We additionally performed genome-wide MR analyses using Causal Analysis Using Summary Effect estimates (CAUSE),孟德尔随机化安装CAUSE包
时间: 2024-05-24 21:10:29 浏览: 14
(version 1.3.0)in R software (version 4.0.2). We obtained the summary statistics for the exposure and outcome from large-scale GWAS datasets, and used the two-sample MR approach to estimate the causal effect of the exposure on the outcome. We used the inverse-variance weighted (IVW) method as the primary analysis, and performed sensitivity analyses using the weighted median and MR-Egger methods to assess the robustness of the results. We also checked for potential pleiotropy using the MR-PRESSO method. The significance threshold was set at a Bonferroni-corrected p-value of 0.05/number of tests performed.
相关问题
Closed-loop Rescheduling using Deep Reinforcement Learning
Closed-loop rescheduling using deep reinforcement learning is an approach to optimize scheduling decisions in a dynamic environment. In this approach, a deep reinforcement learning model is trained to make rescheduling decisions based on the current state of the system, such as machine status, job priority, and resource availability. The model then uses the feedback from the actual execution of the rescheduling decision to update its policy and improve its performance.
The closed-loop aspect of this approach means that the model is constantly learning and adapting to changes in the system, making it more robust and able to handle unforeseen events. This approach has been applied in various domains, such as manufacturing, logistics, and transportation, where scheduling decisions need to be made in real-time.
One of the advantages of using deep reinforcement learning for closed-loop rescheduling is that it can handle complex and dynamic environments, where traditional optimization techniques may not be effective. Additionally, the use of reinforcement learning allows the model to learn from experience and improve its performance over time.
Overall, closed-loop rescheduling using deep reinforcement learning is a promising approach for optimizing scheduling decisions in dynamic environments, and has the potential to improve efficiency and reduce costs in various industries.
Using insecure protocols with repositories, without explicit opt-in, is unsupported.
Using insecure protocols with repositories, without explicit opt-in, is unsupported. This means that the Maven repository configuration needs to use a secure protocol like HTTPS. Additionally, there may be restrictions on the protocols that can be used. One possible solution is to update the settings.gradle file in your project and add the following code:
```
pluginManagement {
repositories {
maven {
allowInsecureProtocol true
url 'your Maven repository URL, using HTTPS'
}
}
}
dependencyResolutionManagement {
repositories {
maven {
allowInsecureProtocol true
url 'your Maven repository URL, using HTTPS'
}
}
}
```
This will allow the use of insecure protocols in the Maven repository configuration.