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With intensifying climate change, droughts over Southwest China (SWC) are drawing increasing interest. The droughts of 2009–2011 have attracted special concern. However, the roles of atmospheric evaporative demand [reflected by reference evapotranspiration (ET0)] and precipitation (P) anomalies in drought evolutions are still unclear. To that end, we revisited the evolution of the 2009–2011 SWC meteorological droughts quantitatively and comprehensively by quantifying the ET0 and P impacts. In terms of three drought parameters [i.e., drought intensity (DI), area (DA), and duration (DD)], SWC experienced the second most severe drought of the 1961–2012 period in 2009. Evident differences existed among the drought conditions in different months, i.e., more severe droughts occurred in February–March 2009, September 2009 to March 2010, and June–October 2011. Based on the annual means over drought regions, the 2009 and 2011 DI (DA) could be attributed to lower P, but the P and ET0 impacts [P, ET0, and P-and-ET0 (which reflects drought occurrence due to their combined impacts only)] were comparable in 2010. In addition, P reduction was responsible for DD during 2009–2011. Except in January–March, which were dominated mostly by P, DI (DA) was generally dominated by ET0 (ET0 or P-and-ET0) over the three years. While P dominated spatially and temporally for the majority of the 36 months for DI (DD), some areas were mainly affected by ET0 (ET0 or P-and-ET0). Therefore, to better understand drought evolutions and improve drought monitoring and forecasting, the impacts of ET0 anomalies should be fully considered. The results of this study provide an important reference framework for comprehensively understanding the underlying mechanisms of droughts not only in SWC but also in other regions around the world.

There is much uncertainty about how climate warming will impact greenhouse gases (GHG) budget in dry environments due to the lack of available data for desert biocrust soil. We implemented a 2.5-year field measurement of CO

Purpose: This study aimed to explore the predictive efficacy of radiomics analyses based on readout-segmented echo-planar diffusion-weighted imaging (RESOLVE-DWI) for prognosis evaluation in nasopharyngeal carcinoma in order to provide further information for clinical decision making and intervention. Methods: A total of 154 patients with untreated NPC confirmed by pathological examination were enrolled, and the pretreatment magnetic resonance image (MRI)—including diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) maps, T2-weighted imaging (T2WI), and contrast-enhanced T1-weighted imaging (CE-T1WI)—was collected. The Random Forest (RF) algorithm selected radiomics features and established the machine-learning models. Five models, namely model 1 (DWI + ADC), model 2 (T2WI + CE-T1WI), model 3 (DWI + ADC + T2WI), model 4 (DWI + ADC + CE-T1WI), and model 5 (DWI + ADC + T2WI + CE-T1WI), were constructed. The average area under the curve (AUC) of the validation set was determined in order to compare the predictive efficacy for prognosis evaluation. Results: After adjusting the parameters, the RF machine learning models based on extracted imaging features from different sequence combinations were obtained. The invalidation sets of model 1 (DWI + ADC) yielded the highest average AUC of 0.80 (95% CI: 0.79–0.81). The average AUCs of the model 2, 3, 4, and 5 invalidation sets were 0.72 (95% CI: 0.71–0.74), 0.66 (95% CI: 0.64–0.68), 0.74 (95% CI: 0.73–0.75), and 0.75 (95% CI: 0.74–0.76), respectively. Conclusion: A radiomics model derived from the MRI DWI of patients with nasopharyngeal carcinoma was generated in order to evaluate the risk of recurrence and metastasis. The model based on MRI DWI can provide an alternative approach for survival estimation, and can reveal more information for clinical decision-making and intervention.

China has released its ambitious target for carbon neutrality by 2060. With decades of top-down energy conservation and pollutant mitigation policies, the techno-mitigation space has gradually shrunk, while more mitigation space is required for a systematic approach. To help to uncover CO

Assessing satellite-based precipitation product capacity for detecting precipitation and linear trends is fundamental for accurately knowing precipitation characteristics and changes, especially for regions with scarce and even no observations. In this study, we used daily gauge observations across the Huai River Basin (HRB) during 1983–2012 and four validation metrics to evaluate the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) capacity for detecting extreme precipitation and linear trends. The PERSIANN-CDR well captured climatologic characteristics of the precipitation amount- (PRCPTOT, R85p, R95p, and R99p), duration- (CDD and CWD), and frequency-based indices (R10mm, R20mm, and Rnnmm), followed by moderate performance for the intensity-based indices (Rx1day, R5xday, and SDII). Based on different validation metrics, the PERSIANN-CDR capacity to detect extreme precipitation varied spatially, and meanwhile the validation metric-based performance differed among these indices. Furthermore, evaluation of the PERSIANN-CDR linear trends indicated that this product had a much limited and even no capacity to represent extreme precipitation changes across the HRB. Briefly, this study provides a significant reference for PERSIANN-CDR developers to use to improve product accuracy from the perspective of extreme precipitation, and for potential users in the HRB.

Land evaporation (ET) plays a crucial role in hydrological and energy cycle. However, the widely used numerical products are still subject to great uncertainties due to imperfect model parameterizations and forcing data. Lack of available observed data has further complicated the estimation. Hence, there is an urgency to define the global benchmark land ET for climate-induced hydrology and energy change. In this study, we have used the coefficient of variation (CV) and carefully selected merging regions with high consistency of multiple data sets. Reliability Ensemble Averaging (REA) method has been used to generate a long-term (1980–2017) daily ET product with a spatial resolution of 0.25 degree by merging the selected three data sets, ERA5, GLDAS2 and MERRA2. GLEAM3.2a and flux tower observation data have been selected as the data for reference and evaluation, respectively. The results showed that the merged product performed well under a variety of vegetation cover conditions as the weights were distributed across the east-west direction banding manner, with greater differences near the equator. The merged product also captured well the trend of land evaporation over different areas, showing the significant decreasing trend in Amazon plain in South America and Congo Basin in central Africa, and the increasing trend in the east of North America, west of Europe, south of Asia and north of Oceania. In addition to model performance, REA method also successfully worked for the model convergence showing as an outstanding reference for data merging of other variables. Data can be accessed at https://doi.org/10.5281/zenodo.4595941 (Lu et al., 2021).

