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Blue water (BW) and green water (GW) are crucial components of the hydrological cycle, but their accurate simulation and interpretation remain challenging in data-scarce basins. We propose the SWAT-ELM-SHAP model, coupling the Soil and Water Assessment Tool (SWAT), Ensemble Learning Model (ELM), and Shapley Additive Explanations (SHAP) method. This novel approach bridges the gap between a physically-based hydrological model, a data-driven machine learning (ML) model, and a holistically-interpreted SHAP method, offering accurate blue-green water simulation and holistic result interpretation for improved water resources management in data-scarce basins. We took the transfer simulation of blue-green water from the Xiangjiang River Basin (source basin) to the Zishui River Basin (target basin) as a case study to test and evaluate the feasibility of the coupled model during 1991–2022. The model performance results indicate that the simulation accuracy of our new coupled model is improved in data-scarce basins. In combination with hydrological response features generated by SWAT and meteorological features as the ELM input, our model enhances the daily blue-green water simulation. The Nash-Sutcliffe Efficiency coefficient (NSE) for BW, Green water flow (GWF), and Green water storage (GWS) consistently exceeds 0.77 during the calibration period (1991–2010) and exceeds 0.8 during the testing period (2011–2022). The interpretation results of coupled model demonstrate that SHAP holistic interpretation provides good interpretability for blue-green water simulation results in data-scarce basins. In general, the SWAT-ELM-SHAP offers a referenced approach that can reliably and efficiently simulate blue-green water in data-scarce basins, but more importantly, can further our understanding of the potential causal relationships, influence mechanisms, and variation mechanisms of blue-green water under changing environmental conditions.

Abstract Background Rice field weed object detection can provide key information on weed species and locations for precise spraying, which is of great significance in actual agricultural production. However, facing the complex and changing real farm environments, traditional object detection methods still have difficulties in identifying small-sized, occluded and densely distributed weed instances. To address these problems, this paper proposes a multi-scale feature enhanced DETR network, named RMS-DETR. By adding multi-scale feature extraction branches on top of DETR, this model fully utilizes the information from different semantic feature layers to improve recognition capability for rice field weeds in real-world scenarios. Methods Introducing multi-scale feature layers on the basis of the DETR model, we conduct a differentiated design for different semantic feature layers. The high-level semantic feature layer adopts Transformer structure to extract contextual information between barnyard grass and rice plants. The low-level semantic feature layer uses CNN structure to extract local detail features of barnyard grass. Introducing multi-scale feature layers inevitably leads to increased model computation, thus lowering model inference speed. Therefore, we employ a new type of Pconv (Partial convolution) to replace traditional standard convolutions in the model. Results Compared to the original DETR model, our proposed RMS-DETR model achieved an average recognition accuracy improvement of 3.6% and 4.4% on our constructed rice field weeds dataset and the DOTA public dataset, respectively. The average recognition accuracies reached 0.792 and 0.851, respectively. The RMS-DETR model size is 40.8 M with inference time of 0.0081 s. Compared with three classical DETR models (Deformable DETR, Anchor DETR and DAB-DETR), the RMS-DETR model respectively improved average precision by 2.1%, 4.9% and 2.4%. Discussion This model is capable of accurately identifying rice field weeds in complex real-world scenarios, thus providing key technical support for precision spraying and management of variable-rate spraying systems.

In a changing environment, a comprehensive and accurate water security assessment is critical to regional sustainable development. Most water security assessment studies concentrate on the sustainable use of blue water and less on green water, while the latter is irreplaceable to sustain food production. Considering the vital role that green water plays in maintaining human production and ecosystem stability. Here we incorporate green water into the water security assessment framework, proposing a blue-green water security index (BGWSI) and conducting a case study in the Xiangjiang River Basin (XRB). Two main conclusions are as follows: (a) combining green water to improve the water security assessment framework. The framework expands and extends the connotation of water security assessment and pioneers linking the attributes of water resources, like natural, social, and spatial benefit distances. In this way, the intrinsic connection between human activities and the risk status of regional blue-green water security can be grasped more comprehensively. (b) The case study reveals that the framework is feasible and applicable. The index BGWSI can better reflect the influence of basin green water resource fluctuation, green water footprint dynamics, spatial distance, and other factors on the overall water security risk. Incorporating green water and its related concepts into the water security assessment framework would provide new insight into water security assessment research, and we hope it will provide a more comprehensive reference for conducting regional blue-green water security assessment research. Also, the framework would be popularized and applied in more similar areas of blue-green water security assessment studies, which may contribute to accurately identifying the actual status of regional water security and promote the development of more scientific water management measures.

