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The fusion of Iterative Closest Point (ICP) registrations in existing state estimation frameworks relies on an accurate estimation of their uncertainty. In this paper, we study the estimation of this uncertainty in the form of a covariance. First, we scrutinize the limitations of existing closed-form covariance estimation algorithms over 3D datasets. Then, we set out to estimate the covariance of ICP registrations through a data-driven approach, with over 5 100 000 registrations on 1020 pairs from real 3D point clouds. We assess our solution upon a wide spectrum of environments, ranging from structured to unstructured and indoor to outdoor. The capacity of our algorithm to predict covariances is accurately assessed, as well as the usefulness of these estimations for uncertainty estimation over trajectories. The proposed method estimates covariances better than existing closed-form solutions, and makes predictions that are consistent with observed trajectories.

The adipose tissue is an important endocrine organ secreting numerous peptide hormones, including leptin. Increased circulating levels of leptin, as a result of hormonal resistance in obese individuals, may contribute to lower androgen production in obese males. However, the molecular mechanisms involved need to be better defined. Androgens are mainly produced by Leydig cells within the testis. In male rodents, activation of the leptin receptor modulates a cascade of intracellular signal transduction pathways which may lead to regulation of transcription factors having influences on steroidogenesis in Leydig cells. Thus, as a result of high leptin levels interacting with its receptor and modulating the activity of the JAK/STAT signaling pathway, the activity of transcription factors important for steroidogenic genes expressions may be inhibited in Leydig cells. Here we show that Lepr is increasingly expressed within Leydig cells according to postnatal development. Although high levels of leptin (corresponding to obesity condition) alone had no effect on Leydig cells' steroidogenic genes expression, it downregulated cAMP-dependent activations of the cholesterol transporter Star and of the rate-limiting steroidogenic enzyme Cyp11a1. Our results suggest that STAT transcriptional activity is downregulated by high levels of leptin, leading to reduced cAMP-dependent steroidogenic genes (Star and Cyp11a1) expressions in MA-10 Leydig cells. However, other transcription factors such as members of the SMAD and NFAT families may be involved and need further investigation to better define how leptin regulates their activities and their relevance for Leydig cells function.

Ovarian fibrosis is a pathological condition associated with aging and is responsible for a variety of ovarian dysfunctions. Given the known contributions of tissue fibrosis to tumorigenesis, it is anticipated that ovarian fibrosis may contribute to ovarian cancer risk. We recently reported that diabetic postmenopausal women using metformin had ovarian collagen abundance and organization that were similar to premenopausal ovaries from nondiabetic women. In this study, we investigated the effects of aging and metformin on mouse ovarian fibrosis at a single-cell level. We discovered that metformin treatment prevented age-associated ovarian fibrosis by modulating the proportion of fibroblasts, myofibroblasts, and immune cells. Senescence-associated secretory phenotype (SASP)–producing fibroblasts increased in aged ovaries, and a unique metformin-responsive subpopulation of macrophages emerged in aged mice treated with metformin. The results demonstrate that metformin can modulate specific populations of immune cells and fibroblasts to prevent age-associated ovarian fibrosis and offers a new strategy to prevent ovarian fibrosis.