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Currently, the classification of grapevine black rot disease relies on assessing the percentage of affected spots in the total area, with a primary focus on accurately segmenting these spots in images. Particularly challenging are cases in which lesion areas are small and boundaries are ill-defined, hampering precise segmentation. In our study, we introduce an enhanced U-Net network tailored for segmenting black rot spots on grape leaves. Leveraging VGG as the U-Net's backbone, we strategically position the atrous spatial pyramid pooling (ASPP) module at the base of the U-Net to serve as a link between the encoder and decoder. Additionally, channel and spatial dual-attention modules are integrated into the decoder, alongside a feature pyramid network aimed at fusing diverse levels of feature maps to enhance the segmentation of diseased regions. Our model outperforms traditional plant disease semantic segmentation approaches like DeeplabV3+, U-Net, and PSPNet, achieving impressive pixel accuracy (PA) and mean intersection over union (MIoU) scores of 94.33% and 91.09%, respectively. Demonstrating strong performance across various levels of spot segmentation, our method showcases its efficacy in enhancing the segmentation accuracy of black rot spots on grapevines. [ABSTRACT FROM AUTHOR]

Birds play a vital role in maintaining biodiversity. Accurate identification of bird species is essential for conducting biodiversity surveys. However, fine-grained image recognition of birds encounters challenges due to large within-class differences and small inter-class differences. To solve this problem, our study took a part-based approach, dividing the identification task into two parts: part detection and identification classification. We proposed an improved bird part detection algorithm based on YOLOv5, which can handle partial overlap and complex environmental conditions between part objects. The backbone network incorporates the Res2Net-CBAM module to enhance the receptive fields of each network layer, strengthen the channel characteristics, and improve the sensitivity of the model to important information. Additionally, in order to boost data on features extraction and channel self-regulation, we have integrated CBAM attention mechanisms into the neck. The success rate of our suggested model, according to experimental findings, is 86.6%, 1.2% greater than the accuracy of the original model. Furthermore, when compared with other algorithms, our model's accuracy shows noticeable improvement. These results show how useful the method we suggested is for quickly and precisely recognizing different bird species. [ABSTRACT FROM AUTHOR]

Ground-object classification using remote-sensing images of high resolution is widely used in land planning, ecological monitoring, and resource protection. Traditional image segmentation technology has poor effect on complex scenes in high-resolution remote-sensing images. In the field of deep learning, some deep neural networks are being applied to high-resolution remote-sensing image segmentation. The DeeplabV3+ network is a deep neural network based on encoder-decoder architecture, which is commonly used to segment images with high precision. However, the segmentation accuracy of high-resolution remote-sensing images is poor, the number of network parameters is large, and the cost of training network is high. Therefore, this paper improves the DeeplabV3+ network. Firstly, MobileNetV2 network was used as the backbone feature-extraction network, and an attention-mechanism module was added after the feature-extraction module and the ASPP module to introduce focal loss balance. Our design has the following advantages: it enhances the ability of network to extract image features; it reduces network training costs; and it achieves better semantic segmentation accuracy. Experiments on high-resolution remote-sensing image datasets show that the mIou of the proposed method on WHDLD datasets is 64.76%, 4.24% higher than traditional DeeplabV3+ network mIou, and the mIou on CCF BDCI datasets is 64.58%. This is 5.35% higher than traditional DeeplabV3+ network mIou and outperforms traditional DeeplabV3+, U-NET, PSP-NET and MACU-net networks. [ABSTRACT FROM AUTHOR]

Wireless sensor network (WSN) location is a significant research area. In complex environments like forests, inaccurate signal intensity ranging is a major challenge. To address this issue, this paper presents a reliable WSN distance measurement-positioning algorithm for forest environments. The algorithm divides the positioning area into several sub-regions based on the discrete coefficient of the collected signal strength. Then, using the fitting method based on the signal intensity value of each sub-region, the algorithm derives the reference points of the logarithmic distance path loss model and path loss index. Finally, the algorithm locates target nodes using anchor nodes in different regions. Additionally, to enhance the positioning accuracy, weight values are assigned to the positioning result based on the discrete coefficient of the signal intensity in each sub-region. Experimental results demonstrate that the proposed WSN algorithm has high precision in forest environments. [ABSTRACT FROM AUTHOR]

