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Crop yield results from the complex interaction between genotype, management, and environment. While farmers have control over what genotype to plant, and how to manage it, their decisions are often sub-optimal due to climate variability. Sub-seasonal climate predictions embrace the great potential to improve risk analysis and decision-making. However, adequate frameworks integrating future weather uncertainty to predict crop outcomes are lacking. Maize (Zea mays L.) yields are highly sensitive to weather anomalies, and very responsive to plant density (plants m-2), thus this variable could be optimized conditional to the seasonal prospects. The aims of this study were to (i) design a model that describes the yield-to-plant density (herein termed as yield-density) relationship as a function of weather variables, (ii) evaluate the predictive performance and analyze the sources of uncertainty, and (iii) provide probabilistic forecasts for predicting the economic optimum plant density (EOPD). We present a novel approach to enable decision-making in agriculture using sub-seasonal climate predictions and Bayesian modeling. This model provides crop management recommendations by accounting for various sources of uncertainty. A Bayesian hierarchical shrinkage model was fitted to the response of maize yield-density trials performed during the 2010-2019 period across 7 states in the United States, identifying the relative importance of key weather, crop, and soil variables. Tercile forecasts of precipitation and temperature from the International Research Institute (IRI) were used to forecast EOPD before the start of the season. The variables with the greatest influence on the yield-density relationship were weather anomalies, especially months with above-normal temperatures. Improvements on climate forecasting may also improve precision, as the coefficient of determination (R2) increased from 0.26 to 0.32 when weather forecasts were correct. This study may contribute to the development of decision-support tools that can trigger discussions between farmers and consultants about management strategies and their associated risks.

The monkeypox outbreak of 2022 saw the first community-sustained transmission of the monkeypox virus outside of Africa, and rapidly developed into multi-country spread. A common presenting sign of monkeypox infection during this outbreak has been rectal pain due to proctitis. Proctitis with large hypoattenuated anorectal ulcers on CT scan should invoke consideration for monkeypox infection in young homosexual or bisexual men with associated skin eruptions.

In the absence of stress, crop growth depends on the amount of light intercepted by the canopy and the conversion efficiency [radiation use efficiency (RUE)]. This study tested the hypothesis that long-term genetic gain for grain yield was partly due to improved RUE. The hypothesis was tested using 30 elite maize hybrids commercialized in the US corn belt between 1930 and 2017. Crops grown under irrigation showed that pre-flowering crop growth increased at a rate of 0.11 g m–2 year–1, while light interception remained constant. Therefore, RUE increased at a rate of 0.0049 g MJ–1 year–1, translating into an average of 3 g m–2 year–1 of grain yield over 100 years of maize breeding. Considering that the harvest index has not changed for crops grown at optimal density for the hybrid, the cumulative RUE increase over the history of commercial maize breeding in the USA can account for ~32% of the documented yield trend for maize grown in the central US corn belt. The remaining RUE gap between this study and theoretical maximum values suggests that a yield improvement of a similar magnitude could be achieved by further increasing RUE.

Crop adaptation to the mixture of environments that defines the target population of environments is the result from a balanced resource allocation between roots, shoots and reproductive organs. Root growth places a critical role in the determination of this balance. Root growth and function responses to temperature can determine the strength of roots as sinks but also influence the crop’s ability to uptake water and nutrients. Surprisingly, this behavior has not been studied in maize since the middle of the last century, and the genetic determinants are unknown. Low temperatures often recorded in deep soil layers limit root growth and soil exploration and may constitute a bottleneck towards increasing drought tolerance, nitrogen recovery, sequestration of carbon and productivity in maize. High throughput phenotyping (HTP) systems were developed to investigate these responses and to examine genetic variability therein across diverse maize germplasm. Here we show that there is: 1) genetic variation of root growth under low temperature and below 10°C, and 2) genotypic variation in water transport under low temperature. Using simulation, we demonstrate that the measured variation for both traits contribute to drought tolerance and explain important components of yield variation in the US corn-belt. The trait set examined herein and HTP platform developed for its characterization reveal a unique opportunity to remove a major bottleneck for crop improvement, and adaptation to climate change.

Com objetivo de avaliar as emoções e comportamentos dos adultos brasileiros com hemofilia durante o 1º ano da pandemia, a ABRAPHEM realizou uma pesquisa online com 17 perguntas de características demográficas e clínicas, a qual foi respondida por 40 pessoas com hemofilia no período de março a abril de 2021.Sendo 35% de 18 a 30 anos, 50% entre 31 e 50 anos e 15% entre 51 e 70 anos. Destes,34 respondentes possuem hemofilia A e 6 possuem hemofilia B. De toda esta amostra, 87,5% relatou estar em tratamento de profilaxia, enquanto 12,5% trata apenas sob demanda.Apesar da maioria dos respondentes reconhecer que esta pandemia teria repercussões psicológicas, 60% afirmaram não precisar de apoio na área da saúde mental. Para 31% dos pacientes a informação fornecida pelos Centros de Hemofilia (CTH) foi suficiente, enquanto 9% dos respondentes solicitaram apoio e ajuda psicológica.Quanto ao relacionamento com o CTH: 61% relatou que nada mudou, 12,2% relatou melhora no relacionamento, enquanto 12,2% relatou dificuldades no relacionamento em função do medo de possível contágio.Com relação ao gerenciamento do controle de sangramentos e das consultas programadas,53,7% declararam não ter sido afetados pela pandemia, enquanto 24,4% relataram que tanto rotina quanto emergência foram alterados. Para 19,5% apesar da suspensão das consultas, os atendimentos de urgência e planos de tratamento foram oferecidos regularmente.

