One of the most profound events in biospheric evolution was the emergence of animals, which is thought to have occurred some 600-650 Ma. Here we report on the discovery of phosphatised body fossils that we interpret as ancient sponge-like fossils and term them Otavia antiqua gen. et sp. nov. The fossils are found in Namibia in rocks that range in age between about 760 Ma and 550 Ma. This age places the advent of animals some 100 to 150 million years earlier than proposed, and prior to the extreme climatic changes and postulated stepwise increases in oxygen levels of Ediacaran time. These findings support the predictions based on genetic sequencing and inferences drawn from biomarkers that the first animals were sponges. Further, the deposition and burial of Otavia as sedimentary particles may have driven the large positive C-isotopic excursions and increases in oxygen levels that have been inferred for Neoproterozoic time.

The main objective of this work was to assess the influence of temperature and pressure on the chemical characteristics of the essential oil obtained from CO2 extraction of grape bagasses in the production of jam. The experiments were performed in a laboratory-scale unit, where the effect of temperature (290 and 303 K) and pressure (15 and 25 Mpa) was investigated in terms of liquid yield and chemical composition of the extracts. The CO2 mass flow rate was kept within a range of 2.5 to 3.0 g/min. The instrumental analysis was performed by gas chromatography with a mass spectrometer detector (GC-MS). The extraction conditions investigated in this work had no significant influence on the mass of essencial oil extracted. The main compounds identified in the extracts by the GC-MS spectra library (match quality higher tan 90%) were octadecane, dihydroxy ergostene-dione and phenylethyl n-decanoate when the temperature was increased from 290 to 303 K. Heptanal, ethyl ester of decosonoic acid and hexatriacontane were the individual compounds with the greatest increase in the chromatographic peak area when the pressure was increased from 15 to 25 Mpa. The most important class of compounds were hydrocarbons at 303 K and 15 MPa and were ketones and aldehydes at 25 Mpa and 290 K.

This work presents the results of a hybrid neural model (HNM) technique as applied to modeling supercritical fluid extraction (SCFE) curves obtained from two Brazilian vegetable matrices. The serial HNM employed uses a neural network to estimate parameters of a phenomenological model. A small set of SCFE data for each vegetable was used to generate a semi-empirical extended data set, large enough for efficient network training, using three different approaches. Afterwards, other sets of experimental data, not used during the training procedure, were used to validate each approach. The HNM correlates well withthe experimental data, and it is shown that the predictions accomplished with this technique may be promising for SCFE purposes.