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| 1 | Improvement of X-Band Polarization Radar Melting Layer Recognition by the Bayesian Method and ITS Impact on Hydrometeor Classification显示文摘Using melting layer(ML)and non-melting layer(NML)data observed with the X-band dual linear polarization Doppler weather radar(X-POL)in Shunyi,Beijing,the reflectivity(ZH),differential reflectivity(ZDR),and correlation coefficient(CC)in the ML and NML are obtained in several stable precipitation processes.The prior probability density distributions(PDDs)of the ZH,ZDR and CC are calculated first,and then the probabilities of ZH,ZDR and CC at each radar gate are determined(PBB in the ML and PNB in the NML)by the Bayesian method.When PBB>PNB the gate belongs to the ML,and when PBBPNB the gate belongs to the ML,and when PBB | Jianli MA Zhiqun HU Meilin YANG Siteng LI | 2020 | Advances in Atmospheric Sciences2020，37，1： | 4 |
| 2 | New Homogeneously Doped Fe(Ⅲ)-TiO2 Photocatalyst for Gaseous Pollutant Degradation显示文摘 | SITENG Tieng ANDREI Kanaev KHAY Chhor | | 0，，： | 1 |
| 3 | Chlorinated and polycyclic aromatic hydrocarbons in riverine and cstuarine sediments from Pearl River Delta, China显示文摘 | MAI B X FU J M SItENG G Y | 2002 | Environmental Pollution2002，117，： | 1 |
| 4 | Distribution of particulate- and vapor-phase n-alkancs and polynuclcar aromatic hydrocarbons in urball atmosphere of Guangzhou, China显示文摘 | BI X It SItENG G Y PENG E | 2003 | Atmospheric Environment2003，37，： | 1 |
| 5 | Integrative analysis of ferroptosis regulators for clinical prognosis based on deep learning and potential chemotherapy sensitivity of prostate cancer显示文摘Exploring useful prognostic markers and developing a robust prognostic model for patients with prostate cancer are crucial for clinical practice.We applied a deep learning algorithm to construct a prognostic model and proposed the deep learning-based ferroptosis score(DLFscore)for the prediction of prognosis and potential chemotherapy sensitivity in prostate cancer.Based on this prognostic model,there was a statistically significant difference in the disease-free survival probability between patients with high and low DLFscore in the The Cancer Genome Atlas(TCGA)cohort(P<0.0001).In the validation cohort GSE116918,we also observed a consistent conclusion with the training set(P=0.02).Additionally,functional enrichment analysis showed that DNA repair,RNA splicing signaling,organelle assembly,and regulation of centrosome cycle pathways might regulate prostate cancer through ferroptosis.Meanwhile,the prognostic model we constructed also had application value in predicting drug sensitivity.We predicted some potential drugs for the treatment of prostate cancer through AutoDock,which could potentially be used for prostate cancer treatment. | Tuanjie Guo Zhihao Yuan Tao Wang Jian Zhang Heting Tang Ning Zhang Xiang Wang Siteng Chen | 2023 | Precision Clinical Medicine2023，6，1： | 0 |
| 6 | Artificial intelligence-based non-invasive tumorsegmentation, grade stratification and prognosisprediction for clear-cell renal-cell carcinoma显示文摘Due to the complicated histopathological characteristics of clear-cell renal-cell carcinoma(ccRcC),non-invasive prognosis before operative treatment is crucial in selecting the appropriate treatment.A total of 126345 computerized tomography(cT)images from four independent patient cohorts were included for analysis in this study.We propose a V Bottieneck multi-resolution and focus-organ network(VB-MrFo-Net)using a cascade framework for deep learning analysis.The VB-MrFo-Net achieved better performance than VB-Net in tumor segmentation,with a Dice score of 0.87.The nuclear-grade prediction model performed best in the logistic regression classifier,with area under curve values from 0.782 to 0.746.Survival analysis revealed that our prediction model could significantly distinguish patients with high survival risk,with a hazard ratio(HR)of 2.49[95%confidence interval(CI):1.13-5.45,P=0.023]in the General cohort.Excellent performance had also been verified in the Cancer Genome Atlas cohort,the Clinical Proteomic Tumor Analysis Consortium cohort,and the Kidney Tumor Segmentation Challenge cohort,with HRs of 2.77(95%CI:1.58-4.84,P=0.0019),3.83(95%CI:1.22-11.96,P=0.029),and 2.80(95%CI:1.05-7.47,P=0.025),respectively.In conclusion,we propose a novel VB-MrFo-Net for the renal tumor segmentation and automatic diagnosis of ccRcc.The risk stratification model could accurately distinguish patients with high tumor grade and high survival risk based on non-invasive CT images before surgical treatments,which couid provide practical advicefordecidingtreatmentoptions. | Siteng Chen Dandan Song Lei Chen Tuanjie Guo Beibei Jiang Aie Liu Xianpan Pan Tao Wang Heting Tang Guihua Chen Zhong Xue Xiang Wang Ning Zhang Junhua Zheng | 2023 | Precision Clinical Medicine2023，6，3： | 0 |
| 7 | A New X-band Weather Radar System with Distributed Phased-Array Front-ends: Development and Preliminary Observation Results显示文摘A novel weather radar system with distributed phased-array front-ends was developed. The specifications and preliminary data synthesis of this system are presented, which comprises one back-end and three or more front-ends. Each front-end, which utilizes a phased-array digital beamforming technology, sequentially transmits four 22.5°-width beams to cover the 0°–90° elevational scan within about 0.05 s. The azimuthal detection is completed by one mechanical scan of0°–360° azimuths within about 12 s volume-scan update time. In the case of three front-ends, they are deployed according to an acute triangle to form a fine detection area(FDA). Because of the triangular deployment of multiple phased-array front-ends and a unique synchronized azimuthal scanning(SAS) rule, this new radar system is named Array Weather Radar(AWR). The back-end controls the front-ends to scan strictly in accordance with the SAS rule that assures the data time differences(DTD) among the three front-ends are less than 2 s for the same detection point in the FDA. The SAS can maintain DTD < 2 s for an expanded seven-front-end AWR. With the smallest DTD, gridded wind fields are derived from AWR data, by sampling of the interpolated grid, onto a rectangular grid of 100 m ×100 m ×100 m at a 12 s temporal resolution in the FDA. The first X-band single-polarized three-front-end AWR was deployed in field experiments in 2018 at Huanghua International Airport, China. Having completed the data synthesis and processing, the preliminary observation results of the first AWR are described herein. | Xiaoqiong ZHEN Shuqing MA Hongbin CHEN Guorong WANG Xiaoping XU Siteng LI | 2022 | Advances in Atmospheric Sciences2022，39，3： | 0 |
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