The South Asian High (SAH) location and intensity are linked with the Tibetan Plateau (TP) and Yangtze River basin latent heating. The existing feedbacks of SAH variability is rarely linked with TP soil moisture regulated energy fluxes. In this study, remotely sensed soil moisture and global atmospheric reanalysis products are used to quantify the relationship between the TP spring (April, May, June) soil moisture with SAH and South Asian (SA) monsoon onset during 1988–2008. The diagnostic analysis infers that the SAH exhibits a significant correlation (R ≥ 0.90) with TP spring soil moisture and monsoon onset indices (R≥ -0.56−-0.61). During the early and late monsoon onset, a significant anomalous soil moisture regime influenced the surface energy fluxes, which affected the vertical diabatic heating profile. The diabatic heating profile affects the TP ascending motion and SAH intensity, which leads to regional monsoon circulation changes and onset. An asymmetric SAH movement in the upper troposphere appears before the early and late monsoon onset composites and drives the lower tropospheric westerlies/easterlies winds towards the continental SA. The wind shear and transition from prevailing easterlies into westerlies during the pre-onset, onset, and post-onset pentad results in strong/weak ascent in the Bay of Bengal and advances into continental regions. The onset- mechanism further suggested intensified/weaker westerlies/easterlies during early/late onset composites. The SAH intensity and movement are linked with TP soil moisture, which exhibits teleconnections with the regional circulation pattern. A detailed model experiment will be conducted to verify the influence of soil moisture as a driver of energy fluxes and SA monsoon onset.

Planktonic microorganisms play a key role in environmental and ecological processes in river ecosystems. Molecular techniques are increasingly used for biological monitoring of rivers. Here, the present study used high-throughput sequencing technology to investigate and study the bacterioplankton community of water bodies of the Huangshui River on the ecotone zone of Qinghai Plateau and Loess Plateau. The results indicated that the Huangshui River had lower alpha diversity than other plain rivers. Proteobacteria was the dominant phyla with relative abundance of 77.48%, followed by Firmicutes, Alphaproteobacteria and Gammaproteobacteria were significantly enriched. The predominant bacterial genera were Acidiphilium, Acidocella, Metallibacterium and Acinetobacter. Diverse taxa of potential pathogens, such as Acinetobacter, Pseudomonas, and Aeromonas, were also been identified. The major driving factors for the bacterial community were Cr6+, altitude, and TN. PICRUSt results revealed that a substantial number of functional profiles were involved in human diseases and antibiotic resistance. Therefore, further studies were needed to isolate these potentially pathogenic organisms, as this will help protect the lives of communities using Huangshui River. The present study provided a certain theoretical basis for understanding the microbial ecology and environmental protection in the Huangshui River on the ecotone zone of Qinghai Plateau and Loess Plateau.

The autogenous shrinkage of alkali-activated slag (AAS) is significantly higher than that of ordinary Portland cement (OPC). The higher risk of concrete cracking due to autogenous shrinkage is a critical drawback to wider use of this promising alternative binder. The effect of CaO content on the autogenous shrinkage of AAS mortar was investigated. The autogenous shrinkage of AAS mortars was determined by comparator. The pore structure of the pastes was determined by mercury intrusion porosimetry. The hydration products of the pastes were determined by Fourier transform-infrared, thermogravimetric analysis, X-ray diffraction, and 29Si solid-state magic-angle spinning nuclear magnetic resonance. The results show that the amount of portlandite increases as CaO content increases. CaO in the paste causes the partial replacement of C-S-H(I) (low stiffness) by C-S-H(II) (high stiffness). The hydration reaction of AAS is inhibited by the addition of CaO. The increase of polymerization degree of C-(A-)S-H and rearrangement of C-S-H(I) during hydration are inhibited by the addition of CaO, and micropores closure is also inhibited. Therefore, the autogenous shrinkage of AAS mortar decreases with the increase of CaO content.

Drought severity still remains a serious concern across sub-Saharan Africa (SSA) due to the destructive impact on multiple sectors of our society The interannual variability and trends in the changes of self-calibrated Palmer Drought Severity Index based on Penman–Monteith (scPDSIPM) and Thornthwaite (scPDSITH) methods for potential evapotranspiration (PET), precipitation (P) and normalized difference vegetation index (NDVI) anomalies, and sea surface temperature (SST) anomaly were investigated through statistical analysis of modelled and remote sensing data. It is shown that scPDSIPM and scPDSITH differed in the representation of drought characteristics over SSA. The scPDSI and remotely-sensed-based anomalies of P and NDVI showed wetting and drying trends over the period 1980-2012. The trend analysis showed increased drought events in the semi-arid and arid regions of SSA over the same period. A correlation analysis reveals a strong relationship between scPDSI variability and P, and NDVI anomalies for monsoon and pre-monsoon seasons. The correlation analysis of scPDSI variability with SST anomalies indicates significant positive and negative relationships, respectively. This study has demonstrated the applicability of multiple data sources for drought assessment and provides useful information for regional drought predictability and mitigation strategies.

Stimuli-responsive luminogens with aggregation-induced emission (AIE) properties have attracted more and more attention for their intense emission in the aggregated state and tunable physicochemical performances. Nevertheless, it still remains challenging to design multifunctional AIE luminogens (AIEgens) with multi-responsive moieties and tunable fluorescence and morphologies. Here, a novel multifunctional AIEgen (DAS-BA) is elaborately designed based on the synergistic effect of twisted intramolecular charge-transfer (TICT), restriction of intramolecular motions (RIM), and excited-state intramolecular proton transfer (ESIPT). DAS-BA not only possesses triple pH-responsivenesses and long-wavelength emission, but also shows corresponding transition in its fluorescence and self-assembled morphologies. Under strong alkaline conditions, owing to the deprotonation of phenolic hydroxyl group, the red emission of DAS-BA is quenched and the molecular assemblies change from micro-rods to nano-rods. However, under weak acidic conditions, the red fluorescence dramatically shifts to yellow-green and the assemblies strikingly transform from micro-rods to nano-spheres due to the protonation of diethylamino moieties. Further acidification can hydrolyze the Schiff base, resulting in fluorescence quenching and destruction of the nano-spheres. Based on the reversible protonation process of diethylamino moiety, DAS-BA exhibits CO2-activated reversible fluorescence switching. Furthermore, DAS-BA has been successfully applied in anti-counterfeit and dual encryption, photo-patterning and molecular logic gates.

Despite numerous assessments of satellite-based and reanalysis precipitation across the globe, few studies have been conducted based on the precipitation linear trend (LT), particularly during daytime and nighttime, when there are different precipitation mechanisms. Herein, we first examine LTs for the whole day (LTwd), daytime (LTd), and nighttime (LTn) over mainland China (MC) in 2003–2017, with sub-daily observations from a dense rain gauge network. For MC and ten Water Resources Regions (WRRs), annual and seasonal LTwd, LTd, and LTn were generally positive but with evident regional differences. Subsequently, annual and seasonal LTs derived from six satellite-based and six reanalysis popular precipitation products were evaluated using metrics of correlation coefficient (CC), bias, root-mean-square-error (RMSE), and sign accuracy. Finally, metric-based optimal products (OPs) were identified for MC and each WRR. Values of each metric for annual and seasonal LTwd, LTd, or LTn differ among products; meanwhile, for any single product, performance varied by season and time of day. Correspondingly, the metric-based OPs varied among regions and seasons, and between daytime and nighttime, but were mainly characterized by OPs of Tropical Rainfall Measuring Mission (TRMM) 3B42, ECMWF Reanalysis (ERA)-Interim, and Modern Era Reanalysis for Research and Applications (MERRA)-2. In particular, the CC-based (RMSE-based) OPs in southern and northern WRRs were generally TRMM3B42 and MERRA-2, respectively. These findings imply that to investigate precipitation change and obtain robust related conclusions using precipitation products, comprehensive evaluations are necessary, due to variation in performance within one year, one day and among regions for different products. Additionally, our study facilitates a valuable reference for product users seeking reliable precipitation estimates to examine precipitation change across MC, and an insight (i.e., capacity in detecting LTs, including daytime and nighttime) for developers improving algorithms.

