EVALUATION OF GENOTYPE BY ENVIRONMENT INTERACTIONS FROM UNREPLICATED MULTI-ENVIRONMENTAL TRIALS OF HYBRID MAIZE

Diverse soils and varying weather conditions not only affect overall performance of hybrid maize in multi-environment field studies, but can also cause strong genotype by environment interactions (GEI). Modern maize breeding experiments utilize multilocation trials with augmented field designs to evaluate the performance of unreplicated test hybrids. Augmented designs are resource efficient; however, these designs do not efficiently quantify or test GEI variation in the test hybrids. New methods are being developed that use random regression models to incorporate multiple environmental effects into GEI models to increase their accuracy and predictive ability. Incorporation of varying weather and soil physical variables into these models can be used to determine the potential causal factors of GEI. The identification of causal factors can assist in developing clusters of locations where homogenous performance of hybrids can be expected. The utility of the proposed approach is demonstrated with a real data analysis

Row selection in remote sensing from four-row plots of maize and sorghum based on repeatability and predictive modeling

Remote sensing enables the rapid assessment of many traits that provide valuable information to plant breeders throughout the growing season to improve genetic gain. These traits are often extracted from remote sensing data on a row segment (rows within a plot) basis enabling the quantitative assessment of any row-wise subset of plants in a plot, rather than a few individual representative plants, as is commonly done in field-based phenotyping. Nevertheless, which rows to include in analysis is still a matter of debate. The objective of this experiment was to evaluate row selection and plot trimming in field trials conducted using four-row plots with remote sensing traits extracted from RGB (red-green-blue), LiDAR (light detection and ranging), and VNIR (visible near infrared) hyperspectral data. Uncrewed aerial vehicle flights were conducted throughout the growing seasons of 2018 to 2021 with data collected on three years of a sorghum experiment and two years of a maize experiment. Traits were extracted from each plot based on all four row segments (RS) (RS1234), inner rows (RS23), outer rows (RS14), and individual rows (RS1, RS2, RS3, and RS4). Plot end trimming of 40 cm was an additional factor tested. Repeatability and predictive modeling of end-season yield were used to evaluate performance of these methodologies. Plot trimming was never shown to result in significantly different outcomes from non-trimmed plots. Significant differences were often observed based on differences in row selection. Plots with more row segments were often favorable for increasing repeatability, and excluding outer rows improved predictive modeling. These results support long-standing principles of experimental design in agronomy and should be considered in breeding programs that incorporate remote sensing

Mapping of QTL for Resistance against the Crucifer Specialist Herbivore Pieris brassicae in a New Arabidopsis Inbred Line Population, Da(1)-12×Ei-2

In Arabidopsis thaliana and other crucifers, the glucosinolate-myrosinase system contributes to resistance against herbivory by generalist insects. As yet, it is unclear how crucifers defend themselves against crucifer-specialist insect herbivores.We analyzed natural variation for resistance against two crucifer specialist lepidopteran herbivores, Pieris brassicae and Plutella xylostella, among Arabidopsis thaliana accessions and in a new Arabidopsis recombinant inbred line (RIL) population generated from the parental accessions Da(1)-12 and Ei-2. This RIL population consists of 201 individual F(8) lines genotyped with 84 PCR-based markers. We identified six QTL for resistance against Pieris herbivory, but found only one weak QTL for Plutella resistance. To elucidate potential factors causing these resistance QTL, we investigated leaf hair (trichome) density, glucosinolates and myrosinase activity, traits known to influence herbivory by generalist insects. We identified several previously unknown QTL for these traits, some of which display a complex pattern of epistatic interactions.Although some trichome, glucosinolate or myrosinase QTL co-localize with Pieris QTL, none of these traits explained the resistance QTL convincingly, indicating that resistance against specialist insect herbivores is influenced by other traits than resistance against generalists

Heat-tolerant maize for rainfed hot, dry environments in the lowland tropics: from breeding to improved seed delivery

Climate change-induced heat stress combines two challenges: high day- and nighttime temperatures, and physiological water deficit due to demand-side drought caused by increase in vapor-pressure deficit. It is one of the major factors in low productivity of maize in rainfed stress-prone environments in South Asia, affecting a large population of smallholder farmers who depend on maize for their sustenance and livelihoods. The International Maize and Wheat Improvement Center (CIMMYT) maize program in Asia, in partnership with public-sector maize research institutes and private-sector seed companies in South Asian countries, is implementing an intensive initiative for developing and deploying heat-tolerant maize that combines high yield potential with resilience to heat and drought stresses. With the integration of novel breeding tools and methods, including genomics-assisted breeding, doubled haploidy, field-based precision phenotyping, and trait-based selection, new maize germplasm with increased tolerance to heat stress is being developed for the South Asian tropics. Over a decade of concerted effort has resulted in the successful development and release of 20 high-yielding heat-tolerant maize hybrids in CIMMYT genetic backgrounds. Via public–private partnerships, eight hybrids are presently being deployed on over 50,000 ha in South Asian countries, including Bangladesh, Bhutan, India, Nepal, and Pakistan

Natural Variation in Partial Resistance to Pseudomonas syringae Is Controlled by Two Major QTLs in Arabidopsis thaliana

