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Wheat stripe rust-damaged leaves present challenges to automatic disease index calculation, including high similarity between spores and spots, and difficulty in distinguishing edge contours. In actual field applications, investigators rely on the naked eye to judge the disease extent, which is subjective, of low accuracy, and essentially qualitative. To address the above issues, this study undertook a task of semantic segmentation of wheat stripe rust damage images using deep learning. To address the problem of small available datasets, the first large-scale open dataset of wheat stripe rust images from Qinghai province was constructed through field and greenhouse image acquisition, screening, filtering, and manual annotation. There were 33,238 images in our dataset with a size of 512 × 512 pixels. A new segmentation paradigm was defined. Dividing indistinguishable spores and spots into different classes, the task of accurate segmentation of the background, leaf (containing spots), and spores was investigated. To assign different weights to high- and low-frequency features, we used the Octave-UNet model that replaces the original convolutional operation with the octave convolution in the U-Net model. The Octave-UNet model obtained the best benchmark results among four models (PSPNet, DeepLabv3, U-Net, Octave-UNet), the mean intersection over a union of the Octave-UNet model was 83.44%, the mean pixel accuracy was 94.58%, and the accuracy was 96.06%, respectively. The results showed that the state-of-art Octave-UNet model can better represent and discern the semantic information over a small region and improve the segmentation accuracy of spores, leaves, and backgrounds in our constructed dataset. [ABSTRACT FROM AUTHOR]

The 2021 Madoi M7.4 Earthquake in Qinghai is a major earthquake that occurred in the Bajankara Block of Qinghai-Tibet Plateau in the past 30 years, which spatially filled the seismogenic gap in the eastern section of the northern boundary of the block. Here we determined the values of ML, MS_BB, mB, MWp, MWW, Mdt, and M (GNSS) by abundant regional and global seismic and geodetic observations, which is 6.61, 7.43, 7.18, 7.33, 7.43, 7.38, and 7.4, respectively. To compare the time efficiency and stability of different magnitude scales, we generated a real-time environment, to iteratively determine the magnitudes over elapsed times. Some methods such as mB, MSBB, MWp gave considerable variations of as large as 0.5 units for the determined magnitudes with elapsed time, as more data were included. Others such as MWW and Mdt were very stable with increasing data over time. The systematic calculations of various magnitude scales in this study quantitively evaluated the stability and accuracy of those methods, shading light on the adaptability and applicability of different magnitude scales. [ABSTRACT FROM AUTHOR]

This study focuses on rapidly determining seismic intensity maps of earthquakes because it offers fundamental information for effective emergency rescue and subsequent scientific research, and remains challenging to accurately determine seismic intensity map in regions with sparse instrumental observations. Here we applied a novel method that consisted of array technology (back-projection), ground-motion prediction equations, and site corrections, to estimate the seismic intensity maps of the 2021 Mw 7.3 Madoi, Qinghai and the Mw 6.1 Yangbi, Yunnan, China earthquakes. We used seismic data recorded at European stations to back-project the source processes of the 2021 Mw 7.3 Madoi, Qinghai and the Mw 6.1 Yangbi, Yunnan, China earthquakes. The back-projected energy radiations were then used as subevents or used to define the fault geometry. Summing the contributions of each subevent or estimating the shortest distances from each site to the rupture fault, we obtained the ground motion (PGA and PGV) for each site under rock site conditions. The estimated ground motions were corrected at each site for local site amplification according to the Vs30 database. Our estimated seismic intensity maps and field reports showed high similarity, which further validated the effectiveness of the novel approach, and pushed the limit of earthquake size down to ∼M 6. Such efforts would substantially help in the fast and accurate evaluation of earthquake damage, and precise rescue efforts. [ABSTRACT FROM AUTHOR]

Puccinia striiformis f. sp. tritici Eriks., the cause of wheat yellow or stripe rust on wheat, undergoes sexual reproduction on barberry, but it is unclear if barberry plays any role in stripe rust epidemics under natural conditions. P. striiformis f. sp. tritici was isolated from its alternate host barberry (Berberis spp.) and primary host wheat in the vicinity of barberry by inoculation of aeciospores and urediniospores on Mingxian 169 cultivar in Qinghai province of China in 2018. The P. striiformis f. sp. tritici isolates from barberry and wheat were characterized to virulence patterns by inoculation on 24 differentials bearing Yr gene under control conditions and analyzed using 12 polymorphic simple sequence repeat (SSR) markers. The occurrence frequency of P. striiformis f. sp. tritici on barberry was 1.87% by inoculation aecia, collected from barberry on Mingxian 169 of wheat. A close virulence relationship was presented between P. striiformis f. sp. tritici isolates from both barberry and wheat based on virulence simple matching coefficient and principal coordinates analysis (PCoA). Additionally, the same genetic ancestry, based on structure analysis by STRUCTURE program and genetic relationship analyses using discriminant analysis of principal components and PCoA, was shared between P. striiformis f. sp. tritici isolates from barberry and those from wheat. Together, all the results indicated that the role of barberry in providing aeciospores as an inoculum source causing wheat stripe rust epidemic in Qinghai in spring is of considerable importance. [ABSTRACT FROM AUTHOR]