An Accurate, Rapid and Cost-Effective Method for T-nos Detection Based on CRISPR/Cas12a.

CRISPR/Cas12a technology is used for nucleic acid detection due to its specific recognition function and non-specific single-stranded DNA cleavage activity. Here, we developed a fluorescence visualisation detection method based on PCR and CRISPR/Cas12a approaches. The method was used to detect the nopaline synthase terminator (T-nos) of genetically modified (GM) crops, circumventing the need for expensive instruments and technicians. For enhanced sensitivity and stability of PCR-CRISPR/Cas12a detection, we separately optimised the reaction systems for PCR amplification and CRISPR/Cas12a detection. Eleven samples of soybean samples were assessed to determine the applicability of the PCR-CRISPR/Cas12a method. The method could specifically detect target gene levels as low as 60 copies in the reaction within 50 min. In addition, accurate detection of all 11 samples confirmed the applicability. The method is not limited by large-scale instruments, making it suitable for mass detection of transgenic components in plants in the field. In conclusion, we developed a new, accurate, rapid, and cost-effective method for GM detection.

Bayesian rank penalization.

Rank minimization is a key component of many computer vision and machine learning methods, including robust principal component analysis (RPCA) and low-rank representations (LRR). However, usual methods rely on optimization to produce a point estimate without characterizing uncertainty in this estimate, and also face difficulties in tuning parameter choice. Both of these limitations are potentially overcome with Bayesian methods, but there is currently a lack of general purpose Bayesian approaches for rank penalization. We address this gap using a positive generalized double Pareto prior, illustrating the approach in RPCA and LRR. Posterior computation relies on hybrid Gibbs sampling and geodesic Monte Carlo algorithms. We assess performance in simulation examples, and benchmark data sets.

Learning Markov Random Walks for robust subspace clustering and estimation.

Markov Random Walks (MRW) has proven to be an effective way to understand spectral clustering and embedding. However, due to less global structural measure, conventional MRW (e.g., the Gaussian kernel MRW) cannot be applied to handle data points drawn from a mixture of subspaces. In this paper, we introduce a regularized MRW learning model, using a low-rank penalty to constrain the global subspace structure, for subspace clustering and estimation. In our framework, both the local pairwise similarity and the global subspace structure can be learnt from the transition probabilities of MRW. We prove that under some suitable conditions, our proposed local/global criteria can exactly capture the multiple subspace structure and learn a low-dimensional embedding for the data, in which giving the true segmentation of subspaces. To improve robustness in real situations, we also propose an extension of the MRW learning model based on integrating transition matrix learning and error correction in a unified framework. Experimental results on both synthetic data and real applications demonstrate that our proposed MRW learning model and its robust extension outperform the state-of-the-art subspace clustering methods.