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The use of cultivar mixtures is increasingly practical in wheat stripe rust management. Field experiments with wheat cultivar mixtures were conducted to determine their effects on temporal and spatial patterns of stripe rust epidemics in three regions. In the Beijing and Gangu fields, where the epidemics were caused by artificial inoculation, disease incidence and the area under the disease progress curve (AUDPC) of the cultivar mixtures were significantly lower (P

This paper presents a new formula for calculating the hardness of metallic crystals, resulted from the research on the critical grain size with stable dislocations. The formula is H = 6 kG/[pi(1 - nu)e(eta)], where H is the hardness, k the coefficient, G the shear modulus, nu the Poisson's ratio, eta a function of the radius of an atom (r) and the electron density at the atom interface (n). The formula will not only be used to testify the critical grain size with stable dislocations, but also play an important role in the understanding of mechanical properties of nanocrystalline metals.

Essential oil of Phellodendron chinense fruit was obtained by hydrodistillation and analyzed by GC-FID and GC-MS. Myrcene (70.7%), β-elemene (4.7%) and p-cymene (4.4%) were the major compounds of the 20 identified components. The antifungal activity of the oil, evaluated against fifteen plant pathogenic fungi, was determined by mycelial radial growth inhibition method. The values of the mean inhibitory concentration (IC50) against the tested fungi were at a range of 0.24–1.46 mg/mL. Furthermore, IC50 of the oil against spore germination of Magnaporthe oryzae was determined to be 2.60 mg/mL. These results indicated that P. chinense oil could be a potential source of antimicrobial ingredients for plant disease control.

Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici, is one of the most destructive wheat diseases in China. Yunnan Province, located in south-western China, possesses unique features of geography, climate, wheat growth and stripe rust epidemics, different from main epidemic regions in China. The isolates of this pathogen were collected from nine counties in Yunnan Province during February to May of 2008. Used as a comparison, isolates were also collected from five counties of Gansu Province, the province important in inter-regional stripe rust epidemics in China. Amplified fragment length polymorphism (AFLP) method was applied to study the population genetics of the pathogen among different populations in these two provinces. Forty one AFLP genotypes were obtained from 150 isolates and the genotype qj3 showed the highest frequency in Yunnan Province. While 22 genotypes were detected from 40 isolates, no genotype showing as predominant was identified in Gansu Province. Genotypic diversity in Gansu Province was higher than that in Yunnan Province. A free recombination signature was detected in Gansu Province but not in Yunnan Province. We concluded that the population of P. striiformis in Yunnan Province can be considered as a clonal population.

Polyurethane foams were used as carriers to fabricate SiC-based ceramic foams. The compressive strength and thermal shock-resistance of SiC-based ceramic foams were investigated with concentration of Fe 2 O 3 . Using scanning electron microscopy and energy dispersive spectroscopy, the microstructure and composition of SiC-based ceramic foams in micro-scale were observed. It is found that when the concentration of Fe 2 O 3 increases, the number of cyclic thermal shock of the ceramic foams decreases obviously, and its compressive strength at room temperature increases rapidly. The concentration of Fe 2 O 3 pays an important rule for the sinter behavior of the SiC-based ceramic foams. Superfluous Fe 2 O 3 causes the formation of excessive glass phase in the ceramic foams.

Gray cast irons were inoculated with FeSi75+RE and FeSi75+Sr inoculants. The changes of apex angle of the drills before and after being used were used to evaluate machinability of gray cast irons. Effect of FeSi75+RE and FeSi75+Sr inoculants on mechanical properties, machinability and sensibility of gray cast iron used in cylinder block were investigated. Experimental results showed that gray cast iron treated with 60%FeSi75+40% RE inoculants exhibited tensile strength consistently at about 295 MPa along with good hardness and improved metallurgical quality. While gray cast iron inoculated with 20%FeSi75+80% Sr inoculants exhibited the best machinability, the lowest cross-section sensibility and the least microhardness difference. The tool flank wear of the drill increased correspondingly with the increase of the microhardness difference of the matrix, indicating the great effect of homogeneity of the matrix on the machinability of gray cast iron.

