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[This corrects the article DOI: 10.1186/s13007-018-0317-4.].

BackgroundGrain yield, ear and kernel attributes can assist to understand the performance of maize plant under different environmental conditions and can be used in the variety development process to address farmer's preferences. These parameters are however still laborious and expensive to measure.ResultsA low-cost ear digital imaging method was developed that provides estimates of ear and kernel attributes i.e., ear number and size, kernel number and size as well as kernel weight from photos of ears harvested from field trial plots. The image processing method uses a script that runs in a batch mode on ImageJ; an open source software. Kernel weight was estimated using the total kernel number derived from the number of kernels visible on the image and the average kernel size. Data showed a good agreement in terms of accuracy and precision between ground truth measurements and data generated through image processing. Broad-sense heritability of the estimated parameters was in the range or higher than that for measured grain weight. Limitation of the method for kernel weight estimation is discussed.ConclusionThe method developed in this work provides an opportunity to significantly reduce the cost of selection in the breeding process, especially for resource constrained crop improvement programs and can be used to learn more about the genetic bases of grain yield determinants.

The marker x environment interaction (MxE) genomic model can be used to generate predictions for untested individuals and identify genomic regions in which effects are stable across environments and others that show environmental specificity. The objectives of this study were (i) to extend the MxE model using priors that produce shrinkage and variable selection such as Bayesian ridge regression (BRR) and BayesB (BB), respectively, and (ii) to evaluate the genomic prediction accuracy of MxE, single-environment, and across-environment models using a multiparental durum wheat (Triticum turgidum L. spp. duram) population characterized for grain yield (GY), grain volume weight (GVW), 1000-kernel weight (GWT), and heading date (HD) in four environments. Breeding value predictions were generated for two prediction problems: cross-validation problem 1 (CV1) and cross-validation problem 2 (CV2). In general, results showed that the MxE model performed better than the single-environment and across-environment models, in terms of minimizing the model residual variance, for both CV1 and CV2. The improved data-fitting gain over the other models was more evident for GWT and HD (up to twofold differences) than to GY and GVW, which showed more complex genetic bases and smaller single-marker effects. Considering the Bayesian models used, BB showed better overall prediction accuracy than BRR. As proof-of-concept for the MxE model, the major controllers of HD--Ppd and FT on chromosomes 2A, 2B, and 7A--showed stable effects across environments as well as environment-specific effects. For GY, besides the regions on chromosomes 2B and 7A, additional chromosome regions with large marker effects were detected in all chromosome groups. [ABSTRACT FROM AUTHOR]

Genomic selection (GS) has been implemented in animal and plant species, and is regarded as a useful tool for accelerating genetic gains. Varying levels of genomic prediction accuracy have been obtained in plants, depending on the prediction problem assessed and on several other factors, such as trait heritability, the relationship between the individuals to be predicted and those used to train the models for prediction, number of markers, sample size and genotype × environment interaction (GE). The main objective of this article is to describe the results of genomic prediction in International Maize and Wheat Improvement Center's (CIMMYT's) maize and wheat breeding programs, from the initial assessment of the predictive ability of different models using pedigree and marker information to the present, when methods for implementing GS in practical global maize and wheat breeding programs are being studied and investigated. Results show that pedigree (population structure) accounts for a sizeable proportion of the prediction accuracy when a global population is the prediction problem to be assessed. However, when the prediction uses unrelated populations to train the prediction equations, prediction accuracy becomes negligible. When genomic prediction includes modeling GE, an increase in prediction accuracy can be achieved by borrowing information from correlated environments. Several questions on how to incorporate GS into CIMMYT's maize and wheat programs remain unanswered and subject to further investigation, for example, prediction within and between related bi-parental crosses. Further research on the quantification of breeding value components for GS in plant breeding populations is required. [ABSTRACT FROM AUTHOR]