Simple Summary: Blurry scenarios often affect the clarity of fish images, posing significant challenges to deep learning models in terms of the accurate recognition of fish species. A method based on deep learning with a diffusion model and an attention mechanism, DiffusionFR, is proposed herein to improve the accuracy of fish species recognition in blurry scenarios caused by light reflections and water ripple noise. Using a self-constructed dataset, BlurryFish, extensive experiments were conducted and the results showed that the proposed two-stage diffusion network model can restore the clarity of blurry fish images to some extent and the proposed learnable attention module is effective in improving the accuracy of fish species recognition. Blurry scenarios, such as light reflections and water ripples, often affect the clarity and signal-to-noise ratio of fish images, posing significant challenges for traditional deep learning models in accurately recognizing fish species. Firstly, deep learning models rely on a large amount of labeled data. However, it is often difficult to label data in blurry scenarios. Secondly, existing deep learning models need to be more effective for the processing of bad, blurry, and otherwise inadequate images, which is an essential reason for their low recognition rate. A method based on the diffusion model and attention mechanism for fish image recognition in blurry scenarios, DiffusionFR, is proposed to solve these problems and improve the performance of species recognition of fish images in blurry scenarios. This paper presents the selection and application of this correcting technique. In the method, DiffusionFR, a two-stage diffusion network model, TSD, is designed to deblur bad, blurry, and otherwise inadequate fish scene pictures to restore clarity, and a learnable attention module, LAM, is intended to improve the accuracy of fish recognition. In addition, a new dataset of fish images in blurry scenarios, BlurryFish, was constructed and used to validate the effectiveness of DiffusionFR, combining bad, blurry, and otherwise inadequate images from the publicly available dataset Fish4Knowledge. The experimental results demonstrate that DiffusionFR achieves outstanding performance on various datasets. On the original dataset, DiffusionFR achieved the highest training accuracy of 97.55%, as well as a Top-1 accuracy test score of 92.02% and a Top-5 accuracy test score of 95.17%. Furthermore, on nine datasets with light reflection noise, the mean values of training accuracy reached a peak at 96.50%, while the mean values of the Top-1 accuracy test and Top-5 accuracy test were at their highest at 90.96% and 94.12%, respectively. Similarly, on three datasets with water ripple noise, the mean values of training accuracy reached a peak at 95.00%, while the mean values of the Top-1 accuracy test and Top-5 accuracy test were at their highest at 89.54% and 92.73%, respectively. These results demonstrate that the method showcases superior accuracy and enhanced robustness in handling original datasets and datasets with light reflection and water ripple noise. [ABSTRACT FROM AUTHOR]

Background: The preservation of platelets (PLTs) by room temperature (RT) oscillation limits their shelf life to between 4 and 7 days because of the decrease in PLT function. TRPC6 is a non‐selective mechanically sensitive cation channel that has been shown to mediate Ca2+ signalling, implying a role in PLT activation during preservation by RT oscillation. Objectives: This study was designed to investigate whether inhibition of TRPC6 can improve the RT preservation of PLTs and the possible underlying mechanism. Methods: Human PLTs from whole blood were stored at 22 ± 2°C with oscillation in plasma or M‐sol (mixture of solutions). BI‐749327, a specific TRPC6 inhibitor, was administered throughout the preservation period. PLT distribution width (PDW), mean platelet volume (MPV), maximum platelet aggregation rate (MAR) and average aggregation rate (AAR) were measured. The MTT method was used to assess the relative viability of PLTs. Flow cytometry was used to measure the changes of Ca2+ concentration in PLTs and phosphatidylserine (PS) exposure on the PLT surface, and western blotting was used to assess the expression changes of platelet TRPC6 and CD62P proteins. Results: Compared with the control group, inhibition of TRPC6 with BI‐749327 significantly reduced the PDW, MPV and Ca2+ concentration, the MAR and AAR were significantly increased, the expression of TRPC6 and CD62P protein was significantly down‐regulated in PLTs, and the PS exposure was significantly reduced on the PLT surface. However, these effects were all reversed by activation of TRPC6. Conclusion: Inhibition of TRPC6 improves the quality of PLT preservation by inhibiting the Ca2+ signal mediated by TRPC6. [ABSTRACT FROM AUTHOR]

