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Simple Summary: Given the variable aggressiveness of PCa, patients with indolent PCa do not require intervention, but rather require active surveillance and close lifelong follow-up, while those with invasive PCa require surgery, various types of radiation therapy, androgen-deprivation therapy (ADT), or multimodal treatment. Hence, it is critical to accurately distinguish indolent from invasive PCa for prognosis evaluation and treatment decision-making. The aim of the present study was to investigate the value of MR radiomics feature-based machine learning (ML) models in predicting the Ki67 index and Gleason grade group (GGG) of PCa. Biparametric magnetic resonance imaging (bpMRI) radiomics-based ML models to predict immuno-histochemically-determined Ki67 expression and the GGG demonstrated the ability to identify aggressive PCa. A preliminary exploration was performed in the conjoint analysis, laying the theoretical foundation for models predicting two or more variables; such models are expected to provide more comprehensive pathological information and provide valuable guidance for clinical decision-making in a noninvasive, synchronous, and objective manner. Purpose: The Ki67 index and the Gleason grade group (GGG) are vital prognostic indicators of prostate cancer (PCa). This study investigated the value of biparametric magnetic resonance imaging (bpMRI) radiomics feature-based machine learning (ML) models in predicting the Ki67 index and GGG of PCa. Methods: A total of 122 patients with pathologically proven PCa who had undergone preoperative MRI were retrospectively included. Radiomics features were extracted from T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps. Then, recursive feature elimination (RFE) was applied to remove redundant features. ML models for predicting Ki67 expression and GGG were constructed based on bpMRI and different algorithms, including logistic regression (LR), support vector machine (SVM), random forest (RF), and K-nearest neighbor (KNN). The performances of different models were evaluated with receiver operating characteristic (ROC) analysis. In addition, a joint analysis of Ki67 expression and GGG was performed by assessing their Spearman correlation and calculating the diagnostic accuracy for both indices. Results: The ML model based on LR and ADC + T2 (LR_ADC + T2, AUC = 0.8882) performed best in predicting Ki67 expression, and ADC_wavelet-LHH_firstorder_Maximum had the highest feature weighting. The SVM_DWI + T2 (AUC = 0.9248) performed best in predicting GGG, and DWI_wavelet HLL_glcm_SumAverage had the highest feature weighting. The Ki67 and GGG exhibited a weak positive correlation (r = 0.382, p < 0.001), and LR_ADC + DWI had the highest diagnostic accuracy in predicting both (0.6230). Conclusion: The proposed ML models are suitable for predicting both Ki67 expression and GGG in PCa. This algorithm could be used to identify indolent or invasive PCa with a noninvasive, repeatable, and accurate diagnostic method. [ABSTRACT FROM AUTHOR]

Climate change is an important issue that affects both global warming and precipitation, and the main cause is increased N2O emissions. Temperature and moisture are key factors in grassland ecosystem's response to climate change, and they can affect N2O fluxes. To clarify the impacts of warming and precipitation changes on N2O fluxes, an experiment was conducted in a semiarid steppe in Inner Mongolia, China over a nine‐year period (2011–2019). Plant productivity and soil nutrient dynamics were examined concurrently from 2017 to 2019, and N2O fluxes were monitored in response to different treatment conditions: control (C), warming (W), precipitation addition (P), and warming and precipitation addition (WP). The results showed that N2O emissions in the growing season were higher than those in the nongrowing season. Warming and precipitation addition had no significant effect on N2O fluxes compared with ambient conditions. Compared with P treatment, warming increased N2O flux in nongrowing season and decreased it in growing season. N2O flux was positively correlated with soil temperature and moisture (p < 0.05). Warming had a significant positive effect on soil NH4+‐N, whereas additional precipitation had a large positive effect on soil total nitrogen and soil nitrate nitrogen. With the WP treatment, soil microbial biomass nitrogen (MBN) and soil microbial biomass carbon (MBC) increased by 53.8% and 41.9%, respectively. The decrease in N2O emissions during the growing season in the W treatment compared with the P treatment may be largely attributed to the greater dominance of Leymus chinensis. The results highlight that changes in species dominance play an important role in regulating N2O emissions, and that the N2O fluxes in the nongrowing season account for a large proportion of the changes in N2O fluxes. Therefore, The warming effects on N2O emissions during nongrowing seasons should be further investigated. [ABSTRACT FROM AUTHOR]

The most common cause of hepatocellular carcinoma (HCC) is chronic hepatitis and cirrhosis. It is proposed that precancerous lesions of HCC include all stages of the disease, from dysplastic foci (DF), and dysplastic nodule (DN), to early HCC (eHCC) and progressed HCC (pHCC), which is a complex multi-step process. Accurately identifying precancerous hepatocellular lesions can significantly impact the early detection and treatment of HCC. The changes in high-grade dysplastic nodules (HGDN) were similar to those seen in HCC, and the risk of malignant transformation significantly increased. Nevertheless, it is challenging to diagnose precancerous lesions of HCC. We integrated the literature and combined imaging, pathology, laboratory, and other relevant examinations to improve the accuracy of the diagnosis of precancerous lesions. [ABSTRACT FROM AUTHOR]

