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Liu Chao,Liu Hongling,Wu Xinglong,Xiao Kejun,He Hengguo,Huang Qiong,Pu Deqiang 한국곤충학회 2022 Entomological Research Vol.52 No.4
The ladybeetle Coccinella septempunctata Linnaeus is an important natural enemy of aphids, scale insects, whitefly, and lepidopteran larvae. Mass rearing of this natural enemy is limited due to the lack of effective artificial feed. We compared the biological performance of C. septempunctata, reared on four artificial diets (A, B, C, and D), while the pea aphid Aphis craccivora served as a control treatment (CK). Results showed that the developmental time before emergence ranked from short to long follow as: CK (12.30d)<D(16.66d)<A(17.38d)<C (17.54d)<B (18.57d). The eclosion rate of larvae ranked from high to low follow as: CK (90.00%) > C (87.50%) = D (87.50%) > B(80.00%)> A (57.50%), and new adult weight from high to low follow as: CK (339.50 g*0.0001) > A (205.33 g*0.0001) > D (197.68 g*0.0001) > B (174.89 g*0.0001) > C (169.46 g*0.0001). The ratios of fecundity between the experimental group and the control group were 80.46% (A), 39.24% (B), 45.31% (C), and 53.02% (D). The hatch rates were 59.50% (A), 46.00% (B), 57.65% (C), 54.50% (D), and 53.88% (CK). The mortality of C. septempunctata adults fed on a combined artificial diet was higher than those fed on the control diet. Compared to the control diet, C. septempunctata did not significantly reduce oviposition when fed on artificial diet A. Therefore, diet A can be used in mass-rearing of C. septempunctata.
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Xinyu Cao,Yin Fang,Chunguang Yang,Zhenghao Liu,Guoping Xu,Yan Jiang,Peiyan Wu,Wenbo Song,Hanshuo Xing,Xinglong Wu 대한배뇨장애요실금학회 2024 International Neurourology Journal Vol.28 No.1
Purpose: Prostate cancer (PCa) is an epithelial malignancy that originates in the prostate gland and is generally categorized into low, intermediate, and high-risk groups. The primary diagnostic indicator for PCa is the measurement of serum prostate-specific antigen (PSA) values. However, reliance on PSA levels can result in false positives, leading to unnecessary biopsies and an increased risk of invasive injuries. Therefore, it is imperative to develop an efficient and accurate method for PCa risk stratification. Many recent studies on PCa risk stratification based on clinical data have employed a binary classification, distinguishing between low to intermediate and high risk. In this paper, we propose a novel machine learning (ML) approach utilizing a stacking learning strategy for predicting the tripartite risk stratification of PCa. Methods: Clinical records, featuring attributes selected using the lasso method, were utilized with 5 ML classifiers. The outputs of these classifiers underwent transformation by various nonlinear transformers and were then concatenated with the lasso-selected features, resulting in a set of new features. A stacking learning strategy, integrating different ML classifiers, was developed based on these new features. Results: Our proposed approach demonstrated superior performance, achieving an accuracy of 0.83 and an area under the receiver operating characteristic curve value of 0.88 in a dataset comprising 197 PCa patients with 42 clinical characteristics. Conclusions: This study aimed to improve clinicians’ ability to rapidly assess PCa risk stratification while reducing the burden on patients. This was achieved by using artificial intelligence-related technologies as an auxiliary method for diagnosing PCa.
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