Cross-validation of methods used for analysis of MTBE and other gasoline components in groundwater.

Head space gas chromatography with flame-ionization detection (HS-GC-FID), ancl purge and trap gas chromatography-mass spectrometry (P&T-GC-MS) have been used to determine methyl-tert-butyl ether (MTBE) and benzene, toluene, and the ylenes (BTEX) in groundwater. In the work discussed in this paper measures of quality, e.g. recovery (94-111%), precision (4.6 - 12.2%), limits of detection (0.3 - 5.7 I~g L 1 for HS and 0.001 I~g L 1 for PT), and robust-ness, for both methods were compared. In addition, for purposes of comparison, groundwater samples from areas suffering from odor problems because of fuel spillage and tank leakage were analyzed by use of both techniques. For high concentration levels there was good correlation between results from both methods.

Elicitation of Customer Requirements with Group Methods in Software Engineering

Vaatimusmäärittely on tärkeä vaihe ohjelmistotuotannossa, koska virheelliset ja puutteelliset asiakasvaatimukset vaikuttavat huomattavasti asiakkaan tyytymättömyyteen ohjelmistotuotteessa. Ohjelmistoinsinöörit käyttävät useita erilaisia menetelmiä ja tekniikoita asiakasvaatimusten kartoittamiseen. Erilaisia tekniikoita asiakasvaatimusten keräämiseen on olemassa valtava määrä.Diplomityön tavoitteena oli parantaa asiakasvaatimusten keräämisprosessia ohjelmistoprojekteissa. Asiakasvaatimusten kartoittamiseen käytettävien tekniikoiden arvioinnin perusteella kehitettiin parannettu asiakasvaatimusten keräämisprosessi. Kehitetyn prosessin testaamiseksi ja parantamiseksi järjestettiin ryhmätyöistuntoja liittyen todellisiin ohjelmistokehitysprojekteihin. Tuloksena vaatimusten kerääminen eri sidosryhmiltä nopeutui ja tehostui. Prosessi auttoi muodostamaan yleisen kuvan kehitettävästä ohjelmistosta, prosessin avulla löydettiin paljon ideoita ja prosessi tehosti ideoiden analysointia ja priorisointia. Prosessin suurin kehityskohde oli fasilitaattorin ja osallistujien valmistautumisessa ryhmätyöistuntoihin etukäteen.

Fulvestrant with or without selumetinib, a MEK 1/2 inhibitor, in breast cancer progressing after aromatase inhibitor therapy: A multicentre randomised placebo-controlled double-blind phase II trial, SAKK 21/08.

BACKGROUND: Second line endocrine therapy has limited antitumour activity. Fulvestrant inhibits and downregulates the oestrogen receptor. The mitogen-activated protein kinase (MAPK) pathway is one of the major cascades involved in resistance to endocrine therapy. We assessed the efficacy and safety of fulvestrant with selumetinib, a MEK 1/2 inhibitor, in advanced stage breast cancer progressing after aromatase inhibitor (AI). PATIENTS AND METHODS: This randomised phase II trial included postmenopausal patients with endocrine-sensitive breast cancer. They were ramdomised to fulvestrant combined with selumetinib or placebo. The primary endpoint was disease control rate (DCR) in the experimental arm. ClinicalTrials.gov Indentifier: NCT01160718. RESULTS: Following the planned interim efficacy analysis, recruitment was interrupted after the inclusion of 46 patients (23 in each arm), because the selumetinib-fulvestrant arm did not reach the pre-specified DCR. DCR was 23% (95% confidence interval (CI) 8-45%) in the selumetinib arm and 50% (95% CI 27-75%) in the placebo arm. Median progression-free survival was 3.7months (95% CI 1.9-5.8) in the selumetinib arm and 5.6months (95% CI 3.4-13.6) in the placebo arm. Median time to treatment failure was 5.1 (95% CI 2.3-6.7) and 5.6 (95% CI 3.4-10.2) months, respectively. The most frequent treatment-related adverse events observed in the selumetinib-fulvestrant arm were skin disorders, fatigue, nausea/vomiting, oedema, diarrhoea, mouth disorders and muscle disorders. CONCLUSIONS: The addition of selumetinib to fulvestrant did not show improving patients' outcome and was poorly tolerated at the recommended monotherapy dose. Selumetinib may have deteriorated the efficacy of the endocrine therapy in some patients.

