Advances in Cas12a-Based Amplification-Free Nucleic Acid Detection.

In biomedicine, rapid and sensitive nucleic acid detection technology plays an important role in the early detection of infectious diseases. However, most traditional nucleic acid detection methods require the amplification of nucleic acids, resulting in problems such as long detection time, complex operation, and false-positive results. In recent years, clustered regularly interspaced short palindromic repeats (CRISPR) systems have been widely used in nucleic acid detection, especially the CRISPR-Cas12a system, which can trans cleave single-stranded DNA and can realize the detection of DNA targets. But, amplification of nucleic acids is still required to further improve detection sensitivity, which makes Cas12a-based amplification-free nucleic acid detection methods a great challenge. This article reviews the recent progress of Cas12a-based amplification-free detection methods for nucleic acids. These detection methods apply electrochemical detection methods, fluorescence detection methods, noble metal nanomaterial detection methods, and lateral flow assay. Under various optimization strategies, unamplified nucleic acids have the same sensitivity as amplified nucleic acids. At the same time, the article discusses the advantages and disadvantages of each method and further discusses the current challenges such as off-target effects and the ability to achieve high-throughput detection. Amplification-free nucleic acid detection technology based on CRISPR-Cas12a has great potential in the biomedical field.

Remaining Useful Life Prediction of Airplane Engine Based on PCA-BLSTM.

The accurate prediction of airplane engine failure can provide a reasonable decision basis for airplane engine maintenance, effectively reducing maintenance costs and reducing the incidence of failure. According to the characteristics of the monitoring data of airplane engine sensors, this work proposed a remaining useful life (RUL) prediction model based on principal component analysis and bidirectional long short-term memory. Principal component analysis is used for feature extraction to remove useless information and noise. After this, bidirectional long short-term memory is used to learn the relationship between the state monitoring data and remaining useful life. This work includes data preprocessing, the construction of a hybrid model, the use of the NASA's Commercial Aerodynamic System Simulation (C-MAPSS) data set for training and testing, and the comparison of results with those of support vector regression, long short-term memory and bidirectional long short-term memory models. The hybrid model shows better prediction accuracy and performance, which can provide a basis for formulating a reasonable airplane engine health management plan.