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In 2015, adam collapsed at Samarco iron ore mine in the municipality of Mariana, Brazil, and contaminated more than 600 km of watercourses and destroyed almost 1600 acres of vegetation. Nineteen people died and more than 600 families lost their homes. This study aimed to estimate health-related quality of life (HRQoL) losses owing to this disaster.<br />We collected data from a probabilistic sample of 459 individuals aged 15 years or older. Household face-to-face interviews were conducted in December 2018. Pre-event data were not available for this population, so respondents were asked to evaluate at present and in retrospect their health status using EQ-5D-3L. The Minas Gerais societal value sets for EQ-5D-3L health preferences, estimated in 2011, were used to calculate utility losses. The health loss estimation from EQ-5D will form the basis for the calculation of compensation payments for the victims.<br />Approximately 74% of the study population suffered some HRQoL loss. On average, EQ-5D index values decreased from 0.95 to 0.76. The greatest effects were observed for the anxiety/depression dimension, followed by pain/discomfort. Before the tragedy, the proportion of individuals with severe anxiety/depression and pain/discomfort was equal to 1% rising to 23% and 11%, respectively.<br />Catastrophic losses owing to the Samarco disaster were found. The EQ-5D-3L instrument showed feasibility and sensitiveness to measure HRQoL losses owing to a negative health shock in a low-income Brazilian population.<br />Catastrophic losses owing to the Samarco disaster were found. The EQ-5D-3L instrument showed feasibility and sensitiveness to measure HRQoL losses owing to a negative health shock in a low-income Brazilian population.Traditional chemical methods used to measure the zinc content in rice plants are time-consuming, laborious, requires reagents, and have a limited monitoring range, while the Raman spectroscopy method has the advantage of being fast, non-destructive, and requires no reagents. Unfortunately, the identification accuracy of the Raman partial least squares (PLS) model based on principal components is only 53.33%. To boost this, a One-Way ANOVA method was used to extract the characteristic variables in the Raman spectra. Based on these Raman variables, a model for identifying zinc stressed samples was established. The identification accuracy was improved to 70% but still fell short of the measurement requirements. To further enhance these results, the Raman spectrum was decomposed into components based on the Hilbert Vibration Decomposition (HVD) method. Using characteristic variables of the Raman spectrum and its HVD components to establish a PLS model, the identification accuracy of the test set is raised to 90.25%. These results are a significant improvement from those obtained using a model solely based on the Raman spectral characteristic variables, revealing that HVD components provide highly effective identification information. A Raman modeling method based on the characteristic variables of the HVD component is an innovative way for improving the accuracy of Raman detection, especially for the measurement of trace substances.A dual-mode microscopic hyperspectral imager (DMHI) combined with a machine learning algorithm for the purpose of classifying origins and varieties of Tetrastigma hemsleyanum (T. hemsleyanum) was developed. By switching the illumination source, the DMHI can operate in reflection imaging and fluorescence detection modes. The DMHI system has excellent performance with spatial and spectral resolutions of 27.8 μm and 3 nm, respectively. To verify the capability of the DMHI system, a series of classification experiments of T. hemsleyanum were conducted. Captured hyperspectral datasets were analyzed using principal component analysis (PCA) for dimensional reduction, and a support vector machine (SVM) model was used for classification. In reflection microscopic hyperspectral imaging (RMHI) mode, the classification accuracies of T. hemsleyanum origins and varieties were 96.3% and 97.3%, respectively, while in fluorescence microscopic hyperspectral imaging (FMHI) mode, the classification accuracies were 97.3% and 100%, respectively. Combining datasets in dual mode, excellent predictions of origin and variety were realized by the trained model, both with a 97.5% accuracy on a newly measured test set. [https://www.selleckchem.com/products/deg-77.html DEG-77 purchase] The results show that the DMHI system is capable of T. hemsleyanum origin and variety classification, and has the potential for non-invasive detection and rapid quality assessment of various kinds of medicinal herbs.In this study, we reported a colorimetry and SERS dual-mode sensing of serotonin (5-HT) based on functionalized gold nanoparticles (AuNPs). Based on the amino and hydroxyl groups in 5-HT can react with dithiobis succinimidyl propionate (DSP) and N-acetyl-L-cysteine (NALC) respectively, we synthesized two kinds of functionalized AuNPs (DSP-AuNPs and NALC-AuNPs). A double interaction between functionalized nanoparticles and the hydroxyl and the amino group of serotonin led to interparticle-crosslinking aggregation. The aggregation of the two functionalized AuNPs can cause the plasmon coupling of AuNPs resulting in a color change visible to the naked eye and the enlargement of SERS "hot spot" area and the enhancement of SERS signal. Furthermore, two kinds of functionalized AuNPs can specifically recognize 5-HT and effectively reduce the interference of biomolecules with similar structure to 5-HT in the experiment. This dual-mode system has the advantages of low detection limit, high sensitivity and good selectivity, and the detection limit is 0.15 nmol L-1. Besides, the system was applied to the determination of 5-HT content in human serum, and the relative standard deviation (RSD) was lower than 3.75%, which indicated that the system had a good application prospect in the determination of biological samples.Two-dimensional (2D) materials of SiMI4(M = Ge, Sn) monolayers are identified as promising visible-light-driven photocatalyst for hydrogen evolution reaction by DFT calculations. The dynamical and thermal stabilities of the two monolayers are confirmed by the phonon dispersion calculations and ab initiomolecular dynamics (AIMD) simulations, respectively.The results show that the two 2D materials have indirect bandgaps of 2.45 and 2.43 eV, and the band edges can match the hydrogen evolution reaction conditions. Absorption spectra show that the monolayers respond tovisible light and can be tuned by different strains.Besides, the hole and electron mobilitiesare different, which is beneficial for photoelectronic performance. The mechanisms of the hydrogen evolution reaction and the direct water splitting process are also explored. The calculational results support the promising applications of SiMI4(M = Ge, Sn) monolayers asvisible-light-driven photocatalyst of hydrogen production.