Uncertainty Quantification Of A Semi-Supervised Support Vector Regression Reservoir Model

Uncertainty quantification of petroleum reservoir models is one of the present challenges, which is usually approached with a wide range of geostatistical tools linked with statistical optimisation or/and inference algorithms. Recent advances in machine learning offer a novel approach to model spatial distribution of petrophysical properties in complex reservoirs alternative to geostatistics. The approach is based of semisupervised learning, which handles both ?labelled? observed data and ?unlabelled? data, which have no measured value but describe prior knowledge and other relevant data in forms of manifolds in the input space where the modelled property is continuous. Proposed semi-supervised Support Vector Regression (SVR) model has demonstrated its capability to represent realistic geological features and describe stochastic variability and non-uniqueness of spatial properties. On the other hand, it is able to capture and preserve key spatial dependencies such as connectivity of high permeability geo-bodies, which is often difficult in contemporary petroleum reservoir studies. Semi-supervised SVR as a data driven algorithm is designed to integrate various kind of conditioning information and learn dependences from it. The semi-supervised SVR model is able to balance signal/noise levels and control the prior belief in available data. In this work, stochastic semi-supervised SVR geomodel is integrated into Bayesian framework to quantify uncertainty of reservoir production with multiple models fitted to past dynamic observations (production history). Multiple history matched models are obtained using stochastic sampling and/or MCMC-based inference algorithms, which evaluate posterior probability distribution. Uncertainty of the model is described by posterior probability of the model parameters that represent key geological properties: spatial correlation size, continuity strength, smoothness/variability of spatial property distribution. The developed approach is illustrated with a fluvial reservoir case. The resulting probabilistic production forecasts are described by uncertainty envelopes. The paper compares the performance of the models with different combinations of unknown parameters and discusses sensitivity issues.

Parenteral is more efficient than mucosal immunization to induce regression of human papillomavirus-associated genital tumors.

Cervical cancer is a public health concern as it represents the second cause of cancer death in women worldwide. High-risk human papillomaviruses (HPV) are the etiologic agents, and HPV E6 and/or E7 oncogene-specific therapeutic vaccines are under development to treat HPV-related lesions in women. Whether the use of mucosal routes of immunization may be preferable for inducing cell-mediated immune responses able to eradicate genital tumors is still debated because of the uniqueness of the female genital mucosa (GM) and the limited experimentation. Here, we compared the protective activity resulting from immunization of mice via intranasal (i.n.), intravaginal (IVAG) or subcutaneous (s.c.) routes with an adjuvanted HPV type 16 E7 polypeptide vaccine. Our data show that s.c. and i.n. immunizations elicited similar frequencies and avidity of TetE71CD81 and E7-specific Interferon-gamma-secreting cells in the GM, whereas slightly lower immune responses were induced by IVAG immunization. In a novel orthotopic murine model, both s.c. and i.n. immunizations allowed for complete long-term protection against genital E7-expressing tumor challenge. However, only s.c. immunization induced complete regression of already established genital tumors. This suggests that the higher E7-specific systemic response observed after s.c. immunization may contribute to the regression of growing genital tumors, whereas local immune responses may be sufficient to impede genital challenges. Thus, our data show that for an efficiently adjuvanted protein-based vaccine, parenteral vaccination route is superior to mucosal vaccination route for inducing regression of established genital tumors in a murine model of HPV-associated genital cancer.

Intravaginal and subcutaneous immunization induced vaccine specific CD8 T cells and tumor regression in the bladder.

PURPOSE: Vaccines targeting tumor associated antigens are in development for bladder cancer. Most of these cancers are nonmuscle invasive at diagnosis and confined in the mucosa and submucosa. However, to our knowledge how vaccination may induce the regression of tumors at such mucosal sites has not been examined previously. We compared different immunization routes for the ability to induce vaccine specific antitumor CD8 T cells in the bladder and bladder tumor regression in mice. MATERIALS AND METHODS: In the absence of a murine bladder tumor model expressing a tumor antigen relevant for human use we established an orthotopic model expressing the HPV-16 tumor antigen E7 as a model. We used an adjuvant E7 polypeptide to induce CD8 T cell mediated tumor regression. RESULTS: Subcutaneous and intravaginal but not intranasal vaccination induced a high number of TetE7(+)CD8(+) T cells in the bladder as well as bladder tumor regression. The entry of vaccine specific T cells in the bladder was not the only key since persistent regression of established bladder tumors by intravaginal or subcutaneous immunization was associated with tumor infiltration of total CD4 and CD8 T cells. This resulted in an increase in TetE7(+)CD8(+) T cells and a decrease in T regulatory cells, leading to an increased number of effector interferon-γ secreting vaccine specific CD8 T cells in the regressing bladder tumor. CONCLUSIONS: These data show that immunization routes should be tailored to each mucosal tumor site. Subcutaneous or intravaginal vaccination may be of additional value to treat patients with bladder cancer.

