Bayesian lithology–fluid inversion - algorithm efficiency

Algorithms for inversion of seismic prestack AVO data into lithology-fluid classes in a vertical profile are evaluated. The inversion is defined in a Bayesian setting where the prior model for the lithology-fluid classes is a Markov chain, and the likelihood model relates seismic data and elastic material properties to these classes. The likelihood model is approximated such that the posterior model can be calculated recursively using the extremely efficient forward–backward algorithm. The impact of the approximation in the likelihood model is evaluated empirically by comparing results from the approximate approach with results generated from the exact posterior model. The exact posterior is assessed by sampling using a sophisticated Markov chain Monte Carlo simulation algorithm. The simulation algorithm is iterative, and it requires considerable computer resources. Seven realistic evaluation models are defined, from which synthetic seismic data are generated. Using identical seismic data, the approximate marginal posterior is calculated and the exact marginal posterior is assessed. It is concluded that the approximate likelihood model preserves 50% to 90% of the information content in the exact likelihood model

Bayesian lithology/fluid inversion - comparison of two algorithms

Algorithms for inversion of seismic prestack AVO data into lithology-fluid classes in a vertical profile are evaluated. The inversion is defined in a Bayesian setting where the prior model for the lithology-fluid classes is a Markov chain, and the likelihood model relates seismic data and elastic material properties to these classes. The likelihood model is approximated such that the posterior model can be calculated recursively using the extremely efficient forward–backward algorithm. The impact of the approximation in the likelihood model is evaluated empirically by comparing results from the approximate approach with results generated from the exact posterior model. The exact posterior is assessed by sampling using a sophisticated Markov chain Monte Carlo simulation algorithm. The simulation algorithm is iterative, and it requires considerable computer resources. Seven realistic evaluation models are defined, from which synthetic seismic data are generated. Using identical seismic data, the approximate marginal posterior is calculated and the exact marginal posterior is assessed. It is concluded that the approximate likelihood model preserves 50% to 90% of the information content in the exact likelihood model

IL-12, IL-15, and IL-18 pre-activated NK cells target resistant T cell acute lymphoblastic leukemia and delay leukemia development in vivo

NK cells have shown promise in therapy of hematological cancers, in particular against acute myeloid leukemia. In contrast, the more NK cell-resistant acute lymphoblastic leukemia (ALL) is difficult to treat with NK-cell-based therapies, and we hypothesized that pre-activation of NK cells could overcome this resistance. We show in pediatric and adult patients with T-cell ALL (T-ALL) perturbed NK cell effector functions at diagnosis. Using an in vivo rat model for T-ALL, Roser leukemia (RL), suppressed NK cell effector functions were observed. NK cells from T-ALL patients had reduced expression of the activating receptors NKp46 and DNAM-1, but not NKG2D. In contrast to T-ALL patients, NKG2D but not NKp46 was downregulated on NK cells during rat RL. Decreased frequencies of terminally differentiated NKG2A+CD57−CD56dim NK cells in human T-ALL was paralleled in the rat by reduced frequencies of bone marrow NK cells expressing the maturation marker CD11b, possibly indicating impairment of differentiation during leukemia. RL was highly resistant to autologous NK cells, but this resistance was overcome upon pre-activation of NK cells with IL-12, IL-15, and IL-18, with concomitant upregulation of activation markers and activating receptors. Importantly, adoptive transfers of IL-12, IL-15, and IL-18 pre-activated NK cells significantly slowed progression of RL in vivo. The data thus shows that T-ALL blasts normally resistant to NK cells may be targeted by cytokine pre-activated autologous NK cells, and this approach could have potential implications for immunotherapeutic protocols using NK cells to more efficiently target leukemia