ADVANCED NUMERICAL METHODS FOR SIMULATING MICROBUBBLE IN AN AERATED MIXING TANK

Fluid mixing in mechanically agitated tanks is one of the major unit operations in many industries. Bubbly flows have been of interest among researchers in physics, medicine, chemistry and technology over the centuries. The aim of this thesis is to use advanced numerical methods for simulating microbubble in an aerated mixing tank. Main components of the mixing tank are a cylindrical vessel, a rotating Rushton turbine and the air nozzle. The objective of Computational Fluid Dynamics (CFD) is to predict fluid flow, heat transfer, mass transfer and chemical reactions. The CFD simulations of a turbulent bubbly flow are carried out in a cylindrical mixing tank using large eddy simulation (LES) and volume of fluid (VOF) method. The Rushton turbine induced flow is modeled by using a sliding mesh method. Numerical results are used to describe the bubbly flows in highly complex liquid flow. Some of the experimental works related to turbulent bubbly flow in a mixing tank are briefly reported. Numerical simulations are needed to complete and interpret the results of the experimental work. Information given by numerical simulations has a major role in designing and scaling-up mixing tanks. The results of this work have been reported in the following scientific articles: ·Honkanen M., Koohestany A., Hatunen T., Saarenrinne P., Zamankhan P., Large eddy simulations and PIV experiments of a two-phase air-water mixer, in Proceedings of ASME Fluids Engineering Summer Conference (2005). ·Honkanen M., Koohestany A., Hatunen T., Saarenrinne P., Zamankhan P., Dynamical States of Bubbling in an Aerated Stirring Tank, submitted to J. Computational Physics.

A phase IIa randomised clinical study of GNbAC1, a humanised monoclonal antibody against the envelope protein of multiple sclerosis-associated endogenous retrovirus in multiple sclerosis patients.

BACKGROUND: GNbAC1 is an immunoglobulin (IgG4) humanised monoclonal antibody against multiple sclerosis-associated retrovirus (MSRV)-Env, a protein of endogenous retroviral origin, expressed in multiple sclerosis (MS) lesions, which is pro-inflammatory and inhibits oligodendrocyte precursor cell differentiation. OBJECTIVE: This is a randomised, double-blind placebo-controlled dose-escalation study followed by a six-month open-label phase to test GNbAC1 in MS patients. The primary objective was to assess GNbAC1 safety in MS patients, and the other objectives were pharmacokinetic and pharmacodynamic assessments. METHODS: Ten MS patients were randomised into two cohorts to receive a single intravenous infusion of GNbAC1/placebo at doses of 2 or 6 mg/kg. Then all patients received five infusions of GNbAC1 at 2 or 6 mg/kg at four-week intervals in an open-label setting. Safety, brain magnetic resonance imaging (MRI), pharmacokinetics, immunogenicity, cytokines and MSRV RNA expression were studied. RESULTS: All patients completed the study. GNbAC1 was well tolerated in all patients. GNbAC1 pharmacokinetics is dose-linear with mean elimination half-life of 27-37 d. Anti-GNbAC1 antibodies were not detected. Cytokine analysis did not indicate an adverse effect. MSRV-transcripts showed a decline after the start of treatment. Nine patients had stable brain lesions at MRI. CONCLUSION: The safety, pharmacokinetic profile, and pharmacodynamic responses to GNbAC1 are favourable in MS patients over a six-month treatment period.

Two cilengitide regimens in combination with standard treatment for patients with newly diagnosed glioblastoma and unmethylated MGMT gene promoter: results of the open-label, controlled, randomized phase II CORE study.

