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Anatase TiO2 as sodium-ion-battery anode has attracted increased attention because of its low volume change and good safety. However, low capacity and poor rate performance caused by low electrical conductivity and slow ion diffusion greatly impede its practical applications. Here, a bi-solvent enhanced pressure strategy that induces defects (oxygen vacancies) into TiO2 via N doping and reduces its size by using mutual-solvent ethanol and dopant dimethylformamide as pressure-increased reagent of tetrabutyl orthotitanate tetramer is proposed to fabricate N-doped TiO2/C nanocomposites. The induced defects can increase ion storage sites, improve electrical conductivity, and decrease bandgap and ion diffuse energy barrier of TiO2. The size reduction increases contact interfaces between TiO2 and C and shortens ion diffuse distance, thus increasing extra ion storage sites and boosting ion diffusion rate of TiO2. The N-doped TiO2 possesses highly stable crystal structure with a slightly increase of 0.86% in crystal lattice spacing and 3.2% in particle size after fully sodiation. Consequently, as a sodium-ion battery anode, the nanocomposite delivers high capacity and superior rate capability along with ultralong cycling life. This work proposes a novel pressure-induced synthesis strategy that provides unique guidance for designing TiO2-based anode materials with high capacity and excellent fast-charging capability.
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Semantic segmentation of target objects in power transmission line corridor point cloud scenes is a crucial step in powerline tree barrier detection. The massive quantity, disordered distribution, and non-uniformity of point clouds in power transmission line corridor scenes pose significant challenges for feature extraction. Previous studies have often overlooked the core utilization of spatial information, limiting the network's ability to understand complex geometric shapes. To overcome this limitation, this paper focuses on enhancing the deep expression of spatial geometric information in segmentation networks and proposes a method called BDF-Net to improve RandLA-Net. For each input 3D point cloud data, BDF-Net first encodes the relative coordinates and relative distance information into spatial geometric feature representations through the Spatial Information Encoding block to capture the local spatial structure of the point cloud data. Subsequently, the Bilinear Pooling block effectively combines the feature information of the point cloud with the spatial geometric representation by leveraging its bilinear interaction capability thus learning more discriminative local feature descriptors. The Global Feature Extraction block captures the global structure information in the point cloud data by using the ratio between the point position and the relative position, so as to enhance the semantic understanding ability of the network. In order to verify the performance of BDF-Net, this paper constructs a dataset, PPCD, for the point cloud scenario of transmission line corridors and conducts detailed experiments on it. The experimental results show that BDF-Net achieves significant performance improvements in various evaluation metrics, specifically achieving an OA of 97.16%, a mIoU of 77.48%, and a mAcc of 87.6%, which are 3.03%, 16.23%, and 18.44% higher than RandLA-Net, respectively. Moreover, comparisons with other state-of-the-art methods also verify the superiority of BDF-Net in point cloud semantic segmentation tasks.
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Since abdominal muscle training is one of the most important ways to treat rectal diastasis, it is necessary to design and provide appropriate exercises to treat this problem. One of the complementary methods to achieve higher intensity training is the use of electrical muscle stimulation along with voluntary activity, which causes maximum recall in muscle units. Therefore, in the current study, the effect of electrical stimulation followed by exercises was evaluated in postnatal diastasis recti abdominis via MMP2 gene expression. For this purpose, we studied on thirty-two women who had rectal diastasis for six months and were referred to a physiotherapy clinic by a gynecologist. They were divided into the control group (n=16) and the intervention group (n=16). The distance between the two blocks of the rectus abdominis muscle and the thickness of the abdominal muscles at rest was determined by ultrasound. In the intervention group, electrical stimulation and strengthening exercises of oblique muscles were performed for six weeks. The control group did not perform any specific exercises. After six weeks, another ultrasound was performed. The expression of the MMP2 gene was measured by the real-time PCR method. Comparison of the distance between the two blocks of rectus abdominis muscle (above the umbilicus and below the umbilicus) before and after six weeks showed that in the experimental group after intervention in both areas had a significant decrease (above the umbilicus = 0.001 and below the umbilicus P = 0.03), while this distance in the control group did not decrease significantly (p >0.05). Also, in the upper part of the umbilicus, the distance between the two rectus abdominis muscle blocks in two groups after the intervention showed a significant difference (p = 0.04). Evaluation of MMP2 gene expression showed that there was no significant difference between the two groups before the intervention (p >0.05). However, after the intervention, the expression of this gene decreased significantly in the intervention group (p = 0.007). In general, the present study results showed that electrical stimulation of abdominal muscles with strengthening exercises of internal and external oblique muscles could reduce rectal diastasis and increase the thickness of these muscles in people with rectal diastasis.
