Harnessing translational research in wheat for climate resilience.

Despite being the world's most widely grown crop, research investments in wheat (Triticum aestivum and Triticum durum) fall behind those in other staple crops. Current yield gains will not meet 2050 needs, and climate stresses compound this challenge. However, there is good evidence that heat and drought resilience can be boosted through translating promising ideas into novel breeding technologies using powerful new tools in genetics and remote sensing, for example. Such technologies can also be applied to identify climate resilience traits from among the vast and largely untapped reserve of wheat genetic resources in collections worldwide. This review describes multi-pronged research opportunities at the focus of the Heat and Drought Wheat Improvement Consortium (coordinated by CIMMYT), which together create a pipeline to boost heat and drought resilience, specifically: improving crop design targets using big data approaches; developing phenomic tools for field-based screening and research; applying genomic technologies to elucidate the bases of climate resilience traits; and applying these outputs in developing next-generation breeding methods. The global impact of these outputs will be validated through the International Wheat Improvement Network, a global germplasm development and testing system that contributes key productivity traits to approximately half of the global wheat-growing area.

Pay Attention!: Sluggish Multisensory Attentional Shifting as a Core Deficit in Developmental Dyslexia.

The aim of this review is to provide a background on the neurocognitive aspects of the reading process and review neuroscientific studies of individuals with developmental dyslexia, which provide evidence for amodal processing deficits. Hari, Renvall, and Tanskanen (2001) propose amodal sluggish attentional shifting (SAS) as a causal factor for temporal processing deficits in dyslexia. Undergirding this theory is the notion that when dyslexics are faced with rapid sequences of stimuli, their automatic attentional systems fail to disengage efficiently, which leads to difficulty when moving from one item to the next (Lallier et al., ). This results in atypical perception of rapid stimulus sequences. Until recently, the SAS theory, particularly the examination of amodal attentional deficits, was studied solely through the use of behavioural measures (Facoetti et al., ; Facoetti, Lorusso, Cattaneo, Galli, & Molteni, ). This paper examines evidence within the literature that provides a basis for further exploration of amodal SAS as an underlying deficit in developmental dyslexia.

Salinity stress tolerance prediction for biomass-related traits in maize (Zea mays L.) using genome-wide markers.

Maize (Zea mays L.) is the third most important cereal crop after rice (Oryza sativa) and wheat (Triticum aestivum). Salinity stress significantly affects vegetative biomass and grain yield and, therefore, reduces the food and silage productivity of maize. Selecting salt-tolerant genotypes is a cumbersome and time-consuming process that requires meticulous phenotyping. To predict salt tolerance in maize, we estimated breeding values for four biomass-related traits, including shoot length, shoot weight, root length, and root weight under salt-stressed and controlled conditions. A five-fold cross-validation method was used to select the best model among genomic best linear unbiased prediction (GBLUP), ridge-regression BLUP (rrBLUP), extended GBLUP, Bayesian Lasso, Bayesian ridge regression, BayesA, BayesB, and BayesC. Examination of the effect of different marker densities on prediction accuracy revealed that a set of low-density single nucleotide polymorphisms obtained through filtering based on a combination of analysis of variance and linkage disequilibrium provided the best prediction accuracy for all the traits. The average prediction accuracy in cross-validations ranged from 0.46 to 0.77 across the four derived traits. The GBLUP, rrBLUP, and all Bayesian models except BayesB demonstrated comparable levels of prediction accuracy that were superior to the other modeling approaches. These findings provide a roadmap for the deployment and optimization of genomic selection in breeding for salt tolerance in maize.

Managing the COVID-19 Pandemic Using Quality Improvement Principles: A New York City Pediatric Primary Care Experience.

In the setting of COVID-19, pediatric primary care in New York City faced multiple challenges, requiring large-scale practice reorganization. We used quality improvement principles to implement changes to care delivery rapidly. METHODS: Plan-do-study-act cycles were used, based on primary drivers of consolidation, reorganization of in-person and urgent care, telehealth expansion, patient outreach, mental health linkages, team communication, and safety. RESULTS: The average visit volume in pediatrics decreased from 662 per week to 370. Telehealth visits increased from 2 to 140 per week, whereas urgent in-person visits decreased from 350 to 8 per week. Adolescent visits decreased from 57 to 46 per week. Newborn Clinic visits increased from 37 per week to 54. Show rates increased significantly for pediatrics and adolescent (P = 0.003 and P = 0.038, respectively). CONCLUSIONS: Quality improvement methodology allowed for the consolidation of pediatric primary care practices during the first wave of the COVID-19 pandemic, ensuring care for patients while prioritizing safety, evidence-based practices, and available resources.

