| 1. |
- Wang, Xiaoliang, et al.
(författare)
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Genome-wide interaction analysis of menopausal hormone therapy use and breast cancer risk among 62,370 women
- 2022
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Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- Use of menopausal hormone therapy (MHT) is associated with increased risk for breast cancer. However, the relevant mechanisms and its interaction with genetic variants are not fully understood. We conducted a genome-wide interaction analysis between MHT use and genetic variants for breast cancer risk in 27,585 cases and 34,785 controls from 26 observational studies. All women were post-menopausal and of European ancestry. Multivariable logistic regression models were used to test for multiplicative interactions between genetic variants and current MHT use. We considered interaction p-values < 5 x 10(-8) as genome-wide significant, and p-values < 1 x 10(-5) as suggestive. Linkage disequilibrium (LD)-based clumping was performed to identify independent candidate variants. None of the 9.7 million genetic variants tested for interactions with MHT use reached genome-wide significance. Only 213 variants, representing 18 independent loci, had p-values < 1 x 10(5). The strongest evidence was found for rs4674019 (p-value = 2.27 x 10(-7)), which showed genome-wide significant interaction (p-value = 3.8 x 10(-8)) with current MHT use when analysis was restricted to population-based studies only. Limiting the analyses to combined estrogen-progesterone MHT use only or to estrogen receptor (ER) positive cases did not identify any genome-wide significant evidence of interactions. In this large genome-wide SNP-MHT interaction study of breast cancer, we found no strong support for common genetic variants modifying the effect of MHT on breast cancer risk. These results suggest that common genetic variation has limited impact on the observed MHT-breast cancer risk association.
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| 2. |
- Jin, J., et al.
(författare)
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A GAN-Based Short-Term Link Traffic Prediction Approach for Urban Road Networks Under a Parallel Learning Framework
- 2022
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Ingår i: IEEE transactions on intelligent transportation systems (Print). - : Institute of Electrical and Electronics Engineers (IEEE). - 1524-9050 .- 1558-0016. ; 23:9, s. 16185-16196
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Tidskriftsartikel (refereegranskat)abstract
- Road link speed is often employed as an essential measure of traffic state in the operation of an urban traffic network. Not only real-time traffic demand but also signal timings and other local planning factors are major influential factors. This paper proposes a short-term traffic speed prediction approach, called PL-WGAN, for urban road networks, which is considered an important part of a novel parallel learning framework for traffic control and operation. The proposed method applies Wasserstein Generative Adversarial Nets (WGAN) for robust data-driven traffic modeling using a combination of generative neural network and discriminative neural network. The generative neural network models the road link features of the adjacent intersections and the control parameters of intersections using a hybrid graph block. In addition, the spatial-temporal relations are captured by stacking a graph convolutional network (GCN), a recurrent neural network (RNN), and an attention mechanism. A comprehensive computational experiment was carried out including comparing model prediction and computational performances with several state-of-the-art deep learning models. The proposed approach has been implemented and applied for predicting short-term link traffic speed in a large-scale urban road network in Hangzhou, China. The results suggest that it provides a scalable and effective traffic prediction solution for urban road networks.
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| 3. |
- Wang, Yifan, et al.
(författare)
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Composite Data Driven-based Adaptive Control for a Piezoelectric Linear Motor
- 2022
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Ingår i: IEEE Transactions on Instrumentation and Measurement. - 1557-9662 .- 0018-9456. ; 71
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Tidskriftsartikel (refereegranskat)abstract
- Piezoelectric linear motors play an important role in ultra-precision manufacturing technology. However, the complex nonlinear relationship between the input and output of the piezoelectric linear motors limits their further application. In this paper, to achieve precise motion control for a piezoelectric linear motor, a composite data driven-based adaptive control method is proposed, consisting of a correction controller, model free adaptive controller (MFAC), and low pass filter. The proposed control method addresses the demand for a precise model of the piezoelectric linear motor and solely relies on the linear model and input/output measurement data. First, an experimental test is implemented to analyze the complex nonlinearity between input and output signals of the controlled system, and a correction control is employed based on the dynamic linear sub-model of the piezoelectric linear motor to improve its dynamic and static characteristics. Then, to avoid the influence of unmodeled dynamics, such as inherent nonlinearity and external vibration, a MFAC is established as a feedback controller using data driven technology. In addition, a low pass filter is incorporated into the feedback loop to eliminate high frequency measurement noise in the system, thus improving the transient response of the MFAC method. Finally, the theoretical analysis of the error convergence is presented. The effectiveness of the proposed method is verified via comparisons with a correction control method, correction control-based digital sliding-mode control method, and correction control-based MFAC method. The experimental results indicate that the proposed control method is suitable for engineering applications. In particular, the root-mean-square error (RMSE) for the third-order S-curve tracking using the proposed is reduced by more than 15%, compared with the RMSEs for the cases with contrast control methods.
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