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This study was aimed to investigate the effects of dietary calcitriol or quercetin supplementation on eggshell and bone quality of laying hens. In trial 1, 72 Hy-Line Brown layers (80-week-old) with weak-shelled strength (25 to 30 N) were assigned into 4 dietary treatments with 6 replicates of 3 birds and fed a basal diet (4% calcium level) or basal diets supplemented with 0.5% calcium, 5 μg/kg calcitriol or 500 mg/kg quercetin for 4 weeks. In trial 2, 360 Hy-Line Brown layers (60-week-old) were divided into 3 groups with 8 replicates of 15 birds: control group (basal diet), calcitriol group (basal diet + 5 μg/kg calcitriol), and quercetin group (basal diet + 500 mg/kg quercetin). This trial lasted for 12 weeks. The results showed that dietary calcitriol or quercetin improved eggshell quality in both trials (P

Abstract Various mechanical effects have been reported with molecular materials, yet organic crystals capable of multiple dynamic effects are rare, and at present, their performance is worse than some of the common actuators. Here, we report a confluence of different mechanical effects across three polymorphs of an organic crystal that can efficiently convert light into work. Upon photodimerization, acicular crystals of polymorph I display output work densities of about 0.06–3.94 kJ m−3, comparable to ceramic piezoelectric actuators. Prismatic crystals of the same form exhibit very high work densities of about 1.5–28.5 kJ m−3, values that are comparable to thermal actuators. Moreover, while crystals of polymorph II roll under the same conditions, crystals of polymorph III are not photochemically reactive; however, they are mechanically flexible. The results demonstrate that multiple and possibly combined mechanical effects can be anticipated even for a simple organic crystal.

Abstract Background Deteriorations in eggshell and bone quality are major challenges in aged laying hens. This study compared the differences of eggshell quality, bone parameters and their correlations as well as uterine physiological characteristics and the bone remodeling processes of hens laying eggs of different eggshell breaking strength to explore the mechanism of eggshell and bone quality reduction and their interaction. A total of 240 74-week-old Hy-line Brown laying hens were selected and allocated to a high (HBS, 44.83 ± 1.31 N) or low (LBS, 24.43 ± 0.57 N) eggshell breaking strength group. Results A decreased thickness, weight and weight ratio of eggshells were observed in the LBS, accompanied with ultrastructural deterioration and total Ca reduction. Bone quality was negatively correlated with eggshell quality, marked with enhanced structures and increased components in the LBS. In the LBS, the mammillary knobs and effective layer grew slowly. At the initiation stage of eggshell calcification, a total of 130 differentially expressed genes (DEGs, 122 upregulated and 8 downregulated) were identified in the uterus of hens in the LBS relative to those in the HBS. These DEGs were relevant to apoptosis due to the cellular Ca overload. Higher values of p62 protein level, caspase-8 activity, Bax protein expression and lower values of Bcl protein expression and Bcl/Bax ratio were seen in the LBS. TUNEL assay and hematoxylin-eosin staining showed a significant increase in TUNEL-positive cells and tissue damages in the uterus of the LBS. Although few DEGs were identified at the growth stage, similar uterine tissue damages were also observed in the LBS. The expressions of runt-related transcription factor 2 and osteocalcin were upregulated in humeri of the LBS. Enlarged diameter and more structural damages of endocortical bones and decreased ash were observed in femurs of the HBS. Conclusion The lower eggshell breaking strength may be attributed to a declined Ca transport due to uterine tissue damages, which could affect eggshell calcification and lead to a weak ultrastructure. Impaired uterine Ca transport may result in reduced femoral bone resorption and increased humeral bone formation to maintain a higher mineral and bone quality in the LBS. Graphical Abstract

As the most economically developed area in China, the environmental water quality of Guangdong-Hong Kong-Macao Greater Bay Area has received extensive attention, and the spatial variations of total phosphorus (TP) in the Greater Bay were significant, conventional laboratory analysis is difficult to meet the requirements of TP monitoring due to time and cost consuming, and Fourier transform infrared attenuated total reflectance spectroscopy (FTIR-ATR) technology, combined with the self-adaptive partial least squares (SA-PLS) model, was used to determine the TP concentration. The results showed that P-O vibrations were observed in the wavenumber range of 1200–900 cm−1, and prediction models were established by using this range. For the conventional partial least squares (PLS) model, the R2, RMSE and RPD were 0.817, 0.022 mg L−1 and 2.335, respectively, while for the SA-PLS model the prediction was improved with the values of 0.965, 0.010 mg L−1, 5.296, respectively, and the SA-PLS mode prediction was satisfied when the TP content in water was more than 0.05 mg L−1. It was found that the TP determination was interfered by sulfate, when the sulfate content was < 100 mg L−1, both SA-PLS and the conventional PLS model could be used for quantitative analysis of TP; when sulfate content was > 100 mg L−1, PLS model could not be used while the SA-PLS model still achieved an excellent prediction. Therefore, FTIR-ATR combined with SA-PLS model can rapidly determine TP in water, providing an alternative strategy for monitoring TP in natural water bodies.

Snow depth is a fundamental parameter in hydrological and climate models, which is crucial for studying climate change, hydrological cycles, and ecosystem changes. Polarimetric synthetic aperture radar interferometry (PolInSAR) is one of the most promising snow depth retrieval methods, which is sensitive to the shape, direction, and vertical distribution of targets. The dense medium random-volume-over-ground (DM-RVoG) model for PolInSAR has been shown to be workable for snow depth retrieval, it still suffers from the inaccuracy of the parameters representing the phase center and decorrelation. In this study, based on the backscattering mechanism of snow, a novel snow depth retrieval method is proposed to improve the DM-RVoG model using polarization decomposition and decorrelation optimization. First, the polarization decomposition is extended to obtain the ground scattering phase. Then, the coherence region boundary estimation method is put forward to obtain pure volume decorrelation. Finally, the proposed method is validated using Ku-band UAV SAR data, and the accuracy is assessed using in situ data. The correlation coefficient, root mean square error, and mean absolute error of the proposed method are 0.88, 4.98, and 4.08 cm, respectively, demonstrating significant improvements compared with the original method.

