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Abstract Bladder cancer (BCa), as the most common malignant tumor of the urinary system, has received significant attention in research on the clinical application of artificial intelligence algorithms. Nevertheless, it has been observed that certain investigations use data from various medical facilities to train models for BCa, which may pose a privacy risk. Given this concern, protecting patient privacy during machine learning algorithm training is a crucial aspect that requires substantial attention. One emerging machine learning paradigm that addresses this concern is federated learning (FL). FL enables multiple entities to collaboratively build machine learning models while preserving data privacy and security. In this study, we present a multicenter BCa magnetic resonance imaging (MRI) dataset. The dataset comprises 275 three-dimensional bladder T2-weighted MRI scans collected from four medical centers, and each scan includes diagnostic pathological labels for muscle invasion and pixel-level annotations of tumor contours. Four FL methods are used to assess the baseline of the dataset for both the task of diagnosing muscle-invasive bladder cancer and automatic bladder tumor lesion segmentation.

The Yangtze River is the mother river of China. To promote the aquatic ecosystem protection of the great river, the Project of Yangtze Fisheries Resources and Environment Investigation (2017–2021) supported by the Ministry of Agriculture and Rural Affairs, P. R. China carried out by 24 institutes and universities that located in the Yangtze River basin surveys the status of (1) fish species composition and spatial distribution, (2) current fish abundance, (3) endangered fishes, (4) Yangtze finless porpoise, (5) aquatic eco-environments, (6) water-level fluctuation areas, (7) capture fisheries and recreational fisheries of the Yangtze River mainstream and 10 of its main tributaries, including Yalong River, Heng River, Min River (including Dadu River), Chishui River, Tuo River, Jialing River, Wu River, Han River, Dongting Lake and Poyang Lake. The results showed that there were 443 fish species (belonging to 163 genus, 37 families, and 18 orders) before 2017, but only 323 fish species (including 15 new recorded exotic species) were recorded in the project of 2017–2021. Among them, Cyprinus carpio, Carassius auratus, Hypophthalmichthys molitrix, Pelteobagrus fulvidraco, Coilia brachygnathus, Silurus asotus, Saurogobio dabryi, Ctenopharyngodon idellus, Pelteobagrus nitidus, Hemiculter leucisculus, Siniperca chuatsi, Coreius heterodon, Culter alburnus, Parabramis pekinensis, and Aristichthys nobilis were the dominant species across the whole Yangtze River system. It is estimated that there were 886 million individuals weighing 124.8 million kg, merely equivalent to 27.3% of the resources in 1950s, 30.9% of the resources in 1960s, or 58.7% of the resources in 1980s. In the new list of protected fishes that recorded in the Yangtze River system, only 15 of 29 were collected in this project of 2017–2021. Psephurus gladius has been affirmed to be Extinct by International Union for Conservation of Nature (IUCN). The wild individuals of Tenualosa reevesii and Luciobrama macrocephalus have disappeared for many years and maybe have been extinct already. Acipenser dabryanus has been affirmed to be Extinct in the Wild by IUCN. The natural propagations of A. sinensis, Myxocyprinus asiaticus and Trachidermus fasciatus have been interrupted for many years. The populations of Yangtze finless porpoise in the Yangtze River mainstream, Dongting Lake and Poyang Lake have steadily rising sizes and expanding distributions in 2017–2021. Parts of them migrate from one region to another with the seasons, which would result in the fluctuation of Yangtze finless porpoise population within some regions. The conventional indicators of water quality in the Yangtze River system were good and conformed to the water quality criteria of fishery in 2017–2021. In the last 40 years, the maximum surface water area in the Yangtze River basin extended to approximately 63,360 km2, the minimum surface water area covered approximately 26,396 km2, and the seasonal water-level fluctuation areas occupied approximately 36,964 km2. Compared with 1984–2000 period, the 2001–2020 period witnessed an overall decreasing trend in the frequency of surface water occurrence within about 25,869 km2 of aquatic areas. From 1984 to 2000 period to 2001–2020 period, permanent surface water has decreased by nearly 8,750 km2. In 2017, the fishermen were mainly 40–60 years old and their educational levels were mainly lower than junior high school. In 2017, most anglers were older than 40 and used hand rod and/or sea rod. Their average catch of each time was mainly less than 1 kg. Results suggested that the fishing ban in key waters of the Yangtze River basin is not only an ecological project of aquatic ecosystem conservation, but also a livelihood project for the fisherman to embrace industrial transformation and improve their living conditions. After the implementation of the fishing ban, the fisheries resources would gradually recover, and the Yangtze finless porpoise population size would also see a steady increase. However, the endangered species would remain threatened for a long time. The degraded waterbodies and water-level fluctuation areas would be the key restrictive factors for future aquatic ecosystem recovery in the Yangtze River basin. Since this survey was carried out before the fishing ban, the current results could provide a baseline for future evaluation of the effect of the Yangtze River fishing ban.

High-resolution Digital Elevation Models (DEMs) are essential for precise geographic analysis. However, obtaining high-resolution DEMs in regions with dense vegetation, complex terrain, or satellite imagery voids presents substantial challenges. This study introduces a deep learning approach using three-dimensional (3D) terrain features combined with Conditional Generative Adversarial Networks (CGANs) to reconstruct DEMs. The 3D terrain features, such as valley and ridge lines, exhibit topographic relief patterns and provide constraints for CGANs to reconstruct DEMs. Experiments conducted in the Loess Plateau of Shaanxi confirmed the performance of the proposed method, demonstrating marked improvements in the accuracy of DEM reconstruction compared to models based on two-dimensional (2D) terrain features. The elevation accuracy of the reconstructed DEMs by the proposed method is 5.30 m, which is higher than that of the 2D terrain features method (18.90 m) by 71.96 %. Meanwhile, the proposed method shows a 15.78 % and 17.64 % improvement in elevation accuracy and slope accuracy, respectively, when reconstructing a 5 m high-resolution DEM from a 30 m low-resolution DEM. The proposed method can be flexibly used for reconstructing, repairing, and filling voids in DEM data.

Recently, deep learning methods have shown outstanding performance in remote sensing change detection (CD). To improve the accuracy of edge regions, researchers have introduced edge information as prior knowledge into CD methods. However, there is still room for improvement in utilizing edge information. In this study, an edge-guided multi-scale foreground attention network (EMAF) with an innovative and effective strategy is proposed. Our edge-guided strategy focuses on incorporating edge information into the feature fusion process at all levels. EMAF firstly employs a Siamese Resnet-34 as the encoder, producing four levels of bi-temporal features. Secondly, the Foreground Module (FM) is introduced to separate foreground and background at each level, resulting in foreground change features. Then, the Edge Fusion Module (EFM) is introduced to fuse the features of two adjacent levels with the edge probability map obtained by the Edge Extraction Module (EEM). Finally, after performing EFM and EEM in the three levels, EMAF generates the final change map and edge probability map. During the deep supervision training, the Dynamic Weighted Binary Cross-Entropy (DWBCE) loss is calculated as the edge loss. The comparison testing results on WHU-CD, SYSU-CD, and LEVIR-CD datasets validate the effectiveness of EMAF. Notably, EMAF achieves impressive Intersection over Union (IoU) of 89.03% on WHU-CD and 71.04% on SYSU-CD datasets.

