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Now showing items 3723-3742 of 30981
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(2018)Syftet med avhandlingen är att undersöka hur Märta Tikkanens diktverk Århundradets kärlekssaga recenserades i nordisk press 1978 – 1981. Min metodik inspireras framför allt av den tradition i litteraturhistorien som Gunilla Domellöf i sin bok Mätt med främmande mått – Idéanalys av kvinnliga författares samtidsmottagande och romaner 1930-1935 (2001) och Anna Williams i Stjärnor utan stjärnbilder: kvinnor och kanon i litteraturhistoriska översiktsverk under 1900-talet (1997) visar; att kvinnliga författare recenseras på olika villkor jämfört med sina manliga kolleger, och att de rent mätbart får betydligt mindre utrymme än de manliga författarna i kanonskrivningen. Centralt har varit att undersöka ifall Domellöfs och Williams resultat går att tillämpa på Århundradets kärlekssaga år 1978, ifall det könspolitiska spänningsfältet mellan den auktoritära kritikern och en kvinnlig författare fortfarande är aktuell, och på vilka sätt det i så fall tar sig uttryck. Förekom maktutövande över den kvinnliga författaren – Märta Tikkanen – fortfarande i slutet av 1970-talet? Avhandlingen är en receptionsstudie, och mitt forskningsmaterial består av recensioner som Århundradets kärlekssaga fått i nordisk (finlandssvensk, finsk, svensk, norsk, dansk och isländsk) press mellan åren 1978 och 1981, sammanlagt 79 recensioner. Genom att dela in min analys i olika teman har jag systematiskt gått igenom hur Århundradets kärlekssaga recenserades och lyft fram de ämnen som är återkommande. Jag har först delat in analysen i tre kategorier – innehåll, genre & form och kritikerns uppgift – och sedan även fördjupat mig i diskussionerna som förs i recensionerna genom att se på vilka teman dessa kategorier rymmer. Ur mitt material och med min tematiska disposition framgick tydligt att tre olika läsningar går att urskilja; Århundradets kärlekssaga lästes och recenserades som ett litterärt verk, ett politiskt manifest och som kändisskvaller, det vill säga utomlitterärt, som icke-litteratur. Undersökningen visar också att Domellöfs analysmetod om kvinnliga författares mottagande på 1930-talet som metodik är fungerande – precis som hon utgår från den första feministiska vågen på 1890-talet för att skapa paralleller i behandlingen av den kvinnliga författaren i kulturdebatter för sin egen undersökning – visar min analys att receptionsundersökningen vid feminismens tredje våg under 1970-talet också kan jämföras med undersökningar skrivna om den andra vågen. Det blev tydligt genom en närläsning som visade att Århundradets kärlekssaga recenserades utöver sig själv, att recensionerna rymde mera än litteraturkritik, vilket understryker Domellöfs och Williams slutledningar om en beklaglig litteraturtradition, där kvinnan skrivs in i marginalen och värderas med olika premisser jämfört med hennes manliga kollegor.
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(2023)I denna magisteravhandling analyserar jag gestaltningen av den funktionsnedsatta karaktären Gudrun Lind i den finlandssvenska författaren Walentin Chorells (1912-1983) skönlitterära roman De barmhärtiga (1962). Jag reflekterar även över hur trovärdig Gudrun och hennes funktionsnedsättning är som litterär representation. Analysen är teoretiskt förankrad i litteraturvetenskapliga begrepp och teorier utvecklade inom disability studies. Metoden är textanalys och jag utgår från ett tidigt 2020-talsperspektiv på litterära karaktärer och funktionsnedsättningar. Min flerdelade analys påvisar att Gudrun Lind är en mångfacetterad och trovärdig litterär representation av en funktionsnedsatt person, trots vissa svagheter. Denna avhandling är ett inlägg i den akademiska diskussionen om hur funktionsnedsatta karaktärer gestaltas i litteraturen samt hur vi som läsare och forskare tolkar dem.