BACKGROUND: Survival outcomes for patients with glioblastoma remain poor, particularly for patients with unmethylated O(6)-methylguanine-DNA methyltransferase (MGMT) gene promoter. This phase II, randomized, open-label, multicenter trial investigated the efficacy and safety of 2 dose regimens of the selective integrin inhibitor cilengitide combined with standard chemoradiotherapy in patients with newly diagnosed glioblastoma and an unmethylated MGMT promoter. METHODS: Overall, 265 patients were randomized (1:1:1) to standard cilengitide (2000 mg 2×/wk; n = 88), intensive cilengitide (2000 mg 5×/wk during wk 1-6, thereafter 2×/wk; n = 88), or a control arm (chemoradiotherapy alone; n = 89). Cilengitide was administered intravenously in combination with daily temozolomide (TMZ) and concomitant radiotherapy (RT; wk 1-6), followed by TMZ maintenance therapy (TMZ/RT→TMZ). The primary endpoint was overall survival; secondary endpoints included progression-free survival, pharmacokinetics, and safety and tolerability. RESULTS: Median overall survival was 16.3 months in the standard cilengitide arm (hazard ratio [HR], 0.686; 95% CI: 0.484, 0.972; P = .032) and 14.5 months in the intensive cilengitide arm (HR, 0.858; 95% CI: 0.612, 1.204; P = .3771) versus 13.4 months in the control arm. Median progression-free survival assessed per independent review committee was 5.6 months (HR, 0.822; 95% CI: 0.595, 1.134) and 5.9 months (HR, 0.794; 95% CI: 0.575, 1.096) in the standard and intensive cilengitide arms, respectively, versus 4.1 months in the control arm. Cilengitide was well tolerated. CONCLUSIONS: Standard and intensive cilengitide dose regimens were well tolerated in combination with TMZ/RT→TMZ. Inconsistent overall survival and progression-free survival outcomes and a limited sample size did not allow firm conclusions regarding clinical efficacy in this exploratory phase II study.

Methods of Design and Testing of Real Time Systems

Teollusuussovelluksissa vaaditaan nykyisin yhä useammin reaaliaikaista tiedon käsittelyä. Luotettavuus on yksi tärkeimmistä reaaliaikaiseen tiedonkäsittelyyn kykenevän järjestelmän ominaisuuksista. Sen saavuttamiseksi on sekä laitteisto, että ohjelmisto testattava. Tämän työn päätavoitteena on laitteiston testaaminen ja laitteiston testattavuus, koska luotettava laitteistoalusta on perusta tulevaisuuden reaaliaikajärjestelmille. Diplomityössä esitetään digitaaliseen signaalinkäsittelyyn soveltuvan prosessorikortin suunnittelu. Prosessorikortti on tarkoitettu sähkökoneiden ennakoivaa kunnonvalvontaa varten. Uusimmat DFT (Desing for Testability) menetelmät esitellään ja niitä sovelletaan prosessorikortin sunnittelussa yhdessä vanhempien menetelmien kanssa. Kokemukset ja huomiot menetelmien soveltuvuudesta raportoidaan työn lopussa. Työn tavoitteena on kehittää osakomponentti web -pohjaiseen valvontajärjestelmään, jota on kehitetty Sähkötekniikan osastolla Lappeenrannan teknillisellä korkeakoululla.

Task-Driven Activity Reduces the Cortical Activity Space of the Brain: Experiment and Whole-Brain Modeling.

How a stimulus or a task alters the spontaneous dynamics of the brain remains a fundamental open question in neuroscience. One of the most robust hallmarks of task/stimulus-driven brain dynamics is the decrease of variability with respect to the spontaneous level, an effect seen across multiple experimental conditions and in brain signals observed at different spatiotemporal scales. Recently, it was observed that the trial-to-trial variability and temporal variance of functional magnetic resonance imaging (fMRI) signals decrease in the task-driven activity. Here we examined the dynamics of a large-scale model of the human cortex to provide a mechanistic understanding of these observations. The model allows computing the statistics of synaptic activity in the spontaneous condition and in putative tasks determined by external inputs to a given subset of brain regions. We demonstrated that external inputs decrease the variance, increase the covariances, and decrease the autocovariance of synaptic activity as a consequence of single node and large-scale network dynamics. Altogether, these changes in network statistics imply a reduction of entropy, meaning that the spontaneous synaptic activity outlines a larger multidimensional activity space than does the task-driven activity. We tested this model's prediction on fMRI signals from healthy humans acquired during rest and task conditions and found a significant decrease of entropy in the stimulus-driven activity. Altogether, our study proposes a mechanism for increasing the information capacity of brain networks by enlarging the volume of possible activity configurations at rest and reliably settling into a confined stimulus-driven state to allow better transmission of stimulus-related information.

