Segmentation of thyroid glands and nodules in ultrasound images using the improved U-Net architecture.

BACKGROUND: Identifying thyroid nodules' boundaries is crucial for making an accurate clinical assessment. However, manual segmentation is time-consuming. This paper utilized U-Net and its improved methods to automatically segment thyroid nodules and glands. METHODS: The 5822 ultrasound images used in the experiment came from two centers, 4658 images were used as the training dataset, and 1164 images were used as the independent mixed test dataset finally. Based on U-Net, deformable-pyramid split-attention residual U-Net (DSRU-Net) by introducing ResNeSt block, atrous spatial pyramid pooling, and deformable convolution v3 was proposed. This method combined context information and extracts features of interest better, and had advantages in segmenting nodules and glands of different shapes and sizes. RESULTS: DSRU-Net obtained 85.8% mean Intersection over Union, 92.5% mean dice coefficient and 94.1% nodule dice coefficient, which were increased by 1.8%, 1.3% and 1.9% compared with U-Net. CONCLUSIONS: Our method is more capable of identifying and segmenting glands and nodules than the original method, as shown by the results of correlational studies.

Study on the Integration Strategy of Online EOL Testing of Pure Electric Vehicle Power Battery.

This paper analyzes the electrical test items of the EOL testing line in automotive manufacturers. On this basis, this paper proposes and designs an integrated and automated testing strategy to deal with the problems of slow testing speed, high dependence on manual labor and low efficiency. This article mainly analyzes the various tests of the two main tests in battery EOL testing: Battery Management System (BMS) testing and electrical testing. We propose an innovative integrated solution based on various testing items, including the reception, transmission, and self-analysis of different UDS protocol messages, a unique automated electrical performance measurement scheme, and a requirement and logic design of an integrated software end based on Python. The experimental results of actual testing show that the implementation of the integrated strategy greatly reduces the complexity of the testing steps, improves the testing efficiency, and reduces errors caused by human operation.

Individualized Short-Term Electric Load Forecasting Using Data-Driven Meta-Heuristic Method Based on LSTM Network.

Short-term load forecasting is viewed as one promising technology for demand prediction under the most critical inputs for the promising arrangement of power plant units. Thus, it is imperative to present new incentive methods to motivate such power system operations for electricity management. This paper proposes an approach for short-term electric load forecasting using long short-term memory networks and an improved sine cosine algorithm called MetaREC. First, using long short-term memory networks for a special kind of recurrent neural network, the dispatching commands have the characteristics of storing and transmitting both long-term and short-term memories. Next, four important parameters are determined using the sine cosine algorithm base on a logistic chaos operator and multilevel modulation factor to overcome the inaccuracy of long short-term memory networks prediction, in terms of the manual selection of parameter values. Moreover, the performance of the MetaREC method outperforms others with regard to convergence accuracy and convergence speed on a variety of test functions. Finally, our analysis is extended to the scenario of the MetaREC_long short-term memory with back propagation neural network, long short-term memory networks with default parameters, long short-term memory networks with the conventional sine-cosine algorithm, and long short-term memory networks with whale optimization for power load forecasting on a real electric load dataset. Simulation results demonstrate that the multiple forecasts with MetaREC_long short-term memory can effectively incentivize the high accuracy and stability for short-term power load forecasting.

[Automatic Segmentation of Digital Pathology Slides Based on Unsupervised Learning].

