En France, la recherche contre le cancer est un enjeu majeur de santé publique. On estime notamment que le nombre de nouveaux cas de cancer a plus que doublé entre 1980 et 2012. L’hétérogénéité des caractéristiques tumorales, pour un même cancer, impose des défis complexes dans la recherche de traitements efficaces. Dans ce contexte, des espoirs importants sont placés dans la recherche de biomarqueurs prédictifs reflétant les caractéristiques des patients ainsi que de leur tumeur afin d’orienter le choix de la stratégie thérapeutique. Par exemple, pour les cancers colorectaux métastatiques, il est maintenant reconnu que l’ajout de cetuximab (un anti-EGFR) à la chimiothérapie classique (ici le FOLFOX4), n’apporte un bénéfice qu’aux patients dont le gène KRAS est non muté. Le gène KRAS est ici un biomarqueur prédictif binaire, mais de nombreux biomarqueurs sont le résultat d’une quantification ou d’un dosage. L’objectif de cette thèse est dans un premier temps, de quantifier la capacité globale d’un biomarqueur quantitatif à guider le choix du traitement. Après une revue de la littérature, une nouvelle méthode basée sur une extension des courbes ROC est proposée, et comparée aux méthodes existantes. Son principal avantage est d’être non paramétrique, et d’être indépendante de l’efficacité moyenne des traitements. Dans un second temps, lorsqu’un biomarqueur prédictif quantitatif est étudié, la définition d’un seuil de marqueur au-delà duquel la première option de traitement sera préférée, et en-deçà duquel la deuxième option de traitement sera préférée se pose. Une approche reposant sur la définition d’une fonction d’utilité est proposée permettant alors de tenir compte de l’efficacité des traitements ainsi que de leur impact sur la qualité de vie des patients. Une méthode Bayésienne d’estimation de ce seuil optimal est proposée / In France, the cancer research is a major public health issue. The number of new cancer cases nearly doubled between 1980 and 2012. The heterogeneity of the tumor characteristics, for a given cancer, presents a great challenge in the research of new effective treatments. In this context, much hope is placed in the research of predictive (or treatment selection) biomarkers that reflect the patients’ characteristics in order to guide treatment choice. For example, in the metastatic colorectal cancer setting, it is admitted that the addition of cetuximab (an anti-EGFR) to classical chemotherapy (the FOLFOX4), only improve the outcome of patients with KRAS wild-type tumors. In that context, the KRAS gene is a binary treatment selection marker, but plenty of biomarkers result from some quantifications or dosage measurements. The first aim of this thesis is to quantify the global treatment selection ability of a biomarker. After a review of the existing litterature, a method based on an extension of ROC curves is proposed and compared to existing methods. Its main advantage is that it is non-parametric, and that it does not depend on the mean risk of event in each treatment arm. In a second time, when a quantitative treatment selection biomarker is assessed, there is a need to estimate a marker thereshold value above which one treatment is preferred, and below which the other treatment is recommended. An approach that relies on the definition of a utility function is proposed in order to take into account both efficacy and toxicity of treatments when estimating the optimal threshold. A Bayesian method for the estimation of the optimal threshold is proposed

This diploma thesis deals with detection of atrial fibrillation from HRV, classification of Poincare map and in the end the divide into two groups, one with detected atrial fibrillation and one not. The result is the decision on which variables are statistically significant for the identification of atrial fibrillations and which are not, and classification of the ECG signals.

This diploma thesis deals with detection of atrial fibrillation from HRV, classification of Poincare map and in the end the divide into two groups, one with detected atrial fibrillation and one not. The result is the decision on which variables are statistically significant for the identification of atrial fibrillations and which are not, and classification of the ECG signals with Bayes and Lavenberg-Marquardt neural networks. Bayes neural network with 23 neurons in hidden layer is best with F1 measure = 83,6 %, Sensitivity = 88,1 % and Specificity 94,5 %.

