The biliary glycoproteins (BGP or CD66a), a group of different splice variants of a single gene, are members of the carcinoembryonic antigen family and the immunoglobulin super-family. Recently, we detected CD66a on IL-2 activated lymphocytes. In this study we characterized the structure and the expression pattern of BGP on human lymphocytes and investigated its role in T cell activation. Lymphocytes express 2 of the 13 known splice variants , i.e. BGPa and BGPb, which are glycosylated in a lymphocyte-specific manner. Both BGPa and BGPb have the long cytoplasmic tail, which contains two immunoreceptor tyrosine-based inhibitory motif (ITIM)-like motifs, but differ in their extracellular region containing 4 and 3 immunoglobulin-like domains, respectively. On PBL BGP is expressed in small amounts only on B cells and Th cells. Stimulation with IL-2 leads to a strong up-regulation of BGP by these cells, and induces de novo BGP expression on + ˇ T cells, CD8 + and CD56 + cells, but not on CD16 + lymphocytes. This up-regulation of BGP seems to be part of the physiological process of T cell activation, since stimulation with anti-CD3 mAb is sufficient to induce BGP up-regulation. Based on the presence of the two ITIM-like motifs, one may expect that BGP inhibits T cell activation, but surprisingly, engagement of BGP enhances the proliferation of anti-CD3-stimulated T cells.
We studied 12 healthy subjects with fMRI while they performed a driving simulation task. In the active condition they steered the car themselves (driving), in the passive condition a person from outside the scanner was steering the car (passive driving). Common activations in both conditions were found in occipital and parietal regions bilaterally. Activity specifically associated with driving was found only in the sensorimotor cortex and the cerebellum. Compared to passive driving, activity during driving was reduced in numerous brain regions including MT/MST. It is concluded that simulated driving requires mainly perceptual-motor integration and that the limited cognitive capacity model of driving has to be revised.
One of the first steps in building a spoken language understanding (SLU) module for dialogue systems is the extraction of flat concepts out of a given word sequence, usually provided by an automatic speech recognition (ASR) system. In this paper, six different modeling approaches are investigated to tackle the task of concept tagging. These methods include classical, well-known generative and discriminative methods like Finite State Transducers (FSTs), Statistical Machine Translation (SMT), Maximum Entropy Markov Models (MEMMs), or Support Vector Machines (SVMs) as well as techniques recently applied to natural language processing such as Conditional Random Fields (CRFs) or Dynamic Bayesian Networks (DBNs). Following a detailed description of the models, experimental and comparative results are presented on three corpora in different languages and with different complexity. The French MEDIA corpus has already been exploited during an evaluation campaign and so a direct comparison with existing benchmarks is possible. Recently collected Italian and Polish corpora are used to test the robustness and portability of the modeling approaches. For all tasks, manual transcriptions as well as ASR inputs are considered. Additionally to single systems, methods for system combination are investigated. The best performing model on all tasks is based on conditional random fields. On the MEDIA evaluation corpus, a concept error rate of 12.6% could be achieved. Here, additionally to attribute names, attribute values have been extracted using a combination of a rule-based and a statistical approach. Applying system combination using weighted ROVER with all six systems, the concept error rate (CER) drops to 12.0%.
The OECD guidelines 308 and 309 define simulation tests aimed at assessing biotransformation of chemicals in water-sediment systems. They should serve the estimation of persistence indicators for hazard assessment and half-lives for exposure modeling. Although dissipation half-lives of the parent compound are directly extractable from OECD 308 data, they are system-specific and mix up phase transfer with biotransformation. In contrast, aerobic biotransformation half-lives should be easier to extract from OECD 309 experiments with suspended sediments. Therefore, there is scope for OECD 309 tests with suspended sediment to serve as a proxy for degradation in the aerobic phase of the more complicated OECD 308 test, but that correspondence has remained untested so far. Our aim was to find a way to extract biotransformation rate constants that are universally valid across variants of water-sediment systems and, hence, provide a more general description of the compound's behavior in the environment. We developed a unified model that was able to simulate four experimental types (two variants of OECD 308 and two variants of OECD 309) for three compounds by using a biomass-corrected, generalized aerobic biotransformation parameter (k'bio). We used Bayesian calibration and uncertainty assessment to calibrate the models for individual experimental types separately and for combinations of experimental types. The results suggested that k'bio was a generally valid parameter for quantifying biotransformation across systems. However, its uncertainty remained significant when calibrated on individual systems alone. Using at least two different experimental types for the calibration of k'bio increased its robustness by clearly separating degradation from the phase-transfer processes taking place in the individual systems. Overall, k'bio has the potential to serve as a system-independent descriptor of aerobic biotransformation at the water-sediment interface that is equally and consistently applicable for both persistence and exposure assessment purposes.
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