Stripe rust, caused byPuccinia striiformisf. sp.tritici(Pst), is one of the important wheat diseases worldwide. In this study, the spectral data were collected from wheat canopy during the latent period inoculated with three different concentrations of urediniospores and classification models based on discriminant partial least squares (DPLS) were built to differentiate leaves with and without infection of the stripe rust pathogen. The effects of different spectra features, wavebands, and the number of the samples used in modeling on the performances of the models were assessed. The results showed that, in the spectral region of 325–1075 nm, the model with the spectral feature of 2nd derivative of Pseudoabsorption index had better accuracy than others. The average accuracy rate was 97.28% for the training set and 92.98% for the testing set. In the waveband of 925–1075 nm, the model with the spectral feature of 1st derivative Pseudoabsorption index had better accuracy than other models, and the average accuracy rates were 98.27% and 94.33% for the training and testing sets, respectively. The results demonstrated that wheat stripe rust in latent period can be qualitatively identified based on the canopy spectral detection. Thus, the method can be used for early monitoring of infections of wheat stripe rust.

Stripe rust caused byPuccinia striiformisf. sp.tritici(Pst) is an important disease on wheat. In this study, quantitative determination of germinability ofPsturediospores was investigated by using near infrared reflectance spectroscopy (NIRS) combined with quantitative partial least squares (QPLS) and support vector regression (SVR). The near infrared spectra of the urediospore samples were acquired using FT-NIR MPA spectrometer and the germination rate of each sample was measured using traditional spore germination method. The best QPLS model was obtained with vector correction as the preprocessing method of the original spectra and 4000–12000 cm−1as the modeling spectral region while the modeling ratio of the training set to the testing set was 4 : 1. The best SVR model was built when vector normalization was used as the preprocessing method, the modeling ratio was 5 : 1 and the modeling spectral region was 8000–11000 cm−1. The results showed that the effect of the best model built using QPLS or SVR was satisfactory. This indicated that quantitative determination of germinability ofPsturediospores using near infrared spectroscopy technology is feasible. A new method based on NIRS was provided for rapid, automatic, and nondestructive determination of germinability ofPsturediospores.

Stripe rust and leaf rust with similar symptoms are two important wheat diseases. In this study, to investigate a method to identify and assess the two diseases, the canopy hyperspectral data of healthy wheat, wheat in incubation period, and wheat in diseased period of the diseases were collected, respectively. After data preprocessing, three support vector machine (SVM) models for disease identification and six support vector regression (SVR) models for disease index (DI) inversion were built. The results showed that the SVM model based on wavelet packet decomposition coefficients with the overall identification accuracy of the training set equal to 99.67% and that of the testing set equal to 82.00% was better than the other two models. To improve the identification accuracy, it was suggested that a combination model could be constructed with one SVM model and two models built usingK-nearest neighbors (KNN) method. Using the DI inversion SVR models, the satisfactory results were obtained for the two diseases. The results demonstrated that identification and DI inversion of stripe rust and leaf rust can be implemented based on hyperspectral data at the canopy level.

Wheat stripe rust caused by Puccinia striiformis f.sp. tritici (Pst) is one of the most important diseases of wheat worldwide. Detection of latent infection in leaves is critical for estimating the initial inoculum potential of epidemics. A loop-mediated isothermal amplification (LAMP) assay was developed and evaluated for the detection of Pst DNA in spores and wheat seedlings with latent infections. LAMP assay could detect as low as 2 pg/μl template DNA and detect latent infections from leaves as early as 24 h after inoculation. This provides a rapid and accurate method of estimating latent infection levels.

