Soil mineral N dynamics beneath mixtures of leaves from legume and fruit trees in Central Amazonian multi-strata agroforests

Long term applications of leguminous green mulch could increase mineralizable nitrogen (N) beneath cupuaçu trees produced on the infertile acidic Ultisols and Oxisols of the Amazon Basin. However, low quality standing cupuaçu litter could interfere with green mulch N release and soil N mineralization. This study compared mineral N, total N, and microbial biomass N beneath cupuaçu trees grown in two different agroforestry systems, north of Manaus, Brazil, following seven years of different green mulch application rates. To test for net interactions between green mulch and cupuaçu litter, dried gliricidia and inga leaves were mixed with senescent cupuaçu leaves, surface applied to an Oxisol soil, and incubated in a greenhouse for 162 days. Leaf decomposition, N release and soil N mineralization were periodically measured in the mixed species litter treatments and compared to single species applications. The effect of legume biomass and cupuaçu litter on soil mineral N was additive implying that recommendations for green mulch applications to cupuaçu trees can be based on N dynamics of individual green mulch species. Results demonstrated that residue quality, not quantity, was the dominant factor affecting the rate of N release from leaves and soil N mineralization in a controlled environment. In the field, complex N cycling and other factors, including soil fauna, roots, and microclimatic effects, had a stronger influence on available soil N than residue quality.

Metabolômica, uma abordagem otimizada para exploração da biodiversidade brasileira: estado da arte, perspectivas e desafios

Brazilian biodiversity is a colossal source of secondary metabolites with remarkable structural features, which are valuable in further biodiscovery studies. In order to fully understand the relations and interactions of a living system with its surroundings, efforts in natural product chemistry are directed toward the challenge of detecting and identifying all the molecular components present in complex samples. It is plausible that this endeavor was born out of recent technological sophistication in secondary metabolite identification with sensitive spectroscopic instruments (MS and NMR) and higher resolving power of chromatographic systems, which allow a decrease in the amount of required sample and time to acquire data. Nevertheless, the escalation of data acquired in these analyses must be sorted with statistical and multi-way tools in order to select key information. Chromatography is also of paramount importance, more so when selected compounds need to be isolated for further investigation. However, in the course of pursuing a "greener" environment, new policies, with an aim to decrease the use of energy and solvents, are being developed and incorporated into analytical methods. Metabolomics could be an effective tool to answer questions on how living organisms in our huge biodiversity work and interact with their surroundings while also being strategic to the development of high value bio-derived products, such as phytotherapeutics and nutraceuticals. The incorporation of proper phytotherapeutics in the so-called Brazilian Unified Health System is considered an important factor for the urgent improvement and expansion of the Brazilian national health system. Furthermore, this approach could have a positive impact on the international interest toward scientific research developed in Brazil as well as the development of high value bio-derived products, which appear as an interesting economic opportunity in national and global markets. Thus, this study attempts to highlight the recent advances in analytical tools used in detection of secondary metabolites, which can be useful as bioproducts. It also emphasizes the potential avenues to be explored in Brazilian biodiversity, known for its rich chemical diversity.

Non-Linear Unsteady Aerodynamic Response Approximation Using Multi-Layer Functionals

Non-linear functional representation of the aerodynamic response provides a convenient mathematical model for motion-induced unsteady transonic aerodynamic loads response, that accounts for both complex non-linearities and time-history effects. A recent development, based on functional approximation theory, has established a novel functional form; namely, the multi-layer functional. For a large class of non-linear dynamic systems, such multi-layer functional representations can be realised via finite impulse response (FIR) neural networks. Identification of an appropriate FIR neural network model is facilitated by means of a supervised training process in which a limited sample of system input-output data sets is presented to the temporal neural network. The present work describes a procedure for the systematic identification of parameterised neural network models of motion-induced unsteady transonic aerodynamic loads response. The training process is based on a conventional genetic algorithm to optimise the network architecture, combined with a simplified random search algorithm to update weight and bias values. Application of the scheme to representative transonic aerodynamic loads response data for a bidimensional airfoil executing finite-amplitude motion in transonic flow is used to demonstrate the feasibility of the approach. The approach is shown to furnish a satisfactory generalisation property to different motion histories over a range of Mach numbers in the transonic regime.