Association of ESR1 polymorphism rs2234693 and rs9340799 with postmenopausal osteoporosis in a Chinese population.

BACKGROUND: Postmenopausal osteoporosis (PMO) is the most common type of primary osteoporosis. ESR1 polymorphism rs2234693 and rs9340799 has been widely studied as a candidate gene associated with PMO, however, the findings were inconclusive. The present study aims to explore the relationship of ESR1 polymorphism rs2234693 and rs9340799 with PMO risk in a Chinese Han population. METHODS: PMO patients and healthy controls were recruited from gynecology department. DNA of all participants were extracted from the peripheral blood samples and genotyped by Mass Array method. A meta-analysis of case control studies was also conducted to further elucidate the relationship of polymorphism with PMO. RESULTS: Our results revealed that there were no associations of rs2234693 with PMO. However, GG genotype of rs9340799 was associated with a higher risk of PMO (OR = 1.51, 95%CI:1.08-4.34, p = 0.03), even adjusting for risk factors (OR = 1.83, 95%CI: 1.12-5.04, p = 0.04). Logistic regression analysis showed that dominant model was associated with a higher risk of PMO (OR = 2.07, 95%CI: 1.02-5.16, p = 0.02) after correcting the risk factors (OR = 2.14, 95%CI:1.12-5.64, p = 0.04); In addition, the Meta-analysis results revealed that both two polymorphisms were not associated with PMO. CONCLUSIONS: In conclusion, ESR1 polymorphism rs9340799 was associated with PMO. However, well designed studies with larger sample sizes are required to further elucidate the associations.

Modified particle filtering algorithm for single acoustic vector sensor DOA tracking.

The conventional direction of arrival (DOA) estimation algorithm with static sources assumption usually estimates the source angles of two adjacent moments independently and the correlation of the moments is not considered. In this article, we focus on the DOA estimation of moving sources and a modified particle filtering (MPF) algorithm is proposed with state space model of single acoustic vector sensor. Although the particle filtering (PF) algorithm has been introduced for acoustic vector sensor applications, it is not suitable for the case that one dimension angle of source is estimated with large deviation, the two dimension angles (pitch angle and azimuth angle) cannot be simultaneously employed to update the state through resampling processing of PF algorithm. To solve the problems mentioned above, the MPF algorithm is proposed in which the state estimation of previous moment is introduced to the particle sampling of present moment to improve the importance function. Moreover, the independent relationship of pitch angle and azimuth angle is considered and the two dimension angles are sampled and evaluated, respectively. Then, the MUSIC spectrum function is used as the "likehood" function of the MPF algorithm, and the modified PF-MUSIC (MPF-MUSIC) algorithm is proposed to improve the root mean square error (RMSE) and the probability of convergence. The theoretical analysis and the simulation results validate the effectiveness and feasibility of the two proposed algorithms.

A novel ultrasonic array signal processing scheme for wind measurement.

This article presents a novel ultrasonic array signal processing scheme for wind speed and wind direction measurement. Two ultrasonic array structures are illustrated and the corresponding array signal processing method is proposed. The statistical performance of proposed scheme, such as estimation variance and Cramer Rao Bound (CRB), is derived. The ambiguity problem for traditional uniform line array (ULA) structure is theoretically discussed. The hardware experiment results verify the feasibility of the proposed wind measurement method.

Wind speed and direction measurement based on arc ultrasonic sensor array signal processing algorithm.

This article investigates a kind of method to measure the wind speed and the wind direction, which is based on arc ultrasonic sensor array and combined with array signal processing algorithm. In the proposed method, a new arc ultrasonic array structure is introduced and the array manifold is derived firstly. On this basis, the measurement of the wind speed and the wind direction is analyzed and discussed by means of the basic idea of the classic MUSIC (Multiple Signal Classification) algorithm, which achieves the measurements of the 360° wind direction with resolution of 1° and 0-60m/s wind speed with resolution of 0.1m/s. The implementation of the proposed method is elaborated through the theoretical derivation and corresponding discussion. Besides, the simulation experiments are presented to show the feasibility of the proposed method. The theoretical analysis and simulation results indicate that the proposed method has superiority on anti-noise performance and improves the wind measurement accuracy.