Bevacizumab, Pemetrexed, and Cisplatin, or Bevacizumab and Erlotinib for Patients With Advanced Non-Small-Cell Lung Cancer Stratified by Epidermal Growth Factor Receptor Mutation: Phase II Trial SAKK19/09.

OBJECTIVE: The goal was to demonstrate that tailored therapy, according to tumor histology and epidermal growth factor receptor (EGFR) mutation status, and the introduction of novel drug combinations in the treatment of advanced non-small-cell lung cancer are promising for further investigation. METHODS: We conducted a multicenter phase II trial with mandatory EGFR testing and 2 strata. Patients with EGFR wild type received 4 cycles of bevacizumab, pemetrexed, and cisplatin, followed by maintenance with bevacizumab and pemetrexed until progression. Patients with EGFR mutations received bevacizumab and erlotinib until progression. Patients had computed tomography scans every 6 weeks and repeat biopsy at progression. The primary end point was progression-free survival (PFS) ≥ 35% at 6 months in stratum EGFR wild type; 77 patients were required to reach a power of 90% with an alpha of 5%. Secondary end points were median PFS, overall survival, best overall response rate (ORR), and tolerability. Further biomarkers and biopsy at progression were also evaluated. RESULTS: A total of 77 evaluable patients with EGFR wild type received an average of 9 cycles (range, 1-25). PFS at 6 months was 45.5%, median PFS was 6.9 months, overall survival was 12.1 months, and ORR was 62%. Kirsten rat sarcoma oncogene mutations and circulating vascular endothelial growth factor negatively correlated with survival, but thymidylate synthase expression did not. A total of 20 patients with EGFR mutations received an average of 16 cycles. PFS at 6 months was 70%, median PFS was 14 months, and ORR was 70%. Biopsy at progression was safe and successful in 71% of the cases. CONCLUSIONS: Both combination therapies were promising for further studies. Biopsy at progression was feasible and will be part of future SAKK studies to investigate molecular mechanisms of resistance.

Adaptive MCMC methods with applications in environmental and geophysical models

This work presents new, efficient Markov chain Monte Carlo (MCMC) simulation methods for statistical analysis in various modelling applications. When using MCMC methods, the model is simulated repeatedly to explore the probability distribution describing the uncertainties in model parameters and predictions. In adaptive MCMC methods based on the Metropolis-Hastings algorithm, the proposal distribution needed by the algorithm learns from the target distribution as the simulation proceeds. Adaptive MCMC methods have been subject of intensive research lately, as they open a way for essentially easier use of the methodology. The lack of user-friendly computer programs has been a main obstacle for wider acceptance of the methods. This work provides two new adaptive MCMC methods: DRAM and AARJ. The DRAM method has been built especially to work in high dimensional and non-linear problems. The AARJ method is an extension to DRAM for model selection problems, where the mathematical formulation of the model is uncertain and we want simultaneously to fit several different models to the same observations. The methods were developed while keeping in mind the needs of modelling applications typical in environmental sciences. The development work has been pursued while working with several application projects. The applications presented in this work are: a winter time oxygen concentration model for Lake Tuusulanjärvi and adaptive control of the aerator; a nutrition model for Lake Pyhäjärvi and lake management planning; validation of the algorithms of the GOMOS ozone remote sensing instrument on board the Envisat satellite of European Space Agency and the study of the effects of aerosol model selection on the GOMOS algorithm.