Eutrophication occurs frequently in urban water bodies, and rapid measurement of phosphorus (P) is needed for water quality control, since P has been one of the limiting factors. In this study, approximately 400 water samples were collected from typical urban water bodies in Nanjing city, and Fourier transform infrared attenuated total reflectance spectroscopy (FTIR-ATR) was applied for rapid P determination. Both silicon ATR (Si-ATR) and ZnSe-ATR were employed in the recording of FTIR-ATR spectra, and different algorithms, including partial least squares regression (PLSR), support vector machines for regression (SVRs), extreme learning machines (ELMs), and self-adaptive partial least squares model (SA–PLS), were applied in the analysis of spectra data. The results showed that the water quality varied significantly for different water bodies in different seasons, and both Si-ATR and ZnSe-ATR could achieve good P prediction. The PLSR and SVR models showed poor P prediction effects while the ELM model was excellent, and the SA-PLS model was the best one. For the SA-PLS model, the prediction accuracy of Si-ATR (Rv2 = 0.973, RMSEV = 0.015 mg L−1, RPDV = 6.05) was slightly better than that of ZnSe-ATR (Rv2 = 0.942, RMSEV = 0.011 mg L−1, RPDV = 4.13). Therefore, the FTIR-ATR technology coupled with the SA-PLS model achieved rapid P determination in urban water, providing an effective option for water quality monitoring.

Tobacco, a pivotal economic crop in China, faces the challenge of securing high-quality raw materials for its industry due to unbalanced and inefficient nitrogen (N) application. To assess the impact of fertilizer management and soil factors on the yield and quality of flue-cured tobacco (FCT), a meta-analysis was conducted across 82 peer-reviewed research studies. The findings demonstrated that both fertilizer management and soil properties exerted a significantly greater influence on yield (63.13% and 62.05%, respectively) than the proportion of superior and medium tobacco (PSMT) (23.57% and 23.83%, respectively). Multiple models were conducted to analyze the N application rate for maximum yield and PSMT, respectively, resulting in an optimum range from 90 to 100 kg N ha−1. The highest yield and PSMT increments were observed with fertilizer timing (FT) applied twice, a basal fertilizer ratio (BFR) exceeding 50%, and a soil pH below 6.5. The nicotine content escalated with increasing N application rates (NR) and soil nitrogen content, peaking at NR over 120 kg N ha−1 and soil total nitrogen (TN) above 2 g kg−1. Stepwise regression modeling indicated that nicotine content was positively influenced by fertilizer management factors (including NR, fertilizer timing, and BFR), as well as initial soil nitrogen content (AN and TN). However, it was negatively correlated with available potassium (AK). Therefore, the results of this meta-analysis suggest that effective fertilizer management, slightly acidic soils enriched with AK, and lower N supply capacity are crucial for enhancing leaf quality while reducing nicotine content. This approach promises improved economic and environmental returns for the tobacco industry in China.

Abstract Background In the student-centered modern teaching environment of higher education, the affective domain was of great value to the overall development of medical students and the sustainable development of medical education. However, in the teaching practice of the medical specialty in Our country, there are still a lot of phenomena that pay attention to knowledge teaching but neglect affective education. Compared with affective domain, teachers tended to focus on the learning of specialized knowledge and skills. This paper investigated the attitudes and evaluations of teachers and students of medical school on affective education, analyzed the current situation and problems of teaching in the affective educauion of medical professions, and explored the path of combining the affective field and medical profession from the perspective of medical teachers. Methods A questionnaire survey was conducted among medical teachers and students across the country. Using the self-appointed teacher scale and the student scale to obtain their ratings of all dimensions of teaching in the affective field through a free online tool called "WenJuanXing". Descriptive statistics and multiple regression analysis methods were used to analyze the data to explore the obstacles in affective education and the factors affecting the outcome of affective education. Results A total of 523 medical teachers and 3268 medical students were surveyed, according to the results of questionnaire data analysis, there are differences in the needs of senior and lower grade students in various dimensions of affective education, and the current Chinese medical teachers carrying out affective education are facing unclear positioning and interpretation of affective education goals, lack of affective experience in teaching methods, lack of affective education evaluation norms, lack of continuity and progressivity of affective cultivation, and school organizational mechanisms need to be improved. Conclusions This paper had some suggestions aimed at the above problem. Firstly, It is necessary to strengthen the construction of the organizational mechanism of medical universities, provide them with guarantees and training according to the characteristics of teachers of different teaching ages, and comprehensively improve teachers' affective literacy from the inside out. Secondly, teachers should design clear progressive goals and content systems of affective education, constructing an evaluation system of affective education in the experiential teaching method.

Abstract Background As a skeletal malignancy, osteosarcoma has high incidence among primary malignant bone tumors. With increasing researches on molecules which mediate cancer progression, molecular mechanism has gradually become the pivot of osteosarcoma research and treatment. Aim Our study aimed at investigating the function of G3BP stress granule assembly factor 2 (G3BP2), which is an oncogene for breast cancer (BC) and prostate cancer but remains unknown in osteosarcoma cells. Methods Related gene expression was confirmed by RT-qPCR. Functional assays including immunofluorescence (IF), colony formation, transferase-mediated dUTP nick-end labeling (TUNEL) as well as transwell assays were utilized to test the cell biological process caused by the genes. Meanwhile, RNA pull-down assay, along with luciferase reporter and RNA immunoprecipitation (RIP) assays, was utilized to detect the interaction G3BP2, miR-124-3p and FGD5 antisense RNA 1 (FGD5-AS1) may exert on the regulation of osteosarcoma cells. Results G3BP2 was with high expression in osteosarcoma cells, and it aggravated the malignant cell behaviors in osteosarcoma. Additionally, miR-124-3p was verified to negatively regulate G3BP2 expression in osteosarcoma cells. Moreover, lncRNA FGD5-AS1 was predicted and testified to be the sponge of miR-124-3p and modulated G3BP2 expression positively. Subsequently, FGA5-AS1 accelerated osteosarcoma cell proliferation through up-regulating G3BP2. Furthermore, we identified EBF transcription factor 1 (EBF1) as the transcription factor for FGA5-AS1, and EBF1 served as a tumor facilitator in osteosarcoma cells. Conclusion EBF1 induced-FGA5-AS1 aggravated osteosarcoma cell malignancy by targeting miR-124-3p and G3BP2.

