Rolling Bearing Fault Diagnosis Based on Domain Adaptation and Preferred Feature Selection under Variable Working Conditions

In real industrial scenarios, with the use of conventional machine learning techniques, data-driven diagnosis models have a limitation that it is difficult to achieve the desirable fault diagnosis performance, and the reason is that the training and testing datasets are assumed to have the same feature distributions. To address this problem, a novel bearing fault diagnosis framework based on domain adaptation and preferred feature selection is proposed, in that the model trained by the labeled data collected from a working condition can be applied to diagnose a new but similar target data collected from other working conditions. In this framework, an improved domain adaptation method, transfer component analysis with preserving local manifold structure (TCAPLMS), is proposed to reduce the differences in the data distributions between different domain datasets and, at the same time, take the label information of feature dataset and the local manifold structure of feature data into consideration. Furthermore, preferred feature selection by fault sensitivity and feature correlation (PSFFC) is embedded into this framework for selecting features which are more beneficial to fault pattern recognition and reduce the redundancy of feature set. Finally, vibration datasets collected from two test platforms are used for experimental analysis. The experimental results validate that the proposed method can obviously improve diagnosis accuracy and has significant potential benefits towards actual industrial scenarios

Research on an Off-Chip Microvalve for Pneumatic Control in Microfluidic Chips

A compact, rapid, and portable off-chip pneumatic control valve is significant for the miniaturization and integration of external pneumatic systems for microfluidic chips. In this work, an off-chip microvalve with a high-speed electromagnetic switch actuator and a polydimethylsiloxane (PDMS) material valve body has been designed to be easily encapsulated, simulated using MATLAB/Simulink software, and tested in a micromixer. Multi-physical coupling mathematical models are developed based on the elastic deformation force of the valve membrane, the driving force of the valve core, and the fluid force in the microchannel. Two single microvalves are used to form a three-way microvalve, which can control the air pressure in a pneumatic microchannel on the microfluidic chip. The relationship between the flow–duty cycle, the flow–pressure difference of the single electromagnetic microvalve, and the load pressure of the three-way microvalve is simulated and analyzed. Sample mixing performance controlled by the proposed off-chip three-way microvalve was tested to evaluate the pneumatic control capability, and the results show that the undertaking can fully satisfy the needs of a pneumatic microfluidic chip for most applications

Treatment of autosomal dominant retinitis pigmentosa caused by RHO-P23H mutation with high-fidelity Cas13X in mice

Mutations in Rhodopsin (RHO) gene commonly cause autosomal dominant retinitis pigmentosa (adRP) without effective therapeutic treatment so far. Compared with genomic DNA-targeting CRISPR-Cas9 system, Cas13 edits RNA for therapeutic applications, avoiding the risk of causing permanent changes in the genome. In particular, a compact and high-fidelity Cas13X (hfCas13X) recently has been developed to degrade targeted RNA with minimal collateral effects and could also be packaged in a single adeno-associated virus for efficient in vivo delivery. In this study, we engineered single-guide RNA for hfCas13X to specifically knock down human mutant Rhodopsin transcripts RHO-P23H with minimal effect on wild-type transcripts. Moreover, treatment with hfCas13X alleviated the adRP progression in both RHO-P23H overexpression-induced and humanized hRHOP23H/WT mouse models. Our study indicates the potential of hfCas13X in treating adRP caused by RHO mutations and other genetic diseases

Structural insight into poly(A) binding and catalytic mechanism of human PARN

Poly(A)-specific ribonuclease (PARN) is a processive, poly(A)-specific 3′ exoribonuclease. The crystal structure of C-terminal truncated human PARN determined in two states (free and RNA-bound forms) reveals that PARNn is folded into two domains, an R3H domain and a nuclease domain similar to those of Pop2p and ɛ186. The high similarity of the active site structures of PARNn and ɛ186 suggests that they may have a similar catalytic mechanism. PARNn forms a tight homodimer, with the R3H domain of one subunit partially enclosing the active site of the other subunit and poly(A) bound in a deep cavity of its nuclease domain in a sequence-nonspecific manner. The R3H domain and, possibly, the cap-binding domain are involved in poly(A) binding but these domains alone do not appear to contribute to poly(A) specificity. Mutations disrupting dimerization abolish both the enzymatic and RNA-binding activities, suggesting that the PARN dimer is a structural and functional unit. The cap-binding domain may act in concert with the R3H domain to amplify the processivity of PARN