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(2022)International policy documents (FN (UN), 1989; WHO & BZgA, 2010, 2013; WAS, 2014; UNESCO, 2018) recommend that children should receive age-appropriate sexuality education, but it is unclear whether this is fulfilled in practice. There is relatively little research on sexuality education for younger students. Therefore, it is relevant to research how sexuality education for younger students is delivered. The purpose of this study is to describe how sexuality education is delivered in grades 1-2 in Finland and to examine teachers’ attitudes towards sexuality education for younger students. In this study I would like to draw attention to the potential of the Finnish curriculum when it comes to sexuality education. Five teachers, who at the time of the study taught grades 1-2 in Finnish primary schools, participated in this study. Data were collected through semi-structured interviews that were recorded, transcribed, and analysed with thematic analysis. The results found that teachers had difficulties defining sexuality education. Teachers taught sexuality education across subjects, spontaneously and sometimes also unconsciously. Teachers need help defining what sexuality education is and what themes are to be addressed. Sexuality education should be made an evident part of the subject environmental studies in the Finnish curriculum. This can hopefully encourage teachers to teach sexuality education. The sexuality education was not really planned, evaluated, or developed in any of the schools. This is a serious issue that needs to be addressed. Local plans for sexuality education should be written. The teachers had generally positive attitudes towards sexuality education for younger students, but in some situations sexuality education could be perceived as a difficult subject. Several of the teachers showed interest in promoting diversity and equal treatment in their teaching. This study failed in providing any deeper insight into how teachers work with norms in sexuality education.
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(2022)Today, Global Navigation Satellite Systems (GNSS) provide services that many critical systems [1] as well as normal users, need in everyday life. These signals are threatened by unintentional and intentional interference. The received satellite signals are complex-valued by nature, however, state-of-the-art anomaly detection approaches operate in the real domain. Changing the anomaly detection into the complex domain allows for preserving the phase component of the signal data. In this thesis, I developed and tested a fully complex-valued Long Short-Term Memory (LSTM) based autoencoder for anomaly detection. I also developed a method for scaling of complex-numbers that forces both real and imaginary units into the range [-1,1] and does not change the direction of a complex vector. The model is trained and tested both in the time and frequency domains, and the frequency domain is divided into two parts: real and complex domain. The developed model’s training data consists only of clean sample data, and the output of the model is the reconstruction of the model’s input. In testing, it can be determined whether the output is clean or anomalous based on the reconstruction error and the computed threshold value. The results show that the autoencoder model in the real domain outperforms the model trained in the complex domain. This does not indicate that the anomaly detection in the complex domain does not work; rather, the model’s architecture needs improvements, and the amount of training data must be increased to reduce the overfitting of the complex domain and thus improve the anomaly detection capability. It was also investigated that some anomalous sample sequences contain a few large valued spikes while other values in the same data snapshot are smaller. After scaling, the values other than in the spikes get closer to zero. This phenomenon causes small reconstruction errors in the model and yields false predictions in the complex domain.
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(2020)Bat monitoring is commonly based on audio analysis. By collecting audio recordings from large areas and analysing their content, it is possible estimate distributions of bat species and changes in them. It is easy to collect a large amount of audio recordings by leaving automatic recording units in nature and collecting them later. However, it takes a lot of time and effort to analyse these recordings. Because of that, there is a great need for automatic tools. We developed a program for detecting bat calls automatically from audio recordings. The program is designed for recordings that are collected from Finland with the AudioMoth recording device. Our method is based on a median clipping method that has previously shown promising results in the field of bird song detection. We add several modifications to the basic method in order to make it work well for our purpose. We use real-world field recordings that we have annotated to evaluate the performance of the detector and compare it to two other freely available programs (Kaleidoscope and Bat Detective). Our method showed good results and got the best F2-score in the comparison.
