眼の表面と眼底の画像分析に基づいた黒毛和牛の血中ビタミンA濃度の非侵襲モニタリング

京都大学新制・課程博士博士(農学)甲第25349号農博第2615号京都大学大学院農学研究科地域環境科学専攻(主査)教授 近藤 直, 准教授 小川 雄一, 教授 飯田 訓久学位規則第4条第1項該当Doctor of Philosophy (Engineering)Kyoto UniversityDGA

KEANEKARAGAMAN JENIS HERPETOFAUNA (ORDO SQUAMATA) DI KAWASAN BUKIT WANGKANG HUTAN LINDUNG GUNUNG AMBAWANG KABUPATEN KUBU RAYA

Gunung Ambawang was protected area including lowland forest type and plantation area border. The activities found to built plantation acces and changing the plantation oil and plantation rubber area to be effect the Biodiversity species of reptile. The purpose of the research to knowed Biodiversity species and measure Herpetofauna (Ordo Squamata)  abundant species in Gunung Ambawang protected area  Kubu Raya Regency. The method used Visual/VES (Visual Encounter Survey) combination with transect system on the two types that was Aquatic and Teresterial (Kusrini, 2008). The survey consisted of 5 transect that length at 1 km. The results showed that Reptile species was found in 12 species , that were classified into 6 family, 4 Lizards (Sauria) family, and 2 Snakes (Ophidia). The Biodiversity teresterial habitat was found with the highest of species were 9 species. Whereas Aquatic habitat was found with the greatest number which 12 cyrtodoctylus yoshii individual.Keywords : Diversity, Herpetofauna, Kubu Raya Regency, Protected fores

Fundus camera-based precision monitoring of blood vitamin A level for Wagyu cattle using deep learning

In the wagyu industry worldwide, high-quality marbling beef is produced by promoting intramuscular fat deposition during cattle fattening stage through dietary vitamin A control. Thus, however, cattle become susceptible to either vitamin A deficiency or excess state, not only influencing cattle performance and beef quality, but also causing health problems. Researchers have been exploring eye photography monitoring methods for cattle blood vitamin A levels based on the relation between vitamin A and retina colour changes. But previous endeavours cannot realise real-time monitoring and their prediction accuracy still need improvement in a practical sense. This study developed a handheld camera system capable of capturing cattle fundus images and predicting vitamin A levels in real time using deep learning. 4000 fundus images from 50 Japanese Black cattle were used to train and test the prediction algorithms, and the model achieved an average 87%, 83%, and 80% accuracy for three levels of vitamin A deficiency classification (particularly 87% for severe level), demonstrating the effectiveness of camera system in vitamin A deficiency prediction, especially for screening and early warning. More importantly, a new method was exemplified to utilise visualisation heatmap for colour-related DNNs tasks, and it was found that chromatic features extracted from LRP heatmap highlighted-ROI could account for 70% accuracy for the prediction of vitamin A deficiency. This system can assist farmers in blood vitamin A level monitoring and related disease prevention, contributing to precision livestock management and animal well-being in wagyu industry

The Global Flood Partnership Annual Meeting 2019

The Global Flood Partnership Conference (GFP) 2019 was held from June 11 to 14 in Guangzhou, China. It was the first time that the GFP annual meeting was not located either in Europe or the US with the aim to strengthen the links to the relevant community in Asia. The conference was hosted by the Hydrometeorological Extremes Simulation Group (HENG) led by Dr. Huan Wu, Sun Yat-Sen University (SYSU). 110 researchers, 11 business representatives, 7 NGO members, and 8 government officers from 14 countries participated to the conference with a total of 136 conference participants.JRC.E.1 - Disaster Risk Managemen

Anthropogenic Influences on 2019 July Precipitation Extremes Over the Mid–Lower Reaches of the Yangtze River

