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.

A Novel Acute Retroviral Syndrome Severity Score Predicts the Key Surrogate Markers for HIV-1 Disease Progression.

OBJECTIVE: Best long-term practice in primary HIV-1 infection (PHI) remains unknown for the individual. A risk-based scoring system associated with surrogate markers of HIV-1 disease progression could be helpful to stratify patients with PHI at highest risk for HIV-1 disease progression. METHODS: We prospectively enrolled 290 individuals with well-documented PHI in the Zurich Primary HIV-1 Infection Study, an open-label, non-randomized, observational, single-center study. Patients could choose to undergo early antiretroviral treatment (eART) and stop it after one year of undetectable viremia, to go on with treatment indefinitely, or to defer treatment. For each patient we calculated an a priori defined "Acute Retroviral Syndrome Severity Score" (ARSSS), consisting of clinical and basic laboratory variables, ranging from zero to ten points. We used linear regression models to assess the association between ARSSS and log baseline viral load (VL), baseline CD4+ cell count, and log viral setpoint (sVL) (i.e. VL measured ≥90 days after infection or treatment interruption). RESULTS: Mean ARSSS was 2.89. CD4+ cell count at baseline was negatively correlated with ARSSS (p = 0.03, n = 289), whereas HIV-RNA levels at baseline showed a strong positive correlation with ARSSS (p<0.001, n = 290). In the regression models, a 1-point increase in the score corresponded to a 0.10 log increase in baseline VL and a CD4+cell count decline of 12/µl, respectively. In patients with PHI and not undergoing eART, higher ARSSS were significantly associated with higher sVL (p = 0.029, n = 64). In contrast, in patients undergoing eART with subsequent structured treatment interruption, no correlation was found between sVL and ARSSS (p = 0.28, n = 40). CONCLUSION: The ARSSS is a simple clinical score that correlates with the best-validated surrogate markers of HIV-1 disease progression. In regions where ART is not universally available and eART is not standard this score may help identifying patients who will profit the most from early antiretroviral therapy.

Encapsulation of packaging cell line results in successful retroviral-mediated transfer of a suicide gene in vivo in an experimental model of glioblastoma.

AIMS: Retroviral-mediated gene therapy has been proposed as a primary or adjuvant treatment for advanced cancer, because retroviruses selectively infect dividing cells. Efficacy of retroviral-mediated gene transfer, however, is limited in vivo. Although packaging cell lines can produce viral vectors continuously, such allo- or xenogeneic cells are normally rejected when used in vivo. Encapsulation using microporous membranes can protect the packaging cells from rejection. In this study, we used an encapsulated murine packaging cell line to test the effects of in situ delivery of a retrovirus bearing the herpes simplex virus thymidine kinase suicide gene in a rat model of orthotopic glioblastoma. MATERIALS AND METHODS: To test gene transfer in vitro, encapsulated murine psi2-VIK packaging cells were co-cultured with baby hamster kidney (BHK) cells, and the percentage of transfected BHK cells was determined. For in vivo experiments, orthotopic C6 glioblastomas were established in Wistar rats. Capsules containing psi2-VIK cells were stereotaxically implanted into these tumours and the animals were treated with ganciclovir (GCV). Tumours were harvested 14 days after initiation of GCV therapy for morphometric analysis. RESULTS: Encapsulation of psi2-VIK cells increased transfection rates of BHK target cells significantly in vitro compared to psi2-VIK conditioned medium (3 x 10(6) vs 2.3 x 10(4) cells; P<0.001). In vivo treatment with encapsulated packaging cells resulted in 3% to 5% of C6 tumour cells transduced and 45% of tumour volume replaced by necrosis after GCV (P<0.01 compared to controls). CONCLUSION: In this experimental model of glioblastoma, encapsulation of a xenogeneic packaging cell line increased half-life and transduction efficacy of retrovirus-mediated gene transfer and caused significant tumour necrosis.

An adenovirus-based fluorescent reporter vector to identify and isolate HIV-infected cells.

