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【Objective】 Transpiration is not only an important hydrological process but also a footprint of primary productivity of terrestrial systems under different environments. Its calculation at large scale is challenging. This paper proposes a method to fill this gap. 【Method】 The method is based on the modified Penman-Monteith (PM) model. The canopy resistance in the model is estimated using soil moisture based on the Ortega-Farias model, and the hydrodynamic resistance is calculated by two methods: one is based on wind speed and canopy temperature-environment relationship, and the other one is the Perrier logarithm method. The model was applied to simulate transpiration of citrus trees under water stress from June to July in 2022; its accuracy was evaluated against ground-true data. 【Result】 Water stress significantly reduced stomatal conductance and transpiration of the citrus trees. The canopy resistance estimated by the Ortega-Farias model agreed with the measured data with R2 = 0.63, accurately capturing the diurnal variation in canopy resistance under water stress. The aerodynamic resistance calculated by the leaf-based method was significantly greater than estimated by the Perrier logarithmic method. The accuracy of aerodynamic resistance calculated by the Perrier logarithmic method varied with soil water content, and the R2 between the measured and calculated transpiration from the PM model decreased from 0.66 when the plant was under slight water stress to 0.2 when the plant was under severe stress. In contrast, for the transpiration calculated using the aerodynamic resistances estimated by the leaf-simulation method, the R2 decreased from 0.71 to 0.24 with the increase in water stress. 【Conclusion】 When the soil moisture was greater than 50% of the field capacity, using the Ortega-Farias model to estimate canopy resistance and the leaf-based method to estimate aerodynamic resistance can improve the accuracy of the PM model for simulating transpiration of the citrus trees under water stress.

Cell-free circulating tumor DNA (ctDNA) is synthesized by tumor cells, including metastatic tumors, and circulates in the bloodstream. Evidence suggests that ctDNA is a potential predictive and prognostic biomarker for colorectal cancer (CRC), but its predictive efficacy in detecting CRC liver metastasis (CLM) remains unclear. Additionally, its utility in the clinical setting needs further investigation. We conducted a meta-analysis to determine the utility of ctDNA as a biomarker for predicting the prognosis of CLM and investigate the relationship between CLM and ctDNA positivity. A literature search was performed in electronic databases to identify relevant studies published up to March 19, 2022. We retrieved data on overall survival (OS), disease-free survival (DFS), and recurrence-free survival (RFS) for both ctDNA-positive and ctDNA-negative colorectal liver metastasis (CLM) patients from the selected articles. Hazard ratios (HRs) were also calculated for these survival outcomes analysis was also performed. The stability of the combined meta-analysis was verified by sensitivity analysis and publication bias evaluation. Ten trials were included, and 615 patients were evaluated. In patients with CLM, pooled HRs revealed a substantial link between ctDNA positivity and RFS/DFS. Subgroup analysis revealed that ctDNA had a prospective detection value. Sensitivity analysis and publication bias evaluation indicated stable results. Although the results on pooled HR for OS suggested that ctDNA-positive patients had a shorter survival time, their pooled HRs had a relatively evident heterogeneity, and sensitivity analysis and publication bias evaluation indicated that pooled HRs were extremely unstable. In conclusion, our results demonstrate that ctDNA appears to be a prognostic biomarker for resectable CLM patients.

The frequency analysis method is commonly used to calculate design floods. Under the double challenge of the non‐stationary situation under the changing environment and the inadequate length of flood series, developing a new method to integrate the historical extraordinary floods into the non‐stationary frequency analysis is essential. First, the Multi‐Model Ensemble projections of temperature and precipitation based on Global Climate Model outputs were employed to drive the Soil & Water Assessment Tool hydrological model for runoff simulation. Then, the Integrated Time‐Varying Moment (ITVM) model was developed to re‐analyze the design floods based on the Pearson‐III distribution. The calibrated SWAT model can satisfactorily simulate the rainfall‐runoff relationship in the Yalong River basin. The developed ITVM model is effective to conduct the design flood frequency analysis to cope with the problems of insufficient length and non‐stationarity of the flood series. The design flood values of Maidilong station show an obvious increase, with variations of 6.5%–9.4%, 2.9%–12.3%, and 16%–33.7% for SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5, respectively. The significant increase of low frequencies (p = 0.2%, p = 0.1%) floods, especially for SSP5‐8.5 scenario, requires more attention, as the increased floods may exceed the discharge capacity of the reservoir determined at the design stage. [ABSTRACT FROM AUTHOR]

