Search

Pseudo-nitzschia pungens is a widely distributed marine pennate diatom. Hybrid zones, regions in which two different genotypes may interbreed, are important areas for speciation and ecology, and have been reported across the globe for this species. However, sexual reproduction between differing clades in the natural environment is yet to be observed and is difficult to predict. Here we carried out experiments using two mono-clonal cultures of P. pungens from different genotypes to measure the frequency and timing of sexual reproduction across varying biotic (growth phases and cell activity potential) and abiotic conditions (nutrients, light, turbulence). We found the mating rates and number of zygotes gradually decreased from exponential to late stationary growth phases. The maximum zygote abundance observed was 1,390 cells mL-1 and the maximum mating rate was 7.1%, both which occurred during the exponential growth phase. Conversely, only 9 cells mL-1 and a maximum mating rate of 0.1% was observed during the late stationary phase. We also found the higher the relative potential cell activity (rPCA) in parent cells, as determined by the concentration of chlorophyll a per cell and the ratio of colony formation during parent cultivations, revealed higher mating rates. Furthermore, sexual events were reduced under nutrient enrichment conditions, and mating pairs and zygotes were not formed under aphotic (dark) or shaking culture conditions (150 rpm). In order to understand the sexual reproduction of Pseudo-nitzschia in the natural environment, our results highlight that it is most likely the combination of both biotic (growth phase, Chl. a content) and abiotic factors (nutrients, light, turbulence) that will determine the successful union of intraspecific populations of P. pungens in any given region.

BACKGROUND: Coronary artery calcium (CAC) score is a clinically validated marker of cardiovascular disease risk. We developed and validated a novel cardiovascular risk stratification system based on deep-learning-predicted CAC from retinal photographs. METHODS: We used 216 152 retinal photographs from five datasets from South Korea, Singapore, and the UK to train and validate the algorithms. First, using one dataset from a South Korean health-screening centre, we trained a deep-learning algorithm to predict the probability of the presence of CAC (ie, deep-learning retinal CAC score, RetiCAC). We stratified RetiCAC scores into tertiles and used Cox proportional hazards models to evaluate the ability of RetiCAC to predict cardiovascular events based on external test sets from South Korea, Singapore, and the UK Biobank. We evaluated the incremental values of RetiCAC when added to the Pooled Cohort Equation (PCE) for participants in the UK Biobank. FINDINGS: RetiCAC outperformed all single clinical parameter models in predicting the presence of CAC (area under the receiver operating characteristic curve of 0·742, 95% CI 0·732-0·753). Among the 527 participants in the South Korean clinical cohort, 33 (6·3%) had cardiovascular events during the 5-year follow-up. When compared with the current CAC risk stratification (0, >0-100, and >100), the three-strata RetiCAC showed comparable prognostic performance with a concordance index of 0·71. In the Singapore population-based cohort (n=8551), 310 (3·6%) participants had fatal cardiovascular events over 10 years, and the three-strata RetiCAC was significantly associated with increased risk of fatal cardiovascular events (hazard ratio [HR] trend 1·33, 95% CI 1·04-1·71). In the UK Biobank (n=47 679), 337 (0·7%) participants had fatal cardiovascular events over 10 years. When added to the PCE, the three-strata RetiCAC improved cardiovascular risk stratification in the intermediate-risk group (HR trend 1·28, 95% CI 1·07-1·54) and bo