Weeds in paddy fields compete with rice for nutrients and cause pests and diseases, greatly affecting rice yield. Accurate weed detection is vital for implementing variable spraying with unmanned aerial vehicles (UAV) for weed control. Therefore, this paper presents an improved weed detection algorithm, YOLOv10n-FCDS (YOLOv10n with FasterNet, CGBlock, Dysample, and Structure of Lightweight Detection Head), using UAV images of Sagittaria trifolia in rice fields as the research object, to address challenges like the detection of small targets, obscured weeds and weeds similar to rice. We enhanced the YOLOv10n model by incorporating FasterNet as the backbone for better small target detection. CGBlock replaced standard convolution and SCDown modules to improve the detection ability of obscured weeds, while DySample enhanced discrimination between weeds and rice. Additionally, we proposed a lightweight detection head based on shared convolution and scale scaling, maintaining accuracy while reducing model parameters. Ablation studies revealed that YOLOv10n-FCDS achieved a 2.6% increase in mean average precision at intersection over union 50% for weed detection, reaching 87.4%. The model also improved small target detection (increasing mAP50 by 2.5%), obscured weed detection (increasing mAP50 by 2.8%), and similar weed detection (increasing mAP50 by 3.0%). In conclusion, YOLOv10n-FCDS enables effective weed detection, supporting variable spraying applications by UAVs in rice fields.

In recent years, accurate field monitoring has been a research hotspot in the domains of aerial remote sensing and satellite remote sensing. In view of this, this study proposes an innovative cross-platform super-resolution reconstruction method for remote sensing images for the first time, aiming to make medium-resolution satellites capable of field-level detection through a super-resolution reconstruction technique. The progressive growing generative adversarial network (PGGAN) model, which has excellent high-resolution generation and style transfer capabilities, is combined with a deep residual network, forming the Res-PGGAN model for cross-platform super-resolution reconstruction. The Res-PGGAN architecture is similar to that of the PGGAN, but includes a deep residual module. The proposed Res-PGGAN model has two main benefits. First, the residual module facilitates the training of deep networks, as well as the extraction of deep features. Second, the PGGAN structure performs well in cross-platform sensor style transfer, allowing for cross-platform high-magnification super-resolution tasks to be performed well. A large pre-training dataset and real data are used to train the Res-PGGAN to improve the resolution of Sentinel-2’s 10 m resolution satellite images to 0.625 m. Three evaluation metrics, including the structural similarity index metric (SSIM), the peak signal-to-noise ratio (PSNR), and the universal quality index (UQI), are used to evaluate the high-magnification images obtained by the proposed method. The images generated by the proposed method are also compared with those obtained by the traditional bicubic method and two deep learning super-resolution reconstruction methods: the enhanced super-resolution generative adversarial network (ESRGAN) and the PGGAN. The results indicate that the proposed method outperforms all the comparison methods and demonstrates an acceptable performance regarding all three metrics (SSIM/PSNR/UQI: 0.9726/44.7971/0.0417), proving the feasibility of cross-platform super-resolution image recovery.

A precision agriculture approach that uses drones for crop protection and variable rate application has become the main method of rice weed control, but it suffers from excessive spraying issues, which can pollute soil and water environments and harm ecosystems. This study proposes a method to generate variable spray prescription maps based on the actual distribution of weeds in rice fields and utilize DJI plant protection UAVs to perform automatic variable spraying operations according to the prescription maps, achieving precise pesticide application. We first construct the YOLOv8n DT model by transferring the “knowledge features” learned by the larger YOLOv8l model with strong feature extraction capabilities to the smaller YOLOv8n model through knowledge distillation. We use this model to identify weeds in the field and generate an actual distribution map of rice field weeds based on the recognition results. The number of weeds in each experimental plot is counted, and the specific amount of pesticide for each plot is determined based on the amount of weeds and the spraying strategy proposed in this study. Variable spray prescription maps are then generated accordingly. DJI plant protection UAVs are used to perform automatic variable spraying operations based on prescription maps. Water-sensitive papers are used to collect droplets during the automatic variable operation process of UAVs, and the variable spraying effect is evaluated through droplet analysis. YOLOv8n-DT improved the accuracy of the model by 3.1% while keeping the model parameters constant, and the accuracy of identifying weeds in rice fields reached 0.82, which is close to the accuracy of the teacher network. Compared to the traditional extensive spraying method, the approach in this study saves approximately 15.28% of herbicides. This study demonstrates a complete workflow from UAV image acquisition to the evaluation of the variable spraying effect of plant protection UAVs. The method proposed in this research may provide an effective solution to balance the use of chemical herbicides and protect ecological safety.

