998 resultados para High resolution gamma spectrometry


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Gamma radiations measurements were carried out in the vicinity of a coal-fired power plant located in the southwest coastline of Portugal. Two different gamma detectors were used to assess the environmental radiation within a circular area of 20 km centred in the coal plant: a scintillometer (SPP2 NF, Saphymo) and a high purity germanium detector (HPGe, Canberra). Fifty urban and suburban measurements locations were established within the defined area and two measurements campaigns were carried out. The results of the total gamma radiation ranged from 20.83 to 98.33 counts per second (c.p.s.) for both measurement campaigns and outdoor doses rates ranged from 77.65 to 366.51 Gy/h. Natural emitting nuclides from the U-238 and Th-232 decay series were identified as well as the natural emitting nuclide K-40. The radionuclide concentration from the uranium and thorium series determined by gamma spectrometry ranged from 0.93 to 73.68 Bq/kg, while for K-40 the concentration ranged from 84.14 to 904.38 Bq/kg. The obtained results were used primarily to define the variability in measured environmental radiation and to determine the coal plant’s influence in the measured radiation levels. The highest values were measured at two locations near the power plant and at locations between the distance of 6 and 20 km away from the stacks, mainly in the prevailing wind direction. The results showed an increase or at least an influence from the coal-fired plant operations, both qualitatively and quantitatively.

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Huntington's disease (HD) is a fatal autosomal-dominant neurodegenerative disorder that affects approximately 3-10 people per 100 000 in the Western world. The median age of onset is 40 years, with death typically following 15-20 years later. In this study, we biochemically profiled post-mortem frontal lobe and striatum from HD sufferers (n = 14) and compared their profiles with controls (n = 14). LC-LTQ-Orbitrap-MS detected a total of 5579 and 5880 features for frontal lobe and striatum, respectively. An ROC curve combining two spectral features from frontal lobe had an AUC value of 0.916 (0.794 to 1.000) and following statistical cross-validation had an 83% predictive accuracy for HD. Similarly, two striatum biomarkers gave an ROC AUC of 0.935 (0.806 to 1.000) and after statistical cross-validation predicted HD with 91.8% accuracy. A range of metabolite disturbances were evident including but-2-enoic acid and uric acid, which were altered in both frontal lobe and striatum. A total of seven biochemical pathways (three in frontal lobe and four in striatum) were significantly altered as a result of HD. This study highlights the utility of high-resolution metabolomics for the study of HD. Further characterization of the brain metabolome could lead to the identification of new biomarkers and novel treatment strategies for HD.

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The development of a compact gamma camera with high spatial resolution is of great interest in Nuclear Medicine as a means to increase the sensitivity of scintigraphy exams and thus allow the early detection of small tumours. Following the introduction of the wavelength-shifting fibre (WSF) gamma camera by Soares et al. and evolution of photodiodes into highly sensitive silicon photomultipliers (SiPMs), this thesis explores the development of a WSF gamma camera using SiPMs to obtain the position information of scintillation events in a continuous CsI(Na) crystal. The design is highly flexible, allowing the coverage of different areas and the development of compact cameras, with very small dead areas at the edges. After initial studies which confirmed the feasibility of applying SiPMs, a prototype with 5 5 cm2 was assembled and tested at room temperature, in an active field-of-view of 10 10 mm2. Calibration and characterisation of intrinsic properties of this prototype were done using 57Co, while extrinsic measurements were performed using a high-resolution parallel-hole collimator and 99mTc. In addition, a small mouse injected with a radiopharmaceutical was imaged with the developed prototype. Results confirm the great potential of SiPMs when applied in a WSF gamma camera, achieving spatial resolution performance superior to the traditional Anger camera. Furthermore, performance can be improved by an optimisation of experimental conditions, in order to minimise and control the undesirable effects of thermal noise and non-uniformity of response of multiple SiPMs. The development and partial characterisation of a larger SiPM WSF gamma camera with 10 10 cm2 for clinical application are also presented.

