K-means Find Density Peaks in Molecular Conformation Clustering

Performing cluster analysis on molecular conformation is an important way to find the representative conformation in the molecular dynamics trajectories. Usually, it is a critical step for interpreting complex conformational changes or interaction mechanisms. As one of the density-based clustering algorithms, find density peaks (FDP) is an accurate and reasonable candidate for the molecular conformation clustering. However, facing the rapidly increasing simulation length due to the increase in computing power, the low computing efficiency of FDP limits its application potential. Here we propose a marginal extension to FDP named K-means find density peaks (KFDP) to solve the mass source consuming problem. In KFDP, the points are initially clustered by a high efficiency clustering algorithm, such as K-means. Cluster centers are defined as typical points with a weight which represents the cluster size. Then, the weighted typical points are clustered again by FDP, and then are refined as core, boundary, and redefined halo points. In this way, KFDP has comparable accuracy as FDP but its computational complexity is reduced from O\begin{document}$(n^2)$\end{document} to O\begin{document}$(n)$\end{document}. We apply and test our KFDP method to the trajectory data of multiple small proteins in terms of torsion angle, secondary structure or contact map. The comparing results with K-means and density-based spatial clustering of applications with noise show the validation of the proposed KFDP.     

Accelerated K-means clustering in metric spaces

The K-means method is a popular technique for clustering data into k-partitions. In the adaptive form of the algorithm, Lloyds method, an iterative procedure alternately assigns cluster membership based on a set of centroids and then redefines the centroids based on the computed cluster membership. The most time-consuming part of this algorithm is the determination of which points being clustered belong to which cluster center. This paper discusses the use of the vantage-point tree as a method of more quickly assigning cluster membership when the points being clustered belong to intrinsically low- and medium-dimensional metric spaces. Results will be discussed from simulated data sets and real-world data in the clustering of molecular databases based upon physicochemical properties. Comparisons will be made to a highly optimized brute-force implementation of Lloyd's method and to other pruning strategies.     

Improving the performance of self-organizing maps via growing representations

Self-organizing maps (SOMs) are a type of artificial neural network that through training can produce simplified representations of large, high dimensional data sets. These representations are typically used for visualization, classification, and clustering and have been successfully applied to a variety of problems in the pharmaceutical and bioinformatics domains. SOMs in these domains have generally been restricted to static sets of nodes connected in either a grid or hexagonal connectivity and planar or toroidal topologies. We investigate the impact of connectivity and topology on SOM performance, and experiments were performed on fixed and growing SOMs. Three synthetic and two relevant data sets from the chemistry domain were used for evaluation, and performance was assessed on the basis of topological and quantization errors after equivalent training periods. Although we found that all SOMs were roughly comparable at quantizing a data space, there was wide variation in the ability to capture its underlying structure, and growing SOMs consistently outperformed their static counterparts in regards to topological errors. Additionally, one growing SOM, the Neural Gas, was found to be far more capable of capturing details of a target data space, finding lower dimensional relationships hidden within higher dimensional representations.     

Simultaneous optimization by neuro-genetic approach of a multiresidue method for determination of pesticides in Passiflora alata infuses using headspace solid phase microextraction and gas chromatography

A simultaneous optimization strategy based on neuro-genetic approach has been applied to a HS-SPME-GC-ECD (Headspace Solid Phase Microextraction coupled to Gas Chromatography with Electron Capture Detection) method for simultaneous determination of the pesticides chlorotalonil, methyl parathion, malathion, alpha-endosulfan and beta-endosulfan in herbal infusions of Passiflora alata (Dryander). Two types of extractive fibers were used: a home-made device coated by sol-gel process with polydimethylsiloxane-poly(vinyl alcohol) (PDMS/PVA) and a commercial PDMS. The effects of extraction parameters such as dilution of the infusion, extraction temperature and time, as well as sample ionic strength were evaluated through the Doehlert design. To find a model that could relate these extraction parameters with the extraction efficiency of all pesticide simultaneously, a Bayesian Regularized Artificial Neural Network (BRANN) approach was employed. Subsequently, Genetic Algorithm (GA) was applied to attain the optimum values from the model developed by the neural network. The use of the proposed approach allowed the determination of a single extraction condition that maximized the peak areas of all pesticides simultaneously, showing a promising and a suitable new procedure to the optimization process of complex analytical problems.     

