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The protoplasm of giant-celled Bryopsis species is totipotent. When protoplasm is extruded from the fresh alga into seawater, the organelles - including protoplasts, nucleus and other cell contents can aggregate and reconstitute a whole cell, even regenerate into a mature individual. It is believed that a lectin carbohydrate complementary system is involved in these surprising processes. To address this issue, a polyclonal antibody against the lectin in Bryopsis hypnoides was prepared and used to evaluate the lectin's role in organellar agglutination. Results showed that the agglutination of cell organelles was blocked completely by the antibody. Real-time quantitative polymerase chain reaction of this lectin gene (EU410470.1) demonstrated that it was down-regulated, with expression reaching the lowest level at 6 h and returning to maximal level at 12 11 after protoplasm release. Western blotting revealed that a 43-kDa protein appeared at 3 h after protoplasm release, and then disappeared at 6 h. Immunogold localization indicated that the lectin was located in both chloroplast and cytoplasm. These results indicate that lectin plays an indispensable role in agglutination of the cell organelles and the regeneration of this giant-celled alga.

Two polymorphs of 3,4-bis(2-benzimidazolyl)pyridine (1) were prepared and characterized by infrared, nuclear magnetic resonance spectroscopy and single-crystal X-ray crystallography. The metastable greenish polymorph 1a (hexagonal space group P6522) was solvothermally formed first, which can be irreversibly transformed into the stable yellow polymorph 1b (orthorhombic space group Pbcn) on heating or recrystallization. The structure of 1a shows a dihedral angle of 45.3° between the benzimidazolyl/pyridyl rings and a dihedral angle of 52.2° between the benzimidazolyl/benzimidazolyl rings, while the corresponding dihedral angles in the structure of 1b are 48.0° and 67.9°, respectively. The two polymorphs exhibit different combination of intermolecular interactions (N–H⋯N hydrogen bonds, π–π stacking or C–H⋯π interaction), leading to significantly different supramolecular assemblies. The photophysical properties of 1a and 1b have also been studied.

The reaction of o-carboranyllithium with two equiv. of 2-pyridinecarboxaldehyde affords 1-[(hydroxy)(pyridyl)methyl]-o-carborane (1) and 1,2-bis[(hydroxy)(pyridyl)methyl]-o-carborane (2), which have been characterized by IR and NMR spectroscopy, mass spectrometry and single-crystal X-ray diffraction. Compound 2 exhibits interesting polymorphism (monoclinic 2a and triclinic 2b). The solid state structures of the new carboranes feature intermolecular and intramolecular hydrogen bonding interactions involving all the N and O atoms, leading to one dimensional (1, 2b) or two-dimensional (2a) supramolecular structures. The photoluminescence of 1 and 2b was investigated in the solid and solution states at room temperature, respectively.

This paper presents a method based on partial least squares regression (PLSR) and multiple kernel extreme learning machine (MKLEM) for multivariate chaotic time series prediction. At first, singular spectrum analysis (SSA) is applied for the time series extraction of complex trends and eliminating the influence of noise. Then, partial least squares regression is used to capture the essential structure of the data and extract the compositions, in order to overcome the multicollinearity problem among time series and reduce the input dimension of neural networks. Finally, multiple kernel extreme learning machine is used to predict the time series. Multiple kernel extreme learning machine overcomes the problem that single extreme learning machine with kernels (KELM) doesn't present an effective generalization performance. Root mean square error (RMSE) is used to measure the performance of the proposed prediction model. The simulation experiment results based on Lorenz chaotic time series and Dalian monthly average temperature-rainfall time series demonstrate that the proposed model is effective for time series prediction, and the prediction accuracy is higher than other models.

This paper presents a multivariate time series modeling and prediction method based on reservoir independent components. As a new type of recurrent neural networks (RNNs), reservoir computing methods have become a new hot topic and attracted wide attention from researchers in the field of time series prediction. It has overcome the problems that traditional gradient descent training algorithms present, for example, the process is computationally expensive, and easy to end in a local minimum. However, there are ill-posed solutions when least square estimation methods are used to calculate the output weights because of the collinear columns or rows in the state matrix. Therefore, we use independent component analysis (ICA) to extract the independent components of the state matrix. In addition, this paper proposes an iterative prediction model based on local error compensation to solve the problem of accumulated errors in multiple-step prediction, in order to realize medium-term prediction. The models have been simulated on benchmark dataset of Lorenz time series and a real-world application of Dalian monthly average temperature-rainfall time series. Simulation results substantiate the proposed methods' effectiveness and characteristics.