Background Coronavirus disease 2019 (COVID-19) is a worldwide public health pandemic with a high mortality rate, among severe cases. The disease is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. It is important to ensure early detection of the virus to curb disease progression to severe COVID-19. This study aimed to establish a clinical-nomogram model to predict the progression to severe COVID-19 in a timely, efficient manner. Methods This retrospective study included 202 patients with COVID-19 who were admitted to the Fifth Affiliated Hospital of Sun Yat-sen University and Shiyan Taihe Hospital from January 17 to April 30, 2020. The patients were randomly assigned to the training dataset (n = 163, with 43 progressing to severe COVID-19) or the validation dataset (n = 39, with 10 progressing to severe COVID-19) at a ratio of 8:2. The optimal subset algorithm was applied to filter for the clinical factors most relevant to the disease progression. Based on these factors, the logistic regression model was fit to distinguish severe (including severe and critical cases) from non-severe (including mild and moderate cases) COVID-19. Sensitivity, specificity, and area under the curve (AUC) were calculated using the R software package to evaluate prediction performance. A clinical nomogram was established and performance assessed with the discrimination curve. Results Risk factors, including demographics data, symptoms, laboratory and image findings were recorded for the 202 patients. Eight of the 52 variables that were entered into the selection process were selected via the best subset algorithm to establish the predictive model; they included gender, age, BMI, CRP, D-dimer, TP, ALB, and involved-lobe. Sensitivity, specificity and AUC were 0.91, 0.84 and 0.86 for the training dataset, and 0.87, 0.66, and 0.80 for the validation dataset. Conclusions We established an efficient and reliable clinical nomogram model which showed that gender, age, and initial indexes including BMI, CRP, D-dimer, involved-lobe, TP, and ALB could predict the risk of progression to severe COVID-19.

In this study, an existing combination approach that maximizes temporal correlations is used to combine six passive microwave satellite soil moisture products from 1998 to 2015 to assess its added value in long-term applications. Five of the products used are included in existing merging schemes such as the European Space Agency’s essential climate variable soil moisture (ECV) program. These include the Special Sensor Microwave Imagers (SSM/I), the Tropical Rainfall Measuring Mission (TRMM/TMI), the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) sensor on the National Aeronautics and Space Administration’s (NASA) Aqua satellite, the WindSAT radiometer, onboard the Coriolis satellite and the soil moisture retrievals from the Advanced Microwave Scanning Radiometer 2 (AMSR2) sensor onboard the Global Change Observation Mission on Water (GCOM-W). The sixth, the microwave radiometer imager (MWRI) onboard China’s Fengyun-3B (FY3B) satellite, is absent in the ECV scheme. Here, the normalized soil moisture products are merged based on their availability within the study period. Evaluation of the merged product demonstrated that the correlations and unbiased root mean square differences were improved over the whole period. Compared to ECV, the merged product from this scheme performed better over dense and sparsely vegetated regions. Additionally, the trends in the parent inputs are preserved in the merged data. Further analysis of FY3B’s contribution to the merging scheme showed that it is as dependable as the widely used AMSR2, as it contributed significantly to the improvements in the merged product.

Background Health-care workers (HCWs) are an epidemiological group with increased exposure to tuberculosis (TB), especially at health-care facilities (HCFs) with poor TB infection control in high-TB-burden settings. China is a high-TB-burden country, and the comprehensive measures for stopping TB transmission at some HCFs were not implemented well owing to limited resources and other factors. The purpose of this study was to review risk of occupational exposure to TB among HCWs and its change trend, and identify epidemiological characteristics of pulmonary tuberculosis (PTB) among HCWs in Henan, central part of China. Methods A retrospective cohort study was conducted from 2010 to 2017. All HCWs and teachers in Henan were enrolled to the study as exposed group and non-exposed control group, respectively. Relative risk (RR), attributable risk (AR) and AR percent (AR%) were used to measure the association between the occupational exposure and PTB, and estimated with Poisson regression. Results The study results showed a total of 1663 cases of PTB were reported among the HCWs in Henan, accounting for 3.2‰ of all PTB cases reported in the whole population, and annual incidence rate of PTB among HCWs declined by 34% from 2010 to 2017. Over the eight years, the incidence rate of PTB among HCWs was 43.7 cases per 100,000 person-years (PYs), significantly higher than that among teachers (18.8 cases/100,000 PYs), and RR, AR and AR% were estimated to 2.3, 24.9 cases per 100,000 PYs and 57%, respectively. Among HCWs, males were more likely to suffer from PTB than females (adjusted RR: 1.3; 95%CI: 1.2–1.4), and HCWs aged under 25 years had the highest relative risk over all age groups with adjusted RR equaling to 8.3 (95%CI: 6.9-9.9) calculated with those aged 45–54 years as the reference. Conclusions Although overall incidence rate of PTB among HCWs showed decreasing temporal trends over the period of 2010–2017, attributable risk of occupational exposure to TB among HCWs did not decrease in Henan, and TB infection at HCFs for males, young or senior HCWs, especially for young HCWs is of much concern.

Lodging in cereal crops can result in yield loss and harvesting difficulties for growers. Application of plant growth regulator (PGR) has been an indispensable management practice to reduce lodging problems that are often exacerbated during high wind growing conditions and/or high nitrogen (N)/water environments, but the data is limited in the Columbia Basin of Oregon. The objective of this research was to evaluate the effect of two PGR products (chlormequat chloride-CC, trinexapac-ethyl-TE) at different rates and application timings on two soft white winter wheat varieties (ORCH-102 and SY Ovation). Crop growth (stem height and thickness), yield-related (spike density as ears m&minus, 2, seeds per spike, grain weight) and quality parameters (test weight, protein) were measured for two cropping seasons from October 2017 to July 2019 following the application of the two PGR products at tillering (GS21-26), stem elongation (GS30-32), and/or flag leaf (GS37-39) stages under a high-N fertilizer scenario. In both growing seasons, no lodging problems were recorded for any treatments. The plant height was reduced after PGR application, but the impact on stem thickness was limited. PGR application slightly affected wheat yield, yield components, testing weight, and protein level in both growing seasons. Our results suggested that the effect of PGR application is relatively limited if no lodging problem occurred.