Snow profiles can provide reliable and detailed snow parameters for global change research, but it is still challenging to reconstruct large-scale spatiotemporally continuous snow profiles. Synthetic aperture radar (SAR) tomography (TomoSAR) has been proven to be a promising method for reconstructing the 3D structure of targets and has been used to reconstruct snow profiles from ground-based SAR. However, the experimental condition of ground-based SAR is ideal and the algorithms developed for ground-based SAR have some problems when applied to airborne and spaceborne sensors, such as large computational load and big angular diversity. In this paper, we first collected the Ku-band tomographic SAR dataset in the snow season of Altay, China using the Unmanned Aerial Vehicle (UAV) platform. Then, we reconstructed the snow profile from the dataset using three spectral estimation-based focusing algorithms. According to the non-stationary characteristic of the UAV platform and the dense medium property of snow, phase calibration and medium correction were performed, respectively. Finally, we assessed the reconstruction accuracy of the snow profile and snow depth using the ground synchronous measurement data. The reconstruction accuracy of the three focusing algorithms from high to low is the MUSIC algorithm, the Capon algorithm, and the Beamforming algorithm. Among them, the average error of the MUSIC algorithm to reconstruct the snow profile is 1.13 cm, and the RMSE and MAE of the snow depth retrieval are 3.71 cm and 3.26 cm, respectively. Although TomoSAR is still widely unexplored for snow using air-based sensors, the results show that it has the ability to reconstruct snow profiles accurately, and the proposed method can provide reliable support for reconstructing the large-scale spatiotemporal continuous snow profiles from subsequent spaceborne SAR.

In fault diagnosis, broad learning systems (BLS) have been applied in recent years. However, the best fault diagnosis cannot be guaranteed by width node extension alone, so a stacked broad learning system (stacked BLS) was proposed. Most of the methods for choosing the number of depth layers used optimization algorithms that tend to increase computation time. In addition, the data under single feature selection are not sufficiently representative, and effective features are easily lost. To solve these problems, this article proposes an infrared fault diagnosis model for rolling bearings based on integration of principal component analysis and singular value decomposition (IPS) and the stacked BLS with self-selected depth model (SSDStacked-BLS). First, 72 second-order statistical features are extracted from the pre-processed infrared images of rolling bearings. Next, feature selection is performed using IPS. he IPS feature selection module consists of principal component analysis (PCA) and singular value decomposition (SVD). The feature selection is performed by PCA and SVD separately, which are then stitched together to form a new feature. This ensures a comprehensive coverage of infrared image features. Finally, the acquired features are input into SSDStacked-BLS. This model establishes a data storage group for the residual training characteristics of stacked BLS, adding one block at a time. The accuracy rate of each newly added block is output and saved to the data storage group. If the diagnostic rate fails to increase three consecutive times, the block stacking is stopped and the results are output. IPS-SSDStacked-BLS achieved an accuracy of 0.9667 in 0.1775 s. This is almost five times faster than stacked BLS optimized using the grid search method. Compared with the original BLS, its accuracy was 0.0445 higher and the time was approximated. Compared with IPS-SVM, IPS-RF, IPS-1DCNN and 2DCNN, IPS-SSDStacked-BLS was more advantageous in terms of accuracy and time consumption.

To address the challenge of optimizing the placement of actuators on an asymmetric spacecraft continuum system, this paper develops a rigid–flexible electromechanical coupling dynamic model that integrates the interactions among rigidity, flexibility, and electromechanical coupling effects. The model is constructed using ordinary differential equations and partial differential equations (ODE–PDEs) and considers the effects of the installation position and physical characteristics (mass and stiffness) of the piezoelectric (PZT) actuator on an asymmetric flexible spacecraft continuum system. The proposed model aims to accurately capture the complex interactions among the rigid body, flexible appendages, and PZT actuators. Based on the developed model, the installation location of the actuators is optimized using a genetic algorithm with a hybrid optimization criterion. In the numerical simulations, the proposed optimization algorithm is employed to determine the optimal installation position for the actuators. Then, the influence of the actuator’s physical characteristics and installation position on the dynamic properties of the spacecraft and the performance of the control system is investigated. The numerical simulation results demonstrate that the optimization algorithm can effectively identify the appropriate actuator installation location for the desired application. Utilizing the actuator with the optimized position allows for effective vibration suppression while consuming less energy.

Abstract Background To study the competency of general practitioners (GPs) in Shanghai, China on prevention and management of type 2 diabetes, also understand factors that may prohibit it. Methods A survey questionnaire with 25 questions was designed based on 2013 Chinese Type 2 Diabetes Prevention Guidelines and Chinese Type 2 Diabetes Prevention Guidelines (Grassroots Edition) and conducted among 789 GPs who work at 54 community healthcare centers (CHCs) within 16 districts at Shanghai, China. Excel 2016 and SPSS 24.0 were used for data analysis, and a difference of P

To overcome the problems of low machine learning fault diagnosis rate and long consumption time of deep learning in rolling bearing fault diagnosis, an RFR-GA-BLS model is proposed. The model is validated by infrared images of rolling bearings to find the most representative features, the most suitable parameters and the best diagnostic rate. Based on the pre-processed infrared thermal images of the faulty bearing, 72 second-order statistical features were obtained as information for fault diagnosis. RFR considered the robustness of the features, and new sequences were obtained. BLS was optimized by GA for fault diagnosis. New sequence features were added to the model sequentially, one at a time. After satisfying the model conditions, the most appropriate number of features was selected as the first 20. The search results for the number of feature nodes, the number of feature node windows and the number of enhancement nodes for the BLS were 24, 19 and 544, respectively, and the fault diagnosis rate of 98.8889% was achieved. According to a comparison with CFR-GA-BLS, BLS, PSO-BLS and Grdy-BLS, our proposed model is more advantageous in the search for the best performance. The fault diagnosis accuracy is higher compared to SVM and RF. The speed of our proposed model is 207 times faster than 1DCNN and 10,147 times faster than 2DCNN.

This study was conducted to evaluate the prebiotic effects of dietary xylooligosaccharide (XOS) supplementation on performance, nutrient digestibility, intestinal morphology, and gut microbiota in laying hens. In a 12-wk experiment, a total of 288 Hy-Line Brown layers at 50 wk of age were randomly assigned into 3 dietary treatments supplemented with XOS at 0, 200 or 400 mg/kg. Each treatment had 8 replicates with 12 birds each. Hens fed XOS diets showed a lower feed-to-egg ratio during wk 7 to 12 and a higher egg yolk color value in wk 12 compared with those fed the control diet (P

Traditional Synthetic Aperture Radar (SAR) imaging is the projection of a real three-dimensional scene onto a two-dimensional domain of azimuth and slant range, which results in the loss of the high-dimensional information. With the advancement of SAR system and its processing technology, tomographic SAR systems obtain multiple data along the height direction to construct the high-dimensional synthetic aperture, and use array signal processing methods to achieve high-resolution three-dimensional images. It can reconstruct the observation scene and extract vertical structure information of the ground target, which is very important for vegetation monitoring, snow and ice detecting, and urban modeling. This paper analyzed the key steps of three-dimensional imaging, such as image registration, flat-earth phase removal, phase compensation, and the three-dimensional focusing, as well as the current research status of each step based on the observation mechanism of tomographic SAR system. This paper particularly focuses on using tomographic SAR on the application of vegetation, glacier, snow, and urban information. The most relevant experimental results in the past two decades were introduced. Further, the application potential and existing problems related to the vegetation height with canopy structure, glacier thickness with internal structure, snow thickness with stratification, and urban three-dimensional reconstruction with deformation monitoring under different platforms are discussed. Finally, the prospects of TomoSAR in the primary applications field are presented.

