Physiological Screening for Drought Tolerance Traits in Vegetable Amaranth (Amaranthus tricolor) Germplasm

Amaranth (Amaranthus tricolor), an underutilized climate smart crop, is highly nutritious and possesses diverse drought tolerance traits, making it an ideal crop to thrive in a rapidly changing climate. Despite considerable studies on the growth and physiology of plants subjected to drought stress, a precise trait phenotyping strategy for drought tolerance in vegetable amaranth is still not well documented. In this study, two drought screening trials were carried out on 44 A. tricolor accessions in order to identify potential drought-tolerant A. tricolor germplasm and to discern their physiological responses to drought stress. The findings revealed that a change in stem biomass was most likely the main mechanism of drought adaptation for stress recovery, and dark-adapted quantum yield (Fv/Fm) could be a useful parameter for identifying drought tolerance in amaranth. Three drought tolerance indices: geometric mean productivity (GMP), mean productivity (MP) and stress tolerance index (STI) identified eight drought-tolerant accessions with stable performance across the two screening trials. The highly significant genotypic differences observed in several physiological traits among the amaranth accessions indicate that the amaranth panel used in this study could be a rich source of genetic diversity for breeding purposes for drought tolerance traits.

Novel Metrics to Characterize In Vitro Pollen Tube Growth Performance of Apple Cultivars

In vitro germination assays are frequently used in screening trials to evaluate the pollen viability of pollinizers. To be effective, screening trials must have defined threshold criteria, from which individuals can then be assessed. However, despite decades of research on pollen viability, no established threshold is available to categorize apple cultivars based on their in vitro pollen tube lengths. This study aimed to identify and characterize the subgroups of cultivars based on their pollen tube growth performance. In vitro pollen tube lengths of 41 individuals were determined by incubating samples on artificial germination media at 15 and 25 °C. A six-number summary statistic was calculated, and hierarchical clustering on principal component (HCPC) analysis was used to determine and characterize subgroups. Furthermore, a decision tree model was used to predict class membership for future datasets. HCPC analysis partitioned the 41 individuals into three subgroups with different performances. The decision tree quickly predicted the cluster membership based on the second quartile at 15 °C and the third quartile at 25 °C. The thresholds from the decision tree can be used to characterize new observations. The use of the methods will be demonstrated using a case study with 29 apple accessions.

UKCTOCS update: applying insights of delayed effects in cancer screening trials to the long-term follow-up mortality analysis

Background During trials that span decades, new evidence including progress in statistical methodology, may require revision of original assumptions. An example is the continued use of a constant-effect approach to analyse the mortality reduction which is often delayed in cancer-screening trials. The latter led us to re-examine our approach for the upcoming primary mortality analysis (2020) of long-term follow-up of the United Kingdom Collaborative Trial of Ovarian Cancer Screening (LTFU UKCTOCS), having initially (2014) used the proportional hazards (PH) Cox model. Methods We wrote to 12 experts in statistics/epidemiology/screening trials, setting out current evidence, the importance of pre-specification, our previous mortality analysis (2014) and three possible choices for the follow-up analysis (2020) of the mortality outcome: (A) all data (2001–2020) using the Cox model (2014), (B) new data (2015–2020) only and (C) all data (2001–2020) using a test that allows for delayed effects. Results Of 11 respondents, eight supported changing the 2014 approach to allow for a potential delayed effect (option C), suggesting various tests while three favoured retaining the Cox model (option A). Consequently, we opted for the Versatile test introduced in 2016 which maintains good power for early, constant or delayed effects. We retained the Royston-Parmar model to estimate absolute differences in disease-specific mortality at 5, 10, 15 and 18 years. Conclusions The decision to alter the follow-up analysis for the primary outcome on the basis of new evidence and using new statistical methodology for long-term follow-up is novel and has implications beyond UKCTOCS. There is an urgent need for consensus building on how best to design, test, estimate and report mortality outcomes from long-term randomised cancer screening trials. Trial registration ISRCTN22488978. Registered on 6 April 2000.

Duration of lead time in screening for lung cancer

Background Screening for lung cancer has used chest radiography (CR), low dose computed tomography (LDCT) and sputum cytology (SC). Estimates of the lead time (LT), i.e., the time interval from detection of lung cancer by screening to the development of symptoms, have been derived from longitudinal studies of populations at risk, tumor doubling time (DT), the ratio between its prevalence at the first round of screening and its annual incidence during follow-up, and by probability modeling derived from the results of screening trials. Objective To review and update the estimates of LT of lung cancer. Methods A non-systematic search of the literature for estimates of LT and screening trials. Search of the reference sections of the retrieved papers for additional relevant studies. Calculation of LTs derived from these studies. Results LT since detection by CR was 0.8–1.1 years if derived from longitudinal studies; 0.6–2.1 years if derived from prevalence / incidence ratios; 0.2 years if derived from the average tumor DT; and 0.2–1.0 if derived from probability modeling. LT since detection by LDCT was 1.1–3.5 if derived from prevalence / incidence ratios; 3.9 if derived from DT; and 0.9 if derived from probability modeling. LT since detection of squamous cell cancer by SC in persons with normal CR was 1.3–1.5 if derived from prevalence/incidence ratios; and 2.1 years if derived from the DT of squamous cell cancer. Conclusions Most estimates of the LT yield values of 0.2–1.5 years for detection by CR; of 0.9–3.5 years for detection by LDCT; and about 2 years or less for detection of squamous cell cancer by SC in persons with normal CR. The heterogeneity of the screening trials and methods of derivation may account for the variability of LT estimates.