Plant physiology can offer invaluable insights to accelerate genetic gain. However, translating physiological understanding into breeding decisions has been an ongoing and complex endeavor. Here we demonstrate an approach to leverage physiology and genomics to hasten crop improvement. A half-diallel maize (Zea mays) experiment resulting from crossing 9 elite inbreds was conducted at 17 locations in the USA corn belt and 6 locations at managed stress environments between 2017 and 2019 covering a range of water environments from 377 to 760 mm of evapotranspiration and family mean yields from 542 to 1,874 g m−2. Results from analyses of 35 families and 2,367 hybrids using crop growth models linked to whole-genome prediction (CGM–WGP) demonstrated that CGM–WGP offered a predictive accuracy advantage compared to BayesA for untested genotypes evaluated in untested environments (r = 0.43 versus r = 0.27). In contrast to WGP, CGMs can deal effectively with time-dependent interactions between a physiological process and the environment. To facilitate the selection/identification of traits for modeling yield, an algorithmic approach was introduced. The method was able to identify 4 out of 12 candidate traits known to explain yield variation in maize. The estimation of allelic and physiological values for each genotype using the CGM created in silico phenotypes (e.g. root elongation) and physiological hypotheses that could be tested within the breeding program in an iterative manner. Overall, the approach and results suggest a promising future to fully harness digital technologies, gap analysis, and physiological knowledge to hasten genetic gain by improving predictive skill and definition of breeding goals.

IntroductionCrop plasticity is fundamental to sustainability discussions in production agriculture. Modern corn (Zea mays L.) genetics can compensate yield determinants to a small degree, but plasticity mechanisms have been masked by breeder selection and plant density management preferences. While tillers are a well-known source of plasticity in cereal crops, the functional trade-offs of tiller expression to the hierarchical yield formation process in corn are unknown. This investigation aimed to further dissect the consequences of tiller expression on corn yield component determination and plasticity in a range of environments from two plant fraction perspectives – i) main stalks only, considering potential functional trade-offs due to tiller expression; and ii) comprehensive (main stalk plus tillers). MethodsThis multi-seasonal study considered a dataset of 17 site-years across Kansas, United States. Replicated field trials evaluated tiller presence (removed or intact) in two hybrids (P0657AM and P0805AM) at three target plant densities (25000, 42000, and 60000 plants ha-1). Record of ears and kernels per unit area and kernel weight were collected separately for both main stalks and tillers in each plot. ResultsIndicated tiller contributions impacted the plasticity of yield components in evaluated genotypes. Ear number and kernel number per area were less dependent on plant density, but kernel number remained key to yield stability. Although ear number was less related to yield stability, ear source and type were significant yield predictors, with tiller axillary ears as stronger contributors than main stalk secondary ears in high-yielding environments. DiscussionsCertainly, managing for the most main stalk primary ears possible – that is, optimizing the plant density (which consequently reduces tiller expression), is desirable to maximize yields. However, the demonstrated escape from the deterministic hierarchy of corn yield formation may offer avenues to reduce corn management dependence on a seasonally variable optimum plant density, which cannot be remediated mid-season.

Breeding climate-resilient crops with improved levels of abiotic and biotic stress resistance as a response to climate change presents both opportunities and challenges. Applying the framework of the “breeder’s equation,” which is used to predict the response to selection for a breeding program cycle, we review methodologies and strategies that have been used to successfully breed crops with improved levels of drought resistance, where the target population of environments (TPEs) is a spatially and temporally heterogeneous mixture of drought-affected and favorable (water-sufficient) environments. Long-term improvement of temperate maize for the US corn belt is used as a case study and compared with progress for other crops and geographies. Integration of trait information across scales, from genomes to ecosystems, is needed to accurately predict yield outcomes for genotypes within the current and future TPEs. This will require transdisciplinary teams to explore, identify, and exploit novel opportunities to accelerate breeding program outcomes; both improved germplasm resources and improved products (cultivars, hybrids, clones, and populations) that outperform and replace the products in use by farmers, in combination with modified agronomic management strategies suited to their local environments.