Fan blade loss is the most serious condition of typical extreme working conditions in turbofan engines. The transient huge unbalance force leads to the complicated and destructive vibration response of the rotor system. The paper proposed a set of safety design method for the rotor system, which ensured the engine to stop safely after the fan blade lost. The variable stiffness supporting structure at the rear of the fan was presented and its parameters were optimized through dynamic characteristics calculation. The results revealed that the vibration response of the rotor system could be suppressed during the speed reduction process. The spherical matching surface between the bearing ring and the rotation shaft could reduce the impact of the large shaft deformation, and enhanced the bearing's safety.

The fast dose calculator (FDC), a track-repeating algorithm that was initially developed for proton therapy, has been expanded to include carbon. The purpose of this study was to validate FDC as a dose engine for treatment planning of scanning proton and carbon beams at the Shanghai Proton and Heavy Ion Center (SPHIC). For a complete set of clinical reference beams, the absorbed dose calculated with FDC was compared with that measured. A gamma-index analysis found over 94.6% and 100.0% of the measured points for protons had a gamma-index value smaller than unity for the 1%/1 mm and 1.5%/1.5 mm dose/distance agreement criteria, respectively. For carbon, the passing rates were above 80.6% and 92% with and without ripple filter if the criteria was set to 1%/1 mm. The values increased to 90.0%and 96.0% with the criteria 1.5%/1.5 mm. In conclusion, we found that FDC can serve as an accurate dose calculation engine for the SPHIC.

Echo state networks have become increasingly popular for its superior performance in the field of time series prediction. However, it is difficult to implement the complicated ESN topologies in practice. To solve the problem, we propose a diagonal connected reservoir structure with composite functions inside the nodes. The input is first processed by wavelet functions and then passes through sigmoid activation functions. This increases the diversity of the reservoir. A selection method that takes into account the domain of the input data is applied to initialize the wavelet translation and dilation parameters. The output weights are efficiently computed by the least square method after the reservoir state matrix is formed. We exhibit the merits of our model on a benchmark multivariate chaotic dataset and a real-world application. Experimental results substantiate that the proposed model can achieve significantly good performance with a low-dimensional reservoir.

This paper describes an efficient implementation of a form of linear semi-infinite programming (LSIP). We look at maximizing (minimizing) a linear function over a set of constraints formed by positive trigonometric polynomials. Previous studies about LSIP are formulated using semi-definite programming (SDP), this is typically done by using the Kalman Yakubovich Popov (KYP) lemma or using a trace operation involving a Grammian matrix, which can be computationally expensive. The proposed algorithm is based on simplex method that directly solves the LSIP without any parameterization. Numerical results show that the proposed LISP algorithm is significantly more efficient than existing SDP solvers using KYP lemma and Grammian matrix, in both execution time and memory.

Alzheimer's disease (AD) is the most common neurodegenerative disease in the elderly. Recently, genome-wide association studies (GWAS) have been used to investigate AD pathogenesis. However, a large proportion of AD heritability has yet to be explained. We previously identified the cell adhesion molecule (CAM) pathway as a consistent signal in two AD GWAS. However, it is unclear whether CAM is present in the Genetic and Environmental Risk for Alzheimer's Disease Consortium (GERAD) GWAS and brain expression GWAS. Meanwhile, we think integrating AD GWAS and AD brain expression datasets may provide complementary information to identify important pathways involved in AD. Here, we conducted a systems analysis using (1) KEGG pathways, (2) large-scale AD GWAS from GERAD (n = 11,789), (3) two brain expression GWAS datasets (n = 399) from the AD cerebellum and temporal cortex, and (4) previous results from pathway analysis of AD GWAS. Our results indicate that (1) CAM is a consistent signal in five AD GWAS; (2) CAM is the most significant signal in AD; (3) we confirmed previous AD risk pathways related to immune system and diseases, and cardiovascular disease, etc.; and (4) we highlighted the purine metabolism pathway in AD for the first time. We believe that our results may advance our understanding of AD mechanisms and will be very informative for future genetic studies in AD.

