Lactation improves pancreatic ß cell mass and function through serotonin production.

Pregnancy imposes a substantial metabolic burden on women through weight gain and insulin resistance. Lactation reduces the risk of maternal postpartum diabetes, but the mechanisms underlying this benefit are unknown. Here, we identified long-term beneficial effects of lactation on ß cell function, which last for years after the cessation of lactation. We analyzed metabolic phenotypes including ß cell characteristics in lactating and non-lactating humans and mice. Lactating and non-lactating women showed comparable glucose tolerance at 2 months after delivery, but after a mean of 3.6 years, glucose tolerance in lactated women had improved compared to non-lactated women. In humans, the disposition index, a measure of insulin secretory function of ß cells considering the degree of insulin sensitivity, was higher in lactated women at 3.6 years after delivery. In mice, lactation improved glucose tolerance and increased ß cell mass at 3 weeks after delivery. Amelioration of glucose tolerance and insulin secretion were maintained up to 4 months after delivery in lactated mice. During lactation, prolactin induced serotonin production in ß cells. Secreted serotonin stimulated ß cell proliferation through serotonin receptor 2B in an autocrine and paracrine manner. In addition, intracellular serotonin acted as an antioxidant to mitigate oxidative stress and improved ß cell survival. Together, our results suggest that serotonin mediates the long-term beneficial effects of lactation on female metabolic health by increasing ß cell proliferation and reducing oxidative stress in ß cells.

A general framework for tracking multiple people from a moving camera.

In this paper, we present a general framework for tracking multiple, possibly interacting, people from a mobile vision platform. To determine all of the trajectories robustly and in a 3D coordinate system, we estimate both the camera's ego-motion and the people's paths within a single coherent framework. The tracking problem is framed as finding the MAP solution of a posterior probability, and is solved using the reversible jump Markov chain Monte Carlo (RJ-MCMC) particle filtering method. We evaluate our system on challenging datasets taken from moving cameras, including an outdoor street scene video dataset, as well as an indoor RGB-D dataset collected in an office. Experimental evidence shows that the proposed method can robustly estimate a camera's motion from dynamic scenes and stably track people who are moving independently or interacting.