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Diástase Muscular , Terapia por Estimulação Elétrica , Metaloproteinase 2 da Matriz , Reto do Abdome , Diástase Muscular/terapia , Feminino , Expressão Gênica , Humanos , Metaloproteinase 2 da Matriz/genética , Reto do Abdome/fisiopatologiaRESUMO
OBJECTIVE: Coronary slow/no reflow is not rare after successfully undergoing primary percutaneous coronary intervention (PCI) in patients with acute myocardial infarction (AMI), and shock index (SI) is an important factor for adverse cardiovascular prognosis. In this study, we are to explore whether SI is associated with coronary slow/no reflow in patients with AMI following primary PCI. METHODS: A total of 153 consecutive AMI patients undergoing primary PCI within 24 hours of symptom onset were included in this study. The participants were divided into normal flow group (n=124) and slow/no reflow group (n=29) according to cineangiograms recorded during the period of PCI. Cardiovascular risk factors, hematologic parameters, preoperative management of antithrombotic therapy, and baseline angiography were collected. RESULTS: SI, plasma glucose, white blood cells (WBC) and neutrophil count, neutrophil to lymphocyte ratio (PLR), high sensitivity C-reactive protein (hs-CRP), probrain natriuretic peptide (pro-BNP), and Killip classification on admission and thrombus burden on initial angiography were significantly different between patients with and without slow/no reflow. Multivariate analysis revealed that SI≥0.66, thrombus burden, and plasma glucose on admission were independent predictors for coronary slow/no reflow. Preoperative management of tirofiban therapy improves initial thrombolysis in myocardial infarction (TIMI). However, it has no effect on prognosis of slow/no reflow. CONCLUSION: Our findings demonstrated that slow/no reflow in patients with AMI following primary PCI was more likely associated with SI≥0.66, thrombus burden, and plasma glucose on admission. SI as a predictor for coronary slow/no reflow should be further confirmed in the following more large-scale and prospective studies. The clinical registration number is ChiCTR1900024447.
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Infarto do Miocárdio/cirurgia , Fenômeno de não Refluxo , Intervenção Coronária Percutânea , Choque Cardiogênico/diagnóstico , Idoso , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Fenômeno de não Refluxo/diagnóstico , Fenômeno de não Refluxo/etiologia , Admissão do Paciente , Intervenção Coronária Percutânea/efeitos adversos , Intervenção Coronária Percutânea/métodos , Cuidados Pré-Operatórios/métodos , Prognóstico , Estudos Prospectivos , Índice de Gravidade de Doença , Choque Cardiogênico/complicações , Resultado do TratamentoRESUMO
This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been retracted at the request of the Editor-in-Chief. The journal was initially contacted by the corresponding author to report that the article was submitted without the consent of an author. Given the comments of Dr Elisabeth Bik regarding this article " the Western blot bands in all 400+ papers are all very regularly spaced and have a smooth appearance in the shape of a dumbbell or tadpole, without any of the usual smudges or stains. All bands are placed on similar looking backgrounds, suggesting they were copy/pasted from other sources, or computer generated ", the journal requested the authors to provide the raw data. However, the authors were not able to fulfil this request and therefore the Editor-in-Chief decided to retract the article.