Abstract In recent years, the advances in light‐responsive soft materials with fascinating properties and functions have attracted tremendous attention, which are also enlightening when attempting to achieve the goals of complex deformations, motions, or attractive applications by precise regulation. Attractively, light is not only a clean and inexhaustible energy but also can be controlled remotely, quickly and accurately in a non‐contact way. Moreover, light‐responsive soft materials are capable of amplifying photo‐triggered molecular changes at the microscopic scale into macroscopic deformations, that is, directly converting the input light energy into the output mechanical work, therefore enabling potential applications in the field of actuators and functional devices. To date, some wonderful reviews have reported the progress in photo‐driven soft materials. However, the research progress in ultraviolet, visible (Vis) and near‐infrared (NIR) light‐driven soft materials containing azobenzene or other non‐azobenzene moieties has not been reported yet. In this review, we summarize recent progress in light‐responsive soft materials in terms of preparation methods, response wavelengths and potential applications. Firstly, the preparation methods of photoresponsive soft materials are introduced. Subsequently, photoinduced macroscopic deformations or motions are summarized, in which Vis and NIR light‐responsive behaviors are especially highlighted. Finally, the potential applications of photoresponsive soft materials are classified. To guide the future work for researchers, the existing problems and future development prospects of light‐responsive soft materials are proposed.

Abstract Aedes sp. mosquitoes are changing their geographic range in response to climate change. This is of concern because these mosquitoes can carry dengue fever and other viral diseases. Changing weather patterns can also increase the numbers of Aedes mosquitoes, leading to greater human exposure and enhancing population health risks. We project the geographic distribution of Aedes and associated changes in populations exposed to dengue in Asian metropolitan areas under warming scenarios from 1.5°C to 5.0°C above pre‐industrial temperatures, using multi‐model ensembles. With global warming, the southern part of the Arabian Peninsula, the coast of the Arabian Sea in southern Iran, southern Pakistan in West Asia, the Korean Peninsula, most of the Japanese islands, and parts of North China in East Asia are projected to become suitable for dengue transmission. The numbers of metropolitan areas exposed to dengue is projected to change from 142 (48%) in the reference period (1995–2014) to 211 (71%) at 5.0°C warming. With the combined impact of socioeconomic and climate change, population exposure to dengue in Asian metropolitan areas is projected to increase from 263 (multi‐model range 252–268) million in 1995–2014 to 411 (394–432) million, 446 (420–490) million, 509 (475–601), 558 (493–685) and 587 (529–773) million, respectively, at 1.5°C, 2.0°C, 3.0°C, 4.0°C and 5°C warming, with an average of 2.9 million new people exposed to dengue fever in metropolitan areas each year.

Hydatid cysts of the spleen are rare. Our report details a case wherein an 8-year-old boy with a ruptured splenic hydatid cyst suffered ongoing abdominal pain and shock. Computed Tomography (CT) imaging showed an irregular cyst at the spleen's lower edge with a “floating membranes” sign. Emergency surgery was performed to remove the hydatid cyst and repair the ruptured spleen. During follow-up, the patient was treated with Albendazole orally for 12 months.

Study Region: In the Yangtze River basin of China. Study focus: The emerging Explainable Artificial Intelligence (XAI) methods provide us an opportunity to understand the nonlinear relationship that the Deep Learning(DL) model learned inside. The construction of the Three Gorges Dam (TGD) has successfully minimized the likelihood of flooding in the Yangtze River basin. The XAI methods can help us to know the nonlinear relationship behind it. We apply the Long Short Term Memory (LSTM) network, in conjunction with two XAI methods, SHapley Additive exPlanation (SHAP) and Expected Gradient (EG), to do our work.In our DL model, we use YiChang (YC) station runoff,Precipitation (Pre) and vapour pressure deficit (VPD) data from the middle and lower river basin as input, while the output of the model generates runoff data at the DaTong (DT) station, XAI methods enable us to calculate the significance of each input feature is for generating the output feature in a DL model. In this study, we examine the difference in importance scores between the Before Three Gorges Dam (BTGD) period and the After Three Gorges Dam (ATGD) period. New Hydrological Insights for the Region: In the BTGD period, YC runoff was the primary contributor to flooding at the DT station. However, in the ATGD period, the largest contribution to flooding in the middle and lower Yangtze River basin has shifted from YC runoff to the the middle and lower reaches of precipitation. Our results suggest that the XAI can show the nonlinear relationship between the TGD and downstream flood clearly and the TGD can effectively mitigate flooding in the middle and lower river basins by regulating runoff from the upper river basin. The work shows the potential of XAI to explain the nonlinear relationship in the hydrology field.

Abstract The sensitivity strength of air temperature (T) to surface soil temperature (sST) (namely β hereafter) constitutes a significant factor in how global climate models quantify changes in the climate. This study examines how this sensitivity is represented in the CMIP6 models. Results show regional differences and even contrasts in the β trends at interannual scales between climate models and two reanalysis products during 1980–2014. At high latitudes in the Northern Hemisphere (NH), β is stronger in the CMIP6 models than in the reanalysis data. Additionally, the β trends differ between the CMIP6 and reanalysis data, which may be related to the different precipitation (PR) and soil water availability (PR-ET) trends between the models. In the regions of increasing β intensity at high latitudes in the NH, sST is more sensitive to PR and PR-ET. Consequently, increasing PR and PR-ET leads to slower sST changes, potentially making β intensity stronger in CMIP6 models. However, in the reanalysis data, decreases in PR and PR-ET accelerate sST changes, leading to a weakening of the β intensity. A resulting implication is that β, based on high-emission scenarios, shows a stronger trend during 2015–2100, although this trend could potentially be overestimated. The findings contribute to a better understanding of the sensitivity of T to sST and facilitate the assessment of energy exchange between the land surface and the atmosphere in climate models.

Deep Learning (DL) models can make short- and long-term predictions in just a few seconds, beyond the capabilities of traditional physical models. However, the capabilities of different DL models for meteorological element forecasting, are still yet to be comprehensively evaluated. Here, DL models were used to forecast multiple meteorological elements, including temperature (T), surface net solar radiation (SSR), soil moisture (SM), and evapotranspiration (ET). We compared the seven models in term of training performance, prediction accuracy, and the effects of parameters. We found that the training of RNN-based models (LSTM, GRU, and Bi-LSTM) was faster than others. However, with sufficient training epochs, Transformer-based models consistently achieve the lowest loss function. Among the Transformers, the Informer demonstrates the best prediction accuracy in most scenarios. Beyond the choice of DL model, the prediction performance is also influenced by the meteorological element itself. The MTGNN is comparable to Transformer-based models for T and SSR forecasting, but it does not perform as well as the Informer for SM and ET. The sliding window size and prediction time step have a slight impact on the performance differences between the models. The results can offer insights into applying DL models in meteorological element forecasting.