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Triple quadrupole mass spectrometers coupled with high performance liquid chromatography are workhorses in quantitative bioanalyses. It provides substantial benefits including reproducibility, sensitivity and selectivity for trace analysis. Selected Reaction Monitoring allows targeted assay development but data sets generated contain very limited information. Data mining and analysis of non-targeted high-resolution mass spectrometry profiles of biological samples offer the opportunity to perform more exhaustive assessments, including quantitative and qualitative analysis. The objectives of this study was to test method precision and accuracy, statistically compare bupivacaine drug concentration in real study samples and verify if high resolution and accurate mass data collected in scan mode can actually permit retrospective data analysis, more specifically, extract metabolite related information. The precision and accuracy data presented using both instruments provided equivalent results. Overall, the accuracy was ranging from 106.2 to 113.2% and the precision observed was from 1.0 to 3.7%. Statistical comparisons using a linear regression between both methods reveal a coefficient of determination (R2) of 0.9996 and a slope of 1.02 demonstrating a very strong correlation between both methods. Individual sample comparison showed differences from -4.5% to 1.6% well within the accepted analytical error. Moreover, post acquisition extracted ion chromatograms at m/z 233.1648 ± 5 ppm (M-56) and m/z 305.2224 ± 5 ppm (M+16) revealed the presence of desbutyl-bupivacaine and three distinct hydroxylated bupivacaine metabolites. Post acquisition analysis allowed us to produce semiquantitative evaluations of the concentration-time profiles for bupicavaine metabolites.

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In recent years, we observed a significant increase of food fraud ranging from false label claims to the use of additives and fillers to increase profitability. Recently in 2013, horse and pig DNA were detected in beef products sold from several retailers. Mass spectrometry has become the workhorse in protein research and the detection of marker proteins could serve for both animal species and tissue authentication. Meat species authenticity will be performed using a well defined proteogenomic annotation, carefully chosen surrogate tryptic peptides and analysis using a hybrid quadrupole-Orbitrap mass spectrometer. Selected mammalian meat samples were homogenized, proteins were extracted and digested with trypsin. The samples were analyzed using a high-resolution mass spectrometer. The chromatography was achieved using a 30 minutes linear gradient along with a BioBasic C8 100 × 1 mm column at a flow rate of 75 µL/min. The mass spectrometer was operated in full-scan high resolution and accurate mass. MS/MS spectra were collected for selected proteotypic peptides. Muscular proteins were methodically analyzed in silico in order to generate tryptic peptide mass lists and theoretical MS/MS spectra. Following a comprehensive bottom-up proteomic analysis, we were able to detect and identify a proteotypic myoglobin tryptic peptide [120-134] for each species with observed m/z below 1.3 ppm compared to theoretical values. Moreover, proteotypic peptides from myosin-1, myosin-2 and -hemoglobin were also identified. This targeted method allowed a comprehensive meat speciation down to 1% (w/w) of undesired product.

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Quantification of uranium in human urine is a valuable technique for assessing occupational and public exposure to uranium. A reliable method has been developed and validated in the ARPANSA Radiochemistry Laboratory by means of standard radiochemical separation and purification techniques and measurement using high-resolution alpha spectrometry. This method can be used to evaluate the levels of naturally occurring 234U, 235U and 238U in urine. Method design and validation is the process of defining an analytical requirement, and then confirming that the method under consideration has performance capabilities consistent with what the application requires. The method was designed to measure levels down to 2 mBq/day of total uranium, corresponding to approximately 1/100th of the annual committed effective dose of 20 mSv. Validation tests were developed to assess selectivity, accuracy, recovery and quantification of uncertainty. The radiochemical recovery of this method was measured using 232U tracer. The typical minimum detectable concentration for total uranium for 24-h urine samples is approximately 0.6 mBq/day or 0.019 μg/day.

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This paper describes informatics for cross-sample analysis with comprehensive two-dimensional gas chromatography (GCxGC) and high-resolution mass spectrometry (HRMS). GCxGC-HRMS analysis produces large data sets that are rich with information, but highly complex. The size of the data and volume of information requires automated processing for comprehensive cross-sample analysis, but the complexity poses a challenge for developing robust methods. The approach developed here analyzes GCxGC-HRMS data from multiple samples to extract a feature template that comprehensively captures the pattern of peaks detected in the retention-times plane. Then, for each sample chromatogram, the template is geometrically transformed to align with the detected peak pattern and generate a set of feature measurements for cross-sample analyses such as sample classification and biomarker discovery. The approach avoids the intractable problem of comprehensive peak matching by using a few reliable peaks for alignment and peak-based retention-plane windows to define comprehensive features that can be reliably matched for cross-sample analysis. The informatics are demonstrated with a set of 18 samples from breast-cancer tumors, each from different individuals, six each for Grades 1-3. The features allow classification that matches grading by a cancer pathologist with 78% success in leave-one-out cross-validation experiments. The HRMS signatures of the features of interest can be examined for determining elemental compositions and identifying compounds.

