Cyclodextrin-catalyzed oxidation of glutathione in solution and in an ion trap

Incubated solutions containing glutathione (GSH) and alpha- or beta-cyclodextrins (CDs) were analyzed using electrospray mass spectrometry and tandem mass spectrometry, The results suggest that both CDs can catalyze oxidation of GSH to the oxidized glutathione (GSSG). The collision-induced dissociation (CID) of the 1:1 and 1:2 (CD/GSH) and 1:1 (CD/GSSG) complexes reveals the strong interactions of the CDs with the peptides tested. The 1:2 (CD/GSH) complex is considered to be the oxidation reaction intermediate, which indicates that the three-dimensional structure of the complexed two GSHs in CD complexes Is different from that of the proton-bound GSH dimer, The oxidation product, GSSG, Is also observed in the CID spectrum of the singly charged 1:1 (CD/GSH) complex, suggesting that a complex ion-complex ion reaction occurs by forming a doubly charged complex dimer, as a result of the ability of ion trap to accumulate and activate ions. The observations indicate that ion trap mass spectrometry can be used to explore cyclodextrin-catalyzed reactions and to carry out complex gaseous chemistry research. Copyright (C) 1999 John Wiley & Sons, Ltd.

STUDIES ON STRUCTURE OF EO/THF RANDOM COPOLYETHER(I) - NMR-STUDIES ON SOLUTION ACTION AND COMPLEX

The special action of TEO solution was investigated by 1D, 2D-NMR in CDCl3. For the present measurements, when the concentration of TEO was higher in CDCl3, the chemical shift difference (Delta delta) and the peak number of C-13 NMR spectrum were changed with increasing the solution concentration, At lower concentration(< 3% V/V ), the peaks will be closed together for -CH2O- resonance carbon and it is not the appearance of the narrowed, When temperature was changed, the Delta delta value was contrary to the solvent effect, So, the shifts of the resonance carbon in the NMR spectra indicated clearly that the complex formation for the system of CDCl3, and TEO molecular interaction were affected by the experiment temperature and the solution concentration.

SYNTHESIS, SOLUTION DYNAMICS AND X-RAY CRYSTAL-STRUCTURES OF NEODYMIUM-YLIDE COMPLEXES, [(C(5)H(4)R)(3)NDCH2P(ME)PH(2)] (R=H, BU(T))

The interaction of [(C(5)H(4)R)(2)NdCl.2LiCl] (R = H, Bu(t)) with one equivalent of Li[(CH2)(CH2)PPh(2)] in refluxing tetrahydrofuran gave the purplish-blue complex [(C(5)H(4)R)(3)NdCH2P(Me)Ph(2)] in 50% yield. The compounds have been fully characterized by analytical, spectroscopic and X-ray diffraction methods. Variable temperature P-31{H-1} NMR spectroscopy indicated the existence of the following equilibrium: [(C(5)H(4)R)(3)NdCH2P(Me)Ph(2)] + THF reversible arrow (C(5)H(4)R)(3)Nd(THF) + CH2=P(Me)Ph(2). At room temperature, the exchange between the coordinated and free ylide ligand is slow on the NMR time scale.

A semi-analytical theory of coastal upwelling and jet

The general forms of the conservation of momentum, temperature and potential vorticity of coastal ocean are obtained in the x-z plane for the nonlinear ocean circulation of Boussinesq fluid, and a elliptic type partial differential equations of second order are derived. Solution of the partial differential equations are obtained under the conditions that the fluid moves along the topography. The numerical results show that there exist both upwelling and downwelling along coastline that mainly depends on the large scale ocean condition. Numerically results of the upwelling (downwelling), coastal jet and temperature front zone are favorable to the observations.

STUDY ON THE FORMATIONAL MECHANISM OF THE NORTHERN YELLOW (HUANGHAI) SEA COLD WATER MASS .2. DISCUSSION ON THE SOLUTION OF THE MODEL

The theoretical solution of the model of the Northern Yellow (Huanghai) Sea Cold Water Mass (NYSCWM) reveals that the NYSCWM is mainly formed through the continuous temperature increase of the overwintered water body above the Northern Yellow Sea Depression (NYSD) after spring when heat is continuously conducted from the sea surface to the deeper layer. In the NYSCWM's growing period, (June-July), nonlinear vertical convection and advection effects continuously increase, and are gradually balanced by the heat diffusion effect as the temperature increases from the surface to the bottom, which leads to the formation of an intensive thermocline and lateral front. Meanwhile, the three-dimensional circulation correspondingly occurs. In the NYSCWM's entire growing period, the horizontal circulation is always in the cyclonic motion, while the vertical circulation passes through a transition from a period with the cold centre as downwelling to a period with the cold centre as upwelling.