Plant function arises from a complex network of structural and physiological traits. Explicit representation of these traits, as well as their connections with other biophysical processes, is required to advance our understanding of plant-soil-climate interactions. We used the Terrestrial Regional Ecosystem Exchange Simulator (TREES) to evaluate physiological trait networks in maize. Net primary productivity (NPP) and grain yield were simulated across five contrasting climate scenarios. Simulations achieving high NPP and grain yield in high precipitation environments featured trait networks conferring high water use strategies: deep roots, high stomatal conductance at low water potential (“risky” stomatal regulation), high xylem hydraulic conductivity, and high maximal leaf area index. In contrast, high NPP and grain yield was achieved in dry environments with low late-season precipitation via water conserving trait networks: deep roots, high embolism resistance, and low stomatal conductance at low leaf water potential (“conservative” stomatal regulation). We suggest that our approach, which allows for the simultaneous evaluation of physiological traits and their interactions (i.e., networks), has potential to improve crop growth predictions in different environments. In contrast, evaluating single traits in isolation of other coordinated traits does not appear to be an effective strategy for predicting plant performance.Summary statementOur process-based model uncovered two beneficial but contrasting trait networks for maize which can be understood by their integrated effect on water use/conservation. Modification of multiple, physiologically aligned, traits were required to bring about meaningful improvements in NPP and yield.

Special Issue: Feeding the World: The Future of Plant Breeding Asymmetry of investment in crop research leads to knowledge gaps and lostopportunities to accelerate genetic gain through identifying new sources andcombinations of traits and alleles. On the basis of consultation with scientistsfrom most major seed companies, we identified several research areas withthree common features: (i) relatively underrepresented in the literature; (ii) highprobability of boosting productivity in a wide range of crops and environments;and (iii) could be researched in‘precompetitive’space, leveraging previousknowledge, and thereby improving models that guide crop breeding and man-agement decisions. Areas identified included research into hormones, recombi-nation, respiration, roots, and source–sink, which, along with new opportunitiesin phenomics, genomics, and bioinformatics, make it more feasible to explorecrop genetic resources and improve breeding strategies. The authors acknowledge the following scientists for helping to achieve a consensus on research bottlenecks with significant implications for crop improvement: Yann Manes (Syngenta), Jeremy Derory (Limagrain), Bruno Marty (BASF), and Hans Braun (recent director of the Global Wheat Program, CIMMYT). We acknowledge Renee Lafitte (Bill and Melinda Gates Foundation) for helpful feedback on the manuscript and Fatima Escalante for valuable assistance in coordinating edits to the manuscript and its formatting. M.R. acknowledges the International Wheat Yield Partnership (https://iwyp.org/) for establishing a precedent of integrating different research strands in wheat for targeted prebreeding and the Foundation for Food and Agricultural Research (https://foundationfar.org/) for supporting a translational research and prebreeding pipeline at CIMMYT to identify and stack climate resilience traits in wheat.

Because plants capture water and nutrients through roots, it was proposed that changes in root systems architecture (RSA) might underpin the 3-fold increase in maize (Zea mays L.) grain yield over the last century. Here we show that both RSA and yield have changed with decades of maize breeding, but not the crop water uptake. Results from X-ray phenotyping in controlled environments showed that single cross (SX) hybrids have smaller root systems than double cross (DX) hybrids for root diameters between 2465 µm and 181µm (P, Emerging opportunity to continue long-term genetic gain in maize yield in the US corn belt by improving the balance between canopy, root, and reproductive growth and development.

Long-term glucocorticoid (GC) therapy is frequently indicated to treat autoimmune and chronic inflammatory diseases in daily clinical practice. Two of the most devastating untoward effects are bone loss and fractures. Doses as low as 2.5 mg of prednisone for more than 3 months can impair bone integrity. Population at risk is defined based on the dose and duration of GC therapy and should be stratified according to FRAX (Fracture Risk Assessment Tool), major osteoporotic fracture, prior fractures, and bone mineral density values (BMD). General measures include to prescribe the lowest dose of GC to control the underlying disease for the shortest possible time, maintain adequate vitamin D levels and calcium intake, maintain mobility, and prescribe a bone acting agent in patients at high risk of fracture. These agents include oral and intravenous bisphosphonates, denosumab, and teriparatide.

