Concurrent multiple binding modes of a single ligand can be detected and quantified by saturation transfer difference (STD) NMR spectroscopy. Analysis of experimental and predicted STD initial growing rates has allowed us to determine the precise orientation of Man α(1→2)Man in the minor complex with the carbohydrate recognition domain of DC‐SIGN.

Protein-carbohydrate recognition is of fundamental importance for a large number of biological processes; carbohydrate-aromatic stacking is a widespread, but poorly understood, structural motif in this recognition. We describe, for the first time, the measurement of carbohydrate-aromatic interactions from their contribution to the stability of a dangling-ended DNA model system. We observe clear differences in the energetics of the interactions of several monosaccharides with a benzene moiety depending on the number of hydroxy groups, the stereochemistry, and the presence of a methyl group in the pyranose ring. A fucose-benzene pair is the most stabilizing of the studied series (-0.4 Kcal mol(-1)) and this interaction can be placed in the same range as other more studied interactions with aromatic residues of proteins, such as Phe-Phe, Phe-Met, or Phe-His. The noncovalent forces involved seem to be dispersion forces and nonconventional hydrogen bonds, whereas hydrophobic effects do not seem to drive the interaction.

DC-SIGN, a lectin, which presents at the surface of immature dendritic cells, constitutes nowadays a promising target for the design of new antiviral drugs. This lectin recognizes highly glycosylated proteins present at the surface of several pathogens such as HIV, Ebola virus, Candida albicans, Mycobacterium tuberculosis, etc. Understanding the binding mode of this lectin is a topic of tremendous interest and will permit a rational design of new and more selective ligands. Here, we present computational and experimental tools to study the interaction of di- and trisaccharides with DC-SIGN. Docking analysis of complexes involving mannosyl di- and trisaccharides and the carbohydrate recognition domain (CRD) of DC-SIGN have been performed. Trisaccharides Manalpha1,2[Manalpha1,6]Man 1 and Manalpha1,3[Manalpha1,6]Man 2 were synthesized from an orthogonally protected mannose as a common intermediate. Using these ligands and the soluble extracellular domain (ECD) of DC-SIGN, NMR experiments based on STD and transfer-NOE were performed providing additional information. Conformational analysis of the mannosyl ligands in the free and bound states was done. These studies have demonstrated that terminal mannoses at positions 2 or 3 in the trisaccharides are the most important moiety and present the strongest contact with the binding site of the lectin. Multiple binding modes could be proposed and therefore should be considered in the design of new ligands.

Biological rhythms are driven by endogenous biological clocks; in mammals, the master clock is located in the suprachiasmatic nucleus (SCN) of the hypothalamus. This master pacemaker can synchronize other peripheral oscillators in several tissues such as some involved in endocrine or reproductive functions. The presence of an endogenous placental clock has received little attention. In fact, there are no studies in human full-term placentas. To test the existence of an endogenous pacemaker in this tissue we have studied the expression of circadian locomoter output cycles kaput (Clock), brain and muscle arnt-like (Bmal)1, period (Per)2, and cryptochrome (Cry)1 mRNAs at 00, 04, 08, 12, 16, and 20 hours by qPCR. The four clock genes studied are expressed in full-term human placenta. The results obtained allow us to suggest that a peripheral oscillator exists in human placenta. Data were analyzed using Fourier series where only the Clock and Bmal1 expression shows a circadian rhythm.

Text Categorization is the process of assigning documents to a set of previously fixed categories. A lot of research is going on with the goal of automating this time-consuming task. Several different algorithms have been applied, and Support Vector Machines (SVM) have shown very good results. In this report, we try to prove that a previous filtering of the words used by SVM in the classification can improve the overall performance. This hypothesis is systematically tested with three different measures of word relevance, on two different corpus (one of them considered in three different splits), and with both local and global vocabularies. The results show that filtering significantly improves the recall of the method, and that also has the effect of significantly improving the overall performance.

Abstract-Text Categorization, which consists of automatically assigning documents to a set of categories, usually involves the management of a huge number of features. Most of them are irrelevant and others introduce noise which could mislead the classifiers. Thus, feature reduction is often performed in order to increase the efficiency and effectiveness of the classification. In this paper, we propose to select relevant features by means of a family of linear filtering measures which are simpler than the usual measures applied for this purpose. We carry out experiments over two different corpora and find that the proposed measures perform better than the existing ones.

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