Studies on two coordination polymers [M(mu(4)-pz25dc)](n) (M = Cd or Zn, pz25dc=pyrazine-2,5-dicarboxylato) with three-dimensional pillared-layer three-nodal framework: Synthesis, structural characterization, strong optical non-linearities and optical limiting properties

Two isomorphous new candidates [M(mu(4)-pz25dc)](n) (M = Cd, 1; Zn, 2; pz25dc = pyrazine-2,5-dicarboxylato)for nonlinear optical (NLO) materials have been synthesized hydrothermally and characterized crystallographically as pillared-layer three-nodal frameworks with one four-connected metal nodes and two crystallographically different four-connected ligand nodes. Their optical non-linearities are measured by the Z-Scan technique with an 8 ns pulsed laser at 532 nm. These two coordination polymers both exhibit strong NLO absorptive abilities [alpha(2) = (63 +/- 6) x 10 (12) mW (1) 1, ( 46 +/- 6) x 10 (11) mW (1) 2] and effective self-focusing performance [n(2) = (67 +/- 5) x 10 (18) 1, (13 +/- 3) x 10 (18) m(2) W (1) 2] in 1.02 x 10 (4) 1 and 1.05 x 10 (4) mol dm (3) 2 DMF solution separately. The values of the limiting threshold are also measured from the optical limiting experimental data. The heavy atom effect plays important role in the enhancement of optical non-linearities and optical limiting properties. (C) 2009 Elsevier B. V. All rights reserved.

软土亚塑性模型与一维非线性固结理论研究

With the great development of Tianjing New Coastal District economy, people need more land to build and live. Land subsidence, which is caused by its special engineering geological conditions, has restricted the further development in the district. Soft soil consolidation is main factor of land subsidence ;thus , on the basis of consolidation theory, the paper make further study on soft soils one-dimension nonlinear consolidation which contains two parts:(1) the nonlinear consolidation of permeability coefficient and compressibility coefficient changing with time and depth, which means real one-dimension nonlinear consolidation;(2) the non-homogeneous consolidation of permeability coefficient and compressibility coefficient only changing with depth. Firstly, nonlinear characteristics of soft soils are elaborated. Hypoplastic theory is introduced to establish a modified soft soils nonlinear constitutive model; the nonlinear governing equation of compressibility coefficient is built, and the nonlinear characteristics of compressibility coefficient are analyzed. Secondly, Considering Load Fluctuation and soil thickness changing ,the consolidation characteristics of single layer is discussed in the paper; meanwhile, on the basis of the Davis and Raymond’s hypothesis and single layer nonlinear consolidation equation, the doubled-layer one-dimension nonlinear consolidation equation is also derived. The solution of the equation is obtained by analytical method, and the consolidation characteristics of doubled-layer soft soil nonlinear theory is also analyzed. Finally, based on assumption that permeability coefficient and compressibility coefficient is varying along depth, single layer soil one-dimension non-homogeneous consolidation differential equation is derived; and the approximate solution is obtained. Furthermore, the single layer non-homogeneous consolidation is extended to double layer non-homogeneous consolidation theory. By using parabolic differential scheme, the matrix equation is established; and the solution of the matrix equation is obtained by chase method. Consolidation characteristics of soil soft single (double) layer non-homogeneous consolidation theory and Terzaghi’s theory are also discussed.

Electrophoretic migration behavior of DNA fragments in polymer solution

A newly developed polymer coil shrinking theory is described and compared with the existing entangled solution theory to explain electrophoretic migration behaviour of DNA in hydroxypropylmethylcellulose (HPMC) polymer solution in buffer containing 100 mM tris(hydroxymethyl)aminomethane 100 mM boric acid, 2 mm ethylenediaminetetraacetic acid at pH 8.3. The polymer coil shrinking theory gave a better model to explain the results obtained. The polymer coil shrinking concentration, C-s, was found to be 0.305% and the uniform entangled concentration, C+, 0.806%. The existence of three regions (the dilute, semidilute, and concentrated solution) at different polymer concentrations enables a better understanding of the system to guide the selection of the best conditions to separate DNA fragments. For separating large fragments (700/800 bp), dilute solutions (HPMC < 0.3%) should be used to achieve a short migration time (10 min). For small fragments (200/300 bp), concentrated solutions are preferred to obtain constant resolution and uniform separation. The best resolution is 0.6% HPMC due to a combined interaction of the polymer coils and the entangled structure. The possibility of DNA separation in semidilute solution is often neglected and the present results indicate that this region has a promising potential for analytical separation of DNA fragments.

A collision model for DNA separation by capillary electrophoresis in dilute polymer solution

A theoretical description. based on chemical kinetics and electrochemistry, is given of DNA separation in dilute polymer solution by capillary electrophoresis. A self-consistent model was developed leading to predictions of the DNA electrophoretic velocity as a function of the experimental conditions - polymer concentration, temperature, and electric field strength. The effect of selected experimental variables is discussed. The phenomena discussed are illustrated for the example of 100 bp DNA ladder separation in dilute HPMC solution by capillary electrophoresis. This model is the first single model that can fully explain the dependence of DNA electrophoretic velocity on electrophoretic conditions.