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A de novo sequencing program for proteins is described that uses tandem MS data from electron capture dissociation and collisionally activated dissociation of electrosprayed protein ions. Computer automation is used to convert the fragment ion mass values derived from these spectra into the most probable protein sequence, without distinguishing Leu/Ile. Minimum human input is necessary for the data reduction and interpretation. No extra chemistry is necessary to distinguish N- and C-terminal fragments in the mass spectra, as this is determined from the electron capture dissociation data. With parts-per-million mass accuracy (now available by using higher field Fourier transform MS instruments), the complete sequences of ubiquitin (8.6 kDa) and melittin (2.8 kDa) were predicted correctly by the program. The data available also provided 91% of the cytochrome c (12.4 kDa) sequence (essentially complete except for the tandem MS-resistant region K13–V20 that contains the cyclic heme). Uncorrected mass values from a 6-T instrument still gave 86% of the sequence for ubiquitin, except for distinguishing Gln/Lys. Extensive sequencing of larger proteins should be possible by applying the algorithm to pieces of ≈10-kDa size, such as products of limited proteolysis.

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Human activities represent a significant burden on the global water cycle, with large and increasing demands placed on limited water resources by manufacturing, energy production and domestic water use. In addition to changing the quantity of available water resources, human activities lead to changes in water quality by introducing a large and often poorly-characterized array of chemical pollutants, which may negatively impact biodiversity in aquatic ecosystems, leading to impairment of valuable ecosystem functions and services. Domestic and industrial wastewaters represent a significant source of pollution to the aquatic environment due to inadequate or incomplete removal of chemicals introduced into waters by human activities. Currently, incomplete chemical characterization of treated wastewaters limits comprehensive risk assessment of this ubiquitous impact to water. In particular, a significant fraction of the organic chemical composition of treated industrial and domestic wastewaters remains uncharacterized at the molecular level. Efforts aimed at reducing the impacts of water pollution on aquatic ecosystems critically require knowledge of the composition of wastewaters to develop interventions capable of protecting our precious natural water resources.

The goal of this dissertation was to develop a robust, extensible and high-throughput framework for the comprehensive characterization of organic micropollutants in wastewaters by high-resolution accurate-mass mass spectrometry. High-resolution mass spectrometry provides the most powerful analytical technique available for assessing the occurrence and fate of organic pollutants in the water cycle. However, significant limitations in data processing, analysis and interpretation have limited this technique in achieving comprehensive characterization of organic pollutants occurring in natural and built environments. My work aimed to address these challenges by development of automated workflows for the structural characterization of organic pollutants in wastewater and wastewater impacted environments by high-resolution mass spectrometry, and to apply these methods in combination with novel data handling routines to conduct detailed fate studies of wastewater-derived organic micropollutants in the aquatic environment.

In Chapter 2, chemoinformatic tools were implemented along with novel non-targeted mass spectrometric analytical methods to characterize, map, and explore an environmentally-relevant “chemical space” in municipal wastewater. This was accomplished by characterizing the molecular composition of known wastewater-derived organic pollutants and substances that are prioritized as potential wastewater contaminants, using these databases to evaluate the pollutant-likeness of structures postulated for unknown organic compounds that I detected in wastewater extracts using high-resolution mass spectrometry approaches. Results showed that application of multiple computational mass spectrometric tools to structural elucidation of unknown organic pollutants arising in wastewaters improved the efficiency and veracity of screening approaches based on high-resolution mass spectrometry. Furthermore, structural similarity searching was essential for prioritizing substances sharing structural features with known organic pollutants or industrial and consumer chemicals that could enter the environment through use or disposal.

I then applied this comprehensive methodological and computational non-targeted analysis workflow to micropollutant fate analysis in domestic wastewaters (Chapter 3), surface waters impacted by water reuse activities (Chapter 4) and effluents of wastewater treatment facilities receiving wastewater from oil and gas extraction activities (Chapter 5). In Chapter 3, I showed that application of chemometric tools aided in the prioritization of non-targeted compounds arising at various stages of conventional wastewater treatment by partitioning high dimensional data into rational chemical categories based on knowledge of organic chemical fate processes, resulting in the classification of organic micropollutants based on their occurrence and/or removal during treatment. Similarly, in Chapter 4, high-resolution sampling and broad-spectrum targeted and non-targeted chemical analysis were applied to assess the occurrence and fate of organic micropollutants in a water reuse application, wherein reclaimed wastewater was applied for irrigation of turf grass. Results showed that organic micropollutant composition of surface waters receiving runoff from wastewater irrigated areas appeared to be minimally impacted by wastewater-derived organic micropollutants. Finally, Chapter 5 presents results of the comprehensive organic chemical composition of oil and gas wastewaters treated for surface water discharge. Concurrent analysis of effluent samples by complementary, broad-spectrum analytical techniques, revealed that low-levels of hydrophobic organic contaminants, but elevated concentrations of polymeric surfactants, which may effect the fate and analysis of contaminants of concern in oil and gas wastewaters.

