C. elegans molting requires rhythmic accumulation of the Grainyhead/LSF transcription factor GRH-1.

C. elegans develops through four larval stages that are rhythmically terminated by molts, that is, the synthesis and shedding of a cuticular exoskeleton. Each larval cycle involves rhythmic accumulation of thousands of transcripts, which we show here relies on rhythmic transcription. To uncover the responsible gene regulatory networks (GRNs), we screened for transcription factors that promote progression through the larval stages and identified GRH-1, BLMP-1, NHR-23, NHR-25, MYRF-1, and BED-3. We further characterize GRH-1, a Grainyhead/LSF transcription factor, whose orthologues in other animals are key epithelial cell-fate regulators. We find that GRH-1 depletion extends molt durations, impairs cuticle integrity and shedding, and causes larval death. GRH-1 is required for, and accumulates prior to, each molt, and preferentially binds to the promoters of genes expressed during this time window. Binding to the promoters of additional genes identified in our screen furthermore suggests that we have identified components of a core molting-clock GRN. Since the mammalian orthologues of GRH-1, BLMP-1 and NHR-23, have been implicated in rhythmic homeostatic skin regeneration in mouse, the mechanisms underlying rhythmic C. elegans molting may apply beyond nematodes.

A semi-automated technique for adenoma quantification in the ApcMin mouse using FeatureCounter.

Colorectal cancer is a major contributor to death and disease worldwide. The ApcMin mouse is a widely used model of intestinal neoplasia, as it carries a mutation also found in human colorectal cancers. However, the method most commonly used to quantify tumour burden in these mice is manual adenoma counting, which is time consuming and poorly suited to standardization across different laboratories. We describe a method to produce suitable photographs of the small intestine of ApcMin mice, process them with an ImageJ macro, FeatureCounter, which automatically locates image features potentially corresponding to adenomas, and a machine learning pipeline to identify and quantify them. Compared to a manual method, the specificity (or True Negative Rate, TNR) and sensitivity (or True Positive Rate, TPR) of this method in detecting adenomas are similarly high at about 80% and 87%, respectively. Importantly, total adenoma area measures derived from the automatically-called tumours were just as capable of distinguishing high-burden from low-burden mice as those established manually. Overall, our strategy is quicker, helps control experimenter bias, and yields a greater wealth of information about each tumour, thus providing a convenient route to getting consistent and reliable results from a study.

Effects of sleep/wake history and circadian phase on proposed pilot fatigue safety performance indicators.

The Karolinska Sleepiness Scale and Samn-Perelli fatigue ratings, and psychomotor vigilance task performance are proposed as measures for monitoring commercial pilot fatigue. In laboratory studies, they are sensitive to sleep/wake history and circadian phase. The present analyses examined whether they reliably reflect sleep/wake history and circadian phase during transmeridian flight operations. Data were combined from four studies (237 pilots, 730 out-and-back flights between 13 city pairs, 1-3-day layovers). Sleep was monitored (wrist actigraphy, logbooks) before, during and after trips. On duty days, sleepiness, fatigue and mean response speed were measured pre-flight and at the top of the descent. Mixed-model analysis of variance examined associations between these measures and sleep/wake history, after controlling for operational factors. Circadian phase was approximated by local (domicile) time in the city where each trip began and ended. More sleep in the 24 h prior to duty was associated with lower pre-flight sleepiness and fatigue and faster response speed. Sleepiness and fatigue were greater before flights departing during the domicile night and early morning. At the top of the descent, pilots felt less sleepy and fatigued after more in-flight sleep and less time awake. Flights arriving in the early-mid-morning (domicile time) had greater sleepiness and fatigue and slower response speeds than flights arriving later. Subjective ratings showed expected associations with sleep/wake history and circadian phase. The response speed showed expected circadian variation but was not associated with sleep/wake history at the top of the descent. This may reflect moderate levels of fatigue at this time and/or atypically fast responses among pilots.

Pilot fatigue: relationships with departure and arrival times, flight duration, and direction.

INTRODUCTION: Flight timing is expected to influence pilot fatigue because it determines the part of the circadian body clock cycle that is traversed during a flight. However the effects of flight timing are not well-characterized because field studies typically focus on specific flights with a limited range of departure times and have small sample sizes. The present project combined data from four studies, including 13 long-range and ultra-long range out-and-back trips across a range of departure and arrival times (237 pilots in 4-person crews, 730 flight segments, 1-3 d layovers). METHODS: All studies had tripartite support and underwent independent ethical review. Sleep was monitored (actigraphy) from 3 d prior to ≥ 3 d post-trip. Preflight and at top of descent (TOD), pilots rated their sleepiness (Karolinska Sleepiness Scale) and fatigue (Samn-Perelli scale), and completed a psychomotor vigilance task (PVT) test. Mixed model ANOVA identified independent associations between fatigue measures and operational factors (domicile times of departure and arrival, flight duration and direction, landing versus relief crew). RESULTS: Preflight subjective fatigue and sleepiness were lowest for flights departing 14:00-17:59. Total in-flight sleep was longest on flights departing 18:00-01:59. At TOD, fatigue and sleepiness were higher and PVT response speeds were slower on flights arriving 06:00-09:59 than on flights arriving later. PVT response speed at TOD was also faster on longer flights. DISCUSSION: The findings indicate the influence of flight timing (interacting with the circadian body clock cycle), as well as flight duration, on in-flight sleep and fatigue measures at TOD.

Crew fatigue safety performance indicators for fatigue risk management systems.

INTRODUCTION: Implementation of Fatigue Risk Management Systems (FRMS) is gaining momentum; however, agreed safety performance indicators (SPIs) are lacking. This paper proposes an initial set of SPIs based on measures of crewmember sleep, performance, and subjective fatigue and sleepiness, together with methods for interpreting them. METHODS: Data were included from 133 landing crewmembers on 2 long-range and 3 ultra-long-range trips (4-person crews, 3 airlines, 220 flights). Studies had airline, labor, and regulatory support, and underwent independent ethical review. SPIs evaluated preflight and at top of descent (TOD) were: total sleep in the prior 24 h and time awake at duty start and at TOD (actigraphy); subjective sleepiness (Karolinska Sleepiness Scale) and fatigue (Samn-Perelli scale); and psychomotor vigilance task (PVT) performance. Kruskal-Wallis nonparametric ANOVA with post hoc tests was used to identify significant differences between flights for each SPI. RESULTS: Visual and preliminary quantitative comparisons of SPIs between flights were made using box plots and bar graphs. Statistical analyses identified significant differences between flights across a range of SPls. DISCUSSION: In an FRMS, crew fatigue SPIs are envisaged as a decision aid alongside operational SPIs, which need to reflect the relevant causes of fatigue in different operations. We advocate comparing multiple SPIs between flights rather than defining safe/unsafe thresholds on individual SPIs. More comprehensive data sets are needed to identify the operational and biological factors contributing to the differences between flights reported here. Global sharing of an agreed core set of SPIs would greatly facilitate implementation and improvement of FRMS.