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Grain size distribution and chemistry of the brackish Lake sediment in Korea
I Chan Shin,Tetsuji Akatsuka,Hisayuki Azumi,Lan Ao,Nozomi Amahashi,Maki Oyagi,Noriko Ishida,Naoshige Goto,Masahiro Maruo,Akihiko Yagi,Yashshi Seike,Seung Hyun Lee,Sung Ae Yoon,Jun Kil Choi,Young Woong 대한환경공학회 2021 Environmental Engineering Research Vol.26 No.5
To determine the grain size distribution and chemical characteristics of bottom surface sediments in shallow brackish lagoons, we studied sediment samples collected from the entire horizontal lake area and in vertical profiles from three stations in Lakes Youngrang and Hwajinpo, on the eastern coast of Korea. Vertical and horizontal grain size distributions of the bottom sediments indicated predominantly sand- and silt in both lakes. The vertical distribution of C/N ratios ranged from 6.14 to 11.92 in Lake Youngrang, and 6.74 to 12.34 in Lake Hwajinpo. The horizontal distribution of C/N ratios in Lake Youngrang ranged from 6.1 to 17.6, whereas they ranged from 4.4 to 12.1 in Lake Hwajinpo. C/N ratios showed locally different responses to the origin of allochthonous (partial region) and autochthonous (entire region) organic materials. Horizontally, bottom sediment with low δ<SUP>13</SUP>C and high δ<SUP>15</SUP>N in Lake Youngrang were likely to be influenced by autochthonous organic material derived from primary production, and would be affected by N inputs from sources. In contrast, high δ<SUP>13</SUP>C and low δ<SUP>15</SUP>N sediments in Lake Hwajinpo were likely to be influenced by cyanobacteria.
Takayuki Takahashi,Hikaru Matsuoka,Rieko Sakurai,Jun Akatsuka,Yusuke Kobayashi,Masaru Nakamura,Takashi Iwata,Kouji Banno,Motomichi Matsuzaki,Jun Takayama,Daisuke Aoki,Yoichiro Yamamoto,Gen Tamiya 대한부인종양학회 2022 Journal of Gynecologic Oncology Vol.33 No.5
Objective: Human papillomavirus subtypes are predictive indicators of cervical intraepithelial neoplasia (CIN) progression. While colposcopy is also an essential part of cervical cancer prevention, its accuracy and reproducibility are limited because of subjective evaluation. This study aimed to develop an artificial intelligence (AI) algorithm that can accurately detect the optimal lesion associated with prognosis using colposcopic images of CIN2 patients by utilizing objective AI diagnosis. Methods: We identified colposcopic findings associated with the prognosis of patients with CIN2. We developed a convolutional neural network that can automatically detect the rate of high-grade lesions in the uterovaginal area in 12 segments. We finally evaluated the detection accuracy of our AI algorithm compared with the scores by multiple gynecologic oncologists. Results: High-grade lesion occupancy in the uterovaginal area detected by senior colposcopists was significantly correlated with the prognosis of patients with CIN2. The detection rate for high-grade lesions in 12 segments of the uterovaginal area by the AI system was 62.1% for recall, and the overall correct response rate was 89.7%. Moreover, the percentage of high-grade lesions detected by the AI system was significantly correlated with the rate detected by multiple gynecologic senior oncologists (r=0.61). Conclusion: Our novel AI algorithm can accurately determine high-grade lesions associated with prognosis on colposcopic images, and these results provide an insight into the additional utility of colposcopy for the management of patients with CIN2.