Abstract Drought has a paramount impact on global agriculture and food security. However, the study on future cropland areas that can incur drought is inadequate. This paper uses input parameters from 7 CMIP6 models for 7 future scenarios (SSP1‐1.9, SSP1‐2.6, SSP4‐3.4, SSP2‐4.5, SSP4‐6.0, SSP3‐7.0, and SSP5‐8.5) to measure South Asian cropland exposure to drought and its underlying factors. Some defined epochs such as 2021–2040 (near‐term), 2041–2060 (mid‐term), 2081–2100 (long‐term), and 1995–2014 (reference period) are designed to explore diverse outlooks of the change. The Standardized Precipitation Evapotranspiration Index and the Run theory methods are applied to detect drought. Results indicate an intensified cropland (under SSP4‐3.4, SSP3‐7.0, and SSP5‐8.5) in the Indo‐Gangetic Plain region of South Asia, where mostly the variation occurs among scenarios and periods. Notably, the future cropland exposed to drought will increase in the 2021–2040, and 2041–2060 periods, but it intends to decline during the 2081–2100. Relatively, the exposed cropland will upturn highest by 49.2% (SSP3‐7.0) in the mid‐term period and decrease by −8.2% (SSP5‐8.5) in the end future. Spatially, distributed cropland in the central, south‐west, and portion of the northeast of South Asia are subjective to be exposed largely, but it can drop greatly across the eastern part by the end future. Importantly, the climate change effect plays a grounding role in future exposure change over the region during the near to mid‐term periods, while the cropland change effect is predominant in the long‐term perspectives. However, these findings signify the urgency of policymaking focusing on drought mitigation to ensure food security.

Abstract Shrub expansion is increasing in grasslands worldwide and has profound effects on biodiversity and ecosystem functions. Shrub expansion has primarily been explained by extrinsic factors such as climate change and human activities, yet the roles of intrinsic shrub demographic population dynamic and shrub–grass interaction remain poorly understood. With a paired mown and exclosure comparison experiment and baseline levels of shrub abundance in a semiarid grassland, we confirmed that long‐term livestock exclusion promotes expansion of two native shrubs, Artemisia sacrorum and Caragana brachypoda. Shrub abundance increased during the livestock exclusion period. Shrub biomass in fenced plots was about 5‐ to 10‐fold greater than in mown plots. Both increase in population density and individual size (especially height) contributed to shrub increase. The mechanism behind this was the divergent effects of litter accumulation on shrubs and grasses. Litter accumulation suppressed grass production, while facilitating shrub recruitment and growth. Our findings highlight the potential role of litter accumulation‐mediated shifts in shrub–grass interactions, which should be incorporated into grazing top‐down control to better predict the restoration outcomes of grassland management changes. The identified internal biotic mechanism raises the possibility that removal of accumulated litter by grazing or mowing may be an alternative way to maintain or restore a grass‐dominated state in shrub expansion areas.

Abstract Background Leeches are an integral component of aquatic biocenosis and can be found in a wide range of ecosystems such as freshwater, saltwater, flowing, and still-water ecosystems. It especially plays an important role in the freshwater benthic community and is an important part of the food web. In this study, a leech species was found in the mantle cavity of wild freshwater mussels in Zigong City, Sichuan Province, China, and its identity was determined through morphological analysis and molecular biological analysis. Results The leech is Hemiclepsis khankiana, a new species of Hemiclepsis that has been discovered in Russia in recent years. Through morphological analysis, the current survey observed that the morphological characteristics of Hemiclepsis khankiana eyespots were significantly different from the first reported description. The first pair of eyespots on the leech were separated and clear, while it had been reduced to unclear shadows in the previous report. The phylogenetic tree based on the COI gene showed that the COI gene sequence obtained in this study was in the same evolutionary branch as Hemiclepsis khankiana (MN295420, MN295421). Genetically, it was most closely related to Hemiclepsis kasmiana (mean COI p-distance = 3.98%). Conclusions The current study reported on the new distribution range of Hemiclepsis khankiana, which was initially discovered in China. This study indicates that the distribution range of the leech species has expanded, laying a foundation for further studies in China.

The Cu–Cr–Ni alloy is a key material for the manufacturing of connectors, which requires excellent resistance to stress relaxation. However, the inherent correlation among microstructure, texture, and properties is still unclear. In this study, we investigated the influence of calefaction and stress relaxation on the grain boundaries (GBs), textures, and properties of the Cu–Cr–Ni alloy. The results showed that calefaction and stress relaxation had opposite effects on GBs and textures. Calefaction led to a decrease in the proportion of low-angle grain boundaries (LAGBs), an increase in the Schmidt factor (SF) value of the grains, and a transition of texture from to . The grains with higher SF values were more susceptible to plastic deformation, which deteriorated the stress relaxation resistance. By comparison, stress relaxation led to an increase in the proportion of LAGBs, a decrease in SF values of the grains, and a transition of texture from to and . After stress relaxation, the variation trends of the GBs and textures were consistent with those of other plastic deformations, indicating that stress relaxation can be verified by the variations in GBs and textures. Our findings provide a theoretical basis for improvements in stress relaxation resistance of the Cu-based alloys used in connector industry.

Extraosseous osteosarcoma is a rare malignant tumor, most commonly occurring in the thigh, upper limbs, and retroperitoneum. However, there are only a few reported cases of renal osteosarcoma. Herein, we present the case of a 54-year-old woman with malignant extraosseous osteosarcoma of the left kidney. CT and MR imaging revealed a soft tissue mass originating from the left kidney.

