A dynamic nonstationary spatio-temporal model for short term prediction of precipitation

Precipitation is a complex physical process that varies in space and time. Predictions and interpolations at unobserved times and/or locations help to solve important problems in many areas. In this paper, we present a hierarchical Bayesian model for spatio-temporal data and apply it to obtain short term predictions of rainfall. The model incorporates physical knowledge about the underlying processes that determine rainfall, such as advection, diffusion and convection. It is based on a temporal autoregressive convolution with spatially colored and temporally white innovations. By linking the advection parameter of the convolution kernel to an external wind vector, the model is temporally nonstationary. Further, it allows for nonseparable and anisotropic covariance structures. With the help of the Voronoi tessellation, we construct a natural parametrization, that is, space as well as time resolution consistent, for data lying on irregular grid points. In the application, the statistical model combines forecasts of three other meteorological variables obtained from a numerical weather prediction model with past precipitation observations. The model is then used to predict three-hourly precipitation over 24 hours. It performs better than a separable, stationary and isotropic version, and it performs comparably to a deterministic numerical weather prediction model for precipitation and has the advantage that it quantifies prediction uncertainty.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS564 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

PD-1 blockade in advanced NSCLC: A focus on pembrolizumab.

Non-small cell lung cancer (NSCLC) is one of the most prevalent cancers and is responsible for a large proportion of all cancer-related deaths. Current treatment options are inadequate, reflecting a substantial unmet clinical need. Increasing knowledge regarding the mechanisms and genetic aberrations underlying tumor development and growth has heralded a new era of therapy in oncology, moving away from indiscriminate cytotoxic chemotherapy toward more finely focused, targeted medicine. The development of small-molecule drugs and monoclonal antibodies directed toward specific components of dysfunctional molecular or immune pathways, and mutated genes specific to particular cancer types, is leading the field to more personalized and less toxic treatment options, many of which have demonstrated greater efficacy and survival benefits than their chemotherapeutic counterparts. Particularly successful examples are agents that interfere with the programmed death 1 (PD-1) pathway, which many tumors can hijack to avoid immune surveillance and editing. Pembrolizumab, a monoclonal antibody directed at PD-1 that blocks the engagement between PD-1 and its ligands, has been explored as a treatment for solid tumors, and demonstrated survival benefits in several studies. The use of PD-1 inhibitors such as nivolumab and pembrolizumab in advanced cancers is widespread, and pembrolizumab is available in more than 60 countries for at least one of the following: advanced melanoma, PD-L1-expressing NSCLC, head and neck squamous cell carcinoma, and adult and pediatric patients with refractory classical Hodgkin's lymphoma. This work provides a brief overview of the role of pembrolizumab in the treatment of advanced (recurrent/metastatic) NSCLC