Objective To establish lymphoblastoid cell line (LCL) and human monoclonal antibody cell lines against PP1Pk antigens from a donor with rare p[Tj(a-)] phenotype. Methods Peripheral blood mononuclear cells (PBMCs)of an O type p[Tj(a-)] donor were prepared and transformed with Epstein-Barr virus (EBV) in vitro. Normal AB and O type red blood cells treated with bromelain as well as p[Tj (a-)] were used to screen the culture supernatant. The transformed LCL secreting antibodies to PP1Pk antigens was picked up and then hybridized with the SHM-D33 cell line using electric fusion technique. The hybridomas were performed by HOTD selective culture, antibody screening and limited dilution subcloning. Results We established a lymphoblastoid cell line 5E1-E5 and several monoclonal hybridoms cell lines secreting anti-PP1Pk immunoglobulin M from EBV transformed B lymphocytes of a p[Tj(a-)] type donor with anti-PP1Pk successfully. Conclusion Our work made it possible for screening rare p[Tj(a-)] blood type in donor population in China using human monoclonal anti-PP1Pk antibodies, which was important for the establishment of rare blood group banks in China.

Abstract Rice leaf blast is prevalent worldwide and a serious threat to rice yield and quality. Hyperspectral imaging is an emerging technology used in plant disease research. In this study, we calculated the standard deviation (STD) of the spectral reflectance of whole rice leaves and constructed support vector machine (SVM) and probabilistic neural network (PNN) models to classify the degree of rice leaf blast at different growth stages. Average accuracies at jointing, booting and heading stages under the full-spectrum-based SVM model were 88.89%, 85.26%, and 87.32%, respectively, versus 80%, 83.16%, and 83.41% under the PNN model. Average accuracies at jointing, booting and heading stages under the STD-based SVM model were 97.78%, 92.63%, and 92.20%, respectively, versus 88.89%, 91.58%, and 92.20% under the PNN model. The STD of the spectral reflectance of the whole leaf differed not only within samples with different disease grades, but also among those at the same disease level. Compared with raw spectral reflectance data, STDs performed better in assessing rice leaf blast severity.

Objective To analyze the influence of β-lactam antibiotics on RBC aging and clearance by detecting various indicators of aging and clearance on RBCs, as well as the differences in phagocytosis for erythrocytes before and after drugs treated in vitro. Methods RBCs were treated by β-lactam antibiotics, including Penicillin, Cefepime, Cefoperazone and Ceftazidime, and the changing of phosphatidylserine (PS) and clearance related CD markers, including CD35, CD47, CD55 and CD59 on the surface of the RBCs, were detected by flow cytometry at 0h and 24h after drugs treatment. The proportion of acanthocytes by microscope also at 0h and 24h after drugs treatment was calculated. The phagocytosis of drug-treated RBC was detected by monocyte monolayer assay (MMA). Untreated RBCs were incubated in PBS by the same condition as a negative control.The influence of β-lactam antibiotics on RBC aging and clearance by all the results above was studied. Results Compare to the untreated RBCs, the drug treated RBCs showed a higher PS level on the cell surface. The results showed by percentage as following(0 h vs 24 h): Penicillin 9.42% vs 93.30%, Cefepime 3.88% vs 57.27%, Cefoperazone 4.71% vs 75.75% and Ceftazidime 3.05% vs 43.19%. The acanthocytes ratio was as following(0 h vs 24 h): Penicillin 7.33% vs 86%, Cefepime 2.67% vs 52.67%, Cefoperazone 3.33% vs 67.67% and Ceftazidime 3.33% vs 90.67%. On the opposite, the clearance related CD markers, showed an obviously lower level after drugs treated(0 h vs 24 h): CD35: Penicillin 7.36% vs 11.87%, Cefepime 0.14% vs 28.51%, Cefoperazone 11.85% vs 21.55% and Ceftazidime 7.63% vs 8.73%; CD47: Penicillin 1.22% vs 9.13%, Cefepime 1.80% vs 0.86%, Cefoperazone 0.08% vs 6.85% and Ceftazidime 1.54% vs 5.50%; CD55: Penicillin 14.46% vs 44.31%, Cefepime 17.27% vs 38.41%, Cefoperazone 19.28% vs 33.28% and Ceftazidime 14.62% vs 34.13%; CD59: Penicillin 4.71% vs 20.56%, Cefepime 4.03% vs 7.60%, Cefoperazone 5.91% vs 22.38% and Ceftazidime 5.93% vs 30.89%. Drug-treated RBCs attached more to monocytes than untreated RBCs. Conclusion The β-lactam antibiotics could induce the changing of PS and the clearance of related CD markers on surface of RBCs. They also could lead acanthocytes and make the RBCs more susceptible to phagocytosis by monocytes. The β-lactam antibiotics could promote the RBCs aging and clearance, which might deteriorate the DIIHA.