Structural insight into the glucokinase-ligands interactions. Molecular docking study

Glucokinase (GK) plays a key role in the regulation of hepatic glucose metabolism. Inactivation of GK is associated with diabetes, and an increase of its activity is linked to hypoglycemia. Possibility to regulate the GK activity using small chemical compounds as allosteric activators induces the scientific interest to the study of the activation mechanism and to the development of new allosteric glucokinase activators.Interaction of glucokinase with ligands is the first step of the complicated mechanism of regulation of the GK functioning. In this paper, we study the interaction of GK with native (glucose) and synthetic (allosteric activators) ligands using molecular docking method. Calculations demonstrate the ability of molecular docking programs to accurately reproduce crystallized ligand poses and conformations and to calculate a free energy of binding with satisfactory accuracy. Correlation between the free energy of binding and the bioactivity of activators is discussed. These results provide a new insight into protein–ligand interactions and can be used for the engineering of new activators.     

The use of docking-based comparative intermolecular contacts analysis to identify optimal docking conditions within glucokinase and to discover of new GK activators

Glucokinase (GK) is involved in normal glucose homeostasis and therefore it is a valid target for drug design and discovery efforts. GK activators (GKAs) have excellent potential as treatments of hyperglycemia and diabetes. The combined recent interest in GKAs, together with docking limitations and shortages of docking validation methods prompted us to use our new 3D-QSAR analysis, namely, docking-based comparative intermolecular contacts analysis (dbCICA), to validate docking configurations performed on a group of GKAs within GK binding site. dbCICA assesses the consistency of docking by assessing the correlation between ligands’ affinities and their contacts with binding site spots. Optimal dbCICA models were validated by receiver operating characteristic curve analysis and comparative molecular field analysis. dbCICA models were also converted into valid pharmacophores that were used as search queries to mine 3D structural databases for new GKAs. The search yielded several potent bioactivators that experimentally increased GK bioactivity up to 7.5-folds at 10 μM.     

钼酸铋基气敏材料研究进展

气体传感器被广泛应用于检测工业和家庭中有毒有害气体。气体敏感材料是气体传感器中重要的组成部分,敏感材料的性质决定了气体传感器的性能。研制精度高、检测快、集成度高的气体检测器迫在眉睫。钼酸铋作为一种新型双金属氧化物气敏材料,具有高选择性、高敏感度的优势。本文从气敏机理、形貌控制、掺杂和复合材料构建方面对近年来钼酸铋作为气敏材料的研究进行了总结,并对钼酸铋基气敏材料未来的研究方向进行了展望。     

From gas chromatography to gaia

Summary This paper is an account of the development of the gaia hypothesis as seen through the eyes of a gas chromatographer. Gaia is a radical notion about the Earth which postulates the climate and chemical composition to be regulated at an optimum by and for life. Gas chromatography played an important role in the gathering of evidence for the hypothesis but most significantly the art of inventing detectors lead to the developement of a planetary life detector through which gaia was revealed.Presented at the 14th International Symposium on Chromatography London, September, 1982     

Measurements by gas chromatography/pyrolysis/mass spectrometry: fundamental conditions in (2)H/(1)H isotope ratio analysis

Gas chromatography/pyrolysis/isotope ratio mass spectrometry (GC/P/IRMS) is a relatively new method for on-line determination of (2)H/(1)H isotope ratios. The influence of different parameters on the (2)H/(1)H isotope ratios obtained in GC/P/IRMS has been thoroughly studied using several flavor compounds, such as 5-nonanone, linalool, menthol, linalyl acetate and 4-decanolide. The requirement of "conditioning" the pyrolysis reactor to obtain reliable delta(2)H(V-SMOW) values is discussed. Furthermore, the influence of the carrier gas flow of the gas chromatograph on the completeness of pyrolysis and subsequently on the delta(2)H(V-SMOW) values is investigated in detail. The linear range of the compounds investigated is determined. The results show that calibrating the GC/P/IRMS system with secondary standard substances is absolutely necessary in order to obtain reliable delta(2)H(V-SMOW) values. In view of interlaboratory comparability, validation procedures are recommended.     

Recursive neural networks prediction of glass transition temperature from monomer structure: an application to acrylic and methacrylic polymers

We propose a new method based on a Recursive Neural Network (RecNN) for predicting polymer properties from their structured molecular representations. RecNN allows for a completely novel approach to QSPR analysis by direct adaptive processing of molecular graphs. This model joins the representational power of structured domains with Neural Network ability to capture underlying complex relationships in the data by a process of training from examples. To this aim, a structured representation was designed for the modelling of polymer structures. The adopted representation can account also for average macromolecule characteristics, such as degree of polymerization, stereoregularity, comonomer distribution. To begin with, this model was applied to the prediction of the glass transition temperature of (meth)acrylic polymers with different degree of main chain tacticity. The results so far obtained indicate that the proposed representation of polymer structure can convey information on both the repeating unit structure and average polymer features. The ability of the proposed RecNN method of treating this structured representation makes this method more general and flexible with respect to standard literature methods. Moreover, the same model can handle at the same time the Tg of polymer samples present in only one tacticity form together with that of polymer with different stereoregularity.     