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Orthogonal frequency division multiplexing (OFDM) has become a very popular method for high-data-rate communication. However, it is well known that OFDM is plagued by a large peak-to-average power ratio (PAPR) problem. This high PAPR results in overdesigned power amplifiers, which amongst other things leads to inefficient amplifier usage, which is undesirable. Various methods have been recommended to reduce the PAPR of an OFDM transmission; however, all these methods result in a number of drawbacks. In this thesis, a novel method called offset modulation (OM-OFDM) is proposed to control the PAPR of an OFDM signal. The proposed OM-OFDM method does not result in a number of the drawbacks being experienced by current methods in the field. The theoretical bandwidth occupancy and theoretical bit error rate (BER) expression for an OM-OFDM transmission is derived. A newly applied power performance decision metric is also introduced, which can be utilised throughout the PAPR field, in order to compare various methods. The proposed OM-OFDM method appears to be similar to a well-known constant envelope OFDM (CE-OFDM) transmission. The modulation, structural and performance differences between an OM-OFDM and a CE-OFDM method are discussed. By applying the power performance decision metric, the OM-OFDM method is shown to offer significant performance gains when compared to CE-OFDM and traditional OFDM transmissions. In addition, the OM-OFDM method is able to accurately control the PAPR of a transmission for a targeted BER. By applying the power performance decision metric and complementary cumulative distribution function (CCDF), the proposed OM-OFDM method is shown to offer further performance gains when compared to existing PAPR methods, under frequency selective fading conditions. In this thesis, the OM-OFDM method has been combined with an existing active constellation extended (ACE) PAPR reduction method. To introduce a novel method called offset modulation with active constellation extension (OM-ACE), to control the PAPR of an OFDM signal. The theoretical BER expression for an OM-ACE transmission is presented and validated. Thereafter, by applying the decision metric and CCDF, the OM-ACE method is shown to offer performance improvements when compared to various PAPR methods. The use of OM-OFDM for cognitive radio applications is also investigated. Cognitive radio applications require transmissions that are easily detectable. The detection characteristics of an OM-OFDM and OFDM transmission are studied by using receiver operating characteristic curves. A derivation of a simplified theoretical closed-form expression, which relates the probability of a missed detection to the probability of a false alarm, for an unknown deterministic signal, at various signal-to-noise ratio (SNR) values is derived and validated. Previous expressions have been derived, which relate the probability of a missed detection to the probability of a false alarm. However, they have not been presented in such a generic closed-form expression that can be used for any unknown deterministic signal (for instance OFDM and OM-OFDM). Thereafter, an examination of the spectrum characteristics of an OM-OFDM transmission indicates its attractive detection characteristics. The proposed OM-OFDM method is further shown to operate at a significantly lower SNR value than an OFDM transmission, while still offering better detection characteristics than that of an OFDM transmission under Rician, Rayleigh and frequency selective fading channel conditions. In addition to its attractive PAPR properties, OM-OFDM also offers good detection characteristics for cognitive radio applications. These aspects make OM-OFDM a promising candidate for future deployment. / Thesis (PhD)--University of Pretoria, 2012. / Electrical, Electronic and Computer Engineering / unrestricted

Background: In many severely mentally disordered children, the clinical presentation is complicated by comorbid affective and behavioral dysregulation. Recently, a highly heritable behavioral phenotype of simultaneous deviance on the anxious/depressed, attention problems, and aggressive behavior syndrome scales has been identified on the Child Behavior Checklist Dysregulation Profile (CBCL-DP). The aim of the present pilot study was to determine an equivalent to the CBCL-DP using the Strengths and Difficulties Questionnaire (SDQ). Sampling and Methods: We applied stepwise linear discriminant analyses and receiver operating characteristic (ROC) analysis to data from 543 consecutively referred children and adolescents, aged 5–17 years. The CBCL and the SDQ were completed by parents as part of the diagnostic routine. ICD-10 discharge diagnoses were established in consensus conferences. Results: A combination of five SDQ items (SDQ-Dysregulation Profile, SDQ-DP) yielded the best discrimination of children with and without CBCL-DP and classified 81.0% of the subjects correctly leading to an area under the curve of 0.93. The content of the five SDQ-DP items mirrors well the mixed behavioral phenotype of anxious-depressive, aggressive and attention problems captured by the CBCL-DP. SDQ-DP status was highly correlated with CBCL-DP status and was best defined by a SDQ-DP score ≧5. Conclusions: The psychometric properties of the SDQ-DP have been robustly tested and validated. Based on these results, clinicians may use the SDQ-DP as a useful and economical screening measure to improve the assessment, prevention, and treatment of severe dysregulation in childhood and adolescence. Future investigations should study the longitudinal stability, heritability, and genetic associations of this behavioral phenotype. / Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich.

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This document deals with experimental verifying to use machine learning algorithms AdaBoost or WaldBoost to make classifier, that is able to find point in the second picture that matches original point in the first picture. This work also depicts finding points of interest in image as a first step of finding correspondence. Next there are described some descriptors of points of interest. Corresponding points could be useful for 3D modeling of shooted scene.

Of concern in this research were the ecological parameters associated with the establishment of a model invasive plant species, Imperata cylindrica, across a scale of ecological organization. Specifically, the study addressed the species’ ability to: differentially respond to abiotic and biotic constraints during seedling establishment, exhibit a novel underground competitive interference mechanism, and alter the decomposition dynamics in newly invaded ecosystems. Finally, the last portion of the research was centered around creating a predictive habitat model that will provide information on the most important variables responsible for creating habitat for this species. The population level seedling study indicated that soil characteristics and light availability play a significant role in seedling establishment. There were large trends in biomass allocation attributable to soil type with seedlings performing best in high nutrient soils representative of the Mississippi Alluvial Valley physiographic region. I. cylindrica seedlings also showed a positive response to increased seedling density during the initial stages of seedling establishment. The community level research examining a hypothesized novel interference mechanism deployed by I. cylindrica showed a significant and robust pattern of I. cylindrica damaging its own belowground tissue more often than that of its surrounding neighbors. Therefore, it is highly unlikely that I. cylindrica gains a competitive advantage by exposing the native plant assemblage to pathogen invasion (via ruptured tissue) as the plant would expose itself to these pathogens (to which it is evolutionarily naive) at much higher volumes. The ecosystem level examination of this globally important invasive species indicated that I. cylindrica invasion into native systems will significantly accelerate ambient rates of decomposition. Furthermore, fungal community composition in invaded areas was drastically altered as well as bacterial community functional activity in relation to several key enzymes responsible for the decomposition of plant tissue which were produced more abundantly in invaded areas.The landscape-scale analyses and modeling work validated decades of anecdotal evidence and indicated that anthropogenic disturbance factors associated with road maintenance and construction (soil disturbance and vegetation removal) are the principal factors responsible for creating habitat suitable for invasion by this species.

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