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        Prevalence and risk factors for incidental prostate cancer in patients after transurethral resection of the prostate with negative results on prostate biopsy: A retrospective study

        Zhenlang Guo,Junwei He,Jun Pan,Lijuan Huang,Jiadong Cao,Zunguang Bai,Shusheng Wang,Songtao Xiang,Chiming Gu,Zhaohui Wang 대한비뇨의학회 2022 Investigative and Clinical Urology Vol.63 No.2

        Purpose: This study aimed to explore the prevalence and predictors of incidental prostate cancer (IPC) after transurethral resection of the prostate (TURP) with negative results on transperineal magnetic resonance imaging (MRI)/transrectal ultrasonography (TRUS) fusion prostate biopsy or TRUS-guided prostate biopsy. Materials and Methods: Data of 253 patients who underwent TURP with a preliminary diagnosis of benign prostatic hyperplasia (BPH) were evaluated. The prevalence of IPC was calculated. Univariate and multivariate logistic regression analyses were conducted to explore independent predictive factors of IPC. Results: A total of 253 patients were included. IPC was diagnosed in 12 patients (4.7%). The mean age of the patients and the mean prostate volume were 69.8±7.07 years and 89.3±49.29 mL, respectively. The prevalence of IPC was higher in the TRUS guided prostate biopsy group than in the transperineal MRI/TRUS fusion prostate biopsy group (11 of 203 [5.4%] vs. 1 of 50 [2.0%], p=0.47), but the difference was not statistically significant. Our results indicated that older age (≥70 y) (odds ratio [OR], 1.14; 95% confidence interval [CI], 1.02–1.27; p=0.025) and smaller prostate volume (OR, 0.97; 95% CI, 0.938–0.998; p=0.039) were associated with an increased incidence of IPC after TURP. Conclusions: Our findings indicate that the prevalence of IPC may be higher among patients who undergo transrectal prostate biopsy before TURP than among those who undergo transperineal MRI/TRUS fusion prostate biopsy. Older age and smaller prostate volume were independent predictors of increasing the risk for IPC after TURP.

      • KCI등재

        Over-expression of a DUF1644 protein gene, SIDP361, enhances tolerance to salt stress in transgenic rice

        Min Li,Lijia Guo,Chiming Guo,Liangjiang Wang,Liang Chen 한국식물학회 2016 Journal of Plant Biology Vol.59 No.1

        Salt stress has adverse effects on the growth and production of rice crops. In this study, we isolated and characterized SIDP361, which encodes a DUF1644 family protein. This gene was expressed in various rice tissues and was induced by high salt (200 mM NaCl), dehydration, and abscisic acid (100 µM ABA) treatments. Stable expression of SIDP361-GFP in rice cells suggested that SIDP361 is a cytoplasmic protein. When compared with the untransformed wild-type (WT) control, transgenic plants over-expressing SIDP361 exhibited significantly improved tolerance to salt stress at both the seedling and heading stages. Under salinity conditions, the transgenics also had elevated amounts of free proline. Moreover, transcript levels for genes encoding proline synthetase enzymes were significantly higher in transformants than in the WT. The transgenic lines were also hypersensitive to exogenous ABA. Quantitative real-time PCR analysis showed that transcription of several stressrelated genes was greater in SIDP361-overexpressing plants than in the WT under both normal and salt-stressed conditions. These results demonstrate that SIDP361 has high potential as a tool for genetically improving salt tolerance in rice.

      • KCI등재

        Overexpression of the receptor-like cytoplasmic kinase gene XCRK enhances Xoc and oxidative stress tolerance in rice

        Yuxia Zhang,Xiaoling Guo,Yuchao Cui,Chiming Guo,Liang Chen 한국식물학회 2017 Journal of Plant Biology Vol.60 No.5

        In this paper, we characterized a differentiallyexpressed receptor-like cytoplasmic kinase XCRK, whichconfers resistance to bacterial leaf streak (BLS). We analyzedthe tissue expression of XCRK and showed that XCRK waswidely expressed in multiple rice (Oryza sativa) organs,including internodes, roots, leaves and flowers. In addition,the expression of XCRK was significantly induced by ABA,salt and H2O2 treatments, suggesting its function in thesepathways. The XCRK-overexpressing transgenic seedlingsexhibited higher tolerance to Xanthomonas oryzae pv.oryzicola(Xoc) compared with the wild-type seedlings. Furthermore,XCRK-overexpressing seedlings showed stronger antioxidantcapacity with reduced MDA and H2O2 content and higherantioxidant enzyme activities. It has been hypothesized thatthe enhanced Xoc tolerance was attributed to the improvedexpression of resistance-responsive factors positively regulatedby XCRK. In accordance with this, the expression of resistanceand oxidation-related genes Wrky77, Wrky13, PAL1, PR5,Fe-SOD and SodCc2 were up-regulated by the overexpressionof XCRK, which might contribute collectively to the increasedXoc tolerance. Overall, overexpression of XCRK couldenhance the antioxidant capacity and Xoc tolerance in rice.

      • KCI등재

        A new fault diagnosis method based on convolutional neural network and compressive sensing

        Yunfei Ma,Xisheng Jia,Huajun Bai,Guozeng Liu,Guanglong Wang,Chiming Guo,Shuangchuan Wang 대한기계학회 2019 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.33 No.11

        Compressive sensing is an efficient machinery monitoring framework, which just needs to sample and store a small amount of observed signal. However, traditional reconstruction and fault detection methods cost great time and the accuracy is not satisfied. For this problem, a 1D convolutional neural network (CNN) is adopted here for fault diagnosis using the compressed signal. CNN replaces the reconstruction and fault detection processes and greatly improves the performance. Since the main information has been reserved in the compressed signal, the CNN is able to extract features from it automatically. The experiments on compressed gearbox signal demonstrated that CNN not only achieves better accuracy but also costs less time. The influencing factors of CNN have been discussed, and we compared the CNN with other classifiers. Moreover, the CNN model was also tested on bearing dataset from Case Western Reserve University. The proposed model achieves more than 90 % accuracy even for 50 % compressed signal.

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