were developed for primary applications as periodic regenerator amplifiers in long-distance fiber-based communication systems. Their in-line amplification performance also makes them attractive for optical fiber sensor systems which require long effective lengths or the synthesis of special length-dependent signal processing functions. Sensor geometries incorporating EDFA's in recirculating and multiple loop sensors are discussed. Noise and polarization birefringence are also considered, and the experimental development of system components is discussed

What are Links in Linked Open Data? A Characterization and Evaluation of Links between Knowledge Graphs on the Web

Linked Open Data promises to provide guiding principles to publish interlinked knowledge graphs on the Web in the form of findable, accessible, interoperable and reusable datasets. We argue that while as such, Linked Data may be viewed as a basis for instantiating the FAIR principles, there are still a number of open issues that cause significant data quality issues even when knowledge graphs are published as Linked Data. Firstly, in order to define boundaries of single coherent knowledge graphs within Linked Data, a principled notion of what a dataset is, or, respectively, what links within and between datasets are, has been missing. Secondly, we argue that in order to enable FAIR knowledge graphs, Linked Data misses standardised findability and accessability mechanism, via a single entry link. In order to address the first issue, we (i) propose a rigorous definition of a naming authority for a Linked Data dataset (ii) define different link types for data in Linked datasets, (iii) provide an empirical analysis of linkage among the datasets of the Linked Open Data cloud, and (iv) analyse the dereferenceability of those links. We base our analyses and link computations on a scalable mechanism implemented on top of the HDT format, which allows us to analyse quantity and quality of different link types at scale.Series: Working Papers on Information Systems, Information Business and Operation

A More Decentralized Vision for Linked Data

We claim that ten years into Linked Data there are still many unresolved challenges towards arriving at a truly machine-readable and decentralized Web of data. With a focus on the the biomedical domain, currently, one of the most promising adopters of Linked Data, we highlight and exemplify key technical and non-technical challenges to the success of Linked Data, and we outline potential solution strategies

A More Decentralized Vision for Linked Data

In this deliberately provocative position paper, we claim that ten years into Linked Data there are still (too?) many unresolved challenges towards arriving at a truly machine-readable and decentralized Web of data. We take a deeper look at the biomedical domain - currently, one of the most promising "adopters" of Linked Data - if we believe the ever-present "LOD cloud" diagram. Herein, we try to highlight and exemplify key technical and non-technical challenges to the success of LOD, and we outline potential solution strategies. We hope that this paper will serve as a discussion basis for a fresh start towards more actionable, truly decentralized Linked Data, and as a call to the community to join forces.Series: Working Papers on Information Systems, Information Business and Operation

Enabling Web-scale data integration in biomedicine through Linked Open Data

The biomedical data landscape is fragmented with several isolated, heterogeneous data and knowledge sources, which use varying formats, syntaxes, schemas, and entity notations, existing on the Web. Biomedical researchers face severe logistical and technical challenges to query, integrate, analyze, and visualize data from multiple diverse sources in the context of available biomedical knowledge. Semantic Web technologies and Linked Data principles may aid toward Web-scale semantic processing and data integration in biomedicine. The biomedical research community has been one of the earliest adopters of these technologies and principles to publish data and knowledge on the Web as linked graphs and ontologies, hence creating the Life Sciences Linked Open Data (LSLOD) cloud. In this paper, we provide our perspective on some opportunities proffered by the use of LSLOD to integrate biomedical data and knowledge in three domains: (1) pharmacology, (2) cancer research, and (3) infectious diseases. We will discuss some of the major challenges that hinder the wide-spread use and consumption of LSLOD by the biomedical research community. Finally, we provide a few technical solutions and insights that can address these challenges. Eventually, LSLOD can enable the development of scalable, intelligent infrastructures that support artificial intelligence methods for augmenting human intelligence to achieve better clinical outcomes for patients, to enhance the quality of biomedical research, and to improve our understanding of living systems