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Background Gleason score (GS) is a histologic prognostic factor and the basis of treatment decision‐making for prostate cancer (PCa). Treatment regimens between lower‐grade (GS ≤7) and high‐grade (GS >7) PCa differ largely and have great effects on cancer progression. Purpose To investigate the use of different sequences in biparametric MRI (bpMRI) of the prostate gland for noninvasively distinguishing high‐grade PCa. Study Type Retrospective. Population In all, 489 patients (training cohort: N = 326; test cohort: N = 163) with PCa between June 2008 and January 2018. Field Strength/Sequence 3.0T, pelvic phased‐array coils, bpMRI including T2‐weighted imaging (T2WI) and diffusion‐weighted imaging (DWI); apparent diffusion coefficient map extracted from DWI. Assessment The whole prostate gland was delineated. Radiomic features were extracted and selected using the Kruskal–Wallis test, the minimum redundancy‐maximum relevance, and the sequential backward elimination algorithm. Two single‐sequence radiomic (T2WI, DWI) and two combined (T2WI‐DWI, T2WI‐DWI‐Clinic) models were respectively constructed and validated via logistic regression. Statistical Tests The Kruskal–Wallis test and chi‐squared test were utilized to evaluate the differences among variable groups. P < 0.05 determined statistical significance. The area under the receiver operating characteristic curve (AUC), specificity, sensitivity, and accuracy were used to evaluate model performance. The Delong test was conducted to compare the differences between the AUCs of all models. Result All radiomic models showed significant (P < 0.001) predictive performances. Between the single‐sequence radiomic models, the DWI model achieved the most encouraging results, with AUCs of 0.801 and 0.787 in the training and test cohorts, respectively. For the combined models, the T2WI‐DWI models acquired an AUC of 0.788, which was almost the same with DWI in the test cohort, and no significant difference was found between them (training cohort: P = 0.199; test cohort: P = 0.924). Data Conclusion Radiomics based on bpMRI can noninvasively identify high‐grade PCa before the operation, which is helpful for individualized diagnosis of PCa. Level of Evidence 4 Technical Efficacy Stage 2 J. Magn. Reson. Imaging 2020;52:1102–1109.

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Vegetation deterioration and soil loss are the main causes of more precipitation leakages and surface water shortages in degraded karst areas. In order to improve the utilization of water resources in such regions, water storage engineering has been considered; however, site selection and cost associated with the special karstic geological structure have made this difficult. According to the principle of the Soil Plant Atmosphere Continuum, increasing both vegetation cover and soil thickness would change water cycle process, resulting in a transformation from leaked blue water (liquid form) into green water (gas or saturated water form) for terrestrial plant ecosystems, thereby improving the utilization of water resources. Using the Soil Vegetation Atmosphere Transfer model and the geographical distributed approach, this study simulated the conversion from leaked blue water (leakage) into green water in the environs of Guiyang, a typical degraded karst area. The primary results were as follows: (1) Green water in the area accounted for <50% of precipitation, well below the world average of 65%; (2) Vegetation growth played an important role in converting leakage into green water; however, once it increased to 56%, its contribution to reducing leakage decreased sharply; (3) Increasing soil thickness by 20 cm converted the leakage considerably. The order of leakage reduction under different precipitation scenarios was dry year > normal year > rainy year. Thus, increased soil thickness was shown effective in improving the utilization ratio of water resources and in raising the amount of plant ecological water use; (4) The transformation of blue water into green water, which avoids constructions of hydraulic engineering, could provide an alternative solution for the improvement of the utilization of water resources in degraded karst area. Although there are inevitable uncertainties in simulation process, it has important significance for overcoming similar problems.

Abstract. A modern pollen dataset with an even distribution of sites is essential for pollen-based past vegetation and climate estimations. As there were geographical gaps in previous datasets covering the central and eastern Tibetan Plateau, lake surface-sediment samples (n=117) were collected from the alpine meadow region on the Tibetan Plateau between elevations of 3720 and 5170 m a.s.l. Pollen identification and counting were based on standard approaches, and modern climate data were interpolated from a robust modern meteorological dataset. A series of numerical analyses revealed that precipitation is the main climatic determinant of pollen spatial distribution; Cyperaceae, Ranunculaceae, Rosaceae, and Salix indicate wet climatic conditions, while Poaceae, Artemisia, and Chenopodiaceae represent drought. Model performance of both weighted-averaging partial least squares (WA-PLS) and the random forest (RF) algorithm suggest that this modern pollen dataset has good predictive power in estimating the past precipitation for pollen spectra from the eastern Tibetan Plateau. In addition, a comprehensive modern pollen dataset can be established by combining our modern pollen dataset with previous datasets, which will be essential for the reconstruction of vegetation and climatic signals for fossil pollen sprecta on the Tibetan Plateau. Pollen datasets including both pollen counts and percentages for each sample together with their site location and climatic data are available at the National Tibetan Plateau Data Center (TPDC; DOI: 10.11888/Paleoenv.tpdc.271191).

Patients suffered from ITON without OCF before ETOCD have better surgical outcome than those with OCF. Older in age, longer time to medical treatment and existence of OCF are independent factors for poor VA prognosis and lower IDVA. Preoperative VA is independent factor for VA prognosis only.