Biotrophic fungi, such as Puccinia striiformis f. sp. tritici, because they cannot be cultured on nutrient media, to obtain adequate quantity of DNA for molecular genetic analysis, are usually propagated on living hosts, wheat plants in case of P. striiformis f. sp. tritici. The propagation process is time-, space- and labor-consuming and has been a bottleneck to molecular genetic analysis of this pathogen. In this study we evaluated multiple displacement amplification (MDA) of pathogen genomic DNA from urediospores as an alternative approach to traditional propagation of urediospores followed by DNA extraction. The quantities of pathogen genomic DNA in the products were further determined via real-time PCR with a pair of primers specific for the β-tubulin gene of P. striiformis f. sp. tritici. The amplified fragment length polymorphism (AFLP) fingerprints were also compared between the DNA products. The results demonstrated that adequate genomic DNA at fragment size larger than 23 Kb could be amplified from 20 to 30 urediospores via MDA method. The real-time PCR results suggested that although fresh urediospores collected from diseased leaves were the best, spores picked from diseased leaves stored for a prolonged period could also be used for amplification. AFLP fingerprints exhibited no significant differences between amplified DNA and DNA extracted with CTAB method, suggesting amplified DNA can represent the pathogen’s genomic DNA very well. Therefore, MDA could be used to obtain genomic DNA from small precious samples (dozens of spores) for molecular genetic analysis of wheat stripe rust pathogen, and other fungi that are difficult to propagate.

Behçet's disease (BD) is a multisystem vasculitis, and pulmonary involvement in BD can have complex clinical manifestations. We aimed to analyze the clinical and radiological features, as well as the outcomes, of BD patients with pulmonary manifestation.We retrospectively reviewed the medical records of 106 patients with BD diagnosed in our institute between January 2000 and January 2013. Patients with pulmonary vascular or parenchymal abnormalities on chest radiography, thorax computed tomography, magnetic resonance imaging or pulmonary scintigraphy were included in this study.Fifteen patients (14%) were identified to have pulmonary involvement. Pulmonary artery aneurysms (PAA) was observed in six patients, and all of them had concomitant thrombi, attenuation or occlusion of PAs, and five of them also had radiographic parenchymal changes. Three patients had solely pulmonary artery thrombus (PAT) without PAA. For the six patients with isolated radiographic parenchyma changes, pulmonary infiltration resolved with immunosuppressant therapy in four subjects; the lesion remained unchanged in one subject with radiographic interstitial changes, and acid-fast bacilli were found in the remaining subject. Patients with PAA or PAT had more frequency of hemoptysis and extra-pulmonary vascular lesions compared with isolated parenchymal involvement. Radiographic parenchyma changes are nonspecific, with ill-defined ground-glass opacity being the most common pulmonary radiographic parenchymal changes. Patients with isolated parenchymal changes had better prognosis than those with PAA or PAT.BD with pulmonary involvement can have a wide spectrum of abnormal clinical and radiographic manifestations, and multiple pulmonary lesions can exit in the same patient.

The scanning tunnelling microscope (STM) has been employed to fabricate grooves by extracting Si atoms out of the Si(111)-7×7 surface and deposit the Si atoms back onto the Si(111)-7×7 surface at room temperature. The deposited Si atoms can form a straight ridge under controlled conditions. The width of the ridge can be controlled better than 2 nm. Ridges can only be formed after the tip extracts enough atoms out of the Si(111)-7×7 surface. If the tip is clean, no atoms will be deposited from the tip under the depositing conditions. This suggests that the deposited atoms are silicon atoms extracted out of the Si(111)-7×7 surface. The deposition mechanism is discussed.

A web-based prediction system for wheat stripe rust was developed based on B/S (Browser/Server) mode in this study. Some existing prediction models of wheat stripe rust were collected, analyzed and then stored in SQL Server 2005 database according to certain rules. All these models could be called through this web-based system and used to predict wheat stripe rust. Meanwhile, Using multiple regression analysis principle, prediction regression model could be built based on the input historical data of wheat stripe rust through the network programming via this system, and significance tests of prediction factors could be conducted to obtain optimal prediction model and the built model could be stored into the model database for further prediction of this disease. Using WebGIS technologies, the prediction results of wheat stripe rust could be displayed in different colors in the web map according to the prediction values of disease prevalence. The web-based prediction system for wheat stripe rust developed in this study provided a convenient and fast way for the prediction of wheat stripe rust.

In order to realize accurately calculating and automatically grading of plant disease severity, a single-leaf disease severity automatic grading system based on image processing was developed by using MATLAB GUIDE platform. Using this system, the single-leaf disease severity could be automatically assessed and graded via image development technologies including segmentation processing technologies of plant disease images and related data mining technologies. Structural diagram of the system, algorithms used in the system and realization of the system functions were described. The problems in the current version of the system were discussed and further research on this subject was suggested. The usefulness and adaptability of the system was evaluated using the images of grape downy mildew caused by Plasmopara viticola. The results showed that the effectiveness of the system was favorable with high accuracy.