Striga hermonthica (Del.) Benth., a parasitic weed, causes substantial yield losses in maize production in sub-Saharan Africa (SSA). Breeding for Striga resistance in maize is constrained by limited genetic diversity for Striga resistance within the elite germplasm and phenotyping capacity under artificial Striga infestation. Genomics-enabled approaches have the potential to accelerate identification of Striga resistant lines for hybrid development. The objectives of this study were to evaluate the accuracy of genomic selection for traits associated with Striga resistance and grain yield (GY) and to predict genetic values of tested and untested doubled haploid (DH) maize lines. We genotyped 606 DH lines with 8,439 rAmpSeq markers. A training set of 116 DH lines crossed to two testers was phenotyped under artificial Striga infestation at three locations in Kenya. Heritability for Striga resistance parameters ranged from 0.38‒0.65 while that for GY was 0.54. The prediction accuracies for Striga resistance-associated traits across locations, as determined by cross validation (CV) were 0.24 to 0.53 for CV0 and from 0.20 to 0.37 for CV2. For GY, the prediction accuracies were 0.59 and 0.56 for CV0 and CV2, respectively. The results revealed 300 DH lines with desirable genomic estimated breeding values (GEBVs) for reduced number of emerged Striga plants (STR) at 8, 10, and 12 weeks after planting. The GEBVs of DH lines for Striga resistance associated traits in the training and testing sets were similar in magnitude. These results highlight the potential application of genomic selection in breeding for Striga resistance in maize. The integration of genomic-assisted strategies and DH technology for line development coupled with forward breeding for major adaptive traits will enhance genetic gains in breeding for Striga resistance in maize., (© The Author(s) 2024. Published by Oxford University Press on behalf of The Genetics Society of America.)

The σ 1 receptor (S1R) is a ligand-regulated non-opioid intracellular receptor involved in several pathological conditions. The development of S1R-based drugs as therapeutic agents is a challenge due to the lack of simple functional assays to identify and classify S1R ligands. We have developed a novel nanoluciferase binary technology (NanoBiT) assay based on the ability of S1R to heteromerize with the binding immunoglobulin protein (BiP) in living cells. The S1R-BiP heterodimerization biosensor allows for rapid and accurate identification of S1R ligands by monitoring the dynamics of association-dissociation of S1R and BiP. Acute treatment of cells with the S1R agonist PRE-084 produced rapid and transient dissociation of the S1R-BiP heterodimer, which was blocked by haloperidol. The effect of PRE-084 was enhanced by calcium depletion, leading to a higher reduction in heterodimerization even in the presence of haloperidol. Prolonged incubation of cells with S1R antagonists (haloperidol, NE-100, BD-1047, and PD-144418) increased the formation of S1R-BiP heteromers, while agonists (PRE-084, 4-IBP, and pentazocine) did not alter heterodimerization under the same experimental conditions. The newly developed S1R-BiP biosensor is a simple and effective tool for exploring S1R pharmacology in an easy cellular setting. This biosensor is suitable for high-throughput applications and a valuable resource in the researcher's toolkit.

Background: As a result of the technological progress, the use of sensors for crop survey has substantially increased, generating valuable information for modelling agricultural data. Plant spectroscopy jointly with statistical modeling can potentially help to assess certain chemical components of interest present in plants, which may be laborious and expensive to obtain by direct measurements. In this research, the phosphorus content in wheat grain is modeled using reflectance information measured by a hyperspectral sensor at different wavelengths. A Bayesian procedure for selecting variables was used to identify the set of the most important spectral bands. Additionally, three different models were evaluated: the first model assumes that the observations are independent, the other two models assume that the observations are spatially correlated: one of the proposed models, assumes spatial dependence using a Conditionally Autoregressive Model (CAR), and the other through an exponential correlogram. The goodness of fit of the models was evaluated by means of the Deviance Information Criterion, and the predictive power is evaluated using cross validation., Results: We have found that CAR was the model that best fits and predicts the data. Additionally, the selection variable procedure in the CAR model reveals which wavelengths in the range of 500-690 nm are the most important. Comparing the vegetative indices with the CAR model, it was observed that the average correlation of the CAR model exceeded that of the vegetative indices by 23.26%, - 1.2% and 22.78% for the year 2010, 2011 and 2012 respectively; therefore, the use of the proposed methodology outperformed the vegetative indices in prediction., Conclusions: The proposal to predict the phosphorus content in wheat grain using Bayesian approach, reflect with the results as a good alternative., (© 2023. The Author(s).)

