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Abstract: One hundred genotypes of Eucalyptus globulus were ranked according to specific consumption of wood (cubic meters of wood needed to produce 1 ton of pulp). Ten of the most contrasting genotypes were separated in two groups of five clones each; group 1 (G1) with high wood density, high pulp yield, and low specific consumption, and group 2 (G2) with low density, low pulp yield, and high specific consumption. The contrasting genotypes also had significant differences in lignin content, percent syringyl unit composition, and frequency of β-O-4 linkages. Gene expression for phenylalanine ammonia-lyase (PAL), cinnamyl alcohol dehydrogenase (CAD), 4-coumarate:CoA ligase (4CL) and ferulate 5-hydrolase (F5H) was analyzed in the contrasting genotypes. In both groups, transcript abundance for CAD, PAL, and 4CL were similar and only F5H presented significant differences between groups, with high values in the best ranked genotypes G1 in comparison to G2. Correlations between traits were estimated for lignin content vs. pulp yield (R 2 = 0.97), pulp yield vs. syringyl units (R 2 = 0.82), β-O-4 linkages vs. pulp yield (R 2 = 0.84), and β-O-4 linkages vs. syringyl units (R 2 = 0.97). Correlations between chemical composition and transcript abundance for F5H were calculated, finding correlation values with lignin content (R 2 = 0.81), syringyl units (R 2 = 0.83), and pulp yield (R 2 = 0.81). The measurement of transcript abundance of F5H represents a potential genomic tool for tree improvement programs to select trees with high pulp yield.

Forest genetics as a science has existed since the 1940s. With the depiction of the structure of DNA in 1953 by Watson and Crick, some forest geneticists were inspired to apply this knowledge to trees. Since that time the science of molecular biology has evolved considerably. This paper summarises the applications of biotechnology to trees, the traits and trials for genetically engineered trees, possible applications to trees, environmental and social benefit and biosafety.

Hearbeat time series obtained by Holter monitoring of 10 healthy subjects are studied using a nonlinear predictor S map . The normalized prediction error was calculated as a function of the model control parameter in order to get information on the amount of nonlinearity in the data. Moreover, to search for possible chaotic behavior, the linear correlation coefficient between predicted and real values was calculated as a function of the prediction time. The results of this analysis reveal no clear evidence of chaoticness or nonlinearity in the data. Moreover, for 8 subjects out of 10, the predictability during sleep is better than during the daytime.