Soil organic matter (SOM), as the greatest carbon storage in the terrestrial environment, is inextricably related to the global carbon cycle and global climate change. Accurate estimation and mapping of SOM content are crucial for guiding agricultural output and management, as well as controlling the climate issue. Traditional chemical analysis is unable to satisfy the dynamic estimation of SOM due to its low timeliness. Remote and proximal sensing have significant advantages in terms of ease of use, estimation accuracy, and geographical resolution. In this study, we developed a framework based on machine learning to estimate SOM with high accuracy and resolution using Fourier mid-infrared attenuation total reflectance spectroscopy (FTIR-ATR), Sentinel-2 images, and DEM derivatives. This framework’s performance was evaluated on a regional scale using 245 soil samples from northeast China. Results indicated that the calibration size could be shrunk to 50% while achieving a fair prediction performance for SOM content. The Lasso, partial least squares (PLS), support vector regression (SVR), and convolutional neural networks (CNN) performed well in predicting SOM from FTIR-ATR spectra, and the performance was enhanced further by using Sentinel-2 images and DEM derivates. The PLS, SVR, and CNN models created SOM maps with higher spatial resolution and variation than the Kriging approach. The PLS and SVR models provided enough variety and were more realistic in the local SOM map, making them usable at the field scale, and the suggested framework took a fresh look at high-resolution SOM mapping.

To improve the spatial density and quality of measurement points in multitemporal interferometric synthetic aperture radar, distributed scatterers (DSs) should be processed. An essential procedure in DS interferometry is phase estimation, which reconstructs a consistent phase series from all available interferograms. Influenced by the well-known suboptimality of coherence estimation, the performance of the state-of-the-art phase estimation algorithms is severely degraded. Previous research has addressed this problem by introducing the coherence bias correction technique. However, the precision of phase estimation is still insufficient because of the limited correction capabilities. In this paper, a modified phase estimation approach is proposed. Particularly, by incorporating the information on both interferometric coherence and the number of looks, a significant bias correction to each element of the coherence magnitude matrix is achieved. The bias-corrected coherence matrix is combined with advanced statistically homogeneous pixel selection and time series phase optimization algorithms to obtain the optimal phase series. Both the simulated and Sentinel-1 real data sets are used to demonstrate the superiority of this proposed approach over the traditional phase estimation algorithms. Specifically, the coherence bias can be corrected with considerable accuracy by the proposed scheme. The mean bias of coherence magnitude is reduced by more than 29%, and the standard deviation is reduced by more than 18% over the existing bias correction method. The proposed approach achieves higher accuracy than the current methods over the reconstructed phase series, including smoother interferometric phases and fewer outliers.

Bearings are essential components of rotating machinery used in mechanical systems, and fault diagnosis of bearings is of great significance to the operation and maintenance of mechanical equipment. Deep learning is a popular method for bearing fault diagnosis, which can effectively extract the in-depth information of fault signals, thus achieving high fault diagnosis accuracy. However, due to the complex deep structure of deep learning, most deep learning methods require more time and resources for bearing fault diagnosis. This paper proposes a bearing fault diagnosis method combining feature engineering and fuzzy broad learning. First, time domain, frequency domain, and time-frequency domain features are extracted from the bearing signals. Then the stability and robustness indexes of these features are evaluated to complete the feature engineering. The features obtained by feature engineering are used as the input of the fault diagnosis model, and three sets of experimental data validate the model. The experimental results show that the proposed method can achieve the bearing fault diagnosis accuracy of 96.43% on the experimental bench data, 100% on the Case Western Reserve University dataset, and 100% on the centrifugal pump bearing fault dataset, with a time of approximately 0.28 s. The results show that this method has the advantages of accuracy, rapidity, and stability of bearing fault diagnosis.

Deep learning-based fault diagnosis usually requires a rich supply of data, but fault samples are scarce in practice, posing a considerable challenge for existing diagnosis approaches to achieve highly accurate fault detection in real applications. This paper proposes an imbalanced fault diagnosis of rotatory machinery that combines time-frequency feature oversampling (TFFO) with a convolutional neural network (CNN). First, the sliding segmentation sampling method is employed to primarily increase the number of fault samples in the form of one-dimensional signals. Immediately after, the signals are converted into two-dimensional time-frequency feature maps by continuous wavelet transform (CWT). Subsequently, the minority samples are expanded again using the synthetic minority oversampling technique (SMOTE) to realize TFFO. After such two-fold data expansion, a balanced data set is obtained and imported to an improved 2dCNN based on the LeNet-5 to implement fault diagnosis. In order to verify the proposed method, two experiments involving single and compound faults are conducted on locomotive wheel-set bearings and a gearbox, resulting in several datasets with different imbalanced degrees and various signal-to-noise ratios. The results demonstrate the advantages of the proposed method in terms of classification accuracy and stability as well as noise robustness in imbalanced fault diagnosis, and the fault classification accuracy is over 97%.

A series of modified montmorillonites were prepared by acid modification method prior to the organosilicon grafting. Aflatoxin B1, zearalenone and deoxynivalenol (DON) were selected to evaluated the mycotoxin adsorption performance of modified montmorrillonites. The experimental results suggest that under acidic conditions, the higher the charge density in the covalent orbital of the active group, the stronger the interaction between it and the DON molecule, and the better the corresponding modified montmorillonite adsorbs DON. Silicified montmorillonite with active groups of cyano group and methacryloyloxy group has the best adsorption performance for DON, and the adsorption degree are 51.6% and 39.0%, respectively. However, under alkaline terms, due to the hydrolysis of active groups, the adsorption degree of montmorillonite on DON dropped steeply.