This study assessed spatiotemporal trends in daily monsoon precipitation extremes at seasonal and sub-seasonal scales (June, July, August, and September) and their links with atmospheric circulations over Pakistan. The study used observed precipitation data from fifty in-situ stations and reanalysis products from the European Centre for Medium-Range Weather Forecasts (ECMWF) and National Centers for Environmental Prediction/the National Center for Atmospheric Research (NCEP/NCAR) during 1981–2018. A suite of seven extreme precipitation indices and non-parametric statistical techniques were used to infer trends in the frequency and intensity of extreme precipitation indices. An increase in frequency and intensity of overall extreme indices was evident, with a maximum tendency in the country’s northwestern (z-score=>2.5), central, and eastern (z-score > 4) monsoon-dominant parts. The northern and southwestern parts of the country exhibited a slight decrease (z-score

Abstract As an internal forcing of the earth climate system, soil moisture (SM) significantly influences the water and energy cycle by controlling evapotranspiration and terrestrial solar energy. The current study used observed precipitation, remotely sensed SM, and reanalysis of atmosphere and land parameters to assess the East Asian Monsoon (EAM) precipitation variability due to meridional SM oscillations across China. A generalized linear method, namely coupled manifold technique (CMT) for assessing the reciprocal forcing between two climate fields and numerical simulations are applied to SM and EAM precipitation. We find that the EAM precipitation interannual variability between north and south China significantly correlates with SM meridional oscillation. The CMT results further showed that SM forcing has a significant (99% confidence) influence on the EAM precipitation explaining about 0.40 of the variance ratio in north and south China. The EAM and SM composite analysis show that the wetter (drier) north (south) oscillates the EAM precipitation over the north (south) of China and vice versa due to SM thermal controls. We then used control and sensitivity simulations with SM observations to further validate the findings implying that SM can potentially improve the interannual EAM forecast skills. The model results show that a wetter (drier) north (south) results in negative (positive) sensible heat (latent heat) anomalies that impact the boundary layer and propagate to change the meridional atmospheric heating profile. When positive (negative) SM anomalies exist over northern (southern) China, the zonal easterlies and extratropical westerlies move to north China causing above-normal precipitation that descends into southern China, suppressing subtropical westerlies and precipitation in southern China. On the contrary, a dry (wet) north (south) favors intensified subtropical westerlies and precipitation in southern China. The findings have dire implications for the water and energy cycle of the region in the projected wetting and drying patterns of the north (south).

ObstructiveTo develop and validate radiomics and machine learning models for identifying encrusted stents and compare their recognition performance with multiple metrics.MethodsA total of 354 patients with ureteral stent placement were enrolled from two medical institutions and divided into the training cohort (n = 189), internal validation cohort (n = 81) and external validation cohort (n = 84). Based on features selected by Wilcoxon test, Spearman Correlation Analysis and least absolute shrinkage and selection operator (LASSO) regression algorithm, six machine learning models based on radiomics features were established with six classifiers (LR, DT, SVM, RF, XGBoost, KNN). After comparison with those models, the most robust model was selected. Considering its feature importance as radscore, the combined model and a nomogram were constructed by incorporating indwelling time. Accuracy, sensitivity, specificity, area under the curve (AUC), decision curve analysis (DCA) and calibration curve were used to evaluate the recognition performance of models.Results1,409 radiomics features were extracted from 641 volumes of interest (VOIs) and 20 significant radiomics features were selected. Considering the superior performance (AUC 0.810, 95%CI, 0.722–0.888) in the external validation cohort, feature importance of XGBoost was used as a radscore, constructing a combined model and a nomogram with indwelling time. The accuracy, sensitivity, specificity and AUC of the combined model were 98, 100, 97.3% and 0.999 for the training cohort, 83.3, 80, 84.5% and 0.867 for the internal cohort and 78.2, 76.3, 78.8% and 0.820 for the external cohort, respectively. DCA indicates the favorable clinical utility of models.ConclusionMachine learning model based on radiomics features enables to identify ureteral stent encrustation with high accuracy.

Study region: In the Yangtze River basin of China. Study focus: We applied a recently popular deep learning (DL) algorithm, Transformer (TSF), and two commonly used DL methods, Long-Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), to evaluate the performance of TSF in predicting runoff in the Yangtze River basin. We also add the main structure of TSF, Self-Attention (SA), to the LSTM and GRU models, namely LSTM-SA and GRU-SA, to investigate whether the inclusion of the SA mechanism can improve the prediction capability. Seven climatic observations (mean temperature, maximum temperature, precipitation, etc.) are the input data in our study. The whole dataset was divided into training, validation and test datasets. In addition, we investigated the relationship between model performance and input time steps. New hydrological insights for the region: Our experimental results show that the GRU has the best performance with the fewest parameters while the TSF has the worst performance due to the lack of sufficient data. GRU and the LSTM models are better than TSF for runoff prediction when the training samples are limited (such as the model parameters being ten times larger than the samples). Furthermore, the SA mechanism improves the prediction accuracy when added to the LSTM and the GRU structures. Different input time steps (5 d, 10 d, 15 d, 20 d, 25 d and 30 d) are used to train the DL models with different prediction lengths to understand their relationship with model performance, showing that an appropriate input time step can significantly improve the model performance.

Drought is a natural disaster that occurs globally and can damage the environment, disrupt agricultural production and cause large economic losses. The accurate prediction of drought can effectively reduce the impacts of droughts. Deep learning methods have shown promise in drought prediction, with convolutional neural networks (CNNs) being particularly effective in handling spatial information. In this study, we employed a deep learning approach to predict drought in the Fenhe River (FHR) basin, taking into account the meteorological conditions of surrounding regions. We used the daily SAPEI (Standardized Antecedent Precipitation Evapotranspiration Index) as the drought evaluation index. Our results demonstrate the effectiveness of the CNN model in predicting drought events 1~10 days in advance. We evaluated the predictions made by the model; the average Nash–Sutcliffe efficiency (NSE) between the predicted and true values for the next 10 days was 0.71. While the prediction accuracy slightly decreased with longer prediction lengths, the model remained stable and effective in predicting heavy drought events that are typically difficult to predict. Additionally, key meteorological variables for drought predictions were identified, and we found that training the CNN model with these key variables led to higher prediction accuracy than training it with all variables. This study approves an effective deep learning approach for daily drought prediction, particularly when considering the meteorological conditions of surrounding regions.

With the development trend of connectors to high-current and high-temperature service, copper alloy connectors are required to have superior stress relaxation resistance. In this work, the optimal aging parameter was explored via stress relaxation resistance, strength, microhardness, and conductivity. And the strengthening mechanism of stress relaxation was investigated through the TEM and EBSD microscopic characterization. The results show that the Cu–Cr–Ni alloy reached the peak aging state under 450 °C for 1 h and obtained excellent properties. Especially, the percentage of residual stress in the relaxation test reached 93.1%. The microhardness associated with aging strengthening showed a positive correlation with yield strength and stress relaxation resistance. Then the microhardness after aging can be used as a simple evaluation index of stress relaxation resistance. The Cu–Cr–Ni alloy with superior stress relaxation resistance is mainly related to dislocation strengthening, second phase strengthening, and the coexistence of spinodal decomposition and ordering.

Reduction of uncertainty in climate projections is a key issue for climate change adaptation. Here the authors show that an applying an emergent constraint effectively reduces projection uncertainty across Asia, and reveals less warming and a slower increase in water availability than previously estimated.