Induction chemoradiation in stage IIIA/N2 non-small-cell lung cancer: a phase 3 randomised trial.

BACKGROUND: One of the standard options in the treatment of stage IIIA/N2 non-small-cell lung cancer is neoadjuvant chemotherapy and surgery. We did a randomised trial to investigate whether the addition of neoadjuvant radiotherapy improves outcomes. METHODS: We enrolled patients in 23 centres in Switzerland, Germany and Serbia. Eligible patients had pathologically proven, stage IIIA/N2 non-small-cell lung cancer and were randomly assigned to treatment groups in a 1:1 ratio. Those in the chemoradiotherapy group received three cycles of neoadjuvant chemotherapy (100 mg/m(2) cisplatin and 85 mg/m(2) docetaxel) followed by radiotherapy with 44 Gy in 22 fractions over 3 weeks, and those in the control group received neoadjuvant chemotherapy alone. All patients were scheduled to undergo surgery. Randomisation was stratified by centre, mediastinal bulk (less than 5 cm vs 5 cm or more), and weight loss (5% or more vs less than 5% in the previous 6 months). The primary endpoint was event-free survival. Analyses were done by intention to treat. This trial is registered with ClinicalTrials.gov, number NCT00030771. FINDINGS: From 2001 to 2012, 232 patients were enrolled, of whom 117 were allocated to the chemoradiotherapy group and 115 to the chemotherapy group. Median event-free survival was similar in the two groups at 12·8 months (95% CI 9·7-22·9) in the chemoradiotherapy group and 11·6 months (8·4-15·2) in the chemotherapy group (p=0·67). Median overall survival was 37·1 months (95% CI 22·6-50·0) with radiotherapy, compared with 26·2 months (19·9-52·1) in the control group. Chemotherapy-related toxic effects were reported in most patients, but 91% of patients completed three cycles of chemotherapy. Radiotherapy-induced grade 3 dysphagia was seen in seven (7%) patients. Three patients died in the control group within 30 days after surgery. INTERPRETATION: Radiotherapy did not add any benefit to induction chemotherapy followed by surgery. We suggest that one definitive local treatment modality combined with neoadjuvant chemotherapy is adequate to treat resectable stage IIIA/N2 non-small-cell lung cancer. FUNDING: Swiss State Secretariat for Education, Research and Innovation (SERI), Swiss Cancer League, and Sanofi.