Fault diagnosis of rolling bearings is significant for mechanical equipment operation and maintenance. Presently, the deep convolutional neural network (CNN) is increasingly used for fault diagnosis of rolling bearings, but CNN has challenges with incomplete training and lengthy training times. This paper proposes a residual network combined with the transfer learning (ResNet-TL) based diagnosis method for rolling bearing, which can preprocess the one-dimensional data of vibration signals into image data. Then, the transfer learning theory in parameter transfer is applied to the training of the network model, and the ResNet34 network is pre-trained and re-trained; the image data are selected to be the inputs of the fault diagnosis model. The experimental validation of the rolling bearing fault dataset collected from the practical bench and Case Western Reserve University shows the superiority of the ResNet34-TL model compared with other classification models.

Abstract Background An effective and expeditious approach to assess plant nitrogen status is urgently needed in rice production and management as the conventional chemical methods are laborious and time-consuming. Results Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS) was used to record the spectra of rice leaves for the effective diagnosis of nitrogen nutrition status. The band in the wavenumber range of 1680 to 1630 cm−1 was associated with amide I and that from 1570 to 1510 cm−1 with amide II. We attempted to use this information to characterize the nitrogen status in rice plants at different growth stages. The ratio of photoacoustic intensity of amide II to amide I was measured and applied as nitrogen status index, and considering the yields, the ratio showed a positive linear correlation (R2 = 0.9) with the total nitrogen of rice leaves. The ratio at the tillering and full panicle stages were more suitable for diagnosis, a ratio of 0.4–0.55 indicated an adequate nitrogen status, ratios lower than 0.4 indicated a poor nitrogen status; whereas ratios greater than 0.55 indicated excessive nitrogen supply. Conclusion Our study provides an effective and rapid strategy for nitrogen-supply assessment in rice based on FTIR-PAS, which can guide rational fertilization in rice production.

The start date of vegetation growing season (SOS) is generally considered as an essential indicator to reflect vegetation growth condition. To date, relatively little research has explored the combined effects of temperature and precipitation on the responses of forest spring phenology to snow cover in detail. To investigate this, we applied the developed plant phenology index (PPI) derived from Moderate Resolution Image Spectroradiometer (MODIS) to estimate SOS over Northeast China from 2004 to 2018, and explored the relationship between SOS and climate variables, such as temperature, precipitation and snow cover. Our results indicated that winter snow cover dynamics had a significant effect on the forest spring growth in the following year. SOS showed a negative correlation with snow cover duration (SCD) and the ending date of snow cover (SCED), whereas had a positive correlation with the onset date of snow cover (SCOD). It implied that a longer SCD, later SCED and earlier SCOD would promote forest growth. Furthermore, we first revealed that SOS was more closely associated with the preseason temperature than winter temperature. Most regions exhibited a significant positive correlation with increasing preseason temperature, but the winter temperature showed an opposite pattern except for the cool-temperate needleleaf forest region. Meanwhile, SOS had a negative relationship with precipitation, especially for preseason precipitation. Furthermore, with increasing of temperature and precipitation in winter, the responses of SOS to snow cover phenology in temperate needleleaf and broadleaf mixed-forest region are contrary to that in the other three regions. During the period from SCED to SOS, the responses of SOS to snow cover phenology varied among different vegetation zones and the gradients of temperature and precipitation.

Benefitting from the special structure of the leaf cuticle layer, plants have natural hydrophobicity and anti-fouling abilities. Inspired by the leaf surface structure, a biomimetic modification strategy was raised to improve the surface hydrophobicity of polyacrylate coating for controlled release fertilizer. Double-layer (polyacrylate and carnauba wax) coated fertilizer was obtained after biomimetic modification. The quality of controlled release fertilizer modified with the carnauba wax was greatly enhanced, and the coating material was effectively saved. The surface appearance of polyacrylate-coated fertilizer was improved for the surface blemish was repaired by the loaded carnauba wax. The characterizations by Fourier transform infrared spectroscopy indicated that the hydrogen bonds were formed between the water-based polyacrylate membrane and the carnauba wax layers. By optimizing the content of polyacrylate and carnauba wax, the release duration of the fertilizer was effectively prolonged, which was improved from 1 month to more than 2 months after the biomimetic modification. Therefore, biological wax as an environmentally-friendly natural material that has showed a broad potential in the application of coated controlled release fertilizer.

Crystal nucleation determining the formation and assembly pathway of first organic materials is the central science of various scientific disciplines such as chemical, geochemical, biological, and synthetic materials. However, our current understanding of the molecular mechanisms of nucleation remains limited. Over the past decades, the advancements of new experimental and computational techniques have renewed numerous interests in detailed molecular mechanisms of crystal nucleation, especially structure evolution and solution chemistry. These efforts bifurcate into two categories: (modified) classical nucleation theory (CNT) and non-classical nucleation mechanisms. In this review, we briefly introduce the two nucleation mechanisms and summarize current molecular understandings of crystal nucleation that are specifically applied in polymorphic crystallization systems of small organic molecules. Many important aspects of crystal nucleation including molecular association, solvation, aromatic interactions, and hierarchy in intermolecular interactions were examined and discussed for a series of organic molecular systems. The new understandings relating to molecular self-assembly in nucleating systems have suggested more complex multiple nucleation pathways that are associated with the formation and evolution of molecular aggregates in solution.

To extract the time-series characteristics of the original bearing signals and predict the remaining useful life (RUL) more effectively, a parallel multichannel recurrent convolutional neural network (PMCRCNN) is proposed for the prediction of RUL. Firstly, the time domain, frequency domain, and time-frequency domain features are extracted from the original signal. Then, the PMCRCNN model is constructed. The front of the model is the parallel multichannel convolution unit to learn and integrate the global and local features from the time-series data. The back of the model is the recurrent convolution layer to model the temporal dependence relationship under different degradation features. Normalized life values are used as labels to train the prediction model. Finally, the RUL was predicted by the trained neural network. The proposed method is verified by full life tests of bearing. The comparison with the existing prognostics approaches of convolutional neural network (CNN) and the recurrent convolutional neural network (RCNN) models proves that the proposed method (PMCRCNN) is effective and superior in improving the accuracy of RUL prediction.