Identification of the carbon (C) sources of methane (CH4) and methanogenic community structures after organic fertilization may provide a better understanding of the mechanism that regulate CH4 emissions from paddy soils. Based on our previous field study, a pot experiment with isotopic 13C labelling was designed in this study. The objective was to investigate the main C sources for CH4 emissions and the key environmental factor with the application of organic fertilizer in paddies. Results indicated that 28.6%, 64.5%, 0.4%, and 6.5% of 13C was respectively distributed in CO2, the plants, soil, and CH4 at the rice tillering stage. In total, organically fertilized paddy soil emitted 3.51 kg·CH4 ha−1 vs. 2.00 kg·CH4 ha−1 for the no fertilizer treatment. Maximum CH4 fluxes from organically fertilized (0.46 mg·m−2·h−1) and non-fertilized (0.16 mg·m−2·h−1) soils occurred on day 30 (tillering stage). The total percentage of CH4 emissions derived from rice photosynthesis C was 49%, organic fertilizer C < 0.34%, and native soil C > 51%. Therefore, the increased CH4 emissions from paddy soil after organic fertilization were mainly derived from native soil and photosynthesis. The 16S rRNA sequencing showed Methanosarcina (64%) was the dominant methanogen in paddy soil. Organic fertilization increased the relative abundance of Methanosarcina, especially in rhizosphere. Additionally, Methanosarcina sp. 795 and Methanosarcina sp. 1H1 co-occurred with Methanobrevibacter sp. AbM23, Methanoculleus sp. 25XMc2, Methanosaeta sp. HA, and Methanobacterium sp. MB1. The increased CH4 fluxes and labile methanogenic community structure in organically fertilized rice soil were primarily due to the increased soil C, nitrogen, potassium, phosphate, and acetate. These results highlight the contributions of native soil- and photosynthesis-derived C in paddy soil CH4 emissions, and provide basis for more complex investigations of the pathways involved in ecosystem CH4 processes. [ABSTRACT FROM AUTHOR]

Eco-agricultural systems aim to reduce the use of chemical fertilizers in order to improve sustainable production and maintain a healthy ecosystem. The aim of this study was to explore the effects of rice-frog farming on the bacterial community and N-cycling microbes in paddy rhizosphere soil. This experiment involved three rice cultivation patterns: Conventionally cultivated rice (CR), green rice-frog farming (GR), and organic rice-frog farming (OR). The rice yield, paddy soil enzyme activities, physicochemical variables and bacterial and N-cycling bacterial abundances were quantitatively analyzed. Rice-frog cultivations significantly increased soil protease, nitrate and reductase activity. Additionally, the nirS gene copy number and the relative abundance of denitrifying bacteria also increased, however urease activity and the relative abundance of nitrifying bacteria significantly decreased. The bacterial community richness and diversity of OR soil was significantly higher than that of the GR or CR soil. Nitrogen use efficiency (NUE) of GR was highest. The N-cycling bacterial community was positively correlated with the total carbon (TC), total nitrogren (TN) and carbon to nitrogen (C:N) ratio. The present work strengthens our current understanding of the soil bacterial community structure and its functions under rice-frog farming. The present work also provides certain theoretical support for the selection of rational rice cultivation patterns. [ABSTRACT FROM AUTHOR]

Neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and Huntington's disease, affect millions of people worldwide. Tremendous efforts have been put into disease-related research, but few breakthroughs have been made in diagnostic and therapeutic approaches. Extracellular vesicles (EVs) are heterogeneous cell-derived membrane structures that arise from the endosomal system or are directly separated from the plasma membrane. EVs contain many biomolecules, including proteins, nucleic acids, and lipids, which can be transferred between different cells, tissues, or organs, thereby regulating cross-organ communication between cells during normal and pathological processes. Recently, EVs have been shown to participate in various aspects of neurodegenerative diseases. Abnormal secretion and levels of EVs are closely related to the pathogenesis of neurodegenerative diseases and contribute to disease progression. Numerous studies have proposed EVs as therapeutic targets or biomarkers for neurodegenerative diseases. In this review, we summarize and discuss the advanced research progress on EVs in the pathological processes of several neurodegenerative diseases. Moreover, we outline the latest research on the roles of EVs in neurodegenerative diseases and their therapeutic potential for the diseases. [ABSTRACT FROM AUTHOR]

Paralytic shellfish poisoning (PSP) toxin is widely contained in seafood and its product. It brings great harm to human health when people eat it. During the past two decades, shellfish toxin quantitative analysis methods develop fast, such as mouse bioassay (MBA), chemical analysis method, immunoassay and cell-based assay (CBA). However, there are some disadvantages in these analytical methods such as time consuming, high cost. Therefore, a more suitable method is in demand. In this study, a novel method using high-frequency surface acoustic wave resonator (SAWR)-based sensor combined with living cells was developed for shellfish toxin continuous monitoring. The mouse tongue isolated taste bud (MTITB) cells cell lines were used as the sensing elements to establish the sensor device for PSP toxin carbamoyl toxin (CT) quantitative monitoring. The sensor device is in serial with SAWR. SAWR output frequency was recorded for detecting characterization. HEK293 cell lines were cultured on electrode and utilized as negative control. The results showed that MTITB cell-based sensor had selective responses to CT. This method is promising in PSP toxins rapid quantitative determination. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) is a malignancy that seriously threatens human health, and metastasis from CRC is a major cause of death and poor prognosis for patients. Studying the potential mechanisms of small extracellular vesicles (sEVs) in tumor development may provide new options for early and effective diagnosis and treatment of CRC metastasis. In this review, we systematically describe how sEVs mediate epithelial mesenchymal transition (EMT), reconfigure the tumor microenvironment (TME), modulate the immune system, and alter vascular permeability and angiogenesis to promote CRC metastasis. We also discuss the current difficulties in studying sEVs and propose new ideas. [ABSTRACT FROM AUTHOR]

Semantic segmentation of standing trees is important to obtain factors of standing trees from images automatically and effectively. Aiming at the accurate segmentation of multiple standing trees in complex backgrounds, some traditional methods have shortcomings such as low segmentation accuracy and manual intervention. To achieve accurate segmentation of standing tree images effectively, SEMD, a lightweight network segmentation model based on deep learning, is proposed in this article. DeepLabV3+ is chosen as the base framework to perform multi-scale fusion of the convolutional features of the standing trees in images, so as to reduce the loss of image edge details during the standing tree segmentation and reduce the loss of feature information. MobileNet, a lightweight network, is integrated into the backbone network to reduce the computational complexity. Furthermore, SENet, an attention mechanism, is added to obtain the feature information efficiently and suppress the generation of useless feature information. The extensive experimental results show that using the SEMD model the MIoU of the semantic segmentation of standing tree images of different varieties and categories under simple and complex backgrounds reaches 91.78% and 86.90%, respectively. The lightweight network segmentation model SEMD based on deep learning proposed in this paper can solve the problem of multiple standing trees segmentation with high accuracy. [ABSTRACT FROM AUTHOR]