OBJECTIVE: To segment images through an unsupervised method as an alternative to manual labeling. METHODS: A total of 100 whole slide image (WSI) data of HE stained and Pap stained slides were selected as the research and test objects, including 70 breast slides, 20 lung slides and 10 thyroid slides. In order to ensure the diversity of data, the breast slides included those of normal tissue, inflammation and tumor, the lung slides were mainly neoplasms in the lower lobe, including those of inflammation and tumor, and the thyroid slides were of cells, all benign, obtained through fine needle aspiration. The maximum total magnification (original magnification) of each image was 400 times, and the file format was NDPI. Each WSI was manually labeled, and the labeled area of each WSI was more than 10 fields of vision. The labeled information was to be used for validity verification. An unsupervised image segmentation technique based on superpixel and fully convolution neural network algorithms was constructed and used to segment any region of interest (ROI) of unlabeled WSI. In comparison with the region adjacency graph merging method, the segmentation effect of the two methods was assessed with the under segmentation error, the boundary recall and the mean Intersection-over-Union, and the efficiency of the two methods was also compared. In the comparison of execution efficiency, the test process included the preprocessing time of superpixel, and excluded the time of loading the deep learning engine. RESULTS: Unsupervised automatic segmentation was implemented for any ROI region of WSI according to the texture and color. The results of the breast slides, lung slides and thyroid slides showed slight differences, and multiple tests yielded stable results. However, the performance of this method in differentiating inflammation and tumor was average. The under-segmentation error, the boundary recall and the mean Intersection-over-Union were 19.10%, 82.06% and 45.06%, respectively. The under segmentation error, the boundary recall and the mean Intersection-over-Union for the region adjacency graph merging method were 21.52%, 78.39% and 44.81%, respectively. The average time consumption of the whole process was 0.27 s in GPU mode and 1.30 s in CPU mode. The average time consumption of the region adjacency graph merging method was 10.5 s in CPU mode because the method of region adjacency graph merging was not realized in the GPU mode. CONCLUSION: This method produced ideal pixel level labeling results through simple human-computer interaction, which could effectively reduce the cost of digital pathology slide data labeling. Compared with the region adjacency graph merging method, this method had better performance in processing image texture and had faster processing speed.

[Application Test of the AI-Automatic Diagnostic System for Ki-67 in Breast Cancer].

OBJECTIVE: To study the different methods of artificial intelligence (AI)-assisted Ki-67 scoring of clinical invasive ductal carcinoma (IDC) of the breast and to compare the results. METHODS: A total of 100 diagnosed IDC cases were collected, including slides of HE staining and immunohistochemical Ki-67 staining and diagnosis results. The slides were scanned and turned into whole slide image (WSI), which were then scored with AI. There were two AI scoring methods. One was fully automatic counting by AI, which used the scoring system of Ki-67 automatic diagnosis to do counting with the whole image of WSI. The second method was semi-automatic AI counting, which required manual selection of areas for counting, and then relied on an intelligent microscope to conduct automatic counting. The diagnostic results of pathologists were taken as the results of pure manual counting. Then the Ki-67 scores obtained by manual counting, semi-automatic AI counting and automatic AI counting were pairwise compared. The Ki-67 scores obtained from the manual counting (pathological diagnosis results), semi-automatic AI and automatic AI counts were pair-wise compared and classified according to three levels of difference: difference ≤10%, difference of >10%-<30% and difference ≥30%. Intra-class correlation coefficient ( ICC) was used to evaluate the correlation. RESULTS: The automatic AI counting of Ki-67 takes 5-8 minutes per case, the semi-automatic AI counting takes 2-3 minutes per case, and the manual counting takes 1-3 minutes per case. When results of the two AI counting methods were compared, the difference in Ki-67 scores was all within 10% (100% of the total), and the ICC index being 0.992. The difference between manual counting and semi-automatic AI was less than 10% in 60 cases (60% of the total), between 10% and 30% in 37 cases (37% of the total), and more than 30% in only 3 cases (3% of the total), ICC index being 0.724. When comparing automatic AI with manual counting, 78 cases (78% of the total) had a difference of ≤10%, 17 cases (17% of the total) had a difference of between 10% and 30%, and 5 cases (5%) had a difference of ≥30%, the ICC index being 0.720. The ICC values showed that there was little difference between the results of the two AI counting methods, indicating good repeatability, but the repeatability between AI counting and manual counting was not particularly ideal. CONCLUSION: AI automatic counting has the advantage of requiring less manpower, for the pathologist is involved only for the verification of the diagnosis results at the end. However, the semi-automatic method is better suited to the diagnostic habits of pathologists and has a shorter turn-over time compared with that of the fully automatic AI counting method. Furthermore, in spite of its higher repeatability, AI counting, cannot serve as a full substitute for pathologists, but should instead be viewed as a powerful auxiliary tool.

[Research on Self-perception and Active Warning Model of Medical Equipment Operation and Maintenance Status Based on Machine Learning Algorithm].