Computer Perception of Three-Dimensional Objects

We first pose the following problem: to develop a program which takes line-drawings as input and constructs three-dimensional objects as output, such that the output objects are the same as the ones we see when we look at the input line-drawing. We then introduce the principle of minimum standard-deviation of angles (MSDA) and discuss a program based on MSDA. We present the results of testing this program with a variety of line- drawings and show that the program constitutes a solution to the stated problem over the range of line-drawings tested. Finally, we relate this work to its historical antecedents in the psychological and computer-vision literature.

Analytical Representation of Contours

The interpretation and recognition of noisy contours, such as silhouettes, have proven to be difficult. One obstacle to the solution of these problems has been the lack of a robust representation for contours. The contour is represented by a set of pairwise tangent circular arcs. The advantage of such an approach is that mathematical properties such as orientation and curvature are explicityly represented. We introduce a smoothing criterion for the contour tht optimizes the tradeoff between the complexity of the contour and proximity of the data points. The complexity measure is the number of extrema of curvature present in the contour. The smoothing criterion leads us to a true scale-space for contours. We describe the computation of the contour representation as well as the computation of relevant properties of the contour. We consider the potential application of the representation, the smoothing paradigm, and the scale-space to contour interpretation and recognition.

Resolution prediction and optimization of temperature programme in comprehensive two-dimensional gas chromatography

A model is developed for predicting the resolution of interested component pair and calculating the optimum temperature programming condition in the comprehensive two-dimensional gas chromatography (GC x GC). Based on at least three isothermal runs, retention times and the peak widths at half-height on both dimensions are predicted for any kind of linear temperature-programmed run on the first dimension and isothermal runs on the second dimension. The calculation of the optimum temperature programming condition is based on the prediction of the resolution of "difficult-to-separate components" in a given mixture. The resolution of all the neighboring peaks on the first dimension is obtained by the predicted retention time and peak width on the first dimension, the resolution on the second dimension is calculated only for the adjacent components with un-enough resolution on the first dimension and eluted within a same modulation period on the second dimension. The optimum temperature programming condition is acquired when the resolutions of all components of interest by GC x GC separation meet the analytical requirement and the analysis time is the shortest. The validity of the model has been proven by using it to predict and optimize GC x GC temperature programming condition of an alkylpyridine mixture. (c) 2005 Elsevier B.V. All rights reserved.

Simultaneous Learning of Nonlinear Manifold and Dynamical Models for High-dimensional Time Series

The goal of this work is to learn a parsimonious and informative representation for high-dimensional time series. Conceptually, this comprises two distinct yet tightly coupled tasks: learning a low-dimensional manifold and modeling the dynamical process. These two tasks have a complementary relationship as the temporal constraints provide valuable neighborhood information for dimensionality reduction and conversely, the low-dimensional space allows dynamics to be learnt efficiently. Solving these two tasks simultaneously allows important information to be exchanged mutually. If nonlinear models are required to capture the rich complexity of time series, then the learning problem becomes harder as the nonlinearities in both tasks are coupled. The proposed solution approximates the nonlinear manifold and dynamics using piecewise linear models. The interactions among the linear models are captured in a graphical model. By exploiting the model structure, efficient inference and learning algorithms are obtained without oversimplifying the model of the underlying dynamical process. Evaluation of the proposed framework with competing approaches is conducted in three sets of experiments: dimensionality reduction and reconstruction using synthetic time series, video synthesis using a dynamic texture database, and human motion synthesis, classification and tracking on a benchmark data set. In all experiments, the proposed approach provides superior performance.

Simultaneous Learning of Non-Linear Manifold and Dynamical Models for High-Dimensional Time Series

The goal of this work is to learn a parsimonious and informative representation for high-dimensional time series. Conceptually, this comprises two distinct yet tightly coupled tasks: learning a low-dimensional manifold and modeling the dynamical process. These two tasks have a complementary relationship as the temporal constraints provide valuable neighborhood information for dimensionality reduction and conversely, the low-dimensional space allows dynamics to be learnt efficiently. Solving these two tasks simultaneously allows important information to be exchanged mutually. If nonlinear models are required to capture the rich complexity of time series, then the learning problem becomes harder as the nonlinearities in both tasks are coupled. The proposed solution approximates the nonlinear manifold and dynamics using piecewise linear models. The interactions among the linear models are captured in a graphical model. The model structure setup and parameter learning are done using a variational Bayesian approach, which enables automatic Bayesian model structure selection, hence solving the problem of over-fitting. By exploiting the model structure, efficient inference and learning algorithms are obtained without oversimplifying the model of the underlying dynamical process. Evaluation of the proposed framework with competing approaches is conducted in three sets of experiments: dimensionality reduction and reconstruction using synthetic time series, video synthesis using a dynamic texture database, and human motion synthesis, classification and tracking on a benchmark data set. In all experiments, the proposed approach provides superior performance.