To achieve automatic diagnosis of plant diseases and improve the image recognition accuracy of plant diseases, two kinds of grape diseases (grape downy mildew and grape powdery mildew) and two kinds of wheat diseases (wheat stripe rust and wheat leaf rust) were selected as research objects, and the image recognition of the diseases was conducted based on image processing and pattern recognition. After image preprocessing including image compression, image cropping and image denoising, K_means clustering algorithm was used to segment the disease images, and then 21 color features, 4 shape features and 25 texture features were extracted from the images. Backpropagation (BP) networks were used as the classifiers to identify grape diseases and wheat diseases, respectively. The results showed that identification of the diseases could be effectively achieved using BP networks. While the dimensions of the feature data were not reduced by using principal component analysis (PCA), the optimal recognition results for grape diseases were obtained as the fitting accuracy and the prediction accuracy were both 100%, and that for wheat diseases were obtained as the fitting accuracy and the prediction accuracy were both 100%. While the dimensions of the feature data were reduced by using PCA, the optimal recognition result for grape diseases was obtained as the fitting accuracy was 100% and the prediction accuracy was 97.14%, and that for wheat diseases was obtained as the fitting accuracy and the prediction accuracy were both 100%.

It is important to implement detection and assessment of plant diseases based on remotely sensed data for disease monitoring and control. Hyperspectral data of healthy leaves, leaves in incubation period and leaves in diseased period of wheat stripe rust and wheat leaf rust were collected under in-field conditions using a black-paper-based measuring method developed in this study. After data preprocessing, the models to identify the diseases were built using distinguished partial least squares (DPLS) and support vector machine (SVM), and the disease severity inversion models of stripe rust and the disease severity inversion models of leaf rust were built using quantitative partial least squares (QPLS) and support vector regression (SVR). All the models were validated by using leave-one-out cross validation and external validation. The diseases could be discriminated using both distinguished partial least squares and support vector machine with the accuracies of more than 99%. For each wheat rust, disease severity levels were accurately retrieved using both the optimal QPLS models and the optimal SVR models with the coefficients of determination (R2) of more than 0.90 and the root mean square errors (RMSE) of less than 0.15. The results demonstrated that identification and severity evaluation of stripe rust and leaf rust at the leaf level could be implemented based on the hyperspectral data acquired using the developed method. A scientific basis was provided for implementing disease monitoring by using aerial and space remote sensing technologies.

Digital image recognition of plant diseases could reduce the dependence of agricultural production on the professional and technical personnel in plant protection field and is conducive to the development of plant protection informatization. In order to find out a method to realize image recognition of plant diseases, four kinds of neural networks including backpropagation (BP) networks, radial basis function (RBF) neural networks, generalized regression networks (GRNNs) and probabilistic neural networks (PNNs) were used to distinguish wheat stripe rust from wheat leaf rust and to distinguish grape downy mildew from grape powdery mildew based on color features, shape features and texture features extracted from the disease images. The results showed that identification and diagnosis of the plant diseases could be effectively achieved using BP networks, RBF neural networks, GRNNs and PNNs based on image processing. For the two kinds of wheat diseases, the best prediction accuracy was 100% with the fitting accuracy equal to 100% while BP networks, GRNNs or PNNs were used, and the best prediction accuracy was 97.50% with the fitting accuracy equal to 100% while RBF neural networks were used. For the two kinds of grape diseases, the best prediction accuracy was 100% with the fitting accuracy equal to 100% while BP networks, GRNNs or PNNs were used, and the best prediction accuracy was 94.29% with the fitting accuracy equal to 100% while RBF neural networks were used.

