The characteristics of influenza seasons vary substantially from year to year, posing challenges for public health preparation and response. Influenza forecasting is used to inform seasonal outbreak response, which can in turn potentially reduce the impact of an epidemic. The United States Centers for Disease Control and Prevention, in collaboration with external researchers, has run an annual prospective influenza forecasting exercise, known as the FluSight challenge. Uniting theoretical results from the forecasting literature with domain-specific forecasts from influenza outbreaks, we applied parametric forecast combination methods that simultaneously optimize model weights and calibrate the ensemble via a beta transformation and made adjustments to the methods to reduce their complexity. We used the beta-transformed linear pool, the finite beta mixture model, and their equal weight adaptations to produce ensemble forecasts retrospectively for the 2016/2017, 2017/2018, and 2018/2019 influenza seasons in the U.S. We compared their performance to methods that were used in the FluSight challenge to produce the FluSight Network ensemble, namely the equally weighted linear pool and the linear pool. Ensemble forecasts produced from methods with a beta transformation were shown to outperform those from the equally weighted linear pool and the linear pool for all week-ahead targets across in the test seasons based on average log scores. We observed improvements in overall accuracy despite the beta-transformed linear pool or beta mixture methods\' modest under-prediction across all targets and seasons. Combination techniques that explicitly adjust for known calibration issues in linear pooling should be considered to improve probabilistic scores in outbreak settings.

Plants with alien genomic components (alien chromosomes / chromosomal fragments / genes) are important materials for genomic research and crop improvement. To date, four strategies based on trait observation, chromosome analysis, specific proteins, and DNA sequences have been developed for the identification of alien genomic components. Among them, DNA sequence-based molecular markers are mainly used to identify alien genomic components. This review summarized several molecular markers for identification of alien genomic components in wheat, cabbage and other important crops. We also compared the characteristics of nine common molecular markers, such as simple sequence repeat (SSR), insertion-deletion (InDel) and single nucleotide polymorphism (SNP). In general, the accuracy of using a combination of different identification methods is higher than using a single identification method. We analyzed the application of different combination of identification methods, and provided the best combination for wheat, brassica and other crops. High-throughput detection can be easily achieved by using the new generation molecular markers such as InDel and SNP, which can be used to determine the precise localization of alien introgression genes. To increase the identification efficiency, other new identification methods, such as microarray comparative genomic hybridization (array-CGH) and suppression subtractive hybridization (SSH), may also be included.

In multi-arm adaptive trials, several treatments are assessed simultaneously and accumulating data are used to inform decisions about the trial, such as whether treatments are dropped or continued. Different methodological approaches have been developed for such trials and research has compared the performance of different subsets of these. One particular approach, for which we use the acronym MAMS(R), has generally not been included in these comparisons because control of the family-wise error rate (FWER) could not be guaranteed. Recently, the MAMS(R) approach has been extended to facilitate the generation of efficient designs which strongly control the FWER. We consider multi-arm two-stage trials with binary outcomes and propose parameterising treatment effects using the log odds ratio. We conduct a simulation study comparing the extended MAMS(R) framework with the well-established combination method both for trials where a different outcome is used for mid-trial analysis and for trials where the same outcome is used throughout. We show how the MAMS(R) framework compares favourably only in scenarios where the same outcome is used. We propose a hybrid selection rule within MAMS(R) methodology and demonstrate that this makes it possible to use the MAMS(R) framework in trials incorporating comparative treatment selection.