Objective To establish human hybridoma cell lines, secreting monoclonal antibody against antigens of Rh blood system, from a donor with rare D--phenotype. Methods Peripheral blood B lymphocytes of an O type female donor, lacking C/c/E/e antigens on her erythrocyte, were transformed with Epstein-Barr virus (EBVs). EBVs were harvested from the cultural supernatant of B95-8 cells. The transformed lymphoblastoid cell line (LCL) secreting antibodies to C antigens were picked up and then hybridized with the myeloma SHM-D33 using electric fusion technique. Hybridoma cells were selected by HATD-Ouabain(HOTD)(Hypoxantine, Aminopterin, Thymidine, 2-Deoxycytide, and Ouabain)culture medium, microplate antibody screening and limited dilution subcloning. The monoclonal antibody was assayed by serological test and was confirmed by flow cytometry (FCM). Results From the cultural supernatant of D--peripheral blood transformed B lymphocytes, 3A6-C6, which agglutinated with R1R1(DCe/DCe)O-type RBCs but not with R2R2(DcE/DcE)O-type RBCs, was screened and preliminarily identified as anti-C. We established a hybridoma cell line secreting anti-C immunoglobulin M from B cells of D--individual successfully after hybridization with SHM-D33 myeloma cells. Conclusion The study had laid the groundwork for future research and development of human monoclonal antibodies against Rh antigens of RBC in future for diagnosis and screening purpose.

BackgroundNoninvasive methods for the early identify diagnosis of prostatitis, benign prostatic hyperplasia (BPH), and prostate cancer (PCa) are current clinical challenges.MethodsThe serum metabolites of 20 healthy individuals and patients with prostatitis, BPH, or PCa were identified using untargeted liquid chromatography-mass spectrometry (LC-MS). In addition, targeted LC-MS was used to verify the organic acid metabolites in the serum of a validation cohort.ResultsOrganic acid metabolites had good sensitivity and specificity in differentiating prostatitis, BPH, and PCa. Three diagnostic models identified patients with PROSTATITIS: phenyllactic acid (area under the curve [AUC]=0.773), pyroglutamic acid (AUC=0.725), and pantothenic acid (AUC=0.721). Three diagnostic models identified BPH: citric acid (AUC=0.859), malic acid (AUC=0.820), and D-glucuronic acid (AUC=0.810). Four diagnostic models identified PCa: 3-hydroxy-3-methylglutaric acid (AUC=0.804), citric acid (AUC=0.918), malic acid (AUC=0.862), and phenyllactic acid (AUC=0.713). Two diagnostic models distinguished BPH from PCa: phenyllactic acid (AUC=0.769) and pyroglutamic acid (AUC=0.761). Three diagnostic models distinguished benign BPH from PROSTATITIS: citric acid (AUC=0.842), ethylmalonic acid (AUC=0.814), and hippuric acid (AUC=0.733). Six diagnostic models distinguished BPH from prostatitis: citric acid (AUC=0.926), pyroglutamic acid (AUC=0.864), phenyllactic acid (AUC=0.850), ethylmalonic acid (AUC=0.843), 3-hydroxy-3-methylglutaric acid (AUC=0.817), and hippuric acid (AUC=0.791). Three diagnostic models distinguished PCa patients with PROSTATITISA < 4.0 ng/mL from those with PSA > 4.0 ng/mL: 5-hydromethyl-2-furoic acid (AUC=0.749), ethylmalonic acid (AUC=0.750), and pyroglutamic acid (AUC=0.929). Conclusions: These results suggest that serum organic acid metabolites can be used as biomarkers to differentiate prostatitis, BPH, and PCa.