Intravaginal TLR agonists increase local vaccine-specific CD8 T cells and human papillomavirus-associated genital-tumor regression in mice.

Human papillomaviruses (HPV)-related cervical cancer is the second leading cause of cancer death in women worldwide. Despite active development, HPV E6/E7 oncogene-specific therapeutic vaccines have had limited clinical efficacy to date. Here, we report that intravaginal (IVAG) instillation of CpG-ODN (TLR9 agonist) or poly-(I:C) (TLR3 agonist) after subcutaneous E7 vaccination increased ∼fivefold the number of vaccine-specific interferon-γ-secreting CD8 T cells in the genital mucosa (GM) of mice, without affecting the E7-specific systemic response. The IVAG treatment locally increased both E7-specific and total CD8 T cells, but not CD4 T cells. This previously unreported selective recruitment of CD8 T cells from the periphery by IVAG CpG-ODN or poly-(I:C) was mediated by TLR9 and TLR3/melanoma differentiation-associated gene 5 signaling pathways, respectively. For CpG, this recruitment was associated with a higher proportion of GM-localized CD8 T cells expressing both CCR5 and CXCR3 chemokine receptors and E-selectin ligands. Most interestingly, IVAG CpG-ODN following vaccination led to complete regression of large genital HPV tumors in 75% of mice, instead of 20% with vaccination alone. These findings suggest that mucosal application of immunostimulatory molecules might substantially increase the effectiveness of parenterally administered vaccines.Mucosal Immunology advance online publication 12 September 2012; doi:10.1038/mi.2012.83.

Language Regression Associated With Autistic Regression and Electroencephalographic (EEG) Abnormalities: A Prospective Study.

We report a boy, referred at 25 months following a dramatic isolated language regression antedating autistic-like symptomatology. His sleep electroencephalogram (EEG) showed persistent focal epileptiform activity over the left parietal and vertex areas never associated with clinical seizures. He was started on adrenocorticotropic hormone (ACTH) with a significant improvement in language, behavior, and in EEG discharges in rapid eye movement (REM) sleep. Later course was characterized by fluctuations/regressions in language and behavior abilities, in phase with recrudescence of EEG abnormalities prompting additional ACTH courses that led to remarkable decrease in EEG abnormalities, improvement in language, and to a lesser degree, in autistic behavior. The timely documentation of regression episodes suggesting an "atypical" autistic regression, striking therapy-induced improvement, fluctuation of symptomatology over time could be ascribed to recurrent and persisting EEG abnormalities.

Long-term outcome after cognitive and behavioral regression in non-lesional epilepsy with CSWS

Purpose: To present the long-term outcome (LTO) of 10 adolescents and young adults with documented cognitive and behavioral regression as children due to non-lesional focal, mainly frontal epilepsy with continuous spike-waves during slow wave sleep (CSWS). Method: Past medical and EEG data of all patients were reviewed and neuropsychological tests exploring main cognitive functions were administered. Result: After a mean duration of follow-up of 15.6 years (range 8-23 years), none of the 10 patients had recovered fully, but four regained borderline to normal intelligence and were almost independent. Patients with prolonged global intellectual regression had the worst outcome, whereas those with more specific and short-lived deficits recovered best. The marked behavioral disorders that were so disturbing during the active period (AP) resolved in all but one patient. Executive functions were neither severely nor homogenously affected. Three patients with a frontal syndrome during the AP disclosed only mild residual executive and social cognition deficits. The main cognitive gains occurred shortly after the AP, but qualitative improvements continued to occur. LTO correlated best with duration of CSWS. Conclusion: Our findings emphasize that cognitive recovery after cessation of CSWS depends on the severity and duration of the initial regression. None of our patients had major executive and social cognition deficits with preserved intelligence as reported in adults with destructive lesions of the frontal lobes during childhood. Early recognition of epilepsy with CSWS and rapid introduction of effective therapy are crucial for a best possible outcome.

In vivo targeting of an anti-tumor antibody coupled to antigenic MHC class I complexes induces specific growth inhibition and regression of established syngeneic tumor grafts.