Fault diagnosis of rolling bearings is not a trivial task because fault-induced periodic transient impulses are always submerged in environmental noise as well as large accidental impulses and attenuated by transmission path. In most hybrid diagnostic methods available for rolling bearings, the problems lie in twofolds. First, most optimization indices used in the individual signal processing stage do not take the periodical characteristic of fault transient impulses into consideration. Second, the individual stages make use of different optimization indices resulting in inconsistent optimization directions and possibly an unsatisfied diagnosis. To solve these problems, a multistage fault feature extraction method of consistent optimization for rolling bearings based on correlated kurtosis (CK) is proposed where maximum correlated kurtosis deconvolution (MCKD) is employed to attenuate the influence of transmission path followed by tunable Q factor wavelet transform (TQWT) to further enhance fault features by decomposing the preprocessed signals into multiple subbands under different Q values. The major contribution of the proposed approach is to consistently use CK as an optimization index of both MCKD and TQWT. The subband signal with the maximum CK which is an index being able to measure the periodical transient impulses in vibration signal yields an envelope spectrum, from which fault diagnosis is implemented. Simulated and experimental signals verified the effectiveness and advantages of the proposed method.

Abstract Development of controlled-release urea (CRU) has attracted research attention because of food scarcity problems and environmental concerns. To slow down the nutrient release of CRU coated with waterborne polyacrylate, conventional emulsion polymerization (CEP), conventional emulsion polymerization containing hexadecane (CEP + HD), and miniemulsion polymerization (MP) were carried out to discern the influence of polymerization technique and hexadecane on the properties of emulsions, films, and on the resultant nutrient release profiles of controlled-release urea. The addition of hexadecane improved water resistance, decreased the glass-transition temperature, and slowed down the nutrient release. CEP + HD was superior to MP in retarding nutrient release since the majority of HD was distributed in the exteriors of the particles of the former and interiors of the particles of the latter. Exterior HD improved water resistance more effectively, while interior HD reduced glass-transition temperature more significantly. Overall, our findings showed that incorporation of HD into polyacrylate emulsion produces excellent coatings that delay the release of urea. It has great potential application in controlled-release fertilizers coated with waterborne polymers.

The seasonal dynamic changes of Tibetan glaciers have seen little prior investigation, despite the increase in geodetic studies of multi-year changes. This study compares seasonal glacier dynamics (“cold” and “warm” seasons) in the ablation zone of Parlung No. 4 Glacier, a temperate glacier in the monsoon-influenced southeastern Tibetan Plateau, by using repeat unpiloted aerial vehicle (UAV) surveys combined with Structure-from-Motion (SfM) photogrammetry and ground stake measurements. Our results showed that the surveyed ablation zone had a mean change of −2.7 m of ice surface elevation during the period of September 2018 to October 2019 but is characterized by significant seasonal cyclic variations with ice surface elevation lifting (+2.0 m) in the cold season (September 2018 to June 2019) but lowering (−4.7 m) in the warm season (June 2019 to October 2019). Over an annual timescale, surface lowering was greatly suppressed by the resupply of ice from the glacier’s accumulation area—the annual emergence velocity compensates for about 55% of surface ablation in our study area. Cold season emergence velocities (3.0 ± 1.2 m) were ~5-times larger than those observed in the warm season (0.6 ± 1.0 m). Distinct spring precipitation patterns may contribute to these distinct seasonal signals. Such seasonal dynamic conditions are possibly critical for different glacier responses to climate change in this region of the Tibetan Plateau, and perhaps further afield.

Many surge-type glaciers are present on the St. Elias Mountains, but a detailed study on the surge behavior of the glaciers is still missing. In this study, we used remote sensing data to reveal detailed glacier surge behavior, focusing on the recent surge at Walsh Glacier, which was reported to have surged once in the 1960s. Glacial velocities were derived using a cross-correlation algorithm, and changes in the medial moraines were interpreted based on Landsat images. The digital elevation model (DEM) difference method was applied to Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEMs to evaluate the surface elevation of the glacier. The results showed that the surge initiated near the conjunction of the eastern and northern branches, and then quickly spread downward. The surge period was almost three years, with an active phase of less than two years. The advancing speed of the surge front was much large than the maximum ice velocity of ≈14 m/d observed during the active phase. Summer speed-ups and a winter speed-up in ice velocity were observed from velocity data, with the speed-ups being more obvious during the active phase. Changes in the glacier velocity and the medial moraines suggested that the eastern branch was more affected by the surge. The DEM differencing results showed that the receiving zone thickened up to about 140 m, and the upstream reservoir zone became thinner. These surge behaviors, as characterized by remote sensing data, gave us more detailed insights into the surge dynamics of Walsh Glacier.

This paper classifies the blastability of fractured rock mass based on the solid coefficient, rock impedance, unit explosive consumption and mean fracture spacing. Specifically, an evaluation model was established according to the principle of clustering analysis, and applied to classify the rock mass blastability of an actual project. According to the field acoustic test, the average longitudinal wave velocity of the layered rock mass is 2,667m/s, and that of the intact rock mass is 3,346m/s. Through the evaluation of blastability, it is concluded that the layered rock mass has a high blastability while the intact rock mass has a medium blastability. The findings lay an important theoretical basis for blasting parameters optimization and field blasting test.

Accurate management of soil nutrients and fast and simultaneous acquisition of soil properties are crucial in the development of sustainable agriculture. However, the conventional methods of soil analysis are generally labor-intensive, environmentally unfriendly, as well as time- and cost-consuming. Laser-induced breakdown spectroscopy (LIBS) is a “superstar” technique that has yielded outstanding results in the elemental analysis of a wide range of materials. However, its application for analysis of farmland soil faces the challenges of matrix effects, lack of large-scale soil samples with distinct origin and nature, and problems with simultaneous determination of multiple soil properties. Therefore, LIBS technique, in combination with partial least squares regression (PLSR), was applied to simultaneously determinate soil pH, cation exchange capacity (CEC), soil organic matter (SOM), total nitrogen (TN), total phosphorus (TP), total potassium (TK), available phosphorus (AP), and available potassium (AK) in 200 soils from different farmlands in China. The prediction performances of full spectra and characteristic lines were evaluated and compared. Based on full spectra, the estimates of pH, CEC, SOM, TN, and TK achieved excellent prediction abilities with the residual prediction deviation (RPDV) values > 2.0 and the estimate of TP featured good performance with RPDV value of 1.993. However, using characteristic lines only improved the predicted accuracy of SOM, but reduced the prediction accuracies of TN, TP, and TK. In addition, soil AP and AK were predicted poorly with RPDV values of < 1.4 based on both full spectra and characteristic lines. The weak correlations between conventionally analyzed soil AP and AK and soil LIBS spectra are responsible for the poor prediction abilities of AP and AK contents. Findings from this study demonstrated that the LIBS technique combined with multivariate methods is a promising alternative for fast and simultaneous detection of some properties (i.e., pH and CEC) and nutrient contents (i.e., SOM, TN, TP, and TK) in farmland soils because of the extraordinary prediction performances achieved for these attributes.

