Monitoring drought responses of barley genotypes with semi-robotic phenotyping platform and association analysis between recorded traits and allelic variants of some stress genes

Genetic improvement of complex traits such as drought adaptation can be advanced by the combination of genomic and phenomic approaches. Semi-robotic phenotyping platform was used for computer-controlled watering, digital and thermal imaging of barley plants grown in greenhouse. The tested barley variants showed 0–76% reduction in green pixel-based shoot surface area in soil with 20% water content, compared to well-watered plants grown in soil with 60% water content. The barley HvA1 gene encoding the group 3 LEA (Late Embryogenesis Abundant) protein exhibited four (A–D) haplotypes as identified by the EcoTILLING and subsequent DNA sequencing. The green pixel mean value of genotypes with haplotype D was higher than the mean value of the remaining haplotypes, indicating a pivotal role of haplotype D in optimizing the green biomass production under drought condition. In water limitation, the canopy temperature of a highly sensitive genotype was 18.0°C, as opposed to 16.9°C of leaves from a tolerant genotype as measured by thermal imaging. Drought-induced changes in leaf temperature showed moderate correlation with the water use efficiency (r2 = 0.431). The haplotype/trait association analysis based on the t-test has revealed a positive effect of a haplotype B (SNPs:GCCCCTGC) in a gene encoding the barley fungal pathogen induced mRNA for pathogen-related protein (HvPPRPX), on harvest index, thousand grain weight, water use efficiency and grain yield. The presented pilot study established a basic methodology for the integrated use of phenotyping and haplotyping data in characterization of genotype-dependent drought responses in barley

A Preliminary Study of Applying Lean Six Sigma Methods to Machine Tool Measurement

Many manufacturers aim to increase their levels of high-quality production in order to improve their market competitiveness. Continuous improvement of maintenance strategies is a key factor to be capable of delivering high quality products and services on-time with minimal operating costs. However, the cost of maintaining quality is often perceived as a non-added-value task. Improving the efficiency and effectiveness of the measurement procedures necessary to guarantee accuracy of production is a more complex task than many other maintenance functions and so deserves particular analysis. This paper investigates the feasibility of producing a concise yet effective framework that will provide a preliminary approach for integrating Lean and Six Sigma philosophies to the specific goal of reducing unnecessary downtime on manufacturing machines while maintaining its ability to machine to the required tolerance. The purpose of this study is to show how a Six Sigma infrastructure is used to investigate the root causes of complication occurring during the machine tool measurement. This work recognises issues of the uncertainty of data, and the measurement procedures in parallel with the main tools of Six Sigma’s Define-Measure-Analyse-Improve-Control (DMAIC). The significance of this work is that machine tool accuracy is critical for high value manufacturing. Over-measuring the machine to ensure accuracy potentially reduces production volume. However, not measuring them or ignoring accuracy aspects possibly lead to production waste. This piece of work aims to present a lean guidance to lessen measurement uncertainties and optimise the machine tool benchmarking procedures, while adopting the DMAIC strategy to reduce unnecessary downtime

The digital data processing concepts of the LOFT mission

The Large Observatory for X-ray Timing (LOFT) is one of the five mission candidates that were considered by ESA for an M3 mission (with a launch opportunity in 2022 - 2024). LOFT features two instruments: the Large Area Detector (LAD) and the Wide Field Monitor (WFM). The LAD is a 10 m 2 -class instrument with approximately 15 times the collecting area of the largest timing mission so far (RXTE) for the first time combined with CCD-class spectral resolution. The WFM will continuously monitor the sky and recognise changes in source states, detect transient and bursting phenomena and will allow the mission to respond to this. Observing the brightest X-ray sources with the effective area of the LAD leads to enormous data rates that need to be processed on several levels, filtered and compressed in real-time already on board. The WFM data processing on the other hand puts rather low constraints on the data rate but requires algorithms to find the photon interaction location on the detector and then to deconvolve the detector image in order to obtain the sky coordinates of observed transient sources. In the following, we want to give an overview of the data handling concepts that were developed during the study phase.Comment: Proc. SPIE 9144, Space Telescopes and Instrumentation 2014: Ultraviolet to Gamma Ray, 91446