Lyconadin A is a pentacyclic Lycopodium alkaloid isolated from the club moss Lycopodium companatum with anticancer activity. Our approach sought to incorporate a 7-exo–6-exo acyl radical cyclization cascade to access the bicyclo[5.4.0]undecane framework of lyconadin A. Our studies created methodology for the synthesis of 5-alkyl and 3,5-dialkyl-6-carbomethoxy-2-pyridones and sterically demanding epoxide substrates. These epoxide substrates underwent an unanticipated Payne rearrangement.Cranomycin is a potent antibiotic with antiprotozoal activity. Structurally it is a cyclopentane ring system with substitution at each carbon in the ring. Another interesting structural aspect is the existence of three contiguous quaternary stereocenters including two tertiary alcohols and a tert-alkylamine. Our strategy led to the development of a highly diastereoselective synthesis of vicinal tertiary diol systems. We have successfully synthesized the cyclopentenone system shown above, from which we hope to assemble cranomycin.

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Lipids are important for the structural and physiological functions of neuronal cell membranes. Alterations in their lipid composition may result in membrane dysfunction and subsequent neuronal deficits that characterise various disorders. This study focused on profiling lipids of aged and LPS-treated rat brain and liver tissue with a view to explore the effect of atorvastatin in neuroinflammation, and examining lipid changes in different areas of rat brain of the Flinders Sensitive Line (FSL) rats, a genetic model of depression. Lipids and other analytes extracted from tissue samples were analysed with proton nuclear magnetic resonance spectroscopy (1H-NMR), gas chromatography (GC) and liquid chromatography-tandem mass spectroscopy (LC/ESI-MS/MS). Changes in the lipid profiles suggested that brain and liver responded differently to ageing and LPS-induced neuroinflammation. In the aged animals, n-3 PUFA were reduced in the brain but were increased in the liver. However, following treatment with LPS, these effects were not observed. Nevertheless, in both models, brain concentration of monounsaturated fatty acids was increased while the liver was able to maintain its monounsaturated fatty acid concentration. Atorvastatin reversed the reduction in n-3 PUFA in the aged brain without reducing brain and liver concentration of cholesterol. These findings further highlight alterations in lipid metabolism in agerelated neuroinflammation and show that the anti-inflammatory actions of atorvastatin may include a modulation of fatty acid metabolism. When studying the FSL model, there were differences in the lipid profile of different brain areas of FSL rats compared to Sprague-Dawley controls. In all brain areas, arachidonic acid was increased in the FSL rats. Docosahexaenoic acid and ether lipids were reduced, while cholesterol and sphingolipids were increased in the hypothalamus of the FSL rats. Furthermore, total diacylglycerophospholipids were reduced in the prefrontal cortex and hypothalamus of the FSL rats. These results show differences in the lipid metabolism of the FSL rat brain and may be suggestive of changes occurring in the brain tissue in depression.