The genetic variation of the European population at a macro-geographic scale follows genetic gradients which reflect main migration events. However, less is known about factors affecting mating patterns at a micro-geographic scale. In this study we have analyzed 726,718 autosomal single nucleotide variants in 435 individuals from the catalan Pyrenees covering around 200 km of a vast and abrupt region in the north of the Iberian Peninsula, for which we have information about the geographic origin of all grand-parents and parents. At a macro-geographic scale, our analyses recapitulate the genetic gradient observed in Spain. However, we also identified the presence of micro-population substructure among the sampled individuals. Such micro-population substructure does not correlate with geographic barriers such as the expected by the orography of the considered region, but by the bishoprics present in the covered geographic area. These results support that, on top of main human migrations, long ongoing socio-cultural factors have also shaped the genetic diversity observed at rural populations., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Fibla, Maceda, Laplana, Guerrero, Álvarez, Burgueño, Camps, Fàbrega, Felisart, Grané, Remón, Serra, Moral and Lao.)

Opioids are the most effective painkillers, but their benefit-risk balance often hinder their therapeutic use. WLB-73502 is a dual, bispecific compound that binds sigma-1 (S1R) and mu-opioid (MOR) receptors. WLB-73502 is an antagonist at the S1R. It behaved as a partial MOR agonist at the G-protein pathway and produced no/unsignificant β -arrestin-2 recruitment, thus demonstrating low intrinsic efficacy on MOR at both signalling pathways. Despite its partial MOR agonism, WLB-73502 exerted full antinociceptive efficacy, with potency superior to morphine and similar to oxycodone against nociceptive, inflammatory and osteoarthritis pain, and superior to both morphine and oxycodone against neuropathic pain. WLB-73502 crosses the blood-brain barrier and binds brain S1R and MOR to an extent consistent with its antinociceptive effect. Contrary to morphine and oxycodone, tolerance to its antinociceptive effect did not develop after repeated 4-week administration. Also, contrary to opioid comparators, WLB-73502 did not inhibit gastrointestinal transit or respiratory function in rats at doses inducing full efficacy, and it was devoid of proemetic effect (retching and vomiting) in ferrets at potentially effective doses. WLB-73502 benefits from its bivalent S1R antagonist and partial MOR agonist nature to provide an improved antinociceptive and safety profile respect to strong opioid therapy., (© 2022 Chinese Pharmaceutical Association and Institute of Materia Medica, Chinese Academy of Medical Sciences. Production and hosting by Elsevier B.V.)

Development of nutrient efficient cultivars depends on effective identification and utilization of genetic variation. We characterized a set of 276 pre-breeding lines (PBLs) for several traits at different levels of nitrogen application. These PBLs originate from synthetic wheats and landraces. We witnessed significant variation in various traits among PBLs to different nitrogen doses. There was ~ 4-18% variation range in different agronomic traits in response to nitrogen application, with the highest variation for the biological yield (BY) and the harvest index. Among various agronomic traits measured, plant height, tiller number, and BY showed a positive correlation with nitrogen applications. GWAS analysis detected 182 marker-trait associations (MTAs) (at p-value < 0.001), out of which 8 MTAs on chromosomes 5D, 4A, 6A, 1B, and 5B explained more than 10% phenotypic variance. Out of all, 40 MTAs observed for differential nitrogen application response were contributed by the synthetic derivatives. Moreover, 20 PBLs exhibited significantly higher grain yield than checks and can be selected as potential donors for improved plant nitrogen use efficiency (pNUE)., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)