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(2023)This study focused on detecting horizontal and vertical collusion within Indonesian government procurement processes, leveraging data-driven techniques and statistical methods. Regarding horizontal collusion, we applied clustering techniques to categorize companies based on their supply patterns, revealing clusters with similar bidding practices that may indicate potential collusion. Additionally, we identified patterns where specific supplier groups consistently won procurements, raising questions about potential competitive advantages or strategic practices that need further examination for collusion. For vertical collusion, we examined the frequency of associations between specific government employees and winning companies. While high-frequency collaborations were observed, it is essential to interpret these results with caution as they do not definitively indicate collusion, and legitimate factors might justify such associations. Despite revealing important patterns, the study acknowledges its limitations, including the representativeness of the dataset and the reliance on quantitative methods. Nevertheless, our findings carry substantial implications for enhancing procurement monitoring, strengthening anti-collusion regulations, and promoting transparency in Indonesian government procurement processes. Future research could enrich these findings by incorporating qualitative methods, exploring additional indicators of collusion, and leveraging machine learning techniques to detect collusion.
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(2023)Hostility in the player communication of video games (and by extension, mobile games) is a well-documented phenomenon that can have negative repercussions for the well-being of the individual being subjected to it, and the society in general. Existing research on detecting hostility in games through machine learning methods is scarce due to the unavailability of data, imbalanced existing data (few positive samples in a large data set), and the challenges involved in defining and identifying hostile communication. This thesis utilizes communication data from the Supercell game Brawl Stars to produce two distinct machine learning models: a support vector classifier and a multi-layer perceptron. Their performance is compared to each other as well as to that of an existing sentiment analysis classifier, VADER. Techniques such as oversampling and using additional data are also used in an attempt to reach better results by improving the balance of the data set. The support vector classifier model was found to have the best performance overall, with an F1 score of 64.15% when used on the pure data set and 65.74% when combined with the SMOTE oversampling algorithm. The thesis includes an appendix with a list of the words that were found to have the strongest influence on the hostile/non-hostile classification.
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(2020)This thesis presents a scalable method for identifying anomalous periods of non-activity in short periodic event sequences. The method is tested with real world point-of-sale (POS) data from grocery retail setting. However, the method can be applied also to other problem domains which produce similar sequential data. The proposed method models the underlying event sequence as a non-homogeneous Poisson process with a piecewise constant rate function. The rate function for the piecewise homogeneous Poisson process can be estimated with a change point detection algorithm that minimises a cost function consisting of the negative Poisson log-likelihood and a penalty term that is linear to the number of change points. The resulting model can be queried for anomalously long periods of time with no events, i.e., waiting times, by defining a threshold below which the waiting time observations are deemed anomalies. The first experimental part of the thesis focuses on model selection, i.e., in finding a penalty value that results in the change point detection algorithm detecting the true changes in the intensity of the arrivals of the events while not reacting to random fluctuations in the data. In the second experimental part the performance of the anomaly detection methodology is measured against stock-out data, which gives an approximate ground truth for the termination of a POS event sequence. The performance of the anomaly detector is found to be subpar in terms of precision and recall, i.e., the true positive rate and the positive predictive value. The number of false positives remains high even with small threshold values. This needs to be taken into account when considering applying the anomaly detection procedure in practice. Nevertheless, the methodology may have practical value in the retail setting, e.g., in guiding the store personnel where to focus their resources in ensuring the availability of the products.
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(2022)Northern peatlands are a large source of methane (CH4) to the atmosphere and can vary strongly depending on local environmental conditions. However, few studies have mapped fine-grained CH4 fluxes at the landscape-level. The aim of this study was to predict land cover and CH4 flux patterns in Pallastunturi, Finland, in a study area dominated by forests, peatlands, fells, and lakes. I used random forest models to map land cover types and CH4 fluxes with multi-source remote sensing data and upscaled CH4 fluxes based on land cover maps. The random forest classifier reliably detected the same land cover patterns as the CORINE Land Cover maps. The main differences between the land cover maps were forest type classification, misclassification between neighboring peatland types, and detection of sparsely vegetated areas on fells. The upscaled CH4 fluxes of sinks were very robust to changes in land cover classification, but shrub tundra and peatland CH4 fluxes were sensitive to the level of detail in the land cover classification. The random forest regression performed well (NRMSE 6.6%, R2 82%) and predicted similar CH4 flux patterns as the upscaled CH4 flux maps, despite predicting larger areas that act as CH4 sources than the upscaled CH4 flux maps. The random forest regressor also better predicted CH4 fluxes in peatlands due to added information about soil moisture content from the remote sensing data. Random forests are a good model choice to detect landscape patterns and predict CH4 patterns in northern peatlands based on remote sensing and topographic data.
