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Background Synechocystis sp. PCC6803 is a model cyanobacterium that has been studied widely and is considered for metabolic engineering applications. Here, Raman spectroscopy and Raman chemometrics (Rametrix™) were used to (i) study broad phenotypic changes in response to growth conditions, (ii) identify phenotypic changes associated with its circadian rhythm, and (iii) correlate individual Raman bands with biomolecules and verify these with more accepted analytical methods. Methods Synechocystis cultures were grown under various conditions, exploring dependencies on light and/or external carbon and nitrogen sources. The Rametrix™ LITE Toolbox for MATLAB® was used to process Raman spectra and perform principal component analysis (PCA) and discriminant analysis of principal components (DAPC). The Rametrix™ PRO Toolbox was used to validate these models through leave-one-out routines that classified a Raman spectrum when growth conditions were withheld from the model. Performance was measured by classification a all other studies. Circadian rhythm analysis showed a return to the initial phenotype after 24 hours for cultures grown in light/dark (12 h/12 h) cycles; this did not occur for cultures grown in the dark. Finally, correlation coefficients (R > 0.7) were found for glycogen, all amino acids, and chlorophyll a when comparing specific Raman bands to other experimental results. ©2020 Tanniche et al.Background During their long evolution, Synechocystis sp. PCC6803 developed a remarkable capacity to acclimate to diverse environmental conditions. In this study, Raman spectroscopy and Raman chemometrics tools (RametrixTM) were employed to investigate the phenotypic changes in response to external stressors and correlate specific Raman bands with their corresponding biomolecules determined with widely used analytical methods. Methods Synechocystis cells were grown in the presence of (i) acetate (7.5-30 mM), (ii) NaCl (50-150 mM) and (iii) limiting levels of MgSO4 (0-62.5 mM) in BG-11 media. Principal component analysis (PCA) and discriminant analysis of PCs (DAPC) were performed with the RametrixTM LITE Toolbox for MATLABⓇ. Next, validation of these models was realized via RametrixTM PRO Toolbox where prediction of accuracy, sensitivity, and specificity for an unknown Raman spectrum was calculated. These analyses were coupled with statistical tests (ANOVA and pairwise comparison) to determine statistically significant changes in the phenotypic responses. Finally, amino acid and fatty acid levels were measured with well-established analytical methods. The obtained data were correlated with previously established Raman bands assigned to these biomolecules. Results Distinguishable clusters representative of phenotypic responses were observed based on the external stimuli (i.e., acetate, NaCl, MgSO4, and controls grown on BG-11 medium) or its concentration when analyzing separately. For all these cases, RametrixTM PRO was able to predict efficiently the corresponding concentration in the culture media for an unknown Raman spectra with accuracy, sensitivity and specificity exceeding random chance. Finally, correlations (R > 0.7) were observed for all amino acids and fatty acids between well-established analytical methods and Raman bands. ©2020 Tanniche et al.Due to their ability to provide efficient mixing at small scales, confined impinging jet mixers (CIJMs) are employed widely in nanoparticle assembly processes such as flash nanoprecipitation and flash nanocomplexation, which require rapid mixing. In this mixing device, two jets from opposite directions impinge directly on each other forming a thin shear layer that breaks down rapidly into small flow structures. This enables effective mixing of the species transported by each jet by drastically reducing the diffusion distance. In the present study, the mixing performance of a commonly used cylindrical CIJM is examined by direct numerical simulations. Analysis of the simulation results indicates that the interaction of the shear layer with the inner walls of the CIJM is critical in inducing a range of instabilities in the impinging jet flow. By examining flow structures, statistical quantities, and metrics, we have characterized and quantified the mixing quality of a binary mixture in the CIJM. Product uniformity in processes such as precipitation and complexation is expected to depend on the residence time of the constituents, and this quantity is also calculated and compared for the cases with different jet Reynolds numbers. The jet Reynolds numbers of Re = 200, 600, and 1000 are considered, and the simulation results show that the CIJM achieves very good mixing for the Re = 600 and Re = 1000 cases. It is also found that the Re = 600 case performs slightly better than the other cases in terms of uniformity of the residence time. These quantitative analyses offer useful insights into the mechanism of nanoparticle size control and uniformity afforded by the unique flow physics and mixing characteristics in the CIJMs. © 2020 Author(s).Gastric cancer (GC) is one of the most common malignancies which has high incidence and mortality worldwide. Peritoneal dissemination is the main route of metastasis in advanced GC. However, few reliable diagnostic or prognostic biomarkers are available for peritoneal metastasis of GC. This study aimed to investigate the effect of lipid phosphate phosphatase-related protein type 4 (LPPR4) on the prognosis of peritoneal metastasis in GC, so as to explore the underlying molecular mechanisms and clinical significance of the process. Differentially expressed genes (DEGs) between tumor tissues and adjacent normal tissues were identified. The prognostic values of the DEGs were tested in two independent cohorts (TCGA-STAD cohort and GSE62254 cohort). Eight DEGs including LPPR4 with prognostic value in GC peritoneal metastasis were identified. The expression of LPPR4 increased in peritoneal metastasis of GC tissues, and high LPPR4 expression was associated with poor overall survival in GC. Loss- and gain-of functionats with GC peritoneal metastasis. FRAX597 AJCR Copyright © 2020.
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