Separation and identification of hydrocarbons and other volatile compounds from cultured blue-green alga Nostoc sp. by gas chromatography-mass spectrometry using serially coupled capillary columns with consecutive nonpolar and semipolar stationary phases

The complex hydrocarbons and volatile compounds produced by cultured blue-green alga Nostoc sp. were separated by serially coupled capillary columns with consecutive nonpolar and semipolar stationary phases. More than 130 metabolites including, cyclohexane, cyclopentane, normal saturated hydrocarbons (C7-C30), fatty acids and benzene derivatives were identified by GC-MS. The most abundant family of hydrocarbons identified were derivatives of cyclohexane (41) and cyclopentane (11). Most of these compounds have not been reported previously in blue-green algae studies.     

Joule Heating and Thermal Denaturation of Proteins in Nano-ESI Theta Tips

Electro-osmotically induced Joule heating in theta tips and its effect on protein denaturation were investigated. Myoglobin, equine cytochrome c, bovine cytochrome c, and carbonic anhydrase II solutions were subjected to electro-osmosis in a theta tip and all of the proteins were denatured during the process. The extent of protein denaturation was found to increase with the applied square wave voltage and electrolyte concentration. The solution temperature at the end of a theta tip was measured directly by Raman spectroscopy and shown to increase with the square wave voltage, thereby demonstrating the effect of Joule heating through an independent method. The electro-osmosis of a solution comprised of myoglobin, bovine cytochrome c, and ubiquitin demonstrated that the magnitude of Joule heating that causes protein denaturation is positively correlated with protein melting temperature. This allows for a quick determination of a protein’s relative thermal stability. This work establishes a fast, novel method for protein conformation manipulation prior to MS analysis and provides a temperature-controllable platform for the study of processes that take place in solution with direct coupling to mass spectrometry.

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Modeling of high-density compaction of granular materials by the Discrete Element Method

Cold compaction of metal powders is now commonly studied at a microscopic scale, to further our understanding of contact mechanics between grains. The Discrete Element Method (DEM) is therefore, a good compromise between calculation time and precision. DEM simulations are in general limited to a relative density of about 0.8, because the existing contact laws do not reproduce all the physical phenomena involved in the densification of granular media. Local contact mechanics can be studied by finite element analyses on meshed distinct elements (MDEM, Meshed Distinct Element Method). However, this method is too time-consuming when in the presence of a large number of grains. In the following work, a new analytical contact law will be formulated with MDEM which will subsequently be used to validate the DEM model. Thus, it will be possible with DEM modeling to reproduce high-density compaction of random packings up to a relative density of about 0.95. By introducing a local relative density parameter in the force–displacement relationship, the incompressibility effects which rule high-density behaviors can be introduced in the modeling of powder compaction.     

Geometric Pattern Matching in d -Dimensional Space

We show that, using the L _∞ metric, the minimum Hausdorff distance under translation between two point sets of cardinality n in d -dimensional space can be computed in time O(n ^(4d-2)/3 log ² n) for 3 < d 8, and in time O(n ^5d/4 log ² n) for any d > 8 . Thus we improve the previous time bound of O(n ^2d-2 log ² n) due to Chew and Kedem. For d=3 we obtain a better result of O(n ³ log ² n) time by exploiting the fact that the union of n axis-parallel unit cubes can be decomposed into O(n) disjoint axis-parallel boxes. We prove that the number of different translations that achieve the minimum Hausdorff distance in d -space is . Furthermore, we present an algorithm which computes the minimum Hausdorff distance under the L ₂ metric in d -space in time , for any δ > 0. Received March 17, 1997, and in revised form January 19, 1998.     

