Comparison on Several Methods for Extracting DNA from Raccoon Dog Hair Follicle

[Objective] This study aimed to investigate a reliable method for DNA ex- traction from Wusuli raccoon dog＇s hair. [Method] Several DNA extraction methods were used to extract DNA from Wusuli raccoon dog hair, including Chelex-100 method, PCR buffer method, organic phenol-chloroform method and centrifugal col- umn type kit method. The extracted DNA was analyzed by using PCR amplification and electrophoresis to compare these four DNA extraction methods. [Result] Accord- ing to the results of spectrophotometer detection and gel electrophoresis, nucleic acid extracted by Chetex-100 method had proteins and other impurities; nucleic acid ex- tracted by PCR buffer method was low in concentration; however, DNA extracted by organic phenol-chloroform method and centrifugal column type kit was high in con- centration with no impurity band. [Conclusion] This study had laid the strong founda- tion of scientific theory to further explore the efficient and simple method for extracting DNA from Wusuli raccoon dog hair follicle.

Impulsive component extraction using shift-invariant dictionary learning and its application to gear-box bearing early fault diagnosis

The impulsive components induced by bearing faults are key features for assessing gear-box bearing faults.However,because of heavy background noise and the interferences of other vibrations,it is difficult to extract these impulsive components caused by faults,particularly early faults,from the measured vibration signals.To capture the high-level structure of impulsive components embedded in measured vibration signals,a dictionary learning method called shift-invariant K-means singular value decomposition(SI-K-SVD)dictionary learning is used to detect the early faults of gear-box bearings.Although SI-K-SVD is more flexible and adaptable than existing methods,the improper selection of two SI-K-SVD-related parameters,namely,the number of iterations and the pattern lengths,has an adverse influence on fault detection performance.Therefore,the sparsity of the envelope spectrum(SES)and the kurtosis of the envelope spectrum(KES)are used to select these two key parameters,respectively.SI-K-SVD with the two selected optimal parameter values,referred to as optimal parameter SI-K-SVD(OP-SI-K-SVD),is proposed to detect gear-box bearing faults.The proposed method is verified by both simulations and an experiment.Compared to the state-of-the-art methods,namely,empirical model decomposition,wavelet transform and K-SVD,OP-SI-K-SVD has better performance in diagnosing the early faults of a gear-box bearing.