An integrated Bayesian model for estimating the long-term health effects of air pollution by fusing modelled and measured pollution data: a case study of nitrogen dioxide concentrations in Scotland

The long-term health effects of air pollution can be estimated using a spatio-temporal ecological study, where the disease data are counts of hospital admissions from populations in small areal units at yearly intervals. Spatially representative pollution concentrations for each areal unit are typically estimated by applying Kriging to data from a sparse monitoring network, or by computing averages over grid level concentrations from an atmospheric dispersion model. We propose a novel fusion model for estimating spatially aggregated pollution concentrations using both the modelled and monitored data, and relate these concentrations to respiratory disease in a new study in Scotland between 2007 and 2011

Sparse Regression with Multi-type Regularized Feature Modeling

Within the statistical and machine learning literature, regularization techniques are often used to construct sparse (predictive) models. Most regularization strategies only work for data where all predictors are treated identically, such as Lasso regression for (continuous) predictors treated as linear effects. However, many predictive problems involve different types of predictors and require a tailored regularization term. We propose a multi-type Lasso penalty that acts on the objective function as a sum of subpenalties, one for each type of predictor. As such, we allow for predictor selection and level fusion within a predictor in a data-driven way, simultaneous with the parameter estimation process. We develop a new estimation strategy for convex predictive models with this multi-type penalty. Using the theory of proximal operators, our estimation procedure is computationally efficient, partitioning the overall optimization problem into easier to solve subproblems, specific for each predictor type and its associated penalty. Earlier research applies approximations to non-differentiable penalties to solve the optimization problem. The proposed SMuRF algorithm removes the need for approximations and achieves a higher accuracy and computational efficiency. This is demonstrated with an extensive simulation study and the analysis of a case-study on insurance pricing analytics

Immunotherapeutic potential of DISC-HSV and OX40L in cancer

Several vectors, viral and bacterial, have been developed over the past few years for means of generating an effective anti-tumor immune response. We have developed and studied a “model for immunotherapy” using a viral vector DISC-HSV, which efficiently transduces various tumor cell lines and offers a useful vehicle for the further development of cell based vaccines. The immunotherapeutic potential of DISC-HSV encoding GMCSF was demonstrated in a number of murine carcinoma models, leading to complete regression of well established tumors in up to 70% of the mice. Moreover, the therapeutic potential of DISC-HSV-GMCSF was significantly enhanced when used in combination therapy with either OX40L or dendritic cells (DC), even in poorly immunogenic tumor model. The ability of this vector to accept large gene inserts, its good safety profile, its ability to undergo only a single round of infection, the inherent viral immunostimulatory properties and its ability to infect various tumor cell lines efficiently, make DISC-HSV an ideal candidate vector for immunotherapy. The DISC- CT-26 tumor model has been used to investigate these mechanisms associated with immunotherapy – induced tumor rejection. Although CTL induction, was positively correlated with regression, MHC class I down regulation and accumulation of immature Gr1+ myeloid cells were shown to be the main immuno-suppressor mechanisms operating against regression and associated with progressive tumor growth

Ontogenetic changes in cutaneous and branchial ionocytes and morphology in yellowfin tuna (Thunnus albacares) larvae.

The development of osmoregulatory and gas exchange organs was studied in larval yellowfin tuna (Thunnus albacares) from 2 to 25 days post-hatching (2.9-24.5 mm standard length, SL). Cutaneous and branchial ionocytes were identified using Na+/K+-ATPase immunostaining and scanning electron microscopy. Cutaneous ionocyte abundance significantly increased with SL, but a reduction in ionocyte size and density resulted in a significant decrease in relative ionocyte area. Cutaneous ionocytes in preflexion larvae had a wide apical opening with extended microvilli; however, microvilli retracted into an apical pit from flexion onward. Lamellae in the gill and pseudobranch were first detected ~ 3.3 mm SL. Ionocytes were always present on the gill arch, first appeared in the filaments and lamellae of the pseudobranch at 3.4 mm SL, and later in gill filaments at 4.2 mm SL, but were never observed in the gill lamellae. Unlike the cutaneous ionocytes, gill and pseudobranch ionocytes had a wide apical opening with extended microvilli throughout larval development. The interlamellar fusion, a specialized gill structure binding the lamellae of ram-ventilating fish, began forming by ~ 24.5 mm SL and contained ionocytes, a localization never before reported. Ionocytes were retained on the lamellar fusions and also found on the filament fusions of larger sub-adult yellowfin tuna; however, sub-adult gill ionocytes had apical pits. These results indicate a shift in gas exchange and NaCl secretion from the skin to branchial organs around the flexion stage, and reveal novel aspects of ionocyte localization and morphology in ram-ventilating fishes