Background-Chronic hypoxia is one of the major causes of pulmonary vascular remodeling associated with stimulating reactive oxygen species (ROS) production. Recent studies have indicated that hypoxia upregulates expression of adrenomedullin (AM), which is not only a potent vasodilator but also an antioxidant. Thus, using heterozygous AM-knockout (AM ϩ/Ϫ ) mice, we examined whether AM could attenuate pulmonary vascular damage induced by hypoxia. Methods and Results-Ten-week-old male wild-type (AM ϩ/ϩ ) or AM ϩ/Ϫ mice were housed under 10% oxygen conditions for 3 to 21 days. In AM ϩ/ϩ mice, hypoxia enhanced AM mRNA expression, which was reduced by the administration of a superoxide dismutase mimetic, 4-hydroxy-2,2,6,6-tetramethyl-piperidine-N-oxyl (hydroxy-TEMPO). Hypoxia induced pulmonary vascular remodeling, which was associated with the increased production of oxidative stress measured by electron spin resonance and immunostaining of 3-nitrotyrosine. The media wall thickness of the pulmonary arteries was significantly greater in AM ϩ/Ϫ mice housed under hypoxia than in AM ϩ/ϩ mice under hypoxia. Concomitantly, pulmonary ROS production induced by hypoxia was more enhanced in AM ϩ/Ϫ mice than in AM

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Automatic and efficient plant monitoring offers accurate plant management. Construction of three-dimensional (3D) models of plants and acquisition of their spatial information is an effective method for obtaining plant structural parameters. Here, 3D images of leaves constructed with multiple scenes taken from different positions were segmented automatically for the automatic retrieval of leaf areas and inclination angles. First, for the initial segmentation, leave images were viewed from the top, then leaves in the top-view images were segmented using distance transform and the watershed algorithm. Next, the images of leaves after the initial segmentation were reduced by 90%, and the seed regions for each leaf were produced. The seed region was re-projected onto the 3D images, and each leaf was segmented by expanding the seed region with the 3D information. After leaf segmentation, the leaf area of each leaf and its inclination angle were estimated accurately via a voxel-based calculation. As a result, leaf area and leaf inclination angle were estimated accurately after automatic leaf segmentation. This method for automatic plant structure analysis allows accurate and efficient plant breeding and growth management.

It is important to grasp the number and location of trees, and measure tree structure attributes, such as tree trunk diameter and height. The accurate measurement of these parameters will lead to efficient forest resource utilization, maintenance of trees in urban cities, and feasible afforestation planning in the future. Recently, light detection and ranging (LiDAR) has been receiving considerable attention, compared with conventional manual measurement techniques. However, it is difficult to use LiDAR for widespread applications, mainly because of the costs. We propose a method for tree measurement using 360 • spherical cameras, which takes omnidirectional images. For the structural measurement, the three-dimensional (3D) images were reconstructed using a photogrammetric approach called structure from motion. Moreover, an automatic tree detection method from the 3D images was presented. First, the trees included in the 360 • spherical images were detected using YOLO v2. Then, these trees were detected with the tree information obtained from the 3D images reconstructed using structure from motion algorithm. As a result, the trunk diameter and height could be accurately estimated from the 3D images. The tree detection model had an F-measure value of 0.94. This method could automatically estimate some of the structural parameters of trees and contribute to more efficient tree measurement.Previous studies on the LiDAR have estimated tree trunk diameter, tree height [3][4][5], leaf area density [6,7], tree volume [8], biomass and leaf inclination angle [9,10]. However, when we use a LiDAR that should be stationed at one point for the measurement, it can require multiple measurements to cover the study sites, depending on the scale and shape of the study site. Recently, a light weight and portable LiDAR has been used for tree measurement [11]. This LiDAR measurement can be conducted while moving, enabling large area-measurement in a short period. Moreover, the field of view of LiDAR is large when the measurement is carried out during movement, and the maximum distance for measurement is not short (e.g., 100 m). Those LiDAR instruments also can be utilized with unmanned aerial systems or drones [12][13][14]. By combining LiDAR with those platforms, the 3D data of large area including numerous trees can be easily obtained. However, the instrument is costly, and the measurement process, such as in forests, is laborious. Moreover, LiDAR itself cannot obtain the color value.Structure from Motion (SfM) is a photogrammetric approach for 3D imaging, where the camera positions and orientations are solved automatically after extracting image features to recover 3D information [15]. Liang et al. [16] showed that the mapping accuracy of a plot level and an individual-tree level was 88 %, and the root mean squared error of the diameter at breast height (DBH) estimates of individual trees was 2.39 cm, which is acceptable for practical applications. Additionally, the results achieved using terrestrial LiDAR are similar. Piermattei ...