No abstract

The translocation of cytochrome c (cyt c) from mitochondria and out of cell is an important signal of cell apoptosis. Monitoring this process extracellularly without invasion and cytotoxicity to cells is of great importance to understand certain diseases at the cellular level; however, it requires sensors with ultrahigh sensitivity and miniature size. This study reports an optical microfiber aptasensor with a silver‐decorated graphene (Ag@RGO) nanointerface for real‐time cellular cyt c monitoring. Owing to an interfacial sensitization effect coupled with the plasmonic electromagnetic enhancement of silver nanoparticles and chemical enhancement of graphene platforms, which enhances the energy density on microfiber surface obviously, the lowest limit of detection achieved is 6.82 × 10−17 m, which is approximately five orders of magnitude lower than those of existing methods. This microfiber successfully detects the ultralow concentrations of cyt c present during the initial stage of apoptosis in situ. As the microfiber functionalized by Ag@RGO nanointerface can be varied to meet any specific detection objective, this work opens up new opportunities to quantitatively monitor biological functions occurring at the cellular level.

To extend agricultural productivity by knowledge-based breeding and tailoring varieties to adapt to specific environmental conditions, it is imperative to improve our ability to acquire the dynamic changes of the crop’s phenotype under field conditions. Canopy leaf biomass (CLB) per ground area is one of the key crop phenotypic parameters in plant breeding. The most promising technique for effectively monitoring CLB is the hyperspectral vegetation index (VI). However, VI-based empirical models are limited by their poor stability and extrapolation difficulties when used to assess complex dynamic environments with different varieties, growth stages, and sites. It has been proven difficult to calibrate and validate some VI-based models. To address this problem, eight field experiments using eight wheat varieties were conducted during the period of 2003–2011 at four sites, and continuous wavelet transform (CWT) was applied to estimate CLB from large number of field experimental data. The analysis of 108 wavelet functions from all 15 wavelet families revealed that the best wavelet features for CLB in terms of wavelength (W) and scale (S) were observed in the near-infrared region and at high scales (7 and 8). The best wavelet-based model was derived from the Daubechies family (db), and was named db7 (W1197 nm, S8). The new model was more accurate (Rv2 = 0.67 and RRMSE = 27.26%) than a model obtained using the best existing VI (Rv2 = 0.54 and RRMSE = 34.71%). Furthermore, the stable performance of the optimal db7 wavelet feature was confirmed by its limited variation among the different varieties, growth stages, and sites, which confirmed the high stability of the CWT to estimate CLB with hyperspectral data. This study highlighted the potential of precision phenotyping to assess the dynamic genetics of complex traits, especially those not amenable to traditional phenotyping.

Interfacing bio-recognition elements to optical materials is a longstanding challenge to manufacture sensitive biosensors and inexpensive diagnostic devices. In this work, a graphene oxide (GO) interface has been constructed between silica microfiber and bio-recognition elements to develop an improved γ-aminobutyric acid (GABA) sensing approach. The GO interface, which was located at the site with the strongest evanescent field on the microfiber surface, improved the detection sensitivity by providing a larger platform for more bio-recognition element immobilization, and amplifying surface refractive index change caused by combination between bio-recognition elements and target molecules. Owing to the interface improvement, the microfiber has a three times improved sensitivity of 1.03 nm/log M for GABA detection, and hence a lowest limit of detection of 2.91 × 10-18 M, which is 7 orders of magnitude higher than that without the GO interface. Moreover, the micrometer-sized footprint and non-radioactive nature enable easy implantation in human brains for in vivo applications.