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Apoptose/efeitos dos fármacos , Proliferação de Células/efeitos dos fármacos , Regulação Neoplásica da Expressão Gênica/efeitos dos fármacos , Ginsenosídeos/farmacologia , MicroRNAs/genética , Linhagem Celular Tumoral , Sobrevivência Celular/efeitos dos fármacos , Regulação para Baixo/efeitos dos fármacos , Humanos , Fosfatidilinositol 3-Quinases/metabolismo , Fosforilação/efeitos dos fármacos , Proteínas Proto-Oncogênicas c-akt/metabolismo , Retinoblastoma/genética , Retinoblastoma/metabolismo , Retinoblastoma/patologia , Serina-Treonina Quinases TOR/metabolismo , Proteína Supressora de Tumor p53/genética , Proteína Supressora de Tumor p53/metabolismo , Regulação para Cima/efeitos dos fármacosRESUMO
BACKGROUND BCL2 19 kD protein-interacting protein 3 (BNIP3) is a BH3-containing protein of the BCL-2 family; it can regulate cell death, autophagy, and cytoprotection. The upregulation of BNIP3 has been reported to relate to progression and poor prognosis in different cancer types. However, the clinical significance of BNIP3 in uveal melanoma (UM) is still unknown. MATERIAL AND METHODS In our study, 47 patients with UM were enrolled; the expression of BNIP3 was detected with immunohistochemistry. According to BNIP3 immunohistochemical scores, the patients were divided into BNIP3 high- and low-expression subgroups. The correlation between the expression of BNIP3 and clinicopathological factors was evaluated with Fisher's test; the associations with survival rates were analyzed with log-rank test. The independent prognostic factors were identified with the Cox-regression model. RESULTS BNIP3 was mainly localized in the cytoplasm, and high expression of BNIP3 accounted for 31.9% (15/47) of the patients in our study. High expression of BNIP3 was demonstrated to be significantly associated with more pigment (P=0.018) and deeper scleral invasion (P=0.013). High expression of BNIP3 was also correlated with lower overall survival rate (P=0.006). Multivariate analysis confirmed positive ciliary body involvement and lymphatic infiltration as independent prognostic factors. CONCLUSIONS High expression of BNIP3 was significantly associated with poor prognosis of patients with UM, indicating that BNIP3 detection could help stratify high-risk patients and identify new therapies targeting BNIP3 as a promising approach to treat UM.
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Melanoma/metabolismo , Melanoma/patologia , Proteínas de Membrana/metabolismo , Proteínas Proto-Oncogênicas/metabolismo , Neoplasias Uveais/metabolismo , Neoplasias Uveais/patologia , Adulto , Idoso , Biomarcadores Tumorais/genética , Biomarcadores Tumorais/metabolismo , Feminino , Humanos , Imuno-Histoquímica , Masculino , Melanoma/genética , Proteínas de Membrana/genética , Pessoa de Meia-Idade , Prognóstico , Proteínas Proto-Oncogênicas/genética , Taxa de Sobrevida , Regulação para Cima , Neoplasias Uveais/genéticaRESUMO
BACKGROUND: Mental health is increasingly becoming a huge public health issue in China. Yet for various cultural, healthcare system, and social economic reasons, people with mental health need have long been under-served in China. In order to inform the current on-going health care reform, empirical evidences on the economic burden of mental illnesses in China are urgently needed to contribute to health policy makers' understanding of the potential benefits to society from allocating more resources to preventing and treating mental illness. However, the cost of mental illnesses and particularly its trend in China remains largely unknown. AIMS OF THE STUDY: To investigate the trend of health care resource utilization among inpatients with mental illnesses in China, and to analyze what are the factors influencing the inpatient costs. METHOD: Our study sample included 15,721 patients, both adults and children, who were hospitalized over an eight-year period (2005-2012) in Shandong Center for Mental Health (SCMH), the only provincial psychiatric hospital in Shandong province, China. Data were extracted from the Health Information System (HIS) at SCMH, with detailed and itemized cost data on all inpatient expenses incurred during hospitalization. The identification of the patients was based on the ICD-10 diagnoses recorded in the HIS. Descriptive analysis was done to analyze the trend of hospitalization cost and length of stay during the study period. Multivariate stepwise regression analysis was conducted to assess the factors that influence hospitalization cost. RESULTS: Among the inpatients in our sample, the most common mental disorders were schizophrenia, schizotypal