Error models in hydrogeology applications

Notre consommation en eau souterraine, en particulier comme eau potable ou pour l'irrigation, a considérablement augmenté au cours des années. De nombreux problèmes font alors leur apparition, allant de la prospection de nouvelles ressources à la remédiation des aquifères pollués. Indépendamment du problème hydrogéologique considéré, le principal défi reste la caractérisation des propriétés du sous-sol. Une approche stochastique est alors nécessaire afin de représenter cette incertitude en considérant de multiples scénarios géologiques et en générant un grand nombre de réalisations géostatistiques. Nous rencontrons alors la principale limitation de ces approches qui est le coût de calcul dû à la simulation des processus d'écoulements complexes pour chacune de ces réalisations. Dans la première partie de la thèse, ce problème est investigué dans le contexte de propagation de l'incertitude, oú un ensemble de réalisations est identifié comme représentant les propriétés du sous-sol. Afin de propager cette incertitude à la quantité d'intérêt tout en limitant le coût de calcul, les méthodes actuelles font appel à des modèles d'écoulement approximés. Cela permet l'identification d'un sous-ensemble de réalisations représentant la variabilité de l'ensemble initial. Le modèle complexe d'écoulement est alors évalué uniquement pour ce sousensemble, et, sur la base de ces réponses complexes, l'inférence est faite. Notre objectif est d'améliorer la performance de cette approche en utilisant toute l'information à disposition. Pour cela, le sous-ensemble de réponses approximées et exactes est utilisé afin de construire un modèle d'erreur, qui sert ensuite à corriger le reste des réponses approximées et prédire la réponse du modèle complexe. Cette méthode permet de maximiser l'utilisation de l'information à disposition sans augmentation perceptible du temps de calcul. La propagation de l'incertitude est alors plus précise et plus robuste. La stratégie explorée dans le premier chapitre consiste à apprendre d'un sous-ensemble de réalisations la relation entre les modèles d'écoulement approximé et complexe. Dans la seconde partie de la thèse, cette méthodologie est formalisée mathématiquement en introduisant un modèle de régression entre les réponses fonctionnelles. Comme ce problème est mal posé, il est nécessaire d'en réduire la dimensionnalité. Dans cette optique, l'innovation du travail présenté provient de l'utilisation de l'analyse en composantes principales fonctionnelles (ACPF), qui non seulement effectue la réduction de dimensionnalités tout en maximisant l'information retenue, mais permet aussi de diagnostiquer la qualité du modèle d'erreur dans cet espace fonctionnel. La méthodologie proposée est appliquée à un problème de pollution par une phase liquide nonaqueuse et les résultats obtenus montrent que le modèle d'erreur permet une forte réduction du temps de calcul tout en estimant correctement l'incertitude. De plus, pour chaque réponse approximée, une prédiction de la réponse complexe est fournie par le modèle d'erreur. Le concept de modèle d'erreur fonctionnel est donc pertinent pour la propagation de l'incertitude, mais aussi pour les problèmes d'inférence bayésienne. Les méthodes de Monte Carlo par chaîne de Markov (MCMC) sont les algorithmes les plus communément utilisés afin de générer des réalisations géostatistiques en accord avec les observations. Cependant, ces méthodes souffrent d'un taux d'acceptation très bas pour les problèmes de grande dimensionnalité, résultant en un grand nombre de simulations d'écoulement gaspillées. Une approche en deux temps, le "MCMC en deux étapes", a été introduite afin d'éviter les simulations du modèle complexe inutiles par une évaluation préliminaire de la réalisation. Dans la troisième partie de la thèse, le modèle d'écoulement approximé couplé à un modèle d'erreur sert d'évaluation préliminaire pour le "MCMC en deux étapes". Nous démontrons une augmentation du taux d'acceptation par un facteur de 1.5 à 3 en comparaison avec une implémentation classique de MCMC. Une question reste sans réponse : comment choisir la taille de l'ensemble d'entrainement et comment identifier les réalisations permettant d'optimiser la construction du modèle d'erreur. Cela requiert une stratégie itérative afin que, à chaque nouvelle simulation d'écoulement, le modèle d'erreur soit amélioré en incorporant les nouvelles informations. Ceci est développé dans la quatrième partie de la thèse, oú cette méthodologie est appliquée à un problème d'intrusion saline dans un aquifère côtier. -- Our consumption of groundwater, in particular as drinking water and for irrigation, has considerably increased over the years and groundwater is becoming an increasingly scarce and endangered resource. Nofadays, we are facing many problems ranging from water prospection to sustainable management and remediation of polluted aquifers. Independently of the hydrogeological problem, the main challenge remains dealing with the incomplete knofledge of the underground properties. Stochastic approaches have been developed to represent this uncertainty by considering multiple geological scenarios and generating a large number of realizations. The main limitation of this approach is the computational cost associated with performing complex of simulations in each realization. In the first part of the thesis, we explore this issue in the context of uncertainty propagation, where an ensemble of geostatistical realizations is identified as representative of the subsurface uncertainty. To propagate this lack of knofledge to the quantity of interest (e.g., the concentration of pollutant in extracted water), it is necessary to evaluate the of response of each realization. Due to computational constraints, state-of-the-art methods make use of approximate of simulation, to identify a subset of realizations that represents the variability of the ensemble. The complex and computationally heavy of model is then run for this subset based on which inference is made. Our objective is to increase the performance of this approach by using all of the available information and not solely the subset of exact responses. Two error models are proposed to correct the approximate responses follofing a machine learning approach. For the subset identified by a classical approach (here the distance kernel method) both the approximate and the exact responses are knofn. This information is used to construct an error model and correct the ensemble of approximate responses to predict the "expected" responses of the exact model. The proposed methodology makes use of all the available information without perceptible additional computational costs and leads to an increase in accuracy and robustness of the uncertainty propagation. The strategy explored in the first chapter consists in learning from a subset of realizations the relationship between proxy and exact curves. In the second part of this thesis, the strategy is formalized in a rigorous mathematical framework by defining a regression model between functions. As this problem is ill-posed, it is necessary to reduce its dimensionality. The novelty of the work comes from the use of functional principal component analysis (FPCA), which not only performs the dimensionality reduction while maximizing the retained information, but also allofs a diagnostic of the quality of the error model in the functional space. The proposed methodology is applied to a pollution problem by a non-aqueous phase-liquid. The error model allofs a strong reduction of the computational cost while providing a good estimate of the uncertainty. The individual correction of the proxy response by the error model leads to an excellent prediction of the exact response, opening the door to many applications. The concept of functional error model is useful not only in the context of uncertainty propagation, but also, and maybe even more so, to perform Bayesian inference. Monte Carlo Markov Chain (MCMC) algorithms are the most common choice to ensure that the generated realizations are sampled in accordance with the observations. Hofever, this approach suffers from lof acceptance rate in high dimensional problems, resulting in a large number of wasted of simulations. This led to the introduction of two-stage MCMC, where the computational cost is decreased by avoiding unnecessary simulation of the exact of thanks to a preliminary evaluation of the proposal. In the third part of the thesis, a proxy is coupled to an error model to provide an approximate response for the two-stage MCMC set-up. We demonstrate an increase in acceptance rate by a factor three with respect to one-stage MCMC results. An open question remains: hof do we choose the size of the learning set and identify the realizations to optimize the construction of the error model. This requires devising an iterative strategy to construct the error model, such that, as new of simulations are performed, the error model is iteratively improved by incorporating the new information. This is discussed in the fourth part of the thesis, in which we apply this methodology to a problem of saline intrusion in a coastal aquifer.

