Ovarian follicles are spherical structures inside the ovaries which contain developing eggs. Monitoring the development of follicles is necessary for both gynecological medicine (ovarian diseases diagnosis and infertility treatment), and veterinary medicine (determining when to introduce superstimulation in cattle, or dividing herds into different stages in the estrous cycle).<p>Ultrasound imaging provides a non-invasive method for monitoring follicles. However, manually detecting follicles from ovarian ultrasound images is time consuming and sensitive to the observer's experience. Existing (semi-) automatic follicle segmentation techniques show the power of automation, but are not widely used due to their limited success.<p>A new automated follicle segmentation method is introduced in this thesis. Human ovarian images acquired <i>in vivo</i> were smoothed using an adaptive neighbourhood median filter. Dark regions were initially segmented using geometric active contour models. Only part of these segmented dark regions were true follicles. A naïve Bayes classifier was applied to determine whether each segmented dark region was a true follicle or not. <p>The Hausdorff distance between contours of the automatically segmented regions and the gold standard was 2.43 ± 1.46 mm per follicle, and the average root mean square distance per follicle was 0.86 ± 0.49 mm. Both the average Hausdorff distance and the root mean square distance were larger than those reported in other follicle segmentation algorithms. The mean absolute distance between contours of the automatically segmented regions and the gold standard was 0.75 ± 0.32 mm, which was below that reported in other follicle segmentation algorithms.<p>The overall follicle recognition rate was 33% to 35%; and the overall image misidentification rate was 23% to 33%. If only follicles with diameter greater than or equal to 3 mm were considered, the follicle recognition rate increased to 60% to 63%, and the follicle misidentification rate increased slightly to 24% to 34%. The proposed follicle segmentation method is proved to be accurate in detecting a large number of follicles with diameter greater than or equal to 3 mm.

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The aim of this study is to examine what social workers consider as ‘challenging behaviour’ among adolescents, but also to examine the connection between investigation and intervention in cases where such behaviour is being displayed. The questions of the study are to examine what social workers think of the instrument of assessment BBIC (Children’s Needs in Focus) and how the usage of BBIC affect social workers understanding of challenging behaviour. Moreover, it is a qualitative study and the respondents are all social workers with various length of professional experience who work with making decisions in juvenile welfare cases. The social workers’ opinion about which behaviours among adolescents that were important to intervene in could be understood with what was considered as moral panic for some behaviour. The social workers use of and opinion towards BBIC could be understood in relation to practical theory, tacit knowledge and naïve theories.

Made available in DSpace on 2014-06-12T15:58:24Z (GMT). No. of bitstreams: 2 arquivo4097_1.pdf: 888475 bytes, checksum: 0cb3006c0211d4a3f7598e6efed04914 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2011 / Os sistemas de classificação de documentos servem, de modo geral, para facilitar o acesso do usuário a uma base de documentos. Esses sistemas podem ser utilizados para detectar spams; recomendar notícias de uma revista, artigos científicos ou produtos de uma loja virtual; refinar buscas e direcioná-las por assunto. Uma das maiores dificuldades na classificação de documentos é sua alta dimensionalidade. A abordagem bag of words, utilizada para extrair as características e obter os vetores que representam os documentos, gera dezenas de milhares de características. Vetores dessa dimensão demandam elevado custo computacional, além de possuir informações irrelevantes e redundantes. Técnicas de seleção de características reduzem a dimensionalidade da representação, de modo a acelerar o processamento do sistema e a facilitar a classificação. Entretanto, a seleção de características utilizada em problemas de classificação de documentos requer um parâmetro m que define quantas características serão selecionadas. Encontrar um bom valor para m é um procedimento complicado e custoso. A idéia introduzida neste trabalho visa remover a necessidade do parâmetro m e garantir que as características selecionadas cubram todos os documentos do conjunto de treinamento. Para atingir esse objetivo, o algoritmo proposto itera sobre os documentos do conjunto de treinamento e, para cada documento, escolhe a característica mais relevante. Se a característica escolhida já tiver sido selecionada, ela é ignorada, caso contrário, ela é selecionada. Deste modo, a quantidade de características é conhecida no final da execução do algoritmo, sem a necessidade de declarar um valor prévio para m. Os métodos propostos seguem essa ideia inicial com certas variações: inserção do parâmetro f para selecionar várias características por documento; utilização de informação local das classes; restrição de quais documentos serão usados no processo de seleção. Os novos algoritmos são comparados com um método clássico (Variable Ranking). Nos experimentos, foram usadas três bases de dados e cinco funções de avaliação de característica. Os resultados mostram que os métodos propostos conseguem melhores taxas de acerto

