2020-2021 field seasons of Maize GxE project within the Genomes to Fields Initiative.

OBJECTIVES: This release note describes the Maize GxE project datasets within the Genomes to Fields (G2F) Initiative. The Maize GxE project aims to understand genotype by environment (GxE) interactions and use the information collected to improve resource allocation efficiency and increase genotype predictability and stability, particularly in scenarios of variable environmental patterns. Hybrids and inbreds are evaluated across multiple environments and phenotypic, genotypic, environmental, and metadata information are made publicly available. DATA DESCRIPTION: The datasets include phenotypic data of the hybrids and inbreds evaluated in 30 locations across the US and one location in Germany in 2020 and 2021, soil and climatic measurements and metadata information for all environments (combination of year and location), ReadMe, and description files for each data type. A set of common hybrids is present in each environment to connect with previous evaluations. Each environment had a collaborator responsible for collecting and submitting the data, the GxE coordination team combined all the collected information and removed obvious erroneous data. Collaborators received the combined data to use, verify and declare that the data generated in their own environments was accurate. Combined data is released to the public with minimal filtering to maintain fidelity to the original data.

Bearded or smooth? Awns improve yield when wheat experiences heat stress during grain fill in the southeastern United States.

The presence or absence of awns-whether wheat heads are 'bearded' or 'smooth' - is the most visible phenotype distinguishing wheat cultivars. Previous studies suggest that awns may improve yields in heat or water-stressed environments, but the exact contribution of awns to yield differences remains unclear. Here we leverage historical phenotypic, genotypic, and climate data for wheat (Triticum aestivum) to estimate the yield effects of awns under different environmental conditions over a 12-year period in the southeastern USA. Lines were classified as awned or awnless based on sequence data, and observed heading dates were used to associate grain fill periods of each line in each environment with climatic data and grain yield. In most environments, awn suppression was associated with higher yields, but awns were associated with better performance in heat-stressed environments more common at southern locations. Wheat breeders in environments where awns are only beneficial in some years may consider selection for awned lines to reduce year-to-year yield variability, and with an eye towards future climates.

Genomes to Fields 2022 Maize genotype by Environment Prediction Competition.

OBJECTIVES: The Genomes to Fields (G2F) 2022 Maize Genotype by Environment (GxE) Prediction Competition aimed to develop models for predicting grain yield for the 2022 Maize GxE project field trials, leveraging the datasets previously generated by this project and other publicly available data. DATA DESCRIPTION: This resource used data from the Maize GxE project within the G2F Initiative [1]. The dataset included phenotypic and genotypic data of the hybrids evaluated in 45 locations from 2014 to 2022. Also, soil, weather, environmental covariates data and metadata information for all environments (combination of year and location). Competitors also had access to ReadMe files which described all the files provided. The Maize GxE is a collaborative project and all the data generated becomes publicly available [2]. The dataset used in the 2022 Prediction Competition was curated and lightly filtered for quality and to ensure naming uniformity across years.

Navigating premature rupture of the expandable sheath delivery system during elective transfemoral transcatheter aortic valve implantation: a case report.

Background: Transcatheter aortic valve implantation (TAVI) has revolutionised the treatment of severe aortic stenosis, but is still associated with a risk of major vascular complication. Case summary: An elective transfemoral TAVI was undertaken for severe aortic stenosis. During delivery of a 26mm Sapien S3 valve, the delivery system sheath (eSheath) split prematurely. The valve could not be advanced, and lay parallel to the sheath body. Following advice, an attempt was made to remove the system en bloc but this was unsuccessful. After careful consideration of alternative options, the valve was advanced with reasonable force and the case was completed successfully. The femoral artery was closed with parclose sutures without major vascular complication. Discussion: Early recognition of a split sheath with effective communication between lab team members and the manufacturer allowed us to successfully navigate this case, with a good outcome for our patient.

Enhancing adaptation of tropical maize to temperate environments using genomic selection.