The panoramic perception of medical equipment operation and maintenance status is the basic guarantee for the implementation of smart medical care, the machine learning algorithm-based autonomous perception and active early warning model of medical equipment operation and maintenance status is proposed. Introduce deep learning multi-dimensional perception of medical equipment multi-source heterogeneous fault data training sample characteristics to realize autonomous perception of medical equipment operation and maintenance status, introduce reinforcement learning to realize autonomous decision-making of test sample fault characteristics, and build the active early warning mechanism for medical equipment faults. Taking the equipment department of hospital as the carrier of model effectiveness verification, the effectiveness simulation of the model was carried out, the results show that the model has the advantages of comprehensive fault information perception, strong compatibility of medical equipment, high efficiency of active early warning.

Implementation of the CRISPR-Cas13a system in fission yeast and its repurposing for precise RNA editing.

In contrast to genome editing, which introduces genetic changes at the DNA level, disrupting or editing gene transcripts provides a distinct approach to perturbing a genetic system, offering benefits complementary to classic genetic approaches. To develop a new toolset for manipulating RNA, we first implemented a member of the type VI CRISPR systems, Cas13a from Leptotrichia shahii (LshCas13a), in Schizosaccharomyces pombe, an important model organism employed by biologists to study key cellular mechanisms conserved from yeast to humans. This approach was shown to knock down targeted endogenous gene transcripts with different efficiencies. Second, we engineered an RNA editing system by tethering an inactive form of LshCas13a (dCas13) to the catalytic domain of human adenosine deaminase acting on RNA type 2 (hADAR2d), which was shown to be programmable with crRNA to target messenger RNAs and precisely edit specific nucleotide residues. We optimized system parameters using a dual-fluorescence reporter and demonstrated the utility of the system in editing randomly selected endogenous gene transcripts. We further used it to restore the transposition of retrotransposon Tf1 mutants in fission yeast, providing a potential novel toolset for retrovirus manipulation and interference.

UGM: a more stable procedure for large-scale multiple testing problems, new solutions to identify oncogene.

Variations of gene expression levels play an important role in tumors. There are numerous methods to identify differentially expressed genes in high-throughput sequencing. Several algorithms endeavor to identify distinctive genetic patterns susceptable to particular diseases. Although these processes have been proved successful, the probability that the number of non-differentially expressed genes measured by false discovery rate (FDR) has a large standard deviation, and the misidentification rate (type I error) grows rapidly when the number of genes to be detected become larger. In this study we developed a new method, Unit Gamma Measurement (UGM), accounting for multiple hypotheses test statistics distribution, which could reduce the dependency problem. Simulated expression profile data and breast cancer RNA-Seq data were utilized to testify the accuracy of UGM. The results show that the number of non-differentially expressed genes identified by the UGM is very close to the real-evidence data, and the UGM also has a smaller standard error, range, quartile range and RMS error. In addition, the UGM can be used to screen many breast cancer-associated genes, such as BRCA1, BRCA2, PTEN, BRIP1, etc., provides better accuracy, robustness and efficiency, the method of identification differentially expressed genes in high-throughput sequencing.

Effects of Alternating Magnetic Fields on the OER of Heterogeneous Core-Shell Structured NiFe2O4@(Ni, Fe)S/P.

Composition optimization, structural design, and introduction of external magnetic fields into the catalytic process can remarkably improve the oxygen evolution reaction (OER) performance of a catalyst. NiFe2O4@(Ni, Fe)S/P materials with a heterogeneous core-shell structure were prepared by the sulfide/phosphorus method based on spinel-structured NiFe2O4 nanomicrospheres. After the sulfide/phosphorus treatment, not only the intrinsic activity of the material and the active surface area were increased but also the charge transfer resistance was reduced due to the internal electric field. The overpotential of NiFe2O4@(Ni, Fe)P at 10 mA cm-2 (iR correction), Tafel slope, and charge transfer resistance were 261 mV, 42 mV dec-1, and 3.163 Ω, respectively. With an alternating magnetic field, the overpotential of NiFe2O4@(Ni, Fe)P at 10 mA cm-2 (without iR correction) declined by 45.5% from 323 mV (0 mT) to 176 mV (4.320 mT). Such enhancement of performance is primarily accounted for the enrichment of the reactive ion OH- on the electrode surface induced by the inductive electric potential derived from the Faraday induction effect of the AMF. This condition increased the electrode potential and thus the charge transfer rate on the one hand and weakened the diffusion of the active substance from the electrolyte to the electrode surface on the other hand. The OER process was dominantly controlled by the charge transfer process under low current conditions. A fast charge transfer rate boosted the OER performance of the catalyst. At high currents, diffusion exerted a significant effect on the OER process and low OH- diffusion rates would lead to a decrease in the OER performance of the catalyst.