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(2023)Arctic ecosystems face drastic changes in community structure due to warming, shrubification, permafrost loss, and other environmental changes. Due to the spatial heterogeneity of these ecosystems, understanding such changes on a local scale requires high-resolution data. Earth observation using satellite imagery and aerial photography has become a staple in mapping large areas and general patterns. Advances in sensor technology, the proliferation of unmanned aerial vehicles (UAVs), and increases in processing capacity enable the use of higher spatial and spectral resolutions. As a result, more detailed ecological observations can be made using remote sensing methods. In this thesis, I assess how increased spectral resolution affects the remote-sensing based modelling of plant communities in low-growth oroarctic tundra heaths. Based on a large field observation dataset, I estimate biomass, leaf area index, species richness, Shannon's biodiversity index, and fuzzy community clusters. I then build random forest models of these with image data of varying spectral, spatial, and temporal specifications and topographical data. Finally, I create maps of the vegetation. Leaf area index and biomass are best estimated of the response variables, with R2 values of 0.64 and 0.59, respectively, with multispectral data proving the most important explanatory dataset. Biodiversity metrics are best estimated with R2 values of 0.40–0.50 with the most important explanatory variables being topographical and hyperspectral, and community cluster with R2 values of 0.27–0.53, with the importance of various explanatory variables depending on the cluster being estimated. These results can help choose a suitable high-resolution remote sensing approach for modelling plant communities in similar conditions.
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(2020)Popularity of mobile instant messaging applications has flourished during the last ten years, and people are using them to exchange private and personal information on daily basis. These applications can be freely installed from online marketplaces, and average users may have several of them installed on their devices. The amount of information available from these messaging applications for a third-party eavesdropper via network traffic analysis has therefore grown significantly as well. Security features of these applications have also been developing over the years, and the communication between the applications and the background server infrastructure nowadays practically always employs encryption. Recently, more advanced end-to-end encryption methods have been developed to hide the content of the exchanged data even from the messaging service providers. Machine learning techniques have successfully been utilized in analyzing encrypted network traffic, and previous research has shown that this approach can effectively be used to detect mobile applications and the actions users are performing in those applications regardless of encryption. While the actual content of the messages and other transferred data cannot be accessed by the eavesdropper, these methods can still lead to serious privacy compromises. This thesis discusses the present state of machine learning-based identification of applications and user actions, how feasible it would be to actually perform such detection in a Wi-Fi network and what kind of privacy concerns would arise.