Tropical maize can be used to diversify the genetic base of temperate germplasm and help create climate-adapted cultivars. However, tropical maize is unadapted to temperate environments, in which sensitivities to long photoperiods and cooler temperatures result in severely delayed flowering times, developmental defects, and little to no yield. Overcoming this maladaptive syndrome can require a decade of phenotypic selection in a targeted, temperate environment. To accelerate the incorporation of tropical diversity in temperate breeding pools, we tested if an additional generation of genomic selection can be used in an off-season nursery where phenotypic selection is not very effective. Prediction models were trained using flowering time recorded on random individuals in separate lineages of a heterogenous population grown at two northern U.S. latitudes. Direct phenotypic selection and genomic prediction model training was performed within each target environment and lineage, followed by genomic prediction of random intermated progenies in the off-season nursery. Performance of genomic prediction models was evaluated on self-fertilized progenies of prediction candidates grown in both target locations in the following summer season. Prediction abilities ranged from 0.30 to 0.40 among populations and evaluation environments. Prediction models with varying marker effect distributions or spatial field effects had similar accuracies. Our results suggest that genomic selection in a single off-season generation could increase genetic gains for flowering time by more than 50% compared to direct selection in summer seasons only, reducing the time required to change the population mean to an acceptably adapted flowering time by about one-third to one-half.

2018-2019 field seasons of the Maize Genomes to Fields (G2F) G x E project.

OBJECTIVES: This report provides information about the public release of the 2018-2019 Maize G X E project of the Genomes to Fields (G2F) Initiative datasets. G2F is an umbrella initiative that evaluates maize hybrids and inbred lines across multiple environments and makes available phenotypic, genotypic, environmental, and metadata information. The initiative understands the necessity to characterize and deploy public sources of genetic diversity to face the challenges for more sustainable agriculture in the context of variable environmental conditions. DATA DESCRIPTION: Datasets include phenotypic, climatic, and soil measurements, metadata information, and inbred genotypic information for each combination of location and year. Collaborators in the G2F initiative collected data for each location and year; members of the group responsible for coordination and data processing combined all the collected information and removed obvious erroneous data. The collaborators received the data before the DOI release to verify and declare that the data generated in their own locations was accurate. ReadMe and description files are available for each dataset. Previous years of evaluation are already publicly available, with common hybrids present to connect across all locations and years evaluated since this project's inception.

ERCP for bile duct stones across a national service, demonstrating a high requirement for repeat procedures.

Background and study aims Bile duct stones (BDS) represent approximately 50 % of the requirement for endoscopic retrograde cholangiopancreatography (ERCP) within most services. Significant variation in outcome rates for BDS clearance at ERCP has been reported, and endoscopy societies have set standards for expected clearance rates. The aim of this study was to analyze procedure outcomes across a national service. Patients and methods Using verified hospital episode statistics (HES) data for the National Health Service (NHS) in England, we analyzed all patients having first ERCPs for BDS from 2015 to 2017, and followed these patients for at least 2 years. Results In total 37,468 patients underwent a first ERCP for BDS, with 69.8 % undergoing only one procedure. This figure of less than 70 % of BDS cleared at first ERCP is below the Key Performance Indicators as set by the British Society of Gastroenterology (> 75 %) and the European Society of Gastrointestinal Endoscopy (> 90 %). Of 55,556 ERCPs done for BDS, 52.9 % were repeat procedures, with 11,322 patients needing multiple procedures. For hospitals performing significant numbers of ERCPs (more than 600 for BDS during the study period) patients undergoing repeat ERCPs for BDS ranged from 9 % to 50 %. Conclusions In this nationwide study, the performance at clearing BDS at first ERCP was suboptimal, with high numbers of repeat procedures required. This may have a negative impact on both patient outcomes and experience, and increase pressure on endoscopy services. Apparent variation of outcome between acute hospital care providers requires further analysis.

Characterization of a Host-Specific Toxic Activity Produced by Bipolaris cookei, Causal Agent of Target Leaf Spot of Sorghum.

Target leaf spot (TLS) of sorghum, caused by the necrotrophic fungus Bipolaris cookei, can cause severe yield loss in many parts of the world. We grew B. cookei in liquid culture and observed that the resulting culture filtrate (CF) was differentially toxic when infiltrated into the leaves of a population of 288 diverse sorghum lines. In this population, we found a significant correlation between high CF sensitivity and susceptibility to TLS. This suggests that the toxin produced in culture may play a role in the pathogenicity of B. cookei in the field. We demonstrated that the toxic activity is light sensitive and, surprisingly, insensitive to pronase, suggesting that it is not proteinaceous. We identified the two sorghum genetic loci most associated with the response to CF in this population. Screening seedlings with B. cookei CF could be a useful approach for prescreening germplasm for TLS resistance.

Environment-specific selection alters flowering-time plasticity and results in pervasive pleiotropic responses in maize.