Plant disease identification based on image processing could quickly and accurately provide useful information for the prediction and control of plant diseases. In this study, 21 color features, 4 shape features and 25 texture features were extracted from the images of two kinds wheat diseases (wheat stripe rust and wheat leaf rust) and two kinds of grape diseases (grape downy mildew and grape powdery mildew), principal component analysis (PCA) was performed for reducing dimensions in feature data processing, and then neural networks including backpropagation (BP) networks, radial basis function (RBF) neural networks, generalized regression networks (GRNNs) and probabilistic neural networks (PNNs) were used as the classifiers to identify wheat diseases and grape diseases, respectively. The results showed that these neural networks could be used for image recognition of these diseases based on reducing dimensions using PCA and acceptable fitting accuracies and prediction accuracies could be obtained. For the two kinds of wheat diseases, the optimal recognition result was obtained when image recognition was conducted based on PCA and BP networks, and the fitting accuracy and the prediction accuracy were both 100%. For the two kinds of grape diseases, the optimal recognition results were obtained when GRNNs and PNNs were used as the classifiers after reducing the dimensions of feature data with PCA, and the prediction accuracies were 94.29% with the fitting accuracies equal to 100%.

Stripe rust caused byPuccinia striiformis f. sp.tritici, is a devastating wheat disease in the world. The prediction of this disease is very important to make control strategies. In order to figure out suitable prediction methods based on neural networks that could provide accurate prediction information with high stability, the predictions of wheat stripe rust by using backpropagation networks with different transfer functions, training functions and learning functions, radial basis networks, generalized regression networks (GRNNs) and probabilistic neural networks (PNNs) were conducted in this study. The results indicated that suitable backpropagation networks, radial basis networks and GRNNs could be used for the prediction of wheat stripe rust. Good fitting accuracy and prediction accuracy could be obtained by using backpropagation networks with trainlm, trainrp or trainbfg as training function. Radial basis networks had more power than backpropagation networks and GRNNs to predict wheat stripe rust. GRNNs were easier to be used than backpropagation networks. New methods based on neural networks were provided for the prediction of wheat stripe rust.

Many studies on remote sensing monitoring of plant diseases have been conducted. Remote sensing (RS) has played an important role in monitoring some kinds of plant diseases and making decisions for the management of the diseases. Progress on remote sensing monitoring of wheat stripe rust in China was summarized from four aspects including remote sensing monitoring stripe rust of single wheat leaves and monitoring this disease using ground, aerial and space remote sensing technologies. The phenomena of same object with different spectra and different objects with same spectrum, the lowest threshold of disease prevalence for remote sensing monitoring, the spectral information distilling technologies, and the methods to develop inversion models based on spectral information were also discussed. Moreover, the development trends of multi-pest remote sensing, space remote sensing and integrated utilization of RS, geographical information system (GIS) and global positioning system (GPS) in monitoring wheat stripe rust were prospected.

In order to realize automatic disease diagnosis and provide related information for disease prediction and control timely and accurately, the identification and diagnosis of grape downy mildew and grape powdery mildew was conducted based on image recognition technologies. The method based on K_means clustering algorithm was used to implement unsupervised segmentation of the disease images. Fifty shape, color and texture features were extracted from the images of the diseases. Support vector machine (SVM) classifier for the diseases was designed based on thirty-one effective selected features. The training recognition rates of these two kinds of grape diseases were both 100%, and the testing recognition rates of grape downy mildew and grape powdery mildew were 90% and 93.33%, respectively. The recognition results using the SVMs with different kernels indicated that the SVM with linear kernel was the most suitable for image recognition of the diseases. This study provided an effective way for rapid and accurate identification and diagnosis of plant diseases, and also provided a basis and reference for further development of automatic diagnosis system for plant diseases.

