An emerging paradigm is that root traits that reduce the metabolic costs of soil exploration improve the acquisition of limiting soil resources. Here, we test the hypothesis that reduced lateral root branching density will improve drought tolerance in maize (Zea mays) by reducing the metabolic costs of soil exploration, permitting greater axial root elongation, greater rooting depth, and thereby greater water acquisition from drying soil. Maize recombinant inbred lines with contrasting lateral root number and length (few but long [FL] and many but short [MS]) were grown under water stress in greenhouse mesocosms, in field rainout shelters, and in a second field environment with natural drought. Under water stress in mesocosms, lines with the FL phenotype had substantially less lateral root respiration per unit of axial root length, deeper rooting, greater leaf relative water content, greater stomatal conductance, and 50% greater shoot biomass than lines with the MS phenotype. Under water stress in the two field sites, lines with the FL phenotype had deeper rooting, much lighter stem water isotopic signature, signifying deeper water capture, 51% to 67% greater shoot biomass at flowering, and 144% greater yield than lines with the MS phenotype. These results entirely support the hypothesis that reduced lateral root branching density improves drought tolerance. The FL lateral root phenotype merits consideration as a selection target to improve the drought tolerance of maize and possibly other cereal crops.

RI is a powerful tool for diagnosis and screening of breast cancer (1). However, widespread use of breast MRI has been restricted because of the limited availability of sites that offer this method. One major reason for limited availability is the lack of radiologists who can offer substantial expertise in interpreting breast MR images. Sophisticated machine learning approaches show promise in complementing human diagnosis (2). Broadly speaking, machine learning can be divided into two major classes: one is radiomic analysis (RA), where handmade image features are extracted; and the other is the concept of convolutional neural networks (CNN), in which the computer learns to recognize image features on its own, usually on the basis of a set of labeled training examples. Both approaches have been pursued with considerable success for image interpretation, although in different areas: In the field of diagnostic radiology, RA has been successfully used to further classify tumor types (3,4). However, CNNs require a larger pool of training images before they achieve a clinically useful performance. Within radiology, breast imaging, specifically mammographic screening, lends itself to be used with CNNs because similarly large data sets are available (5,6). With such large mammographic data sets, and with the advent of increased computing power, deep learning may have the potential to outperform regular computer-assisted diagnosis systems for mammographic interpretation (5). Studies are limited regarding the use of RA or CNNs for diagnostic classification of contrast agent-enhancing breast lesions (ie, for differential diagnosis of benign vs malignant lesions). Bickelhaupt et al (7) used machine learning for further characterization of lesions suspicious for cancer that were found on digital mammographic images and used unenhanced and diffusion-weighted MRI for this purpose. However, the use of RA or CNNs for classification of enhancing lesions observed at regular, clinical, dynamic contrast agent-enhanced, or multiparametric breast MRI is not established. Because breast MRI is performed less than mammographic screening, available breast MRI data sets are smaller

Background Suboptimal water and nutrient availability are primary constraints in global agriculture. Root anatomy plays key roles in soil resource acquisition. In this article we summarize evidence that root anatomical phenotypes present opportunities for crop breeding. Scope Root anatomical phenotypes influence soil resource acquisition by regulating the metabolic cost of soil exploration, exploitation of the rhizosphere, the penetration of hard soil domains, the axial and radial transport of water, and interactions with soil biota including mycorrhizal fungi, pathogens, insects, and the rhizosphere microbiome. For each of these topics we provide examples of anatomical phenotypes which merit attention as selection targets for crop improvement. Several cross-cutting issues are addressed including the importance of phenotypic plasticity, integrated phenotypes, C sequestration, in silico modeling, and novel methods to phenotype root anatomy including image analysis tools. Conclusions An array of anatomical phenes have substantial importance for the acquisition of water and nutrients. Substantial phenotypic variation exists in crop germplasm. New tools and methods are making it easier to phenotype root anatomy, determine its genetic control, and understand its utility for plant fitness. Root anatomical phenotypes are underutilized yet attractive breeding targets for the development of the efficient, resilient crops urgently needed in global agriculture.

BackgroundPlant root systems are key drivers of plant function and yield. They are also under-explored targets to meet global food and energy demands. Many new technologies have been developed to characterize crop root system architecture (CRSA). These technologies have the potential to accelerate the progress in understanding the genetic control and environmental response of CRSA. Putting this potential into practice requires new methods and algorithms to analyze CRSA in digital images. Most prior approaches have solely focused on the estimation of root traits from images, yet no integrated platform exists that allows easy and intuitive access to trait extraction and analysis methods from images combined with storage solutions linked to metadata. Automated high-throughput phenotyping methods are increasingly used in laboratory-based efforts to link plant genotype with phenotype, whereas similar field-based studies remain predominantly manual low-throughput.DescriptionHere, we present an open-source phenomics platform “DIRT”, as a means to integrate scalable supercomputing architectures into field experiments and analysis pipelines. DIRT is an online platform that enables researchers to store images of plant roots, measure dicot and monocot root traits under field conditions, and share data and results within collaborative teams and the broader community. The DIRT platform seamlessly connects end-users with large-scale compute “commons” enabling the estimation and analysis of root phenotypes from field experiments of unprecedented size.ConclusionDIRT is an automated high-throughput computing and collaboration platform for field based crop root phenomics. The platform is accessible at http://dirt.iplantcollaborative.org/ and hosted on the iPlant cyber-infrastructure using high-throughput grid computing resources of the Texas Advanced Computing Center (TACC). DIRT is a high volume central depository and high-throughput RSA trait computation platform for plant scientists working on crop roots. It enables scientists to store, manage and share crop root images with metadata and compute RSA traits from thousands of images in parallel. It makes high-throughput RSA trait computation available to the community with just a few button clicks. As such it enables plant scientists to spend more time on science rather than on technology. All stored and computed data is easily accessible to the public and broader scientific community. We hope that easy data accessibility will attract new tool developers and spur creative data usage that may even be applied to other fields of science.Electronic supplementary materialThe online version of this article (doi:10.1186/s13007-015-0093-3) contains supplementary material, which is available to authorized users.

To test the hypothesis that multiple integrated root phenotypes would co-optimize drought tolerance, we phenotyped the root anatomy and architecture of 400 mature maize (Zea mays) genotypes under well-watered and water-stressed conditions in the field. We found substantial variation in all 23 root phenes measured. A phenotypic bulked segregant analysis revealed that bulks representing the best and worst performers in the field displayed distinct root phenotypes. In contrast to the worst bulk, the root phenotype of the best bulk under drought consisted of greater cortical aerenchyma formation, more numerous and narrower metaxylem vessels, and thicker nodal roots. Partition against medians (PAM) clustering revealed several clusters of unique root phenotypes related to plant performance under water stress. Clusters associated with improved drought tolerance consisted of phene states that likely enable greater soil exploration by reallocating internal resources to greater root construction (increased aerenchyma content, larger cortical cells, fewer cortical cell files), restrict uptake of water to conserve soil moisture (reduced hydraulic conductance, narrow metaxylem vessels), and improve penetrability of hard, dry soils (thick roots with a larger proportion of stele, and smaller distal cortical cells). We propose that the most drought tolerant integrated phenotypes merit consideration as breeding ideotypes.