and delusional disorders. The disease which had the highest per capita hospital expense was behavioral and emotional disorders with onset usually occurring in childhood and adolescence (RMB 8,828.4; US$ 1,419.4, as compared to the average reported household annual income of US$ 2,095.3 in China). The average annual growth rate of per capita hospitalization cost was 23.6%, with the inpatient cost reaching RMB 11,949 (US$ 1921.1) in 2012. The hospitalization cost was found to be strongly associated with hospital length of stay, level of care, age, employment status, admission diagnoses, and frequency of hospitalization. DISCUSSION: Our study found that mental health inpatient resources use, particularly hospitalization cost, has been growing at an increasing rate. In our sample, hospitalization cost nearly tripled from 2005 to 2012. Mental illnesses and the related economic burden on the population will continue to grow, making mental health a major public health issue in China. Hospital length of stay was found to be increasing in our sample, and positively correlated with hospitalization cost. Childhood and adolescence behavioral and emotional disorders were found to be significantly associated with higher inpatient cost. IMPLICATIONS FOR HEALTH POLICIES AND FUTURE RESEARCH: The policy implications generated from the results of this study are two-fold: first of all, in order to meet the growing need of mental health care in China, the government needs to significantly increase its spending in preventing and treating mental illnesses. Second, cost containment in inpatient care would become a major challenge for mental health policy makers in China. Government support, clinical practices and guideline development, as well as research are urgently needed to promote mental health prevention and improve the efficiency of mental health system in China. The current mental health system, like the overall healthcare system in China, relies heavily on hospital inpatient care. In order to build a sustainable mental health care system to meet increasing population need in China, it is crucial to integrate mental health care reform with the ongoing primary health care reform. Future mental health policy reform and research in China should put more focus on how to strengthen primary care system as well as community support, establish effective two-tier referring mechanism between hospital and primary care system, and to ensure continuity of care.
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Preços Hospitalares/tendências , Hospitalização/economia , Hospitais Psiquiátricos/economia , Transtornos Mentais/economia , Transtornos Mentais/terapia , Adulto , China/epidemiologia , Feminino , Hospitalização/tendências , Hospitais Psiquiátricos/tendências , Humanos , Pacientes Internados , Masculino , Pessoa de Meia-Idade , Fatores SocioeconômicosRESUMO
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.
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BACKGROUND: Nanguo pear is a distinctive pear variety in northeast China, grown mainly in mountainous areas. Due to terrain limitations, ground-based pesticide application equipment is difficult to use. This limitation could be overcome by using unmanned aerial vehicles (UAVs) for pesticide application in Nanguo pear orchards. This study evaluated the spraying performance of two UAVs in the Nanguo pear orchards and compared them with a manually used backpack electric sprayer (BES). The study also analyzed the effect of canopy size on droplet deposition and ground loss, and evaluated two sampling methods, leaf sampling and telescopic rod sampling. RESULTS: Compared to BESs, droplet deposition is lower for UAVs, but the actual pesticide active ingredient deposition is not necessarily lower given the solution concentration. The droplet deposition varies among different UAVs due to structural differences. Under the same UAV operating parameters, droplet deposition on trees with smaller canopy sizes is typically greater than that on trees with larger canopy sizes, and the ground loss was also more severe. Although telescopic rod sampling is a quick and convenient method, it can only reflect the trend of droplet deposition, and the data error is greater compared with leaf sampling. CONCLUSION: UAVs can achieve better droplet deposition in mountainous Nanguo pear orchards and does almost no harm to the operators compared with the BES. However, canopy size needs to be considered to adjust the application volume rate. Telescopic rods can be used for qualitative analyses, but are not recommended for quantitative analyses. © 2024 Society of Chemical Industry.
