Genomics tools available for unravelling mechanisms underlying agronomical traits in strawberry with more to come

In the last few years, high-throughput genomics promised to bridge the gap between plant physiology and plant sciences. In addition, high-throughput genotyping technologies facilitate marker-based selection for better performing genotypes. In strawberry, Fragaria vesca was the first reference sequence obtained in the Rosoideae sub-family. This genome has a high level of synteny with genomes of other species of diploid and polyploid Fragaria, but it also provides a reference for species like Rubus and Rosa for functional genomics. Many tools for genetic, genomic and functional analyses were introduced over the last 10 years and these tools are still evolving. For genotyping, many studies have used simple sequence repeats (SSRs) but whole genome sequencing is now a mature technology and facilitates the development of genotyping chips and other genetic approaches such as genome wide association studies (GWAS). Furthermore, microarray-based technologies have been eclipsed by RNA-seq, the high-throughput sequencing of RNA. These new approaches have led to advances in our understanding of the genetically complex octoploid species, and have revolutionized functional genomics. For all genetic and genomic studies, novel material such as complex crosses such as NILs and EMS have appeared in addition to the classical segregating population. With all these tools, strawberry now emerges as a plant model, not only for studying fruit quality but also for deciphering the mechanisms controlling various aspects of plant biology. Selective examples will be described to illustrate the latest research on strawberry and what is coming from other model species.Peer reviewe

Response inhibition is associated with white matter microstructure in children

Cognitive control of thoughts, actions and emotions is important for normal behaviour and the development of such control continues throughout childhood and adolescence. Several lines of evidence suggest that response inhibition is primarily mediated by a right-lateralized network involving inferior frontal gyrus (IFG), presupplementary motor cortex (preSMA), and subthalamic nucleus. Though the brain's fibre tracts are known to develop during childhood, little is known about how fibre tract development within this network relates to developing behavioural control. Here we examined the relationship between response inhibition, as measured with the stop-signal task, and indices of regional white matter microstructure in typically-developing children. We hypothesized that better response inhibition performance would be associated with higher fractional anisotropy (FA) in fibre tracts within right IFG and preSMA after controlling for age. Mean FA and diffusivity values were extracted from right and left IFG and preSMA. As hypothesized, faster response inhibition was significantly associated with higher FA and lower perpendicular diffusivity in both the right IFG and the right preSMA, possibly reflecting faster speed of neural conduction within more densely packed or better myelinated fibre tracts. Moreover, both of these effects remained significant after controlling for age and whole brain estimates of these DTI parameters. Interestingly, right IFG and preSMA FA contributed additively to the prediction of performance variability. Observed associations may be related to variation in phase of maturation, to activity-dependent alterations in the network subserving response inhibition, or to stable individual differences in underlying neural system connectivity

From model-based perceptual decision-making to spatial interference control

Model-based neuroscience is aimed at understanding latent cognitive processes using quantitative cognitive models in combination with neuroscientific measures. This approach has been successful in the domain of perceptual decision making, in which the properties of accumulator models of choice tasks have been related to neural networks that are involved in decision making. Here, we propose that this approach can also be applied to spatial interference control such as required in the Simon task. Spatial interference control is essential for understanding cognitive control processes. A model-based approach may aid in understanding the latent cognitive processes in spatial interference control. Ultimately this approach may uncover the relationship between decision making that requires interference control and default decision making such as perceptual choice