BackgroundFibroblast growth factor 10 (FGF10) is implicated in the growth and development of the eye. Four singles nucleotide polymorphisms (SNPs) in the FGF10 gene (including rs1384449, rs339501, rs12517396 and rs10462070) were found to be associated with extreme myopia (EM, refractive error ≤ − 10.0 diopters) in Japanese and Chinese Taiwan population. This case-control association study was conducted to explore the relationship between these four SNPs and high myopia in a western Chinese population.MethodsA total of 869 high myopia patients (HM, including 485 EM patients) and 899 healthy controls were recruited. These four SNPs were genotyped using the ABI SNaPshot method. Five genetic models (allelic, homozygous, heterozygous, dominant, and recessive) were applied to further evaluate the possible correlation between the SNPs and high myopia. The linkage-disequilibrium block (LD) structure was tested by Haploview Software.ResultsIn our study, no statistically significant differences were found between HM/EM patients and controls after Bonferroni multiple-correction (P > 0.05) in the allele frequencies of these four SNPs in the FGF10 gene. We further found that rs12517396AA and rs10462070GG carriers showed a decreased risk of HM/EM compared with rs12517396AC + CC and rs10462070GA + AA carriers (P = 0.045, OR = 0.366; P = 0.021, OR = 0.131; P = 0.03, OR = 0.341; P = 0.015, OR = 0.122; respectively). Additionally, rs12517396AA and rs10462070GG carriers showed the same decreased risk of HM/EM compared with rs12517396CC and rs10462070AA carriers (P = 0.048, OR = 0.370; P = 0.023, OR = 0.133; P = 0.032, OR = 0.346; P = 0.017, OR = 0.126). However, these significant associations between rs12517396/rs10462070 and HM/EM disappeared after Bonferroni multiple-correction (P > 0.05).ConclusionOur findings indicate that rs12517396 and rs10462070 had marginal association with HM and EM. The other two common polymorphisms in FGF10 unlikely have significant effects in the genetic predisposition to HM/EM in western Chinese population. Further replication studies are needed to validate our findings in both animal models and human genetic epidemiologic studies.
This study aimed to construct a kernel Fisher discriminant analysis (KFDA) method from well logs for lithology identification purposes. KFDA, via the use of a kernel trick, greatly improves the multiclassification accuracy compared with Fisher discriminant analysis (FDA). The optimal kernel Fisher projection of KFDA can be expressed as a generalized characteristic equation. However, it is difficult to solve the characteristic equation; therefore, a regularized method is used for it. In the absence of a method to determine the value of the regularized parameter, it is often determined based on expert human experience or is specified by tests. In this paper, it is proposed to use an improved KFDA (IKFDA) to obtain the optimal regularized parameter by means of a numerical method. The approach exploits the optimal regularized parameter selection ability of KFDA to obtain improved classification results. The method is simple and not computationally complex. The IKFDA was applied to theIrisdata sets for training and testing purposes and subsequently to lithology data sets. The experimental results illustrated that it is possible to successfully separate data that is nonlinearly separable, thereby confirming that the method is effective.
To address the problems of high overflow rate of pipe network inspection well and low drainage efficiency, a rainwater control optimization design approach based on a self-organizing feature map neural network model (SOFM) was proposed in this paper. These problems are caused by low precision parameter design in various rainwater control measures such as the diameter of the rainwater pipe network and the green roof area ratio. This system is to be combined with the newly built rainwater pipe control optimization design project of China International Airport in Daxing District of Beijing, China. Through the optimization adjustment of the pipe network parameters such as the diameter of the rainwater pipe network, the slope of the pipeline, and the green infrastructure (GI) parameters such as the sinking green area and the green roof area, reasonable control of airport rainfall and the construction of sustainable drainage systems can be achieved. This research indicates that compared with the result of the drainage design under the initial value of the parameter, the green roof model and the conceptual model of the mesoscale sustainable drainage system, in the case of a hundred-year torrential rainstorm, the overflow rate of pipe network inspection wells has reduced by 36% to 67.5%, the efficiency of drainage has increased by 26.3% to 61.7%, which achieves the requirements for reasonable control of airport rainwater and building a sponge airport and a sustainable drainage system.
Using high-precision sensors to monitor and predict the deformation trend of supertall buildings is a hot research topic for a long time. And in terms of deformation trend prediction, the main way to realized deformation trend prediction is the deep learning algorithm, but the accuracy of prediction result needs to be improved. To solve the problem described above, firstly, based on the conditional deep belief network (CDBN) model, the levenberg-marquardt (LM) was used to optimize the CDBN model; the LM-CDBN model has been constructed. Then taking CITIC tower, the tallest building in Beijing as the research object, the real-time monitoring data of the shape acceleration array (SAA) as an example, we used LM-CDBN model to analyse and predict the building deformation. Finally, to verify the accuracy and robustness of LM-CDBN model, the prediction results of the LM-CDBN model are compared with the prediction results of the CDBN model, the extreme learning machine (ELM) model, and the unscented Kalman filter-support vector regression (UKF-SVR) model, and we evaluated the result from three aspects: training error, fitness, and stability of prediction results. The results show that the LM-CDBN model has higher precision and fitting degree in the prediction of deformation trend of supertall buildings. And the MRE, MAE, and RMSE of the LM-CDBN model prediction results are only 0.0060, 0.0023mm, and 0.0031mm, and the prediction result was more in line with the actual deformation trend.
Geomagnetism has become a popular technology for indoor positioning, and its accuracy mainly depends on the accuracy of the geomagnetic matching algorithm. Pedestrian dead reckoning technology can calculate the relative position of pedestrians based on sensor information, but only obtain relative position information. According to the advantages and disadvantages of these two techniques, a high-precision GPDR indoor positioning method is proposed, and the improved particle filter algorithm is used to solve the problem of geomagnetic fingerprint fuzzy solution. Finally, a simulation experiment was conducted. The experimental results show that the accuracy of the proposed fusion localization algorithm is 42% higher than that of the PDR algorithm. Compared with a single geomagnetic fingerprint matching algorithm, the positioning accuracy is improved by 57%.
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