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- Middha, Pooja K., et al.
(författare)
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A genome-wide gene-environment interaction study of breast cancer risk for women of European ancestry
- 2023
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Ingår i: Breast Cancer Research. - : BioMed Central (BMC). - 1465-5411 .- 1465-542X. ; 25:1
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Tidskriftsartikel (refereegranskat)abstract
- Background Genome-wide studies of gene-environment interactions (GxE) may identify variants associated with disease risk in conjunction with lifestyle/environmental exposures. We conducted a genome-wide GxE analysis of similar to 7.6 million common variants and seven lifestyle/environmental risk factors for breast cancer risk overall and for estrogen receptor positive (ER +) breast cancer. Methods Analyses were conducted using 72,285 breast cancer cases and 80,354 controls of European ancestry from the Breast Cancer Association Consortium. Gene-environment interactions were evaluated using standard unconditional logistic regression models and likelihood ratio tests for breast cancer risk overall and for ER + breast cancer. Bayesian False Discovery Probability was employed to assess the noteworthiness of each SNP-risk factor pairs. Results Assuming a 1 x 10(-5) prior probability of a true association for each SNP-risk factor pairs and a Bayesian False Discovery Probability < 15%, we identified two independent SNP-risk factor pairs: rs80018847(9p13)-LINGO2 and adult height in association with overall breast cancer risk (ORint = 0.94, 95% CI 0.92-0.96), and rs4770552(13q12)-SPATA13 and age at menarche for ER + breast cancer risk (ORint = 0.91, 95% CI 0.88-0.94). Conclusions Overall, the contribution of GxE interactions to the heritability of breast cancer is very small. At the population level, multiplicative GxE interactions do not make an important contribution to risk prediction in breast cancer.
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| 2. |
- King, Sontoria D., et al.
(författare)
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Genetic Susceptibility to Nonalcoholic Fatty Liver Disease and Risk for Pancreatic Cancer: Mendelian Randomization
- 2023
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Ingår i: Cancer Epidemiology, Biomarkers and Prevention. - : American Association For Cancer Research (AACR). - 1055-9965 .- 1538-7755. ; 32:9, s. 1265-1269
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: There are conflicting data on whether nonalcoholic fatty liver disease (NAFLD) is associated with susceptibility to pancreatic cancer. Using Mendelian randomization (MR), we investigated the relationship between genetic predisposition to NAFLD and risk for pancreatic cancer.METHODS: Data from genome-wide association studies (GWAS) within the Pancreatic Cancer Cohort Consortium (PanScan; cases n = 5,090, controls n = 8,733) and the Pancreatic Cancer Case Control Consortium (PanC4; cases n = 4,163, controls n = 3,792) were analyzed. We used data on 68 genetic variants with four different MR methods [inverse variance weighting (IVW), MR-Egger, simple median, and penalized weighted median] separately to predict genetic heritability of NAFLD. We then assessed the relationship between each of the four MR methods and pancreatic cancer risk, using logistic regression to calculate ORs and 95% confidence intervals (CI), adjusting for PC risk factors, including obesity and diabetes.RESULTS: No association was found between genetically predicted NAFLD and pancreatic cancer risk in the PanScan or PanC4 samples [e.g., PanScan, IVW OR, 1.04; 95% confidence interval (CI), 0.88-1.22; MR-Egger OR, 0.89; 95% CI, 0.65-1.21; PanC4, IVW OR, 1.07; 95% CI, 0.90-1.27; MR-Egger OR, 0.93; 95% CI, 0.67-1.28]. None of the four MR methods indicated an association between genetically predicted NAFLD and pancreatic cancer risk in either sample.CONCLUSIONS: Genetic predisposition to NAFLD is not associated with pancreatic cancer risk.IMPACT: Given the close relationship between NAFLD and metabolic conditions, it is plausible that any association between NAFLD and pancreatic cancer might reflect host metabolic perturbations (e.g., obesity, diabetes, or metabolic syndrome) and does not necessarily reflect a causal relationship between NAFLD and pancreatic cancer.
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| 3. |
- Wang, Hao Luo, et al.
(författare)
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A Data-driven Survival Modelling Approach for Predictive Maintenance of Battery Electric Trucks
- 2023
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Konferensbidrag (refereegranskat)abstract
- Predictive Maintenance (PdM) aims to estimate the optimal moment when the maintenance of an industrial asset should be performed according to its actual health status. The goal is to minimize the costs, by finding the optimal point where the sum of the prevention and repair cost is at the lowest. Data-driven model may predict whether an asset is close to a real breakdown, therefore helping to build more cost-efficient maintenance strategies. This paper focuses on survival analysis based predictive maintenance applied to the operation of Battery Electric Trucks (BET). Cox Proportional Hazards and Random Survival Forests methods are adopted for modelling time-to-failure and the associated survival functions. Detailed telematics data from BET vehicles in real operations are used for modelling and analysis. The model performance is further improved by the feature selection and hyperparameter tuning processes.
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| 4. |
- Wang, Yifan, et al.
(författare)
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Time delay recursive neural network-based direct adaptive control for a piezo-actuated stage
- 2023
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Ingår i: Science China Technological Sciences. - 1869-1900 .- 1674-7321. ; 66:5, s. 1397-1407
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Tidskriftsartikel (refereegranskat)abstract
- Piezo-actuated stage is a core component in micro-nano manufacturing field. However, the inherent nonlinearity, such as rate-dependent hysteresis, in the piezo-actuated stage severely impacts its tracking accuracy. This study proposes a direct adaptive control (DAC) method to realize high precision tracking. The proposed controller is designed by a time delay recursive neural network. Compared with those existing DAC methods designed under the general Lipschitz condition, the proposed control method can be easily generalized to the actual systems, which have hysteresis behavior. Then, a hopfield neural network (HNN) estimator is proposed to adjust the parameters of the proposed controller online. Meanwhile, a modular model consisting of linear submodel, hysteresis submodel, and lumped uncertainties is established based on the HNN estimator to describe the piezo-actuated stage in this study. Thus, the performance of the HNN estimator can be exhibited visually through the modeling results. The proposed control method eradicates the adverse effects on the control performance arising from the inaccuracy in establishing the offline model and improves the capability to suppress the influence of hysteresis on the tracking accuracy of piezo-actuated stage in comparison with the conventional DAC methods. The stability of the control system is studied. Finally, a series of comparison experiments with a dual neural networks-based data driven adaptive controller are carried out to demonstrate the superiority of the proposed controller.
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