Background-Broad inhibition of matrix metalloproteinases (MMPs) attenuates left ventricular remodeling after myocardial infarction (MI). However, it is not clear if selective MMP inhibition strategies will be effective or if MMP inhibition will impair angiogenesis after MI. Methods and Results-We used a selective MMP inhibitor (MMPi) that does not inhibit MMP-1 in rabbits, which, like humans but unlike rodents, express MMP-1 as a major collagenase. On day 1 after MI, rabbits were randomized to receive either inhibitor (nϭ10) or vehicle (nϭ8). At 4 weeks after MI, there were no differences in infarct size or collagen fractional area. However, MMPi reduced ventricular dilation. The increase in end-diastolic dimension from day 1 to week 4 was 3.1Ϯ0.

Hemp (Cannabis sativa L.) is an emerging dioecious crop grown primarily for grain, fiber, and cannabinoids. There is good evidence for medicinal benefits of the most abundant cannabinoid in hemp, cannabidiol (CBD). For CBD production, female plants producing CBD but not tetrahydrocannabinol (THC) are desired. We developed and validated high‐throughput PACE (PCR Allele Competitive Extension) assays for C. sativa plant sex and cannabinoid chemotype. The sex assay was validated across a wide range of germplasm and resolved male plants from female and monoecious plants. The cannabinoid chemotype assay revealed segregation in hemp populations, and resolved plants producing predominantly THC, predominantly CBD, and roughly equal amounts of THC and CBD. Cultivar populations that were thought to be stabilized for CBD production were found to be segregating phenotypically and genotypically. Many plants predominantly producing CBD accumulated more than the current US legal limit of 0.3% THC by dry weight. These assays and data provide potentially useful tools for breeding and early selection of hemp.

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Alfalfa (Medicago sativa L.) is a widely planted perennial forage legume grown throughout temperate and dry subtropical regions in the world. Long breeding cycles limit genetic improvement of alfalfa, particularly for complex traits such as biomass yield. Genomic selection (GS), based on predicted breeding values obtained using genome-wide molecular markers, could enhance breeding efficiency in terms of gain per unit time and cost. In this study, we genotyped tetraploid alfalfa plants that had previously been evaluated for yield during two cycles of phenotypic selection using genotyping-by-sequencing (GBS). We then developed prediction equations using yield data from three locations. Approximately 10,000 single nucleotide polymorphism (SNP) markers were used for GS modeling. The genomic prediction accuracy of total biomass yield ranged from 0.34 to 0.51 for the Cycle 0 population and from 0.21 to 0.66 for the Cycle 1 population, depending on the location. The GS model developed using Cycle 0 as the training population in predicting total biomass yield in Cycle 1 resulted in accuracies up to 0.40. Both genotype  environment interaction and the number of harvests and years used to generate yield phenotypes had effects on prediction accuracy across generations and locations, Based on our results, the selection efficiency per unit time for GS is higher than phenotypic selection, although accuracies will likely decline across multiple selection cycles. This study provided evidence that GS can accelerate genetic gain in alfalfa for biomass yield.