Rolling bearings are the most important parts in rotating machinery, and one of the most vulnerable parts to failure. The rolling bearing is a cyclic symmetrical structure that is stable under normal operating conditions. However, when the rolling bearing fails, its symmetry is destroyed, resulting in unstable performance and causing major accidents. If the performance of rolling bearings can be monitored and evaluated in real time, maintenance strategies can be implemented promptly. In this paper, by using wavelet packet energy entropy (WPEE), the early fault-free features of bearing and the failure samples of similar bearings are decomposed firstly, and the energy value is extracted as the original feature, simultaneously. Secondly, a radial basis function (RBF) neural network model is established by using early fault-free features and similar bearing failure characteristics. The bearing full-life data characteristics of the extracted features are added into the RBF model in an iterative manner to obtain performance degradation Indicator. Boxplot was introduced as an adaptive threshold method to determine the failure threshold. Finally, the results are verified by empirical mode decomposition and Hilbert envelope demodulation. A bearing accelerated life experiment is performed to validate the feasibility and validity of the proposed method. The experimental results show that the method can diagnose early fault points in time and evaluate the degree of bearing degradation, which is of great significance for industrial practical applications.

The behavior of a metal-organic framework (MOF) compound synthesized in hydrothermal reaction conditions and rich in N, P, and Fe nutrients was explored in the field. The attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy and laser induced breakdown spectroscopy (LIBS) characterization results showed that the chemical structures changed during the degradation process in crop field soil. The scanning electron microscope images showed that the micro-rod of the MOF peeled off and degraded in layers. During the growth period of wheat, the MOF degraded by 50.9%, with the degradation rate being closely related to soil temperature. It was also found that the degradation rate increased with soil temperature. Moreover, the nutrient concentration of the soil indicated that the MOF had stable nutrients release efficiencies and could provide a continuous supply of nutrients throughout the wheat growth period, which showed a great alternative for MOF as a fertilizer both benefiting agricultural production and environmental protection.

This 2-year field experiment investigated the effects of full straw incorporation on soil fertility and crop yield in a rice-wheat (Oryza sativa L.–Triticum aestivum L.) rotation on sandy, loamy soil. Two treatments were tested: (i) straw removal (CK) and (ii) straw incorporation (STR). The STR significantly increased the wheat yield by an average of 58% compared with CK; however, no significant difference was found in the rice yield. Soil available nitrogen, phosphorus, and potassium in the 0–20 cm soil layer increased by more than 15% with STR compared to CK. The soil cation exchange capacity and organic carbon in the 0–20 cm soil layer increased by 8% and 22%, for STR compared to CK, respectively. Straw incorporation significantly elevated the soil saturated water content but decreased the soil bulk density compared with CK. Soil aggregates >2 mm were significantly increased after straw return. STR also notably increased the soil urease, invertase, and catalase activities in the 0–15 cm soil layer by 11.4%, 41.0%, and 12.9%, respectively, and the soil microbial carbon and nitrogen contents in the 0–20 cm soil layer by 59% and 54%. Therefore, full straw incorporation could significantly improve soil fertility and maintain crop yields for the study area.

Recently, polyacrylates (PA) have been applied in coated controlled-release fertilizer (CRF), but the impacts of the soil on the degradation of PA have not been evaluated. In this study, an outdoor agriculture soil buried test was carried out for 12 months to investigate the degradation of PA films. The residual degraded films were taken regularly from the soil and analyzed by SEM, Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS) and laser-induced breakdown spectroscopy (LIBS). The concentration of C⁻H and C=O molecular groups of PA were decreased, and the element concentrations of C, O, K, Si of PA were increased under the degradation process. The surface of PA became rough and the degradation of PA occurred on the surface layer. Principal component analysis (PCA) showed that soil invaded PA. The results indicated that PA were environmentally friendly when applied to CRF. FTIR-PAS and LIBS were advanced in the in-situ surface analysis of the degradation process of the polymer.

High N loss and low N use efficiency (NUE), caused by high N fertilizer inputs and inappropriate fertilization patterns, have become important issues in the rice (Oryza sativa L.) growing regions of southern China. Changing current farmer fertilizer practice (FFP, 225 kg ha-1 N as three applications, 40% as basal fertilizer, 30% as tillering fertilizer and 30% as jointing fertilizer) to one-time root-zone fertilization (RZF, 225 kg ha-1 N applied once into 10 cm deep holes positioned 5 cm from the rice root as basal fertilizer) will address this problem. A two-year field experiment covering two rice growing regions was conducted to investigate the effect of urea one-time RZF on rice growth, nutrient uptake, and NUE. The highest NH4+-N content for RZF at fertilizer point at 30 d and 60 d after fertilization were 861.8 and 369.9 mg kg-1 higher than FFP, respectively. Rice yield and total N accumulation of RZF increased by 4.3-44.9% and 12.7-111.2% compared to FFP, respectively. RZF reduced fertilizer-N loss by 56.3-81.9% compared to FFP. The NUEs following RZF (mean of 65.8% for the difference method and 43.7% for the labelled method) were significantly higher than FFP (mean of 35.7% for the difference method and 14.4% for the labelled method). In conclusion, RZF maintained substantial levels of fertilizer-N in the root-zone, which led to enhanced rice biomass and N uptake during the early growth stages, increased fertilizer-N residual levels and reduced fertilizer-N loss at harvest. RZF produced a higher yield increment and showed an increased capacity to resist environmental threats than FFP in sandy soils. Therefore, adopting suitable fertilizer patterns plays a key role in enhancing agricultural benefits.

Bearing performance degradation assessment is of great significance for proactive maintenance and near-zero downtime. For this purpose, a novel assessment method is proposed based on lifting wavelet packet symbolic entropy (LWPSE) and support vector data description (SVDD). LWPSE is presented for feature extraction by jointing use of lifting wavelet packet transform and symbolic entropy. Firstly, the LWPSEs of bearing signals from normal bearing condition are extracted to train an SVDD model by fitting a tight hypersphere around normal samples. Then, the relative distance from the LWPSEs of testing signals to the hypersphere boundary is calculated as a quantitative index for bearing performance degradation assessment. The feasibility and efficiency of the proposed method were validated by the life-cycle data obtained from NASA’s prognostics data repository and the comparison with Hidden Markov Model (HMM). Finally, the assessment results were verified by the envelope spectrum analysis method based on empirical mode decomposition and Hilbert envelope demodulation.