Rheumatoid arthritis (RA) is a chronic inflammatory condition of unknown etiology for which there is no cure. It is one of the most disabling diseases and affects about 1% of the world‟s population. Recent developments in the field of molecular biology have resulted in the production of new drugs used in the treatment RA. Despite these advancements, achieving optimal disease control and prevention of disease progression is still difficult in many patients, leading to a continued search for treatment methods that will improve patient outcomes. Non-biologic forms of treatment that are still being investigated include the use of statins as an adjunct therapy due to their reported antiinflammatory effects. Some studies have shown that the use of statins in patients with RA help in reducing disease activity and swollen joint count in addition to lowering cardiovascular risk. As evidence continue to increase on the anti-inflammatory effect of statins, researchers have started investigating possible benefits that may result from the use of statins in treatment of RA, a chronic disease marked by high levels of systemic and local inflammation. This study investigated the anti-inflammatory effect of statins in rheumatoid arthritis patients with associated risks for cardiovascular disease, using simvastatin as the investigational product. Patients with moderately active RA despite being on maximum disease-modifying antirheumatic drugs (DMARDs) therapy and having associated risks for cardiovascular disease were screened for the study. Eligible patients were randomized into two groups, 1 and 2. Patients in group 1 received simvastatin treatment (20mg/day) for a period of 3 months in addition to their usual DMARDs after which they stopped simvastatin treatment and were followed up for a further 3 months off simvastatin treatment. Those in group 2 were allowed to continue on their usual DMARDs without simvastatin treatment for the first 3 months of the study after which they received 20mg/day simvastatin for a period of 3 months in addition to their usual DMARDs. The anti-inflammatory effect of simvastatin was assessed by monitoring the inflammatory variables; erythrocyte sedimentation rate (ESR) and c-reactive protein (CRP) and disease activity in the two groups at screening, at the crossover point and at end of the study. Disease activity was significantly reduced with simvastatin treatment in the two groups. The mean change in disease activity score with simvastatin treatment was 1.30 (p = 0.01) in group 1 and 1.74 (p = 0.01) in group 2. ESR was significantly reduced with simvastatin treatment in group 1 with a mean change of 19.0 (p = 0.005) and marginally reduced in group 2 with a mean change 26.0 (p = 0.09). There was no significant change in CRP with simvastatin treatment in the two groups. The mean change in CRP with simvastatin treatment was 7.0 (p = 0.24) in group 1 and 14.7 (p =0.20) in group 2. All the patients benefited from the lipid-lowering effect of simvastatin. These findings suggest that statins may have mild anti-inflammatory properties and will be good adjuvant in RA patients with associated risks for cardiovascular disease. / Dissertation (MSc)--University of Pretoria, 2013. / gm2014 / Pharmacology / unrestricted

This study is a comparison of forecasting methods for predicting the daily maximum air temperatures in Uppsala using real data from the Swedish Meteorological and Hydrological Institute. The methods for comparison are univariate time series approaches suitable for the data and represent both standard and more recently developed methods. Specifically, three methods are included in the thesis: neural network, ARIMA, and naïve. The dataset is split into a training set and a pseudo out of sample test set. The assessment of which method best forecast the daily temperature in Uppsala is done by comparing the accuracy of the models when doing walk forward validation on the test set. Results show that the neural network is most accurate for the used dataset for both one-step and all multi-step forecasts. Further, the only same-step forecasts from different models that have a statically significant difference are from the neural network and naïve for one- and two-step forecasts, in favor of the neural network.

In this paper, Natural Language Processing and classification algorithms were used to create a program that automatically can tag different errands that are connected to Fortnox (an IT company based in Växjö) support service. Controlled experiments were conducted to find the best classification algorithm together with different Bag-of-Word pre-processing algorithms to find what was best suited for this problem. All data were provided by Fortnox and were manually labeled with tags connected to it as training and test data. The result of the final algorithm was 69.15% correctly/accurately predicted errands using all original data. When looking at the data that were incorrectly predicted a pattern was noticed where many errands have identical text attached to them. By removing the majority of these errands, the result was increased to 94.08%.

In this paper, we will look at the compatibility of different forecasting methods applied to  time series data in esports, specifically three esports, League of Legends, Counter Strike:Global Offensive and Defence of the Ancients 2. The purpose of the study is to assess whether forecasting the amount of professional esport matches for the first three months of 2021 is possible and if so, how accurately. The forecasting methods used in the report are seasonal ARIMA (SARIMA), autoregressive neural networks (NNAR) and a seasonal naïve model as a benchmark. The results show that, for the chosen methods, all the three datasets were able to fulfill the statistical requirements for producing forecasts as well as outperforming the benchmark model, although with various results. Considering the three games, the one that the study was able to predict with highest accuracy was the CS:GO dataset with a NNAR model where we achieved a mean absolute percentage error of 31%.

In this paper, Natural Language Processing and classification algorithms were used to create a program that automatically can tag different errands that are connected to Fortnox (an IT company based in Växjö) support service. Controlled experiments were conducted to find the best classification algorithm together with different Bag-of-Word pre-processing algorithms to find what was best suited for this problem. All data were provided by Fortnox and were manually labeled with tags connected to it as training and test data. The result of the final algorithm was 69.15% correctly/accurately predicted errands using all original data. When looking at the data that were incorrectly predicted a pattern was noticed where many errands have identical text attached to them. By removing the majority of these errands, the result was increased to 94.08%

T cells play a crucial role in viral clearance and vaccine responses; however, the mechanisms that regulate their homeostasis during viral infections remain unclear. In this study, we investigated the machineries of T-cell homeostasis and telomeric DNA damage using a human model of hepatitis C virus (HCV) infection. We found that naïve CD4 T cells in chronically HCV-infected patients (HCV T cells) were significantly reduced due to apoptosis compared with age-matched healthy subjects (HSs). These HCV T cells were not only senescent, as demonstrated by overexpression of aging markers and particularly shortened telomeres; but also DNA damaged, as evidenced by increased dysfunctional telomere-induced foci (TIF). Mechanistically, the telomere shelterin protein, in particular telomeric repeat binding factor 2 (TRF2) that functions to protect telomeres from DNA damage, was significantly inhibited posttranscriptionally via the p53-dependent Siah-1a ubiquitination. Importantly, knockdown of TRF2 in healthy T cells resulted in increases in telomeric DNA damage and T-cell apoptosis, whereas overexpression of TRF2 in HCV T cells alleviated telomeric DNA damage and T-cell apoptosis. To the best of our knowledge, this is the first report revealing that inhibition of TRF2 promotes T-cell telomere attrition and telomeric DNA damage that accelerates T-cell senescent and apoptotic programs, which contribute to naïve T-cell loss during viral infection. Thus, restoring the impaired T-cell telomeric shelterin machinery may offer a new strategy to improve immunotherapy and vaccine response against human viral diseases.