Increasing temperatures in the US Midwest are projected to reduce maize yields because warmer temperatures hasten reproductive development and, as a result, shorten the grain fill period. However, there is widespread expectation that farmers will mitigate projected yield losses by planting longer season hybrids that lengthen the grain fill period. Here, we ask: (a) how current hybrid maturity length relates to thermal availability of the local climate, and (b) if farmers are shifting to longer season hybrids in response to a warming climate. To address these questions, we used county-level Pioneer brand hybrid sales (Corteva Agriscience) across 17 years and 650 counties in 10 Midwest states (IA, IL, IN, MI, MN, MO, ND, OH, SD, and WI). Northern counties were shown to select hybrid maturities with growing degree day (GDD°C) requirements more closely related to the environmentally available GDD compared to central and southern counties. This measure, termed "thermal overlap," ranged from complete 106% in northern counties to a mere 63% in southern counties. The relationship between thermal overlap and latitude was fit using split-line regression and a breakpoint of 42.8°N was identified. Over the 17-years, hybrid maturities shortened across the majority of the Midwest with only a minority of counties lengthening in select northern and southern areas. The annual change in maturity ranged from -5.4 to 4.1 GDD year-1 with a median of -0.9 GDD year-1 . The shortening of hybrid maturity contrasts with widespread expectations of hybrid maturity aligning with magnitude of warming. Factors other than thermal availability appear to more strongly impact farmer decision-making such as the benefit of shorter maturity hybrids on grain drying costs, direct delivery to ethanol biorefineries, field operability, labor constraints, and crop genetics availability. Prediction of hybrid choice under future climate scenarios must include climatic factors, physiological-genetic attributes, socio-economic, and operational constraints.

Objectives To investigate efficacy and safety of the Janus kinase-1 inhibitor filgotinib in patients with active rheumatoid arthritis (RA) with limited or no prior methotrexate (MTX) exposure. Methods This 52-week, phase 3, multicentre, double-blind clinical trial (NCT02886728) evaluated once-daily oral filgotinib in 1252 patients with RA randomised 2:1:1:2 to filgotinib 200 mg with MTX (FIL200 +MTX), filgotinib 100 mg with MTX (FIL100 +MTX), filgotinib 200 mg monotherapy (FIL200), or MTX. The primary endpoint was proportion achieving 20% improvement in American College of Rheumatology criteria (ACR20) at week 24. Results The primary endpoint was achieved by 81% of patients receiving FIL200+ MTX versus 71% receiving MTX (p Conclusions FIL200+MTX and FIL100+MTX both significantly improved signs and symptoms and physical function in patients with active RA and limited or no prior MTX exposure; FIL200 monotherapy did not have a superior ACR20 response rate versus MTX. Filgotinib was well tolerated, with acceptable safety compared with MTX.

Since first recognized in July 2019, numerous cases of a lung illness associated with electronic-cigarette use or vaping particularly tetrahydrocannabinol in adolescents and adults have been reported. As of January 2020, the Center for Disease Control has reported over 2500 cases of electronic-cigarette or vaping product use-associated lung injury (EVALI), including 60 deaths. Affected patients most commonly present with respiratory or gastrointestinal complaints although neurological symptoms including headache, confusion and lethargy have been reported. We present a new as yet unpublished finding in the brain of a previously healthy teenage boy with EVALI. Brain imaging may be warranted in this patient population.

Introduction Currently, approximately more than one billion people around the world are considered to have deficient levels of vitamin D. International consensus recommends vitamin D supplementation to high-risk patients (advanced age, osteoporosis, liver failure, malabsorption syndromes, etc.) and those with levels below 30 ng/mL. There are several vitamin D formulations and dosages available, including megadoses. At the moment, there is no consensus on the definition of megadoses. The purpose of this review is to define what is a megadose and analyze its effectiveness in bone metabolism, risk of fractures and falls. Conclusion The administration of doses higher than 100,000 IU of vitamin D is considered a megadose. It is evident that the use of megadoses increases serum concentrations of vitamin D; however, there has been no evidence of a decrease in the risk of falls, vertebral fractures or changes in bone mineral density.

Predicting the consequences of manipulating genotype (G) and agronomic management (M) on agricultural ecosystem performances under future environmental (E) conditions remains a challenge. Crop modelling has the potential to enable society to assess the efficacy of G × M technologies to mitigate and adapt crop production systems to climate change. Despite recent achievements, dedicated research to develop and improve modelling capabilities from gene to global scales is needed to provide guidance on designing G × M adaptation strategies with full consideration of their impacts on both crop productivity and ecosystem sustainability under varying climatic conditions. Opportunities to advance the multiscale crop modelling framework include representing crop genetic traits, interfacing crop models with large-scale models, improving the representation of physiological responses to climate change and management practices, closing data gaps and harnessing multisource data to improve model predictability and enable identification of emergent relationships. A fundamental challenge in multiscale prediction is the balance between process details required to assess the intervention and predictability of the system at the scales feasible to measure the impact. An advanced multiscale crop modelling framework will enable a gene-to-farm design of resilient and sustainable crop production systems under a changing climate at regional-to-global scales.

