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The goal of this thesis is to build a near-real time defoliation detector that could be used, early in the spring, to find out which areas of a birch forest are being affected by an insect outbreak. The importance of a reliable detection at the beginning of the spring lies on the possibility for an early intervention. The data on which the study is based is the Normalized Difference Vegetation Index (NDVI), which is calculated from the 8-days interval measurements, obtained from the MODerate resolution Imaging Spectroradiometer (MODIS). Further available information that is included in the analysis are the forest fraction and the altitude of each pixel. The first step of the analysis is fitting a function to the measurements of each pixel whose parameters could capture the important aspects of the changes in NDVI values. Among them, the final NDVI value that is reached during the summer is of particular interest since an abnormally low value could be the indication of an insect infestation. Different assumptions are made on the error distribution. The first ones, more simplystic, do not manage to counteract the noise. A more complex error structure is thus taken into account, leading to a better estimate which is then used to build the estimator. The idea behind the detector is to identify those pixels for which the NDVI does not reach its high late spring/summer values fast enough, with respect to other pixels and previous years. It is known that the two years, among those available in the dataset, that have suffered a moth outbreak are 2004 and 2013. Hence, the estimation of the fitting function is run for 2000-2003, the detector is tried on 2004 and finally tested on 2013, since for this year a few locations of the outbreak were known. The discrepancy between field data and the results generated by the detector suggests further adjustments that would improve the capacity to detect moth infestation., Early and reliable detection of pest infestation in forests is crucial to protect the health of trees. Insect outbreaks are an important cause of defoliation: they delay the blossoming of leaves and thereby affect the growth of trees. Such delays lower the economic potential of forests as well as their capacity to absorb atmospheric CO 2 . The aim of this thesis is to use mathematical models to build a near real-time detector that can identify abnormal behavior in forest growth, which can be the sign of pest infestation. In order for the detector to provide effective early warnings, the analysis uses high time-resolution satellite images. In particular, the detector is based on data collected at the Abisko National Park in the north of Sweden. The insect that is known to threaten this birch forest is a moth called Epirrita Autumnata, whose larvae feed on the bursting leaves early in the spring. The data used to study the behaviour of green mass is the Normalized Difference Vegetation Index (NDVI). A dataset of 14 years of measurements was available over an area of 350 km 2 . The index is calculated based on images collected by the MODerate resolution Imaging Spectroradiometer (MODIS), which is placed on two satellites that orbit Earth and collects images of the surface daily. The analysis also includes information on forest fraction and altitude for each pixel of the area. The first step of the analysis is fitting a function to the NDVI measurements of each pixel. The chosen function captures all important aspects of the change in NDVI during the spring. Different methods are used to fit the function to the NDVI. The first, simplistic models fail to fit the fuction because of the strong noise that affects the measurements. Therefore more robust and complex estimators are tried out. The final, best performing technique is used to construct the detector. The idea behind the detector is to identify those pixels for which NDVI grows slower than expected, based on the va

Background: Membrane proteins are key components in a large spectrum of diverse functions and thus account for the major proportion of the drug-targeted portion of the genome. From a structural perspective, the a-helical transmembrane proteins can be categorized into major groups based on the number of transmembrane helices and these groups are often associated with specific functions. When compared to the well-characterized seven-transmembrane containing proteins (7TM), other TM groups are less explored and in particular the 4TM group. In this study, we identify the complete 4TM complement from the latest release of the human genome and assess the 4TM structure group as a whole. We functionally characterize this dataset and evaluate the resulting groups and ubiquitous functions, and furthermore describe disease and drug target involvement. Results: We classified 373 proteins, which represents similar to 7 % of the human membrane proteome, and includes 69 more proteins than our previous estimate. We have characterized the 4TM dataset based on functional, structural, and/or evolutionary similarities. Proteins that are involved in transport activity constitute 37 % of the dataset, 23 % are receptor-related, and 13 % have enzymatic functions. Intriguingly, proteins involved in transport are more than double the 15 % of transporters in the entire human membrane proteome, which might suggest that the 4TM topological architecture is more favored for transporting molecules over other functions. Moreover, we found an interesting exception to the ubiquitous intracellular N- and C-termini localization that is found throughout the entire membrane proteome and 4TM dataset in the neurotransmitter gated ion channel families. Overall, we estimate that 58 % of the dataset has a known association to disease conditions with 19 % of the genes possibly involved in different types of cancer. Conclusions: We provide here the most robust and updated classification of the 4TM complement of