Satellite observations have provided global and regional soil-moisture estimates in the last four decades. However, the accuracy of these observations largely depends on reducing uncertainties in the retrieval algorithms. In this study, we address two challenges affecting the quality of soil-moisture estimates from a widely used soil-moisture-retrieval model, the land parameter retrieval model (LPRM). We studied two improvement schemes that were aimed at reducing uncertainties in open water signals (the LPRMv6_OWF) and vegetation signals (the LPRMv6_Veg), as well as a scheme to reduce their combined impacts (the LPRMv6_OWFVeg) on LPRM-retrieved soil moisture using the FengYun-3B (FY-3B) satellite observations. To assess the impacts of the improvement schemes, we utilized in situ soil moisture from the Jiangsu and Jiangxi provinces in China. We found that the retrievals (Rs) of the LPRMv6_Veg and the LPRMv6_OWFVeg were mainly in the range of 0.2 to 0.5 in Jiangsu and Jiangxi, with increases of 0.1 compared to those of the LPRMv6. The standard deviation (SD) of the LPRMv6_OWFVeg increased in Jiangsu, while the R of the LPRMv6_OWF increased in Jiangsu by 0.05–0.1 compared to that of the LPRMv6, but the SD tended to become worse. In Jiangxi, there was an increase of 0.1 in R. The results show that each of these algorithms improved the accuracy of soil-moisture inversion to some extent, compared to the original algorithm, with the LPRMv6_OWFVeg showing the greatest improvement, followed by the LPRMv6_Veg. The accuracy of both the LPRMv6_OWF and the LPRMv6_OWFVeg decreased to some extent when the open-water fraction (OWF) was greater than 0.2. Full areal extent analyses based on triple collocation showed significant improvements in correlations and minimized errors across different vegetation scenarios over the entire region of China in both the LPRMv6_OWF and the LPRMv6_Veg. However, reduced qualities were found in arid regions in northern China because of the nonlinear relationships between land-surface temperature, vegetation, and soil moisture in the LPRM. These results highlight important lessons for developing comprehensive improvement schemes for soil-moisture retrievals from passive microwave satellite observations.

The North China Plain (NCP) represents a significant agricultural production region in China, with winter wheat serving as one of its main grain crops. Accurate identification of winter wheat through remote sensing technology holds significant importance in ensuring food security in the NCP. In this study, we have utilized Landsat 8 and Landsat 9 imagery to identify winter wheat in the NCP. Multiple convolutional neural networks (CNNs) and transformer networks, including ResNet, HRNet, MobileNet, Xception, Swin Transformer and SegFormer, are used in order to understand their uncertainties in identifying winter wheat. At the same time, these deep learning (DL) methods are also compared to the traditional random forest (RF) method. The results indicated that SegFormer outperformed all methods, of which the accuracy is 0.9252, the mean intersection over union (mIoU) is 0.8194 and the F1 score (F1) is 0.8459. These DL methods were then applied to monitor the winter wheat planting areas in the NCP from 2013 to 2022, and the results showed a decreasing trend.

The ability of soil moisture (SM) to affect precipitation (P) is a vital part of the water-energy cycles. Accurately quantifying this coupling enhances the ability to predict hydroclimatic extremes like floods and droughts. In this study, the ability of soil moisture to affect precipitation (SM-P) is characterized by two parts: the influence of soil moisture on evapotranspiration (SM-ET), and the influence of evapotranspiration on precipitation (ET-P). We determined localized ET-P by incorporating the coupling between latent heat flux (LH) and LCL height, to optimize the estimation of the SM-P. This approach links SM more closely to P by considering the influence of surface fluxes. The results indicate that CMIP6 models exhibited the anticipated hotspot patterns for the three coupling metrics in transition regions. However, we observed that climate models generally exhibit weaker SM-P coupling compared to reanalysis models. Both SM-ET and SM-P showcase higher values wherein wet climate regions during dry years, and the converse occurs in dry regions. Due to sensitivity to climate change, the ET-P exhibits a more pronounced upward trend in the future. This study helps understand P’s response to SM shifts in climate models, crucial for predicting hydrological extremes and coupled global warming impact.

Soil moisture over the Tibetan Plateau (TP) can affect hydrological cycles on local and remote scales through land–atmosphere interactions. However, TP long-term surface soil moisture characteristics and their response to climate change are still unclear. In this study, we firstly evaluate two satellite-based products—SSM/I (the Special Sensor Microwave Imagers) and ECV COMBINED (the Essential Climate Variable combined)—and three reanalysis products—ERA5-Land (the fifth generation of the land component of the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis), MERRA2 (the second version of Modern-Era Retrospective Analysis for Research and Applications), and GLDAS Noah (the Noah land surface model driven by Global Land Data Assimilation System)—against two in situ observation networks. SSM/I and GLDAS Noah outperform the other soil moisture products, followed by MERRA2 and ECV COMBINED, and ERA5-Land has a certain degree of uncertainty in evaluating TP surface soil moisture. Analysis of long-term soil moisture characteristics during 1988–2008 shows that annual and seasonal mean soil moisture have similar spatial distributions of soil moisture decreasing from southeast to northwest. Additionally, a significant increasing trend of soil moisture is found in most of the TP region. With a non-linear machine learning method, we quantify the contribution of each climatic variable to warm-season soil moisture. It indicates that precipitation dominates soil moisture changes rather than air temperature. Pixel-wise partial correlation coefficients further show that there are significant positive correlations between precipitation and soil moisture over most of the TP region. The results of this study will help to understand the role of TP soil moisture in land–atmosphere coupling and hydrological cycles under climate change.

Understanding the effects of thermal and water stress on maize yield in the context of climate change is crucial to ensure food security in China. However, very few studies looked into the combined effects of heat and water stress on maize yield in China. Here, we utilized historical reanalysis data from ERA5 and four future shared socioeconomic pathway scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) of the Coupled Model Intercomparison Project 6 (CMIP6) models to predict the maize yield. We used the linear mixed-effects model to quantify the grid cell sensitivity of vapor pressure deficit (VPD) and root-zone soil moisture to maize yield in China during 2010–2016. The results infer that VPD and root-zone soil moisture are excellent representatives of heat and moisture stress. Maize yield is beneficial only when the atmospheric moisture demand and soil moisture are in relative balance. Based on the historical results’ polynomial function for VPD and soil moisture, we predict the maize yield response to soil moisture and VPD in the four SSPs. The results show that considering soil moisture in the future the projected yield estimates reduce the overestimated yield loss by half compared to considering only atmospheric moisture requirements. Maize yield will decrease under representative SSPs due to an increase in temperature (1.5, 2.0, 2.5, 3.0, 3.5, and 4.0°C). This study suggests that both atmospheric moisture demand and supply need to be considered when analyzing the specific influence of climate change on crop yield to secure and assure global food supplies.

