Circulating levels of urocortin neuropeptides are impaired in children with overweight

Objective The corticotropin-releasing factor neuropeptides (corticotropin-releasing hormone [CRH] and urocortin [UCN]-1,2,3) and spexin contribute to the regulation of energy balance and inhibit food intake in mammals. However, the status of these neuropeptides in children with overweight has yet to be elucidated. This study investigated the effect of increased body weight on the circulating levels of these neuropeptides. Methods A total of 120 children with a mean age of 12 years were enrolled in the study. Blood samples were collected to assess the circulating levels of neuropeptides and were correlated with various anthropometric, clinical, and metabolic markers. Results Plasma levels of UCNs were altered in children with overweight but less so in those with obesity. Furthermore, the expression pattern of UCN1 was opposite to that of UCN2 and UCN3, which suggests a compensatory effect. However, no significant effect of overweight and obesity was observed on CRH and spexin levels. Finally, UCN3 independently associated with circulating zinc-alpha-2-glycoprotein and UCN2 levels, whereas UCN1 was strongly predicted by TNF alpha levels. Conclusions Significant changes in neuropeptide levels were primarily observed in children with overweight and were attenuated with increased obesity. This suggests the presence of a compensatory mechanism for neuropeptides to curb the progression of obesity.Peer reviewe

Urocortin 3 Levels Are Impaired in Overweight Humans With and Without Type 2 Diabetes and Modulated by Exercise

Urocortin3 (UCN3) regulates metabolic functions and is involved in cellular stress response. Although UCN3 is expressed in human adipose tissue, the association of UCN3 with obesity and diabetes remains unclear. This study investigated the effects of Type 2 diabetes (T2D) and increased body weight on the circulatory and subcutaneous adipose tissue (SAT) levels of UCN3 and assessed UCN3 modulation by a regular physical exercise. Normal-weight (n = 37) and overweight adults with and without T2D (n = 98 and n = 107, respectively) were enrolled in the study. A subset of the overweight subjects (n = 39 for each group) underwent a supervised 3-month exercise program combining both moderate intensity aerobic exercise and resistance training with treadmill. UCN3 levels in SAT were measured by immunofluorescence and RT-PCR. Circulatory UCN3 in plasma was assessed by ELISA and was correlated with various clinical and metabolic markers. Our data revealed that plasma UCN3 levels decreased in overweight subjects without T2D compared with normal-weight controls [median; 11.99 (0.78–86.07) and 6.27 (0.64–77.04), respectively; p <0.001], whereas plasma UCN3 levels increased with concomitant T2D [median; 9.03 (0.77–104.92) p <0.001]. UCN3 plasma levels were independently associated with glycemic index; fasting plasma glucose and hemoglobin A1c (r = 0.16 and r = 0.20, p <0.05, respectively) and were significantly different between both overweight, with and without T2D, and normal-weight individuals (OR = 2.11 [1.84–4.11, 95% CI] and OR = 2.12 [1.59–3.10, 95% CI], p <0.01, respectively). Conversely, the UCN3 patterns observed in SAT were opposite to those in circulation; UCN3 levels were significantly increased with body weight and decreased with T2D. After a 3-month supervised exercise protocol, UCN3 expression showed a significant reduction in SAT of both overweight groups (2.3 and 1.6-fold change; p <0.01, respectively). In conclusion, UCN levels are differentially dysregulated in obesity in a tissue-dependent manner and can be mitigated by regular moderate physical exercise.Peer reviewe

Urocortin Neuropeptide Levels Are Impaired in the PBMCs of Overweight Children

The corticotropin-releasing hormone (CRH) and urocortins (UCNs) have been implicated in energy homeostasis and the cellular stress response. However, the expression of these neuropeptides in children remains unclear. Therefore, we determined the impact of obesity on their expression in 40 children who were normal weight, overweight, and had obesity. Peripheral blood mononuclear cells (PBMCs) and plasma were used to assess the expression of neuropeptides. THP1 cells were treated with 25 mM glucose and 200 µM palmitate, and gene expression was measured by real-time polymerase chain reaction (RT-PCR). Transcript levels of neuropeptides were decreased in PBMCs from children with increased body mass index as indicated by a significant decrease in UCN1, UCN3, and CRH mRNA in overweight and obese children. UCN3 mRNA expression was strongly correlated with UCN1, UCN2, and CRH. Exposure of THP1 cells to palmitate or a combination of high glucose and palmitate for 24 h increased CRH, UCN2, and UCN3 mRNA expression with concomitant increased levels of inflammatory and endoplasmic reticulum stress markers, suggesting a crosstalk between these neuropeptides and the cellular stress response. The differential impairment of the transcript levels of CRH and UCNs in PBMCs from overweight and obese children highlights their involvement in obesity-related metabolic and cellular stress

Calibrating Distributed Camera Networks Using Belief Propagation

We discuss how to obtain the accurate and globally consistent self-calibration of a distributed camera network, in which camera nodes with no centralized processor may be spread over a wide geographical area. We present a distributed calibration algorithm based on belief propagation, in which each camera node communicates only with its neighbors that image a sufficient number of scene points. The natural geometry of the system and the formulation of the estimation problem give rise to statistical dependencies that can be efficiently leveraged in a probabilistic framework. The camera calibration problem poses several challenges to information fusion, including overdetermined parameterizations and non-aligned coordinate systems. We suggest practical approaches to overcome these difficulties, and demonstrate the accurate and consistent performance of the algorithm using a simulated 30-node camera network with varying levels of noise in the correspondences used for calibration, as well as an experiment with 15 real images. I

Research Article Determining Vision Graphs for Distributed Camera Networks Using Feature Digests

We propose a decentralized method for obtaining the vision graph for a distributed, ad-hoc camera network, in which each edge of the graph represents two cameras that image a sufficiently large part of the same environment. Each camera encodes a spatially well-distributed set of distinctive, approximately viewpoint-invariant feature points into a fixed-length “feature digest ” that is broadcast throughout the network. Each receiver camera robustly matches its own features with the decompressed digest and decides whether sufficient evidence exists to form a vision graph edge. We also show how a camera calibration algorithm that passes messages only along vision graph edges can recover accurate 3D structure and camera positions in a distributed manner. We analyze the performance of different message formation schemes, and show that high detection rates (&gt; 0.8) can be achieved while maintaining low false alarm rates (&lt; 0.05) using a simulated 60-node outdoor camera network. Copyright © 2007 Zhaolin Cheng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1

Determining Vision Graphs for Distributed Camera Networks Using Feature Digests

<p/> <p>We propose a decentralized method for obtaining the vision graph for a distributed, ad-hoc camera network, in which each edge of the graph represents two cameras that image a sufficiently large part of the same environment. Each camera encodes a spatially well-distributed set of distinctive, approximately viewpoint-invariant feature points into a fixed-length "feature digest" that is broadcast throughout the network. Each receiver camera robustly matches its own features with the decompressed digest and decides whether sufficient evidence exists to form a vision graph edge. We also show how a camera calibration algorithm that passes messages only along vision graph edges can recover accurate 3D structure and camera positions in a distributed manner. We analyze the performance of different message formation schemes, and show that high detection rates ( <inline-formula><graphic file="1687-6180-2007-057034-i1.gif"/></inline-formula>) can be achieved while maintaining low false alarm rates ( <inline-formula><graphic file="1687-6180-2007-057034-i2.gif"/></inline-formula>) using a simulated 60-node outdoor camera network.</p