On Bayesian Search for the Feasible Space Under Computationally Expensive Constraints

We are often interested in identifying the feasible subset of a decision space under multiple constraints to permit effective design exploration. If determining feasibility required computationally expensive simulations, the cost of exploration would be prohibitive. Bayesian search is data-efficient for such problems: starting from a small dataset, the central concept is to use Bayesian models of constraints with an acquisition function to locate promising solutions that may improve predictions of feasibility when the dataset is augmented. At the end of this sequential active learning approach with a limited number of expensive evaluations, the models can accurately predict the feasibility of any solution obviating the need for full simulations. In this paper, we propose a novel acquisition function that combines the probability that a solution lies at the boundary between feasible and infeasible spaces (representing exploitation) and the entropy in predictions (representing exploration). Experiments confirmed the efficacy of the proposed function

NK Cell Terminal Differentiation: Correlated Stepwise Decrease of NKG2A and Acquisition of KIRs

BACKGROUND: Terminal differentiation of NK cells is crucial in maintaining broad responsiveness to pathogens and discriminating normal cells from cells in distress. Although it is well established that KIRs, in conjunction with NKG2A, play a major role in the NK cell education that determines whether cells will end up competent or hyporesponsive, the events underlying the differentiation are still debated. METHODOLOGY/PRINCIPAL FINDINGS: A combination of complementary approaches to assess the kinetics of the appearance of each subset during development allowed us to obtain new insights into these terminal stages of differentiation, characterising their gene expression profiles at a pan-genomic level, their distinct surface receptor patterns and their prototypic effector functions. The present study supports the hypothesis that CD56dim cells derive from the CD56bright subset and suggests that NK cell responsiveness is determined by persistent inhibitory signals received during their education. We report here the inverse correlation of NKG2A expression with KIR expression and explore whether this correlation bestows functional competence on NK cells. We show that CD56dimNKG2A-KIR+ cells display the most differentiated phenotype associated to their unique ability to respond against HLA-E+ target cells. Importantly, after IL-12+IL-18 stimulation, reacquisition of NKG2A strongly correlates with IFN-gamma production in CD56dimNKG2A- NK cells. CONCLUSIONS/SIGNIFICANCE: Together, these findings call for the reclassification of mature human NK cells into distinct subsets and support a new model, in which the NK cell differentiation and functional fate are based on a stepwise decrease of NKG2A and acquisition of KIRs

Nasal lavage natural killer cell function is suppressed in smokers after live attenuated influenza virus

<p>Abstract</p> <p>Background</p> <p>Modified function of immune cells in nasal secretions may play a role in the enhanced susceptibility to respiratory viruses that is seen in smokers. Innate immune cells in nasal secretions have largely been characterized by cellular differentials using morphologic criteria alone, which have successfully identified neutrophils as a significant cell population within nasal lavage fluid (NLF) cells. However, flow cytometry may be a superior method to fully characterize NLF immune cells. We therefore characterized immune cells in NLF by flow cytometry, determined the effects of live attenuated influenza virus (LAIV) on NLF and peripheral blood immune cells, and compared responses in samples obtained from smokers and nonsmokers.</p> <p>Methods</p> <p>In a prospective observational study, we characterized immune cells in NLF of nonsmokers at baseline using flow cytometry and immunohistochemistry. Nonsmokers and smokers were inoculated with LAIV on day 0 and serial nasal lavages were collected on days 1-4 and day 9 post-LAIV. LAIV-induced changes of NLF cells were characterized using flow cytometry. Cell-free NLF was analyzed for immune mediators by bioassay. Peripheral blood natural killer (NK) cells from nonsmokers and smokers at baseline were stimulated <it>in vitro </it>with LAIV followed by flow cytometric and mediator analyses.</p> <p>Results</p> <p>CD45(+)CD56(-)CD16(+) neutrophils and CD45(+)CD56(+) NK cells comprised median 4.62% (range 0.33-14.52) and 23.27% (18.29-33.97), respectively, of non-squamous NLF cells in nonsmokers at baseline. LAIV did not induce changes in total NK cell or neutrophil percentages in either nonsmokers or smokers. Following LAIV inoculation, CD16(+) NK cell percentages and granzyme B levels increased in nonsmokers, and these effects were suppressed in smokers. LAIV inoculation enhanced expression of activating receptor NKG2D and chemokine receptor CXCR3 on peripheral blood NK cells from both nonsmokers and smokers <it>in vitro </it>but did not induce changes in CD16(+) NK cells or granzyme B activity in either group.</p> <p>Conclusions</p> <p>These data are the first to identify NK cells as a major immune cell type in the NLF cell population and demonstrate that mucosal NK cell cytotoxic function is suppressed in smokers following LAIV. Altered NK cell function in smokers suggests a potential mechanism that may enhance susceptibility to respiratory viruses.</p

NK cell receptor NKG2D sets activation threshold for the NCR1 receptor early in NK cell development