Fire is a common circumstance in the world. It causes direct casualties and economic losses, and also brings severe negative influences on the atmospheric environment. In the background of climate warming and rising population, it is important to understand the fire responses regarding the spatio-temporal changes. Thus, a long-term change analysis of fires is needed in China. We use the remote sensed MOD14A1/MYD14A1 fire products to analyze the seasonal variations and long-term trends, based on five main land cover types (forest, cropland, grassland, savannas and urban areas). The fires are found to have clear seasonal variations; there are more fires in spring and autumn in vegetated lands, which are related to the amount of dry biomass and temperature. The fire numbers have significantly increased during the study period, especially from spring to autumn, and those have decreased in winter. The long-term fire trends are different when delineated into different land cover types. There are significant increasing fire trends in grasslands and croplands in North, East and Northeast China during the study period. The urban fires also show increasing trends. On the contrary, there are significant decreasing fire trends in forests and savannas in South China where it is most densely vegetated. This study provides an overall analysis of the spatio-temporal fire changes from satellite products, and it may help to understand the fire risk in the changing climate for a better risk management.

In many industrial fields, treatment of composite contaminations involving inorganic heavy metal ions and organic pollutants simultaneously remains a tremendous challenge. In this paper, an innovative strategy based on the gelation process of Sodium Alginate (SA) was proposed for synchronous treatment of composite pollutants. Through a pre-gelation step, SA gel balls encapsulated with S

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is the leading cause of a public health emergency in the world, accompanying with high mortality in severe corona virus disease 2019(COVID-19 ), thereby early detection and stopping the progress to severe COVID-19 is important. Our aim is to establish a clinical nomogram model to calculate and predict the progress to severe COVID-19 timely and efficiently.Methods: In this study, 65 patients with COVID-19 had been included retrospectively in the Fifth Affiliated Hospital of Sun Yat-sen University from January 17, to February 11, 2020. Patients were randomly assigned to train dataset (n=51 with 15 progressing to severe COVID-19) and test dataset (n=14 with 4 progressing to severe COVID-19). Lasso algorithm was applied to filter the most classification relevant clinical factors. Based on selected factors, logistic regression model was fit to predict the severe from mild/common. Meanwhile in nomogram sensitivity, specificity, AUC (Area under Curve), and calibration curve were depicted and calculated by R language, to evaluate the prediction performance to severe COVID-19.Results:High ratio of sever COVID-19 patients (26.5%) had been found in our retrospective study, and 84% of these cases progress to severe or critical after 5 days from their first clinical examination. In these 65 patients with COVID-19, 77 clinical characteristics in first examination were collected and analyzed, and 37 ones had been found different between non-severe and severe COVID-19. But when all these factors were analyzed in establishment of prediction model, six factors are crucial for predicting progress of severe COVID-19 via Lasso algorithm. Based on these six factors, including increased fibrinogen, hyponatremia, decreased PaO2,multiple lung lobes involved, down-regulated CD3（+）T-lymphocyte and fever, a logistic regression model was fit to discriminate severe and common COVID-19 patients. The sensitivity, specificity and AUC were 0.93, 0.86, 0.96 in the train dataset and 0.9, 1.0, 1.0 in test dataset respectively. Nomogram-predicted probability was more consistent with actual probability by R language.Conclusions:In summary, an efficient and reliable clinical nomogram model had been established, which indicate increased fibrinogen, hyponatremia, decreased PaO2, multiple lung lobes involved, down-regulated CD3（+）T-lymphocyte and fever at the first clinical examination, could predict progress of patients to severe COVID-19.

The accurate acquisition of water information from remote sensing images has become important in water resources monitoring and protections, and flooding disaster assessment. However, there are significant limitations in the traditionally used index for water body identification. In this study, we have proposed a deep convolutional neural network (CNN), based on the multidimensional densely connected convolutional neural network (DenseNet), for identifying water in the Poyang Lake area. The results from DenseNet were compared with the classical convolutional neural networks (CNNs): ResNet, VGG, SegNet and DeepLab v3+, and also compared with the Normalized Difference Water Index (NDWI). Results have indicated that CNNs are superior to the water index method. Among the five CNNs, the proposed DenseNet requires the shortest training time for model convergence, besides DeepLab v3+. The identification accuracies are evaluated through several error metrics. It is shown that the DenseNet performs much better than the other CNNs and the NDWI method considering the precision of identification results; among those, the NDWI performance is by far the poorest. It is suggested that the DenseNet is much better in distinguishing water from clouds and mountain shadows than other CNNs.

To describe the frequency and risk factors of endometrial hyperplasia (EH) in infertile women undergoing their first IVF/ICSI treatment.A total of 3198 infertile women undergoing their first IVF/ICSI cycle were recruited retrospectively from a fertility treatment center. Endometrial scratching and biopsy were conducted before IVF/ICSI treatment. The relationship between EH and clinical characteristics were analyzed.EH without atypia, atypical hyperplasia, and endometrial cancer (EC) were diagnosed in 94 (2.94%), three (0.093%), and two (0.063%) women, respectively. The frequencies of EH were higher in patients with abnormal uterine bleeding (i.e. menstrual cycle irregularity in terms of frequency, volume, or length; 6.8% vs. 1.6%, P0.001, OR = 4.42), obesity (4.1% vs. 2.7%, P = 0.044, OR = 1.55), and elevated fasting glucose (4.6% vs. 2.5%, P = 0.017, OR = 1.85). In the multivariate analyses, all the three factors (abnormal uterine bleeding, obesity, elevated fasting glucose) were independently associated with risk of EH. In the subgroup analysis, among women with abnormal uterine bleeding, the frequency of EH in the elevated fasting glucose group was 10.7%, which was higher than that of the normal fasting glucose group (5.6%), even after adjustment for BMI (P = 0.014, OR = 1.53).The frequency of EH in infertile women undergoing their first IVF/ICSI treatment was approximately 3%. Elevated fasting glucose was an independent risk factor for EH in infertile women undergoing IVF/ICSI.

