Anti-TST monoclonal antibody, clone FQS22756(C)

Driver sleepiness contributes to traffic accidents. However, sleepiness in urban public transport remains an understudied subject. To fill this gap, we examined the sleepiness, sleep, and on-duty sleepiness countermeasures (SCMs) in 23 tram drivers working morning, day, and evening shifts for three weeks. Sleepiness was measured using Karolinska Sleepiness Scale (KSS). Nocturnal total sleep time (TST) was measured with wrist actigraphy. SCMs and naps were self-reported with a smartphone application. Caffeine and napping were considered effective SCMs. Severe sleepiness (KSS >= 7) was observed in 22% of shifts with no differences between shift types. Rest breaks were associated with slight reductions in sleepiness. TST between days off averaged 7 h but was 1 h 33 min and 38 min shorter prior to morning and day shifts, respectively. The use of effective SCMs showed little variance between shift types. These results highlight the need for fatigue management in non-night-working tram drivers.

Key message Genomic selection with a multiple-year training population dataset could accelerate early-stage testcross testing by skipping the first-stage yield testing, which significantly saves the time and cost of early-stage testcross testing. With the development of doubled haploid (DH) technology, the main task for a maize breeder is to estimate the breeding values of thousands of DH lines annually. In early-stage testcross testing, genomic selection (GS) offers the opportunity of replacing expensive multiple-environment phenotyping and phenotypic selection with lower-cost genotyping and genomic estimated breeding value (GEBV)-based selection. In the present study, a total of 1528 maize DH lines, phenotyped in multiple-environment trials in three consecutive years and genotyped with a low-cost per-sample genotyping platform of rAmpSeq, were used to explore how to implement GS to accelerate early-stage testcross testing. Results showed that the average prediction accuracy estimated from the cross-validation schemes was above 0.60 across all the scenarios. The average prediction accuracies estimated from the independent validation schemes ranged from 0.23 to 0.32 across all the scenarios, when the one-year datasets were used as training population (TRN) to predict the other year data as testing population (TST). The average prediction accuracies increased to a range from 0.31 to 0.42 across all the scenarios, when the two-years datasets were used as TRN. The prediction accuracies increased to a range from 0.50 to 0.56, when the TRN consisted of two-years of breeding data and 50% of third year's data converted from TST to TRN. This information showed that GS with a multiple-year TRN set offers the opportunity to accelerate early-stage testcross testing by skipping the first-stage yield testing, which significantly saves the time and cost of early-stage testcross testing.