Functional Characterization of E. coli LptC: Interaction with LPS and a Synthetic Ligand

Lipopolysaccharide (LPS), the main cell‐surface molecular constituent of Gram‐negative bacteria, is synthesized in the inner membrane (IM) and transported to the outer membrane (OM) by the Lpt (lipopolysaccharide transport) machinery. Neosynthesized LPS is first flipped by MsbA across the IM, then transported to the OM by seven Lpt proteins located in the IM (LptBCFG), in the periplasm (LptA), and in the OM (LptDE). A functional OM is essential to bacterial viability and requires correct placement of LPS in the outer leaflet. Therefore, LPS biogenesis represents an ideal target for the development of novel antibiotics against Gram‐negative bacteria. Although the structures of Lpt proteins have been elucidated, little is known about the mechanism of LPS transport, and few data are available on Lpt–LPS binding. We report here the first determination of the thermodynamic and kinetic parameters of the interaction between LptC and a fluorescent lipo‐oligosaccharide (fLOS) in vitro. The apparent dissociation constant (K_d) of the fLOS–LptC interaction was evaluated by two independent methods. The first was based on fLOS capture by resin‐immobilized LptC; the second used quenching of LptC intrinsic fluorescence by fLOS in solution. The K_d values by the two methods (71.4 and 28.8 μm, respectively) are very similar, and are of the same order of magnitude as that of the affinity of LOS for the upstream transporter, MsbA. Interestingly, both methods showed that fLOS binding to LptC is mostly irreversible, thus reflecting the fact that LPS can be released from LptC only when energy is supplied by ATP or in the presence of a higher‐affinity LptA protein. A fluorescent glycolipid was synthesized: this also interacted irreversibly with LptC, but with lower affinity (apparent K_d=221 μM ). This compound binds LptC at the LPS binding site and is a prototype for the development of new antibiotics targeting LPS transport in Gram‐negative bacteria.     

Mesoporous carbons as low temperature fuel cell platinum catalyst supports

Platinum catalysts supported on ordered mesoporous carbons (OMC) are described. The mesoporous carbon support, CMK3 type, was synthesised as an inverse replica of a SBA-15 silica template. The platinum catalysts (i.e. Pt 20 wt% and Pt 10 wt%, respectively), obtained through a conventional wet impregnation method, have been investigated to determine their structural characteristics and electrochemical behaviour. The electro-catalytic performance towards the oxygen reduction reaction (ORR) was compared to those of commercial Pt/C-Vulcan XTC72R (E-Tek) catalysts with the same Pt wt%, under the same experimental conditions. The two catalyst samples have allowed the effect of the variation of both the Pt to Nafion and Pt to the supporting carbon ratios to be studied. Electrochemical tests have been carried out in three different systems: a catalyst ink deposited on a glassy carbon rotating disk electrode (RDE), a gas diffusion electrode (GDE) in a three-electrode cell with H₂SO₄ as the electrolyte and a complete PEM single fuel cell. The first results indicate that the OMC performs slightly less well than commercial carbon supports, mainly in the complete fuel cell system. The data from the cell tests indicate a less effective distribution of Nafion on the OMC surface which, probably, decreases the platinum utilisation and the proton conductivity.     

Overview of the first AGILE catalog of high-confidence gamma-ray sources

AGILE is an Italian Space Agency mission launched in April 2007, devoted to γ-ray observations in the 30 MeV–50 GeV energy range, with simultaneous X-ray imaging capability in the 18–60 keV band. During its first year in orbit AGILE surveyed the γ-ray sky and detected many galactic and extragalactic sources. The first catalog of high-confidence γ-ray sources detected by AGILE during observations performed from July 9, 2007 to June 30, 2008, for energies greater than 100 MeV was recently published [1], and preliminary versions of the catalog were published on the AGILE Data Center webpage to allow AGILE Cycle-2 guest observers to benefit of the Catalog in the preparation of their proposals. We present here the main results of the final catalog, that includes 47 high-confidence sources, 21 of which are associated with confirmed or candidate pulsars, 13 with Blazars (7 FSRQ, 4 BL Lacs, 2 unknown type), 2 with HMXRBs, 2 with SNRs, 1 with a colliding-wind binary system, 8 with unidentified sources.     