Background The Center for Disease Control (CDC) has identified a national outbreak in the United States of over 2600 cases of e-cigarette or vaping product use-associated lung injury (EVALI), including 60 deaths as of January 2020. We describe our experience in six adolescents. Material and methods We identified all pediatric patients diagnosed with EVALI by CDC guidelines over a 6-month period at our health system. Clinical presentation, hospital course, and imaging were reviewed. Results Six patients were identified (three males, three females; median age 18.5 years). Presenting symptoms varied, including constitutional, gastrointestinal, neurologic, and respiratory complaints with pulmonary symptomatology becoming the dominant feature of the illness. Three patients required intensive care unit-level care, one of whom expired 36 days after presentation. Three had bronchoalveolar lavage, two with evidence of lipid-laden macrophages. Four had pulmonary function testing with various results. Admission chest radiographs in all revealed bibasilar interstitial infiltrate which rapidly progressed. Five patients had computed tomography chest imaging demonstrating: confluent pulmonary infiltrates with subpleural sparing (n = 2), generalized ground-glass opacities (n = 1), patchy ground-glass opacities (n = 1) and a reticulonodular pattern (n = 1). Brain magnetic resonance imaging (MRI) obtained in two patients was normal in one and showed a focal signal abnormality in the corpus callosum in one. Conclusion We describe the clinical course and radiologic findings of EVALI in our adolescent patients and present a new finding in the brain not yet described in the literature. Given the diversity of presenting symptoms, a high level of suspicion for EVALI is necessary for patients reporting vaping product use regardless of the presence of pulmonary complaints. Brain MRI should be strongly considered in patients with neurologic symptoms.

A Crop Growth Model (CGM) is used to demonstrate a biophysical framework for predicting grain yield outcomes for Genotype by Environment by Management (G×E×M) scenarios. This required development of a CGM to encode contributions of genetic and environmental determinants of biophysical processes that influence key resource (radiation, water, nutrients) use and yield‐productivity within the context of the target agricultural system. Prediction of water‐driven yield‐productivity of maize for a wide range of G×E×M scenarios in the U.S. corn‐belt is used as a case study to demonstrate applications of the framework. Three experimental evaluations are conducted to test predictions of G×E×M yield expectations derived from the framework: (1) A maize hybrid genetic gain study, (2) A maize yield potential study, and (3) A maize drought study. Examples of convergence between key G×E×M predictions from the CGM and the results of the empirical studies are demonstrated. Potential applications of the prediction framework for design of integrated crop improvement strategies are discussed. The prediction framework opens new opportunities for rapid design and testing of novel crop improvement strategies based on an integrated understanding of G×E×M interactions. Importantly the CGM ensures that the yield predictions for the G×E×M scenarios are grounded in the biophysical properties and limits of predictability for the crop system. The identification and delivery of novel pathways to improved crop productivity can be accelerated through use of the proposed framework to design crop improvement strategies that integrate genetic gains from breeding and crop management strategies that reduce yield gaps.

Environmental characterization for defining the target population of environments (TPE) is critical to improve the efficiency of breeding programs in crops, such as sorghum (Sorghum bicolorL.). The aim of this study was to characterize the spatial and temporal variation for a TPE for sorghum within the United States. APSIM-sorghum, included in the Agricultural Production Systems sIMulator software platform, was used to quantify water-deficit and heat patterns for 15 sites in the sorghum belt. Historical weather data (∼35 years) was used to identify water (WSP) and heat (HSP) stress patterns to develop water–heat clusters. Four WSPs were identified with large differences in the timing of onset, intensity, and duration of the stress. In the western region of Kansas, Oklahoma, and Texas, the most frequent WSP (∼35%) was stress during grain filling with late recovery. For northeast Kansas, WSP frequencies were more evenly distributed, suggesting large temporal variation. Three HSPs were defined, with the low HSP being most frequent (∼68%). Field data from Kansas State University sorghum hybrid yield performance trials (2006–2013 period, 6 hybrids, 10 sites, 46 site × year combinations) were classified into the previously defined WSP and HSP clusters. As the intensity of the environmental stress increased, there was a clear reduction on grain yield. Both simulated and observed yield data showed similar yield trends when the level of heat or water stressed increased. Field yield data clearly separated contrasting clusters for both water and heat patterns (with vs. without stress). Thus, the patterns were regrouped into four categories, which account for the observed genotype by environment interaction (GxE) and can be applied in a breeding program. A better definition of TPE to improve predictability of GxE could accelerate genetic gains and help bridge the gap between breeders, agronomists, and farmers.