A potential use of small extracellular vesicles (sEVs) for diagnostic and therapeutic purposes has recently generated a great interest. sEVs, when purified directly from various tissues with proper procedures, can reflect the physiological and pathological state of the organism. However, the quality of sEV is affected by many factors during isolation, including separation of sEV from cell and tissues debris, the use of enzymes for tissue digestion, and the storage state of tissues. In the present study, we established an assay for the isolation and purification of liver cancer tissues-derived sEVs (tdsEVs) and cultured explants-derived sEVs (cedsEVs) by comparing the quality of sEVs derived from different concentration of digestion enzyme and incubation time. The nano-flow cytometry (NanoFCM) showed that the isolated tdsEVs by our method are purer than those obtained from differential ultracentrifugation. Our study thus establishes a simple and effective approach for isolation of high-quality sEVs that can be used for analysis of their constituents. [ABSTRACT FROM AUTHOR]

In this paper, an electrochemical sensor array utilizing metal materials was designed, and its applications in sugar content analysis from mixtures were conducted. The system adopts the conventional three-electrode system. Cyclic Voltammetry (CV) and Amperometric i-t Curve (i-t) were utilized as testing methods. The curve of the relationship between current and time could be obtained by i-t scanning. The relationship between the sugar concentration and the current density of the sensor was developed. The data was analyzed using multiple linear regression (MLR). The sugar mixtures were automatically measured by using this instrument. CV and i-t methods were utilized to carry out the experiments. Different scan rates CV results showed that it was a typical diffusion controlled electrochemical process. Different sugars CV results showed that different electrodes for CV experiments with different sugars produced different anode peak potential and current. Control experiments showed that neither Na2CO3 nor NaCl had any effect on the experimental results. The developed system has good stability and repeatability. I-t fitting results indicated that there was a good linear relationship between system responses and sugar concentrations. The linear range was between 0.14 mM and 4.76 mM. A python-based MLR model was developed for sugar content determination. Results were obtained by solving the system of equations, indicating that this method presented content determination abilities for sugar mixtures. The method proposed in this paper had the advantages of easy operation, low cost, rapid analysis, and low detection limit. It provided a new idea for sugar quantitative determination in solution. [ABSTRACT FROM AUTHOR]

Construction of food electronic nose (FEN) for prawn (Macrobrachium rosenbergii) quality rapid assessment assisted with physicochemical parameters was explored in this paper. Sensory analysis, weight, GC/MS, and K value, and microbial were examined to provide the assisting basis for FEN system design. Results indicated that sensory acceptance decreased with the increase in time. The decline of prawn physiochemical parameters was associated with microbiological propagation. The volatile gases emitted by prawn could be utilized for sample quality characterization. FEN successfully discriminated all samples, and presented good detecting accuracy. The method proposed in this work is promising in aquatic products quality rapid analysis. [ABSTRACT FROM AUTHOR]

Nitrogen (N) leakage in paddy fields can cause groundwater pollution. In this study, we conducted a split-plot field experiment over 2 years to compare N leakage in a rice–duck co-culture system and a rice monoculture system with different fertilizer treatments. Four treatments were applied to each field, with consistent N inputs in each fertilizer treatment: no fertilizer (RD and RM, respectively), chemical fertilizer (RDF and RMF, respectively), organic fertilizer (RDO and RMO, respectively), and a mixture of 70% chemical and 30% organic fertilizers (RDFO and RMFO, respectively). In both years, rice–duck co-culture system had lower N leakage than the rice monoculture for the same fertilizer treatment, with average reductions of 14.3 ± 0.1%, 13.5 ± 4.5% and 10.5 ± 3.3% for RDFO, RDF and RDO, respectively. Within the rice–duck co-culture system, the average N leakage across both years was 36.3 ± 6.3% lower in RDO and 16.9 ± 11.5% lower in RDFO than in RMF. RDFO gave the highest grain yield compared with RDF and RDO, average reached 10.35 t ha−1 across both years. In conclusion, our results suggested that rice–duck co-culture reduces environmental risks by controlling N leakage and increasing agricultural productivity. Compared with other treatments in this research, RDFO was the most recommended agricultural production mode in this region because it can reduce the inputs of chemical fertilizer, control nitrogen leakage and increase rice yield. Unlabelled Image • Rice-duck co-culture can reduce N leakage without yield loss. • N leakage was different under different fertilizers. • Rice-duck co-culture under chemical mixed organic fertilizer had lower N leakage. • Rice-duck co-culture promoted N use efficiency of rice. [ABSTRACT FROM AUTHOR]

