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Abstract The influence of environmental factors like smoking and alcohol on myopia and astigmatism is controversial. However, due to ethical concerns, alternative study designs are urgently needed to assess causal inference, as mandatory exposure to cigarettes and alcohol is unethical. Following comprehensive screenings, 326 single nucleotide polymorphisms (SNPs) related to myopia and astigmatism were included in the dataset. To validate the causal association between exposures such as cigarette smoking, alcohol consumption, and coffee intake, and outcomes namely astigmatism and myopia, five regression models were employed. These models encompassed MR-Egger regression, random-effects inverse-variance weighted (IVW), weighted median estimator (WME), weighted model, and simple model. The instrumental variables utilized in these analyses were the aforementioned SNPs. Apply Cochran’s Q test to determine heterogeneity of SNPs; if heterogeneity exists, focus on IVW model results. The IVW model showed a 1.379-fold increase in the risk of astigmatism (OR = 1.379, 95%CI 0.822~2.313, P = 0.224) and a 0.963-fold increase in the risk of myopia (OR = 0.963, 95%CI 0.666~1.393, P = 0.841) for each unit increase in smoking. For each unit increase in coffee intake, the risk of astigmatism increased 1.610-fold (OR = 1.610, 95%CI 0.444~5.835, P = 0.469) and the risk of myopia increased 0.788-fold (OR = 0.788, 95%CI 0.340~1.824, P = 0.578). For each additional unit of alcohol consumption, the risk of astigmatism increased by 0.763-fold (OR = 0.763, 95%CI 0.380~1.530, P = 0.446), and none of the differences were statistically significant. However, for each unit of alcohol consumption, the risk of myopia increased by 1.597 times, and the difference was statistically significant (OR = 1.597, 95%CI 1.023~2.493, P = 0.039). The findings indicate that alcohol consumption is a risk factor for myopia but smoking and coffee intake do not affect its development. Additionally, there is no association between smoking, alcohol consumption, coffee intake, and the risk of astigmatism.

In the field of object detection, domain adaptation is one of popular solution to align the distribution between the real scene (target domain) and the training scene (source domain) by adversarial training. However, only global features are applied to the Domain Adaptive Faster R-CNN (DA Faster R-CNN) method. The lack of local features reduces the performance of domain adaptation. Therefore, a novel method for domain adaptive detection called Skip-Layer Network with Optimization (SLNO) method is proposed in this paper. Three improvements are presented in SLNO. Firstly, different level convolutional features are fused by a multi-level features fusion component for domain classifier. Secondly, a multi-layer domain adaptation component is developed to align the image-level and the instance-level distributions simultaneously. Among this component, domain classifiers are used in both image-level and instance-level distributions through the skip layer. Thirdly, the cuckoo search (CS) optimization method is applied to search for the best coefficient of SLNO. As a result, the capability of domain alignment is strengthened. The Cityscapes, Foggy Cityscapes, SIM10K, KITTI data sets are applied to test our proposed novel approach. Consequently, excellent results are achieved by our proposed methods against state-of-the-art object detection methods. The results demonstrate our improvements are effective on domain adaptation detection.