DeepEdit: single-molecule detection and phasing of A-to-I RNA editing events using nanopore direct RNA sequencing.

Single-molecule detection and phasing of A-to-I RNA editing events remain an unresolved problem. Long-read and PCR-free nanopore native RNA sequencing offers a great opportunity for direct RNA editing detection. Here, we develop a neural network model, DeepEdit, that not only recognizes A-to-I editing events in single reads of Oxford Nanopore direct RNA sequencing, but also resolves the phasing of RNA editing events on transcripts. We illustrate the robustness of DeepEdit by applying it to Schizosaccharomyces pombe and Homo sapiens transcriptome data. We anticipate DeepEdit to be a powerful tool for the study of RNA editing from a new perspective.

DENA: training an authentic neural network model using Nanopore sequencing data of Arabidopsis transcripts for detection and quantification of N6-methyladenosine on RNA.

Models developed using Nanopore direct RNA sequencing data from in vitro synthetic RNA with all adenosine replaced by N6-methyladenosine (m6A) are likely distorted due to superimposed signals from saturated m6A residues. Here, we develop a neural network, DENA, for m6A quantification using the sequencing data of in vivo transcripts from Arabidopsis. DENA identifies 90% of miCLIP-detected m6A sites in Arabidopsis and obtains modification rates in human consistent to those found by SCARLET, demonstrating its robustness across species. We sequence the transcriptome of two additional m6A-deficient Arabidopsis, mtb and fip37-4, using Nanopore and evaluate their single-nucleotide m6A profiles using DENA.

Artificial intelligence applications in pathological diagnosis of gastric cancer.

Globally, gastric cancer is the third leading cause of death from tumors. Prevention and individualized treatment are considered to be the best options for reducing the mortality rate of gastric cancer. Artificial intelligence (AI) technology has been widely used in the field of gastric cancer, including diagnosis, prognosis, and image analysis. Eligible papers were identified from PubMed and IEEE up to April 13, 2022. Through the comparison of these articles, the application status of AI technology in the diagnosis of gastric cancer was summarized, including application types, application scenarios, advantages and limitations. This review presents the current state and role of AI in the diagnosis of gastric cancer based on four aspects: 1) accurate sampling from early diagnosis (endoscopy), 2) digital pathological diagnosis, 3) molecules and genes, and 4) clinical big data analysis and prognosis prediction. AI plays a very important role in facilitating the diagnosis of gastric cancer; however, it also has shortcomings such as interpretability. The purpose of this review is to provide assistance to researchers working in this domain.

Enhanced Alkaline Oxygen Evolution Using Spin Polarization and Magnetic Heating Effects under an AC Magnetic Field.

Renewable electricity from splitting water to produce hydrogen is a favorable technology to achieve carbon neutrality, but slow anodic oxygen evolution reaction (OER) kinetics limits its large-scale commercialization. Electron spin polarization and increasing the reaction temperature are considered as potential ways to promote alkaline OER. Here, it is reported that in the alkaline OER process under an AC magnetic field, a ferromagnetic ordered electrocatalyst can simultaneously act as a heater and a spin polarizer to achieve significant OER enhancement at a low current density. Moreover, its effect obviously precedes antiferromagnetic, ferrimagnetic, and diamagnetic electrocatalysts. In particular, the noncorrected overpotential of the ferromagnetic electrocatalyst Co at 10 mA cm-2 is reduced by a maximum of 36.6% to 243 mV at 4.320 mT. It is found that the magnetic heating effect is immediate, and more importantly, it is localized and hardly affects the temperature of the entire electrolytic cell. In addition, the spin pinning effect established on the ferromagnetic/paramagnetic interface generated during the reconstruction of the ferromagnetic electrocatalyst expands the ferromagnetic order of the paramagnetic layer. Also, the introduction of an external magnetic field further increases the orderly arrangement of spins, thereby promoting OER. This work provides a reference for the design of high-performance OER electrocatalysts under a magnetic field.

Creating RNA Specific C-to-U Editase from APOBEC3A by Separation of Its Activities on DNA and RNA Substrates.

