The Evolutionary Basis of Naturally Diverse Rice Leaves Anatomy

Rice contains genetically and ecologically diverse wild and cultivated species that show a wide variation in plant and leaf architecture. A systematic characterization of leaf anatomy is essential in understanding the dynamics behind such diversity. Therefore, leaf anatomies of 24 Oryza species spanning 11 genetically diverse rice genomes were studied in both lateral and longitudinal directions and possible evolutionary trends were examined. A significant inter-species variation in mesophyll cells, bundle sheath cells, and vein structure was observed, suggesting precise genetic control over these major rice leaf anatomical traits. Cellular dimensions, measured along three growth axes, were further combined proportionately to construct three-dimensional (3D) leaf anatomy models to compare the relative size and orientation of the major cell types present in a fully expanded leaf. A reconstruction of the ancestral leaf state revealed that the following are the major characteristics of recently evolved rice species: fewer veins, larger and laterally elongated mesophyll cells, with an increase in total mesophyll area and in bundle sheath cell number. A huge diversity in leaf anatomy within wild and domesticated rice species has been portrayed in this study, on an evolutionary context, predicting a two-pronged evolutionary pathway leading to the ‘sativa leaf type’ that we see today in domesticated species

Investigating the microstructure of plant leaves in 3D with lab-based X-ray Computed Tomography

Background Leaf cellular architecture plays an important role in setting limits for carbon assimilation and, thus, photosynthetic performance. However, the low density, fine structure, and sensitivity to desiccation of plant tissue has presented challenges to its quantification. Classical methods of tissue fixation and embedding prior to 2D microscopy of sections is both laborious and susceptible to artefacts that can skew the values obtained. Here we report an image analysis pipeline that provides quantitative descriptors of plant leaf intercellular airspace using lab-based X-ray Computed Tomography (microCT). We demonstrate successful visualisation and quantification of differences in leaf intercellular airspace in 3D for a range of species (including both dicots and monocots) and provide a comparison with a standard 2D analysis of leaf sections. Results We used the microCT image pipeline to obtain estimates of leaf porosity and mesophyll exposed surface area (Smes) for three dicot species (Arabidopsis, tomato and pea) and three monocot grasses (barley, oat and rice). The imaging pipeline consisted of (1) a masking operation to remove the background airspace surrounding the leaf, (2) segmentation by an automated threshold in ImageJ and then (3) quantification of the extracted pores using the ImageJ ‘Analyze Particles’ tool. Arabidopsis had the highest porosity and lowest Smes for the dicot species whereas barley had the highest porosity and the highest Smes for the grass species. Comparison of porosity and Smes estimates from 3D microCT analysis and 2D analysis of sections indicates that both methods provide a comparable estimate of porosity but the 2D method may underestimate Smes by almost 50%. A deeper study of porosity revealed similarities and differences in the asymmetric distribution of airspace between the species analysed. Conclusions Our results demonstrate the utility of high resolution imaging of leaf intercellular airspace networks by lab-based microCT and provide quantitative data on descriptors of leaf cellular architecture. They indicate there is a range of porosity and Smes values in different species and that there is not a simple relationship between these parameters, suggesting the importance of cell size, shape and packing in the determination of cellular parameters proposed to influence leaf photosynthetic performance

I am, therefore I buy: Low self-esteem and the pursuit of self-verifying consumption

The idea that consumers use products to feel good about themselves is a basic tenet of marketing. Yet, in addition to the motive to self-enhance, consumers also strive to confirm their self-views (i.e., self-verification). Although self-verification provides self-related benefits, its role in consumer behavior is poorly understood. To redress that gap, we examine a dispositional variable–trait self-esteem–that predicts whether consumers self-verify in the marketplace. We propose that low (vs. high) self-esteem consumers gravitate toward inferior products because those products confirm their pessimistic self-views. Five studies supported our theorizing: Low (vs. high) self-esteem participants gravitated toward inferior products (study 1) because of the motivation to self-verify (study 2). Low self-esteem consumers preferred inferior products only when those products signaled pessimistic (vs. positive) self-views and could therefore be self-verifying (study 3). Even more telling, low self-esteem consumers’ propensity to choose inferior products disappeared after they were induced to view themselves as consumers of superior products (study 4) but remained in the wake of negative feedback (study 5). Our investigation thus highlights self-esteem as a boundary condition for compensatory consumption. By pinpointing factors that predict when self-verification guides consumer behavior, this work enriches the field’s understanding of how products serve self-motives

Fruit Microstructure Evaluation Using Synchrotron X-Ray Computed Tomography

peer reviewe