BACKGROUND: Low-level, partial resistance is pre-eminent in natural populations, however, the mechanisms underlying this form of resistance are still poorly understood. METHODOLOGY/PRINCIPAL FINDINGS: In the present study, we used the model pathosystem Pseudomonas syringae pv. tomato DC3000 (Pst) - Arabidopsis thaliana to study the genetic basis of this form of resistance. Phenotypic analysis of a set of Arabidopsis accessions, based on evaluation of in planta pathogen growth revealed extensive quantitative variation for partial resistance to Pst. It allowed choosing a recombinant inbred line (RIL) population derived from a cross between the accessions Bayreuth and Shahdara for quantitative genetic analysis. Experiments performed under two different environmental conditions led to the detection of two major and two minor quantitative trait loci (QTLs) governing partial resistance to Pst and called PRP-Ps1 to PRP-Ps4. The two major QTLs, PRP-Ps1 and PRP-Ps2, were confirmed in near isogenic lines (NILs), following the heterogeneous inbred families (HIFs) strategy. Analysis of marker gene expression using these HIFs indicated a negative correlation between the induced amount of transcripts of SA-dependent genes PR1, ICS and PR5, and the in planta bacterial growth in the HIF segregating at PRP-Ps2 locus, suggesting an implication of PRP-Ps2 in the activation of SA dependent responses. CONCLUSIONS/SIGNIFICANCE: These results show that variation in partial resistance to Pst in Arabidopsis is governed by relatively few loci, and the validation of two major loci opens the way for their fine mapping and their cloning, which will improve our understanding of the molecular mechanisms underlying partial resistance

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

Correction to: Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

The original version of this article unfortunately contained a mistake

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

Multi-Temporal Predictive Modelling of Sorghum Biomass Using UAV-Based Hyperspectral and LiDAR Data

High-throughput phenotyping using high spatial, spectral, and temporal resolution remote sensing (RS) data has become a critical part of the plant breeding chain focused on reducing the time and cost of the selection process for the “best” genotypes with respect to the trait(s) of interest. In this paper, the potential of accurate and reliable sorghum biomass prediction using visible and near infrared (VNIR) and short-wave infrared (SWIR) hyperspectral data as well as light detection and ranging (LiDAR) data acquired by sensors mounted on UAV platforms is investigated. Predictive models are developed using classical regression-based machine learning methods for nine experiments conducted during the 2017 and 2018 growing seasons at the Agronomy Center for Research and Education (ACRE) at Purdue University, Indiana, USA. The impact of the regression method, data source, timing of RS and field-based biomass reference data acquisition, and the number of samples on the prediction results are investigated. R2 values for end-of-season biomass ranged from 0.64 to 0.89 for different experiments when features from all the data sources were included. Geometry-based features derived from the LiDAR point cloud to characterize plant structure and chemistry-based features extracted from hyperspectral data provided the most accurate predictions. Evaluation of the impact of the time of data acquisition during the growing season on the prediction results indicated that although the most accurate and reliable predictions of final biomass were achieved using remotely sensed data from mid-season to end-of-season, predictions in mid-season provided adequate results to differentiate between promising varieties for selection. The analysis of variance (ANOVA) of the accuracies of the predictive models showed that both the data source and regression method are important factors for a reliable prediction; however, the data source was more important with 69% significance, versus 28% significance for the regression method

Association mapping for grain quality in a diverse sorghum collection

Citation: Sukumaran, Sivakumar, Wenwen Xiang, Scott R. Bean, Jeffrey F. Pedersen, Stephen Kresovich, Mitchell R. Tuinstra, Tesfaye T. Tesso, Martha T. Hamblin, and Jianming Yu. “Association Mapping for Grain Quality in a Diverse Sorghum Collection.” The Plant Genome 5, no. 3 (2012): 126–35. https://doi.org/10.3835/plantgenome2012.07.0016.Knowledge of the genetic bases of grain quality traits will complement plant breeding efforts to improve the end-use value of sorghum [Sorghum bicolor (L.) Moench]. Candidate gene association mapping was used on a diverse panel of 300 sorghum accessions to assess marker–trait associations for 10 grain quality traits measured using the single kernel characterization system (SKCS) and near-infrared reflectance spectroscopy (NIRS). The analysis of the accessions through 1290 genomewide single nucleotide polymorphisms (SNPs) separated the panel into five subpopulations that corresponded to three major sorghum races (durra, kafir, and caudatum), one intermediate race (guinea-caudatum), and one working group (zerazera-caudatum). These subpopulations differed in kernel hardness, acid detergent fiber, and total digestible nutrients. After model testing, association analysis between 333 SNPs in candidate genes and/or loci and grain quality traits resulted in eight significant marker–trait associations. A SNP in starch synthase IIa (SSIIa) gene was associated with kernel hardness (KH) with a likelihood ratio-based R[superscript 2] (R[subscript LR][superscript 2]) value of 0.08, a SNP in starch synthase (SSIIb) gene was associated with starch content with an R[subscript LR][superscript 2] value of 0.10, and a SNP in loci pSB1120 was associated with starch content with an R[subscript LR][superscript 2] value of 0.09