Silver nanoparticles (Ag NPs) are extensively used as an antibacterial additive in commercial products and their release has caused environmental risk. However, conventional methods for the toxicity detection of Ag NPs are very time consuming and the mechanisms of action are not clear. We developed a new, in situ, rapid, and sensitive fingerprinting approach, using surface-enhanced Raman spectroscopy (SERS), to study the antibacterial activity and mechanism of Ag NPs of 80 and 18 nm (Ag80 and Ag18, respectively), by using the strong electromagnetic enhancement generated by Ag NPs. Sensitive spectra changes representing various biomolecules in bacteria were observed with increasing concentrations of Ag NPs. They not only allowed SERS to monitor the antibacterial activity of Ag NPs of different sizes in different water media but also to study the antibacterial mechanism at the molecular level. Ag18 were found to be more toxic than Ag80 in water, but their toxicity declined to a similar level in the PBS medium. The antibacterial mechanism was proposed on the basis of a careful identification of the chemical origins by comparing the SERS spectra with model compounds. The dramatic change in protein, hypoxanthine, adenosine, and guanosine bands suggested that Ag NPs have a significant impact on the protein and metabolic processes of purine. Finally, by adding nontoxic and SERS active Au NPs, SERS was successfully utilized to study the action mode of the NPs unable to produce an observable SERS signal. This work opens a window for the future extensive SERS studies of the antibacterial mechanism of a great variety of non-SERS-active NPs.

Grain appearance quality and milling quality are the main determinants of market value of rice. Breeding for improved grain quality is a major objective of rice breeding worldwide. Identification of genes/QTL controlling quality traits is the prerequisite for increasing breeding efficiency through marker-assisted selection. Here, we reported a genome-wide association study in indica rice to identify QTL associated with 10 appearance and milling quality related traits, including grain length, grain width, grain length to width ratio, grain thickness, thousand grain weight, degree of endosperm chalkiness, percentage of grains with chalkiness, brown rice rate, milled rice rate and head milled rice rate. A diversity panel consisting of 272 indica accessions collected worldwide was evaluated in four locations including Hangzhou, Jingzhou, Sanya and Shenzhen representing indica rice production environments in China and genotyped using genotyping-by-sequencing and Diversity Arrays Technology based on next-generation sequencing technique called DArTseq™. A wide range of variation was observed for all traits in all environments. A total of 16 different association analysis models were compared to determine the best model for each trait-environment combination. Association mapping based on 18,824 high quality markers yielded 38 QTL for the 10 traits. Five of the detected QTL corresponded to known genes or fine mapped QTL. Among the 33 novel QTL identified, qDEC1.1 (qGLWR1.1), qBRR2.2 (qGL2.1), qTGW2.1 (qGL2.2), qGW11.1 (qMRR11.1) and qGL7.1 affected multiple traits with relatively large effects and/or were detected in multiple environments. The research provided an insight of the genetic architecture of rice grain quality and important information for mining genes/QTL with large effects within indica accessions for rice breeding.

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