Crop genetic diversity for climate adaptations is globally partitioned. We performed experimental evolution in maize to understand the response to selection and how plant germplasm can be moved across geographical zones. Initialized with a common population of tropical origin, artificial selection on flowering time was performed for two generations at eight field sites spanning 25° latitude, a 2800 km transect. We then jointly tested all selection lineages across the original sites of selection, for the target trait and 23 other traits. Modeling intergenerational shifts in a physiological reaction norm revealed separate components for flowering-time plasticity. Generalized and local modes of selection altered the plasticity of each lineage, leading to a latitudinal pattern in the responses to selection that were strongly driven by photoperiod. This transformation led to widespread changes in developmental, architectural, and yield traits, expressed collectively in an environment-dependent manner. Furthermore, selection for flowering time alone alleviated a maladaptive syndrome and improved yields for tropical maize in the temperate zone. Our findings show how phenotypic selection can rapidly shift the flowering phenology and plasticity of maize. They also demonstrate that selecting crops to local conditions can accelerate adaptation to climate change.

MRI Evaluation of Gene Therapy in the Canine Model of Duchenne Muscular Dystrophy.

Magnetic resonance imaging (MRI) is a well-established and widely used technique to characterize and quantify skeletal and cardiac muscle changes in Duchenne muscular dystrophy (DMD). Recently, MRI has been explored to study disease progression and response to gene therapy in the canine DMD model. Using traditional sequences, delayed gadolinium enhancement, novel sequences, and spectroscopy, investigators have begun to (i) establish the baseline MRI characteristics of the muscles in normal and affected dogs and (ii) evaluate gene therapy outcomes in treated dogs. As a noninvasive assay, MRI offers an excellent opportunity to study longitudinal muscle changes in long-term gene therapy studies in the canine model. In this chapter, we outline the MRI method used to study DMD in the canine model.

Genomic prediction for the Germplasm Enhancement of Maize project.

The Germplasm Enhancement of Maize (GEM) project was initiated in 1993 as a cooperative effort of public- and private-sector maize (Zea mays L.) breeders to enhance the genetic diversity of the U.S. maize crop. The GEM project selects progeny lines with high topcross yield potential from crosses between elite temperate lines and exotic parents. The GEM project has released hundreds of useful breeding lines based on phenotypic selection within selfing generations and multienvironment yield evaluations of GEM line topcrosses to elite adapted testers. Developing genomic selection (GS) models for the GEM project may contribute to increases in the rate of genetic gain. Here we evaluated the prediction ability of GS models trained on 6 yr of topcross evaluations from the two GEM programs in Raleigh, NC, and Ames, IA, documenting prediction abilities ranging from 0.36 to 0.75 for grain yield and from 0.78 to 0.96 for grain moisture when models were cross-validated within program and heterotic group. Predicted genetic gain from GS ranged from 0.95 to 2.58 times the gain from phenotypic selection. Prediction ability across program and heterotic group was generally poorer than within groups. Based on observed genomic relationships between GEM breeding lines and their tropical ancestors, GS for either yield or moisture would reduce recovery of exotic germplasm only slightly. Using GS models trained within program, the GEM programs should be able to more effectively deliver on its mission to broaden the genetic base of U.S. germplasm.

Increasing age does not influence hip-specific functional outcome or health-related quality of life following total hip arthroplasty : a five-year prospective cohort study.

AIMS: The primary aim of our study was to assess the influence of age on hip-specific outcome following total hip arthroplasty (THA). Secondary aims were to assess health-related quality of life (HRQoL) and level of activity according to age. METHODS: A prospective cohort study was conducted. All patients were fitted with an Exeter stem with a 32 mm head on highly cross-linked polyethylene (X3RimFit) cemented acetabulum. Patients were recruited into three age groups: < 65 years, 65 to 74 years, and ≥ 75 years, and assessed preoperatively and at three, 12, 24, and 60 months postoperatively. Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Harris Hip Score (HHS), and Hip disability and Osteoarthritis Outcome Score (HOOS), were used to assess hip-specific outcome. EuroQol five-dimension five-level questionnaire (EQ-5D-5L) and 36-Item Short Form Survey (SF-36) scores were used to assess HRQoL. The Lower Extremity Activity Scale (LEAS) and Timed Up and Go (TUG) were used to assess level of activity. RESULTS: There were no significant (p > 0.05) differences in the WOMAC scores, HSS, HOOS, or EQ-5D-5L at any postoperative timepoint between the age groups. Patients aged ≥ 75 years had significantly lower physical function (p ≤ 0.010) and physical role (p ≤ 0.047) SF-36 scores at 12, 24, and 60 months, but were equal to that expect of an age-matched population. No differences according to age were observed for the other six domains of the SF-36 (p > 0.060). The ≥ 75 years group had a lower LEAS (p < 0.001) and longer TUG test times (p ≤ 0.032) compared to the < 65 years group, but older age groups had significant (p < 0.001) improvement relative to their preoperative baseline measures. CONCLUSION: Age did not influence postoperative hip-specific outcome or HRQoL (according to the EQ-5D) following THA. Despite a significant improvement, older patients had lower postoperative activity levels compared to younger patients, but this may be reflective of the overall physical effect of ageing.Cite this article: Bone Jt Open 2022;3(9):692-700.

