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It is essential to assess flash drought risk based on a reliable flash drought intensity (severity) index incorporating comprehensive information of the rapid decline ("flash") in soil moisture toward drought conditions and soil moisture thresholds belonging to the "drought" category. In this study, we used the Gan River basin as an example to define a flash drought intensity index that can be calculated for individual time steps (pentads) during a flash drought period over a given grid (or station). The severity of a complete flash drought event is the sum of the intensity values during the flash drought. We explored the spatial and temporal characteristics of flash droughts with different grades based on their respective severities. The results show that decreases in total cloud cover, precipitation, and relative humidity, as well as increases in 500-hPa geopotential height, convective inhibition, temperature, vapor pressure deficit, and wind speed can create favorable conditions for the occurrence of flash droughts. Although flash droughts are relatively frequent in the central and southern parts of the basin, the severity is relatively high in the northern part of the basin due to longer duration. Flash drought severity shows a slightly downward trend due to decreases in frequency, duration, and intensity from 1961 to 2018. Extreme and exceptional flash droughts decrease significantly while moderate and severe flash droughts trend slightly upward. Flash drought severity appears to be more affected by the interaction between duration and intensity as the grade increases from mild to severe. The frequency and duration of flash droughts are higher in July–October. The southern part of the basin is more prone to moderate and severe flash droughts, while the northern parts of the basin are more vulnerable to extreme and exceptional flash droughts due to longer durations and greater severities than other parts. Moderate, severe, extreme, and exceptional flash droughts occurred at approximately 3–6-, 5–15-, 10–50-, and 30–200-yr intervals, respectively, based on the copula analysis. [ABSTRACT FROM AUTHOR]

Future changes in the frequency of extreme drought events are of vital importance for risk assessment and relevant policy making. But a reliable estimation of their probability is intrinsically challenging due to limited available observations or simulations. Here, we use two large ensemble simulations, 50 members from CanESM2 and 40 members from CESM1 under the future RCP8.5 scenario, to elaborate a reliable projection of the 100-yr drought events (once in a century) under different warming levels. It is however necessary to first remove systematic biases for the simulated temperature and precipitation through a bias-correction method based on quantile mapping. Droughts are diagnosed with the standardized precipitation evapotranspiration index (SPEI), which considers both precipitation and potential evapotranspiration (PET, involving temperature). The results show that the frequency of extreme droughts increases with the continued global warming. Some differences between the two ensembles are also observed, especially for high warming levels. The China-averaged probability of 100-yr droughts that occur once in a century in the current climate increase by a factor of 1.52 (1.44) and 1.90 (2.02) under 1.5°C and 2°C warming levels in CanESM2-LE (CESM1-LE), respectively. A simple statistical scheme shows that the increasing future risk of extreme droughts is mainly due to the increasing effect of PET on the occurrence of extreme drought events, while the effect of precipitation almost keeps constant with global warming. [ABSTRACT FROM AUTHOR]

Objective: Compared with normal birth weight fetuses (2,500-4,000 g), accurate fetal weight estimation for fetuses with low or excessive weight is considered more important for fetal compromise prediction and labor management. New formulas were developed to estimate weight for low birth weight (LBW) fetuses and macrosomic fetuses, respectively. Methods: A total of 523 fetuses with birth weight less than 2,500 g, 652 fetuses with normal birth weight, and 239 fetuses with birth weight more than 4,000 g were included in the study. As much as 25 existing formulas which incorporate regularly defined fetal measurements were evaluated and compared. Performance evaluation of existing formulas showed that no formulas can provide consistently accurate weight estimation both for LBW fetuses and macrosomic fetuses. A total of 1,034 cases were utilized to generate an overall regression formula. If the pre-estimated weight fell into the suspicious LBW and macrosomia range, the value was then updated by using the new regression formulas for LBW fetuses and macrosomic fetuses. As the training group, 262 LBW fetuses and 120 macrosomic fetuses were employed in a stepwise linear regression to obtain two update regression formulas for suspicious LBW fetuses and macrosomic fetuses. As the validation group, another 261 LBW fetuses and 119 macrosomic fetuses were assessed. Results: The new overall formula has the form of Log(10)BW = 0.180(HC) + 0.00628(AC) - 0.00318(HC)(2) + 0.00173(AC)(FL) + 0.0000430(BPD)(HC)(2). The update formula for suspicious LBW fetuses is LnBW = 1.470(BPD) + 0.0169(HC) - 0.0873(BPD)(2) + 0.00518(AC)(FL) and for macrosomic fetuses is Log(10)BW = 0.730(BPD) - 0.0375(BPD)(2) + 0.000264(AC)(FL). For LBW fetuses, the new method gave 7.6 ± 209.0 (g) of estimation error and 8.3 ± 7.8 (%) of absolute percentage error, while the best existing formula provided -0.7 ± 226.0 (g) and 9.1 ± 8.3 (%). With the new method, 71.3% of estimates fell within ±10% of the actual birth weight, while the best existing formula gave 65.5%. For macrosomic fetuses, the new method gave -87.9 ± 231.0 (g) of estimation error and 4.4 ± 3.9 (%) of absolute percentage error, while the best existing formula provided 115.6 ± 345.1 (g) and 6.8 ± 5.4 (%). With the new method, 89.1% of estimates fell within ±10% of the actual birth weight, while the best existing formula gave 75.6%. Conclusions: To improve the weight estimation accuracy for low or excessive weight fetuses, separate formulas are necessary. The new method provides significant improvement on fetal weight estimation for LBW fetuses and macrosomic fetuses. [ABSTRACT FROM AUTHOR]

Microvascularity is highly correlated with the grading and subtyping of gliomas, making this one of its most important histological features. Accurate quantitative analysis of microvessels is helpful for the development of a targeted therapy for antiangiogenesis. The deep-learning algorithm is by far the most effective segmentation and detection model and enables location and recognition of complex microvascular networks in large images obtained from hematoxylin and eosin (H&E) stained specimens. We proposed an automated deep-learning-based method to detect and quantify the microvascularity in glioma and applied it to comprehensive clinical analyses. A total of 350 glioma patients were enrolled in our study, for which digitalized imaging of H&E stained slides were reviewed, molecular diagnosis was performed and follow-up was investigated. The microvascular features were compared according to their histologic types, molecular types, and patients' prognosis. The results show that the proposed method can quantify microvascular characteristics automatically and effectively. Significant increases of microvascular density and microvascular area were observed in glioblastomas (95% p < 0.001 in density, 170% p < 0.001 in area) in comparison with other histologic types; increases were also observed in cases with TERT-mut only (68% p < 0.001 in density, 54% p < 0.001 in area) compared with other molecular types. Survival analysis showed that microvascular features can be used to cluster cases into two groups with different survival periods (hazard ratio [HR] 2.843, log-rank <0.001), which indicates the quantified microvascular features may potentially be alternative signatures for revealing patients' prognosis. This deep-learning-based method may be a useful tool in routine clinical practice for precise diagnosis and antiangiogenic treatment.