APOBEC3A (A3A) is a cytidine deaminase involved in innate immune response and is able to catalyze deamination on both DNA and RNA substrates. It was used in creating the CRISPR-mediated base editor, but has since been held back due to its dual activities. On the other hand, it has been a challenge to separate A3A's dual activities in order to enable it for single-base RNA editors. Here we developed the reporter system for C-to-U RNA editing and employed rational design for mutagenesis to differentiate deaminase activities on RNA and DNA substrates to obtain an RNA-specific editase. Generation and examination of 23 previous A3A mutants showed their deamination activity on RNA was mostly abolished when their activity on DNA was impaired, with the exception of mutant N57Q that displayed an inverse change. We designed new mutations on Loops 1 and 7 based on A3A's crystal structure and found mutants H29R and Y132G had differential effects on catalytic activity on RNA and DNA substrates. In order to engineer an A3A with RNA-specific deaminase activity, we combined Y132G with mutations in Loop 1 or helix 6 by rational design. Two multipoint mutants, Y132G/K30R and Y132G/G188A/R189A/L190A, were successful in retaining high deaminase activity on RNA substrate while eliminating deaminase activity on DNA. We, for the first time, created novel human A3A variants with RNA-specific cytidine deaminase activity, providing insight into A3A's mechanism on substrate recognition and a new addition of a toolset to the creation of a RNA-specific C-to-U base editor.

[Epidemiological features and changing patterns of intestinal parasitic diseases in Jiangxi Province].

OBJECTIVE: To understand the epidemiological features and changing patterns of intestinal parasitic diseases in Jiangxi Province from 1989 to 2014, so as to provide scientific evidence for formulating the preventive and control strategy for intestinal parasitic diseases. METHODS: The data regarding the status of intestinal parasitic diseases in Jiangxi Province were captured from the 1989 national survey on the distribution of human parasites in China and the 2014 national survey on major human parasitic diseases in China, and the status of human intestinal parasite infections was compared between the two surveys in Jiangxi Province. RESULTS: The prevalence rate of human intestinal parasite infections was 79.59% and 9.64% in Jiangxi Province in 1989 and 2014, with a decline rate of 87.89% during the 25-year period (χ2 = 30 870.5, P < 0.01). There were 24 and 20 species of human intestinal parasites detected in Jiangxi Province in 1989 and 2014, respectively, with totally 26 species detected during the two surveys. In Jiangxi Province, the 3 most highly prevalent human intestinal parasites included Ascaris lumbricoides (71.06%), hookworm (17.61%) and Enterobius vermicularis (17.59%) in 1989, and E. vermicularis (13.73%), hookworm (4.66%), whipworm (0.95%) in 2014. A higher rate of human intestinal parasite infections was found in females than in males in both surveys in Jiangxi Province (P < 0.01), and the prevalence of human intestinal parasite infections reduced by 82.50% to 95.31% in different age groups in Jiangsu Province during the 25-year period. CONCLUSIONS: The prevalence of human intestinal parasite infections shows a remarkable decline tendency in Jiangxi Province, and soil-transmitted nematodes remain the main species of human intestinal parasites in Jiangxi Province.

[Current status of human hookworm infection in Jiangxi Province in 2014].

OBJECTIVE: To understand the status of human hookworm (Ancylostoma duodenale or Necator americanus) infection in Jiangxi Province. METHODS: A stratified cluster random sampling was conducted based on the geographical location and economic condition in 2014. Kato-Katz's thick smear method was used for stool examinations, and filter paper cultivation was conducted for distinguishing the species of hookworms. RESULTS: A total of 21 615 persons were involved in the investigation, and there were 1 095 persons infected with hookworm with the prevalence of 5.07% (standardized rate of 4.05%). The infection rate of hookworm in Nanfeng County was the highest (13.96%). Most of the infected people had low worm burden (95.43%). The infection rate of hookworm of the female was higher than that of the male (χ2 = 28.03, P < 0.05). The prevalence increased with the age, while the infection rate of hookworm in people at ages of 75 years and higher was the highest (14.79%). Housewife was the highest risk occupation with the infection rate of 7.55%. The differences of infection rates among different educational level groups were significant (χ2 = 135.88, P < 0.05). The prevalence rates of hookworm in different landforms were significantly different (χ2 = 34.37, P < 0.05). The priority species was N. americanus (80.41%) in Jiangxi Province. CONCLUSIONS: The infection rate of hookworm decreases obviously in Jiangxi Province. The high risk groups of hookworm infection are the people with low educational level, female and elder age-group.