In a randomized clinical trial in patients initiating glucocorticoid therapy (GC-I) or on long-term therapy (GC-C), denosumab every 6 months increased spine and hip bone mineral density at 12 and 24 months significantly more than daily risedronate. The aim of this study was to evaluate the effects of denosumab compared with risedronate on bone strength and microarchitecture measured by high-resolution peripheral quantitative computed tomography (HR-pQCT) in GC-I and GC-C. A subset of 110 patients had high-resolution peripheral quantitative computed tomography (HR-pQCT) scans of the distal radius and tibia at baseline and at 12 and 24 months. Cortical and trabecular microarchitecture were assessed with standard analyses and failure load (FL) with micro-finite element analysis. At the radius at 24 months, FL remained unchanged with denosumab and significantly decreased with risedronate in GC-I (-4.1%, 95% confidence interval [CI] -6.4, -1.8) and, in GC-C, it significantly increased with denosumab (4.3%, 95% CI 2.1, 6.4) and remained unchanged with risedronate. Consequently, FL was significantly higher with denosumab than with risedronate in GC-I (5.6%, 95% CI 2.4, 8.7, p < 0.001) and in GC-C (4.1%, 95% CI 1.1, 7.2, p = 0.011). We also found significant differences between denosumab and risedronate in percentage changes in cortical and trabecular microarchitectural parameters in GC-I and GC-C. Similar results were found at the tibia. To conclude, this HR-pQCT study shows that denosumab is superior to risedronate in terms of preventing FL loss at the distal radius and tibia in GC-I and in increasing FL at the radius in GC-C, based on significant differences in changes in the cortical and trabecular bone compartments between treatment groups in GC-I and GC-C. These results suggest that denosumab could be a useful therapeutic option in patients initiating GC therapy or on long-term GC therapy and may contribute to treatment decisions in this patient population. (c) 2022 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR). PG reports grants from Amgen, grants and other from AbbVie, grants from Celgene, grants from Lilly, grants from Merck, grants from Pfizer, grants from Roche, grants from UCB, grants from Fresenius, grants from Mylan, and grants from Sandoz outside the submitted work. BR reports personal fees from Scanco Medical AG outside the submitted work. EL reports personal fees from Amgen; personal fees from Lilly; grants, personal fees, and nonfinancial support from Celgene; personal fees from Expanscience; personal fees from AbbVie; personal fees from Sublimed; grants, personal fees, and non-financial support from UCB; and grants, personal fees, and non-financial support from MSD outside the submitted work. BO reports personal fees from Raffo, personal fees from Baliarda, personal fees from Shire, personal fees from Takeda, personal fees from Gador, personal fees from Amgen, and personal fees from Ultragenyx outside the submitted work. RC reports grants from Amgen during the conduct of the study; grants and personal fees from UCB; personal fees from Lilly; personal fees from BMS; personal fees from AbbVie; personal fees from Pfizer; grants and personal fees from Chugai; personal fees from Sanofi; personal fees from Novartis; and grants and other from Fresenius Kiabi outside the submitted work. AC is employed by Amgen and has equity in Amgen. SH is an employee of and has equity in Amgen. KGS reports grants from Amgen, Horizon Pharma, Swedish Orphan Biovitrum AB, Shanton Pharma Co. LTD, Dyve Bioscience, and LG Chem outside the submitted work. JPB reports grants from Amgen, grants from UCB, and grants from Lilly the outside the submitted work. All authors had full access to the data, and there were no restrictions regarding content to include in the manuscript. This study was funded by Amgen Inc. Authors’ roles: Investigation: PG, OM, EL, BO, RC, and KGS. Formal analysis: KE, AC, and SH. Writing—original draft: PG and MB. Writing—review & editing: PG, MB, BR, OM, EL, BO, RC, KE, AC, SH, KGS, and JPB.

Over the past century of maize (Zea mays L.) breeding, grain yield progress has been the result of improvements in several other intrinsic physiological and morphological traits. In this study, we describe (i) the contribution of kernel weight (KW) to yield genetic gain across multiple agronomic settings and breeding programs, and (ii) the physiological bases for improvements in KW for US hybrids. A global-scale literature review concludes that rates of KW improvement in US hybrids were similar to those of other commercial breeding programs but extended over a longer period of time. There is room for a continued increase of kernel size in maize for most of the genetic materials analysed, but the trade-off between kernel number and KW poses a challenge for future yield progress. Through phenotypic characterization of Pioneer Hi-Bred ERA hybrids in the USA, we determine that improvements in KW have been predominantly related to an extended kernel-filling duration. Likewise, crop improvement has conferred on modern hybrids greater KW plasticity, expressed as a better ability to respond to changes in assimilate availability. Our analysis of past trends and current state of development helps to identify candidate targets for future improvements in maize.

Vascular Calcification (VC), low bone mass and fragility fractures are frequently observed in ageing subjects. Although this clinical observation could be the mere coincidence of frequent age-dependent disorders, clinical and experimental data suggest that VC and bone loss could share pathophysiological mechanisms. Indeed, VC is an active process of calcium and phosphate precipitation that involves the transition of the vascular smooth muscle cells (VSMCs) into osteoblast-like cells. Among the molecules involved in this process, parathyroid hormone (PTH) plays a key role acting through several mechanisms which includes the regulation of the RANK/RANKL/OPG system and the Wnt/ß-catenin pathway, the main pathways for bone resorption and bone formation, respectively. Furthermore, some microRNAs have been implicated as common regulators of bone metabolism, VC, left ventricle hypertrophy and myocardial fibrosis. Elucidating the common mechanisms between ageing; VC and bone loss could help to better understand the potential effects of osteoporosis drugs on the CV system.