Tuberculosis caused by Mycobacterium tuberculosis (M. tuberculosis) is the leading cause of death among infectious diseases in the worldwide. Lack of more sensitive and effective diagnostic reagents has increased the awareness of rapid diagnosis for tuberculosis. In this study, T7 phage displayed genomic DNA library of M. tuberculosis was constructed to screen the antigens that specially bind with TB-positive serum from the whole genome of M. tuberculosis and to improve the sensitivity and specificity of tuberculosis serological diagnosis. After three rounds of biopanning, results of DNA sequencing and BLAST analysis showed that 19 positive phages displayed four different proteins and the occurrence frequency of the phage which displayed ribokinase was the highest. The results of indirect ELISA and dot immunoblotting indicated that representative phages could specifically bind to tuberculosis-positive serum. The prokaryotic expression vector containing the DNA sequence of ribokinase gene was then constructed and the recombinant protein was expressed and purified to evaluate the serodiagnosis value of ribokinase. The reactivity of the recombinant ribokinase with different clinical serum was detected and the sensitivities and specificities in tuberculosis serodiagnosis were 90% and 86%, respectively by screening serum from tuberculosis patients (n = 90) and uninfected individuals (n = 90) based on ELISA. Therefore, this study demonstrated that ribokinase had good potential for the serodiagnosis of tuberculosis. [ABSTRACT FROM AUTHOR]

Bamboo shoots quality determination method using EN and M.O.S sensor array optimization was studied in this paper. EN responses to winter bamboo shoots samples under different storage time were measured. Sensor loading analysis results demonstrated that sensor array optimization improved principal component analysis discrimination degree of shoots samples in different quality. Non-linear stochastic resonance was utilized for optimized EN measurement data analysis. SR signal-to-noise ratio quantitatively discriminated all samples. Winter bamboo shoots quality determination model was developed by linear fitting regression of SR signal-to-noise ratio eigen values. Validating experiments results indicated that the developed model presented a quality-predicting accuracy of 83.3% for shoots samples. [ABSTRACT FROM AUTHOR]

In this paper, ridgetail white prawn (Exopalaemon carinicauda) quality evaluation was performed by electronic nose (e-nose). Physical/chemical, such as human sensory evaluation (HSE), weight loss, colour, total viable counts (TVC), GC-MS, K value, microbial, and e-nose were persistently tested. Principal component analysis (PCA) and stochastic resonance (SR) were used for measurement data processing. Results suggested that prawn quality persistently decreased due to microbial propagation. GC-MS results demonstrated that the volatile gases emitted by the samples increased during measurement. PCA could not discriminate all prawn samples, and SR eigen values discriminated all samples. Sensitivity evaluation on colour, TVC, weight loss, HSE, GC/MS, K value, and e-nose was made, and results indicated that e-nose presents some advantages including low cost, rapid examination, and good repeatability. E-nose Prawn quality evaluation model was also built for e-nose. [ABSTRACT FROM AUTHOR]