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Pyrus , Pyrus/química , Dispositivos Aéreos não Tripulados , China , Folhas de Planta/químicaRESUMO
Introduction: The use of chemical fertilizers in rice field management directly affects rice yield. Traditional rice cultivation often relies on the experience of farmers to develop fertilization plans, which cannot be adjusted according to the fertilizer requirements of rice. At present, agricultural drones are widely used for early monitoring of rice, but due to their lack of rationality, they cannot directly guide fertilization. How to accurately apply nitrogen fertilizer during the tillering stage to stabilize rice yield is an urgent problem to be solved in the current large-scale rice production process. Methods: WOFOST is a highly mechanistic crop growth model that can effectively simulate the effects of fertilization on rice growth and development. However, due to its lack of spatial heterogeneity, its ability to simulate crop growth at the field level is weak. This study is based on UAV remote sensing to obtain hyperspectral data of rice canopy and assimilation with the WOFOST crop growth model, to study the decision-making method of nitrogen fertilizer application during the rice tillering stage. Extracting hyperspectral features of rice canopy using Continuous Projection Algorithm and constructing a hyperspectral inversion model for rice biomass based on Extreme Learning Machine. By using two data assimilation methods, Ensemble Kalman Filter and Four-Dimensional Variational, the inverted biomass of the rice biomass hyperspectral inversion model and the localized WOFOST crop growth model were assimilated, and the simulation results of the WOFOST model were corrected. With the average yield as the goal, use the WOFOST model to formulate fertilization decisions and create a fertilization prescription map to achieve precise fertilization during the tillering stage of rice. Results: The research results indicate that the training set R2 and RMSE of the rice biomass hyperspectral inversion model are 0.953 and 0.076, respectively, while the testing set R2 and RMSE are 0.914 and 0.110, respectively. When obtaining the same yield, the fertilization strategy based on the ENKF assimilation method applied less fertilizer, reducing 5.9% compared to the standard fertilization scheme. Discussion: This study enhances the rationality of unmanned aerial vehicle remote sensing machines through data assimilation, providing a new theoretical basis for the decision-making of rice fertilization.
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The radiative transfer model of vegetation leaves simulates the transmission mechanism of light inside the vegetation and simulates the reflectivity of blades according to the change law of different components in the process of plant growth. Based on the PIOSL model, this paper combines PIOSL with the structure of rice leaves to construct a radiation transfer model for rice leaves. The parameters of each layer of the RPIOSL model are determined by the Non-dominated Sorting Genetic Algorithm-III. (NSGA-III.) algorithm. The reflectance spectra of 218 rice leaf samples in different periods were simulated using the RPIOSL model. The results show that the mean (RMSE) between the simulated and measured spectra of the constructed RPIOSL model is 0.1074, which is 0.0191 lower than that of the PROSPECT model. Among them, the spectral simulation effect of RPIOSL model in yellow and red light band is the best, and the RMSE at tillering period, jointing period, heading period and grouting period are 0.0584, 0.0576, 0.0724 and 0.0820, respectively. Therefore, the establishment of the RPIOSL model can accurately describe the interaction mechanism between light, which is of great significance for the rapid acquisition of rice growth information and accurate crop management.