Objective To investigate the profiles of RhC, c, E, and e antigens and phenotypes in 4 704 inpatients from multiple regions, i. e. Nanning, Guangzhou and Shenzhen, and provide data information for compatibility blood transfusion of Rh blood group. Methods The Rh blood group antigens were detected by microcolumn gel cards from three manufactures. If the test and the control results are inconsistent, a third-party reagent would be used, and traditional tube method for confirmation if needed. The Chi-square test and Fisher's exact test were used to analyze antigen frequency and Rh phenotypes in each region. Results Among the 4 704 inpatients, the frequency of C, c, E, and e antigen was e(91.77%)>C(85.64%)>c(49.62%)>E(41.60%), and Rh phenotypes distribution was CCee(49.40%)>CcEe(27.53%)>Ccee(8.16%)>ccEE(7.74%)>ccEe(4.89%)>CCEe(0.96%)>ccee(0.83%)>CcEE(0.47%)>CCEE(0.02%). There were significant differences in Rh blood type distribution among Nanning, Guangzhou and Shenzhen(P

Rice is the world’s most important food crop and is of great importance to ensure world food security. In the rice cultivation process, weeds are a key factor that affects rice production. Weeds in the field compete with rice for sunlight, water, nutrients, and other resources, thus affecting the quality and yield of rice. The chemical treatment of weeds in rice fields using herbicides suffers from the problem of sloppy herbicide application methods. In most cases, farmers do not consider the distribution of weeds in paddy fields, but use uniform doses for uniform spraying of the whole field. Excessive use of herbicides not only pollutes the environment and causes soil and water pollution, but also leaves residues of herbicides on the crop, affecting the quality of rice. In this study, we created a weed identification index based on UAV multispectral images and constructed the WDVINIR vegetation index from the reflectance of three bands, RE, G, and NIR. WDVINIR was compared with five traditional vegetation indices, NDVI, LCI, NDRE, and OSAVI, and the results showed that WDVINIR was the most effective for weed identification and could clearly distinguish weeds from rice, water cotton, and soil. The weed identification method based on WDVINIR was constructed, and the weed index identification results were subjected to small patch removal and clustering processing operations to produce weed identification vector results. The results of the weed identification vector were verified using the confusion matrix accuracy verification method and the results showed that the weed identification accuracy could reach 93.47%, and the Kappa coefficient was 0.859. This study provides a new method for weed identification in rice fields.

Single-cell omics is the profiling of individual cells through sequencing and other technologies including high-throughput analysis for single-cell resolution, cell classification, and identification as well as time series analyses. Unlike multicellular studies, single-cell omics overcomes the problem of cellular heterogeneity. It provides new methods and perspectives for in-depth analyses of the behavior and mechanism of individual cells in the cell population and their relationship with the body, and plays an important role in basic research and precision medicine. Single-cell sequencing technologies mainly include single-cell transcriptome sequencing, single-cell assay for transposase accessible chromatin with high-throughput sequencing, single-cell immune profiling (single-cell T-cell receptor [TCR]/B-cell receptor [BCR] sequencing), and single-cell transcriptomics. Single-cell TCR/BCR sequencing can be used to obtain a large amount of single-cell gene expression and immunomics data at one time, and combined with transcriptome sequencing and TCR/BCR diversity data, can resolve immune cell heterogeneity. This paper summarizes the progress in applying single-cell TCR/BCR sequencing technology to the tumor immune microenvironment, autoimmune diseases, infectious diseases, immunotherapy, and chronic inflammatory diseases, and discusses its shortcomings and prospects for future application.

Colorectal cancer (CRC) is one of the most common malignant tumors of the digestive tract worldwide and is a serious threat to human life and health. CRC occurs and develops in a multi-step, multi-stage, and multi-gene process, in which abnormal gene expression plays an important role. CRC is currently diagnosed via endoscopy combined with tissue biopsy. Compared with tissue biopsy, liquid biopsy technology has received increasingly more attention and applications in the field of molecular detection due to its non-invasive, safe, comprehensive, and real-time dynamic nature. This review article discusses the application and limitations of current liquid biopsy analytes in the diagnosis, treatment, and prognosis of CRC, as well as directions for their future development.

