Abstract: In the study, we developed new markers for phylogenetic relationships and intraspecies differentiation in Coffea. Nana and Divo, two novel Ty1-copia LTR-retrotransposon families, were isolated through C. canephora BAC clone sequencing. Nana- and Divo-based markers were used to test their: (1) ability to resolve recent phylogenetic relationships; (2) efficiency in detecting intra-species differentiation. Sequence-specific amplification polymorphism (SSAP), retrotransposon-microsatellite amplified polymorphism (REMAP) and retrotransposon-based insertion polymorphism (RBIP) approaches were applied to 182 accessions (31 Coffea species and one Psilanthus accession). Nana- and Divo-based markers revealed contrasted transpositional histories. At the BAC clone locus, RBIP results on C. canephora demonstrated that Nana insertion took place prior to C. canephora differentiation, while Divo insertion occurred after differentiation. Combined SSAP and REMAP data showed that Nana could resolve Coffea lineages, while Divo was efficient at a lower taxonomic level. The combined results indicated that the retrotransposon-based markers were useful in highlighting Coffea genetic diversity and the chronological pattern of speciation/differentiation events. Ongoing complete sequencing of the C. canephora genome will soon enable exhaustive identification of LTR-RTN families, as well as more precise in-depth analyses on contributions to genome size variation and Coffea evolution. © 2011 Springer-Verlag.

Abstract: We present the synthesis and structural characterization, assessed by various techniques (FTIR, TGA, UV-vis, elemental analysis, single-crystal X-ray diffraction for three compounds, magnetic susceptibility, and electrochemistry) of five manganese-containing Wells-Dawson sandwich-type (WDST) complexes. The dimanganese(II)-containing complex, [Na2(H2O) 2MnII2(As2W15O 56)2]18- (1), was obtained by reaction of MnCl2 with 1 equiv of [As2W15O 56]12- in acetate medium (pH 4.7). Oxidation of 1 by Na2S2O8 in aqueous solution led to the dimanganese(III) complex [Na2(H2O)2Mn III2(As2W15O56) 2]16- (2), while its trimanganese(II) homologue, [Na(H2O)2MnII(H2O)Mn II2(As2W15O56) 2]17- (3), was obtained by addition of ca. 1 equiv of MnCl2 to a solution of 1 in 1 M NaCl. The trimanganese(III) and tetramanganese(III) counterparts, [MnIII(H2O)Mn III2(As2W15O56) 2]15- (4) and [MnIII2(H 2O)2MnIII2(As2W 15O56)2]12- (6), are, respectively, obtained by oxidation of aqueous solutions of 3 and [MnII2(H2O)2MnII2(As 2W15O56)2]16- (5) by Na2S2O8. Single-crystal X-ray analyses were carried out on 2, 3, and 4. BVS calculations and XPS confirmed that the oxidation state of Mn centers is +II for complexes 1, 3, and 5 and +III for 2, 4, and 6. A complete comparative electrochemical study was carried out on the six compounds cited above, and it was possible to observe the distinct redox steps MnIV/III and MnIII/II. Magnetization measurements, as a function of temperature, confirm the presence of antiferromagnetic interactions between the Mn ions in these compounds in all cases with the exception of compound 2. © 2011 American Chemical Society.

Abstract: Alkaloids extract of Aspidosperma album was tested as corrosion inhibitor for C38 steel in 1 M HCl by using polarization and electrochemical impedance spectroscopy (EIS). Potentiodynamic polarization curves indicated that the plant extract behaves as mixed-type inhibitors. Impedance measurements showed that the double-layer capacitance decreased and charge-transfer resistance increased with increase in the inhibitors concentration and hence increasing in inhibition efficiency. The effect of temperature on the corrosion behavior of C38 steel in 1 M HCl with and without addition of plant extract was studied in the temperature range 25 – 55°C. The adsorption of the inhibitor molecules was in accordance with the Langmuir adsorption isotherm. The results obtained show that this plant extract could serve as an effective inhibitor for the corrosion of C38 steel in hydrochloric acid. © 2011 by ESG.