The diverse consequences of genotype-by-environment (GxE) interactions determine trait phenotypes across levels of biological organization for crops, challenging our ambition to predict trait phenotypes from genomic information alone. GxE interactions have many implications for optimizing both genetic gain through plant breeding and crop productivity through on-farm agronomic management. Advances in genomics technologies have provided many suitable predictors for the genotype dimension of GxE interactions. Emerging advances in high-throughput proximal and remote sensor technologies have stimulated the development of “enviromics” as a community of practice, which has the potential to provide suitable predictors for the environment dimension of GxE interactions. Recently, several bespoke examples have emerged demonstrating the nascent potential for enhancing the prediction of yield and other complex trait phenotypes of crop plants through including effects of GxE interactions within prediction models. These encouraging results motivate the development of new prediction methods to accelerate crop improvement. If we can automate methods to identify and harness suitable sets of coordinated genotypic and environmental predictors, this will open new opportunities to upscale and operationalize prediction of the consequences of GxE interactions. This would provide a foundation for accelerating crop improvement through integrating the contributions of both breeding and agronomy. Here we draw on our experience from improvement of maize productivity for the range of water-driven environments across the US corn-belt. We provide perspectives from the maize case study to prioritize promising opportunities to further develop and automate “enviromics” methodologies to accelerate crop improvement through integrated breeding and agronomic approaches for a wider range of crops and environmental targets.

Environmental characterization for defining the target population of environments (TPE) is critical to improve the efficiency of breeding programs in crops, such as sorghum (

RESUMEN Introducción: La herramienta FRAX ha sido validada y adaptada a diferentes países, cubriendo a casi el 80% de la población mundial, incluido Ecuador, donde fue adaptada en 2009. El objetivo de este estudio fue elaborar curvas de evaluación e intervención basadas en FRAX Ecuador. Métodos: Utilizando el modelo FRAX Ecuador, calculamos la probabilidad de fractura osteoporótica mayor y fractura de cadera femenina sin ningún factor de riesgo y sin la inclusión de DMO. Las probabilidades se calcularon en intervalos de 5 años de 40 a 90 años. Las probabilidades de fractura mayor y de cadera se calcularon en 3 escenarios diferentes: 1. Historia de fractura previa sin la inclusión de DMO, 2. T-Score de -2,5 SD sin otros factores de riesgo clínico, 3. T-Score -1,5 SD sin otros factores de riesgo clínico. Resultados: En mujeres sin factores de riesgo, la probabilidad de fractura osteoporótica mayor aumentó con la edad del 0,4% a los 40 años al 7,3% a los 90 años. La probabilidad de fractura de cadera aumentó con la edad de 0% a los 40 años a 3,6% a los 90 anos. La probabilidad de fractura osteoporótica mayor aumentó en mujeres con un puntaje T de -2,5 SD de 0,9% a los 40 años a 5,5% a los 90 años; con puntaje T de -1,5 DE, de 0,6% a los 40 años a 3,9% a los 90 anos. Conclusión: Los datos muestran la importancia de aplicar herramientas como FRAX, específicas para cada país y también la creación de curvas de evaluación e intervención que permitan discernir según cada paciente la necesidad de utilizar recursos como DXA y tratamientos específicos. ABSTRACT Introduction: FRAX has been validated and adapted to different countries, covering almost 80% of the world's population, including Ecuador where it was adapted in 2009. The purpose of this study is to elaborate evaluation and intervention curves based on FRAX Ecuador. Methods: Using the FRAX Ecuador model, we calculated the probability of a major osteoporotic fracture and a female hip fracture without any risk factor and without the inclusion of BMD. The probabilities were calculated in 5-year intervals from 40 to 90 years. The probabilities of major fractures and hip fractures were calculated in 3 different scenarios: 1. History of previous fracture without the inclusion of BMD, 2. T score -2.5 SD without other clinical risk factors, 3. T score -1.5 SD without other clinical risk factors. Results: In women without risk factors, the probability of a major osteoporotic fracture increased with age from 0.4% at 40 years to 7.3% at 90 years. The probability of hip fracture increased with age from 0% at 40 years to 3.6% at 90 years. The probability of a major osteoporotic fracture increased in women with a T score of -2.5 SD from 0.9% at 40 years to 5.5% at 90 years; with a T-score of -1.5 SD, from 0.6% at 40 years to 3.9% at 90 years. Conclusion: Data shows the importance of applying tools such as FRAX, specific for each country and also the creation of evaluation and intervention curves that allow discerning according to each patient the need for the use of resources such as DXA and specific treatments.