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(2022)In the past 20 years, three known disease emergence events of highly pathogenic coronaviruses have highlighted the importance of monitoring wildlife for the presence of these viruses. Their peculiar characteristics, like high mutation and recombination rate, have increased their potential for species adaptation and interspecies transmission. Understanding the diversity of these viruses in wildlife and increased surveillance might be key to predicting and preventing future spillovers and pandemics. Studies on wildlife coronaviruses commonly focus on the order Chiroptera, mainly in temperate and tropical regions of the Asian continent. Even though animals belonging to this order are considered the main reservoir, the importance of other small terrestrial mammals should not be overlooked. Rodents, for instance, are animals of great interest for many zoonoses, as they often host parasites, bacteria and other groups of viruses that cause diseases in humans. A recent description of several lineages of coronaviruses recovered from rodents from China highlighted and suggested the presence of these viruses in small terrestrial rodents. In this project, we aimed to investigate the presence of coronaviruses in small mammals from France. Samples were collected during spring 2021 in twelve different locations, within two regions of eastern France, Auvergne Rhône-Alpes and Franche Comté. A total of 448 rodents, 13 shrews and 416 bat samples were collected. The samples were screened and coronaviruses sequences were recovered in 20 different samples. Nine Betacoronavirus genus sequences were recovered from rodent colon samples, and one Alpha- and ten Betacoronavirus sequences from bat guano. These results confirmed previous evidence of these viruses’ presence in small mammals from France and provide the first evidence of betacoronaviruses circulating in wild French bats. The study covers two eastern regions that have not been surveilled in previously released studies therefore this highlights the need to increase the efforts in monitoring these viruses and their wildlife host
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(2012)Sweetpotato is a subsistence crop for many thousands of families across the globe. The present studies in the thesis provide basic knowledge about Sweet potato chlorotic stunt virus (SPCSV) and Sweet potato feathery mottle virus (SPFMV) that were detected and characterized from sweetpotatoes in Guatemala and Honduras. Sweetpotato plants from Central American countries were showing typical virus-like symptoms. Different strategies were adopted for virus detection. SPCSV and SPFMV were found to be infecting sweetpotato plants. SPFMV was detected only in sweetpotato plants from Honduras. SPFMV infection was detected serologically and results were confirmed by RT-PCR and sequencing. A recently developed detection method, based on restrictotypes of PCR products by two different endonucleases, revealed co-infection of SPFMV strains C and RC in a sweetpotato plant from Honduras which was corroborated by sequencing 3'-proximal end (1.8 kb) of the genome and the coat protein (CP) ~940 nt based phylogenetic analysis. SPCSV was detected by double-stranded RNA extraction, confirmed by RT-PCR and subsequent sequencing of the partial HSP70h gene of genomic RNA2 gene of SPCSV. Phylogenetic analysis was done by constructing neighbour-joining tree of aligned nucleotide sequences, including SPCSV-EA isolates and SPCSV-WA isolates from database that clearly differentiated SPCSV isolates of Central American countries. These isolates from Guatemala and Honduras were grouped together with SPCSV-WA isolates from Argentina, United States, Spain, Israel, Nigeria and Egypt. Additionally, the RNase3 gene with UTR at 3´ end of genomic RNA1 gene of SPCSV was sequenced (1264 nt) and aligned against other WA isolates. It was found that the gene for the silencing suppressor protein p22 (676nt) was missing, reflecting intraspecific variation in the genomic structure of SPCSV. These findings revealed the two most important sweetpotato viruses in Guatemala, Honduras, and Central America for the first time and urge further studies of sweetpotato viruses in the region.
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(2018)Histamine is an important neurotransmitter in the central nervous system (CNS). It is involved e.g. in the sleep-wake cycle, endocrine and energy homeostasis as well as in synaptic plasticity and learning. It is produced from L-histidine by histidine decarboxylase (HDC). Almost all species have histamine in their body although the amount varies between species. Histaminergic neurons are located in the tuberomamillary nucleus (TMN) of the posterior hypothalamus. There are four different histamine receptors in mammals and they are all metabotropic GPCR receptors. The first three (Hrh1, Hrh2 and Hrh3) are located in the brain while Hrh1 and Hrh2 along with Hrh4 that is mainly found in mast cells, are found in the periphery. Receptors have different functions e.g. Hrh1 regulates wakefulness and alertness while Hrh2 is involved in learning and memory. It is established that histaminergic neurons contain GABA-producing enzyme GAD1 and GABA itself. In the present study we aimed to evaluate GABAergic phenotype of the hypothalamic histaminergic neurons with double fluorescent in situ hybridization. Specifically, we were interested in co-existence of VGAT, which is responsible for vesicular release of GABA, and HDC mRNA. The animals used in this study were mouse and zebrafish. The percentage of mouse HDC-neurons that expressed GAD1 was 99.65% and co-expression for VGAT was also high (94.53%). This coexistence was verified also in the zebrafish model. Our data suggest that histaminergic neurons containing VGAT mRNA and are potentially able to release GABA. If GABA is released in a paracrine manner like histamine, it causes tonic inhibition that counterbalances the effects of histamine during wakefulness. The fact that VGAT mRNA was also found in zebrafish histaminergic neurons indicates that histamine-GABA system is preserved among species.