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Improving the long-term cycling stability and energy density of all-solid-state lithium (Li)-metal batteries (ASSLMBs) at room temperature is a severe challenge because of the notorious solid-solid interfacial contact loss and sluggish ion transport. Solid electrolytes are generally studied as two-dimensional (2D) structures with planar interfaces, showing limited interfacial contact and further resulting in unstable Li/electrolyte and cathode/electrolyte interfaces. Herein, three-dimensional (3D) architecturally designed composite solid electrolytes are developed with independently controlled structural factors using 3D printing processing and post-curing treatment. Multiple-type electrolyte films with vertical-aligned micro-pillar (p-3DSE) and spiral (s-3DSE) structures are rationally designed and developed, which can be employed for both Li metal anode and cathode in terms of accelerating the Li+ transport within electrodes and reinforcing the interfacial adhesion. The printed p-3DSE delivers robust long-term cycle life of up to 2600 cycles and a high critical current density of 1.92 mA cm-2. The optimized electrolyte structure could lead to ASSLMBs with a superior full-cell areal capacity of 2.75 mAh cm-2 (LFP) and 3.92 mAh cm-2 (NCM811). This unique design provides enhancements for both anode and cathode electrodes, thereby alleviating interfacial degradation induced by dendrite growth and contact loss. The approach in this study opens a new design strategy for advanced composite solid polymer electrolytes in ASSLMBs operating under high rates/capacities and room temperature.
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Background and objective: The rapid diagnosis of rice nitrogen nutrition is of great significance to rice field management and precision fertilization. The nitrogen nutrition index (NNI) based on the standard nitrogen concentration curve is a common parameter for the quantitative diagnosis of rice nitrogen nutrition. However, the current NNI estimation methods based on hyperspectral techniques mainly focus on finding a better estimation model while ignoring the relationship between the critical nitrogen concentration curve and rice hyperspectral reflectance. Methods: This study obtained canopy spectral data using unmanned aerial vehicle (UAV) hyperspectral remote sensing and determined the rice critical nitrogen concentration curve and NNI. Taking the spectrum at critical nitrogen concentration as the standard spectrum, the original spectral reflectance and logarithmic spectral reflectance data were transformed by the difference method, and the features of the spectral data were extracted by a Autoencoder. Finally, the NNI inversion models of rice based on Extreme Learning Machine (ELM) and Bald Eagle Search-Extreme Learning Machine (BES-ELM) were constructed by taking the feature bands of four spectral extractions as input variables. Results: 1) from the feature extraction results of the self-encoder, simple logarithmic or difference transformation had little effect on NNI estimation, and logarithmic difference transformation effectively improved the NNI estimation results; 2) the estimation model based on the logarithmic difference spectrum and BES-ELM had the highest estimation accuracy, and the coefficient of determination (R2) values of the training set and verification set were 0.839 and 0.837, and the root mean square error (RMSE) values were 0.075 and 0.073, respectively; 3) according to the NNI, the samples were divided into a nitrogen-rich group (NNI ≥ 1) and nitrogen-deficient group (NNI < 1). Conclusion: The logarithmic difference transformation of the spectrum can effectively improve the estimation accuracy of the NNI estimation model, providing a new approach for improving NNI estimation methods based on hyperspectral technology.
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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 WDVI NIR vegetation index from the reflectance of three bands, RE, G, and NIR. WDVI NIR was compared with five traditional vegetation indices, NDVI, LCI, NDRE, and OSAVI, and the results showed that WDVI NIR was the most effective for weed identification and could clearly distinguish weeds from rice, water cotton, and soil. The weed identification method based on WDVI NIR 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.
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Leaf blast is a disease of rice leaves caused by the Pyricularia oryzae. It is considered a significant disease is affecting rice yield and quality and causing economic losses to food worldwide. Early detection of rice leaf blast is essential for early intervention and limiting the spread of the disease. To quickly and non-destructively classify rice leaf blast levels for accurate leaf blast detection and timely control. This study used hyperspectral imaging technology to obtain hyperspectral image data of rice leaves. The descending dimension methods got rice leaf disease characteristics of different disease classes, and the disease characteristics obtained by screening were used as model inputs to construct a model for early detection of leaf blast disease. First, three methods, ElasticNet, principal component analysis loadings (PCA loadings), and successive projections algorithm (SPA), were used to select the wavelengths of spectral features associated with leaf blast, respectively. Next, the texture features of the images were extracted using a gray level co-occurrence matrix (GLCM), and the texture features with high correlation were screened by the Pearson correlation analysis. Finally, an adaptive-weight immune particle swarm optimization extreme learning machine (AIPSO-ELM) based disease level classification method is proposed to further improve the model classification accuracy. It was also compared and analyzed with a support vector machine (SVM) and extreme learning machine (ELM). The results show that the disease level classification model constructed using a combination of spectral characteristic wavelengths and texture features is significantly better than a single disease feature in terms of classification accuracy. Among them, the model built with ElasticNet + TFs has the highest classification accuracy, with OA and Kappa greater than 90 and 87%, respectively. Meanwhile, the AIPSO-ELM proposed in this study has higher classification accuracy for leaf blast level classification than SVM and ELM classification models. In particular, the AIPSO-ELM model constructed with ElasticNet+TFs as features obtained the best classification performance, with OA and Kappa of 97.62 and 96.82%, respectively. In summary, the combination of spectral characteristic wavelength and texture features can significantly improve disease classification accuracy. At the same time, the AIPSO-ELM classification model proposed in this study has sure accuracy and stability, which can provide a reference for rice leaf blast disease detection.