Background: Climate factors drive the spread of dengue. Global warming affects the mosquito habitat and the transmission of dengue, especially in densely populated urban areas. Methods: Transfer functions from climate to mosquito and from mosquito to disease are set up using the Generalized Additive Model (GAM) and Susceptible Infected Recovered Model (SIR), respectively, to project distribution patterns of mosquito density in Asia and changes of dengue incidence in Asian metropolises at global warming of 1·5°C, 2°C, 3°C and 4°C above the pre-industrial level, based on multi-model outputs from Coupled Model Inter-comparison Project Phase 6 (CMIP6). Findings: With high temperature, high humidity, and dense population, urban areas in South and Southeast Asia have favorable conditions for dengue fever transmission. In a warming world, the area suitable for dengue transmission is expected to expand in Asia. It is projected that Arabian Peninsula, north of South Asia, and north of East Asia will be under an emergent risk for dengue transmission. In the reference period (1995-2014), about 180 (multi-model range 170~193) million people were exposed to dengue, accounting for a quarter of the total population in Asian metropolises. More population is projected to be exposed to dengue in future. Population exposure in Asian metropolises might increase to 261 (range: 181~300) million, 268 (range: 228~326) million, 287 (range: 241~361) million, and 287 (range: 241~367) million, respectively, at 1·5°C, 2°C, 3°C, and 4°C warming levels. Metropolises with population of more than 10 million have 51% higher morbidity of dengue than smaller cities. Taking all urban population as a whole, increase of dengue fever cases in Asian metropolises is projected to be 90% (range: 77%~127%), 107% (range: 80%~180%), 152% (range: 119%~314%), and 186% (range: 146%~325%), respectively, under different warming scenarios, relative to 260 thousand per year in the reference period. Interpretation: Transmission of mosquitoes is determined mostly by temperature and precipitation conditions. Increasing temperature and precipitation in large parts of Asia in a global warming context expands the range of dengue fever. Although different cities show various climatic and socioeconomic characteristics, results consistently show that number of dengue cases may increase in Asian metropolises in future, which will bring huge challenges related to prevention and control of infectious diseases. Funding Statement: National Key Research and Development Program of China (2017YFA0603804) and the Cooperation Project of Cooperation Group (GZ 1486) in the Chinese and German Center of the Research Promotion, NSFC/DFG. Declaration of Interests: The authors declare no competing interests.

Additional file 1: Table S1. PCCA variants identified in twin siblings with propionic academia.

Additional file 1: Table S1. PCR primers and conditions used for Sanger sequencing. Primer pairs and the size of PCR products were shown. F, forward; R, reverse.

Molecular solar thermal (MOST) systems have attracted tremendous attention for solar energy conversion and storage, which can generate high-energy metastable isomers upon capturing photon energy, and release the stored energy as heat on demand during back conversion. However, the pristine molecular photoswitches are limited by low storage energy density and UV light photon energy storage. Recently, numerous pioneering works have been focused on the development of MOST systems towards phase change (PC) and visible light photon energy storage to increase their properties. On the one hand, the strategy of simultaneously capturing isomerization enthalpy and PC energy between solid and liquid can not only offer high latent heat, but also promote the development of sustainable energy systems. On the other hand, the efficient photon energy storage in the visible light range opens a tremendously fascinating avenue to fabricate MOST systems powered under natural sunlight. Here, the recent advances of MOST systems towards PC and visible light photon energy storage are systematically summarized, the most promising advantages and current challenges are analyzed, and emerging strategies and future research directions are proposed.

The effect of CaO and MgO content on the compressive strength (CS) and flexural strength (FS) of alkali-activated slag mortar (ASM), which was activated by NaOH (Na2O% = 4%), was investigated. The pore structure of alkali-activated slag paste was investigated via mercury intrusion porosimetry (MIP). The morphology of ASM was observed by field emission scanning electron microscope (FSEM). The study shows that the CS and FS of ASM, which was activated by NaOH, increase first and then decrease with the improvement of CaO and MgO content. The optimal CaO and MgO content for CS are both 9% of the slag mass. The CS of ASM with optimal CaO and MgO content can achieve 46.54MPa and 47.79MPa, respectively. The optimal CaO and MgO content for FS are 6% and 3% of the slag mass, respectively. The FS of ASM with optimal CaO and MgO content can achieve 11.23MPa and 11.11MPa, respectively. The comprehensive mechanics performance of ASM are the best, when the CaO content is 6%.

Soil moisture is an important factor in land-atmosphere interactions and other land processes. Improved estimates from climate models have, in the last two decades, become an important alternate source of information. In this study, we extend the evaluation of soil moisture anomalies of different generations of three families of model datasets (the European Center for Medium-Range Weather Forecasts&rsquo, (ECMWF) reanalysis, the Modern Era Retrospective Analysis for Research and Applications of NASA, and the Global Land Data Assimilation System of theNational Oceanic and Atmospheric Administration (NOAA)) in recent studies to the People&rsquo, s Republic of China. Two validation techniques, namely, root-mean-square error (RMSE) from triple collocation analysis (TCA) and correlations (R) with ground observations, were used. The study confirmed the results of previous studies that focused on other regions and showed that the newer generations of each modeling family generally had better skill than the older generations with higher correlations and lower RMSEs. A cross-validation of the results from the two techniques for the newer products showed that the higher correlations and lower RMSEs from the TCA were found over regions with moderate vegetation cover, while regions with less vegetation cover had lower correlations and larger RMSEs (ECMWF (R: &minus, 0.93), NASA (R: &minus, 0.73), and NOAA (R: &minus, 0.61)), indicating that these two techniques complement each other to fairly validate the products.

Here we report a facile method to construct reversible visible-light-responsive switching from antibacterial to bioadhesion by host-guest self-assembly of β-cyclodextrin (β-CD) and azobenzene functionalized polycation/polyanion. The visible-light-responsible azobenzene functionalized polycation, poly{6-[(2,6-dimethoxyphenyl)azo-4-(2′,6′-dimethoxy)phenoxy]propyl dimethylaminoethyl methacrylate-random-poly(2-(N,N-dimethylaminoethyl) methacrylate) (Azo-PDMAEMA), was synthesized via quaternization reaction between 2,6,2′,6′-tetramethoxy-4-(3-bromopropoxy)azobenzene (AzoOMeBr) and poly(2-(N,N-dimethylaminoethyl) methacrylate) (PDMAEMA), and the polyanion, poly{6-[(2,6-dimethoxyphenyl)azo-4-(2′,6′-dimethoxy) phenoxy]hexyl acrylate-random-acrylic acid} (Azo-PAA), was synthesized via esterification reaction between 2,6,2′,6′-tetramethoxy-4-(6-hydroxyhexyloxy) azobenzene (AzoOMeOH) and poly(acryloyl chloride) (PAC) and subsequent hydrolysis reactions. The switch surface could be achieved via the alternate host-guest assembly of Azo-PDMAEMA and Azo-PAA onto a β-CD-terminated substratum (Sub-CD) through visible light irradiation. The positively charged Azo-PDMAEMA with quaternary ammonium groups exhibited antimicrobial properties and few bacteria were adhered on the surface, while the negatively charged Azo-PAA with carboxyl acid groups exhibited excellent bioadhesive properties and a large number of bacteria were adhered. Interestingly, the switch between antibacterial and bioadhesive could be realized upon visible light irradiation via alternate assembly of Azo-PDMAEMA and Azo-PAA. The proposed approach to manufacturing visible-light-responsive surface with reversible and alterable biofunctionality switching between antibacterial and bioadhesive is simple and efficient, which is promising for preparation of multifunctional polymeric surfaces to encounter multifarious demands for the biomedical and biotechnological applications. Statement of Significance Light has attracted great attention in building biointerfaces for its precise spatiotemporal control and convenient operation. However, UV light may damage to biological samples and living tissues, which will limit its applications. This study demonstrates a novel visible-light-responsive surface fabricated through reversible assembly of azobenzene functionalized polycations/polyanions on cyclodextrin (CD)-terminated substrate by host-guest interactions between the visible-light-responsive azobenzene mAzo and CD, which has not been examined previously. It is noted that the azobenzene functionalized polycations show strong antibacterial activities, while the polyanions show excellent bioadhesive properties, as can be switched through the alternate assembly upon visible-light irradiation. This facile and versatile approach to visible-light-responsive surfaces holds great potential for switching of bioadhesion.

