The 2008 DGFI Realization of the ITRS: DTRF2008 (data)

The DTRF2008 is a realization of the International Terrestrial Reference System ITRS. The DTRF2008 consists of station positions and velocities of global distributed observing stations of the space geodetic observation techniques VLBI, SLR, GPS and DORIS. The DTRF2008 was released in May 2010 and includes the observation data of the techniques up to and including 2008. The observation data are processed and submitted by the corresponding international services: IGS (International GNSS Service, http://igscb.jpl.nasa.gov) IVS (International VLBI Service, http://ivscc.gsfc.nasa.gov) ILRS (International Laser Ranging Service, http://ilrs.gsfc.nasa.gov) IDS (International DORIS Service, http://ids-doris.org). The DTRF2008 is an independent ITRS realization, which is computed on the basis of the same input data as the ITRF2008 (IGN, Paris). Both realizations differ with respect to their computation strategies: while the ITRF2008 is based on the combination of solutions, the DTRF2008 is computed by the combination of normal equations. The DTRF2008 comprises the coordinates of 559 GPS-, 106 VLBI-, 122 SLR- and 132 DORIS-stations. The reference epoch is 1.1.2005, 0h UTC. The Earth Orientation Parameters (EOP) - that means the coordinates of the terrestrial and the celestial pole, UT1-UTC and the Length of Day (LOD) - were simultaneously estimated with the station coordinates. The EOP time series cover the period of 1983 to 2008. The station names are the official IERS indications: cdp numbers or 4-character IDs and DOMES numbers (http://itrf.ensg.ign.fr/doc_ITRF/iers_sta_list.txt). The solution is available in different file formats (SINEX and SSC), see below. A detailed description of the solution is given by Seitz M. et al. (2012). The results of a comparison of DTRF2008 and ITRF2008 is given by Seitz M. et al. (2013). More information as well as residual time series of the station positions can be made available by request.

Multi-Resolution Texture Coding for Multi-Resolution 3D Meshes

We present an innovative system to encode and transmit textured multi-resolution 3D meshes in a progressive way, with no need to send several texture images, one for each mesh LOD (Level Of Detail). All texture LODs are created from the finest one (associated to the finest mesh), but can be re- constructed progressively from the coarsest thanks to refinement images calculated in the encoding process, and transmitted only if needed. This allows us to adjust the LOD/quality of both 3D mesh and texture according to the rendering power of the device that will display them, and to the network capacity. Additionally, we achieve big savings in data transmission by avoiding altogether texture coordinates, which are generated automatically thanks to an unwrapping system agreed upon by both encoder and decoder.

Biomolecular interaction analysis of gestrinone-anti-gestrinone using arrays of high aspect ratio SU-8 nanopillars

In this paper, label-free biosensing for antibody screening by periodic lattices of high-aspect ratio SU-8 nano-pillars (BICELLs) is presented. As a demonstration, the determination of anti-gestrinone antibodies from whole rabbit serum is carried out, and for the first time, the dissociation constant (KD = 6 nM) of antigen-antibody recognition process is calculated using this sensing system. After gestrinone antigen immobilization on the BICELLs, the immunorecognition was performed. The cells were interrogated vertically by using micron spot size Fourier transform visible and IR spectrometry (FT-VIS-IR), and the dip wavenumber shift was monitored. The biosensing assay exhibited good reproducibility and sensitivity (LOD = 0.75 ng/mL).

Combining data mining and ontology engineering to enrich ontologies and linked data

In this position paper, we claim that the need for time consuming data preparation and result interpretation tasks in knowledge discovery, as well as for costly expert consultation and consensus building activities required for ontology building can be reduced through exploiting the interplay of data mining and ontology engineering. The aim is to obtain in a semi-automatic way new knowledge from distributed data sources that can be used for inference and reasoning, as well as to guide the extraction of further knowledge from these data sources. The proposed approach is based on the creation of a novel knowledge discovery method relying on the combination, through an iterative ?feedbackloop?, of (a) data mining techniques to make emerge implicit models from data and (b) pattern-based ontology engineering to capture these models in reusable, conceptual and inferable artefacts.

Characterization of the Performance of Optical Label-Free Biosensors

The field of optical label free biosensors has become a topic of interest during past years, with devices based on the detection of angular or wavelength shift of optical modes [1]. Common parameters to characterize their performance are the Limit of Detection (LOD, defined as the minimum change of refractive index upon the sensing surface that the device is able to detect, and also BioLOD, which represents the minimum amount of target analyte accurately resolved by the system; with units of concentration (common un its are p pm, ng/ml, or nM). LOD gives a first value to compare different biosensors, and is obtained both theoretically (using photonic calculation tools), and experimentally,covering the sensing area with fluids of different refractive indexes.

Silicon nanopillar arrays with SiO2 overlayer for biosensing application

We present the fabrication of silicon dioxide (SiO2) coated silicon nanopillar array structures and demonstrate their application as sensitive optical biosensors. Colloidal lithography, plasma dry etching and deposition processes are used to fabricate SiO2 coated Si nanopillar arrays with two different diameters and periods. Proof of concept bio recognition experiments are carried out with the bovine serum albumin (BSA)/antiBSA model system using Fourier transform visible and IR spectrometry (FT-VIS-IR) in reflection mode. A limit of detection (LoD) value of 5.2 ng/ml is estimated taking in to account the wavenumber uncertainty in the measurements.

Best practises for multilingual linked open data: a community effort

The W3C Best Practises for Multilingual Linked Open Data community group was born one year ago during the last MLW workshop in Rome. Nowadays, it continues leading the effort of a numerous community towards acquiring a shared view of the issues caused by multilingualism on the Web of Data and their possible solutions. Despite our initial optimism, we found the task of identifying best practises for ML-LOD a difficult one, requiring a deep understanding of the Web of Data in its multilingual dimension and in its practical problems. In this talk we will review the progresses of the group so far, mainly in the identification and analysis of topics, use cases, and design patterns, as well as the future challenges.