The carotid web is commonly found in the carotid bulb or the beginning of the internal carotid artery. It presents as a thin layer of proliferative intimal tissue originating from the arterial wall and extending into the vessel lumen. A large body of research has proven that the carotid web is a risk factor for ischemic stroke. This review summarizes the current research status of the carotid web and focuses on its imaging presentation.

Soil moisture is an essential component for the planetary balance between land surface water and energy. Obtaining long-term global soil moisture data is important for understanding the water cycle changes in the warming climate. To date several satellite soil moisture products are being developed with varying retrieval algorithms, however with considerable missing values. To resolve the data gaps, here we have constructed two global satellite soil moisture products, i.e., the CCI (Climate Change Initiative soil moisture, 1989–2021; CCIori hereafter) and the CM (Correlation Merging soil moisture, 2006–2019; CMori hereafter) products separately using a Convolutional Neural Network (CNN) with autoencoding approach, which considers soil moisture variability in both time and space. The reconstructed datasets, namely CCIrec and CMrec, are cross-evaluated with artificial missing values, and further againt in-situ observations from 12 networks including 485 stations globally, with multiple error metrics of correlation coefficients (R), bias, root mean square errors (RMSE) and unbiased root mean square error (ubRMSE) respectively. The cross-validation results show that the reconstructed missing values have high R (0.987 and 0.974, respectively) and low RMSE (0.015 and 0.032 m3/m3, respectively) with the original ones. The in-situ validation shows that the global mean R between CCIrec (CCIori) and in-situ observations is 0.590 (0.581), RMSE is 0.093 (0.093) m3/m3, ubRMSE is 0.059 (0.058) m3/m3, bias is 0.032 (0.037) m3/m3 respectively; CMrec (CMori) shows quite similar results. The added value of this study is to provide long-term gap-free satellite soil moisture products globally, which helps studies in the fields of hydrology, meteorology, ecology and climate sciences.

Accurately delimiting phylogenetic relationships and taxonomic status is important for understanding species diversity and distributions and devising effective strategies for biodiversity conservation. However, species delimitation is controversial in Gymnocypris eckloni, a schizothoracine fish endemic to the Qinghai–Tibetan Plateau. The aim of this study is robustly identifying the phylogeny of G. eckloni in the Yellow River (YR) population and Qaidam basin (QB) population. The specific-locus amplified fragments sequencing (SLAF-seq) is employed with comprehensively sampling of schizothoracine fishes. In total, 350,181,802 clean reads and 5,114,096 SNPs are identified from SLAF-seq. Phylogenetic analysis recovers a non-monophyletic population of G. eckloni between YR and QB populations, representing an independent phylogenetic relationship between the two populations. Species delimitation analyses by SNAPPER and GMYC methods using the genome-wide SNP data confirm that their taxonomic statuses are separated. This study highlights the importance of further reconsidering clearer taxonomy, which would improve the genetic diversity conservation of Tibetan highland fishes.

Proliferating trichilemmal tumor is a very rare benign tumor that has the potential to transition into a malignant tumor. PTT most commonly affects the scalps of women above 60 years old and is frequently misdiagnosed due to its rarity. Herein, we present a case of a 68-year-old man with a malignant proliferating trichilemmal tumor on his right thumb. X-ray image showed a soft tissue mass on his thumb accompanied by bone destruction, while 18F-FDG PET revealed a hypermetabolic mass in the first index with axillary lymph node metastasis.

UAV-SfM photogrammetry is widely used in remote sensing and geoscience communities. Scholars have tried to optimize UAV-SfM for terrain modeling based on analysis of error statistics like root mean squared error (RMSE), mean error (ME), and standard deviation (STD). However, the errors of terrain modeling tend to be spatially distributed. Although the error statistic can represent the magnitude of errors, revealing spatial structures of errors is still challenging. The “best practice” of UAV-SfM is lacking in research communities from the perspective of spatial structure of errors. Thus, this study designed various UAV-SfM photogrammetric scenarios and investigated the effects of image collection strategies and GCPs on terrain modeling. The error maps of different photogrammetric scenarios were calculated and quantitatively analyzed by ME, STD, and Moran’s I. The results show that: (1) A high camera inclination (20–40°) enhances UAV-SfM photogrammetry. This not only decreases the magnitude of errors, but also mitigates its spatial correlation (Moran’s I). Supplementing convergent images is valuable for reducing errors in a nadir camera block, but it is unnecessary when the image block is with a high camera angle. (2) Flying height increases the magnitude of errors (ME and STD) but does not affect the spatial structure (Moran’s I). By contrast, the camera angle is more important than the flying height for improving the spatial structure of errors. (3) A small number of GCPs rapidly reduce the magnitude of errors (ME and STD), and a further increase in GCPs has a marginal effect. However, the structure of errors (Moran’s I) can be further improved with increasing GCPs. (4) With the same number, the distribution of GCPs is critical for UAV-SfM photogrammetry. The edge distribution should be first considered, followed by the even distribution. The research findings contribute to understanding how different image collection scenarios and GCPs can influence subsequent terrain modeling accuracy, precision, and spatial structure of errors. The latter (spatial structure of errors) should be routinely assessed in evaluations of the quality of UAV-SfM photogrammetry.

Accurate assessment of the extent of crop distribution and mapping different crop types are essential for monitoring and managing modern agriculture. Medium and high spatial resolution remote sensing (RS) for Earth observation and deep learning (DL) constitute one of the most major and effective tools for crop mapping. In this study, we used high-resolution Sentinel-2 imagery from Google Earth Engine (GEE) to map paddy rice and winter wheat in the Bengbu city of Anhui Province, China. We compared the performance of different popular DL backbone networks with the traditional machine learning (ML) methods, including HRNet, MobileNet, Xception, and Swin Transformer, within the improved DeepLabv3+ architecture, Segformer and random forest (RF). The results showed that the Segformer based on the combination of the Transformer architecture encoder and the lightweight multilayer perceptron (MLP) decoder achieved an overall accuracy (OA) value of 91.06%, a mean F1 Score (mF1) value of 89.26% and a mean Intersection over Union (mIoU) value of 80.70%. The Segformer outperformed other DL methods by combining the results of multiple evaluation metrics. Except for Swin Transformer, which was slightly lower than RF in OA, all DL methods significantly outperformed RF methods in accuracy for the main mapping objects, with mIoU improving by about 13.5~26%. The predicted images of paddy rice and winter wheat from the Segformer were characterized by high mapping accuracy, clear field edges, distinct detail features and a low false classification rate. Consequently, DL is an efficient option for fast and accurate mapping of paddy rice and winter wheat based on RS imagery.