STRATEGIC-1: A multiple-lines, randomized, open-label GERCOR phase III study in patients with unresectable wild-type RAS metastatic colorectal cancer.

BACKGROUND: The management of unresectable metastatic colorectal cancer (mCRC) is a comprehensive treatment strategy involving several lines of therapy, maintenance, salvage surgery, and treatment-free intervals. Besides chemotherapy (fluoropyrimidine, oxaliplatin, irinotecan), molecular-targeted agents such as anti-angiogenic agents (bevacizumab, aflibercept, regorafenib) and anti-epidermal growth factor receptor agents (cetuximab, panitumumab) have become available. Ultimately, given the increasing cost of new active compounds, new strategy trials are needed to define the optimal use and the best sequencing of these agents. Such new clinical trials require alternative endpoints that can capture the effect of several treatment lines and be measured earlier than overall survival to help shorten the duration and reduce the size and cost of trials. METHODS/DESIGN: STRATEGIC-1 is an international, open-label, randomized, multicenter phase III trial designed to determine an optimally personalized treatment sequence of the available treatment modalities in patients with unresectable RAS wild-type mCRC. Two standard treatment strategies are compared: first-line FOLFIRI-cetuximab, followed by oxaliplatin-based second-line chemotherapy with bevacizumab (Arm A) vs. first-line OPTIMOX-bevacizumab, followed by irinotecan-based second-line chemotherapy with bevacizumab, and by an anti-epidermal growth factor receptor monoclonal antibody with or without irinotecan as third-line treatment (Arm B). The primary endpoint is duration of disease control. A total of 500 patients will be randomized in a 1:1 ratio to one of the two treatment strategies. DISCUSSION: The STRATEGIC-1 trial is designed to give global information on the therapeutic sequences in patients with unresectable RAS wild-type mCRC that in turn is likely to have a significant impact on the management of this patient population. The trial is open for inclusion since August 2013. TRIAL REGISTRATION: STRATEGIC-1 is registered at Clinicaltrials.gov: NCT01910610, 23 July, 2013. STRATEGIC-1 is registered at EudraCT-No.: 2013-001928-19, 25 April, 2013.