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BACKGROUND: Aerial application, a spraying method with aircraft as the application platform, is one of the most important methods of pest control in pine forests. This work aimed to investigate the movement and distribution patterns of droplets and the pest control effectiveness of helicopter spraying on Masson pine (MP) and Korean pine (KP) trees. In particular, three nozzle types (CP02, CP03 and CP04) were evaluated and compared at the same spray volume rate (15 L ha-1 ) in both wind tunnel and field trials. RESULTS: The CP04 nozzle with a larger orifice size showed significantly better deposition and penetration, but the precision of flight operation parameters must be strictly controlled when using this type of nozzle. The majority (≥48%) of the droplets produced by the three nozzle types were concentrated in the range of <200 µm, and the spray performance of each nozzle type was more stable in the range 201-300 µm. The final pest correction control rate (CR) of MP was in the range 59.24-95.74% and that of KP was 45.41-54.39% by helicopter spraying. CONCLUSION: This study confirmed that it is necessary to select the appropriate nozzle type and ensure accurate flight parameters for aerial application in mountain pine forests. Meanwhile, it also confirms the importance of the timing of operation which can lead to incorrect implementation, resulting in poor control effectiveness. © 2022 Society of Chemical Industry.
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Pinus , Aeronaves , Controle de PragasRESUMO
Objective: The burden of both general and drug-resistant tuberculosis in rural areas is higher than that in urban areas in China. To characterize the genetic structure and transmission risk of Mycobacterium tuberculosis in rural China, we used whole genome sequencing to analyze clinical strains collected from patients in two counties of Yichang for three consecutive years. Methods: From 2018 to 2020, sputum samples were collected for cultures from patients with suspected tuberculosis in Yidu and Zigui county, and DNA was extracted from the positive strains for genome sequencing. The online SAM-TB platform was used to identify the genotypes and drug resistance-related mutations of each strain, establish a phylogenetic tree, and calculated the genetic distances between pairwise strains. Twelve single nucleotide polymorphisms (SNPs) were used as thresholds to identify transmission clusters. The risk of related factors was estimated by univariable and multivariable logistic regression. Results: A total of 161 out of the collected 231 positive strains were enrolled for analysis, excluding non-tuberculous mycobacterium and duplicate strains from the same patient. These strains belonged to Lineage 2 (92, 57.1%) and Lineage 4 (69, 42.9%), respectively. A total of 49 (30.4%) strains were detected with known drug resistance-related mutations, including 6 (3.7%) multidrug-resistant-TB (MDR-TB) strains and 11 (6.8%) RIF-resistant INH-susceptible TB (Rr-TB) strains. Six of the MDR/Rr-TB (35.3%) were also resistant to fluoroquinolones, which made them pre-extensively drug-resistant TB (pre-XDR-TB). There were another seven strains with mono-resistance to fluoroquinolones and one strain with resistance to both INH and fluoroquinolones, making the overall rate of fluoroquinolones resistance 8.7% (14/161). A total of 50 strains (31.1%) were identified as transmission clusters. Patients under 45 years old (adjusted odds ratio 3.46 [95% confidential intervals 1.28-9.35]), treatment-naive patients (6.14 [1.39-27.07]) and patients infected by lineage 4 strains (2.22 [1.00-4.91]) had a higher risk of transmission. Conclusion: The drug resistance of tuberculosis in rural China, especially to the second-line drug fluoroquinolones, is relatively serious. The standardized treatment for patients and the clinical use of fluoroquinolones warrant attention. At the same time, the recent transmission risk of tuberculosis is high, and rapid diagnosis and treatment management at the primary care needs to be strengthened.