Abstract: Termites are degradation agents that inflict severe damage on wood. Some long-lasting Amazonian trees can resist these insects by producing toxic secondary metabolites. These metabolites could potentially replace synthetic termiticidal products which are becoming more restricted to use. Results: Sextonia rubra is resistant to termite-induced degradation. It has been demonstrated that this species naturally produces an ethyl-acetate-soluble termiticidal metabolite, rubrynolide, to protect its wood. Assays in the presence of tropical and invasive termites established that both rubrynolide and crude ethyl acetate extract from S. rubra wood can be used as a treatment for the protection of sensitive woods against termites. Conclusion: Rubrynolide and S. rubra extract are promising candidates for the replacement of synthetic termiticides. © 2011 Society of Chemical Industry.

Abstract: Characterisation of forest structure is a major stake for forestry, species conservation, carbon stock estimates and many forest ecology and management issues. At large scale natural forest structure tends to vary according to climate and geomorphomology (Paget, 1999; Steege et al., 2006) while soil characteristics (and notably water regime) and syMgenetic stage add some finer scale variation (Oldeman, 1989; Sabatier et al., 1997). Forest structure characterisation traditionally relies on field-based collection of individual tree dimensions such as stem diameter and stem height sampled across tracks of forest (Hall et al., 1998). However, such field intensive methods are costly, and of low accuracy regarding measures of tree heights. Airborne light detection and ranging (LiDAR) technology provides horizontal and vertical Information at high spatial resolutions and vertical accuracies (Lim et al., 2003; Hyyppä et al., 2004). It has the potential for gathering vegetation structure data over large areas rapidly at moderate cost and hence is of particular relevance for poorly sampled, difficult to access and largely unexplored tropical rainforests. In this study we examined the ability of airborne LiDAR to detect spatial changes in the structure of dense tropical rain forest and we probed this remote sensing approach against local statistics derived from stem diameters (i.e. classical field data information) mapped across a large track of forest at a long term experimental site in French Guyana. The large variability in forest structure occurring at the experimental site is du to natural variation of the soil cover (and notably drainage properties) combined with various logging intensities applied 15 years before the LiDAR data were acquired. On this basis ten different forest types were identified at the site (figure 1 and 3). Various stem based statistics were computed for a series of meshes with cells ranging from 30 by 30 m plots to 250 by 250 m plots. These statistics included basal area, stem density, quadratic mean diameter, and diameter distribution percentiles. Similarly local statistics were extracted either from the Canopy Height Model (e.g. median height, mean height, standard height deviation, height coefficient of variation, height percentiles, frequency of hits below 5 m above ground level). Additionally a wetness index (Böhner et al., 2002) was computed at each node of a 5 by 5 m grid from the Digital Terrain Model also extracted from the LiDAR data set. We used both types of cell statistics to discriminate the various forest types. Comparison between the two approaches for a range of spatial resolution is available from in table 1. Results indicate that LiDAR based statistics are essentially as powerful as field based statistics to discriminate forest types at coarse scale. This reflects the very strong correlation between the CHM and the field based stem diameter data. For example (figure 5) the Pearson correlation coefficient between median height and quadratic mean diameter for cells of 125 by 125 m is 0.945 (n=0.72). When a finer resolution is required however as for the detection of seasonally flooded bottomland forest along thalwegs, then LiDAR technology proves more efficient than field based inventories as it combines information from the DTM and the CHM. The wetness index alone correctly retrieves about 2 thirds of the seasonally flooded areas. All in all, discriminant analysis performance of the LiDAR derived information approaches 80% when classifying forests cover at the finest scale of 5 by 5m into 10 different types and reaches 87% when a coarser classification Into 6 forest types is considered (figure 4).