Empirical comparison of genomic and phenotypic selection for resistance to Fusarium ear rot and fumonisin contamination in maize.

KEY MESSAGE: GS and PS performed similarly in improving resistance to FER and FUM content. With cheaper and faster genotyping methods, GS has the potential to be more efficient than PS. Fusarium verticillioides is a common maize (Zea mays L.) pathogen that causes Fusarium ear rot (FER) and produces the mycotoxin fumonisin (FUM). This study empirically compared phenotypic selection (PS) and genomic selection (GS) for improving FER and FUM resistance. Three intermating generations of recurrent GS were conducted in the same time frame and from a common base population as two generations of recurrent PS. Lines sampled from each PS and GS cycle were evaluated in three North Carolina environments in 2020. We observed similar cumulative responses to GS and PS, representing decreases of about 50% of mean FER and FUM compared to the base population. The first cycle of GS was more effective than later cycles. PS and GS both achieved about 70% of predicted total gain from selection for FER, but only about 26% of predicted gains for FUM, suggesting that heritability for FUM was overestimated. We observed a 20% decrease in genetic marker variation from PS and 30% decrease from GS. Our greatest challenge was our inability to quickly obtain dense and consistent set of marker genotypes across generations of GS. Practical implementation of GS in individual small-scale breeding programs will require cheaper and faster genotyping methods, and such technological advances will present opportunities to significantly optimize selection and mating schemes for future GS efforts beyond what we were able to achieve in this study.

Outlook for Implementation of Genomics-Based Selection in Public Cotton Breeding Programs.

Researchers have used quantitative genetics to map cotton fiber quality and agronomic performance loci, but many alleles may be population or environment-specific, limiting their usefulness in a pedigree selection, inbreeding-based system. Here, we utilized genotypic and phenotypic data on a panel of 80 important historical Upland cotton (Gossypium hirsutum L.) lines to investigate the potential for genomics-based selection within a cotton breeding program's relatively closed gene pool. We performed a genome-wide association study (GWAS) to identify alleles correlated to 20 fiber quality, seed composition, and yield traits and looked for a consistent detection of GWAS hits across 14 individual field trials. We also explored the potential for genomic prediction to capture genotypic variation for these quantitative traits and tested the incorporation of GWAS hits into the prediction model. Overall, we found that genomic selection programs for fiber quality can begin immediately, and the prediction ability for most other traits is lower but commensurate with heritability. Stably detected GWAS hits can improve prediction accuracy, although a significance threshold must be carefully chosen to include a marker as a fixed effect. We place these results in the context of modern public cotton line-breeding and highlight the need for a community-based approach to amass the data and expertise necessary to launch US public-sector cotton breeders into the genomics-based selection era.

Life-threatening complications for diabetic patients taking SGLT2 inhibitors when undergoing surgery: A poorly recognised problem?

A knowledge of perioperative problems and complications is an important requirement for surgeons. Diabetic patients are a particular group of patients that are specifically at risk of problems. These risks are not only related to the underlying pathophysiological process associated with the disease, but can also occur secondarily to medications used to manage the condition and require careful monitoring, and is of increased importance in the perioperative period. Although a number of medications have historically been used to manage diabetes, a relatively novel group of diabetic medications 'SGLT2 inhibitors' are now being used and have been shown to have many positive attributes, when considering the sequalae of diabetes. However, they have also been associated with significant perioperative problems, which are a consequence of euglycaemic ketoacidosis, a potentially life-threatening condition. Given the significant complications associated with these medications, it is important that practitioners should have an awareness of the problems related to their use. In addition, messages contained in safety releases pertaining to SGLT2 inhibitor use and their risks in patients undergoing surgery, may have been weakened due the timing of their publication in March 2020, during the first UK national lockdown amidst the Coronavirus pandemic.

