Semiparametric theory and empirical processes in causal inference

In this paper we review important aspects of semiparametric theory and empirical processes that arise in causal inference problems. We begin with a brief introduction to the general problem of causal inference, and go on to discuss estimation and inference for causal effects under semiparametric models, which allow parts of the data-generating process to be unrestricted if they are not of particular interest (i.e., nuisance functions). These models are very useful in causal problems because the outcome process is often complex and difficult to model, and there may only be information available about the treatment process (at best). Semiparametric theory gives a framework for benchmarking efficiency and constructing estimators in such settings. In the second part of the paper we discuss empirical process theory, which provides powerful tools for understanding the asymptotic behavior of semiparametric estimators that depend on flexible nonparametric estimators of nuisance functions. These tools are crucial for incorporating machine learning and other modern methods into causal inference analyses. We conclude by examining related extensions and future directions for work in semiparametric causal inference

Dynamic Graph Cuts and Their Applications in Computer Vision

Over the last few years energy minimization has emerged as an indispensable tool in computer vision. The primary reason for this rising popularity has been the successes of efficient graph cut based minimization algorithms in solving many low level vision problems such as image segmentation, object reconstruction, image restoration and disparity estimation. The scale and form of computer vision problems introduce many challenges in energy minimization. In this chapter we address the problem of efficient and exact minimization of groups of similar functions which are known to be solvable in polynomial time. We will present a novel dynamic algorithm for minimizing such functions. This algorithm reuses computation from previous problem instances to solve new instances resulting in a substantial improvement in the running time. We will present the results of using this approach on the problems of interactive image segmentation, image segmentation in video, human pose estimation and segmentation, and measuring uncertainty of solutions obtained by minimizing energy functions

Regulatory myeloid cells paralyze T cells through cell-cell transfer of the metabolite methylglyoxal.

Regulatory myeloid immune cells, such as myeloid-derived suppressor cells (MDSCs), populate inflamed or cancerous tissue and block immune cell effector functions. The lack of mechanistic insight into MDSC suppressive activity and a marker for their identification has hampered attempts to overcome T cell inhibition and unleash anti-cancer immunity. Here, we report that human MDSCs were characterized by strongly reduced metabolism and conferred this compromised metabolic state to CD8(+) T cells, thereby paralyzing their effector functions. We identified accumulation of the dicarbonyl radical methylglyoxal, generated by semicarbazide-sensitive amine oxidase, to cause the metabolic phenotype of MDSCs and MDSC-mediated paralysis of CD8(+) T cells. In a murine cancer model, neutralization of dicarbonyl activity overcame MDSC-mediated T cell suppression and, together with checkpoint inhibition, improved the efficacy of cancer immune therapy. Our results identify the dicarbonyl methylglyoxal as a marker metabolite for MDSCs that mediates T cell paralysis and can serve as a target to improve cancer immune therapy.Myeloid-derived suppressor cells (MDSCs) residing within tumors can impede immune responses. Knolle and colleagues show that MDSCs poison immune cells by producing methylglyoxal, which functionally alters their cellular metabolism and hence their effector responses

Inhibition of Anchorage-independent Growth of Transformed NIH3T3 Cells by Epithelial Protein Lost in Neoplasm (EPLIN) Requires Localization of EPLIN to Actin Cytoskeleton

Epithelial protein lost in neoplasm (EPLIN) is a cytoskeleton-associated protein characterized by the presence of a single centrally located lin-11, isl-1, and mec-3 (LIM) domain. We have reported previously that EPLIN is down-regulated in transformed cells. In this study, we have investigated whether ectopic expression of EPLIN affects transformation. In untransformed NIH3T3 cells, retroviral-mediated transduction of EPLIN did not alter the cell morphology or growth. NIH3T3 cells expressing EPLIN, however, failed to form colonies when transformed by the activated Cdc42 or the chimeric nuclear oncogene EWS/Fli-1. This suppression of anchorage-independent growth was not universal because EPLIN failed to inhibit the colony formation of Ras-transformed cells. Interestingly, the localization of EPLIN to the actin cytoskeleton was maintained in the EWS/Fli-1– or Cdc42-transformed cells, but not in Ras-transformed cells where it was distributed heterogeneously in the cytoplasm. Using truncated EPLIN constructs, we demonstrated that the NH(2)-terminal region of EPLIN is necessary for both the localization of EPLIN to the actin cytoskeleton and suppression of anchorage-independent growth of EWS/Fli-1–transformed cells. The LIM domain or the COOH-terminal region of EPLIN could be deleted without affecting its cytoskeletal localization or ability to suppress anchorage-dependent growth. Our study indicates EPLIN may function in growth control by associating with and regulating the actin cytoskeleton