New cytotoxic sesquiterpenes from the red algae Laurencia obtusa and Laurencia microcladia

Three new sesquiterpenes (1-3), along with five known (5-9), were isolated from the organic extract of the red alga Laurencia obtusa, collected from the coastal rocks of Serifos in the Aegean Sea. A new dimeric sesquiterpene of the cyclolaurane-type (4) along with four previously reported (7, 10-12) metabolites, were isolated from the extract of Laurencia microcladia, collected at Chios island in the North Aegean Sea. The structures and the relative stereochemistry of the compounds are proposed on the basis of their spectral data. The cytotoxicity of the isolated metabolites was evaluated against several cell lines including human tumor cell lines.     

T-cell epitopes of the La/SSB autoantigen: prediction based on the homology modeling of HLA-DQ2/DQ7 with the insulin-B peptide/HLA-DQ8 complex

T-cell epitopes are important components of the inappropriate response of the immune system to self-proteins in autoimmune diseases. In this study, the candidate T-cell epitopes of the La/SSB autoantigen, the main target of the autoimmune response in patients with Sjogren's Syndrome (SS), and Systemic Lupus Erythematosus (SLE) were predicted using as a template the HLA-DQ2 and DQ7 molecules, which are genetically linked to patients with SS and SLE. Modeling of DQ2 and DQ7 was based on the crystal structure of HLA-DQ8, an HLA molecule of high risk factor of type I diabetes, which is also an autoimmune disease. The quality and reliability of the modeled DQ2 and DQ7 was confirmed by the Ramachandran plot and the TINKER molecular modeling software. Common and/or similar candidate T-cell epitopes, obtained by comparing three different approaches the Taylor's sequence pattern, the TEPITOPE quantitative matrices, and the MULTIPRED artificial neural network, were subjected to homology modeling with the crystal structure of the insulin-B peptide complexed with HLA-DQ8, and the best superposed candidate epitopes were placed into the modeled HLA-DQ2 and DQ7 binding grooves to perform energy minimization calculations. Six T-cell epitopes were predicted for HLA-DQ7 and nine for HLA-DQ2 covering parts of the amino-terminal and the central regions of the La/SSB autoantigen. Residues corresponding to the P1, P4, and P9 pockets of the HLA-DQ2 and DQ7 binding grooves experience very low SASA because they are less exposed to the microenvironment of the groove. The proposed T-cell epitopes complexed with HLA-DQ2/DQ7 were further evaluated for their binding efficiency according to their potential interaction energy, binding affinity, and IC50 values. Our approach constitutes the ground work for a rapid and reliable experimentation concerning the T-cell epitope mapping of autoantigens, and could lead to the development of T-cell inhibitors as immunotherapeutics in autoimmune diseases.     

Synthesis of highly loaded Cu/Ce mesoporous silica. Active catalyst for the simultaneous reduction of SO2 and NO with CO

We report the synthesis of a thermally stable, highly loaded (15 wt.%) Cu/Ce bimetallic mesoporous silica with high surface area and metal dispersion, which was the first template assisted mesoporous network successfully tested for the simultaneous reduction of SO2 and NO with CO, achieving complete conversion of the reactants to elemental sulfur and N2 at high space velocity 32,000 h(-1).     

Coupling of sequential injection analysis and capillary electrophoresis - Laser-induced fluorescence via a valve interface for on-line derivatization and analysis of amino acids and peptides

The on-line coupling of sequential injection analysis (SIA) and capillary electrophoresis (CE) via an in-line injection valve is presented. The SIA system is used for automated derivatization of amino acids and peptides. Dichlorotriazinylaminofluorescein serves as the derivatization agent, thus enabling sensitive laser-induced fluorescence detection of the derivatized analytes. The SIA procedure includes the following steps: (a) introduction of reagent and sample zones in a holding coil, (b) sample and reagent mixing in a reaction coil, (c) stop-flow step for increase of the reaction time, and (d) delivery of derivatized sample into the loop of the micro-valve interface. A small portion of the analyte zone is introduced electrokinetically in the separation capillary via the valve interface and CE analysis is performed. Factors affecting the CE separation, such as pH, the borate and sodium dodecyl sulphate concentration of the background electrolyte have been optimized. The derivatization conditions have been studied to obtain a high reaction yield in a relative short time. The transfer of a part of the reaction plug into the loop of the valve interface has been optimized. Using des-Tyr(1)-[Met]-enkephalinamide as test compound, it is demonstrated that after automated derivatization, on-line electrophoretic analysis could be achieved. Glycine has been selected as the internal standard in order to correct for variations in reaction time and filling of the injection loop. For the enkephalin, good reproducibility (RSD<4.5% calculated by the ratio of the peak areas) and linearity (0.5-5 microg mL(-1), R(2)>or=0.994) are obtained with a detection limit of 30 ng mL(-1) (S/N=3).     

Local Mass Transfer Correlations for Nonaqueous Phase Liquid Pool Dissolution in Saturated Porous Media

Local mass transfer correlations are developed to describe the rate of interface mass transfer of single component nonaqueous phase liquid (NAPL) pools in saturated subsurface formations. A threedimensional solute transport model is employed to compute local mass transfer coefficients from concentration gradients at the NAPL–water interface, assuming that the aqueous phase concentration along the NAPL–water interface is constant and equal to the solubility concentration. Furthermore, it is assumed that the porous medium is homogeneous, the interstitial fluid velocity steady and the dissolved solute may undergo firstorder decay or may sorb under local equilibrium conditions. Powerlaw expressions relating the local Sherwood number to appropriate local Peclet numbers are developed for both rectangular and elliptic/circular source geometries. The proposed power law correlations are fitted to numerically generated data and the correlation coefficients are determined using nonlinear least squares regression. The estimated correlation coefficients are found to be direct functions of the interstitial fluid velocity, pool dimensions, and pool geometry.     

Objective Supervised Machine Learning-Based Classification and Inference of Biological Neuronal Networks

The classification of biological neuron types and networks poses challenges to the full understanding of the human brain’s organisation and functioning. In this paper, we develop a novel objective classification model of biological neuronal morphology and electrical types and their networks, based on the attributes of neuronal communication using supervised machine learning solutions. This presents advantages compared to the existing approaches in neuroinformatics since the data related to mutual information or delay between neurons obtained from spike trains are more abundant than conventional morphological data. We constructed two open-access computational platforms of various neuronal circuits from the Blue Brain Project realistic models, named Neurpy and Neurgen. Then, we investigated how we could perform network tomography with cortical neuronal circuits for the morphological, topological and electrical classification of neurons. We extracted the simulated data of 10,000 network topology combinations with five layers, 25 morphological type (m-type) cells, and 14 electrical type (e-type) cells. We applied the data to several different classifiers (including Support Vector Machine (SVM), Decision Trees, Random Forest, and Artificial Neural Networks). We achieved accuracies of up to 70%, and the inference of biological network structures using network tomography reached up to 65% of accuracy. Objective classification of biological networks can be achieved with cascaded machine learning methods using neuron communication data. SVM methods seem to perform better amongst used techniques. Our research not only contributes to existing classification efforts but sets the road-map for future usage of brain–machine interfaces towards an in vivo objective classification of neurons as a sensing mechanism of the brain’s structure.