Stripe rust, caused by Puccinia striiformis f. sp., is a widespread and serious fungal disease that affects wheat ( Triticum aestivum L.) cultivation worldwide. Its rapid development, high adaptability, and ability to cause significant yield losses threaten global food security. Although conventional breeding strategies play a role, they are limited by the evolutionary dynamics of the pathogen. Therefore, genomic and molecular approaches have been employed to improve resistance breeding techniques. This review summarizes relevant research findings on the pathogenicity of Pst and the genetic basis of resistance to stripe rust, categorized as all-stage resistance (ASR) and adult plant resistance (APR). More than 300 QTLs and 86 yellow rust ( Yr ) resistance genes have been identified through marker-assisted selection (MAS), quantitative trait loci (QTL) mapping, and genome-wide association studies (GWAS). Meta-QTL analyses have advanced to the point where fine-tuning candidate gene regions across different backgrounds is possible, facilitating marker-assisted breeding in closed systems. In addition, genomic selection (GS) enables the combined use of major and minor resistance loci when selecting for multiple traits, allowing rapid gene pyramiding to improve durability. Recent developments in CRISPR/Cas9 genome editing and pan-genomics offer the opportunity to induce a broad spectrum of durable resistance in elite wheat lines. Collectively, these integrative genomic approaches hold promise for sustainable strategies to combat the persistent threat of stripe rust and protect wheat production worldwide.

Rhizosphere microbiome engineering is a promising approach that can enhance crop resilience and input use efficiency by redirecting plant–microbe–soil interactions toward predictable functions. Here, we review the mechanistic bases underlying rhizosphere assembly and stability, including root exudate-mediated selection, priority effects, keystone taxa, and metabolite-driven signaling, and connect these principles to proposed design rules for microbial inoculants. We present a generalizable Design–Build–Test–Learn (DBTL) framework for engineering synthetic microbial consortia, covering trait-to-module mapping (nutrient acquisition, phytohormone modulation, ACC deaminase activity, stress-protective metabolites, and biocontrol), compatibility screening, minimal yet robust community architectures, and iterative optimization driven by multi-omics and high-throughput phenotyping. Translation to field settings is framed as an engineering challenge defined by formulation and administration limitations, including carrier type, seed coating and encapsulation methods, shelf life, strain invasiveness, and permanence of colonization amid environmental diversity. We also summarize how integrative measurement pipelines (amplicon and shotgun sequencing, transcriptomics, metabolomics, and network or causal analyses) can advance microbiome studies from correlation to actionability. We describe how precision agriculture (sensors, remote sensing, and variable-rate inputs) and AI/ML (split-sample comparisons, transfer learning, and active learning) approaches can accelerate strain discovery, mixture optimization, and adaptive experimentation, driven by the need for stringent controls, metadata-rich reporting, and cross-site comparability. Use cases focus on stress conditions (drought, salinity, thermal extremes, and biotic stress) to demonstrate how microbial functions translate to agronomic outcomes and to highlight critical bottlenecks for reproducible, scalable microbiome products.

Background: Program evaluation is critical for ensuring that graduate programs remain responsive, effective, and aligned with the evolving needs of students, faculty, and the broader scientific community. At a major Canadian institution, we aimed to conduct a comprehensive evaluation to assess how well our program meets stakeholder needs and to identify curricular gaps and actionable recommendations across our biomedical and clinical research graduate programs. Methods: A mixed-methods approach was used, guided by the U.S. Centers for Disease Control and Prevention (CDC) Program Evaluation Framework. Data were collected through surveys, interviews, and a focus group, which included a combination of students, alumni, supervisors, and/or faculty members. The evaluation assessed the curriculum structure, program strengths, and areas for improvements. Results: The findings, when considered together, demonstrated that the student learning experience is shaped by numerous factors beyond course content. Synthesis of the results produced 26 recommendations in three main categories of factors, including course content and experiences, infrastructure and support, and faculty and supervisor engagement. In addition, we identified factors that enabled successful program evaluation, including overt institutional support of the evaluation process including commitment to establishment of a recurring cycle of implementation that aligned with other existing quality assurance processes, and establishment of formal activities to convert findings to actionable initiatives and to monitor progress for each. Conclusion: This evaluation demonstrated how the CDC Program Evaluation Framework can be used to enable a systematic, data-driven process that translated stakeholder feedback into actionable recommendations to support meaningful program refinements in response to identified needs regarding program modernization designed to better meet new realities for graduate students. Ongoing evaluation will be essential to monitor the impact of implemented changes and ensure alignment between graduate biomedical education and evolving scientific and workforce needs.