[Investigation of Enterobius vermicularis infection in children in Jiangxi Province in 2014].

OBJECTIVE: To know the status of Enterobius vermicularis infection in children in Jiangxi Province in 2014, so as to provide the evidence for the formulation of prevention and control measures. METHODS: A survey was performed according to the scheme of the 3rd Principal Human Parasites of Jiangxi Province in 2014. Based on the ecological regions, a stratified cluster sampling method was applied by the economic and geographic situation. There were 84 survey sites from 28 counties, and the basic data were also collected in the different investigation sites, and the round-end tube adhesive cellophane anal swab was used to examine E. vermicularis eggs for the children aged 3-6 years. RESULTS: A total of 1 486 children aged 3-6 years were detected, the E. vermicularis infection rate was 13.73% (204/1 486), and the infection rates were 13.89% (114/821) and 13.53% (90/665) in the male and female, respectively. The infection rate in the different age groups showed a gradual rise then fall trend, the lowest infection rate was 10.05% (38/378) in the 3-year age group, and the highest infection rate was 18.24% (81/444) in the 5-year age group. The infection rates in the high, medium and low-income survey sites were 13.79% (87/631), 17.23% (51/296), and 11.81% (66/559), respectively. The E. vermicularis infection rates in the 4 ecological regions were from 12.34% to 17.74%, but there was no significant difference among the different ecological regions (P > 0.05). CONCLUSIONS: The status of E. vermicularis infection in children in Jiangxi Province is relatively serious, and therefore, the parasitic disease control sectors should continue to strengthen the monitoring and control work of E. vermicularis infection in children.

Irrelevance of anti­nAChR cytoplasmic loop antibody in the degree of myasthenia gravis.

Anti­nicotinic acetylcholine receptor (nAChR) antibody in myasthenia gravis (MG) usually refers to that against the extracellular domain (ECD) of nAChR. However, growing evidence has indicated that there also exists the nAChR antibody against the nAChR cytoplasmic loop (CL) in patients with MG. Some studies have demonstrated that the anti­CL antibody may play a protective role in animal models of experimental autoimmune MG. However, to date, limited or no information is available as to whether anti­CL antibody plays a beneficial role in patients with MG. In this, we examined the levels of anti­CL and anti­ECD antibodies in the sera of 76 patients with MG from the MG sample bank of our department, including 29 generalized MG (gMG) and 47 ocular MG (OMG) cases, as well as 40 healthy controls by radioimmunoassay. We also analyzed the correlation between anti­CL antibody levels and the degree of severity in patients with MG. Our results revealed that among the 76 patients with MG, 61 serum samples (80.3%) were positive for anti­ECD nAChR antibody, among which 38 (38/61, 62.3%) were positive for anti­CL antibody. The levels of anti­ECD antibodies in gMG were higher than those in OMG; however, the levels of anti­CL antibodies in gMG were comparable with those in OMG. No clear correlation was observed between the anti­ECD antibody levels and those of anti­CL antibody. Taken together, our findings provide the first clinical evidence of the possibly different role of anti­CL antibody in patients with MG compared to its role in animal models of MG. Anti­CL antibody may not exert a beneficial effect in patients with MG, but may play an as yet udetermined role in the development of MG.

[Role of new strategy in transmission control of schistosomiasis in Poyang Lake region].

OBJECTIVE: To evaluate the role of new strategy in the transmission control of schistosomiasis in Poyang Lake region. METHODS: The information and epidemic data of schistosomiasis control were collected and analyzed in Poyang Lake region from 2005 to 2016. RESULTS: After eleven years of carrying out the new strategy, thirteen counties achieved the objective of transmission control in Poyang Lake region. In 2016, the number of schistosomiasis cases and human infection rate were 10 301 and 0.03%, decreased by 89.64% and 99.45% compared with those in 2005, respectively. The number of cattle and schistosome -infected cattle were 68 152 and 5, decreased by 50.84% and 99.83% compared with those in 2005, respectively. The average density of Oncomelania hupensis snails was decreased by 61.52%. No schistosome-infected snails were found since 2014. CONCLUSIONS: The new strategy accurately locates the key points and targets of schistosomiasis transmission chain, which has controlled the human and animal's fecal eggs from polluting grassland, and cut off the transmission chain, reduced both the infection rates of human and animal and the re-infection risk, and promoted to achieve the target of schistosomiasis transmission control in Poyang Lake region.