We propose a novel spatiotemporal fusion method based on deep convolutional neural networks (CNNs) under the application background of massive remote sensing data. In the training stage, we build two five-layer CNNs to deal with the problems of complicated correspondence and large spatial resolution gaps between MODIS and Landsat images. Specifically, we first learn a nonlinear mapping CNN between MODIS and low-spatial-resolution (LSR) Landsat images and then learn a super-resolution CNN between LSR Landsat and original Landsat images. In the prediction stage, instead of directly taking the outputs of CNNs as the fusion result, we design a fusion model consisting of high-pass modulation and a weighting strategy to make full use of the information in prior images. Specifically, we first map the input MODIS images to transitional images via the learned nonlinear mapping CNN and further improve the transitional images to LSR Landsat images via the fusion model; then, via the learned SR CNN, the LSR Landsat images are supersolved to transitional images, which are further improved to Landsat images via the fusion model. Compared with the previous learning-based fusion methods, mainly referring to the sparse-representation-based methods, our CNNs-based spatiotemporal method has the following advantages: 1) automatically extracting effective image features; 2) learning an end-to-end mapping between MODIS and LSR Landsat images; and 3) generating more favorable fusion results. To examine the performance of the proposed fusion method, we conduct experiments on two representative Landsat–MODIS datasets by comparing with the sparse-representation-based spatiotemporal fusion model. The quantitative evaluations on all possible prediction dates and the comparison of fusion results on one key date in both visual effect and quantitative evaluations demonstrate that the proposed method can generate more accurate fusion results.

Northwest China is considered as the arid and semi-arid temperate continental climate, where the precipitation is closely related to precipitable water vapour (PWV) content. In this paper, the Medi...

The availability of reliable long-term and large scale soil moisture observation datasets over the Tibetan Plateau has been of more concern in recent years. It is therefore necessary to verify any such data to have a better guide to their usage. In this study, we have compared satellite soil moisture retrievals of the Special Sensor Microwave Imager (SSM/I), with ERA-Interim/Land and Global Land Data Assimilation System (GLDAS-1) soil moisture products over the Tibetan Plateau. They are found to be largely different from each other regarding the soil moisture magnitudes in spring, summer and autumn, with those of the ERA-Interim/Land particularly higher than the other datasets in the eastern plateau. The SSM/I and the ERA-Interim/Land data are in good agreement considering their seasonal dynamics, which are however considerably different from the GLDAS-1 models (CLM, Noah, Mosaic and VIC). For interannual variations, it is striking to find strong negative correlations (R

A near-infrared (NIR) responsive DNA probe has been developed for DNA hybridization detection with characteristics of high efficient Forster resonance energy transfer (FRET) from small (∼10 nm) upconversion nanoparticles (UCNPs) to nucleic acid stain SYBR Green I (SG, a specific intercalator of double-stranded DNA). The positively charged UCNPs were prepared and facilely attached with single DNA strands by coordination interaction, which acted as probes to detect the DNA targets in the presence of SG. This probe possesses not only the superiority of the NIR-excitation nature of UCNPs which could minimize the autofluorescence background from biomolecules and the photodamage to biological specimens, but also the advantages of facile preparation and high FRET signals. The FRET efficiencies could increase significantly from 2.6% to 12.5% and then to 26% when the size of UCNPs reduced from 94 nm to 30 nm and to 10 nm, respectively. The 10 nm-UCNP-based DNA probe could reach a lower detection limit of complementary ssDNA2 at 3.2 nM and three-base mismatched ssDNA2-M3 at 7.6 nM. The high sensitivity and selectivity of the probe may endow the system with great potential in fluorescence-based biosensing under the irradiation of NIR light.

An accurate temporal and spatial characterization of errors is required for the efficient processing, evaluation, and assimilation of remotely-sensed surface soil moisture retrievals. However, empirical evidence exists that passive microwave soil moisture retrievals are prone to periodic artifacts which may complicate their application in data assimilation systems (which commonly treat observational errors as being temporally white). In this paper, the link between such temporally-periodic errors and spatial land surface heterogeneity is examined. Both the synthetic experiment and site-specified cases reveal that, when combined with strong spatial heterogeneity, temporal periodicity in satellite sampling patterns (associated with exact repeat intervals of the polar-orbiting satellites) can lead to spurious high frequency spectral peaks in soil moisture retrievals. In addition, the global distribution of the most prominent and consistent 8-day spectral peak in the Advanced Microwave Scanning Radiometer – Earth Observing System soil moisture retrievals is revealed via a peak detection method. Three spatial heterogeneity indicators – based on microwave brightness temperature, land cover types, and long-term averaged vegetation index – are proposed to characterize the degree to which the variability of land surface is capable of inducing periodic error into satellite-based soil moisture retrievals. Regions demonstrating 8-day periodic errors are generally consistent with those exhibiting relatively higher heterogeneity indicators. This implies a causal relationship between spatial land surface heterogeneity and temporal periodic error in remotely-sensed surface soil moisture retrievals.