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Mycobacterium tuberculosis , Tuberculose Resistente a Múltiplos Medicamentos , Humanos , Antituberculosos/uso terapêutico , Sequência de Bases , China/epidemiologia , Farmacorresistência Bacteriana Múltipla/genética , Fluoroquinolonas/uso terapêutico , Mycobacterium tuberculosis/genética , Filogenia , Tuberculose Resistente a Múltiplos Medicamentos/epidemiologia , Tuberculose Resistente a Múltiplos Medicamentos/tratamento farmacológico , Sequenciamento Completo do GenomaRESUMO
OBJECTIVE: To evaluate the effective dose and safety of S-Gel in the treatment of bacterial vaginosis. METHODS: Clinical research method of multi- center, randomly double-blind, and dose group parallel comparison was adopted. In the study, 96 bacterial vaginosis patients were randomized into three groups: Group A, S-Gel 5.0 g, 32 patients; Group B, S-Gel 7.5 g, 32 patients; Group C, placebo, 32 patients. The patients were treated with different methods. Safety and efficacy were analyzed 3 to 4 days and 8 days after the treatment, respectively. RESULTS: The efficacy of comprehensive clinical treatment showed that: 8 days after the treatment, the cure rates of group A (5.0 g), group B (7.5 g), and group C (placebo) were 84.38%, 86.67%, and 3.13% respectively. No difference of statistic significance was found in groups A and B, difference of statistical significance was found in group A and group C, group B and group C respectively (P<0.001). CONCLUSION: As compared with placebo, S-Gel 7.5 g and 5.0 g bid (in the morning and evening) could obviously improve the clinical efficacy index and laboratory index of bacterial vaginosis. Other effects included the release of clinical symptoms, and the recovery of the normal micro-environment in the vagina. No significant difference was found in the cure rates of the two dose groups.
Assuntos
Sacarose/administração & dosagem , Cremes, Espumas e Géis Vaginais/administração & dosagem , Vaginose Bacteriana/tratamento farmacológico , Adolescente , Adulto , Método Duplo-Cego , Feminino , Humanos , Pessoa de Meia-Idade , Adulto JovemRESUMO
Chlorophyll content is an important indicator of the growth status of japonica rice. The objective of this paper is to develop an inversion model that can predict japonica rice chlorophyll content by using hyperspectral image of rice canopy collected with unmanned aerial vehicle (UAV). UAV-based hyperspectral remote sensing can provide timely and cost-effective monitoring of chlorophyll content over a large region. The study was based on hyperspectral data collected at the Shenyang Agricultural College Academician Japonica Rice Experimental Base in 2018 and 2019. In order to extract the salient information embedded in the high-dimensional hyperspectral data, we first perform dimension reduction by using a successive projection algorithm (SPA). The SPA extracts the characteristic hyperspectral bands that are used as input to the inversion model. The characteristic bands extracted by SPA are 410 nm, 481 nm, 533 nm, 702 nm, and 798 nm, respectively. The inversion model is developed by using an extreme learning machine (ELM), the parameters of which are optimized by using particle swarm optimization (PSO). The PSO-ELM algorithm can accurately model the nonlinear relationship between hyperspectral data and chlorophyll content. The model achieves a coefficient of determination R2 = 0.791 and a root mean square error of RMSE = 8.215 mg/L. The model exhibits good predictive ability and can provide data support and model reference for research on nutrient diagnosis of japonica rice.