As a novel class of endogenous non-coding RNAs discovered in recent years, circular RNAs (circRNAs) are highly conserved and stable covalently closed ring structures with no 5′-end cap or 3′-end poly(A) tail. CircRNAs are formed by reverse splicing, mainly by means of a noose structure or intron complementary pairing. Exosomes are tiny discoid vesicles with a diameter of 40-100 nm that are secreted by cells under physiological and pathological conditions. Exosomes play an important role in cell-cell communication by carrying DNA, microRNAs, mRNAs, proteins and circRNAs. In this review, we summarize the biological functions of circRNAs and exosomes, and further reveal the potential roles of exosomal circRNAs in different diseases, providing a scientific basis for the diagnosis, treatment, and prognosis of a wide variety of diseases.

Autophagy and phagocytosis are two important processes that capture and digest materials found in cellular interiors and exteriors, respectively. Aged red blood cells (RBCs) are cleared by phagocytes in vivo. We focused on determining whether autophagy occurs after phagocytes swallow sunset erythrocytes, and whether the degree of autophagy is related to scavenging ability of phagocytes to erythrocytes. In addition, the ability of NLR family pyrin domain containing protein 3 (NLRP3) inflammasome to regulate erythrocyte clearance by phagocytes and its association with autophagy-related protein 16-like protein 1 (ATG16L1) are confirmed. We constructed a stable and low-NLRP3 expression THP-1 cell line using CRISPR/Cas9 technology. The analysis of erythrocyte clearance and autophagy of THP-1 cells with low NLRP3 expression showed that autophagy changes together when THP-1 engulfs aged RBCs. The occurrence of autophagy was dominated by microtubule-associated protein 1A/1B-light chain 3- (LC3-) associated phagocytosis accompanied by canonical autophagy. A negative correlation exists between the clearance of RBCs by THP-1 cells and the degree of autophagy. Downregulating the expression of NLRP3 in THP-1 cells can simultaneously inhibit the scavenging ability of THP-1 to erythrocytes and the degree of autophagy. In addition, the autophagy inhibitor bafilomycin A1 (BafA1) can enhance the phagocytosis ability of THP-1 to erythrocytes and promote the NLRP3 activation in THP-1 cells, while the autophagy inducer rapamycin inhibits the phagocytosis ability of THP-1 to RBCs and downregulates the NLRP3 activation. Results showed that autophagy and phagocytosis may be dynamic balance processes that can provide sufficient nutrition and energy to cells. Choosing NLRP3 as a target may regulate the phagocytic ability and the degree of autophagy in the meantime. These findings may be a potential strategy for regulating the clearance rate of phagocytes to aged RBCs and the secretion of proinflammatory cytokines to ensure transfusion safety.

Polygonum multiflorum (PM) has been used as a tonic and anti-aging remedy for centuries in Asian countries. However, its application in the clinic has been hindered by its potential to cause liver injury and the lack of investigations into this mechanism. Here, we established a strategy using a network pharmacological technique combined with integrated pharmacokinetics to provide an applicable approach for addressing this issue. A fast and sensitive HPLC-QQQ-MS method was developed for the simultaneous quantification of five effective compounds (trans-2,3,5,4′-tetrahydroxystilbene-2-O-β-d-glucoside, emodin-8-O-β-d-glucoside, physcion-8-O-β-d-glucoside, aloe-emodin and emodin). The method was fully validated in terms of specificity, linearity, accuracy, precision, extraction recovery, matrix effects, and stability. The lower limits of quantification were 0.125–0.500 ng/mL. This well-validated method was successfully applied to an integrated pharmacokinetic study of PM extract in rats. The network pharmacological technique was used to evaluate the potential liver injury due to the five absorbed components. Through pathway enrichment analysis, it was found that potential liver injury is primarily associated with PI3K-Akt, MAPK, Rap1, and Ras signaling pathways. In brief, the combined strategy might be valuable in revealing the mechanism of potential liver injury due to PM.

