Flavonoids: Classification, Biosynthesis and Chemical Ecology

Flavonoids are natural products widely distributed in the plant kingdom and form one of the main classes of secondary metabolites. They display a large range of structures and ecological significance (e.g., such as the colored pigments in many flower petals), serve as chemotaxonomic marker compounds and have a variety of biological activities. Therefore, they have been extensively investigated but the interest in them is still increasing. The topics that will be discussed in this chapter describe the regulation of flavonoid biosynthesis, the roles of flavonoids in flowers, fruits and roots and mechanisms involved in pollination and their specific functions in the plant

Efficient application deployment in fog-enabled infrastructures

Fog computing is a paradigm that extends cloud computing services to the edge of the network in order to support delay-sensitive Internet of Things (IoT) services. One of the most promising use-cases of fog computing is Smart City scenarios. Fog computing can substantially improve the quality of citywide services by reducing response delays. Owing to geographically distributed and resource-constrained fog nodes and a multitude of IoT devices in Smart Cities, efficient service deployment and end device traffic routing are quite challenging. Therefore, in this paper, we present an Integer Linear Programming (ILP) formulation for the Joint Application Component Placement and Traffic Routing (JAcPTR) problem in which users' delay requirements and the limited traffic processing capacity of application instances are considered. Besides, the JAcPTR enables users and infrastructure managers to easily enforce their locality and management requirements in the deployment of application instances. To cope with the considerably high execution time in large instances of the JAcPTR problem, we propose a fast polynomial-time heuristic to efficiently solve the problem. The performance of the proposed heuristic has been evaluated through extensive simulation. Results show that in large instances of the problem, while the state-of-the-art Mixed Integer Linear Programming (MILP) solver fails to obtain a solution in 50% of the simulation runs in 300 seconds, our proposed heuristic can obtain a near-optimal solution in less than one second

AVALIAÇÃO DE REGISTROS SEDIMENTARES NA ÁREA DA ENCOSTA DA FACE LESTE DO DOMO DE ITABAIANA-SE: RESULTADOS PRELIMINARES

O Quaternário é reconhecido por variações das condições climáticas que deixaram na paisagem marcas dessas oscilações. O trabalho objetivou reconhecer características dos depósitos e estruturas presentes nas seções. A encosta encontra-se numa área susceptível as mudanças na sua estrutura, remodelando a forma do relevo, onde foram encontradas depósitos referentes a eventos de grande magnitude ou de alta torrencialidade capaz de remobilizar mantos de alteração em forma de fluxos em curtos

MONITORAMENTO DE PROCESSOS EROSIVOS EM UMA ENCOSTA DA BORDA LESTE NO DOMO DE ITABAIANA/SE

A região do Domo de Itabaiana localizada no agreste sergipano se apresenta como um importante compartimento morfoestrutural, de geodiversidade bem particularizada sob condições de marcante metamorfismo regional, neste são evidenciadas dinâmicas diferenciadas da Paisagem geomorfológica. Dá analise realizada emerge a relação pedogênese-morfogênese, onde hoje na área ocorre o predomínio da morfogênese

Angiotensin-(1–7)/Mas axis integrity is required for the expression of object recognition memory

AbstractIt has been shown that the brain has its own intrinsic renin–angiotensin system (RAS) and angiotensin-(1–7) (Ang-(1–7)) is particularly interesting, because it appears to counterbalance most of the Ang II effects. Ang-(1–7) exerts its biological function through activation of the G-protein-coupled receptor Mas. Interestingly, hippocampus is one of the regions with higher expression of Mas. However, the role of Ang-(1–7)/Mas axis in hippocampus-dependent memories is still poorly understood. Here we demonstrated that Mas ablation, as well as the blockade of Mas in the CA1-hippocampus, impaired object recognition memory (ORM). We also demonstrated that the blockade of Ang II receptors AT1, but not AT2, recovers ORM impairment of Mas-deficient mice. Considering that high concentrations of Ang-(1–7) may activate AT1 receptors, nonspecifically, we evaluate the levels of Ang-(1–7) and its main precursors Ang I and Ang II in the hippocampus of Mas-deficient mice. The Ang I and Ang II levels are unaltered in the whole hipocampus of MasKo. However, Ang-(1–7) concentration is increased in the whole hippocampus of MasKo mice, as well as in the CA1 area. Taken together, our findings suggest that the functionality of the Ang-(1–7)/Mas axis is essential for normal ORM processing

Thermodynamic properties of excess-oxygen-doped La2CuO4.11 near a simultaneous transition to superconductivity and long-range magnetic order

We have measured the specific heat and magnetization {\it versus} temperature in a single crystal sample of superconducting La$_{2}$CuO$_{4.11}$ and in a sample of the same material after removing the excess oxygen, in magnetic fields up to 15 T. Using the deoxygenated sample to subtract the phonon contribution, we find a broad peak in the specific heat, centered at 50 K. This excess specific heat is attributed to fluctuations of the Cu spins possibly enhanced by an interplay with the charge degrees of freedom, and appears to be independent of magnetic field, up to 15 T. Near the superconducting transition $T_{c}$($H$=0)= 43 K, we find a sharp feature that is strongly suppressed when the magnetic field is applied parallel to the crystallographic c-axis. A model for 3D vortex fluctuations is used to scale magnetization measured at several magnetic fields. When the magnetic field is applied perpendicular to the c-axis, the only observed effect is a slight shift in the superconducting transition temperature.Comment: 8 pages, 8 figure

Transfer learning for galaxy morphology from one survey to another

© 2018 The Author(s). Published by Oxford University Press on behalf of the Royal Astronomical Society.Deep Learning (DL) algorithms for morphological classification of galaxies have proven very successful, mimicking (or even improving) visual classifications. However, these algorithms rely on large training samples of labelled galaxies (typically thousands of them). A key question for using DL classifications in future Big Data surveys is how much of the knowledge acquired from an existing survey can be exported to a new dataset, i.e. if the features learned by the machines are meaningful for different data. We test the performance of DL models, trained with Sloan Digital Sky Survey (SDSS) data, on Dark Energy survey (DES) using images for a sample of $\sim$5000 galaxies with a similar redshift distribution to SDSS. Applying the models directly to DES data provides a reasonable global accuracy ($\sim$ 90%), but small completeness and purity values. A fast domain adaptation step, consisting in a further training with a small DES sample of galaxies ($\sim$500-300), is enough for obtaining an accuracy > 95% and a significant improvement in the completeness and purity values. This demonstrates that, once trained with a particular dataset, machines can quickly adapt to new instrument characteristics (e.g., PSF, seeing, depth), reducing by almost one order of magnitude the necessary training sample for morphological classification. Redshift evolution effects or significant depth differences are not taken into account in this study.Peer reviewedFinal Accepted Versio