A simple and sensitive label-free FRET probe for glutathione was constructed based on MnO2 nanosheet-modified CdSe/ZnS QDs@SiO2 nanobeads.

In this study, a novel ratiometric fluorescent nanoprobe for pH monitoring has been developed by synthesizing red fluorescent Ag 2 S quantum dots (Ag 2 S QDs) and green fluorescent carbon dots (CDs) nanohybrids (Ag 2 S CDs) in one pot using CDs as templates. The nanoprobe exhibits dual-emission peaks at 500 and 670 nm under a single-excitation wavelength of 450 nm. The red fluorescence can be selectively quenched by increasing pH, while the green fluorescence is an internal reference. Therefore, the change of the relative fluorescence intensity (I 500 /I 670 ) in the ratiometric Ag 2 S CDs probes can be used for pH sensing. The results revealed that I 500 /I 670 of Ag 2 S CDs probes was linearly related to pH variation between pH 5.4 and 6.8. Meanwhile, the Ag 2 S CDs probes possessed a good reversibility along with pH changing between 5.0 and 7.0 without any interruption from common metal ions, proteins and other interferences.

Photon-counting detector based spectral computed tomography (CT) can obtain energydiscriminative attenuation map of an object in different energy channels, extending the conventional volumetric image along a spectral dimension. However, compared with the full spectrum data, the noise in a narrower energy channel is significantly increased. In order to improve image quality of spectral CT images, this paper proposes an iterative reconstruction algorithm based on the prior image constrained compressed sensing (PICCS) and dictionary learning (DL) theories, which is called PICCS-DL. The PICCS-DL utilizes the correlation of the images reconstructed from different energy channels by taking the broad spectrum image as a prior constraint, and it utilizes the sparse of the images by taking the total variation (TV) and DL as prior constraints. The alternating minimization, Split-Bregman and the steepest descent (SD) methods are used to solve the objective function. The effectiveness of the proposed method is validated with numerical simulations and preclinical applications. The results demonstrate that the proposed algorithm generally produces superior image quality, especially for noisy and sparse projection data. INDEX TERMS Spectral CT, prior image constrained compressed sensing, total variation, dictionary learning.

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Soil salinity is a critical obstacle in modern agriculture which devastates crop growth. Many plants have developed different strategies to sense, transduce, and develop tolerance to salinity. Plant adaptation to salinity stress includes complicated metabolic pathways, genes and molecular networks. Here, we used gas chromatography-mass spectrometry (GC-MS) to understand the metabolic responses of soybean seedlings upon various levels of salt stress treatments. To this end, one salt tolerant and one salt sensitive soybean cultivar, namely Dongnong 69 and Dongnong 63 were used in this study. A total of 10 metabolites, including sugars, amino acids and organic acid, were identified as differential biomarkers. Our results indicated that these biomarkers were closely related to salinity tolerance in soybean seedlings. In particular, three metabolites, namely isoleucine, serine and aspartic acid, were found respond significantly differently between the different soybean cultivars. These three metabolites can be therefore served as potential biomarkers to screen for salt tolerant soybean cultivars. Overall, results of this study help to improve our knowledge with respect to plant salt tolerance in general, and soybean in particular