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(2010)Lung transplantation (LTx) is a generally accepted therapy for end-stage lung patients meeting the international criterias. Chronic dysfunction of the allograft, called Brochiolitis Obliterans Syndrome (BOS), is the most important complication limiting the long term survival of these patients. Known risk factors for developing BOS are episodes of acute rejection, CMV-pneumonitis and HLA-immunization. Other risk factors have also been suggested, as one of them gastroesophageal reflux disease (GERD) and the possible microaspiration caused by it. In this study we followed during one year 15 patients who underwent a bilateral LTx in Helsinki University Central Hospital. Our aim was to find out if it is possible to determine bile acids from lung allograft recipients' bronchoalveolar lavage fluid (BALF) by using a commercially available kit and thus possibly find a useful method to verify the microaspiration in these patients. Our study demonstrates that most patients do have bile acids in their BALF samples during the first year after LTx and that this does not correlate with the reflux symptoms experienced by the patients. We were unable to show correlation between the bile acids in BALF and BOS developed by some patients, but our results indicate that BOS is preceded by repeated episodes of BALF neutrophilia.
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(2020)A global pandemic, COVID-19 began in December 2019 in Wuhan, China. Since then it has expanded all around the globe and was declared a global pandemic in early March by the World Health Organization (WHO). Ever since this pandemic started, the number of infections grew exponentially. Currently, there is a global rise in COVID-19 cases with 3.6 million new cases and new deaths with a weekly growth of 21%. The disease outbreak caused over 55.6 million infected cases and more than 1.34 million deaths worldwide since the beginning of this pandemic. Reverse transcription polymerase chain reaction (RT-PCR) test is the best protocol currently in use to detect COVID-19 positive patients. In a setup with low resources especially in developing countries with huge populations, RT-PCR test is not always a viable option for being expensive, time-consuming and it requires trained professionals. With the overwhelming number of infected cases, there is a significant need for a substitute that is cheaper, faster and accessible. In that regard, machine learning classification models are developed in this study to detect COVID-19 positive patients and predict the patient deterioration in the presence of missing data using a dataset published by hospital Israelita Albert Einstein, at São Paulo, Brazil. The dataset consists of 5644 anonymous patient samples who visited the hospital and tested for RT-PCR along with additional laboratory test results providing 111 clinical features. Additionally, there are more than 90% missing values in this dataset. To explore missing data analysis on COVID-19 clinical data, a comparison between a complete case analysis and imputed case analysis is reported in this study. It is established that the logistic regression model with multivariate imputations by chained equations (MICE) on the data, provides 91% and 85% sensitivity respectively for detecting COVID-19 positive patients and predicting the patient deterioration. The area under the receiver operating characteristics curve (AUC) score is reported at 93% and 89% for both tasks respectively. Sensitivity and AUC scores are selected for evaluating the model’s performance as false negatives are harmful for patient screening and triaging. The proposed pipeline is an alternative approach towards COVID-19 diagnosis and prognosis. Clinicians can employ this pipeline for early screening of COVID-19 suspected patients, triaging the medical procedures and as a secondary diagnostic tool for deciding patient’s priority for treatments by utilizing low-cost, readily available laboratory test results.