• IRFF can reduce GWP and GHGI while increased NEEB. • IRFF reduced the GWP by 18.74 %–48.11 % over T0 in 2018 and 2019. • The higher the frog density in the paddy field, the lower the CH 4 emitted. • Incorporate IRFF with 15,000 frogs ha−1 was the most recommended. Integrated rice-frog farming (IRFF) is gaining attention to remediate the negative environmental impacts induced by traditional rice cultivation. However, the net ecosystem economic budget (NEEB) of the IRFF still has to be evaluated. In this study, we conducted a two-year field experiment to assess the impacts of IRFF on greenhouse gas (GHG) emissions, global warming potential (GWP), greenhouse gas intensity (GHGI), and NEEB in rice fields. This study was implemented using six different frogs' densities, namely, control check (T0), 3750 frogs ha−1 (T1), 7500 frogs ha−1 (T2), 15,000 frogs ha−1 (T3), 30,000 frogs ha−1 (T4) and 60,000 frogs ha−1 (T5), respectively. Compared with the T0 control, the average IRFF improved yield gains by 57.8 % and significantly decreased GHGI by approximately 94.1 %. During the rice growing seasons, the CH 4 emissions accounted for over 90 % of the GWP based on the 100-year scale of the IPCC. Considering the GWP costs of CH 4 and N 2 O emissions, the NEEB of T1, T2, T3, T4, and T5 treatments increased (relative to T0) by 29112, 26921, 30596, 22161, and 2693 CNY ha−1 y−1, respectively. Our findings suggest that applying IRFF can simultaneously mitigate GWP and improve the economic benefits of rice farming. While the treatment T3 viz. 15,000 frogs ha−1 can be a suitable ecological rice farming in place of traditional one. [ABSTRACT FROM AUTHOR]

Double pollution with phosphorus (P) losses and potential lead (Pb) accumulation in rice fields could lead to eutrophication and crop toxicity, respectively, and affect people's health. To promote the sustainable and environmentally friendly development of agriculture, we conducted field experiments using a randomized block design to explore P losses, Pb accumulation and any potential association between P and Pb forms in rice–duck (RD) co-culture system and rice monoculture (RM) system combined with different fertilizers applied: the no fertilizer (RD and RM), chemical fertilizer (RDF and RMF), organic fertilizer (RDO and RMO), and a mixture of 70% chemical and 30% organic fertilizers (RDFO and RMFO) treatments with consistent P inputs. The results showed that RDFO had the best advantages in reducing the losses of TP (total phosphorous) (by 6.67%) and DRP (dissolved reactive phosphate) (32.72%) as well as the contents of available Pb (by 7.57%) and the accumulation of Pb in grains (26.32%) compared with RMF. RDFO also achieved the highest grain yield, reaching 10.97 t ha−1, and exhibited a lower soil weak-acid-extracted Pb (readily be taken up by plants) concentration than RDF and RMF. RDO resulted in greater TP leaching (increase by 10.62%) and lower DRP leaching (decrease by 36.57%) than RMF. It also exhibited the lowest concentration of weak-acid-extracted Pb and higher the concentration of grain Pb than that in other treatments. RDF reduced TP (by 5.33%) and DRP (by 16.36%) losses to a greater extent and the concentration of available and grain Pb were respectively 6.58% and 25.57% lower than RMF. Therefore, RDFO was the most recommended agricultural system for the studied region. Furthermore, different soil Pb forms were correlated with different P forms of soil and leakage and runoff water, which depended mainly on the fertilizer type and specific soil redox environment in the rice fields. The ratio of organic to inorganic fertilizer, the choice of organic fertilizer type, the assessment and timing of the detection of potential farmland pollution risks and association between different forms of P and Pb are worthy of further discussion. Image 1 • Rice-duck co-culture can control P leakage and runoff losses without yield loss. • Rice-duck co-culture changes the pH of leakage and runoff water and thus reduce P losses. • Rice-duck co-culture had potential advantage to control potential Pb transfer and accumulation. • Rice-duck co-culture combined with mixed fertilization is the highly recommended. [ABSTRACT FROM AUTHOR]