The activation of natural killer (NK) cells depends on a change in the balance of signals from inhibitory and activating receptors. The activation threshold values of NK cells are thought to be set by engagement of inhibitory receptors during development. Here, we found that the activating receptor NKG2D specifically set the activation threshold for the activating receptor NCR1 through a process that required the adaptor DAP12. As a result, NKGD2-deficient (Klrk1-/-) mice controlled tumors and cytomegalovirus infection better than wild-type controls through the NCR1-induced production of the cytokine IFN-γ. Expression of NKG2D before the immature NK cell stage increased expression of the adaptor CD3ζ. Reduced expression of CD3ζ in Klrk1-/- mice was associated with enhanced signal transduction through NCR1, and CD3ζ deficiency resulted in hyper-responsiveness to stimulation via NCR1. Thus, an activating receptor developmentally set the activity of another activating receptor on NK cells and determined NK cell reactivity to cellular threats

Variations in killer-cell immunoglobulin-like receptor and human leukocyte antigen genes and immunity to malaria

Malaria is one of the deadliest infectious diseases in the world. Immune responses to Plasmodium falciparum malaria vary among individuals and between populations. Human genetic variation in immune system genes is likely to play a role in this heterogeneity. Natural killer (NK) cells produce inflammatory cytokines in response to malaria infection, kill intraerythrocytic Plasmodium falciparum parasites by cytolysis, and participate in the initiation and development of adaptive immune responses to plasmodial infection. These functions are modulated by interactions between killer-cell immunoglobulin-like receptors (KIR) and human leukocyte antigens (HLA). Therefore, variations in KIR and HLA genes can have a direct impact on NK cell functions. Understanding the role of KIR and HLA in immunity to malaria can help to better characterize antimalarial immune responses. In this review, we summarize the different KIR and HLA so far associated with immunity to malaria.This work was supported through the DELTAS Africa Initiative (Grant no. 107743), that funded Stephen Tukwasibwe through PhD fellowship award, and Annettee Nakimuli through group leader award. The DELTAS Africa Initiative is an independent funding scheme of the African Academy of Science (AAS), Alliance for Accelerating Excellence in Science in Africa (AESA) and supported by the New Partnership for Africa’s Development Planning and Coordinating Agency (NEPAD Agency) with funding from the Wellcome Trust (Grant no. 107743) and the UK government. Francesco Colucci is funded by Wellcome Trust grant 200841/Z/16/Z. The project received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No. 695551) for James Traherne and John Trowsdale. Jyothi Jayaraman is a recipient of fellowship from the Centre for Trophoblast Research

Optimization of an aperiodic sequential inspection and condition-based maintenance policy driven by value of information

The issue of the optimal planning of inspection and maintenance actions for a randomly deteriorating system constitutes a difficult sequential decision-making problem in which the objective is generally to achieve minimal life-cycle cost. For mathematical tractability, most approaches rely either on the consideration of specific maintenance strategies, e.g. Periodic Inspection and Replacement (PIR), whose defining parameters are optimized, or on time-and-space-state discretization using Markov Decision Process (MDP) models and resolution through policy iteration. In both cases, optimality may be hard to guarantee. In this paper, the decision-theoretic concept of Value of Information (VoI) is used as a metric to guide resource prioritization in time, that is, to schedule inspections in a piecewise optimal manner. An aperiodic sequential inspection policy is proposed, where the determination of the next best time for inspection, or replacement, is based on the current condition and on the computed expected gain from possible inspections, i.e. on a VoI metric. This policy can be implemented when the current condition is known from imperfect inspection or processing of condition-monitoring data. Also, more generally, a discussion is proposed on the use of VoI as a guide for information collection in life-cycle management

Sequential design of Gaussian process surrogates using pre-posterior analysis and Bayesian model averaging

International audienceTo build a surrogate model from experimental data-from tests or computer simulations-numerous options may exist when choosing a mathematical form. This is true for Gaussian Processes (GP) models, which may or may not include a regression basis-or mean trend-and be built on different correlation structures-through the selected covariance kernel. When data is scarce and prior information on the modeled phenomena is poor, it may be difficult to come to a conclusion on important decisions such as model selection or model improvement. If additional experimental information can be collected, often at significant cost, it is interesting to carry out model selection sequentially and efficiently. In this paper, we propose to leverage the ability of GPs to provide probabilistic descriptions and use it to look for the next "best" point in the design space using a pre-posterior analysis scheme, or Value Of Information (VoI) evaluation. At such point, we expect to get the most relevant information, when the aim is to reduce expected prediction error, given a previous state of knowledge on the likelihood of various modeling options, e.g. using the idea of Bayesian Model Averaging (BMA). With successive queried points, we update our respective beliefs in these options through an "information-optimal" exploration of the design space-given current expectations according to priors. Hence, we attempt to learn efficiently both model structure and parameters

Estimation of the value of prognostic information for condition-based and predictive maintenance

For components subject to degradation, cost-efficient maintenance is necessary. Periodic or continuous collection of information, reducing uncertainty on the component's state of health, generally leads to a better-informed and, thus, more efficient maintenance. Processing condition monitoring data to estimate the current and future health states of the component, can prove valuable. In this paper, it is proposed to quantify the Value of Information (VoI) that may be obtained from state estimation and prediction procedures, with known precision, applied for condition-based and predictive maintenance. VoI is computed numerically using gamma process paths and on the basis of the optimization of the parameters of different maintenance strategies