Here, a novel near-infrared (NIR)/ultraviolet (UV) light-, pH- and temperature-responsive amphiphilic polymer, poly(ethylene glycol)-block-poly(dimethylaminoethyl methacrylate)@2-diazo-1,2-naphthoquinone (PEG-PDMAEMA@DNQ), is synthesized via an atom transfer radical polymerization (ATRP) and quaternization reaction. In aqueous solution, this amphiphilic polymer could self-assemble into spherical assemblies with hydrophobic NIR/UV light and base responsive DNQ moieties as the core and hydrophilic PEG and pH/temperature-responsive PDMAEMA as the corona. The NIR/UV light- and pH-sensitive absorbances of the polymer were characterized by UV-vis spectroscopy. Under 365 nm UV or 808 nm NIR irradiation, the characteristic absorption intensity of DNQ gradually decreased, indicating that DNQ converted to 3-indenecarboxylic acid (3-IC) via the Wolff rearrangement. Under acidic conditions, the absorption spectra changed little, while the absorption peak at around 400 nm decreased and a new absorption peak at 350 nm appeared under basic conditions. The morphological changes of the assemblies, which could be dissociated after UV/NIR irradiation, swollen under acidic conditions, while turned into nanocapsules under basic conditions and shrunk above the lower critical solution temperature (LCST), were revealed by transmission electron microscopy (TEM) and dynamic light scattering (DLS). The loaded hydrophobic model molecules coumarin 102 could be released from the assemblies in response to NIR, UV light and pH. More importantly, the release efficiency could be enhanced dramatically under combined stimulation. The reported NIR/UV light- and pH-responsive polymer assemblies would have great potential as nanocarriers for controlled release.

Visible light/pH dual-responsive polymer dots (PDs) were facilely prepared from branched polyethyleneimine (PEI) functionalized with donor-acceptor Stenhouse adducts (DASAs). The synthesized polymer dots were characterized by X-ray photoelectron spectroscopy, 1H NMR spectroscopy, Fourier transform infrared spectroscopy, transmission electron microscopy (TEM), dynamic light scattering (DLS), UV–vis absorption spectroscopy and fluorescence spectroscopy. The emission peak of the DASA-PEI polymer dots changed as the excitation wavelength varied from 360 nm to 520 nm. The strongest fluorescence emission was located around 522 nm upon 420 nm excitation. Moreover, the DASA-PEI polymer dots exhibited a typical photo-responsive behavior. After visible light irradiation, the triene of DASAs isomerized to the cyclopentenone form. Interestingly, the DASA-PEI polymer dots also responded to acid and alkali and showed a reversible transition between the triene and cyclopentenone isomers. It is demonstrated that the prepared DASA-PEI polymer dots could be applied as a turn-off chemosensor for the rapid and selective detection of Fe3+ and Cu2+ in aqueous solutions with detection limits of 10.1 nM and 1.3 nM, respectively.

We present the first report incorporating strongly electron-donating moiety pyrrolo[3,2-b]pyrrole into fused-ring electron acceptors, and design and synthesize two narrow-bandgap nonfullerene acceptor isomers PPIC1 and PPIC2. These two molecules bear the same electron-withdrawing terminal groups and isomeric eight-fused-ring cores. The two compounds exhibit strong light-harvesting ability in the near-infrared region beyond 1000 nm. Compared with the symmetric PPIC2, the asymmetrical PPIC1 shows remarkably blue-shifted absorption, wider bandgap, down-shifted highest occupied molecular orbital, up-shifted lowest unoccupied molecular orbital, and lower electron mobility. Paired with the classical polymer donor PTB7-Th, PPIC1-based photovoltaic devices exhibit higher open-circuit voltage, short-circuit current and efficiency than the PPIC2-based devices.

Precise monitoring of floods is significant in disaster management and loss reduction; however, remote sensing data resource and methods can largely affect the monitoring accuracy of flooded areas. In this study, we use cloud-free Sentinel-1 Synthetic Aperture Radar (SAR) imagery, preferable to the optical imagery. We have used 5 convolutional neural networks (CNNs), including HRNet, DenseNet, SegNet, ResNet and DeepLab v3 + for flood monitoring in the Poyang Lake area, and compared their performances with the traditional methods — the bimodal threshold segmentation (BTS) and the OSTU method. The HRNet has superior performance in water body identification with the highest precision and efficiency, based on a parallel structure to not only extract rich semantic information but also maintain high-resolution features in the whole process. Besides, speckle noise reduction by deep convolutional neural networks in SAR imagery is better compared with the Refined Lee filter. The CNNs are then used to monitor the temporal evolution of summer flooding (May-Nov.) in 2020. Results show the smallest water coverage of Poyang Lake in late May; it gradually increases to the maximum in mid-July, and then shows a downward trend until November.

Drought has a substantial socioeconomic impact under the changing climate. The estimation of population exposure to drought could be the pivotal signal to predict future water scarcity in the climate hotspot of South Asia. This study examines the changing population exposure to drought across South Asia using 20 climate model ensembles from the latest CMIP6 and demographic data under shared socioeconomic pathways (SSPs). Underpinning the latest version of the IPCC 6th Assessment Report (AR6), this paper focuses on the 2021-2040 (near-term), 2041-2060 (mid-term), and 2081-2100 (long-term) periods to project population exposure changes relative to the reference period (1995-2014) under four SSP-RCP scenarios. Drought events are detected by adopting the standardized precipitation evapotranspiration index (SPEI) and run theory method. Model validation suggests that CMIP6-GCM performs well in projecting climate variables and capturing drought events. The results show that the projected increases in frequent drought events and affected areal coverage are stronger during the early part of the century and weaker at the end under all scenario combinations. In relative terms, the projected increase in the number of people exposed to drought is dominant (>1.5-fold) in the near-term and mid-term periods but decreases in the long-term period. Compared to the reference period, the leading increase in population exposure (2.3-fold) is projected under the newly designed gap scenario (SSP3-7.0) in the mid-term period. A surprising decline in the number of exposed populations was estimated to be 18.8% under SSP5-8.5 by the end of the century. The mitigating effect of the predicted heavy precipitation will decrease droughts in the late future. Spatially, increasing exposure will become more pronounced across India and Afghanistan. Furthermore, the population change effect is mainly responsible for the exposure changes in South Asia. However, this study strongly recommends future 'plausible world' regional rivalry pathways (SSP3) scenario-combinations into consideration for policymaking in regard to water management as well as migration planning over South Asia.

Selenium (Se) concentrations in soils and plants are low in the Pacific Northwest, and consequently, in livestock consuming locally-grown forages. Applying sodium selenate with traditional nitrogen-phosphorus-potassium-sulfur (NPKS) fertilizers provides a cost-effective Se agronomic biofortification method for increasing forage Se concentrations and maintaining optimum health and productivity of livestock. This 2-year study was conducted across Oregon on four representative forage fields: orchardgrass (Dactylis glomerata L.) in Terrebonne (central Oregon), grass-clover mixture in Roseburg (southwestern Oregon), and both grass mixture and alfalfa (Medicago sativa L.) fields in Union (eastern Oregon). Using a split plot design with double-repeated measures (2017 and 2018 with first, second, third, and residual harvest cuts each year), we determined the effect of springtime Se foliar application rate (0, 45, or 90 g Se ha−1 from sodium selenate; whole plot) and NPKS fertilization (none, NPK/PK fertilization, or NPKS/PKS fertilization; subplot; NPKS amounts adapted to meet forage and soil requirements) on total Se concentration and beneficial Se species in forages. Forage Se concentrations increased linearly with foliar sodium selenate application, doubling with each additional 45 g Se ha−1 application (first cut median: 0.11, 2.06, and 4.15 mg kg−1 dry matter, respectively). Nearly all incorporated Se was contained in the first two cuts (87 % and 9 % in first and second cuts, respectively) and most applied selenate was metabolized to selenomethionine. NPK/PK fertilization decreased Se concentrations in second and latter cut forages from Se-amended fields, whereas sulfur fertilization decreased forage Se concentrations from non-Se amended fields. Among forages, alfalfa had greater Se concentrations under low soil Se conditions, whereas grass-dominated forages had higher Se concentrations after Se-amendment. In conclusion, springtime sodium selenate foliar application is an effective management strategy to increase forage total Se concentrations and highly bioavailable selenomethionine concentrations across Oregon, and should be implemented to meet local forage and livestock requirements.