Nitrogen (N) metabolism is a major research target for increasing productivity in crop plants. In maize (Zea mays L.), yield gain over the last few decades has been associated with increased N absorption and utilization efficiency (i.e. grain biomass per unit of N absorbed). However, a dynamical framework is still needed to unravel the role of internal processes such as uptake, allocation, and translocation of N in these adaptations. This study aimed to 1) characterize how genetic enhancement in N efficiency conceals changes in allocation and translocation of N, and 2) quantify internal fluxes behind grain N sources in two historical genotypes under high and low N supply. The genotypes 3394 and P1197, landmark hybrids representing key eras of genetic improvement (1990s and 2010s), were grown under high and low N supply in a two-year field study. Using stable isotope

Genetic gain in breeding programs depends on the predictive skill of genotype-to-phenotype algorithms and precision of phenotyping, both integrated with well-defined breeding objectives for a target population of environments (TPE). The integration of physiology and genomics could improve predictive skill by capturing additive and non-additive interaction effects of genotype (G), environment (E), and management (M). Precision phenotyping at managed stress environments (MSEs) can elicit physiological expression of processes that differentiate germplasm for performance in target environments, thus enabling algorithm training. Gap analysis methodology enables design of GxM technologies for target environments by assessing the difference between current and attainable yields within physiological limits. Harnessing digital technologies such as crop growth model-whole genome prediction (CGM-WGP) and gap analysis, and MSEs, can hasten genetic gain by improving predictive skill and definition of breeding goals in the U.S. maize production TPE. A half-diallel maize experiment resulting from crossing 9 elite maize inbreds was conducted at 17 locations in the TPE and 6 locations at MSEs between 2017 and 2019. Analyses over 35 families represented by 2367 hybrids demonstrated that CGM-WGP offered a predictive advantage (y) compared to WGP that increased with occurrence of drought as measured by decreasing whole-season evapotranspiration (ET; log(y) = 0.80(±0.6) − 0.006(±0.001) ×ET;r2= 0.59;df= 21). Predictions of unobserved physiological traits using the CGM, akin to digital phenotyping, were stable. This understanding of germplasm response to ET enables predictive design of opportunities to close productivity gaps. We conclude that enabling physiology through digital methods can hasten genetic gain by improving predictive skill and defining breeding objectives bounded by physiological realities.

Purpose To analyze the change in utilization of healthcare resources through a review of ultrasound examinations performed in the emergency department of an urban healthcare system in NYC during the time of peak COVID-19 outbreak. Methods This is a retrospective review analyzing ED ultrasound exams performed by the radiology department of an urban healthcare system during the 8-week time period of the peak COVID-19 outbreak in NYC, compared to a time-matched period one year prior. Data regarding the examination type and indication were obtained in addition to patient demographics and indicators of outcomes including admission, length of stay, and mortality. Results There was a 58% decrease in ED ultrasounds performed by the radiology department during the COVID-19 time period. Exams performed during the pandemic compared to the pre-pandemic period were more likely to be performed on men (28.3 vs 18.0%, p

Maize is considered less drought-tolerant than sorghum, but sorghum is commonly grown as a short triple dwarf (3dwarf) type, so difference in plant height confounds the species comparison. The objectives of this study were to experimentally determine effects of species and plant height differences on transpiration efficiency (TE) and transpiration rate per unit green leaf area (TGLA) and use findings to explain input parameters in a simulation study on the comparative adaptation of 3dwarf sorghum and maize in environments with contrasting water availability. Maize, tall double dwarf (2dwarf) and short 3dwarf sorghum genotypes were grown in two lysimeter experiments in 2011 in SE Queensland, Australia. Each plant was harvested after anthesis and total transpiration, shoot and root dry mass were measured to estimate TE. Daily TGLA was used to compare transpiration rates. Species and height had limited effect on TE, but significantly affected TGLA. This was associated with differences in biomass allocation. The similar TE but higher TGLA in maize compared with 3dwarf sorghum meant it potentially produces more biomass, consistent with published differences in biomass accumulation and radiation use efficiency (RUE). The simulation study, which used similar TE for maize and 3dwarf sorghum, but captured differences in TGLA through differences in RUE, predicted crossover interactions for grain yield between species and total water use. The greater TGLA of maize decreased grain yield in water-limited environments, but increased yields in well-watered situations. Results highlight that similarity in TE and differences in TGLA can influence comparative adaptation to water limitation.

ABSTRACTThe timing of crop development has significant impacts on management decisions and subsequent yield formation. A large intercontinental dataset recording the timing of soybean developmental stages was used to establish ensembling approaches that leverage both knowledge-based, human-defined models of soybean phenology and data-driven, machine-learned models to achieve accurate and interpretable predictions. We demonstrate that the knowledge-based models can improve machine learning by generating expert-engineered features. The collection of knowledge-based and data-driven models was combined via super learning to both improve prediction and identify the most performant models. Stacking the predictions of the component models resulted in a mean absolute error of 4.41 and 5.27 days to flowering (R1) and physiological maturity (R7), providing an improvement relative to the benchmark knowledge-based model error of 6.94 and 15.53 days, respectively, in cross-validation. The hybrid intercontinental model applies to a much wider range of management and temperature conditions than previous mechanistic models, enabling improved decision support as alternative cropping systems arise, farm sizes increase and changes in the global climate continue to accelerate.