Life Style Factors Influencing Serum Pepsinogen Levels in Healthy Japanese: a Prospective Study

Background: Gastric cancer mass screening using serum pepsinogen has been recognized and several advantages of this methods over photofluorography have been shown by previous study. Aims: To determine the factors influence the serum pepsinogen levels in healthy subjects. Subjects & Methods: One thousand and one hundred fourteen subjects who were screened for gastric cancer as part of a periodic health check. Blood samples were taken after fasting and stored below –20 ° C, until pepsinogen levels were assayed. Results: The subjects consist of 338 males (mean age 52.6+14.0) and 776 females (mean age 49.0+11.9). Age ranges from 19 to 81 years. The overall prevalence of chronic atrophic gastritis using a criterion PG I £ 70 hg/ml and PG I/II ratio £ 3.0 was 21.99 % in 1996 and 23.97 % in 2000. Bivariate analysis revealed a significant association between age, more salt consumption, fish favorable over meat and less than three time meal intake covariates with the lowering of PG I/II ratio. Smoking, drinking, BMI, weight and gender did not affect the changes of PG I/II ratio. Conclusion: Age and more salt consumption covariates have a strongest association with the decreased of PG I/II by multivariate analysis

Mutual superimposing of SAR and ground-level shooting images mediated by intermediate multi-altitude images

When satellite-based SAR (Synthetic Aperture Radar) images and images acquired from the ground are registered, they offer a wealth of information such as topographic, vegetation or water surface to be extracted from the ground-level shooting images. Simultaneously, high temporal-resolution and high spatial-resolution information obtained by the ground-level shooting images can be superimposed on satellite images. However, due to the differences in imaging modality, spatial resolutions, and observation angle, it was not easy to directly extract the corresponding points between them. This paper proposes an image registration method to estimate the correspondence between SAR images and ground-level shooting images through a set of multi-altitude images taken at different heights

Use of a DNN-Based Image Translator with Edge Enhancement Technique to Estimate Correspondence between SAR and Optical Images

In this paper, the local correspondence between synthetic aperture radar (SAR) images and optical images is proposed using an image feature-based keypoint-matching algorithm. To achieve accurate matching, common image features were obtained at the corresponding locations. Since the appearance of SAR and optical images is different, it was difficult to find similar features to account for geometric corrections. In this work, an image translator, which was built with a DNN (deep neural network) and trained by conditional generative adversarial networks (cGANs) with edge enhancement, was employed to find the corresponding locations between SAR and optical images. When using conventional cGANs, many blurs appear in the translated images and they degrade keypoint-matching accuracy. Therefore, a novel method applying an edge enhancement filter in the cGANs structure was proposed to find the corresponding points between SAR and optical images to accurately register images from different sensors. The results suggested that the proposed method could accurately estimate the corresponding points between SAR and optical images

Model Scaling in Smartphone GNSS-Aided Photogrammetry for Fragmentation Size Distribution Estimation

Fragmentation size distribution estimation is a critical process in mining operations that employ blasting. In this study, we aim to create a low-cost, efficient system for producing a scaled 3D model without the use of ground truth data, such as GCPs (Ground Control Points), for the purpose of improving fragmentation size distribution measurement using GNSS (Global Navigation Satellite System)-aided photogrammetry. However, the inherent error of GNSS data inhibits a straight-forward application in Structure-from-Motion (SfM). To overcome this, the study proposes that, by increasing the number of photos used in the SfM process, the scale error brought about by the GNSS error will proportionally decrease. Experiments indicated that constraining camera positions to locations, relative or otherwise, improved the accuracy of the generated 3D model. In further experiments, the results showed that the scale error decreased when more images from the same dataset were used. The proposed method is practical and easy to transport as it only requires a smartphone and, optionally, a separate camera. In conclusion, with some modifications to the workflow, technique, and equipment, a muckpile can be accurately recreated in scale in the digital world with the use of positional data

Individual-level distance-independent-based growth and yield prediction models for long-term Japanese cedar (Cryptomeria japonica)

Yield prediction has been determined to be vital in sustainable forest management. Recently, research trends have shifted from stand-level to individual-level yield prediction. In this study, we examined the effectiveness of yield prediction models based on a distance-independent approach for Japanese cedar (Cryptomeria japonica) trees in western Japan. We further examined the accuracy of the models by reference to existing data collected long-term. First, we constructed distance-independent height, diameter growth, and survival models. Then, we simulated for approximately 50 years individual tree height, diameter at breast height (DBH), and volume growth using the test data. We then compared the predicted and observed values and calculated root mean square error (RMSE) and bias to evaluate the model accuracy. The models were noted to perform well when predicting mean height, DBH, and volume for Japanese cedar trees; in fact, they adequately predicted the diameter distribution. Our results suggest that distance-independent models could adequately predict long-term mean values and diameter distribution. However, RMSE and bias indicated that error propagation occurred over longer time spans. Thus, it is effective to conduct actual measurements at some point in the forest development phase and use the measurements as initial values for short- or medium-term predictions.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Evaluation of Individual Distance-Independent Diameter Growth Models for Japanese Cedar (Cryptomeria japonica) Trees under Multiple Thinning Treatments

Understanding the tree growth process is essential for sustainable forest management. Future yields are affected by various forest management regimes such as thinning; therefore, accurate predictions of tree growth are needed under various thinning intensities. This study compared the accuracy of individual-level distance-independent diameter growth models constructed for different thinning intensities (thinning intensity-dependent multiple models: TDM model) against the model designed to include all thinning intensities (thinning intensity-independent single model: TIS model) to understand how model accuracy is affected by thinning intensity. We used long-term permanent plot data of Japanese cedar (Cryptomeria japonica) stands in Japan, which was gathered from four plots where thinning was conducted at different thinning intensities: (1) intensive (41% and 38% of trees removed at 25 and 37 years old, respectively), (2) moderate (38% and 34%), (3) light (32% and 34%), and (4) no thinning. First, we specified high interpretability distance-independent competition indices, and we compared the model accuracy both in TDM and TIS models. The results show that the relative spacing index was the best competition index both in TDM and TIS models across all thinning intensities, and the differences in the RMSE (Root mean square error) and rRMSE (relative RMSE) in both TDM and TIS models were 0.001–0.01 cm and 0.2–2%, respectively. In the TIS model, rRMSE varied with thinning intensity; the rRMSE was the lowest for moderate thinning intensity (45.8%) and the highest for no thinning (59.4%). In addition, bias values were negative for the TIS model for all thinning intensities. These results suggest that the TIS model could express diameter growth regardless of thinning intensities. However, the rRMSE had varied with thinning intensity and bias had negative values in the TIS model. Therefore, more model improvements are required for accurate predictions of long-term growth of actual Japanese cedar stands