The assessment of myocardial viability has become an important aspect of the diagnostic and prognostic work-up of patients with coronary artery disease. Technetium-99m labelled sestamibi ((99m)Tc-MIBI) myocardial perfusion imaging may underestimate the viability of ischaemic myocardium. Technetium-99m labelled 4,9-diaza-3,3,10,10-tetramethyldodecan-2,11-dione dioxime ((99m)Tc-HL91) is a hypoxia-avid agent which can identify acutely ischaemic viable myocardium in a canine model using a standard gamma camera. The aim of this study was to evaluate uptake character of ischaemic viable myocardium and diagnostic performance of single-photon emission computed tomography (SPECT) imaging by (99m)Tc-HL91 and (99m)Tc-MIBI in detecting ischaemic viable myocardium in coronary heart disease.A total of 41 patients with coronary artery disease were recruited from March 2008 to May 2009. For detecting ischaemic viable myocardium, SPECT imaging by (99m)Tc-HL91 and (99m)Tc-MIBI were performed in all patients before coronary revascularization. Six patients with single ischaemic myocardial segment received a 2-day SEPCT/CT imaging protocol and the uptake of (99m)Tc-HL91 in ischaemic myocardium was quantitatively analysed. The remaining 35 patients received a 1-day (99m)Tc-HL91 and (99m)Tc-MIBI SPECT imaging protocol. Resting (99m)Tc-MIBI myocardial perfusion imaging in 3-18 months after revascularization was used as the standard methodology to evaluate the myocardial viability.In 41 patients, 66 ischaemic myocardial segments were proven to be viable and 12 to be necrotic by resting (99m)Tc-MIBI myocardial perfusion imaging after coronary revascularization. Furthermore, 60 viable segments with negative uptake of (99m)Tc-MIBI showed positive uptake of (99m)Tc-HL91. The remaining six viable segments and 12 necrotic segments showed both negative uptake of (99m)Tc-HL91 and (99m)Tc-MIBI. The sensitivity, specificity, accuracy, Younden Index, positive predictive value and negative predictive value for evaluating ischaemic viable myocardium were 90·9%, 100%, 92·3%, 90·9%, 100% and 66·7%, respectively. Ischaemic viable myocardium had the negative (99m)Tc-MIBI uptake and positive (99m)Tc-HL91 uptake, which demonstrated a mismatched uptake character. Quantitative analysis indicated the uptake of (99m)Tc-HL91 in viable myocardium was increasing in the first 1-3 h and remained stable at the 3-4 h after injection.Functional SPECT imaging with (99m)Tc-HL91 and (99m)Tc-MIBI can be used to detect the seriously ischaemic but viable myocardium with a mismatched uptake character. The uptake of (99m)Tc-HL91 in the viable myocardium reached a stable level at 3-4 h after injection.

Objectives The purpose is to identify the role of cardiovascular parameters of computed tomographic pulmonary angiography (CTPA) to assess pulmonary vascular resistance (PVR) in patients with chronic thromboembolic pulmonary hypertension (CTEPH). Background The assessment of PVR is of great importance in the management of patients with CTEPH. The role of CPTA in assessment of PVR of CTEPH remains to be explored. Methods Clinical and radiological data of 90 patients (55 men, age 17–84years) with CTEPH were retrospectively reviewed in this study. All patients received CTPA before right-heart catheterization. Cardiovascular parameters and Pulmonary Artery Obstruction Indices including Qanadli Index and Mastora Index were evaluated on CTPA. Hemodynamic PVR was calculated with the standard formula according to data from right-heart catheterization. The correlation of cardiovascular parameters of CTPA and PVR was analyzed. Results In Cardiovascular parameters, neither Qanadli Index(r=0.134, p=0.208) nor Mastora Index (r=0.149, p=0.90) did correlate with PVR. Cobb angle(r=0.613, p=0.000), the ratio of right to left ventricular area(r=0.422, p=0.000)and the ratio of right to left ventricular transverse diameter (r=0.410, p=0.000) respectively correlated with PVR. By receiver operating characteristic curve analysis, a Cobb angle cutoff value of 67.55° had a sensitivity of 72.5% and a specificity of 84.0% to determine PVR ≥1000 (dyn.sec/cm 5 ) and its Area Under Curve is (0.800±0.048). By stepwise linear regression analysis, Cobb angle was only one variable (R=0.601) shown to be independently associated with PVR, leading to the following equation: PVR=25.796×Cobb angle−585.935(F=37.929, p=0.000). Conclusion The analysis of CTPA-derived cardiovascular parameters, especially the Cobb angle, is a reliable tool for estimating PVR in patients with CTEPH, but Pulmonary Artery Obstruction Indices do not correlate with PVR.