Application of multi-trait Bayesian decision theory for parental genomic selection.

In all breeding programs, the decision about which individuals to select and intermate to form the next selection cycle is crucial. The improvement of genetic stocks requires considering multiple traits simultaneously, given that economic value and net genetic merits depend on many traits; therefore, with the advance of computational and statistical tools and genomic selection (GS), researchers are focusing on multi-trait selection. Selection of the best individuals is difficult, especially in traits that are antagonistically correlated, where improvement in one trait might imply a reduction in other(s). There are approaches that facilitate multi-trait selection, and recently a Bayesian decision theory (BDT) has been proposed. Parental selection using BDT has the potential to be effective in multi-trait selection given that it summarizes all relevant quantitative genetic concepts such as heritability, response to selection and the structure of dependence between traits (correlation). In this study, we applied BDT to provide a treatment for the complexity of multi-trait parental selection using three multivariate loss functions (LF), Kullback-Leibler (KL), Energy Score, and Multivariate Asymmetric Loss (MALF), to select the best-performing parents for the next breeding cycle in two extensive real wheat data sets. Results show that the high ranking lines in genomic estimated breeding value (GEBV) for certain traits did not always have low values for the posterior expected loss (PEL). For both data sets, the KL LF gave similar importance to all traits including grain yield. In contrast, the Energy Score and MALF gave a better performance in three of four traits that were different than grain yield. The BDT approach should help breeders to decide based not only on the GEBV per se of the parent to be selected, but also on the level of uncertainty according to the Bayesian paradigm.

Hyperspectral Reflectance-Derived Relationship Matrices for Genomic Prediction of Grain Yield in Wheat.

Hyperspectral reflectance phenotyping and genomic selection are two emerging technologies that have the potential to increase plant breeding efficiency by improving prediction accuracy for grain yield. Hyperspectral cameras quantify canopy reflectance across a wide range of wavelengths that are associated with numerous biophysical and biochemical processes in plants. Genomic selection models utilize genome-wide marker or pedigree information to predict the genetic values of breeding lines. In this study, we propose a multi-kernel GBLUP approach to genomic selection that uses genomic marker-, pedigree-, and hyperspectral reflectance-derived relationship matrices to model the genetic main effects and genotype × environment (G × E) interactions across environments within a bread wheat (Triticum aestivum L.) breeding program. We utilized an airplane equipped with a hyperspectral camera to phenotype five differentially managed treatments of the yield trials conducted by the Bread Wheat Improvement Program of the International Maize and Wheat Improvement Center (CIMMYT) at Ciudad Obregón, México over four breeding cycles. We observed that single-kernel models using hyperspectral reflectance-derived relationship matrices performed similarly or superior to marker- and pedigree-based genomic selection models when predicting within and across environments. Multi-kernel models combining marker/pedigree information with hyperspectral reflectance phentoypes had the highest prediction accuracies; however, improvements in accuracy over marker- and pedigree-based models were marginal when correcting for days to heading. Our results demonstrate the potential of using hyperspectral imaging to predict grain yield within a multi-environment context and also support further studies on the integration of hyperspectral reflectance phenotyping into breeding programs.

Management of Infectious Aspects of Atopic Dermatitis in Primary Care: A Resident Survey.

Atopic dermatitis (AD) is the most common skin disease encountered by pediatric primary care providers. To describe the knowledge, attitudes, and practices of primary care residents in the management of infectious aspects of pediatric AD, an anonymous web-based survey was offered to all residents in the pediatric and family medicine departments at 3 New York City hospitals. Eighty residents responded. Most (62%) reported seeing 5 to 14 patients with AD monthly. Twenty-seven percent reported obtaining cultures prior to prescribing oral antibiotics most of the time, while 8% reported doing so before giving topical antibiotics. Most respondents (60%) reported never/rarely recommending dilute bleach baths, and family medicine residents were significantly more likely to report never doing so (67% vs 16%, P < .001). Greater education on the use of cultures to guide treatment and potential benefits of dilute bleach baths is needed, especially given increasing antibiotic resistance.