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Theanine, a unique and the most abundant non-proteinogenic amino acid in tea plants, endows tea infusion with the umami taste and anti-stress effects. Its content in tea correlates highly with green tea quality. Theanine content in new shoots of tea plants is high in mid-spring and greatly decreases in late spring. However, how the decrease is regulated is largely unknown. In a genetic screening, we observed that a yeast mutant, glutamate dehydrolase 2 (gdh2), was hypersensitive to 40 mM theanine and accumulated more theanine. This result implied a role of CsGDH2s in theanine accumulation in tea plants. Therefore, we identified the two homologs of GDH2, CsGDH2.1 and CsGDH2.2, in tea plants. Yeast complementation assay showed that the expression of CsGDH2.1 in yeast gdh2 mutant rescued the theanine hypersensitivity and hyperaccumulation of this mutant. Subcellular localization and tissue-specific expression showed CsGDH2.1 localized in the mitochondria and highly expressed in young tissues. Importantly, CsGDH2.1 expression was low in early spring, and increased significantly in late spring, in the new shoots of tea plants. These results all support the idea that CsGDH2.1 regulates theanine accumulation in the new shoots. Moreover, the in vitro enzyme assay showed that CsGDH2.1 had glutamate catabolic activity, and knockdown of CsGDH2.1 expression increased glutamate and theanine accumulation in the new shoots of tea plants. These findings suggested that CsGDH2.1-mediated glutamate catabolism negatively regulates theanine accumulation in the new shoots in late spring, and provides a functional gene for improving late-spring green tea quality. [ABSTRACT FROM AUTHOR]

The diameter prediction of silicon ingots in the Czochralski process is a complex problem because the process is highly nonlinear, time-varying, and time-delay. To address this problem, this paper presents a novel hybrid deep learning model, which combines the deep belief network (DBN), support vector regression (SVR), and the ant lion optimizer (ALO). Continuous restricted Boltzmann machines (CRBMs) are used in DBN for working with continuous industrial data. The feature aggregates the outputs from various DBNs through an SVR model. Additionally, the ALO algorithm is used for the parameter's optimization of SVR. The newly developed model is verified with the actual production data and compared with the back propagation neural network (BPNN) and the SVR model. The simulation results demonstrate the availability and accuracy of the CRBM-DBN-ALO-SVR hybrid deep learning model. [ABSTRACT FROM AUTHOR]

This paper describes a rapid and sensitive strategy for the absolute and simultaneous quantification of specific pathogenic strain and total bacterial cells in a mixture. A laboratory-built compact, high-sensitivity, dual channel flow cytometer (HSDCFCM) was modified to enable dual fluorescence detection. A bacterial cell mixture comprising heat-killed pathogenic Escherichia coli E. coli O157: H7 and harmless E. coli DH5[alpha] was used as a model system. Pathogenic E. coli O157:H7 cells were selectively labeled by red fluorescent probe via antibody antigen interaction, and all bacterial cells were stained with membrane-permeable nucleic acid dye that fluoresces green. When each individual bacterium passes through the interrogating laser beam, E. coli O157:H7 emits both red and green fluorescence, while E. coli DH5[alpha] exhibits only green fluorescence. Because the fluorescence burst generated from each individual bacterial cell was easily distinguished from the background, accurate enumeration and consequently absolute quantification were achieved for both pathogenic and total bacterial cells. By using this strategy, accurate counting of bacteria at a density above 1.0 x [10.sup.5] cells/mL can be accomplished with 1 min of data acquisition time after fluorescent staining. Excellent correlation between the concentrations measured by the HSDCFCM and the conventional platecounting method were obtained for pure-cultured E. coli O157:H7 ([R.sup.2] = 0.9993) and E. coli DH5[alpha] ([R.sup.2] = 0.9998). Bacterial cell mixtures with varying proportions of E. coli O157:H7 and E. coli DH5[alpha] were measured with good ratio correspondence. We applied the established approach to detecting artificially contaminated drinking water samples; E. coli O157:H7 of 1.0 x [10.sup.2] cells/mL were accurately quantified upon sample enrichment. It is believed that the proposed method will find wide applications in many fields demanding bacterial identification and quantification. 10.1021/ac902524a

A compact, high-sensitivity, dual-channel flow cytometer (HSDCFCM) was developed for the individual analysis of nanosized particles and biomolecules. A hydrodynamic focusing technique was applied to confine the sample stream and enable small probe volume. Fluorescence bursts from single R-phycoerythrin (R-PE) molecules passing through the laser beam were well resolved from the background with signal-to-noise ratio of 17. Excellent size discrimination was demonstrated with a mixture of three sizes of polystyrene nanoparticles. Simultaneous measurement of fluorescence and light scattering signals from individual nanoparticles was demonstrated with the 100 nm fluorescent latex beads. Doxorubicin-loaded Zr[O.sub.2] nanoparticles and fluorescently stained Escherichia coil ER2738 cells were analyzed successfully with dual-channel detection. Particle counting is demonstrated with the 210 nm fluorescent latex beads, and excellent correlation ([R.sup.2] > 0.998) between the manufacturer-reported concentrations and those measured by HSDCFCM enumeration was obtained. The measured sample detection efficiency was ~90% on average for particle concentrations ranging from 1.62 x [10.sup.5] to 3.93 x [10.sup.7] particles/mL. Sample mixtures with varying proportions of fluorescently labeled and unlabeled nanoparticles were also analyzed with good ratio correspondence. By providing rapid, quantitative, and multiparameter characterization of nanoparticles, it is believed that the HSDCFCM will find many applications in the fields of bionanotechnology, bioanalytical chemistry, and biomedicine.