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In allergology practice and research, it would be convenient to receive pollen identification and monitoring results in much shorter time than it comes from human identification. Image based analysis is one of the approaches to an automated identification scheme for pollen grain and pattern recognition on such images is widely used as a powerful tool. The goal of such attempt is to provide accurate, fast recognition and classification and counting of pollen grains by computer system for monitoring. The isolated pollen grain are objects extracted from microscopic image by CCD camera and PC computer under proper conditions for further analysis. The algorithms are based on the knowledge from feature vector analysis of estimated parameters calculated from grain characteristics, including morphological features, surface features and other applicable estimated characteristics. Segmentation algorithms specially tailored to pollen object characteristics provide exact descriptions of pollen characteristics (border and internal features) already used by human expert. The specific characteristics and its measures are statistically estimated for each object. Some low level statistics for estimated local and global measures of the features establish the feature space. Some special care should be paid on choosing these feature and on constructing the feature space to optimize the number of subspaces for higher recognition rates in low-level classification for type differentiation of pollen grains. The results of estimated parameters of feature vector in low dimension space for some typical pollen types are presented, as well as some effective and fast recognition results of performed experiments for different pollens. The findings show the ewidence of using proper chosen estimators of central and invariant moments (M21, NM2, NM3, NM8 NM9), of tailored characteristics for good enough classification measures (efficiency > 95%), even for low dimensional classifiers (>3) for type differentiation of pollens grain.
INTRODUCTION: The solute carrier 6 (SLC6) family of genes codes transporters for neurotransmitters, amino acids, osmolytes and energy metabolites. In the brain SLC6 transporters play an important role in the reuptake of GABA, serotonin, dopamine, noradrenaline, glycine and proline. SLC6A14 is a Na/Cl-dependent amino acid transporter ATB(0,+) – specific towards neutral and cationic amino acids. It is present in astrocytes and in the blood-brain barrier. In the first step of trafficking to plasma membrane – ER exit, the neurotransmitter transporters interact with specific isoforms of SEC24 proteins, the components of Coatomer II (COPII). AIM(S): To verify, which proteins are involved in trafficking of ATB(0,+) to the cell surface from ER. METHOD(S): Trafficking of rat SLC6A14 to plasma membrane was studied in heterologous expression system. RESULTS: Immunofluorescence analysis showed that SLC6A14 appears in the plasma membrane after 48 h, with the majority of the transporter not leaving ER. This observation was confirmed by biotinylation of surface proteins and analysis of SLC6A14 glycosylation status in order to distinguish core glycosylation (taking place in ER) from full glycosylation (taking place in Golgi apparatus). Trafficking was attenuated after co‑transfection with the dominant negative mutants of Sar1 GTPase – the first protein involved in COPII formation. Further studies demonstrated that out of four SEC24 proteins, exclusively SEC24C co-localized and interacted with SLC6A14, a result confirmed in the proximity ligation assay. CONCLUSIONS: These observations confirm a hypothesis that lysine in position +2 towards an ER export signal is crucial forspecific interaction of SLC6 transporters with SEC24C. FINANCIAL SUPPORT: This work has been supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no 665735 (Bio4Med) and by the funding from Polish Ministry of Science and Higher Education within 2016–2020 funds for the implementation of international projects (agreement no 3548/H2020/COFUND2016/2).
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