Partial melting within the lower crust – Constraints from bulk rock geochemical data and trace element mineral analyses of granulites and migmatites from Central Finland

This PhD thesis concerns geochemical constraints on recycling and partial melting of Archean continental crust. A natural example of such processes was found in the Iisalmi area of Central Finland. The rocks from this area are Middle to Late Archean in age and experienced metamorphism and partial melting between 2.7-2.63 Ga. The work is based on extensive field work. It is furthermore founded on bulk rock geochemical data as well as in-situ analyses of minerals. All geochemical data were obtained at the Institute of Geosciences, University of Mainz using X-ray fluorescence, solution ICP-MS and laser ablation-ICP-MS for bulk rock geochemical analyses. Mineral analyses were accomplished by electron microprobe and laser ablation ICP-MS. Fluid inclusions were studied by microscope on a heating-freezing-stage at the Geoscience Center, University Göttingen. Part I focuses on the development of a new analytical method for bulk rock trace element determination by laser ablation-ICP-MS using homogeneous glasses fused from rock powder on an Iridium strip heater. This method is applicable for mafic rock samples whose melts have low viscosities and homogenize quickly at temperatures of ~1200°C. Highly viscous melts of felsic samples prevent melting and homogenization at comparable temperatures. Fusion of felsic samples can be enabled by addition of MgO to the rock powder and adjustment of melting temperature and melting duration to the rock composition. Advantages of the fusion method are low detection limits compared to XRF analyses and avoidance of wet-chemical processing and use of strong acids as in solution ICP-MS as well as smaller sample volumes compared to the other methods. Part II of the thesis uses bulk rock geochemical data and results from fluid inclusion studies for discrimination of melting processes observed in different rock types. Fluid inclusion studies demonstrate a major change in fluid composition from CO2-dominated fluids in granulites to aqueous fluids in TTG gneisses and amphibolites. Partial melts were generated in the dry, CO2-rich environment by dehydration melting reactions of amphibole which in addition to tonalitic melts produced the anhydrous mineral assemblages of granulites (grt + cpx + pl ± amph or opx + cpx + pl + amph). Trace element modeling showed that mafic granulites are residues of 10-30 % melt extraction from amphibolitic precursor rocks. The maximum degree of melting in intermediate granulites was ~10 % as inferred from modal abundances of amphibole, clinopyroxene and orthopyroxene. Carbonic inclusions are absent in upper-amphibolite facies migmatites whereas aqueous inclusion with up to 20 wt% NaCl are abundant. This suggests that melting within TTG gneisses and amphibolites took place in the presence of an aqueous fluid phase that enabled melting at the wet solidus at temperatures of 700-750°C. The strong disruption of pre-metamorphic structures in some outcrops suggests that the maximum amount of melt in TTG gneisses was ~25 vol%. The presence of leucosomes in all rock types is taken as the principle evidence for melt formation. However, mineralogical appearance as well as major and trace element composition of many leucosomes imply that leucosomes seldom represent frozen in-situ melts. They are better considered as remnants of the melt channel network, e.g. ways on which melts escaped from the system. Part III of the thesis describes how analyses of minerals from a specific rock type (granulite) can be used to determine partition coefficients between different minerals and between minerals and melt suitable for lower crustal conditions. The trace element analyses by laser ablation-ICP-MS show coherent distribution among the principal mineral phases independent of rock composition. REE contents in amphibole are about 3 times higher than REE contents in clinopyroxene from the same sample. This consistency has to be taken into consideration in models of lower crustal melting where amphibole is replaced by clinopyroxene in the course of melting. A lack of equilibrium is observed between matrix clinopyroxene / amphibole and garnet porphyroblasts which suggests a late stage growth of garnet and slow diffusion and equilibration of the REE during metamorphism. The data provide a first set of distribution coefficients of the transition metals (Sc, V, Cr, Ni) in the lower crust. In addition, analyses of ilmenite and apatite demonstrate the strong influence of accessory phases on trace element distribution. Apatite contains high amounts of REE and Sr while ilmenite incorporates about 20-30 times higher amounts of Nb and Ta than amphibole. Furthermore, trace element mineral analyses provide evidence for magmatic processes such as melt depletion, melt segregation, accumulation and fractionation as well as metasomatism having operated in this high-grade anatectic area.