In this study, a method is developed to fabricate sulfonated poly (ether ether ketone)/phosphotungstic acid-polyaniline (SPEEK/HPW-PANI) membranes by in situ polymerization of aniline for the purpose of decreasing the weight loss of HPW in the membranes. The synthesis involves the production of a SPEEK/HPW hybrid membrane followed by different layer of PANI coatings on the membrane surface, and subsequent treatment using drying in vacuum procedures. The scanning electronic microscopy images showed that HPW had good compatibility with SPEEK polymers and energy dispersive X-ray spectroscopy revealed the successfully doping with HPW and polymerization of PANI. The surface of SPEEK/HPW-PANI becomes more compact than that of SPEEK/HPW and pure SPEEK, which may lead to reduce the water uptake and swelling property. The proton conductivity was found for the SPEEK/HPW-PANI-5 composite membrane (91.53 mS/cm at 80°C) higher than that of pure SPEEK membrane (68.72 mS/cm at 80°C). Better thermal stability was determined in both SPEEK/HPW and SPEEK/HPW-PANI membranes than pristine SPEEK membrane. Therefore, PANI is a good potential coating for organic–inorganic hybrid e.g. SPEEK/HPW membrane materials to improve their hydrothermal stable properties and SPEEK/HPW PANI is a material that shows promise as a proton exchange membranes. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014, 131, 41033.

This is an educational art by designing teaching situations cleverly to improve teaching effectiveness. It has many advantages in politics class. Politics teachers can use a variety of materials cleverly to create teaching situation. In order to create a valuable teaching situation, teachers must master the principles of the creation of teaching situation.

To develop a specific, rapid and convenient method based on molecular motor biosensor to detect food-borne rotavirus. A specific probe was encompassed the conservative region of rotavirus's VP7 segment, and a molecular motor detect device was constructed by connecting probes to F0F1-ATPase molecular motor through biotin-streptavidin system. This biosensor's sensitivity was 0.005 ng/mL for rotavirus RNA. Extracted virus RNA was conjugated with the biosensor separately, at the same time ATP was synthesized. By comparing fluorescence intensity, we can detect rotavirus RNA in samples. This method possessed specificity for rotavirus, without any cross-reaction with Hepatitis A virus and noroviris, and it could be accomplished within 1 h. We detected 15 samples using this method and the results were compared with RT-PCR results. This method is sensitive and specific for rotavirus, and it can be used to detect food-borne rotavirus.

In this paper, we present an echo state network model based on sparse Gaussian process regression, which has been successfully applied to multivariate time series prediction. While combining the Gaussian process with Echo State Network, the computational complexity of the model is very high. We consider using a group of limited basis functions instead of the original covariance function, which reduces the computational complexity and maintains the prediction performance of the model. In the framework of Bayesian inference, the model can combine prior knowledge and observation data perfectly and provide prediction confidence. The model realizes adaptive estimation of the hyper-parameters by using maximum likelihood approach and avoids complex computation process. Two simulation results show the effectiveness and practicality of the proposed method.

For a graph, the least signless Laplacian eigenvalue is the least eigenvalue of its signless Laplacian matrix. This paper investigates how the least signless Laplacian eigenvalue of a graph changes under some perturbations, and minimizes the least signless Laplacian eigenvalue among all the nonbipartite graphs with given matching number and edge cover number, respectively.

Calcium-dependent protein kinases (CDPKs) are Ca(2+)-binding proteins known to play crucial roles in Ca(2+) signal transduction pathways which have been identified throughout plant kingdom and in certain types of protists. Genome-wide analysis of CDPKs have been carried out in Arabidopsis, rice and wheat, and quite a few of CDPKs were proved to play crucial roles in plant stress responsive signature pathways. In this study, a comprehensive analysis of Populus CDPK and its closely related gene families was performed, including phylogeny, chromosome locations, gene structures, and expression profiles. Thirty Populus CDPK genes and twenty closely related kinase genes were identified, which were phylogenetically clustered into eight distinct subfamilies and predominately distributed across fifteen linkage groups (LG). Genomic organization analyses indicated that purifying selection has played a pivotal role in the retention and maintenance of Populus CDPK gene family. Furthermore, microarray analysis showed that a number of Populus CDPK and its closely related genes differentially expressed across disparate tissues and under various stresses. The expression profiles of paralogous pairs were also investigated to reveal their evolution fates. In addition, quantitative real-time RT-PCR was performed on nine selected CDPK genes to confirm their responses to drought stress treatment. These observations may lay the foundation for future functional analysis of Populus CDPK and its closely related gene families to unravel their biological roles.