A novel correlation approach for prediction of natural gas compressibility factor

Gas compressibility factor (z-Factor) is one of the most important parameters in upstream and downstream calculations of petroleum industries. The importance of z-Factor cannot be overemphasized in oil and gas engineering calculations. The experimental measurements, Equations of State (EoS) and empirical correlations are the most common sources of z-Factor calculations. There are more than twenty correlations available with two variables for calculating the z-Factor from fitting in an EoS or just through fitting techniques. However, these correlations are too complex, which require initial value and more complicated and longer computations or have magnitude error. The purpose of this study is to develop a new accurate correlation to rapidly estimate z-Factor. Result of this correlation is compared with large scale of database and experimental data also. Proposed correlation has 1.660 of Absolute Percent Relative Error (E_(ABS)) versus Standing and Katz chart and has also 3.221 of E_(ABS) versus experimental data. The output of this correlation can be directly assumed or be used as an initial value of other implicit correlations. This correlation is valid for gas coefficient of isothermal compressibility (c_g) calculations also.     

Polymers for neural implants

Neural implants are technical systems that restore sensory or motor functions after injury and modulate neural behavior in neuronal diseases. Neural interfaces or prostheses have lead to new therapeutic options and rehabilitation approaches in the last 40 years. The interface between the nervous tissue and the technical material is the place that determines success or failure of the neural implant. Recording of nerve signals and stimulation of nerve cells take place at this neuro‐technical interface. Polymers are the most common material class for substrate and insulation materials in combination with metals for interconnection wires and electrode sites. This work focuses on the neuro‐technical interface and summarizes its fundamental specifications first. The most common polymer materials are presented and described in detail. We conclude with an overview of the different applications and their specific designs with the accompanying manufacturing processes from precision mechanics, laser structuring and micromachining that are introduced in either the peripheral or central nervous system. © 2010 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys, 2010     

Kinetic model of gas phase polymerization of 1,3-butadiene catalyzed by supported rare earth coordination system

Gas phase polymerization of 1,3-butadiene (Bd) catalyzed by supported rare earth coordination system is studied and a new kinetic model is proposed. Four elementary reactions or processes: the process of exposure and activation of potential active catalytic center, propagation, deactivation and chain transfer reaction to alkyl aluminum, are considered in this model. Some important parameters, such as monomer-consuming rate, are well expressed as the functions of macroscopic polymerization conditions such as pressure, temperature, and duration. The model can simulate the whole polymerization procedure satisfactorily.     

Determination of bromocriptine in plasma: comparison of gas chromatography, mass fragmentography and liquid chromatography

Gas chromatographic, mass fragmentographic and liquid chromatographic techniques for the determinations of bromocriptine (2-bromo-alpha-ergocriptine; Parlodel) in human plasma are described. These methods were found to be suitable for determining concentrations of bromocriptine down to 0.5, 1.0 and 10.0 microgram/l, respectively. Accuracy, specificity and analytical capacity were satisfactory for all three methods. Gas chromatography was compared with liquid chromatography, and the two methods were demonstrated to give identical results in patients treated with bromocriptine for Parkinson's disease. Gas chromatography was also compared with mass fragmentography, and the results from these two assays were also in agreement.     

Quantitative structure-activity relationships by neural networks and inductive logic programming. I. The inhibition of dihydrofolate reductase by pyrimidines

Summary Neural networks and inductive logic programming (ILP) have been compared to linear regression for modelling the QSAR of the inhibition of E. coli dihydrofolate reductase (DHFR) by 2,4-diamino-5-(substitured benzyl)pyrimidines, and, in the subsequent paper [Hirst, J.D., King, R.D. and Sternberg, M.J.E., J. Comput.-Aided Mol. Design, 8 (1994) 421], the inhibition of rodent DHFR by 2,4-diamino-6,6-dimethyl-5-phenyl-dihydrotriazines. Cross-validation trials provide a statistically rigorous assessment of the predictive capabilities of the methods, with training and testing data selected randomly and all the methods developed using identical training data. For the ILP analysis, molecules are represented by attributes other than Hansch parameters. Neural networks and ILP perform better than linear regression using the attribute representation, but the difference is not statistically significant. The major benefit from the ILP analysis is the formulation of understandable rules relating the activity of the inhibitors to their chemical structure.