In China, rice is one of the most important cereal crops. Rice bacterial brown leaf spot caused by P. s. pv. syringae is among the most damaging rice diseases in the Heilongjiang Province of China and results in substantial yield losses. In this study, a comprehensive analysis of the pathogen, population structure, and genetic diversity within the species was performed. For this purpose, 176 bacterial isolates of P. s. pv. syringae collected from 15 locations were characterized by using biochemical tests such as the LOPAT test, and genetic characterizations such as multilocus sequence analysis (MLSA) and repetitive PCR, using BOX, REP and ERIC primers. Biochemical testing and detection of syrB genes confirm the presence of P. s. pv. syringae, genetic characterization by MLSA and genetic fingerprinting by repetitive PCR confirmed that high genetic heterogeneity exists in the P. s. pv. syringae isolates, and clustering of the tested isolates and reference strains are related with the same genomospecies 1. This work contributes to the physiological classification of the P. s. pv. syringae isolated from Heilongjiang Province, China, and the results present new data concerning the phylogeny and genetic diversity. This type of study about P. s. pv. syringae has been not reported from this region until now.

Rice false smut (RFS) is pervasive and has emerged as the primary disease affecting rice productivity. Due to the lack of effective chemical control, disease-resistant varieties are the primary method of managing the disease. This study aimed to investigate the influence of biological characteristics such as hyphal growth rate, spore production and germination ability on the pathogenicity of Ustilaginoidea virens. The genetic diversity of 86 U. virens isolates was analyzed, and the tolerant varieties were identified using Rep-PCR molecular markers. The results indicated that among the U. virens isolates analyzed, about 31, 27 and 28 strains exhibited fast, medium and slow mycelium growth rates, respectively. Among the U. virens isolates analyzed, about 33, 27 and 26 strains exhibited strong, medium and weak sporulation abilities, respectively. In addition, among the U. virens isolates analyzed, about 29, 30 and 27 strains exhibited strong, medium and weak spore germination, respectively. No significant correlation was noted between mycelium growth rate and pathogenicity, but sporulation ability, spore germination ability and liquid culture were positively correlated (p < 0.05). Moreover, the genetic structure of U. virens was diverse and complex. There was a significant correlation between the genetic differentiation and pathogenicity of U. virens in Heilongjiang Province, China, with a maximum coefficient of 0.1786 and a minimum coefficient of 0.0129 between Harbin and Suihua, but no significant correlation was found with gene groups or geographical regions. The study found that genes and geographical population variation were primarily within the population, with 95.63% and 4.37% variation (p > 0.05), respectively, while pathogenicity population variation was mainly within the population, with 86.08% and 13.92% variation (p > 0.05), respectively. Pathogenicity had a significant effect on the differentiation of U. virens. Most rice varieties were susceptible, with 3 resistant, 8 moderately resistant, 9 moderately susceptible, 8 susceptible and 12 highly susceptible to RFS disease. Collectively, these results indicate that the tolerance of rice against U. virens is poor, which merits further investigation.

The wireless automatic auditory brainstem response measurement system developed in the previous research uses Kalman filter with exponential weight averaging method (Kalman filter with EWA) to filter signal and used the differential method to detect the Wave V of ABR. However, the signals are too noisy to be accurately determined. Therefore, this study compares the Wavelet Kalman filter and Moving average to the Kalman filter with EWA, and the fitted parametric peak (FPP) to the differential method, respectively. The simulation results showed that the signal-to-noise ratio of ABR is the highest after Wavelet Kalman filtering, and the audiologist can mark the waves III and V of ABR the most. The latency of the wave V detected by FPP was used to calculate the Pearson product-moment correlation coefficient with the audiologist’s manual mark. The two latencies are highly correlated, and the correlation coefficient is higher than the differential method. Therefore, the Wavelet-Kalman filter and FPP algorithm were implemented in the AABR measurement system. To evaluate the accuracy of the algorithm in this study, subjective experiments were conducted with four normal hearing subjects. The Mann-Whitney U test was used to test thedifference between the average value of automatic detection and reproducibility.

The wireless automatic auditory brainstem response measurement system developed in previous research uses Kalman filter with an exponential weight averaging method (Kalman filter with EWA) to filter signals and used the differential method to detect the Wave V of the ABR. However, the signals are too noisy to be accurately determined. Therefore, this study compares the Wavelet Kalman filter and Moving average to the Kalman filter with EWA, and the fitted parametric peak (FPP) to the differential method, respectively. The simulation results showed that the signal-to-noise ratio of ABR is the highest after Wavelet Kalman filtering, and the audiologist can mark the waves III and V of ABR the most. The latency of the wave V detected by FPP was used to calculate the Pearson product-moment correlation coefficient with the audiologist’s manual mark. The two latencies are highly correlated, and the correlation coefficient is higher than the differential method. Therefore, the Wavelet-Kalman filter and FPP algorithm were implemented in the AABR measurement system. To evaluate the accuracy of the algorithm in this study, subjective experiments were conducted with four normal hearing subjects. The Mann-Whitney U test was used to test the difference between the average value of automatic detection and reproducibility.