Identification and validation of a QTL influencing bitter pit symptoms in apple (Malus x domestica)

Bitter pit is one of the most economically important physiological disorders affecting apple fruit production, causing soft discrete pitting of the cortical flesh of the apple fruits which renders them unmarketable. The disorder is heritable; however, the environment and cultural practices play a major role in expression of symptoms. Bitter pit has been shown to be controllable to a certain extent using calcium sprays and dips; however, their use does not entirely prevent the incidence of the disorder. Previously, bitter pit has been shown to be controlled by two dominant genes, and markers on linkage group 16 of the apple genome were identified that were significantly associated with the expression of bitter pit symptoms in a genome-wide association study. In this investigation, we identified a major QTL for bitter pit defined by two microsatellite (SSR) markers. The association of the SSRs with the bitter pit locus, and their ability to predict severe symptom expression, was confirmed through screening of individuals with stable phenotypic expression from an additional mapping progeny. The data generated in this current study suggest a two gene model could account for the control of bitter pit symptom expression; however, only one of the loci was detectable, most likely due to dominance of alleles carried by both parents of the mapping progeny used. The SSR markers identified are cost-effective, robust and multi-allelic and thus should prove useful for the identification of seedlings with resistance to bitter pit using marker-assisted selection in apple breeding programs

Integrating node-place and trip end models to explore drivers of rail ridership in Flanders, Belgium’

The node-place model is an analytical framework that was devised to identify spatial development opportunities for railway stations and their surroundings at the regional scale. Today, the model is predominantly invoked and applied in the context of ‘transit-oriented development’ planning debates. As a corollary, these model applications share the pursuit of supporting a transition towards increased rail ridership (and walking and cycling), and therefore assumingly a transition to more sustainable travel behavior. Surprisingly, analyses of the importance of node and place interventions in explaining rail ridership remain thin on the ground. Against this backdrop, this paper aims to integrate the node-place model approach with current insights that derive from the trip end modeling literature. To this end, we apply a series of regression analyses in order to appraise the most important explanatory factors that impact rail ridership in Flanders, Belgium, today. This appraisal is based on both geographical and temporal data segmentations, in order to test for different types of railway stations and for different periods of the day. Additionally, we explore spatial nonstationarity by calibrating geographically weighted regression models, and this for different time windows. The models developed should allow policy and planning professionals to investigate the possible demand impacts of changes to existing stations and the walkable area surrounding them

Estimating soil moisture by electrical resistance measurements

On perpendicular to the row transects in eight Trentino vineyards, we measured electrical resistance in the soil in three 70 cm wide strips and at three depths (0-20 cm, 20-40 cm, 40-60 cm). A custom-made instrument, based on an Arduino single-board microcontroller, transmitted resistance measurements every 15 minutes to the Meteo Centre of San Michele. By using a Richard's pressure plate at 10 kPa (field capacity) and at 1400 kPa (wilting point) we estimated the water available at the three depths. With fixed 50 cm TDR probes inserted vertically near the transects, we measured soil moisture at field capacity and at wilting point. We then perfected an algorithm that, when receiving values of electrical resistance from each layer, gives an output corresponding to soil moistur