University of Alcalá

About: University of Alcalá is a education organization based out in Alcalá de Henares, Spain. It is known for research contribution in the topics: Population & Receptor. The organization has 10795 authors who have published 20718 publications receiving 410089 citations. The organization is also known as: University of Alcala & University of Alcala de Henares.

Signal Transduction Pathways in Breast Cancer: The Important Role of PI3K/Akt/mTOR.

Abstract: Breast cancer is the cancer with the highest prevalence in women and is the number-one cause of cancer mortality worldwide. Cell transduction is a fundamental process in the development and progression of cancer. Modifications in various cell signalling pathways promote tumour cell proliferation, progression, and survival. The PI3K/Akt/mTOR pathway is an example of that, and it is involved in growth, proliferation, survival, motility, metabolism, and immune response regulation. Activation of this pathway is one of the main causes of cancer cell resistance to antitumour therapies. This makes PI3K/Akt/mTOR signalling a crucial object of study for understanding the development and progression of this disease. Thus, this pathway may have a role as a potential therapeutic target, as well as prognostic and diagnostic value, in patients with breast cancer. Despite the existence of selective PI3K/Akt/mTOR pathway inhibitors and current clinical trials, the cellular mechanisms are not yet known. The present review aims to understand the current state of this important disease and the paths that must be forged.

Estimation of shrub height for fuel-type mapping combining airborne LiDAR and simultaneous color infrared ortho imaging

Abstract: A fuel-type map of a predominantly shrub-land area in central Portugal was generated for a fire research experimental site, by combining airborne light detection and ranging (LiDAR), and simultaneous color infrared ortho imaging. Since the vegetation canopy and the ground are too close together to be easily discerned by LiDAR pulses, standard methods of processing LiDAR data did not provide an accurate estimate of shrub height. It was demonstrated that the standard process to generate the digital ground model (DGM) sometimes contained height values for the top of the shrub canopy rather than from the ground. Improvement of the DGM was based on separating canopy from ground hits using color infrared ortho imaging to detect shrub cover, which was measured simultaneously with the LiDAR data. Potentially erroneous data in the DGM was identified using two criteria: low vegetation height and high Normalized Difference Vegetation Index (NDVI), a commonly used spectral index to identify vegetated areas. Based on the height of surrounding pixels, a second interpolation of the DGM was performed to extract those erroneously identified as ground in the standard method. The estimation of the shrub height improved significantly after this correction, and increased determination coefficients from R2 = 0.48 to 0.65. However, the estimated shrub heights were still less than those observed in the field.

Prediction of fire occurrence from live fuel moisture content measurements in a Mediterranean ecosystem

Abstract: The present paper presents and discusses the relationships between live Fuel Moisture Content (FMC) measurements and fire occurrence (number of fires and burned area) in a Mediterranean area of central Spain. Grasslands and four shrub species (Cistus ladanifer L., Rosmarinus officinalis L., Erica australis L. and Phillyrea angustifolia L.) were sampled in the field from the spring to the summer season over a 9-year period. Higher seasonal FMC variability was found for the herbaceous species than for shrubs, as grasslands have very low values in summertime. Moisture variations of grasslands were found to be good predictors of number of fires and total burned surface, while moisture variation of two shrubs (C. ladanifer L. and R. officinalis L.) was more sensitive to both the total burned area and the occurrence of large fires. All these species showed significant differences between the FMC of high and low occurrence periods. Three different logistic regression models were built for the 202 periods of analysis: one to predict periods with more and less than seven fires, another to predict periods with and without large fires (>500 ha), and the third to predict periods with more and less than 200 ha burned. The results showed accuracy in predicting periods with a high number of fires (94%), and extensive burned area (85%), with less accuracy in estimating periods with large fires (58%). Finally, empirical functions based on logistic regression analysis were successfully related to fire ignition or potential burned area from FMC data. These models should be useful to integrate FMC measurements with other variables of fire danger (ignition causes, for instance), to provide a more comprehensive assessment of fire danger conditions.

Meta-studies in land use science: Current coverage and prospects

Abstract: Land use science has traditionally used case-study approaches for in-depth investigation of land use change processes and impacts. Meta-studies synthesize findings across case-study evidence to identify general patterns. In this paper, we provide a review of meta-studies in land use science. Various meta-studies have been conducted, which synthesize deforestation and agricultural land use change processes, while other important changes, such as urbanization, wetland conversion, and grassland dynamics have hardly been addressed. Meta-studies of land use change impacts focus mostly on biodiversity and biogeochemical cycles, while meta-studies of socioeconomic consequences are rare. Land use change processes and land use change impacts are generally addressed in isolation, while only few studies considered trajectories of drivers through changes to their impacts and their potential feedbacks. We provide a conceptual framework for linking meta-studies of land use change processes and impacts for the analysis of coupled human–environmental systems. Moreover, we provide suggestions for combining meta-studies of different land use change processes to develop a more integrated theory of land use change, and for combining meta-studies of land use change impacts to identify tradeoffs between different impacts. Land use science can benefit from an improved conceptualization of land use change processes and their impacts, and from new methods that combine meta-study findings to advance our understanding of human–environmental systems.