Hard sphere perturbation theory for fluids with soft-repulsive-core potentials

The thermodynamic properties of fluids with very soft repulsive-core potentials, resembling those of some liquid metals, are predicted with unprecedented accuracy using a new first-order thermodynamic perturbation theory. This theory is an extension of Mansoori-Canfield/Rasaiah-Stell (MCRS) perturbation theory, obtained by including a configuration integral correction recently identified by Mon, who evaluated it by computer simulation. In this work we derive an analytic expression for Mon's correction in terms of the radial distribution function of the soft-core fluid, g(0)(r), approximated using Lado's self-consistent extension of Weeks-Chandler-Andersen (WCA) theory. Comparisons with WCA and MCRS predictions show that our new extended-MCRS theory outperforms other first-order theories when applied to fluids with very soft inverse-power potentials (n< or =6), and predicts free energies that are within 0.3 kT of simulation results up to the fluid freezing point.     

Endocrine disruption profile analysis of 11,416 chemicals from chemometrical tools⊥

A number of chemicals released into the environment have the potential to disturb the normal functioning of the endocrine system. These chemicals termed endocrine disruptors (EDs) act by mimicking or antagonizing the normal functions of natural hormones and may pose serious threats to the reproductive capability and development of living species. Batteries of laboratory bioassays exist for detecting these chemicals. However, due to time and cost limitations, they cannot be used for all the chemicals which can be found in the ecosystems. SAR and QSAR models are particularly suited to overcome this problem but they only deal with specific targets/endpoints. The interest to account for profiles of endocrine activities instead of unique endpoints to better gauge the complexity of endocrine disruption is discussed through a SAR study performed on 11,416 chemicals retrieved from the US-NCI database and for which 13 different PASS (Prediction of Activity Spectra for Substances) endocrine activities were available. Various multivariate analyses and graphical displays were used for deriving structure-activity relationships based on specific structural features.     

Multiplexed concentration quantification using isotopic surface‐enhanced resonance Raman scattering

The recently developed isotopically edited internal standard approach for surface‐enhanced resonance Raman scattering (SERRS) based chemical quantification is extended to demonstrate multiplexed detection of four different isotopic variants of a single chromophore. More specifically, it is shown that rhodamine‐6G (R6G) with 0, 2, 4, or 6 deuterium substitutions may be reliably quantified in either two‐ or three‐component mixtures. Thus, one isotopic species of known concentration may be used as an internal standard to determine the concentrations of two other isotopic components in a mixture. The concentrations of isotopic R6G SERRS chromophores are determined using partial least squares calibration and shown to yield a predictive accuracy of about ± 10% of the total R6G concentration (over 1–50 nM concentration range). These results set the stage for the use of such isotopic variants as tags for the SERRS/SERS quantitation of mixtures containing proteins, peptides, and other compounds. Copyright © 2009 John Wiley & Sons, Ltd.     

First measurement of the (Ξ−→∑−γ)/(Ξ−→Λπ−) branching ratio

In an experiment performed at the CERN SPS charged hyperon beam, we have observed the radiative decay Ξ^?→∑^?γ. From a sample of 11 candidates, containing an estimated background of 1.6 events, the branching ratio is found to be: $$\Gamma (\Xi ^ - \to \Sigma ^ - \gamma )/(\Xi ^ - \to \Lambda \pi ^ - ) = (2.3 \pm 1.0) \times 10^{ - 4} .$$      

Nonlinear Neural Mapping Analysis of the Adverse Effects of Drugs

Abstract

Numerous drugs have been identified as presenting adverse effects towards the driving of vehicles. A large set of these drugs was compiled and classified into ten categories. Nonlinear neural mapping (N2M) was used to derive a typology of these molecules and also to link their adverse effects to therapeutic categories and structural information.     

Prediction of biological activity profiles of cyanobacterial secondary metabolites

Over the past decade cyanobacteria have become an interesting source of new classes of pharmacologically active natural products. Some cyanobacterial secondary metabolites (CSMs) are also well known for their toxic effects on living species. The PASS (Prediction of Activity Spectra for Substances) computer program, which is able to simultaneously predict more than one thousand biological and toxicological activities from only the structural formulas of the chemicals, was used to predict the biological activity profile of 681 CSMs. Multivariate methods were employed to structure and analyse this wealth of biological and chemical information. PASS predictions were successfully compared to the available information on the pharmacological and toxicological activity of these compounds.