Echo state network is a novel recurrent neural network, with a fixed reservoir structure and an adaptable linear readout layer, facilitating the application of RNNs. Often the network works beautifully. But sometimes it works poorly because of ill-posed problem. To solve it, we introduce a new approach towards ESNs, termed FSDESN, herein. It combines the merits of ESNs and fast subspace decomposition algorithm to provide a more precise alternative to conventional ESNs. The basic idea is to extract the subspace of the redundant large-scale reservoir state matrix by Krylov subspace decomposition algorithm, subsequently, calculate the readout weights using the subspace to replace original reservoir matrix. Hence, it can eliminate approximate collinear components and overcome the ill-posed problems so as to improve generalization performance. We exhibit the merits of our model in two multivariate benchmark datasets. Experimental results substantiate the effectiveness and characteristics of FSDESN.

To develop a multiplex PCR method used to fast diagnose pathogenic fungi of fungal rhinosinusitis.Three pairs of primers (universal fungus-specific primers, aspergillus specific primers, mucor specific primers)were used to establish multiplex PCR system. Detected six important medical fungi species strains to examine the applicability of the system and explore the optimum conditions. Multiplex PCR system was also used to identify pathogenic fungi of fungal rhinosinusitis, and used to compare the sensitivity with fungal culture.Three pairs of primers of the multiplex PCR system had high versatility and specificity, and could amplify the corresponding DNA fragments in the appropriate conditions. Sensitivity of the reaction system was 10 microg/L. Twenty clinical specimens of fungal sinusitis were tested by fungal culture and the multiplex PCR system, 7 (35%) cases were culture positive whereas 15 (75%) cases were positive by the multiplex PCR system, the difference was statistically significant between the two methods (P0.05).The multiplex PCR system was proved to be a rapid, sensitive and specific technology, it could be used to detect and identify clinical pathogenic fungi of fungal rhinosinusitis.

A high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/ MS) method was established for the determination of inabenfide in fruits, vegetables, tea, honey, cereals and animal-derived foods. The food samples were extracted with acetonitrile, then purified by dispersion solid phase extraction using primary secondary amine (PSA) and C18 as solid phase. The residue was determined and confirmed by HPLC-MS/MS and quantified by external standard method. The mass spectrometric detection was operated with electrospray in positive ionization mode and inabenfide was identified in multiple reaction monitoring (MRM) mode. The interference of matrix was reduced by the matrix-matched calibration standard curves. The linear range of the method was 1 - 100 microg/kg, with the correlation coefficients (r2) of 0.998 - 0.999. The recoveries of inabenfide spiked in food samples were 85.2% - 112.4% at the spiked levels of 5, 10, 50 microg/kg. The relative standard deviations (RSDs) were less than 8.5%. The limits of detection (LODs) were 0.08 - 1.64 microg/kg, and the limits of quantification (LOQs) were 0.30 - 5.48 microg/kg. The results showed that the proposed method is sensitive and accurate for the determination of inabenfide in foodstuffs.

The reaction of dithio-o-carborane with diphenyldiselenide (PhSeSePh) affords the title compound, closo-1,2-(PhSe)2C2B10H10 (1), which has been characterized by IR and NMR (1H, 11B) spectroscopy and by single-crystal X-ray diffraction. The phenylselenyl groups are bonded to the cage carbon atoms of the o-carborane cluster with a Ccage-Ccage distance of 1.751(6) Å and an Se-Ccage- Ccage-Se torsion angle of 10.1(3)°. Compound 1 displays a violet emission on excitation with UV light. For comparison the photoluminescence of the known analog closo-1,2-(PhS)2C2B10H10 (2) was also studied

The reactions of the dithiolato-o-carborane salt (Et3NH)2(S2C2B10H10) (2) with alkyl halides BrCH2CH=CH2, BrCH2CH2CH2Cl and C6H5CH2Cl, afford the o-carboranyl-bisthioether derivatives 3a - c, which have been characterized by IR and NMR (1H, 13C, 11B) spectroscopy, mass spectrometry, and single-crystal X-ray diffraction (3c). The photoluminescent properties of the known compound 3c has been investigated. It exhibits a violet (chloroform solution) or blue (solid state) emission when excited with UV light.