Managing change in persistent object systems

Persistent object systems are highly-valued technology because they o er an e ec- tive foundation for building very long-lived persistent application systems (PAS). The technology becomes more e ective as it o ers a more consistently integrated computational context. For it to be feasible to design and construct a PAS it must be possible to in- crementally add program and data to the existing collection. For a PAS to endure it must o er exibility: a capacity to evolve and change. This paper examines the capacity of persistent object systems to accommodate incremental construction and change. Established store based technologies can support incremental construction but methodologies are needed to deploy them e ectively. Evolving data description is one motivation for inheritance but inheritance alone is not enough to support change management. The case for supporting incremental change is very persuasive. The challenge is to provide technologies that will facilitate it and methodologies that will organise it. This paper identi es change absorbers as a means of describing how changes should propagate. It is argued that if we systematically develop an adequate reper- toire of change absorbers then they will facilitate much better quality change man- agement

Using simple PID-inspired controllers for online resilient resource management of distributed scientific workflows

Scientific workflows have become mainstream for conducting large-scale scientific research. As a result, many workflow applications and Workflow Management Systems (WMSs) have been developed as part of the cyberinfrastructure to allow scientists to execute their applications seamlessly on a range of distributed platforms. Although the scientific community has addressed this challenge from both theoretical and practical approaches, failure prediction, detection, and recovery still raise many research questions. In this paper, we propose an approach inspired by the control theory developed as part of autonomic computing to predict failures before they happen, and mitigated them when possible. The proposed approach is inspired on the proportional–integral–derivative controller (PID controller) control loop mechanism, which is widely used in industrial control systems, where the controller will react to adjust its output to mitigate faults. PID controllers aim to detect the possibility of a non-steady state far enough in advance so that an action can be performed to prevent it from happening. To demonstrate the feasibility of the approach, we tackle two common execution faults of large scale data-intensive workflows—data storage overload and memory overflow. We developed a simulator, which implements and evaluates simple standalone PID-inspired controllers to autonomously manage data and memory usage of a data-intensive bioinformatics workflow that consumes/produces over 4.4 TB of data, and requires over 24 TB of memory to run all tasks concurrently. Experimental results obtained via simulation indicate that workflow executions may significantly benefit from the controller-inspired approach, in particular under online and unknown conditions. Simulation results show that nearly-optimal executions (slowdown of 1.01) can be attained when using our proposed method, and faults are detected and mitigated far in advance of their occurrence

The persistent store as an enabling technology for integrated project support environments

The software engineering community has recognised the need for integrated project support environments (IPSEs) for some time. With such a system the user is provided with an integrated set of software tools with which to operate. Given this set of integrated software tools rather than a set of ad hoc tools the cost of software and project support throughout its life cycle is reduced. The technique of integration as a method of cost saving, applies to all levels in the hierarchy of problem solving, both hardware and software. This paper discusses one such level, that in which the IPSE is implemented and in particular the use of a persistent store as an enabling technology for IPSEs. The facilities of the language PS-algol necessary to support an IPSE are illustrated by example and it is demonstrated how an IPSE's base may be provided by a persistent store that supports first class procedures as data objects. The need for a type secure object system which allows static and dynamic binding is demonstrated and finally the secure transactional base of PS-algol is shown to be a necessary and sufficient condition to provide secure version control and concurrent access to both programs and data.Othe

Identification of nitrogen-dependent QTL and underlying genes for root system architecture in hexaploid wheat

The root system architecture (RSA) of a crop has a profound effect on the uptake of nutrients and consequently the potential yield. However, little is known about the genetic basis of RSA and resource dependent response in wheat (Triticum aestivum L.). Here, a high-throughput hydroponic root phenotyping system was used to identify N-dependent root traits in a wheat mapping population. Using quantitative trait locus (QTL) analysis, a total of 55 QTLs were discovered for seedling root traits across two N treatments, 25 of which were N-dependent. Transcriptomic analyses were used on a N-dependent root angle QTL on chromosome 2D and 17 candidate genes were identified. Of these N-dependent genes a nitrate transporter 1/peptide transporter (NPF) family gene was upregulated making it an interesting candidate for N signalling and response processes for root angle change. The RNA-seq results provide valuable genetic insight for root angle control, N-dependent responses and candidate genes for improvement of N capture in wheat

Identification of QTL and underlying genes for root system architecture associated with nitrate nutrition in hexaploid wheat