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(2013)After the Human Genome Project completed the mapping of human DNA sequence in 2001, a new era began in biological and medical research. The genetic basis of various diseases, such as cancer, could be studied with higher precision than ever before. The map of human genome enabled next-generation sequencing (NGS) techniques and not only DNA sequencing got faster and cheaper to perform, also the amount of data started to increase exponentially. The field of bioinformatics, which combines both computer and life sciences, got a great challenge to handle all the data available and to dig out relevant information out of it. Various tools with heavily enhanced or completely new kinds of algorithms were developed for the demanding task of the analysis of NGS data, which are in the focus of this thesis. For the search of cancer causing mutations, NGS methods enable genome scale studies with the precision of a single molecule. However, the spectacular scale and preciseness of the data offer another challenge – how to distinguish trivial data from the non-trivial, and furthermore, how to separate reliable data from erroneous. The raw data must be put through a pipeline of various processing tools, which organize and humanize the data with the help of the map of human genome. After data processing, the data is feasible for the actual cancer specific analysis, where causative mutations can be hunted down. For this purpose, I have developed an analysis and visualization software, Rikurator, which provides various features and tools to handle the NGS data. Rikurator is designed for comparative analysis of dozens of cancer samples, quality filtering, controlling and visualization to name a few. In addition to tools in data processing pipeline, this thesis will describe features and implementation of Rikurator.
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(2010)Molecular biology has created a new pathway for plant breeding in cut flower industry. It focuses on studying flower gene functions and provides a more direct and effective way of breeding new flower cultivars using genetic transformation. Besides flower color, disease resistance, quality and vase life, modification of flower architecture is an important target for flower breeding. Previous studies have showed that various transcription factors encoded by the corresponding genes are involved regulating flower development and flower architecture. The most studied are MADS domain and TCP domain transcription factors. For targeted breeding, it is crucial to study the functions of the corresponding genes in detail. For both MADS and TCP domain proteins, previous studies have indicated that protein-protein interactions are important for their function. GhCYC1, GhCYC2, GhCYC3 and GhCYC4, isolated from gerbera (Gerbera hybrida), are CYCLOIDEA –like genes affecting inflorescence development. The protein-protein interactions among these four genes have previously been studied by yeast two-hybrid system. The aim of this thesis was to verify the interactions in living plant cells, using both BiFC and split luciferase assays. Protoplast electroporation and agroinfiltration were used to introduce the genes in planta. The results from the two assays were compared in order to find an effective in planta method for detecting protein-protein interactions. The experiment also provided information about DNA transformation efficiency using protoplast electroporation and agroinfiltration. The results of the split luciferase assay showed that GhCYC1+GhCYC4, GhCYC3+GhCYC4 as well as GhCYC4+GhCYC4 interacted quite strongly in plant cells while GhCYC1+GhCYC1, GhCYC2+GhCYC2 as well as GhCYC4+GhCYC2 had almost no interactions. The interactions between GhCYC3+GhCYC4, and GhCYC4+ GhCYC4 were also shown in yeast two-hybrid, but the other results were different. According to the BiFC assay, no signals of interactions were detected from GhCYC2+GhCYC2, while strong signals were observed from GhCYC2+GhCYC3, and weak signals were seen from GhCYC2+GhCYC4. The interactions between GhCYC2+GhCYC3, GhCYC2+GhCYC4 were also observed in yeast two-hybrid, but the other results were unconfirmed. Large standard deviations were observed in the split luciferase assay and thereby reliable conclusions cannot be drawn from it. However, BiFC turned out to be a better method to detect the protein-protein interactions in planta and clear signals from interactions could be observed. Comparison of the transformation methods indicated that agroinfiltration is a better way of introducing DNA into plant cells than protoplast electroporation. For further study, BiFC assay still needs to be repeated to confirm the efficiency of this assay, and factors affecting the transformation efficiency in protoplast electroporation need to be optimized in the future studies.
