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Arctic sea ice extent has been of considerable interest to scientists in recent years, mainly due to its decreasing temporal trend over the past 20 years. In this article, we propose a hierarchical spatio-temporal generalized linear model for binary Arctic sea-ice-extent data, where statistical dependencies in the data are modeled through a latent spatio-temporal linear mixed effects model. By using a fixed number of spatial basis functions, the resulting model achieves both dimension reduction and non-stationarity for spatial fields at different time points. An EM algorithm is proposed to estimate model parameters, and an empirical-hierarchical-modeling approach is applied to obtain the predictive distribution of the latent spatio-temporal process. We illustrate the accuracy of the parameter estimation through a simulation study. The hierarchical model is applied to spatial Arctic sea-ice-extent data in the month of September for 20 years in the recent past, where several posterior summaries are obtained to detect the changes of Arctic sea ice cover. In particular, we consider a time series of latent 2 x 2 tables to infer the spatial changes of Arctic sea ice over time.

Poly(3,4-ethylenedioxythiophene) (PEDOT) films were electrochemically polymerised with several synthetic (dodecylbenzosulfonic acid (DBSA)) and biological (dextran sulphate (DS), chondroitin sulphate (CS), alginic acid (ALG) and ulvan (ULV)) dopant anions, and their physical, mechanical and electrochemical properties characterised. PEDOT films incorporating the biological dopants ALG and ULV produced films of the greatest surface roughness (46 ± 5.1 and 31 ± 1.9 nm, respectively), and demonstrated significantly lower shear modulus values relative to all other PEDOT films (2.1 ± 0.1 and 1.2 ± 0.2 MPa, respectively). Quartz crystal microgravimetry was used to study the adsorption of the important extracellular matrix protein fibronectin, revealing protein adsorption to be greatest on PEDOT doped with DS, followed by DBSA, ULV, CS and ALG. Electrical stimulation experiments applying a pulsed current using a biphasic waveform (250 Hz) were undertaken using PEDOT doped with either DBSA or ULV. Electrical stimulation had a significant influence on cell morphology and cell differentiation for PEDOT films with either dopant incorporated, with the degree of branching per cell increased by 10.5x on PEDOT-DBSA and 6.5x on PEDOT-ULV relative to unstimulated cells, and mean neurite length per cell increasing 2.6x and 2.2x on stimulated vs. unstimulated PEDOT-DBSA and PEDOT-ULV, respectively. We demonstrate the cytocompatibility of synthetic and biologically doped PEDOT biomaterials, including the new algal derived polysaccharide dopant ulvan, which, along with DBSA doped PEDOT, is shown to significantly enhance the differentiation of PC12 neuronal cells under electrical stimulation.

Motivated by a satellite remote sensing mission, this article proposes a multivariable errors-in-variables (EIV) regression model with heteroscedastic errors for relating the satellite data products to similar products from a well-characterized but globally sparse ground-based dataset. In the remote sensing setting, the regression model is used to estimate the global divisor for the satellite data. The error structure of the proposed EIV model comprises two components: A random-error component whose variance is inversely proportional to sample size of underlying individual observations which are aggregated to obtain the regression data, and a systematic-error component whose variance remains the same as the underlying sample size increases. In this article, we discuss parameter identifiability for the proposed model and obtain estimates from two-stage parameter estimation. We illustrate our proposed procedure through both simulation studies and an application to validating measurements of atmospheric column-averaged CO2from NASA's Orbiting Carbon Observatory-2 (OCO-2) satellite. The validation uses coincident target-mode OCO-2 data that are temporally and spatially sparse and ground-based measurements from the Total Carbon Column Observing Network (TCCON) that are spatially sparse but more accurate. Supplementary materials for the article are available online.

Environmental Informatics uses a panoply of tools from the Statistics, Mathematics, Computing, and Visualisation disciplines. It uses these tools to reveal, quantify, and validate scientific hypotheses in the environmental sciences, with the quantification of uncertainty central to its approach. There is now a strong recognition that scientific models need to incorporate stochastic components throughout: While it has always been recognised that data have a component of measurement error, attention is now being given to the quantification of model error, and it is becoming accepted by environmental scientists that probability models for the latter allows for a coherent way to make scientific inference. In Environmental Informatics, uncertainty may be assigned not only to datasets of measurements, but also to computer-generated climate-model output. Methodological advances, in the form of hierarchical statistical models and the accompanying computational developments, have expanded the scope of statistical analyses into very large spatial domains. This has led to studies of the dynamical evolution of entire spatial fields of geophysical variables, where results are given in terms of predictive distributions. Environmental Informatics is not only involved in characterising the environment, it can also be used to make decisions about mitigation and adaptation strategies. The steps taken by environmental scientists, from data to information, from information to knowledge, and from knowledge to decisions, are all taken in the presence of uncertainty. Environmental Informatics encompasses all these aspects.

Remote sensing platforms can collect measurements on a global scale within a few days, which provides an unprecedented opportunity to characterize and understand the spatio-temporal variability of environmental variables. Because of the additional challenges of making precise and accurate measurements from space, it is essential to validate satellite remote sensing datasets with highly precise and accurate ground-based measurements. The focus of this article is on two sets of measurements: Atmospheric column-averaged carbon dioxide (CO 2) collected by the Orbiting Carbon Observatory-2 (OCO-2) mission in its target mode of operation; and ground-based data used for validation from the Total Carbon Column Observing Network (TCCON). The current statistical modeling of the relationship between the OCO-2 data and the TCCON data assumes a linear regression and different measurement errors that reside in both the TCCON data and the OCO-2 data. To obtain consistent estimates of the regression coefficients, it is critical to determine the error variance of each datum in the regression. In this article, a rigorous statistical procedure is presented for obtaining the error variances through modeling the spatial and/or temporal dependence structure in the OCO-2 and TCCON datasets. Numerical results for analyzing a pair of datasets at the Lamont TCCON station and OCO-2 orbit number 3590 illustrate our procedure.