The concept of antibody-mediated targeting of antigenic MHC/peptide complexes on tumor cells in order to sensitize them to T-lymphocyte cytotoxicity represents an attractive new immunotherapy strategy. In vitro experiments have shown that an antibody chemically conjugated or fused to monomeric MHC/peptide can be oligomerized on the surface of tumor cells, rendering them susceptible to efficient lysis by MHC-peptide restricted specific T-cell clones. However, this strategy has not yet been tested entirely in vivo in immunocompetent animals. To this aim, we took advantage of OT-1 mice which have a transgenic T-cell receptor specific for the ovalbumin (ova) immunodominant peptide (257-264) expressed in the context of the MHC class I H-2K(b). We prepared and characterized conjugates between the Fab' fragment from a high-affinity monoclonal antibody to carcinoembryonic antigen (CEA) and the H-2K(b) /ova peptide complex. First, we showed in OT-1 mice that the grafting and growth of a syngeneic colon carcinoma line transfected with CEA could be specifically inhibited by systemic injections of the conjugate. Next, using CEA transgenic C57BL/6 mice adoptively transferred with OT-1 spleen cells and immunized with ovalbumin, we demonstrated that systemic injections of the anti-CEA-H-2K(b) /ova conjugate could induce specific growth inhibition and regression of well-established, palpable subcutaneous grafts from the syngeneic CEA-transfected colon carcinoma line. These results, obtained in a well-characterized syngeneic carcinoma model, demonstrate that the antibody-MHC/peptide strategy can function in vivo. Further preclinical experimental studies, using an anti-viral T-cell response, will be performed before this new form of immunotherapy can be considered for clinical use.

Improving generalized regression analysis for the spatial prediction of forest communities

Aim This study used data from temperate forest communities to assess: (1) five different stepwise selection methods with generalized additive models, (2) the effect of weighting absences to ensure a prevalence of 0.5, (3) the effect of limiting absences beyond the environmental envelope defined by presences, (4) four different methods for incorporating spatial autocorrelation, and (5) the effect of integrating an interaction factor defined by a regression tree on the residuals of an initial environmental model. Location State of Vaud, western Switzerland. Methods Generalized additive models (GAMs) were fitted using the grasp package (generalized regression analysis and spatial predictions, http://www.cscf.ch/grasp). Results Model selection based on cross-validation appeared to be the best compromise between model stability and performance (parsimony) among the five methods tested. Weighting absences returned models that perform better than models fitted with the original sample prevalence. This appeared to be mainly due to the impact of very low prevalence values on evaluation statistics. Removing zeroes beyond the range of presences on main environmental gradients changed the set of selected predictors, and potentially their response curve shape. Moreover, removing zeroes slightly improved model performance and stability when compared with the baseline model on the same data set. Incorporating a spatial trend predictor improved model performance and stability significantly. Even better models were obtained when including local spatial autocorrelation. A novel approach to include interactions proved to be an efficient way to account for interactions between all predictors at once. Main conclusions Models and spatial predictions of 18 forest communities were significantly improved by using either: (1) cross-validation as a model selection method, (2) weighted absences, (3) limited absences, (4) predictors accounting for spatial autocorrelation, or (5) a factor variable accounting for interactions between all predictors. The final choice of model strategy should depend on the nature of the available data and the specific study aims. Statistical evaluation is useful in searching for the best modelling practice. However, one should not neglect to consider the shapes and interpretability of response curves, as well as the resulting spatial predictions in the final assessment.

Random measurement error and regression dilution bias.

Summary points: - The bias introduced by random measurement error will be different depending on whether the error is in an exposure variable (risk factor) or outcome variable (disease) - Random measurement error in an exposure variable will bias the estimates of regression slope coefficients towards the null - Random measurement error in an outcome variable will instead increase the standard error of the estimates and widen the corresponding confidence intervals, making results less likely to be statistically significant - Increasing sample size will help minimise the impact of measurement error in an outcome variable but will only make estimates more precisely wrong when the error is in an exposure variable

Recent decrease in the prevalence of congenital heart defects in europe.