Variable heat fluxes over the Tibetan Plateau (TP) interact thermally with the atmosphere, affecting weather in surrounding areas, particularly in the Middle and Lower Yangtze River (MLYR). However, the circulation patterns and time-lag effects between TP heat fluxes and MLYR precipitation remain unclear. This study identified 577 large-scale daily heavy precipitation events (LSDHPEs) in the MLYR from 1980 to 2022. We analyzed the weather causation and spatiotemporal correlations between the TP surface heat fluxes and MLYR LSDHPEs using self-organizing map clustering, singular value decomposition, and harmonic analysis of time series. The results found two dominant synoptic patterns of LSDHPEs at 500 hPa: one, driven by anticyclonic and cyclonic circulations coinciding with shifts in the West Pacific subtropical high and South Asian high, increased from 2000 to 2022; the other, influenced by MLYR cyclonic circulation, showed a significant decrease. For the first time, we revealed lagged effects of the latent heat anomalies (with a lag time of 1–10 d and 130–200 d) and sensible heat anomalies (with a lag time of 2–4 months) over the TP during LSDHPEs in the MLYR. The results may enhance our understanding of TP heat flux anomalies as precursor signals for early warning of heavy rainfall and flooding in the MLYR. [ABSTRACT FROM AUTHOR]

In this paper, the Artificial Neural Network (ANN) was utilized to predict the peak discharge of dam failures, which was based on the combined Genetic Algorithm (GA) and Back Propagation (BP) neural network. The dataset comprises 40 samples from self-conducted experiments and available literature. To compare the efficiency of the suggested approach, three evaluation metrics, including the coefficient of determination (R2), the root mean square error (RMSE) and the mean absolute error (MAE), were analyzed for both the BP neural network and the GA-BP neural network. The findings suggest that (1) The prediction accuracy of the GA-BP was better than that of the BP; and (2) Compared to BP, GA-BP demonstrated a 9.07% average improvement in R2, a 57.36% average reduction in MAE, and a 57.53% average reduction in RMSE. In addition, the results of GA-BP and semi-empirical formulas were compared and the effect of three parameters on the peak discharge was analyzed. The results showed that the GA-BP model could effectively predict the peak discharge of dam failures. [ABSTRACT FROM AUTHOR]

Neonatal hypoxic-ischemic encephalopathy (HIE) is a critical condition characterized by significant brain damage due to insufficient blood flow and oxygen delivery at birth, leading to high rates of neonatal mortality and long-term neurological deficits worldwide. 2,3-Diphosphoglyceric acid (2,3-DPG), a small molecule metabolite prevalent in erythrocytes, plays an important role in regulating oxygen delivery, but its potential neuroprotective role in hypoxic-ischemic brain damage (HIBD) has yet to be fully elucidated. Our research reveals that the administration of 2,3-DPG effectively reduces neuron damage caused by hypoxia-ischemia (HI) both in vitro and in vivo. We observed a notable decrease in HI-induced neuronal cell apoptosis, attributed to the downregulation of Bax and cleaved-caspase 3, alongside an upregulation of Bcl-2 expression. Furthermore, 2,3-DPG significantly alleviates oxidative stress and mitochondrial damage induced by oxygen-glucose deprivation/reperfusion (OGD/R). The administration of 2,3-DPG in rats subjected to HIBD resulted in a marked reduction in brain edema and infarct volume, achieved through the suppression of neuronal apoptosis and neuroinflammation. Using RNA-seq analysis, we validated that 2,3-DPG offers protection against neuronal apoptosis under HI conditions by modulating the p38 MAPK pathway. These insights indicated that 2,3-DPG might act as a promising novel therapeutic candidate for HIE. [ABSTRACT FROM AUTHOR]

The stationarity hypothesis of hydrometeorological elements has been questioned in the context of global warming and intense human disturbance. The conventional drought index and methods of frequency analysis may no longer be applicable for hydrological drought risk evaluation under a changing environment. In this study, a new dynamic hydrological drought risk evaluation framework is proposed for application to the Hanjiang River basin (HRB), which simultaneously considers the non‐stationarity in the construction of drought index as well as in the frequency analysis. First, a non‐stationary standardized runoff index (NSRI) is developed using a generalized additive model for location, scale and shape (GAMLSS) framework. Then, hydrological drought characteristics including duration and severity are identified, and their marginal distributions are established. Finally, based on the dynamic copula, considering the non‐stationarity of the dependence structure, the dynamic joint probability distribution, conditional probability distribution and return period of the bivariate hydrological drought properties are analysed. Results showed that NSRI, which integrates the impacts of climate change and anthropogenic activities on the non‐stationarity of runoff series, had a better ability to capture runoff extremes than had SRI. In addition, it is indispensable to consider the non‐stationarity of the dependence structure between variables when discussing the multivariate joint risk of hydrological drought. The risk of hydrological drought in the study area has shown an increasing trend in the past 65 years, and the drought conditions from upstream to downstream have been alleviated first and then intensified. This study provides valuable information for regional drought risk estimation and water resources management from a non‐stationary perspective. [ABSTRACT FROM AUTHOR]