Abiotic Stress‐Related Expressed Sequence Tags from the Diploid Strawberry Fragaria vesca

Strawberry ( spp.) is a eudicotyledonous plant that belongs to the Rosaceae family, which includes other agronomically important plants such as raspberry ( L.) and several tree-fruit species. Despite the vital role played by cultivated strawberry in agriculture, few stress-related gene expression characterizations of this crop are available. To increase the diversity of available transcriptome sequence, we produced 41,430 L. expressed sequence tags (ESTs) from plants growing under water-, temperature-, and osmotic-stress conditions as well as a combination of heat and osmotic stresses that is often found in irrigated fields. Clustering and assembling of the ESTs resulted in a total of 11,836 contigs and singletons that were annotated using Gene Ontology (GO) terms. Furthermore, over 1200 sequences with no match to available Rosaceae ESTs were found, including six that were assigned the “response to stress” GO category. Analysis of EST frequency provided an estimate of steady state transcript levels, with 91 sequences exhibiting at least a 20-fold difference between treatments. This EST collection represents a useful resource to advance our understanding of the abiotic stress-response mechanisms in strawberry. The sequence information may be translated to valuable tree crops in the Rosaceae family, where whole-plant treatments are not as simple or practical

Stochastic particle packing with specified granulometry and porosity

This work presents a technique for particle size generation and placement in arbitrary closed domains. Its main application is the simulation of granular media described by disks. Particle size generation is based on the statistical analysis of granulometric curves which are used as empirical cumulative distribution functions to sample from mixtures of uniform distributions. The desired porosity is attained by selecting a certain number of particles, and their placement is performed by a stochastic point process. We present an application analyzing different types of sand and clay, where we model the grain size with the gamma, lognormal, Weibull and hyperbolic distributions. The parameters from the resulting best fit are used to generate samples from the theoretical distribution, which are used for filling a finite-size area with non-overlapping disks deployed by a Simple Sequential Inhibition stochastic point process. Such filled areas are relevant as plausible inputs for assessing Discrete Element Method and similar techniques