Understanding the driving factors for precipitation extremes matters for adaptation and mitigation measures against the changing hydrometeorological hazards in Yangtze River basin, a habitable area that provides water resources for domestic, farming, and industrial needs. However, the region is naturally subject to major floods linked to monsoonal heavy precipitation during May–September. This study aims to quantify anthropogenic influences on the changing risk of 2-week-long precipitation extremes such as the July 2019 extreme cases, as well as events of shorter durations, over the middle and lower reaches of Yangtze River basin (MLYRB). Precipitation extremes with different durations ranging from 1-day to 14-days maximum precipitation accumulations are investigated. Gridded daily precipitations based on nearly 2,400 meteorological stations across China are used to define maximum accumulated precipitation extremes over the MLYRB in July during 1961–2019. Attribution analysis is conducted by using the Met Office HadGEM3-GA6 modeling system, which comprises two sets of 525-member ensembles for 2019. One is forced with observed sea-surface temperatures (SSTs), sea-ice and all forcings, and the other is forced with preindustrialized SSTs and natural forcings only. The risk ratio between the exceedance probabilities estimated from all-forcing and natural-forcing simulations is calculated to quantify the anthropogenic contribution to the changing risks of the July 2019–like precipitation extremes. The results reveal that anthropogenic warming has reduced the likelihood of 2019-like 14-days heavy precipitation over the mid–lower reaches of the Yangtze River by 20%, but increased that of 2-days extremes by 30%

Computer-Aided Diagnosis Evaluation of the Correlation Between Magnetic Resonance Imaging With Molecular Subtypes in Breast Cancer

BackgroundThere is a demand for additional alternative methods that can allow the differentiation of the breast tumor into molecular subtypes precisely and conveniently.PurposeThe present study aimed to determine suitable optimal classifiers and investigate the general applicability of computer-aided diagnosis (CAD) to associate between the breast cancer molecular subtype and the extracted MR imaging features.MethodsWe analyzed a total of 264 patients (mean age: 47.9 ± 9.7 years; range: 19–81 years) with 264 masses (mean size: 28.6 ± 15.86 mm; range: 5–91 mm) using a Unet model and Gradient Tree Boosting for segmentation and classification.ResultsThe tumors were segmented clearly by the Unet model automatically. All the extracted features which including the shape features,the texture features of the tumors and the clinical features were input into the classifiers for classification, and the results showed that the GTB classifier is superior to other classifiers, which achieved F1-Score 0.72, AUC 0.81 and score 0.71. Analyzed the different features combinations, we founded that the texture features associated with the clinical features are the optimal features to different the breast cancer subtypes.ConclusionCAD is feasible to differentiate the breast cancer subtypes, automatical segmentation were feasible by Unet model and the extracted texture features from breast MR imaging with the clinical features can be used to help differentiating the molecular subtype. Moreover, in the clinical features, BPE and age characteristics have the best potential for subtype

Anthropogenic influences on heavy precipitation during the 2019 extremely wet rainy season in Southern China

Anthropogenic forcings have reduced the likelihood of heavy precipitation in southern China like the 2019 March-July event by about 60

Comparing Approaches to Deal with Non-Gaussianity of Rainfall Data in Kriging-Based Radar-Gauge Rainfall Merging

Merging radar and rain gauge rainfall data is a technique used to improve the quality of spatial rainfall estimates and in particular the use of Kriging with External Drift (KED) is a very effective radar-rain gauge rainfall merging technique. However, kriging interpolations assume Gaussianity of the process. Rainfall has a strongly skewed, positive, probability distribution, characterized by a discontinuity due to intermittency. In KED rainfall residuals are used, implicitly calculated as the difference between rain gauge data and a linear function of the radar estimates. Rainfall residuals are non-Gaussian as well. The aim of this work is to evaluate the impact of applying KED to non-Gaussian rainfall residuals, and to assess the best techniques to improve Gaussianity. We compare Box-Cox transformations with λ parameters equal to 0.5, 0.25, and 0.1, Box-Cox with time-variant optimization of λ, normal score transformation, and a singularity analysis technique. The results suggest that Box-Cox with λ = 0.1 and the singularity analysis is not suitable for KED. Normal score transformation and Box-Cox with optimized λ, or λ = 0.25 produce satisfactory results in terms of Gaussianity of the residuals, probability distribution of the merged rainfall products, and rainfall estimate quality, when validated through cross-validation. However, it is observed that Box-Cox transformations are strongly dependent on the temporal and spatial variability of rainfall and on the units used for the rainfall intensity. Overall, applying transformations results in a quantitative improvement of the rainfall estimates only if the correct transformations for the specific data set are used.ISSN:0043-1397ISSN:1944-797