Pre-eclampsia is a leading cause of fetal and maternal morbidity and mortality that preferentially affects primiparous patients. It is associated with systemic inflammation and impaired trophoblast invasion of the decidua. Decidual cells are the major cell type of the pregnant endometrium. Macrophages and dendritic cells are major specialized antigen-presenting cells that promote both innate immunity and immune tolerance. Macrophage infiltration is implicated in impaired trophoblast invasion that leads to pre-eclampsia. By contrast, the potential modulating role of decidual dendritic cells in the genesis of pre-eclampsia has not been investigated. Interleukin-1beta (IL-1beta), a pro-inflammatory cytokine, has been implicated in the genesis of pre-eclampsia. Thus, we postulate that pre-eclampsia would be associated with enhanced decidual dendritic cells infiltration and that IL-1beta would enhance the production of relevant dendritic cell-recruiting chemokines. We used immunohistochemistry to demonstrate a marked infiltrate of immature and mature dendritic cells in pre-eclamptic decidua. Further, immunohistochemistry and immunoassays of placental bed biopsies revealed that pre-eclamptic decidua displays elevated levels of several monocyte- and dendritic cell-recruiting chemokines. Leukocyte-free first-trimester decidual cells were then treated with IL-1beta, which enhanced the mRNA and protein expression of these chemokines. The current study also confirmed previous reports that macrophages directly impaired trophoblast invasion and that this inhibitory effect is augmented by the conditioned medium of IL-1beta-treated first-trimester decidual cells. However, unlike macrophages, dendritic cells did not directly impede trophoblast invasion. This study demonstrates that the inflammatory milieu of pre-eclampsia induces decidual cells to promote dendritic cell infiltration. Given their unusual versatility in mediating both immunity and tolerance, these novel findings suggest that dendritic cells may play a critical role either in the pathogenesis of pre-eclampsia or its prevention in subsequent pregnancies.

Abstract-In this paper, we investigate the convergence and consistency properties of an Invariant-Extended Kalman Filter (RI-EKF) based Simultaneous Localization and Mapping (SLAM) algorithm. Basic convergence properties of this algorithm are proven. These proofs do not require the restrictive assumption that the Jacobians of the motion and observation models need to be evaluated at the ground truth. It is also shown that the output of RI-EKF is invariant under any stochastic rigid body transformation in contrast to SO(3) based EKF SLAM algorithm (SO(3)-EKF) that is only invariant under deterministic rigid body transformation. Implications of these invariance properties on the consistency of the estimator are also discussed. Monte Carlo simulation results demonstrate that RI-EKF outperforms SO(3)-EKF, Robocentric-EKF and the "First Estimates Jacobian" EKF, for 3D point feature based SLAM.

Estimation-over-graphs (EoG) is a class of estimation problems that admit a natural graphical representation. Several key problems in robotics and sensor networks, including sensor network localization, synchronization over a group, and simultaneous localization and mapping (SLAM) fall into this category. We pursue two main goals in this work. First, we aim to characterize the impact of the graphical structure of SLAM and related problems on estimation reliability. We draw connections between several notions of graph connectivity and various properties of the underlying estimation problem. In particular, we establish results on the impact of the weighted number of spanning trees on the D-optimality criterion in 2D SLAM. These results enable agents to evaluate estimation reliability based only on the graphical representation of the EoG problem. We then use our findings and study the problem of designing sparse SLAM problems that lead to reliable maximum likelihood estimates through the synthesis of sparse graphs with the maximum weighted tree connectivity. Characterizing graphs with the maximum number of spanning trees is an open problem in general. To tackle this problem, we establish several new theoretical results, including the monotone log-submodularity of the weighted number of spanning trees. We exploit these structures and design a complementary greedy–convex pair of efficient approximation algorithms with provable guarantees. The proposed synthesis framework is applied to various forms of the measurement selection problem in resource-constrained SLAM. Our algorithms and theoretical findings are validated using random graphs, existing and new synthetic SLAM benchmarks, and publicly available real pose-graph SLAM datasets.

Active SLAM poses the challenge for an autonomous robot to plan efficient paths simultaneous to the SLAM process. The uncertainties of the robot, map and sensor measurements, and the dynamic and motion constraints need to be considered in the planning process. In this paper, the active SLAM problem is formulated as an optimal trajectory planning problem. A novel technique is introduced that utilises an attractor combined with local planning strategies such as Model Predictive Control (a.k.a. Receding Horizon) to solve this problem. An attractor provides high level task intentions and incorporates global information about the environment for the local planner, thereby eliminating the need for costly global planning with longer horizons. It is demonstrated that trajectory planning with an attractor results in improved performance over systems that have local planning alone.

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