A potential relationship between MMP-9 rs2250889 and ischemic stroke susceptibility

PurposeIschemic stroke (IS), a serious cerebrovascular disease, greatly affects people's health and life. Genetic factors are indispensable for the occurrence of IS. As a biomarker for IS, the MMP-9 gene is widely involved in the pathophysiological process of IS. This study attempts to find out the relationship between MMP-9 polymorphisms and IS susceptibility.MethodsA total of 700 IS patients and 700 healthy controls were recruited. The single nucleotide polymorphism (SNP) markers of the MMP-9 gene were genotyped by the MassARRAY analyzer. Multifactor dimensionality reduction (MDR) was applied to generate SNP–SNP interaction. Furthermore, the relationship between genetic variations (allele and genotype) of the MMP-9 gene and IS susceptibility was analyzed by calculating odds ratios (ORs) and 95% confidence intervals (CIs).ResultsOur results demonstrated that rs2250889 could significantly increase the susceptibility to IS in the codominant, dominant, overdominant, and log-additive models (p < 0.05). Further stratification analysis showed that compared with the control group, rs2250889 was associated with IS risk in different case groups (age, female, smoking, and non-drinking) (p < 0.05). Based on MDR analysis, rs2250889 was the best model for predicting IS risk (cross-validation consistency: 10/10, OR = 1.56 (1.26–1.94), p < 0.001).ConclusionOur study preliminarily confirmed that SNP rs2250889 was significantly associated with susceptibility to IS

Localization Approach for Underwater Sensors in the Magnetic Silencing Facility Based on Magnetic Field Gradients

Localization of the underwater magnetic sensor arrays plays a pivotal role in the magnetic silencing facility. A localization approach is proposed for underwater sensors based on the optimization of magnetic field gradients in the inverse problem of localization. In the localization system, a solenoid coil carrying direct current serves as the magnetic source. By measuring the magnetic field generated by the magnetic source in different positions, an objective function is established. The position vector of the sensor is determined by a novel multi-swarm particle swarm optimization with dynamic learning strategy. Without the optimization of the magnetic source’s positions, the sensors’ positions, especially in the z-axis direction, struggle to meet the requested localization. A strategy is proposed to optimize the positions of the magnetic source based on magnetic field gradients in the three directions of x, y and z axes. Compared with the former method, the model experiments show that the proposed method could achieve a 10 cm location error for the position type 2 sensor and meet the request of localization

Research on Mobile Machinery NOx Emission Control Based on a Physical Model and Closed-Loop Control

Mobile machinery means a power-driven vehicle that is specifically designed and constructed to perform work on or off the road. To reduce the nitrogen oxide (NOx) emissions that come from mobile machinery, a combination of a physical model and closed-loop control is applied to the selective catalytic reduction (SCR) system. Based on the design of the variable cross-section extended exhaust structure, the differential pressure measurement was achieved, and the physical model of the exhaust flow based on the differential pressure was established. Based on the analysis of the heat and mass transfer process of the SCR catalyst, a prediction model for the internal temperature field of the catalyst was established by combining the upstream and downstream exhaust temperature sensors. Using the SCR downstream nitrogen oxides signal and the proportional–integral–derivative (PID) closed-loop control algorithm, segmented PID closed-loop control under the large hysteresis response of the SCR system was realized. The above algorithms were used to form the control code through MATLAB/Simulink and downloaded to the embedded microprocessor. The test results show that the established model can realize the real-time calculation of the exhaust gas flow rate and the internal temperature of the catalyst. Under steady-state conditions, the calculation error of the exhaust flow rate is less than ±3%, and the calculation error of the catalyst temperature is less than ±5%. Under transient conditions, the calculation error of the exhaust flow rate is less than ±9%, and the calculation error of the catalyst temperature is less than ±8%. The nitrogen–oxygen signal-based PID closed-loop algorithm can improve the nitrogen–oxygen conversion efficiency and control accuracy of the model