A procedure is described that allows the simple identification and sorting of live human cells that transcribe actively the HIV virus, based on the detection of GFP fluorescence in cells. Using adenoviral vectors for gene transfer, an expression cassette including the HIV-1 LTR driving the reporter gene GFP was introduced into cells that expressed stably either the Tat transcriptional activator, or an inactive mutant of Tat. Both northern and fluorescence-activated cell sorting (FACS) analysis indicate that cells containing the functional Tat protein presented levels of GFP mRNA and GFP fluorescence several orders of magnitude higher than control cells. Correspondingly, cells infected with HIV-1 showed similar enhanced reporter gene activation. HIV-1-infected cells of the lymphocytic line Jurkat were easily identified by fluorescence-activated cell sorting (FACS) as they displayed a much higher green fluorescence after transduction with the reporter adenoviral vector. This procedure could also be applied on primary human cells as blood monocyte-derived macrophages exposed to the adenoviral LTR-GFP reporter presented a much higher fluorescence when infected with HIV-1 compared with HIV-uninfected cells. The vector described has the advantages of labelling cells independently of their proliferation status and that analysis can be carried on intact cells which can be isolated subsequently by fluorescence-activated cell sorting (FACS) for further culture. This work suggests that adenoviral vectors carrying a virus-specific transcriptional control element controlling the expressions of a fluorescent protein will be useful in the identification and isolation of cells transcribing actively the viral template, and to be of use for drug screening and susceptibility assays.

Geomodelling of a fluvial system with semi-supervised support vector regression

Fluvial deposits are a challenge for modelling flow in sub-surface reservoirs. Connectivity and continuity of permeable bodies have a major impact on fluid flow in porous media. Contemporary object-based and multipoint statistics methods face a problem of robust representation of connected structures. An alternative approach to model petrophysical properties is based on machine learning algorithm ? Support Vector Regression (SVR). Semi-supervised SVR is able to establish spatial connectivity taking into account the prior knowledge on natural similarities. SVR as a learning algorithm is robust to noise and captures dependencies from all available data. Semi-supervised SVR applied to a synthetic fluvial reservoir demonstrated robust results, which are well matched to the flow performance

Example-based support vector machine for drug concentration analysis

Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. In this paper, we focus on the prediction of drug concentrations using Support Vector Machines (S VM) and the analysis of the influence of each feature to the prediction results. Our study shows that SVM-based approaches achieve similar prediction results compared with pharmacokinetic model. The two proposed example-based SVM methods demonstrate that the individual features help to increase the accuracy in the predictions of drug concentration with a reduced library of training data.

Wave-vector dependence of spin and density multipole excitations in quantum dots

We have employed time-dependent local-spin density-functional theory to analyze the multipole spin and charge density excitations in GaAs-AlxGa1-xAs quantum dots. The on-plane transferred momentum degree of freedom has been taken into account, and the wave-vector dependence of the excitations is discussed. In agreement with previous experiments, we have found that the energies of these modes do not depend on the transferred wave vector, although their intensities do. Comparison with a recent resonant Raman scattering experiment [C. Schüller et al., Phys. Rev. Lett. 80, 2673 (1998)] is made. This allows us to identify the angular momentum of several of the observed modes as well as to reproduce their energies

Generation of a tightly regulated all-cis beta cell-specific tetracycline-inducible vector.

Ability to induce protein expression at will in a cell is a powerful strategy used by scientists to better understand the function of a protein of interest. Various inducible systems have been designed in eukaryotic cells to achieve this goal. Most of them rely on two distinct vectors, one encoding a protein that can regulate transcription by binding a compound X, and one hosting the cDNA encoding the protein of interest placed downstream of promoter sequences that can bind the protein regulated by compound X (e.g., tetracycline, ecdysone). The commercially available systems are not designed to allow cell- or tissue-specific regulated expression. Additionally, although these systems can be used to generate stable clones that can be induced to express a given protein, extensive screening is often required to eliminate the clones that display poor induction or high basal levels. In the present report, we aimed to design a pancreatic beta cell-specific tetracycline-inducible system. Since the classical two-vector based tetracycline-inducible system proved to be unsatisfactory in our hands, a single vector was eventually designed that allowed tight beta cell-specific tetracycline induction in unselected cell populations.

Superantigens and retroviral infection: insights from mouse mammary tumor virus.

Superantigens induce a vigorous immune response by stimulating T cells that express particular T-cell receptor V beta chains. Mouse mammary tumor virus is a milk-transmitted retrovirus that encodes such a superantigen. Paradoxically, as discussed by Werner Held and colleagues, the strong superantigen-induced immune response permits the survival of the virus via T-cell dependent clonal expansion of infected B cells.