Haploid identification in maize.

Doubled haploid (DH) line production through in vivo maternal haploid induction is widely adopted in maize breeding programs. The established protocol for DH production includes four steps namely in vivo maternal haploid induction, haploid identification, genome doubling of haploid, and self-fertilization of doubled haploids. Since modern haploid inducers still produce relatively small portion of haploids among undesirable hybrid kernels, haploid identification is typically laborious, costly, and time-consuming, making this step the second foremost in the DH technique. This manuscript reviews numerous methods for haploid identification from different approaches including the innate differences in haploids and diploids, biomarkers integrated in haploid inducers, and automated seed sorting. The phenotypic differentiation, genetic basis, advantages, and limitations of each biomarker system are highlighted. Several approaches of automated seed sorting from different research groups are also discussed regarding the platform or instrument used, sorting time, accuracy, advantages, limitations, and challenges before they go through commercialization. The past haploid selection was focusing on finding the distinguishable marker systems with the key to effectiveness. The current haploid selection is adopting multiple reliable biomarker systems with the key to efficiency while seeking the possibility for automation. Fully automated high-throughput haploid sorting would be promising in near future with the key to robustness with retaining the feasible level of accuracy. The system that can meet between three major constraints (time, workforce, and budget) and the sorting scale would be the best option.

E-facts: business process management in clinical data repositories.

The Partners Healthcare Research Patient Data Registry (RPDR) is a centralized data repository that gathers clinical data from various hospital systems. The RPDR allows clinical investigators to obtain aggregate numbers of patients with user-defined characteristics such as diagnoses, procedures, medications, and laboratory values. They may then obtain patient identifiers and electronic medical records with prior IRB approval. Moreover, the accurate identification and efficient population of worthwhile and quantifiable facts from doctor's report into the RPDR is a significant process. As part of our ongoing e-Fact project, this work describes a new business process management technology that helps coordinate and simplify this procedure.