Active and Passive Causal Inference Learning

This paper serves as a starting point for machine learning researchers, engineers and students who are interested in but not yet familiar with causal inference. We start by laying out an important set of assumptions that are collectively needed for causal identification, such as exchangeability, positivity, consistency and the absence of interference. From these assumptions, we build out a set of important causal inference techniques, which we do so by categorizing them into two buckets; active and passive approaches. We describe and discuss randomized controlled trials and bandit-based approaches from the active category. We then describe classical approaches, such as matching and inverse probability weighting, in the passive category, followed by more recent deep learning based algorithms. By finishing the paper with some of the missing aspects of causal inference from this paper, such as collider biases, we expect this paper to provide readers with a diverse set of starting points for further reading and research in causal inference and discovery

Surrogate Model Considering Trim Condition for Design Optimization of High-Aspect-Ratio Flexible Wing

An improved, bilevel surrogate model to optimize aerodynamic/nonlinear structural coupled problems, considering trim conditions, is proposed in this paper. The proposed model is applied to a high-aspect-ratio flexible wing. Computational fluid and structural dynamic codes are loosely coupled and interact with the coupling variables of pressure and displacement. Level 1 reduces the coupling variables, using proper orthogonal decomposition, enabling the expansion of any data field as a linear combination of several modes. Level 2 builds the surrogate model, considering proper orthogonal displacement coefficients and the angle of attack as input parameters and the pressure coefficients as output parameters. Unlike the conventional bilevel surrogate model, the improved bilevel surrogate model includes the angle of attack as an input parameter to facilitate the immediate calculation of the trim angle of attack. The wing model of Global Hawk is optimized using the improved bilevel surrogate model, and the conventional bilevel surrogate model was used to compare the results of design optimization under two conditions: including and excluding the trim condition.N

Glucocorticoids alleviate particulate matter-induced COX-2 expression and mitochondrial dysfunction through the Bcl-2/GR complex in A549 cells

Abstract Exposure to particulate matter (PM) causes mitochondrial dysfunction and lung inflammation. The cyclooxygenase-2 (COX-2) pathway is important for inflammation and mitochondrial function. However, the mechanisms by which glucocorticoid receptors (GRs) suppress COX-2 expression during PM exposure have not been elucidated yet. Hence, we examined the mechanisms underlying the dexamethasone-mediated suppression of the PM-induced COX-2/prostaglandin E2 (PGE2) pathway in A549 cells. The PM-induced increase in COX-2 protein, mRNA, and promoter activity was suppressed by glucocorticoids; this effect of glucocorticoids was antagonized by the GR antagonist RU486. COX-2 induction was correlated with the ability of PM to increase reactive oxygen species (ROS) levels. Consistent with this, antioxidant treatment significantly abolished COX-2 induction, suggesting that ROS is involved in PM-mediated COX-2 induction. We also observed a low mitochondrial membrane potential in PM-treated A549 cells, which was reversed by dexamethasone. Moreover, glucocorticoids significantly enhanced Bcl-2/GR complex formation in PM-treated A549 cells. Glucocorticoids regulate the PM-exposed induction of COX-2 expression and mitochondrial dysfunction and increase the interaction between GR and Bcl-2. These findings suggest that the COX-2/PGE2 pathway and the interaction between GR and Bcl-2 are potential key therapeutic targets for the suppression of inflammation under PM exposure