The role of p38 MAPK and its substrates in neuronal plasticity and neurodegenerative disease

A significant amount of evidence suggests that the p38-mitogen-activated protein kinase (MAPK) signalling cascade plays a crucial role in synaptic plasticity and in neurodegenerative diseases. In this review we will discuss the cellular localisation and activation of p38 MAPK and the recent advances on the molecular and cellular mechanisms of its substrates: MAPKAPK 2 (MK2) and tau protein. In particular we will focus our attention on the understanding of the p38 MAPK-MK2 and p38 MAPK-tau activation axis in controlling neuroinflammation, actin remodelling and tau hyperphosphorylation, processes that are thought to be involved in normal ageing as well as in neurodegenerative diseases. We will also give some insight into how elucidating the precise role of p38 MAPK-MK2 and p38 MAPK-tau signalling cascades may help to identify novel therapeutic targets to slow down the symptoms observed in neurodegenerative diseases such as Alzheimer's and Parkinson's disease

Produção de carne em pastagens adubadas.

Adubação com fósforo; Adubação com potássio; Adubação com Enxofre; Adubação com nitrogênio; Adubação com micronutrientes; Manejo das pastagens nas águas; Produção de bovinos de corte em pastagens; Estratégias de manejo na seca.bitstream/item/37352/1/25.pd

Manejo intensivo de pastagen de capim-coastcross

bitstream/item/147068/1/FD05-Manejo-intensivo-de-pastagem-de-capim-croastcross.pd

Características agronômicas das principais plantas forrageiras tropicais.

bitstream/CPPSE/14330/1/PROCIComT35LAC2002.00004.pd

Avaliação do impacto ambiental de sistemas intensivos de produção de carne bovina conduzidos em pastagens.

bitstream/CPPSE/17576/1/Boletim14.pdfISSN 1981-207

Time series forecasting with the WARIMAX-GARCH method

It is well-known that causal forecasting methods that include appropriately chosen Exogenous Variables (EVs) very often present improved forecasting performances over univariate methods. However, in practice, EVs are usually difficult to obtain and in many cases are not available at all. In this paper, a new causal forecasting approach, called Wavelet Auto-Regressive Integrated Moving Average with eXogenous variables and Generalized Auto-Regressive Conditional Heteroscedasticity (WARIMAX-GARCH) method, is proposed to improve predictive performance and accuracy but also to address, at least in part, the problem of unavailable EVs. Basically, the WARIMAX-GARCH method obtains Wavelet “EVs” (WEVs) from Auto-Regressive Integrated Moving Average with eXogenous variables and Generalized Auto-Regressive Conditional Heteroscedasticity (ARIMAX-GARCH) models applied to Wavelet Components (WCs) that are initially determined from the underlying time series. The WEVs are, in fact, treated by the WARIMAX-GARCH method as if they were conventional EVs. Similarly to GARCH and ARIMA-GARCH models, the WARIMAX-GARCH method is suitable for time series exhibiting non-linear characteristics such as conditional variance that depends on past values of observed data. However, unlike those, it can explicitly model frequency domain patterns in the series to help improve predictive performance. An application to a daily time series of dam displacement in Brazil shows the WARIMAX-GARCH method to remarkably outperform the ARIMA-GARCH method, as well as the (multi-layer perceptron) Artificial Neural Network (ANN) and its wavelet version referred to as Wavelet Artificial Neural Network (WANN) as in [1], on statistical measures for both in-sample and out-of-sample forecasting

Manejo de pastagens tropicais.

Definição de alguns termos técnicos; Massa e acúmulo de forragem; Oferta ou disponibilidade de forragem; Resíduo pós-pastejo; Taxa de lotação animal; Eficiência de pastejo; Seleção; Pastejo (ou ocupação) contínuo e pastejo rotacionado; Pastejo rotacionado com dois lotes de animais; Lotação (ou carga) fixa e variável; A planta forrageira; Manejo de pastagens; Pastejo rotacionado; Implantação do sistema de pastejo rotacionado.bitstream/CPPSE-2010/19151/1/PROCIDoc46PMS2009.00415.pdf2. ed. rev