For the first time, a nanobiosensor was established for Sorghum mosaic virus (SrMV) detection. The biosensor consists of cadmium telluride quantum dots (CdTe QDs) conjugated to the specific antibody (Ab) against SrMV coat protein (CP) and carbon quantum dots (C QDs) labeled with SrMV coat protein. The formation of the fluorophore-quencher immunocomplex CdTe QDs-Ab+C QDs-CP led to a distinct decrease in the fluorescence intensity of CdTe QDs. Conversely, the emission intensity of CdTe QDs recovered upon the introduction of unlabeled CP. The developed biosensor showed a limit of detection of 44 nM in a linear range of 0.10–0.54 μM and exhibited the strongest fluorescence intensity (about 47,000 a.u.) at 552 nm. This strategy was applied to detect purified CP in plant sap successfully with a recovery rate between 93–103%. Moreover, the feasibility of the proposed method was further verified by the detection of field samples, and the results were consistent with an enzyme-linked immunosorbent assay (ELISA). Contrarily to ELISA, the proposed biosensor did not require excessive washing and incubation steps, thus the detection could be rapidly accomplished in a few minutes. The high sensitivity and short assay time of this designed biosensor demonstrated its potential application in situ and rapid detection. In addition, the fluorescence quenching of CdTe QDs was attributed to dynamic quenching according to the Stern-Volmer equation.
Our problem of interest is to cluster vertices of a graph by identifying underlying community structure. Among various vertex clustering approaches, spectral clustering is one of the most popular methods because it is easy to implement while often outperforming more traditional clustering algorithms. However, there are two inherent model selection problems in spectral clustering, namely estimating both the embedding dimension and number of clusters. This paper attempts to address the issue by establishing a novel model selection framework specifically for vertex clustering on graphs under a stochastic block model. The first contribution is a probabilistic model which approximates the distribution of the extended spectral embedding of a 1
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