OBJECTIVES: To examine trends in the prevalence of congenital heart defects (CHDs) in Europe and to compare these trends with the recent decrease in the prevalence of CHDs in Canada (Quebec) that was attributed to the policy of mandatory folic acid fortification. STUDY DESIGN: We used data for the period 1990-2007 for 47 508 cases of CHD not associated with a chromosomal anomaly from 29 population-based European Surveillance of Congenital Anomalies registries in 16 countries covering 7.3 million births. We estimated trends for all CHDs combined and separately for 3 severity groups using random-effects Poisson regression models with splines. RESULTS: We found that the total prevalence of CHDs increased during the 1990s and the early 2000s until 2004 and decreased thereafter. We found essentially no trend in total prevalence of the most severe group (group I), whereas the prevalence of severity group II increased until about 2000 and decreased thereafter. Trends for severity group III (the most prevalent group) paralleled those for all CHDs combined. CONCLUSIONS: The prevalence of CHDs decreased in recent years in Europe in the absence of a policy for mandatory folic acid fortification. One possible explanation for this decrease may be an as-yet-undocumented increase in folic acid intake of women in Europe following recommendations for folic acid supplementation and/or voluntary fortification. However, alternative hypotheses, including reductions in risk factors of CHDs (eg, maternal smoking) and improved management of maternal chronic health conditions (eg, diabetes), must also be considered for explaining the observed decrease in the prevalence of CHDs in Europe or elsewhere.

Prediction of 18-month survival in patients with primary myelodysplastic syndrome. A regression model and scoring system based on the combination of chromosome findings and the Bournemouth score.

The predictive potential of six selected factors was assessed in 72 patients with primary myelodysplastic syndrome using univariate and multivariate logistic regression analysis of survival at 18 months. Factors were age (above median of 69 years), dysplastic features in the three myeloid bone marrow cell lineages, presence of chromosome defects, all metaphases abnormal, double or complex chromosome defects (C23), and a Bournemouth score of 2, 3, or 4 (B234). In the multivariate approach, B234 and C23 proved to be significantly associated with a reduction in the survival probability. The similarity of the regression coefficients associated with these two factors means that they have about the same weight. Consequently, the model was simplified by counting the number of factors (0, 1, or 2) present in each patient, thus generating a scoring system called the Lausanne-Bournemouth score (LB score). The LB score combines the well-recognized and easy-to-use Bournemouth score (B score) with the chromosome defect complexity, C23 constituting an additional indicator of patient outcome. The predicted risk of death within 18 months calculated from the model is as follows: 7.1% (confidence interval: 1.7-24.8) for patients with an LB score of 0, 60.1% (44.7-73.8) for an LB score of 1, and 96.8% (84.5-99.4) for an LB score of 2. The scoring system presented here has several interesting features. The LB score may improve the predictive value of the B score, as it is able to recognize two prognostic groups in the intermediate risk category of patients with B scores of 2 or 3. It has also the ability to identify two distinct prognostic subclasses among RAEB and possibly CMML patients. In addition to its above-described usefulness in the prognostic evaluation, the LB score may bring new insights into the understanding of evolution patterns in MDS. We used the combination of the B score and chromosome complexity to define four classes which may be considered four possible states of myelodysplasia and which describe two distinct evolutional pathways.

Uncertainty Quantification with Support Vector Regression Prediction Models

Uncertainty quantification of petroleum reservoir models is one of the present challenges, which is usually approached with a wide range of geostatistical tools linked with statistical optimisation or/and inference algorithms. The paper considers a data driven approach in modelling uncertainty in spatial predictions. Proposed semi-supervised Support Vector Regression (SVR) model has demonstrated its capability to represent realistic features and describe stochastic variability and non-uniqueness of spatial properties. It is able to capture and preserve key spatial dependencies such as connectivity, which is often difficult to achieve with two-point geostatistical models. Semi-supervised SVR is designed to integrate various kinds of conditioning data and learn dependences from them. A stochastic semi-supervised SVR model is integrated into a Bayesian framework to quantify uncertainty with multiple models fitted to dynamic observations. The developed approach is illustrated with a reservoir case study. The resulting probabilistic production forecasts are described by uncertainty envelopes.

Complete and long-lasting regression of disseminated multiple skin melanoma metastases under treatment with cyclooxygenase-2 inhibitor.

Experimental and clinical evidence indicates that non-steroidal anti-inflammatory drugs and cyclooxygenase-2 inhibitors may have anti-cancer activities. Here we report on a patient with a metastatic melanoma of the leg who experienced a complete and sustained regression of skin metastases upon continuous single treatment with the cyclooxygenase-2 inhibitor rofecoxib. Our observations indicate that the inhibition of cyclooxygenase-2 can lead to the regression of disseminated skin melanoma metastases, even after failure of chemotherapy.