The near-infrared (NIR) light in the wavelength range of 780−1700 nm is regarded as transparency therapeutic window for light-activated delivery system in vivo due to the deep tissue penetration and minimum cellular damage of it. Numerous reports about NIR light-sensitive nanocarriers have emerged in the past few years. Here, strategies for the design and fabrication of nanocarriers for NIR light-controlled release are reviewed, which are based on three triggering mechanisms: (1) photoreactions of chromophores, including NIR light-induced photoreactions and upconversion nanoparticles (UCNPs)-mediated photochemical reactions; (2) photothermal effect, triggered by inorganic or organic photothermal conversion agents (PCAs) with the excitation of NIR light; (3) photo-oxidation, induced by reactive oxygen species (ROS) generated by photosensitizers under NIR light radiation. Finally, the challenges and perspectives of NIR light-sensitive nanocarriers for future development are given.

Here a novel quadruple-responsive nanocarrier based on reduced graphene oxide/mesoporous silica sandwich-like nanocomposites (rGO@MS) modified by pH- and temperature-responsive poly(N,N-dimethylaminoethyl methacrylate) (PDMAEMA) with a linker of disulfide was constructed via surface-initiated atom transfer radical polymerization. The polymer chains would be used as gatekeepers to control the release of the loaded cargo molecules under pH, temperature, NIR light and redox stimuli. The cargo molecules (rhodamine B) were demonstrated to release from the polymer-modified nanocomposites triggered by the quadruple-stimuli. It is noted that the release of the loaded rhodamine B from the nanocarriers could be enhanced greatly under the synergistic effect of multiple stimuli. The prepared quadruple-responsive polymer-modified nanocomposites show a bright prospect in the field of smart nanocarriers for controlled release.

Understanding the reasons for past changes in soil moisture conditions is key to improving forecasts of this extremely important variable coupling land and atmosphere. To contribute to this understanding, we analyzed the variability of soil moisture and its relationship to regional climate features. In this study we focused on the Poyang Lake basin, which is located on the southern bank of the Yangtze River at the junction of its middle and lower reaches in Jiangxi Province. Using in situ soil moisture observations from Nankang and Hukou stations for the period from 1998 to 2012, we tested the regional applicability of the high-resolution long-term Essential Climate Variable (ECV) soil moisture (SM) dataset (1978–2013) by means of trend and correlation analyses. We only used the ECV SM product here that is based on passive microwave technology. Both observed and ECV SM data show the same variations on monthly and inter-annual time scales including identical trend direction and strong correlations. To investigate the relationship between soil moisture conditions in the Poyang Lake basin and the Western Pacific Subtropical High (WPSH), correlation coefficients were calculated between the ECV SM data, the strength index ( I S ), and the northern limit index ( I N ) of the WPSH. The I S of the last autumn and winter shows a strong positive correlation with soil moisture during the subsequent rainy season. A strong WPSH in the preceding autumn and winter could result in more humid climate conditions in the Poyang lake basin during the following rainy season. The I N shows a strong negative correlation with concurrent soil moisture from July to September. The further the WPSH extends towards the north, the drier the area.

Controversial results in the drying and wetting trends were found with different indices and potential evapotranspiration calculations in previous studies. Here we make an attempt to find robust conclusions of drying and wetting trends over regions by coherent results of various independent indices by using China (1961–2012) as a study area. Precipitation, statistical, and physical drought indices, including the Standardized Precipitation Evapotranspiration Index (SPEI) and the Palmer Drought Severity Index (PDSI) and self-calibrating PDSI (sc_PDSI), with both the Penman-Monteith (PM) and Thornthwaite (TH) approaches in PDSI calculation are considered. In consequence, four PDSI variants of PDSI_pm, sc_PDSI_pm, PDSI_th, and sc_PDSI_th are involved. To illustrate regional characteristics, six climatic regions based on the Koppen climate classification are defined. At the national scale, precipitation and SPEI indicate wetting trends but all PDSI variants have drying trends. On the other hand, these six indices exhibit coherent results in five of these six regions. Increases in wetness occur in arid region and the Qinghai-Tibet Plateau. Drying trends were found in semiarid and cold and temperate semihumid regions. Only the humid region in southeastern China is seen to have increasing precipitation and SPEI and decreasing PDSI variants. From the perspective of climatic regions, the drying trends mainly occur in the transition regions between the humid and arid regions in China. The spatial pattern of changes in droughts could be categorized by climatic zones, and the changes at regional scale are robust based on these six indices.

This study investigated monthly and annual reference evapotranspiration changes over southwestern China (SWC) from 1960 to 2012, using the Food and Agriculture Organization of the United Nations’ report 56 (FAO-56) Penman–Monteith equation and routine meteorological observations at 269 weather sites. During 1960–2012, the monthly and annual decreased at most sites. Moreover, the SWC regional average trend in annual was significantly negative (p < 0.05); this trend was the same in most months. A new separation method using several numerical experiments was proposed to quantify each driving factor’s contribution to changes and exhibited higher accuracy based on several validation criteria, after which an attribution analysis was performed. Across SWC, the declining annual was mainly due to decreased net radiation (RN). Spatially, the annual changes at most sites in eastern SWC (excluding southeastern West Guangxi) were generally due to RN, whereas wind speed (WND) or vapor pressure deficit (VPD) was the determinant at other sites. Nevertheless, the determinants differed among 12 months. For the whole SWC, increased VPD in February and decreased WND in April, May, and October were the determinant of decreased ; however, decreased RN was the determinant in other months. Overall, the determinant of the monthly changes exhibited a complex spatial pattern. A complete analysis of changes and the related physical mechanisms in SWC is necessary to better understand hydroclimatological extremes (e.g., droughts) and to develop appropriate strategies to sustain regional development (e.g., water resources and agriculture). Importantly, this separation method provides new perspective for quantitative attribution analyses and thus may be implemented in various scientific fields (e.g., climatology and hydrology).