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Tether-nets deployed from a chaser spacecraft are a promising solution to capturing space debris. The success of the one-shot capture process depends on the net’s structural dynamic properties, attributed to its physical design, and on the ability to perform an optimal launch and closure subject to sensing and actuation uncertainties. Hence, this paper presents a reliability-based optimization framework to simultaneously optimize the net design and its launch and closing actions to minimize the system mass (case 1) or closing time (case 2) while preserving a specified probability of capture success. Success is assessed in terms of a capture quality index and the number of locked node pairs. Gaussian noise is used to model the uncertainties in the dynamics, state estimation, and actuation of the tether-net, which is propagated via Monte Carlo sampling. To account for uncertainties and ensure computational efficiency, given the cost of simulating the tether-net dynamics, Bayesian optimization is used to solve this problem. Optimization results show that the mission success rate in the presence of uncertainties has increased from 75% to over 98%, while the capture completion time has almost halved. 

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by the production of autoantibodies predominantly to nuclear material. Many aspects of disease pathology are mediated by the deposition of nucleic acid containing immune complexes, which also induce the type 1interferon response, a characteristic feature of SLE. Notably, SLE is remarkably heterogeneous, with a variety of organs involved in different individuals, who also show variation in disease severity related to their ancestries. Here, we probed one potential contribution to disease heterogeneity as well as a possible source of immunoreactive nucleic acids by exploring the expression of human endogenous retroviruses (HERVs). We investigated the expression of HERVs in SLE and their potential relationship to SLE features and the expression of biochemical pathways, including the interferon gene signature (IGS). Towards this goal, we analyzed available and new RNA-Seq data from two independent whole blood studies using Telescope. We identified 481 locus specific HERV encoding regions that are differentially expressed between case and control individuals with only 14% overlap of differentially expressed HERVs between these two datasets. We identified significant differences between differentially expressed HERVs and non-differentially expressed HERVs between the two datasets. We also characterized the host differentially expressed genes and tested their association with the differentially expressed HERVs. We found that differentially expressed HERVs were significantly more physically proximal to host differentially expressed genes than non-differentially expressed HERVs. Finally, we capitalized on locus specific resolution of HERV mapping to identify key molecular pathways impacted by differential HERV expression in people with SLE.