A field micro-plot experiment using nitrogen isotope (15N) labeling was conducted to determine the effects of placement methods (broadcast and band) and N rates (60, 150 and 240 kg ha-1) on the fate of urea-15N in the wheat-soil system in Guangde County of Anhui Province, China. N fertilizer applied in bands increased grain yield by 15% compared with broadcast application. The N fertilizer application rate had a significant effect on grain yield, straw yield and aboveground biomass, as well as on N uptake and N concentration of wheat. The recovery of urea-15N was a little higher for broadcast (34.0-39.0%) than for band treatment (31.2-38.2%). Most of the soil residual N was retained in the 0-20 cm soil layer. At the N rates of 60 and 240 kg ha-1, the residual 15N was higher for band (34.4 and 108.7 kg ha-1, respectively) than for broadcast application (29.6 and 88.4 kg ha-1, respectively). Compared with broadcast treatment, banded placement of N fertilizer decreased the N loss in the wheat-soil system. Band application one time is an alternative N management practice for winter wheat in this region.

Leaf area index (LAI) is a critical vegetation structural parameter in biogeochemical and biophysical ecosystems. High-resolution LAI products play an essential role in regional studies. Empirical methods, which normally use field measurements as their training samples and have been identified as the most commonly used approaches to retrieve structural parameters of vegetation from high-resolution remote-sensing data, are limited by the quality of training samples. Few efforts have been made to generate training samples from existing global LAI products. In this study, two methods (a homogeneous and pure pixel filter method (method A) and a pixel unmixing method (method B)) were developed to extract training samples from moderate-resolution imaging spectroradiometer (MODIS) surface reflectance and LAI products, and a support vector regression (SVR) algorithm trained by the samples was used to retrieve the high-resolution LAI from Landsat data at Baoding, situated in the Hebei Province in China, and Des Moines, situated in Iowa, United States. For the homogeneous and pure pixel filter method, two different sets of training samples were designed. One was composed of upscaled Landsat reflectance at the 500-m resolution and MODIS LAI products (dataset A1); the other was composed of MODIS reflectance and LAI products (dataset A2). With them, two inversion models were developed using SVR. For the pixel unmixing method, the training samples (dataset B) were extracted from unmixed MODIS surface reflectance and LAI products at 30-m resolution, and the third inversion model was obtained with them. LAI inversion results showed that good agreement with field measurements was achieved using these three inversion models. The R2 (coefficient of determination) value and the root mean square error (RMSE) value were computed to assess the results. For all tests, the R2 values are higher than 0.74 and RMSE values are less than 0.73. These tests showed that three models for the two methods combined with MODIS products can retrieve 30-m resolution LAI from Landsat data. The results of the pixel unmixing method was slightly better than that of the homogeneous and pure pixel filter method.

Maghemite (γ-Fe2O3) nanopowders were synthesized under aeration (oxidizing) conditions by aqueous synthesis in this study. The microstructures of the prepared powders were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), and BET-BJH. The XRD analysis and the chemical experiments showed that well-crystallized γ-Fe2O3 nanoparticles were successfully obtained with a mean particle size of approximately 17 nm. The prepared γ-Fe2O3 was spherical with a BET surface area of 14.357 m2/g and a total pore volume of 0.050 cm3/g. Varying the reaction conditions, such as pH, temperature, and reaction time, we obtained crystallized γ-Fe2O3 powders with different crystallization extent and different particle sizes. When the pH of the reaction suspension was increased, the reaction time was prolonged, and the reaction temperature was increased, the γ-Fe2O3 powders underwent superior crystallization and had larger particle sizes. All the obtained γ-Fe2O3 powders had significant photocatalytic activities under both UV and visible light irradiation for Orange I degradation, and the powders with better crystallization and larger particle size had relatively lower activities for Orange I photocatalytic degradation. The one-step aqueous synthesis method presented in this paper may provide an advantageous pathway to synthesize large quantities of this important iron oxide.

Pseudomonas syringae pv. tomato DC3000 is a model pathogen infecting tomato and Arabidopsis plants. Genes encoding the type III secretion system and substrate proteins (collectively called TTSS genes) of this bacterium are induced in plants and in minimal medium (MM). The induction of TTSS genes is mediated by HrpL, an alternative sigma factor recognizing the hrp box in the promoter of TTSS genes. The transcription of hrpL is activated by HrpR and HrpS, two homologous DNA-binding proteins encoded by the hrpRS operon. Microarray analysis was conducted to evaluate the DC3000 genes regulated by hrpL and hrpRS in MM. The analysis identified a number of novel hrpL-activated genes with a putative TTSS-independent function. Genes regulated by hrpL were mostly regulated by hrpRS in the same manner, but a large number of genes regulated by hrpRS were hrpL-independent, indicating that hrpL represents one branch of the regulatory pathways downstream of hrpRS. The induction of the TTSS genes was associated with downregulation of the housekeeping genes, indicating that the activation of the TTSS has a cost on the basic cellular activities. The novel genes and pathways identified by the microarray provide new insight into the bacterial functions coordinating with the TTSS.

Type III secretion systes are highly conserved among gram-negative plant and animal pathogenic bacteria. Through the type III secretion system, bacteria inject a number of virulence proteins into the host cells. Analysis of the whole genome sequence of Pseudomonas syringae pv. tomato DC3000 strain identified a locus, named HopPtoF, that is homologous to the avirulence gene locus avrPphF in P. syringae pv. phaseolicola. The HopPtoF locus harbors two genes, ShcFPto and HopFPto, that are preceded by a single hrp box promoter. We present evidence here to show that ShcFPto and HopFPto encode a type III chaperone and a cognate effector, respectively. ShcFPto interacts with and stabilizes the HopFPto protein in the bacterial cell. Translation of HopFPto starts at a rare initiation codon ATA that limits the synthesis of the HopFPto protein to a low level in bacterial cells.

The tomato Pto kinase confers resistance to bacterial speck disease caused by strains of Pseudomonas syringae pv. tomato that express the avirulence gene avrPto. Pto contains a putative myristylation site at its amino terminus that was hypothesized to play a role in localizing Pto in the plant cell. Site-directed mutagenesis was used to change the invariant glycine residue in the myristylation motif to an alanine. Transgenes encoding the mutant Pto(G2A) and wild-type Pto were placed behind the cauliflower mosaic virus 35S promoter and transformed into tomato plants that are susceptible to bacterial speck disease. Both the mutant and wild-type forms of Pto conferred resistance to a strain of P. syringae pv. tomato expressing avrPto. These results indicate that the myristylation motif of Pto is not required for bacterial speck disease resistance.