This study determined time-dependent IC50 and confirmed 3.5 mg/L as IC50 value for kaempferol inhibiting toxigenic Microcystis growth, based on which algicidal effects and mechanisms against toxigenic Microcystis exposed to various kaempferol doses (0.5–2 × IC50) were explored along 14 day-test. Results showed that growth inhibition ratio (GIR) almost elevated with increasing kaempferol dose, and at each dose GIR elevated firstly and fluctuated around 17.8%– > 40%, 53.6%–65.6% and 84.8%–89.3% at 1.75, 3.5 and 7 mg/L kaempferol during mid-late stage, respectively. With rising kaempferol dose, photosynthetic pigments contents (chlorophyll-a, phycobiliproteins), antioxidant response (superoxide dismutase and catalase (CAT) activities, glutathione (GSH) contents) and microcystins (MCs) production were almost increasingly stimulated as cellular protective responses during early-mid stage. However, these parameters (excluding CAT and GSH) were almost increasingly inhibited at late stage by prolonged stress and Microcystis cell was still more severely damaged as dose elevated along test, which could be reasons for increasing GIR with rising kamepferol dose. Persistent stimulation of CAT and GSH at each dose could alleviate cell damage until late stage, thus GIR no longer increased at late stage at each kaempferol dose. Moreover, fewer MCs release under kaempferol stress than control suggested kaempferol as eco-safe algaecide for migrating toxigenic Microcystis-dominated blooms (MCBs) and decreasing MCs risks. Compared with our previous data for luteolin inhibiting toxigenic Microcystis, this study supported formerly-proposed 'flavonoids structure - algicidal activity' relationship that the only OH-location difference between kaempferol and luteolin could affect algicidal activity and mechanisms against toxigenic Microcystis. Also, kaempferol and luteolin was revealed to exert additive effect on toxigenic Microcystis growth at equitoxic ratio. Our findings gave novel algicidal scenario of flavonoids and were greatly implicated in eco-friendly migrating toxigenic MCBs.

Tillering fertilization is an important part of field management in rice production. As the first peak fertilizer requirement period of rice, tillering fertilization directly affects the number of tillers and the growth of rice in the middle and late stages. In order to investigate a method of constructing an accurate fertilizer prescription map in the tillering stage using an unmanned aerial vehicle (UAV) remote sensing nitrogen demand diagnosis and reduce the amount of chemical fertilizer while ensuring the rice yield, this study realized the diagnosis of the rice nitrogen nutrient demand using UAV hyperspectral remote sensing during the tilling stage fertilization window. The results showed that the fertilizer amount was determined using the characteristic waveband and remote sensing. The results showed that five rice hyperspectral variables were extracted in the range of 450–950 nm by the feature band selection and feature extraction for the inversion of rice nitrogen content, and the inversion model of rice nitrogen content constructed by the whale-optimized extreme learning machine (WOA-ELM) was better than that constructed by the whale-optimized extreme learning machine (ELM). The model coefficient of determination was 0.899 and the prescription map variable fertilizer application method based on the nitrogen content inversion results reduced the nitrogen fertilizer by 23.21%. The results of the study can provide data and a model basis for precise variable fertilizer tracking by agricultural drones in the cold rice tillering stage.

To achieve rapid, accurate, and non-destructive diagnoses of nitrogen deficiency in cold land japonica rice, hyperspectral data were collected from field experiments to investigate the relationship between the nitrogen (N) content and the difference in the spectral reflectance relationship and to establish the hyperspectral reflectance difference inversion model of differences in the N content of rice. In this study, the hyperspectral reflectance difference was used to invert the nitrogen deficiency of rice and provide a method for the implementation of precision fertilization without reducing the yield of chemical fertilizer. For the purpose of constructing the standard N content and standard spectral reflectance the principle of minimum fertilizer application at maximum yield was used as a reference standard, and the acquired rice leaf nitrogen content and leaf spectral reflectance were differenced from the standard N content and standard spectral reflectance to obtain N content. The difference and spectral reflectance differential were then subjected to discrete wavelet multiscale decomposition, successive projections algorithm, principal component analysis, and iteratively retaining informative variables (IRIVs); the results were treated as partial least squares (PLSR), extreme learning machine (ELM), and genetic algorithm-extreme learning machine (GA-ELM). The results of hyperspectral dimensionality reduction were used as input to establish the inverse model of N content differential in japonica rice. The results showed that the GA-ELM inversion model established by discrete wavelet multi-scale decomposition obtained the optimal results in data set modeling and training. Both the R2 of the training data set and the validation data set were above 0.68, and the root mean square errors (RMSEs) were