Drought has a great impact on global terrestrial ecosystems. A large number of studies have shown that the impact of drought on vegetation growth has a lagged and cumulative effect, but it is unclear how much it contributes to different vegetation types. Therefore, based on the standardized precipitation evapotranspiration index (SPEI) base version 2.5 and the Global Inventory Monitoring and Modeling System (GIMMS3g) normalized difference vegetation index (NDVI) datasets, this study aimed to analyze the response process of different vegetation types to the cumulative and lagged effects of drought in the Mongolian Plateau during 1982–2015 using Pearson correlation and the Mann–Kendall mutation method and deeply explore the magnitude of the contribution of drought cumulative and lagged effects on vegetation using the multiple regression method. Our results show that, from 1982 to 2015 as a whole, NDVI showed an insignificant increasing trend, and SPEI had a significant mutation in 1998 and showed an insignificant increasing trend before and after 1998. Before 1998, the cumulative months were shorter (1–3 months) in the central steppe and agricultural vegetation zones, and the lagged months were longer (10–12 months) in the southeastern steppe and northeastern forest zones; after 1998, the cumulative months of NDVI increased (7–12 months) and the lag months decreased (3–8 months) in most vegetation zones. A comparison of the contribution of drought accumulation and lag to NDVI revealed that the main driver of NDVI has shifted from lagged to cumulative effect. [ABSTRACT FROM AUTHOR]

Peritoneal lymphomatosis is extremely rare and associated with poor prognosis. Most practitioners only pay more attention to peritoneal carcinomatosis. However, peritoneal lymphomatosis can be neglected and misdiagnosed. We report a teenager with 10 days of abdominal distension and pain accompanied by computed tomography scan suggesting diffuse thickening of the peritoneum and omentum and abdominopelvic effusion. Tuberculous peritonitis and peritoneal carcinomatosis were initially suspected. However, it was finally confirmed as non-Hodgkin's B-cell lymphoma by omentum biopsies. He achieved complete remission after chemotherapy and autologous stem cell transplantation. But unfortunately, he suffered a relapse and died 10 months after diagnosis. Following a review of the literature, it can be concluded that the discovery of lymphomatosis in peritoneum is a rare finding. Lymphoma should be considered in the differential diagnosis of unexplained peritoneal thickening on computed tomography, and this case emphasizes the importance of early pathological diagnosis to make sure that the right treatment can be started opportunely. [ABSTRACT FROM AUTHOR]

Reliability and accuracy of soil moisture datasets are essential for understanding changes in regional climate such as precipitation and temperature. Soil moisture datasets from the Essential Climate Variable (ECV), the Coupled Model Intercomparison Project Phase 5 (CMIP5), the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), the Global Land Data Assimilation System (GLDAS), and reanalysis products are widely used. These datasets generated by different techniques are compared in a common framework over China in this study. The comparison focuses on four aspects: spatial pattern, temporal correlation, long-term trend, and the relationships with precipitation and the Normalized Difference Vegetation Index (NDVI). The results indicate that all soil moisture datasets reach a good agreement on the spatial patterns of wet and dry soil. These patterns are also consistent with that of precipitation. However, there are considerable discrepancies in the absolute values of soil moisture among these datasets. In terms of unbiased Root-Mean-Square Difference (unRMSE, i.e., removing the differences in absolute values), all modeled datasets obtain performances comparable with ECV observations. Our results also suggest that a multi-model ensemble of soil moisture datasets can improve the representation of soil moisture conditions. The optimal dataset from which the wetting/drying trends in soil moisture have the highest consistency in terms of changes in precipitation and NDVI varies by season. Specifically, in spring, CMIP5 in northwest China shows that the trends in soil moisture are consistent with the changes in precipitation and NDVI. In summer, ECV presents the most identical performance compared to the changes in precipitation and NDVI. In autumn, GLDAS and Reanalysis have better performance in south China and parts of north China. In winter, GLDAS performs the best in the east of south China, followed by the Reanalysis dataset. These discrepancies among the datasets present various changes in different regions, which should be well noted and discussed before use. [ABSTRACT FROM AUTHOR]

The present study aimed to investigate the expression and regulation of microRNA-193b (miR-193b) in tissues and cells from esophageal cancer and Barrett's esophagus (BE). Surgical biopsies of esophageal lesions and adjacent normal tissues were obtained, and the miR‑193b expression and promoter methylation status were examined. Human BE and esophageal cancer cells were analyzed for miR‑193b expression and promoter methylation, with or without treatment with the hypomethylating agent 5‑azacytidine. Immunohistochemistry was performed to determine the expression and distribution of Kirsten rat sarcoma viral oncogene homolog (K‑Ras), a target of miR‑193b. miR‑193b expression was significantly downregulated in BE and esophageal cancer tissues compared with corresponding normal tissues. The miR‑193b level was significantly reduced in esophageal cancer compared with BE tissue. 5‑Azacytidine treatment resulted in a significant upregulation of miR‑193b in BE and esophageal cancer cells. Methylation‑specific polymerase chain reaction analysis and bisulfite pyrosequencing confirmed hypermethylation of miR‑193b promoter regions in esophageal cancer and BE cells, whereas hypermethylation was not observed in normal esophageal squamous epithelial cells. The methylation rate in BE and esophageal cancer tissues was significantly increased compared with the adjacent normal esophageal tissues. BE and esophageal cancer tissues exhibited increased K‑Ras protein expression levels compared with the adjacent normal tissues. To the best of our knowledge, this is the first report describing DNA methylation-mediated silencing of miR‑193b in esophageal cancer and BE tissues.