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(2018)Remote sensing of soil permittivity and soil freezing was investigated using two different satellite based microwave radars: ASCAT and ASAR. ASCAT is a scatterometer with a good temporal resolution but coarse spatial resolution. ASAR is a synthetic aperture radar and has fine spatial resolution, but lacks good temporal coverage. Soil permittivity is related to soil moisture, which is considered an essential climate vari- able since it has an effect on both weather and climate. Soil freezing affects hydrological and carbon cycles, surface energy balance, photosynthesis of vegetation and the activity of soil microbes. A semi-empirical model for backscattering of forested land was used to acquire soil permittivity retrievals from satellite measurements using the method of least squares. The onset of soil freezing was determined from the permittivity retrievals using a simple threshold method. A five year time series of satellite observations from July 2007 to June 2012 (April 2012 for ASAR) was investigated in Sodankylä in Northern Finland. The satellite based retrievals were compared against in situ measurements of soil permittivity, soil temperature, soil frost and snow depth. According to the results the satellite permittivity retrievals correlate with each other, but not with in situ permittivity measurements. ASCAT retrieval shows some correlation with in situ temperature measurements, which could impair its correlation with in situ permittivity. The explanation for this phenomenon needs further research. Comparison of soil freezing onset dates from satellite retrievals with in situ soil temperature and soil frost measurements showed quite good agreement for most years, and did not seem to be affected by first snowfall, even though the permittivity retrievals appeared to react in a similar way to snow cover and soil freezing. This indicates that with better calibration of the permittivity threshold limit this method could be used for soil freeze detection. Auxiliary information about air temperature and snow cover could also be used to filter out possible false estimates before freezing and after the snow cover starts to affect the satellite retrievals.
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(2018)Accurate and sensitive analysis of mono-, di-, and oligosaccharides is desired in several different scientific areas due to the wide appearance of saccharides. This work focuses on the detection of mono-, di-, and oligosaccharides utilizing capillary electrophoresis (CE). Saccharide analysis with CE is challenging due to the lack of UV-absorbing chromophores in the molecular structure. CE also requires that the analytes are in their charged form, which is demanding in the case of mono-, di-, and oligosaccharides due to their high pKa-values. The first part of this work presents several detection methods and procedures to succeed in saccharide analysis with CE. A selection of the scientific work published in this area is presented to highlight the different detection possibilities. Derivatization of the analytes is commonly used to transform the saccharides into UV absorbing species. Special compositions of the background electrolyte, e.g. borate buffers and copper(II) containing buffers can be exploited to form charged complexes with the saccharides, which enhance the separation. Indirect UV detection is not as sensitive as direct UV detection of saccharide derivatives, but it is fast and useful in applications where high sensitivity is not required. Electrochemical detection (pulsed amperometric detection and contactless conductivity detection) is especially useful in miniaturized and portable systems. An advantage of electrochemical detection is also that no sample pretreatment or special reagents are required. Mass spectrometry (MS) detection is a powerful tool when detailed information about oligosaccharide structures is required and when the sample amounts are small. MS detection is therefore especially suitable in biochemical applications. In the second part of this work, CE was utilized for the separation and quantification of five novel ionic liquids and the quantification of acetate and xylose in ionic liquid matrices. The internal standard method was used in the quantitative work. The novel ionic liquids were detected with direct UV detection and the limit of detection ranged from 2-5 µg/mL. Resolution and number of effective plates were calculated from the separation studies. In the quantitative work, calibration curves were obtained for four of the novel ionic liquids. CE with indirect UV detection was used for the quantification of acetate, which is a typical counter ion in ionic liquids. A calibration curve for acetate was obtained and the linearity ranged from 0.0025 to 0.2 mg/mL. The method was successfully applied to the determination of the concentration of acetate in a standard sample containing the ionic liquid [MTBDH][OAc]. In the last part of the work, solid phase extraction was utilized to extract ionic liquids from industrial samples. CE with direct and indirect detection was used to check if the extraction was complete and if saccharides were present in the extracts. A calibration curve for xylose was constructed and the linear range for xylose was 0.05 to 3 mg/mL. It was found that the developed method for xylose detection was not sensitive enough to detect possible saccharide residues in the extracts and the analytical procedure requires further development.
Now showing items 3723-3742 of 30981