Anatase-supported vanadium oxide catalysts for o-xylene oxidation: From consolidated certainties to unexpected effects

In this paper some aspects of the reaction of o-xylene oxidation, catalysed by anatase-supported vanadium oxide, are re-examined and compared with the scientific and patent literature. Specifically, the effect of vanadia loading on turnover frequency and on the distribution of products has been investigated, using catalysts having sub-monolayer and above-monolayer vanadium oxide deposited on a high-purity anatase support. It was found that even catalysts having less-than-monolayer vanadia loading, containing isolated or oligomeric VO₄ species, may give good selectivity to phthalic anhydride, provided the support does not catalyze the formation of heavy compounds. Moreover, the effect of water on the reactivity and chemical–physical characteristics of catalysts was studied by means of in situ Raman spectroscopy. Steam promoted dynamic reversible phenomena occurring at the catalyst surface, by facilitating the dispersion of bulk vanadium oxide and therefore increasing the catalyst activity.     

Detection of buffalo milk adulteration with cow milk by capillary electrophoresis analysis

The addition of cow milk during the production of buffalo mozzarella is a common fraud in dairy industries because of the lower price and greater availability of cow milk throughout the year. The aim of this study was to develop a new, rapid, and robust capillary electrophoresis method for detecting and quantifying cow milk in buffalo milk by exploiting cow α-lactalbumin as a marker of adulteration. In particular, a linear calibration curve was generated, using a training set of calibrators consisting of 7 series of 17 buffalo/bovine whey mixtures, obtained after casein precipitation, with increasing percentages of cow whey. The capillary electrophoresis method showed high linearity (R² = 0.968), repeatability [relative standard deviation (RSD) = 2.11, 3.02, 4.38, and 1.18%, respectively for 5, 10, 20, and 50% of buffalo/bovine whey mixtures], and intermediate precision (RSD = 2.18, 2.49, 5.09, and 3.19%, respectively, for 5, 10, 20, and 50% buffalo/bovine whey mixtures). Moreover, the minimum amount of detectable fraudulent cow milk was 1%, and the limit of quantification was 3.1%.     

Locally adaptive metrics for clustering high dimensional data

Clustering suffers from the curse of dimensionality, and similarity functions that use all input features with equal relevance may not be effective. We introduce an algorithm that discovers clusters in subspaces spanned by different combinations of dimensions via local weightings of features. This approach avoids the risk of loss of information encountered in global dimensionality reduction techniques, and does not assume any data distribution model. Our method associates to each cluster a weight vector, whose values capture the relevance of features within the corresponding cluster. We experimentally demonstrate the gain in perfomance our method achieves with respect to competitive methods, using both synthetic and real datasets. In particular, our results show the feasibility of the proposed technique to perform simultaneous clustering of genes and conditions in gene expression data, and clustering of very high-dimensional data such as text data.     

An effective double-bounded tree-connected Isomap algorithm for microarray data classification

Isometric mapping (Isomap) is a popular nonlinear dimensionality reduction technique which has shown high potential in visualization and classification. However, it appears sensitive to noise or scarcity of observations. This inadequacy may hinder its application for the classification of microarray data, in which the expression levels of thousands of genes in a few normal and tumor sample tissues are measured. In this paper we propose a double-bounded tree-connected variant of Isomap, aimed at being more robust to noise and outliers when used for classification and also computationally more efficient. It differs from the original Isomap in the way the neighborhood graph is generated: in the first stage we apply a double-bounding rule that confines the search to at most k nearest neighbors contained within an ε-radius hypersphere; the resulting subgraphs are then joined by computing a minimum spanning tree among the connected components. We therefore achieve a connected graph without unnaturally inflating the values of k and ε. The computational experiences show that the new method performs significantly better in terms of accuracy with respect to Isomap, k-edge-connected Isomap and the direct application of support vector machines to data in the input space, consistently across seven microarray datasets considered in our tests.     

Composite kernels for semi-supervised clustering

A critical problem related to kernel-based methods is how to select optimal kernels. A kernel function must conform to the learning target in order to obtain meaningful results. While solutions to the problem of estimating optimal kernel functions and corresponding parameters have been proposed in a supervised setting, it remains a challenge when no labeled data are available, and all we have is a set of pairwise must-link and cannot-link constraints. In this paper, we address the problem of optimizing the kernel function using pairwise constraints for semi-supervised clustering. We propose a new optimization criterion for automatically estimating the optimal parameters of composite Gaussian kernels, directly from the data and given constraints. We combine our proposal with a semi-supervised kernel-based algorithm to demonstrate experimentally the effectiveness of our approach. The results show that our method is very effective for kernel-based semi-supervised clustering.