Visfatin is a recently discovered adipokine that contributes to glucose and obesity-related conditions. This study investigates Visfatin RS4730153 polymorphism from the perspectives of its relations with glucose/lipid metabolism and its influence on the effects of exercise-induced weight loss. Eighty-eight obese Han Chinese children and adolescents were randomly selected from a 2008 Shanghai Weight Loss Summer Camp and were supervised to complete a 4 week aerobic exercise training program. Significant differences were observed in before-exercise TG value and exercise-induced HOMA-β change, with the AG group having a much higher TG value than the GG group (P ≤ 0.05), and the latter exhibiting a significantly larger before-and-after exercise HOMA-β change than the former (P ≤ 0.05). However, no significant difference was observed between the two groups in before exercise indices of body shape, function and quality, nor in exercise-induced changes of body shape, function, and quality. Findings suggest that Visfatin RS4730153 homozygous GG genotype may effect adjustment of glucose and lipid metabolism in obese children and adolescents by reducing TG levels and increasing insulin sensitivity to exercise.

No abstract

Mapping biodiversity is essential for assessing conservation and ecosystem services in global terrestrial ecosystems. Compared with remotely sensed mapping of forest biodiversity, that of grassland plant diversity has been less studied, because of the small size of individual grass species and the inherent difficulty in identifying these species. The technological advances in unmanned aerial vehicle (UAV)-based or proximal imaging spectroscopy with high spatial resolution provide new approaches for mapping and assessing grassland plant diversity based on spectral diversity and functional trait diversity. However, relatively few studies have explored the relationships among spectral diversity, remote-sensing-estimated functional trait diversity, and species diversity in grassland ecosystems. In this study, we examined the links among spectral diversity, functional trait diversity, and species diversity in a semi-arid grassland monoculture experimental site. The results showed that (1) different grassland plant species harbored different functional traits or trait combinations (functional trait diversity), leading to different spectral patterns (spectral diversity). (2) The spectral diversity of grassland plant species increased gradually from the visible (VIR, 400–700 nm) to the near-infrared (NIR, 700–1100 nm) region, and to the short-wave infrared (SWIR, 1100–2400 nm) region. (3) As the species richness increased, the functional traits and spectral diversity increased in a nonlinear manner, finally tending to saturate. (4) Grassland plant species diversity could be accurately predicted using hyperspectral data (R2 = 0.73, p < 0.001) and remotely sensed functional traits (R2 = 0.66, p < 0.001) using cluster algorithms. This will enhance our understanding of the effect of biodiversity on ecosystem functions and support regional grassland biodiversity conservation.

Purpose Accurately segmenting curvilinear structures, for example, retinal blood vessels or nerve fibers, in the medical image is essential to the clinical diagnosis of many diseases. Recently, deep learning has become a popular technology to deal with the image segmentation task, and it has obtained remarkable achievement. However, the existing methods still have many problems when segmenting the curvilinear structures in medical images, such as losing the details of curvilinear structures, producing many false‐positive segmentation results. To mitigate these problems, we propose a novel end‐to‐end curvilinear structure segmentation network called Curv‐Net. Methods Curv‐Net is an effective encoder–decoder architecture constructed based on selective kernel (SK) and multibidirectional convolutional LSTM (multi‐Bi‐ConvLSTM). To be specific, we first employ the SK module in the convolutional layer to adaptively extract the multi‐scale features of the input image, and then we design a multi‐Bi‐ConvLSTM as the skip concatenation to fuse the information learned in the same stage and propagate the feature information from the deep stages to the shallow stages, which can enable the feature captured by Curv‐Net to contain more detail information and high‐level semantic information simultaneously to improve the segmentation performance. Results The effectiveness and reliability of our proposed Curv‐Net are verified on three public datasets: two color fundus datasets (DRIVE and CHASE_DB1) and one corneal nerve fiber dataset (CCM‐2). We calculate the accuracy (ACC), sensitivity (SE), specificity (SP), Dice similarity coefficient (Dice), and area under the receiver (AUC) for the DRIVE and CHASE_DB1 datasets. The ACC, SE, SP, Dice, and AUC of the DRIVE dataset are 0.9629, 0.8175, 0.9858, 0.8352, and 0.9810, respectively. For the CHASE_DB1 dataset, the values are 0.9810, 0.8564, 0.9899, 0.8143, and 0.9832, respectively. To validate the corneal nerve fiber segmentation performance of the proposed Curv‐Net, we test it on the CCM‐2 dataset and calculate Dice, SE, and false discovery rate (FDR) metrics. The Dice, SE, and FDR achieved by Curv‐Net are 0.8114 ± 0.0062, 0.8903 ± 0.0113, and 0.2547 ± 0.0104, respectively. Conclusions Curv‐Net is evaluated on three public datasets. Extensive experimental results demonstrate that Curv‐Net outperforms the other superior curvilinear structure segmentation methods.