Environment-specific genomic prediction ability in maize using environmental covariates depends on environmental similarity to training data.

Technology advances have made possible the collection of a wealth of genomic, environmental, and phenotypic data for use in plant breeding. Incorporation of environmental data into environment-specific genomic prediction is hindered in part because of inherently high data dimensionality. Computationally efficient approaches to combining genomic and environmental information may facilitate extension of genomic prediction models to new environments and germplasm, and better understanding of genotype-by-environment (G × E) interactions. Using genomic, yield trial, and environmental data on 1,918 unique hybrids evaluated in 59 environments from the maize Genomes to Fields project, we determined that a set of 10,153 SNP dominance coefficients and a 5-day temporal window size for summarizing environmental variables were optimal for genomic prediction using only genetic and environmental main effects. Adding marker-by-environment variable interactions required dimension reduction, and we found that reducing dimensionality of the genetic data while keeping the full set of environmental covariates was best for environment-specific genomic prediction of grain yield, leading to an increase in prediction ability of 2.7% to achieve a prediction ability of 80% across environments when data were masked at random. We then measured how prediction ability within environments was affected under stratified training-testing sets to approximate scenarios commonly encountered by plant breeders, finding that incorporation of marker-by-environment effects improved prediction ability in cases where training and test sets shared environments, but did not improve prediction in new untested environments. The environmental similarity between training and testing sets had a greater impact on the efficacy of prediction than genetic similarity between training and test sets.

Prediction ability of genome-wide markers in Pinus taeda L. within and between population is affected by relatedness to the training population and trait genetic architecture.

Genomic prediction has the potential to significantly increase the rate of genetic gain in tree breeding programs. In this study, a clonally replicated population (n = 2063) was used to train a genomic prediction model. The model was validated both within the training population and in a separate population (n = 451). The prediction abilities from random (20% vs 80%) cross validation within the training population were 0.56 and 0.78 for height and stem form, respectively. Removal of all full-sib relatives within the training population resulted in ∼50% reduction in their genomic prediction ability for both traits. The average prediction ability for all 451 individual trees was 0.29 for height and 0.57 for stem form. The degree of genetic linkage (full-sib family, half sib family, unrelated) between the training and validation sets had a strong impact on prediction ability for stem form but not for height. A dominant dwarfing allele, the first to be reported in a conifer species, was discovered via genome-wide association studies on linkage Group 5 that conferred a 0.33-m mean height reduction. However, the QTL was family specific. The rapid decay of linkage disequilibrium, large genome size, and inconsistencies in marker-QTL linkage phase suggest that large, diverse training populations are needed for genomic selection in Pinus taeda L.

Identifying trends in ultrafine particle infiltration and carbon dioxide ventilation in 92 vehicle models.

There has been ongoing research aimed at reducing pollution concentrations in vehicles due to the high exposure which occurs in this setting. These studies have found using recirculate (RC) settings substantially reduces in-cabin traffic-related pollution concentrations but possibly leads to an adverse accumulation of carbon dioxide (CO2) from driver respiration. The aim of this study was to highlight how vehicle models and ventilation settings affect in-cabin concentrations to ultrafine particles (UFP) and CO2 in real-world conditions. We assessed the ability of different vehicles to balance reductions in UFP against the build-up of in-cabin CO2 concentrations by measuring these pollutants concurrently both inside and outside the vehicle to derive an in/out ratio. When ventilation settings were set to RC, UFP concentrations inside the vehicles (median: 3205 pt./cm3) were 86% lower compared to outside air (OA) (23,496 pt./cm3) across a 30-min real-world driving route. However, CO2 concentrations demonstrated a rapid linear increase under RC settings, at times exceeding 2500 ppm. These concentrations have previously been associated with decreased cognitive performance. Our study did not find an effect of gasoline fuelled vehicles affecting in-cabin UFP levels compared to hybrid or electric vehicles, suggesting that self-pollution was not an issue. We also found that certain vehicle models were better at reducing both in-cabin UFP and CO2 concentrations. The results suggest that under RC settings in/out CO2 ratios are largely determined by the leakiness of the vehicle cabin, whereas in/out UFP ratios are primarily determined by the efficacy of the in-built air filter in the vehicles ventilation system.