The root system architecture (RSA) of a crop has a profound effect on the uptake of nutrients and consequently the potential yield. However, little is known about the genetic basis of RSA and resource adaptive responses in wheat (Triticum aestivum L.). Here, a high-throughput germination paper-based plant phenotyping system was used to identify seedling traits in a wheat doubled haploid mapping population, Savannah×Rialto. Significant genotypic and nitrate-N treatment variation was found across the population for seedling traits with distinct trait grouping for root size-related traits and root distribution-related traits. Quantitative trait locus (QTL) analysis identified a total of 59 seedling trait QTLs. Across two nitrate treatments, 27 root QTLs were specific to the nitrate treatment. Transcriptomic analyses for one of the QTLs on chromosome 2D, which was found under low nitrate conditions, revealed gene enrichment in N-related biological processes and 28 differentially expressed genes with possible involvement in a root angle response. Together, these findings provide genetic insight into root system architecture and plant adaptive responses to nitrate, as well as targets that could help improve N capture in wheat

X‐ray CT reveals 4D root system development and lateral root responses to nitrate in soil

Abstract The spatial arrangement of the root system, termed root system architecture, is important for resource acquisition as it directly affects the soil zone explored. Methods for phenotyping roots are mostly destructive, which prevents analysis of roots over time as they grow. Here, we used X‐ray microcomputed tomography (μCT) to non‐invasively characterize wheat (Triticum aestivum L.) seedling root development across time under high and low nitrate nutrition. Roots were imaged multiple times with the 3D models co‐aligned and timestamped in the architectural plant model OpenSimRoot for subsequent root growth and nitrate uptake simulations. Through 4D imaging, we found that lateral root traits were highly responsive to nitrate limitation in soil with greater lateral root length under low N. The root growth model using all μCT root scans was comparable to a parameterized model using only the final root scan in the series. In a second μCT experiment, root growth and nitrate uptake simulations of candidate wheat genotypes found significant root growth and uptake differences between lines. A high nitrate uptake wheat line selected from field data had a greater lateral root count and length at seedling growth stage compared with a low uptake line

Phenotyping pipeline reveals major seedling root growth QTL in hexaploid wheat

Seedling root traits of wheat (Triticum aestivum L.) have been shown to be important for efficient establishment and linked to mature plant traits such as height and yield. A root phenotyping pipeline, consisting of a germination paper-based screen combined with image segmentation and analysis software, was developed and used to characterize seedling traits in 94 doubled haploid progeny derived from a cross between the winter wheat cultivars Rialto and Savannah. Field experiments were conducted to measure mature plant height, grain yield, and nitrogen (N) uptake in three sites over 2 years. In total, 29 quantitative trait loci (QTLs) for seedling root traits were identified. Two QTLs for grain yield and N uptake co-localize with root QTLs on chromosomes 2B and 7D, respectively. Of the 29 root QTLs identified, 11 were found to co-localize on 6D, with four of these achieving highly significant logarithm of odds scores (>20). These results suggest the presence of a major-effect gene regulating seedling root vigour/growth on chromosome 6D

OpenSimRoot: widening the scope and application of root architectural models

Research Conducted and Rationale: OpenSimRoot is an open sourced, functional- structural plant model and mathematical description of root growth and function. We describe OpenSimRoot and its functionality to broaden the benefits of root modeling to the plant science community. Description: OpenSimRoot is an extended version of SimRoot, established to simulate root system architecture, nutrient acquisition, and plant growth. OpenSimRoot has a plugin, modular infrastructure, coupling single plant and crop stands to soil nutrient, and water transport models. It estimates the value of root traits for water and nutrient acquisition in environments and plant species. Key results and unique features: The flexible OpenSimRoot design allows upscaling from root anatomy to plant community to estimate 1) resource costs of developmental and anatomical traits, 2) trait synergisms, 3) (inter species) root competition. OpenSimRoot can model 3D images from MRI and X-ray CT of roots in soil. New modules include: 1) soil water dependent water uptake and xylem flow, 2) tiller formation, 3) evapotranspiration, 4) simultaneous simulation of mobile solutes, 5) mesh refinement, and 6) root growth plasticity. Conclusion: OpenSimRoot integrates plant phenotypic data with environmental metadata to support experimental designs and gain mechanistic understanding at system scales

Uncovering the hidden half of plants using new advances in root phenotyping

Major increases in crop yield are required to keep pace with population growth and climate change. Improvements to the architecture of crop roots promise to deliver increases in water and nutrient use efficiency but profiling the root phenome (i.e., its structure and function) represents a major bottleneck. We describe how advances in imaging and sensor technologies are making root phenomic studies possible. However, methodological advances in acquisition, handling and processing of the resulting ‘big-data’ is becoming increasingly important. Advances in automated image analysis approaches such as Deep Learning promise to transform the root phenotyping landscape. Collectively, these innovations are helping drive the selection of the next-generation of crops to deliver real world impact for ongoing global food security efforts