Abstract Background Copper was used for many years in aquaculture operations as an effective algaecide or a parasite treatment of fish. It is an essential nutrient with numerous functions in organisms, but is toxic at high concentrations. However, the toxicity of copper to fish remains unclear. In this study, we used the piebald naked carp, Gymnocypris eckloni, as a model. RNA-seq data from different tissues, including gills, kidney, and liver, were used to investigate the underlying mechanism of copper toxicology in G. eckloni. Results We compared the transcriptomes from different tissues with different time durations of copper ion treatment. After 72 h copper ion treatment, the number of genes with different expression in gills and liver changed dramatically, but not in kidneys. In KEGG functional enrichment, the pattern of differentially expressed genes (DEGs) was also similar in the gills and liver. The most enriched pathway of DEGs was “Ribosome” in both tissues. Furthermore, we analyzed the expression levels of genes involved in oxidative stress response and protein synthesis using qPCR and RNA-seq data. Our results showed that several genes involved in oxidative stress response were up-regulated both in gills and liver. Up-regulation of these genes indicated that copper treatment caused oxidative stress, which is likely to result in ribosome damage. In addition, our results showed that the expression of Eef1b2, a transcription elongation factor, was decreased in the liver under oxidative stress, and the expression of translation initiation factors Eif4ebp1 and eIF2α, and elongation factor eEF2 was up-regulated. These results supported the idea that oxidative stress inhibits protein synthesis in cells. Conclusions Our results indicate that copper exposure caused different responses in different tissues, since the gene expression patterns changed substantially either in the gills or liver, while the effect on the kidney was relatively weak. Furthermore, our results indicated that the expression pattern of the genes involved in the ribosome, which is a complex molecular machine orchestrating protein synthesis in the cell, together with translation initiation factor and elongation factors, were affected by copper exposure both in the gills and liver of piebald naked carp. This result leads us to speculate that the downregulation of global protein synthesis is an acute response strategy of fish to metal-induced oxidative stress. Moreover, we speculate that this strategy not only exists in the selective translation of proteins but also exists in the specific translation of functional proteins in tissues and cells.

Coronary heart disease (CHD) is the leading cause of mortality in the world. Early detection and treatment of CHD are crucial. Currently, coronary CT angiography (CCTA) has been the prior choice for CHD screening and diagnosis, but it cannot meet the clinical needs in terms of examination quality, the accuracy of reporting, and the accuracy of prognosis analysis. In recent years, artificial intelligence (AI) has developed rapidly in the field of medicine; it played a key role in auxiliary diagnosis, disease mechanism analysis, and prognosis assessment, including a series of studies related to CHD. In this article, the application and research status of AI in CCTA were summarized and the prospects of this field were also described.

Biochar is a carbon-rich stable substance, defined as charred organic matter, produced during biomass thermochemical decomposition, and its application is currently considered as a mean of enhancing soil productivity, which is an important requirement for increasing crop yields whereas, simultaneously, it improves the quality of contaminated soil and water. However, depending on pedoclimatic conditions, its applicability exhibits negative aspects as well. It can also support biofuel production, therefore helping in reducing the demand for fossil fuels. Biochar is providing ecosystem services such as immobilization and transformation of contaminants and mitigation of climate change by sequestering carbon and reducing the release of greenhouse gases such as nitrous oxide and methane. It can further reduce waste as it could be produced from everything that contains biomass thereby assisting in waste management. Due to such wide-ranging applications, this review was conceptualized to emphasize the importance of biochar as an alternative to classic products used for energy, environmental and agricultural purposes. Based on the detailed information on the factors impacting biochar properties, the benefits and limitations of biochar, and the potential application guidelines for growers, this work aimed to help in partial achievement of multiple environmental goals and a practical recommendation to growers although its large-scale application is still controversial.

Soil moisture (SM) is an important variable in mediating the land-atmosphere interactions. Earth System Models (ESMs) are the key tools for predicting the response of SM to future climate change. Many ESMs provide outputs for SM; however, the estimated SM accuracy from different ESMs varies geographically as each ESM has its advantages and limitations. This study aimed to develop a merged SM product with improved accuracy and spatial resolution in China for 2015-2100 through data fusion of 25 ESMs with a deep-learning (DL) method. A DL model that can simultaneously perform data fusion and spatial downscaling was used to analyze SM’s future trend in China. Through the model, monthly SM data in four future scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) from 2015 to 2100, with a high resolution at 0.25°, was obtained. The evaluation metrics include mean absolute error (MAE), root mean square difference (RMSD), unbiased root mean square difference (ubRMSD), and coefficient of correlation (r). The evaluation results showed that our merged SM product is significantly better than each of the ESMs and the ensemble mean of all ESMs in terms of accuracy and spatial distribution. In the temporal dimension, the merged product is equivalent to the original data after deviation correction and equivalent to reconstructing the fluctuation of the whole series in a high error area. By further analyzing the spatiotemporal patterns of SM with the merged product in China, we found that northeast China will become wetter whereas South China will become drier. Northwest China and the Qinghai-Tibet Plateau would change from wetting to drying under a medium emission scenario. From the temporal scale of the results, the rate of SM variations is accelerated with time in the future under different scenarios. This study demonstrates the feasibility and effectiveness of the proposed procedure for simultaneous data fusion and spatial downscaling to generate improved SM data. The merged data have great practical and scientific implications.

Abstract Background Propionic acidemia (PA) is a severe monogenic disorder characterized by a deficiency of the mitochondrial protein propionyl-CoA carboxylase (PCC) enzyme, which is caused by mutations in the PCCA or PCCB gene. Preconception carrier screening could provide couples with meaningful information for their reproductive options; however, it is not widely performed in China. Case presentation This report describes a case of dizygotic twin siblings conceived by in vitro fertilization (IVF) and diagnosed with propionic acidemia (PA). Their parents had no history of PA. Tandem mass spectrometry and urine gas chromatography/mass spectrometry (GC/MS) of the twin siblings revealed markedly elevated propionyl carnitine (C3), C3/C2, and 3-hydroxypropionate in the plasma and urine. Whole-exome sequencing was performed for the twin siblings. A homozygous missense mutation, c.2002G > A (p.Gly668Arg) in PCCA, was identified in the twin siblings. Sanger sequencing confirmed the homozygous mutation in the twin siblings and identified their parents as heterozygous carriers of the c.2002G > A mutation in PCCA. Both neonates in this case died. This is an emotionally and financially devastating outcome that could have been avoided with genetic carrier screening before conception. If couples are screened before IVF and found to be silent carriers, then reproductive options (such as preimplantation genetic diagnosis or prenatal diagnosis) can be offered to achieve a healthy newborn. Conclusion This case is a reminder to infertile couples seeking IVF that it is beneficial to clarify whether they are silent carriers before undergoing IVF.