Purpose The aim was to investigate the role of computed tomographic pulmonary angiography (CTPA) in the assessment of severity and right ventricular function in chronic thromboembolic pulmonary hypertension (CTEPH). Materials and methods Clinical and radiological data of 56 patients with CTEPH January 2006–October 2009 were retrospectively reviewed in the present study. All patients received CTPA with a 64-row CT using the retrospective ECG-Gated mode before digital subtraction pulmonary angiography and right-heart catheterization. CTPA findings including Right Ventricular diameter (RVd) and left ventricular diameter (LVd) were measured at the end diastole. CT Pulmonary Artery Obstruction Indexes including Qanadli Index and Mastora Index were used in the assessment of severity of pulmonary arterial obstruction. Hemodynamic parameters and pulmonary hypertension classification were evaluated by right-heart catheterization in all patients. Right ventricular function was measured with echocardiography in 49 patients. Results Qanadli Index and Mastora Index respectively were (37.93±14.74)% and (30.92±16.91)%, which showed a significant difference ( Z =−5.983, P =0.000) and a good correlation ( r =0.881, P =0.000). Neither Qanadli nor Mastora Index correlated with pulmonary hypertension classification ( r =−0.009, P =0.920) or New York Heart Association heart function classification ( r =−0.031, P =0.756). Neither Qanadli nor Mastora Index correlated with any echocardiographic right ventricular parameters ( P >0.05), while RVd/LVd by CTPA correlated with echocardiographic right ventricular functional parameters ( P r =−0.288, P =0.006) and Mastora Index ( r =−0.203, P =0.032) demonstrated a weakly negative correlation with SPO 2 . CTPA findings correlated with hemodynamic variables. Backward linear regression analysis revealed that the RVd/LVd, Right Ventricular Anterior Wall Thickness (RVAWT), Main Pulmonary Artery trunk diameter (MPAd) were shown to be independently associated with mean Pulmonary Artery Pressure (mPAP) levels (model: r 2 =0.351, P =0.025; RVd/LVd: beta=11.812, P =0.000; RVAWT: beta=2.426, P =0.000; MPAd: beta=0.677, P =0.003). Conclusion Computed tomographic pulmonary angiography is a valuable tool to evaluate hemodynamics, right ventricular function of CTEPH, but neither Qanadli Index nor Mastora Index can reflect pulmonary arterial obstruction in CTEPH accurately.

We have prepared polyethylene thin films with the ionized cluster beam (ICB) deposition technique. There are perfect polyethylene crystal lamellas in the thin films. We have observed the polyethylene lamellar surface topography with scanning tunneling microscopy (STM), and found the fold chain structures on the (001) surface of polyethylene crystal lamellas first. We have analyzed polyethylene thin films with transmission electron microscopy (TEM) and found the (001) plane parallel to the surface of the substrate. There are strict periodic structures in the (110) plane, but no good periodic structures in the [110] direction.

Bioassay-guided fractionation of the crude extract of the whole plant of Macleaya cordata R. Br. led to the isolation of four alkaloids, which were identified as sanguinarine (1), chelerythrine (2), protopine (3) and α-allocryptopine (4) on the basis of their physicochemical and spectrometric data. Compounds 1 and 2 demonstrated a significant antifungal activity against the six test fungi with median inhibitory concentrations (IC50) ranging from 0.47 to 6.13 μg/mL. Compound 1 was the most effective with an IC50 of 0.47 μg/mL on Rhizoctonia solani. Furthermore, compounds 1 and 2 also demonstrated strong antibacterial activity, with IC50 values ranging from 5.01 to 11.3 μg/mL, and minimum inhibitory concentrations (MIC) ranging from 8.0 to 32.0 μg/mL. This is the first report on the activity of the alkaloids from M. cordata against economically important phytopathogenic fungi and bacteria.