Taken together, my work represents significant progress in the characterization of polar organic chemical pollutants associated with wastewater-impacted environments by high-resolution mass spectrometry. Application of these comprehensive methods to examine micropollutant fate processes in wastewater treatment systems, water reuse environments, and water applications in oil/gas exploration yielded new insights into the factors that influence transport, transformation, and persistence of organic micropollutants in these systems across an unprecedented breadth of chemical space.

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The Amazon Basin plays key role in atmospheric chemistry, biodiversity and climate change. In this study we applied nanoelectrospray (nanoESI) ultra-high-resolution mass spectrometry (UHRMS) for the analysis of the organic fraction of PM2.5 aerosol samples collected during dry and wet seasons at a site in central Amazonia receiving background air masses, biomass burning and urban pollution. Comprehensive mass spectral data evaluation methods (e.g. Kendrick mass defect, Van Krevelen diagrams, carbon oxidation state and aromaticity equivalent) were used to identify compound classes and mass distributions of the detected species. Nitrogen- and/or sulfur-containing organic species contributed up to 60 % of the total identified number of formulae. A large number of molecular formulae in organic aerosol (OA) were attributed to later-generation nitrogen- and sulfur-containing oxidation products, suggesting that OA composition is affected by biomass burning and other, potentially anthropogenic, sources. Isoprene-derived organosulfate (IEPOX-OS) was found to be the most dominant ion in most of the analysed samples and strongly followed the concentration trends of the gas-phase anthropogenic tracers confirming its mixed anthropogenic–biogenic origin. The presence of oxidised aromatic and nitro-aromatic compounds in the samples suggested a strong influence from biomass burning especially during the dry period. Aerosol samples from the dry period and under enhanced biomass burning conditions contained a large number of molecules with high carbon oxidation state and an increased number of aromatic compounds compared to that from the wet period. The results of this work demonstrate that the studied site is influenced not only by biogenic emissions from the forest but also by biomass burning and potentially other anthropogenic emissions from the neighbouring urban environments.

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Measurement of marine algal toxins has traditionally focussed on shellfish monitoring while, over the last decade, passive sampling has been introduced as a complementary tool for exploratory studies. Since 2011, liquid chromatography-tandem mass spectrometry (LC-MS/MS) has been adopted as the EU reference method (No.15/2011) for detection and quantitation of lipophilic toxins. Traditional LC-MS approaches have been based on low-resolution mass spectrometry (LRMS), however, advances in instrument platforms have led to a heightened interest in the use of high-resolution mass spectrometry (HRMS) for toxin detection. This work describes the use of HRMS in combination with passive sampling as a progressive approach to marine algal toxin surveys. Experiments focused on comparison of LRMS and HRMS for determination of a broad range of toxins in shellfish and passive samplers. Matrix effects are an important issue to address in LC-MS; therefore, this phenomenon was evaluated for mussels (Mytilus galloprovincialis) and passive samplers using LRMS (triple quadrupole) and HRMS (quadrupole time-of-flight and Orbitrap) instruments. Matrix-matched calibration solutions containing okadaic acid and dinophysistoxins, pectenotoxin, azaspiracids, yessotoxins, domoic acid, pinnatoxins, gymnodimine A and 13-desmethyl spirolide C were prepared. Similar matrix effects were observed on all instruments types. Most notably, there was ion enhancement for pectenotoxins, okadaic acid/dinophysistoxins on one hand, and ion suppression for yessotoxins on the other. Interestingly, the ion selected for quantitation of PTX2 also influenced the magnitude of matrix effects, with the sodium adduct typically exhibiting less susceptibility to matrix effects than the ammonium adduct. As expected, mussel as a biological matrix, quantitatively produced significantly more matrix effects than passive sampler extracts, irrespective of toxin. Sample dilution was demonstrated as an effective measure to reduce matrix effects for all compounds, and was found to be particularly useful for the non-targeted approach. Limits of detection and method accuracy were comparable between the systems tested, demonstrating the applicability of HRMS as an effective tool for screening and quantitative analysis. HRMS offers the advantage of untargeted analysis, meaning that datasets can be retrospectively analysed. HRMS (full scan) chromatograms of passive samplers yielded significantly less complex data sets than mussels, and were thus more easily screened for unknowns. Consequently, we recommend the use of HRMS in combination with passive sampling for studies investigating emerging or hitherto uncharacterised toxins.