For some time now, there has been a growing awareness in society about climate change, pollution, energy and the use of natural resources. This thinking has permeated society, mainly because the extreme natural phenomena that we are experiencing nowadays are no longer outliers in our time series of meteorological records. In this regard, it has been proven that the actual high temperatures are not only unparalleled, but also consistent around the globe which is something that had not happened until now (Neukom et al., 2019). The XX century was a turning point when it comes to the increase of the landuse for crops. In a context where the population doubled, the crop production for food from 1960 to 2010 tripled, helping to reduce the hungry population. When the world’s population is expected to continue to grow up to 9 billion people (Goodfray et al., 2010) by middle XXI century, it is essential to provide ourselves with the necessary tools to maximise crop production by taking advantage of all the resources available under a sustainable point of view. Under this context, all farmers in the European Union (EU) have the possibility to benefit from the Common Agricultural Policy (CAP), which came into force in 1960. The CAP is responsible for the financing of aid to farmers on a cross-compliance basis, based on the declaration of crop types. Traditionally, the authorities have checked the veracity of declarations in person through field inspections, which is clearly a highly inefficient, impractical and very expensive system. However, in 2018 the European Commission drafted an amendment to the CAP (European Commission, 2018), to be implemented in 2020, recommending the establishment of newprocedures for checking declarations, including the use of satellite data from the Copernicus programme or other new technologies. Among the various satellite technologies, Synthetic Aperture Radar (SAR) (Brown,1967; Curlander and McDonough, 1992) has proven the most reliable,as the images are acquired with a constant pass period and they are not subject to cloud problems (as is the case with sensors working in the optical domain) and information can be acquired both day and night. They are based in a SAR microwave sensor installed on a satellite platform with a forward trajectory which offers side-looking imaging geometries. Working in a range between 300 MHz and 30 GHz, the SAR sensor is in charge of emitting electromagnetic pulses and receiving the resulting echoes from the imaged target, which can help retrieve information about its dielectric properties, geometry, orientation, shape, and its behaviour along time. For a given target, the SAR backscattering response σ0 is function of many parameters (Lee and Pottier, 2017; Dobson et al., 1985): wave frequency, polarisation, imaging configuration, roughness, geometrical structure and dielectric properties. This makes the information extraction a major problem, as identical radar responses from two different targets may lead to the same result. To cope with this problem, the main techniques are based on extending the observation space by working with the full diversity of data. Thus, the main axes of SAR data are: • Time • Polarimetry • Interferometry • Frequency. Time series of radar data constitutes a major source of information for the classification of crops and land cover, since it makes it possible to distinguish between classes by their temporal behaviour: some land covers show a uniform response along time (e.g. urban areas), whereas there are others subject to seasonal changes (e.g. crops). It may happen that different crop species give the same radar response at a given time, however, when the time window becomes larger, and consecutive acquisitions are taken over a shorter time span, the more one can detect abrupt changes in the target over a longer time interval. Polarimetry is sensitive to the shape, orientation and the scattering mechanisms of the scatterers (Boerner et al.,1981; Zyl, Zebker, and Elachi, 1987). In that sense, when using different polarisations it is possible to discern better the true nature of the target, as some features may be visible in one polarisation but not in the others. Regarding multi-spectral data, it also constitutes a major source of information which can be exploited for classification purposes. Working with sensors operating at different frequencies, or wavelengths, provides diversity in the size of the elements of the scene to which the radar is sensitive as the radar backscattering will come from elements the size of the wavelength used it. For all of the above, multifrequency data provide complementary information, as each frequency operates and interacts with elements of the same wavelength or longer, and being transparent to all others. In addition, different bands are also associated with different spatial resolutions, so a high-frequency sensor can complement the classification performance of a low-frequency sensor when there are sufficiently small details in the scene that cannot be appreciated with the spatial resolution available at the lower frequency. From all the 4 axes exposed above, Interferometry (Graham, 1974) is without a doubt the least exploited for classification purposes. While polarimetry is sensitive to the scattering mechanisms of the scene by means of the polarisation information, interferometry adds the third dimension by being sensitive to the spatial distribution of the scatterers (Treuhaft et al., 1996). Coherence and phase difference computed between two complex-valued SAR images are the main descriptors of interferometry (Bamler and Hartl, 1998), and together, can be used to derive topographic information, vegetation structure, and deformation (volcanoes, landslides, etc.). For this reason, interferometry is especially suited for classification of covers in which there is vertical distribution of elements, e.g. urban areas and vegetation (forests and crops). Polarimetric interferometric SAR (PolInSAR) (Cloude and Papathanassiou, 1998; Treuhaft and Cloude, 1999), constitutes the next step forward, and is based on the application of interferometry to all polarisation channels. Polarimetry can identify the different scattering mechanisms in the scene by using the polarisation information, whilst interferometry is able to locate the effective scattering phase centres, which are mainly dependent on frequency, the polarisation employed, the physical, geometrical structure and orientation of the scatterer. By using the combination of both we can retrieve the vertical structure of the scene, which shows a great potential for classification purposes, since classes characterised by similar backscattering or polarimetric responses can be separated if their heights are different (e.g. types of buildings, forests, crops, etc.), whereas classes with similar heights, and hence similar interferometric coherence values (e.g. grass, crops, bare soil, etc.) can be resolved using their polarimetric response. In summary, PolInSAR-based classification is attractive since polarimetric ambiguities are resolved by interferometric information and vice-versa. The lack of exploitation of the 4 data axes in the literature, plus the arrival of a new generation of SAR sensors in the near future such as ROSE-L, BIOMASS and NISAR among others, offers a new range of possibilities in terms of new types of features for classification whose results and impact must be analysed. In this context, there are many types of SAR data (i.e. features) that have not been used yet, acquired from different sensors (Sentinel-1, PAZ, TanDEMX, TerraSAR-X and ALOS-2), and whose diversity axes, either used individually or jointly, have not yet been explored for classification applications. Therefore, the exploration of these new types of SAR data, whose contribution to classification is unknown regarding crop-type mapping, is the main objective of this doctoral thesis, and consequently also its main novelty. Based on the current state of the art of the research topic the main objective of this PhD thesis is to explore the added value of new SAR features, and their potential, alone or used together, for crop type and land cover classification. In the end, several experiments will be carried out, in different test sites, in which the proposed new features will be evaluated and compared with the traditional observables used so far, with the aim of evaluating their internal potential in classification applications. / Work supported by the Spanish Ministry of Science and Innovation, the State Agency of Research (AEI) and the European Funds for Regional Development (EFRD) under Projects TEC2017-85244-C2-1-P and PID2020-117303GB-C22. Mario Busquier received a grant from the University of Alicante UAFPU20-08.

The determination and application of Analytical Threshold (AT) is a vital part of the forensic Deoxyribonucleic Acid (DNA) internal validation process. AT is the relative fluorescence unit (RFU) signal at which allelic peaks can be confidently distinguished from baseline noise. Several methods of calculating AT are currently being implemented within the forensic DNA community. These methods may utilize DNA negative sample data, DNA positive sample data, or both in their calculations. In this study, two of the DNA positive-based AT calculation techniques were chosen for assessment and comparison. The simplified upper limit approach (ULA) and the cost-based approach. ATs were calculated for each dye channel using a dilution series of 3 single source DNA samples ranging from 0.05-0.8ng. The ATs calculated via the cost-based approach consistently exhibited lower values than those determined via the ULA. As a result, the incidence of allelic drop-out exhibited by these AT values was also consistently lower, with an equivalent or only marginally increased incidence of baseline noise drop-in. These results indicated that the cost-based approach may be a more effective and practical method of calculating AT than the ULA, particularly in the analysis of low DNA template samples.

A generalized low order model (LOM) for the fluctuating lift coefficient caused by vortex shedding from a tandem cylinder pair is proposed to expand upon models from previous authors. This model could provide a reduced computational time method for collecting qualitative and quantitive data from a tandem shedding pair. A delay coupled system with sufficient bifurcation characteristics is developed to account for the different flow regimes (extended-body, reattachment, and co-shedding) which occur as cylinder spacing is varied. Coefficient and parameter fitting is performed to fit experimental data. Finally, results and physical interpretations of the interactions in the model are discussed. It was found that many aspects of the flow at varying L/D ratios could be modeled by the LOM, including vortex suppression in the forward cylinder at the critical spacing, and amplitude growth in the rear cylinder in the co-shedding regime.

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