CNDOL: A fast and reliable method for the calculation of electronic properties of very large systems. Applications to retinal binding pocket in rhodopsin and gas phase porphine.

Very large molecular systems can be calculated with the so called CNDOL approximate Hamiltonians that have been developed by avoiding oversimplifications and only using a priori parameters and formulas from the simpler NDO methods. A new diagonal monoelectronic term named CNDOL/21 shows great consistency and easier SCF convergence when used together with an appropriate function for charge repulsion energies that is derived from traditional formulas. It is possible to obtain a priori molecular orbitals and electron excitation properties after the configuration interaction of single excited determinants with reliability, maintaining interpretative possibilities even being a simplified Hamiltonian. Tests with some unequivocal gas phase maxima of simple molecules (benzene, furfural, acetaldehyde, hexyl alcohol, methyl amine, 2,5 dimethyl 2,4 hexadiene, and ethyl sulfide) ratify the general quality of this approach in comparison with other methods. The calculation of large systems as porphine in gas phase and a model of the complete retinal binding pocket in rhodopsin with 622 basis functions on 280 atoms at the quantum mechanical level show reliability leading to a resulting first allowed transition in 483 nm, very similar to the known experimental value of 500 nm of "dark state." In this very important case, our model gives a central role in this excitation to a charge transfer from the neighboring Glu(-) counterion to the retinaldehyde polyene chain. Tests with gas phase maxima of some important molecules corroborate the reliability of CNDOL/2 Hamiltonians.

Once-daily dolutegravir versus darunavir plus ritonavir for treatment-naive adults with HIV-1 infection (FLAMINGO): 96 week results from a randomised, open-label, phase 3b study.

BACKGROUND: The primary analysis of the FLAMINGO study at 48 weeks showed that patients taking dolutegravir once daily had a significantly higher virological response rate than did those taking ritonavir-boosted darunavir once daily, with similar tolerability. We present secondary efficacy and safety results analysed at 96 weeks. METHODS: FLAMINGO was a multicentre, open-label, phase 3b, non-inferiority study of HIV-1-infected treatment-naive adults. Patients were randomly assigned (1:1) to dolutegravir 50 mg or darunavir 800 mg plus ritonavir 100 mg, with investigator-selected combination tenofovir and emtricitabine or combination abacavir and lamivudine background treatment. The main endpoints were plasma HIV-1 RNA less than 50 copies per mL and safety. The non-inferiority margin was -12%. If the lower end of the 95% CI was greater than 0%, then we concluded that dolutegravir was superior to ritonavir-boosted darunavir. This trial is registered with ClinicalTrials.gov, number NCT01449929. FINDINGS: Of 595 patients screened, 488 were randomly assigned and 484 included in the analysis (242 assigned to receive dolutegravir and 242 assigned to receive ritonavir-boosted darunavir). At 96 weeks, 194 (80%) of 242 patients in the dolutegravir group and 164 (68%) of 242 in the ritonavir-boosted darunavir group had HIV-1 RNA less than 50 copies per mL (adjusted difference 12·4, 95% CI 4·7-20·2; p=0·002), with the greatest difference in patients with high viral load at baseline (50/61 [82%] vs 32/61 [52%], homogeneity test p=0·014). Six participants (three since 48 weeks) in the dolutegravir group and 13 (four) in the darunavir plus ritonavir group discontinued because of adverse events. The most common drug-related adverse events were diarrhoea (23/242 [10%] in the dolutegravir group vs 57/242 [24%] in the darunavir plus ritonavir group), nausea (31/242 [13%] vs 34/242 [14%]), and headache (17/242 [7%] vs 12/242 [5%]). INTERPRETATION: Once-daily dolutegravir is associated with a higher virological response rate than is once-daily ritonavir-boosted darunavir. Dolutegravir compares favourably in efficacy and safety to a boosted darunavir regimen with nucleoside reverse transcriptase inhibitor background treatment for HIV-1-infected treatment-naive patients. FUNDING: ViiV Healthcare and Shionogi & Co.