The carbon implications of large‐scale afforestation of agriculturally marginal land with short‐rotation willow in Saskatchewan

Afforestation with short‐rotation coppice (SRC) willow plantations for the purpose of producing bioenergy feedstock was contemplated as one potential climate change mitigation option. The objectives of this study were to assess the magnitude of this mitigation potential by addressing: (i) the land area potentially available for SRC systems in the province of Saskatchewan, Canada; (ii) the potential biomass yields of SRC plantations; and (iii) the carbon implications from such a large‐scale afforestation program. Digital soils and land‐use data were used to identify, map, and group into clusters of similar polygons 2.12 million hectares (Mha) of agriculturally marginal land that was potentially suitable for willow in the Boreal Plains and Prairies ecozones in Saskatchewan. The Physiological Principles in Predicting Growth (3PG) model was calibrated with data from SRC experiments in Saskatchewan, to quantify potential willow biomass yields, and the Carbon Budget Model of the Canadian Forest Sector (CBM‐CFS3), was used to simulate stand and landscape‐level C fluxes and stocks. Short‐rotation willow plantations managed in 3 year rotations for seven consecutive harvests (21 years) after coppicing at Year 1 produced about 12 Mg ha^?1 yr^?1 biomass. The more significant contribution to the C cycle was the cumulative harvest. After 44 years, the potential average cumulative harvested biomass C in the Prairies was 244 Mg C ha^?1 (5.5 Mg C ha^?1 yr^?1) about 20% higher than the average for the Boreal Plains, 203 Mg C ha^?1 (4.6 Mg C ha^?1 yr^?1). This analysis did not consider afforestation costs, rate of establishment of willow plantations, and other constraints, such as drought and disease effects on biomass yield. The results must therefore be interpreted as a biophysical mitigation potential with the technical and economic potential being both lower than our estimates. Nevertheless, short‐rotation bioenergy plantations offer one potential mitigation option to reduce the rate of CO₂ accumulation in the earth's atmosphere and further research is needed to operationalise such a mitigation effort.     

Ex-post assessment of genetically modified,low level presence in Canadian flax

Canada is the world’s largest producer and exporter of flaxseed. In 2009, DNA from deregistered genetically modified (GM) CDC Triffid was detected in a shipment of Canadian flaxseed exported to Europe, causing a large decrease in the amount of flax planted in Canada and a major shift in export markets. The flax industry in Canada undertook major changes to ensure the removal of transgenic flax from the supply chain. To demonstrate compliance, Canada adopted a protocol involving testing grain samples (post-harvest) using an RT-PCR test for the construct found in CDC Triffid. Efforts to remove the presence of GM flax from the value chain included reconstituting major flax varieties from GM-free plants. The reconstituted varieties represented the majority of planting seed in 2014. This study re-evaluates GM flax presence in Canadian grain stocks for an updated dataset (2009–2015) using a previously described simulation model to estimate low-level GM presence. Additionally, losses to the Canadian economy resulting from the reduction in flax production and export opportunities, costs associated with reconstituting major flax varieties, and testing for the presence of GM flax along the flax value chain are estimated.     

Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses

Variations in sample quality are frequently encountered in small RNA-sequencing experiments, and pose a major challenge in a differential expression analysis. Removal of high variation samples reduces noise, but at a cost of reducing power, thus limiting our ability to detect biologically meaningful changes. Similarly, retaining these samples in the analysis may not reveal any statistically significant changes due to the higher noise level. A compromise is to use all available data, but to down-weight the observations from more variable samples. We describe a statistical approach that facilitates this by modelling heterogeneity at both the sample and observational levels as part of the differential expression analysis. At the sample level this is achieved by fitting a log-linear variance model that includes common sample-specific or group-specific parameters that are shared between genes. The estimated sample variance factors are then converted to weights and combined with observational level weights obtained from the mean–variance relationship of the log-counts-per-million using ‘voom’. A comprehensive analysis involving both simulations and experimental RNA-sequencing data demonstrates that this strategy leads to a universally more powerful analysis and fewer false discoveries when compared to conventional approaches. This methodology has wide application and is implemented in the open-source ‘limma’ package.     

A joint fairness model with applications to risk predictions for underrepresented populations

In data collection for predictive modeling, underrepresentation of certain groups, based on gender, race/ethnicity, or age, may yield less accurate predictions for these groups. Recently, this issue of fairness in predictions has attracted significant attention, as data-driven models are increasingly utilized to perform crucial decision-making tasks. Existing methods to achieve fairness in the machine learning literature typically build a single prediction model in a manner that encourages fair prediction performance for all groups. These approaches have two major limitations: (i) fairness is often achieved by compromising accuracy for some groups; (ii) the underlying relationship between dependent and independent variables may not be the same across groups. We propose a joint fairness model (JFM) approach for logistic regression models for binary outcomes that estimates group-specific classifiers using a joint modeling objective function that incorporates fairness criteria for prediction. We introduce an accelerated smoothing proximal gradient algorithm to solve the convex objective function, and present the key asymptotic properties of the JFM estimates. Through simulations, we demonstrate the efficacy of the JFM in achieving good prediction performance and across-group parity, in comparison with the single fairness model, group-separate model, and group-ignorant model, especially when the minority group's sample size is small. Finally, we demonstrate the utility of the JFM method in a real-world example to obtain fair risk predictions for underrepresented older patients diagnosed with coronavirus disease 2019 (COVID-19).