Background: Parkinson's disease (PD) is a degenerative disease of the brain that often do harm to the motor skills of patients. A disturbed gait is very common among PD patients, who are easy to fall down and may lose their functional independence. Purpose: In this paper, we extract features from the gait signals in PD patients and control subjects for the purpose of comparison and realization of automatic recognition in computer. Also, we want to extract the pattern of walking in PD and help explain the disturbed gait through the perspective of bioinformatics. Method: In this paper, autoregressive (AR) model is applied to analysis of foot-pressure based on Yule-Walker equation for the calculation of power spectra. In the comparison process, we also give our own definition to Associated Discrete Index (ADI) in order to measure the discrete degree at a same frequency. Results: We analyze the data from three research groups: Ga, Ju and Si. The database includes 18, 25, 29 control subjects and 29, 29, 35 PD patients respectively. In the framework of 31–50Hz in the power spectra of Ga; 7–12Hz, 25–38Hz, 43–50Hz in Ju; and 2–10Hz in Si, we can see obvious difference between PD patient and control subject through t-test. Conclusions: The AR model can be used as a novel and useful tool to study the time series of foot-pressure. Associated Discrete Index could be an indicator in classification the gait signal in PD patient.

In this paper we show the possibility of using wavelet packet to analysis and characterize the gait signal of patients with Parkinson disease. Entropy has been defined to help extract the feature from results of wavelet packet decomposition. And the assessment of results has been performed by the means of T-test, showing that control group and PD group are statistically different from each other and our procedure based on wavelet packet could be applied to feature extraction of gait data.

Summary form only given. The ability of killing some microorganisms by atmospheric pressure glow discharges (APGD) has been demonstrated in a number of previous researches and can be extensively applied to medical treatment, sanitation, environmental protection and food reservation. At present, we designed a set of plasma device, which produce the dielectric barrier discharge plasma between the coaxial bar electrode and barrel electrode. This device adopts alternating current supply of 10 kHz to produce atmospheric continuous filament discharges plasma (ACFDP) at the gap distance of 5 mm and voltage of 15 kV. Because of the high exciting frequency, the plasma is generated constantly. We install nine of these devices in parallel and the applied power required to sustain stable discharges is around 400 W. When working gas (nitrogen, air, helium, etc) is inflated at a relatively high speed, the particle density decreases and the evenness degree of plasma increases, which is similar to the glow discharge. We use this plasma to treat some bacteria and fungus in crops, especially in wheat and the sterilization result is demonstrated to be remarkable

Au ultrafine particles-polyethylene thin films and C 60 -polyethylene thin films have been prepared in an ionized-cluster-beam time-of-flight mass spectrometer system and analysed by transmission electron microscopy. We found that the small Au clusters with a size of 2.0–5.0 nm are suspended randomly in the polyethylene thin films, and the C 60 -polyethylene thin films are f.c.c. structures with a lattice constant a of 1.454 nm.

Polyethylene thin films were prepared by the ionized cluster beam (ICB) deposition technique. There are perfect polyethylene crystal lamellae in the thin films. We have analyzed polyethylene thin films with the transmission electron microscope (TEM) and found the (001) plane parallel to the surface of the substrate. There are strict periodic structures in the (110) plane, but no good periodic structures in the [110] direction. We have observed the polyethylene lamellar surface topography with the scanning tunneling microscope (STM), and found the fold chain structures on the (001) surface of polyethylene crystal lamellae.

[1] The effects of ocean feedback on the energetic characteristics of the tropical cyclone (TC) are investigated based on idealized TC-ocean coupling simulations. Results reveal notable impacts of ocean response on TC energetics. The ocean feedback reduces the latent energy of TCs, and consequently less latent heat is released in TC clouds, which leads to an evident decrease of the kinetic energy. A bulk equivalent potential temperature (θe) budget analysis demonstrates that the upward heat fluxes at the top of the boundary layer, of comparable magnitudes with the surface heat fluxes, are significantly diminished by the ocean feedback. A relatively higher portion of energy extracted from the ocean has been retained by the boundary layer under influences of the ocean feedback. The air parcels in contact with the cold wake possess evidently lowered moist static energy. To evaluate the evolution of moist static energy following these cooled air parcels, a three-dimensional Lagrangian analysis is conducted using a large sample of trajectory seeds. Statistical results indicate that there is a mixing process between the cooled and ambient air parcels that lasts mostly less than 240 min. Over 70% of the seeded air parcels approach to a radius of 30–90 km from the TC center before they obtain adequate energy from surrounding air to balance their negative asymmetric θe. Most of the cooled air parcels are warmed up in the boundary layer, thereby producing impacts on TC energetics through changing directly the moist static energy of the boundary layer. More work of real case simulations is required to generalize the findings reported herein.