Life cycle assessment of Portland cement production in Myanmar

The International Journal of Life Cycle Assessment - Cement manufacturing is associated with global and local environmental issues. Many studies have employed life cycle assessment (LCA) to...     

Climate genomics—Geoscientists,ecologists, and geneticists must reinforce their collaborations to confront climate change

Geoscientists and ecologists alike must confront the impact of climate change on ecosystems and the services they provide. In the marine realm, major changes are projected in net primary and export production, with significant repercussions on food security, carbon storage, and climate system feedbacks. However, these projections do not include the potential for rapid linear evolution to facilitate adaptation to environmental change. Climate genomics confronts this challenge by assessing the vulnerability of ecosystem services to climate change. Because DNA is the primary biological repository of detectable environmentally selected mutations (showing evidence of change before impacts arise in morphological or metabolic patterns), genomics provides a window into selection in response to climate change, while also recording neutral processes deriving from stochastic mechanisms (Lowe et al., Trends in Ecology & Evolution, 2017; 32:141–152). Due to the revolution afforded by sequencing technology developments, genomics can now meet ecologists and climate scientists in a cross-disciplinary space fertile for collaborations. Collaboration between geoscientists, ecologists, and geneticists must be reinforced in order to combine modeling and genomics approaches at every scale to improve our understanding and the management of ecosystems under climate change. To this end, we present advances in climate genomics from plankton to larger vertebrates, stressing the interactions between modeling and genomics, and identifying future work needed to develop and expand the field of climate genomics.     

Assessing exposure-time treatment effect heterogeneity in stepped-wedge cluster randomized trials

A stepped-wedge cluster randomized trial (CRT) is a unidirectional crossover study in which timings of treatment initiation for clusters are randomized. Because the timing of treatment initiation is different for each cluster, an emerging question is whether the treatment effect depends on the exposure time, namely, the time duration since the initiation of treatment. Existing approaches for assessing exposure-time treatment effect heterogeneity either assume a parametric functional form of exposure time or model the exposure time as a categorical variable, in which case the number of parameters increases with the number of exposure-time periods, leading to a potential loss in efficiency. In this article, we propose a new model formulation for assessing treatment effect heterogeneity over exposure time. Rather than a categorical term for each level of exposure time, the proposed model includes a random effect to represent varying treatment effects by exposure time. This allows for pooling information across exposure-time periods and may result in more precise average and exposure-time-specific treatment effect estimates. In addition, we develop an accompanying permutation test for the variance component of the heterogeneous treatment effect parameters. We conduct simulation studies to compare the proposed model and permutation test to alternative methods to elucidate their finite-sample operating characteristics, and to generate practical guidance on model choices for assessing exposure-time treatment effect heterogeneity in stepped-wedge CRTs.     

Penalized estimation of frailty-based illness–death models for semi-competing risks

Semi-competing risks refer to the time-to-event analysis setting, where the occurrence of a non-terminal event is subject to whether a terminal event has occurred, but not vice versa. Semi-competing risks arise in a broad range of clinical contexts, including studies of preeclampsia, a condition that may arise during pregnancy and for which delivery is a terminal event. Models that acknowledge semi-competing risks enable investigation of relationships between covariates and the joint timing of the outcomes, but methods for model selection and prediction of semi-competing risks in high dimensions are lacking. Moreover, in such settings researchers commonly analyze only a single or composite outcome, losing valuable information and limiting clinical utility—in the obstetric setting, this means ignoring valuable insight into timing of delivery after preeclampsia has onset. To address this gap, we propose a novel penalized estimation framework for frailty-based illness–death multi-state modeling of semi-competing risks. Our approach combines non-convex and structured fusion penalization, inducing global sparsity as well as parsimony across submodels. We perform estimation and model selection via a pathwise routine for non-convex optimization, and prove statistical error rate results in this setting. We present a simulation study investigating estimation error and model selection performance, and a comprehensive application of the method to joint risk modeling of preeclampsia and timing of delivery using pregnancy data from an electronic health record.