Implementazione su fpga di un pre-distorsore digitale a banda larga per sistemi a microonde di backhaul

The development of next generation microwave technology for backhauling systems is driven by an increasing capacity demand. In order to provide higher data rates and throughputs over a point-to-point link, a cost-effective performance improvement is enabled by an enhanced energy-efficiency of the transmit power amplification stage, whereas a combination of spectrally efficient modulation formats and wider bandwidths is supported by amplifiers that fulfil strict constraints in terms of linearity. An optimal trade-off between these conflicting requirements can be achieved by resorting to flexible digital signal processing techniques at baseband. In such a scenario, the adaptive digital pre-distortion is a well-known linearization method, that comes up to be a potentially widely-used solution since it can be easily integrated into base stations. Its operation can effectively compensate for the inter-modulation distortion introduced by the power amplifier, keeping up with the frequency-dependent time-varying behaviour of the relative nonlinear characteristic. In particular, the impact of the memory effects become more relevant and their equalisation become more challenging as the input discrete signal feature a wider bandwidth and a faster envelope to pre-distort. This thesis project involves the research, design and simulation a pre-distorter implementation at RTL based on a novel polyphase architecture, which makes it capable of operating over very wideband signals at a sampling rate that complies with the actual available clock speed of current digital devices. The motivation behind this structure is to carry out a feasible pre-distortion for the multi-band spectrally efficient complex signals carrying multiple channels that are going to be transmitted in near future high capacity and reliability microwave backhaul links.

Online Stream Processing di Big Data su Apache Storm per Applicazioni di Instant Coupon

Big data è il termine usato per descrivere una raccolta di dati così estesa in termini di volume,velocità e varietà da richiedere tecnologie e metodi analitici specifici per l'estrazione di valori significativi. Molti sistemi sono sempre più costituiti e caratterizzati da enormi moli di dati da gestire,originati da sorgenti altamente eterogenee e con formati altamente differenziati,oltre a qualità dei dati estremamente eterogenei. Un altro requisito in questi sistemi potrebbe essere il fattore temporale: sempre più sistemi hanno bisogno di ricevere dati significativi dai Big Data il prima possibile,e sempre più spesso l’input da gestire è rappresentato da uno stream di informazioni continuo. In questo campo si inseriscono delle soluzioni specifiche per questi casi chiamati Online Stream Processing. L’obiettivo di questa tesi è di proporre un prototipo funzionante che elabori dati di Instant Coupon provenienti da diverse fonti con diversi formati e protocolli di informazioni e trasmissione e che memorizzi i dati elaborati in maniera efficiente per avere delle risposte in tempo reale. Le fonti di informazione possono essere di due tipologie: XMPP e Eddystone. Il sistema una volta ricevute le informazioni in ingresso, estrapola ed elabora codeste fino ad avere dati significativi che possono essere utilizzati da terze parti. Lo storage di questi dati è fatto su Apache Cassandra. Il problema più grosso che si è dovuto risolvere riguarda il fatto che Apache Storm non prevede il ribilanciamento delle risorse in maniera automatica, in questo caso specifico però la distribuzione dei clienti durante la giornata è molto varia e ricca di picchi. Il sistema interno di ribilanciamento sfrutta tecnologie innovative come le metriche e sulla base del throughput e della latenza esecutiva decide se aumentare/diminuire il numero di risorse o semplicemente non fare niente se le statistiche sono all’interno dei valori di soglia voluti.

A Data-Analytic Strategy for Protein-Biomarker Discovery: Profiling of High-Dimensional Proteomic Data for Cancer Detection

With recent advances in mass spectrometry techniques, it is now possible to investigate proteins over a wide range of molecular weights in small biological specimens. This advance has generated data-analytic challenges in proteomics, similar to those created by microarray technologies in genetics, namely, discovery of "signature" protein profiles specific to each pathologic state (e.g., normal vs. cancer) or differential profiles between experimental conditions (e.g., treated by a drug of interest vs. untreated) from high-dimensional data. We propose a data analytic strategy for discovering protein biomarkers based on such high-dimensional mass-spectrometry data. A real biomarker-discovery project on prostate cancer is taken as a concrete example throughout the paper: the project aims to identify proteins in serum that distinguish cancer, benign hyperplasia, and normal states of prostate using the Surface Enhanced Laser Desorption/Ionization (SELDI) technology, a recently developed mass spectrometry technique. Our data analytic strategy takes properties of the SELDI mass-spectrometer into account: the SELDI output of a specimen contains about 48,000 (x, y) points where x is the protein mass divided by the number of charges introduced by ionization and y is the protein intensity of the corresponding mass per charge value, x, in that specimen. Given high coefficients of variation and other characteristics of protein intensity measures (y values), we reduce the measures of protein intensities to a set of binary variables that indicate peaks in the y-axis direction in the nearest neighborhoods of each mass per charge point in the x-axis direction. We then account for a shifting (measurement error) problem of the x-axis in SELDI output. After these pre-analysis processing of data, we combine the binary predictors to generate classification rules for cancer, benign hyperplasia, and normal states of prostate. Our approach is to apply the boosting algorithm to select binary predictors and construct a summary classifier. We empirically evaluate sensitivity and specificity of the resulting summary classifiers with a test dataset that is independent from the training dataset used to construct the summary classifiers. The proposed method performed nearly perfectly in distinguishing cancer and benign hyperplasia from normal. In the classification of cancer vs. benign hyperplasia, however, an appreciable proportion of the benign specimens were classified incorrectly as cancer. We discuss practical issues associated with our proposed approach to the analysis of SELDI output and its application in cancer biomarker discovery.