Abstract Background Fertilization failure after intracytoplasmic sperm injection continues to affect couples and the etiology is not well-understood. Case presentation We characterized a couple with 2-year history of primary unexplained infertility. Three different assisted reproduction attempts (IVF + rescue ICSI, ICSI and ICSI-AOA) showed repeated fertilization failure for MII oocyte retrieval after controlled ovarian hyperstimulation. After whole-exome sequencing and sanger sequencing of the couple and their family members, variant pathogenicity was assessed using SIFT, PolyPhen2, Mutation Taster, and Human Splicing Finder software. We identified novel compound heterozygous mutations, c.1535 + 3A > G and c.946C > T (p. Leu316Phe), in WEE2 in the female proband. Trios analysis of the variations revealed an autosomal recessive pattern. c.1535 + 3A > G in WEE2 was predicted to break the wild-type donor site and affect splicing, and the missense mutation c.946C > T (p. Leu316Phe) of WEE2 was predicted to be pathogenic. Conclusion A novel compound heterozygous mutation in WEE2 was identified in an infertile female who experienced repeated fertilization failure even after ICSI-AOA. These novel mutations in WEE2 provided genetic evidence for fertilization failure.

Abstract 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.

Effective nitrogen (N) management practices are critical to sustain crop production and minimize nitrate (NO3−) leaching loss from irrigated fields in the Columbia Basin (U.S.), but studies on the applied practices are limited. Therefore, from 2014 to 2016, two separate field studies were conducted in sandy loam soils in the region to evaluate the performance of various N fertilizers in spring and winter wheat. The treatments consisted of two nitrification inhibitors (NIs) (Instinct® II and Agrotain® Ultra) in combination with two N fertilizers (urea and urea ammonium nitrate [UAN]) under two application methods (single vs. split-application) and two rates (100% vs. 85% of growers’ standard). The results from these field trials demonstrated that N fertilizer treatments did not affect wheat grain yield (GY) and grain protein (GP). In the spring wheat trial, higher NH4⁺-N content but lower NO3−-N content was observed in the UAN treatments (0–30 cm). However, the application of NIs had no considerable effect on soil N content. In the winter wheat trial, the split N application generally reduced NO3−-N and total mineral nitrogen (TMN) content, especially at 30–60 cm, in comparison to a single application. The use of Instinct® II tended to reduce NO3−-N and TMN contents, while Agrotain® Ultra was not effective in inhibiting nitrification. Our findings suggest that more studies on the effectiveness of NIs and N applications would enable growers to optimize N use efficiency and crop production in the region.

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.

The present work aims to systematically investigate the influence of P content on the microstructure and texture evolution of oxygen-free copper during intermediate annealing and final cold rolling. The microstructure and texture evolution were studied by electron backscattered diffraction and transmission electron microscopy. With the addition of P, the grains refined and a large fraction of low angle grain boundaries (LAGBs) emerged after intermediate annealing. The texture transformed from pure metal type for pure Cu to the α-fiber texture which included brass and Goss texture as P was added. The recrystallization temperature increased with the addition of P, and refined grains after the final cold rolling process. The addition of elemental P would reduce the stacking fault energy, and then influence the transformation of the deformation and recrystallization texture of the copper. Accompanied by the evolution of the deformation texture, the recrystallization cubic texture {001} was suppressed and a strong {236} brass recrystallization texture emerged with the addition of elemental P after the intermediate annealing and subsequent final cold rolling process.

Land evaporation (LET) is an important variable in climate change, water cycle and water resources management. Mastering the projected changes in LET is significant for crop water requirements and the energy cycle. The global climate model (GCM) is a vital tool for future climate change research. However, the GCMs have low spatial resolution and relatively high errors. We use a deep learning (DL)-based model to deal with this problem. The DL approach can downscale the model data and merge simultaneously. We applied the DL approach to a suit of models from the Coupled Model Intercomparison Project 6th edition (CMIP6) LET data. From the result of all the evaluation metrics, the DL merged data greatly improved in both spatial and time dimensions. The mean RMSE is 5.85 mm and the correlation is 0.95 between the DL merged data and reference data (historical reliable evaporation data). The future LET evidently increases in four scenarios (SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5), and the upward intensity rises from the low to high emission scenarios. The highest increasing regions are in the Tibet Plateau and the south of China and the trend is larger than 10 mm/decade in the high scenarios. From the seasonal point of view, the increasing trend in spring and summer is far larger than for autumn and winter. The Tibet Plateau and the northeast of China have the largest upward trend in the spring of SSP5–8.5, higher than 1.6 mm/decade.

Autotetraploid alfalfa is a major hay crop planted all over the world due to its adaptation in different environments and high quality for animal feed. However, the genetic basis of alfalfa quality is not fully understood. In this study, a diverse panel of 200 alfalfa accessions were planted in field trials using augmented experimental design at three locations in 2018 and 2019. Thirty-four quality traits were evaluated by Near Infrared Reflectance Spectroscopy (NIRS). The plants were genotyped using a genotyping by sequencing (GBS) approach and over 46,000 single nucleotide polymorphisms (SNPs) were obtained after variant calling and filtering. Genome-wide association studies (GWAS) identified 28 SNP markers associated with 16 quality traits. Among them, most of the markers were associated with fiber digestibility and protein content. Phenotypic variations were analyzed from three locations and different sets of markers were identified by GWAS when using phenotypic data from different locations, indicating that alfalfa quality traits were also affected by environmental factors. Among different sets of markers identified by location, two markers were associated with nine traits of fiber digestibility. One marker associated with lignin content was identified consistently in multiple environments. Putative candidate genes underlying fiber-related loci were identified and they are involved in the lignin and cell wall biosynthesis. The DNA markers and associated genes identified in this study will be useful for the genetic improvement of forage quality in alfalfa after the validation of the markers.

Abstract Background Osteosarcoma is a most common bone malignant tumor which threatens children and adolescents. Circular RNAs (circRNAs) fundamentally play essential roles in the progress and development of human cancers by sponging with microRNAs (miRNAs). However, the role of circRNAs in osteosarcoma is not clear. The aim of the study was to investigate the roles and molecular mechanism of circRNAs in osteosarcoma. Results The data from qRT-PCR showed that circ_0051079 expression was higher in osteosarcoma cells and tissues compared to their normal controls. Meanwhile, bioinformatic analysis indicated that circ_0051079 was a sponge of miR-26a-5p, which was verified by luciferase activity assay. Subsequently, TGF-β1 was verified as a putative target mRNA of miR-26a-5p by luciferase assay. Cellular function assays were conducted and the findings revealed that circ_0051079/miR-26a-5p/TGF-β1 regulated osteosarcoma proliferation and metastasis. Conclusion The study demonstrated that circ_0051079 could act as an oncogene via regulating miR-26a-5p/TGF-β1 and a potential biomarker for osteosarcoma diagnose.

Heatwaves are expected to increase under climate change, and so are the associated deaths. Here the authors determine the regional high temperature thresholds for 27 metropolises in China and analyze the changes to heat-related mortality, showing that the additional global-warming temperature increase of 0.5°C, from 1.5°C to 2.0°C, will lead to tens of thousands of additional deaths, annually.