Digital processing and data compilation approach for using remotely sensed imagery to identify geological lineaments in hard-rock terrains : an application for groundwater explorations in Nicaragua

Sustainable yields from water wells in hard-rock aquifers are achieved when the well bore intersects fracture networks. Fracture networks are often not readily discernable at the surface. Lineament analysis using remotely sensed satellite imagery has been employed to identify surface expressions of fracturing, and a variety of image-analysis techniques have been successfully applied in “ideal” settings. An ideal setting for lineament detection is where the influences of human development, vegetation, and climatic situations are minimal and hydrogeological conditions and geologic structure are known. There is not yet a well-accepted protocol for mapping lineaments nor have different approaches been compared in non-ideal settings. A new approach for image-processing/synthesis was developed to identify successful satellite imagery types for lineament analysis in non-ideal terrain. Four satellite sensors (ASTER, Landsat7 ETM+, QuickBird, RADARSAT-1) and a digital elevation model were evaluated for lineament analysis in Boaco, Nicaragua, where the landscape is subject to varied vegetative cover, a plethora of anthropogenic features, and frequent cloud cover that limit the availability of optical satellite data. A variety of digital image processing techniques were employed and lineament interpretations were performed to obtain 12 complementary image products that were evaluated subjectively to identify lineaments. The 12 lineament interpretations were synthesized to create a raster image of lineament zone coincidence that shows the level of agreement among the 12 interpretations. A composite lineament interpretation was made using the coincidence raster to restrict lineament observations to areas where multiple interpretations (at least 4) agree. Nine of the 11 previously mapped faults were identified from the coincidence raster. An additional 26 lineaments were identified from the coincidence raster, and the locations of 10 were confirmed by field observation. Four manual pumping tests suggest that well productivity is higher for wells proximal to lineament features. Interpretations from RADARSAT-1 products were superior to interpretations from other sensor products, suggesting that quality lineament interpretation in this region requires anthropogenic features to be minimized and topographic expressions to be maximized. The approach developed in this study has the potential to improve siting wells in non-ideal regions.

Quantitative proteome analysis in cardiovascular physiology and pathology. I. Data processing

Methodological evaluation of the proteomic analysis of cardiovascular-tissue material has been performed with a special emphasis on establishing examinations that allow reliable quantitative analysis of silver-stained readouts. Reliability, reproducibility, robustness and linearity were addressed and clarified. In addition, several types of normalization procedures were evaluated and new approaches are proposed. It has been found that the silver-stained readout offers a convenient approach for quantitation if a linear range for gel loading is defined. In addition, a broad range of a 10-fold input (loading 20-200 microg per gel) fulfills the linearity criteria, although at the lowest input (20 microg) a portion of protein species will remain undetected. The method is reliable and reproducible within a range of 65-200 microg input. The normalization procedure using the sum of all spot intensities from a silver-stained 2D pattern has been shown to be less reliable than other approaches, namely, normalization through median or through involvement of interquartile range. A special refinement of the normalization through virtual segmentation of pattern, and calculation of normalization factor for each stratum provides highly satisfactory results. The presented results not only provide evidence for the usefulness of silver-stained gels for quantitative evaluation, but they are directly applicable to the research endeavor of monitoring alterations in cardiovascular pathophysiology.

Integrated data acquisition and processing to determine metabolite contents, relaxation times, and macromolecule baseline in single examinations of individual subjects

Absolute quantitation of clinical (1)H-MR spectra is virtually always incomplete for single subjects because the separate determination of spectrum, baseline, and transverse and longitudinal relaxation times in single subjects is prohibitively long. Integrated Processing and Acquisition of Data (IPAD) based on a combined 2-dimensional experimental and fitting strategy is suggested to substantially improve the information content from a given measurement time. A series of localized saturation-recovery spectra was recorded and combined with 2-dimensional prior-knowledge fitting to simultaneously determine metabolite T(1) (from analysis of the saturation-recovery time course), metabolite T(2) (from lineshape analysis based on metabolite and water peak shapes), macromolecular baseline (based on T(1) differences and analysis of the saturation-recovery time course), and metabolite concentrations (using prior knowledge fitting and conventional procedures of absolute standardization). The procedure was tested on metabolite solutions and applied in 25 subjects (15-78 years old). Metabolite content was comparable to previously found values. Interindividual variation was larger than intraindividual variation in repeated spectra for metabolite content as well as for some relaxation times. Relaxation times were different for various metabolite groups. Parts of the interindividual variation could be explained by significant age dependence of relaxation times.