Genetic architecture of fresh-market tomato yield.

BACKGROUND: The fresh-market tomato (Solanum lycopersicum) is bred for direct consumption and is selected for a high yield of large fruits. To understand the genetic variations (distinct types of DNA sequence polymorphism) that influence the yield, we collected the phenotypic variations in the yields of total fruit, extra-large-sized fruit, small-sized fruit, or red-colored fruit from 68 core inbred contemporary U.S. fresh-market tomatoes for three consecutive years and the genomic information in 8,289,741 single nucleotide polymorphism (SNP) positions from the whole-genome resequencing of these tomatoes. RESULTS: Genome-wide association (GWA) mapping using the SNP data with or without SNP filtering steps using the regularization methods, validated with quantitative trait loci (QTL) linkage mapping, identified 18 significant association signals for traits evaluated. Among them, 10 of which were not located within genomic regions previously identified as being associated with fruit size/shape. When mapping-driven association signals [558 SNPs associated with 28 yield (component) traits] were used to calculate genomic estimated breeding values (GEBVs) of evaluated traits, the prediction accuracies of the extra-large-sized fruit and small-sized fruit yields were higher than those of the total and red-colored fruit yields, as we tested the generated breeding values in inbred tomatoes and F2 populations. Improved accuracy in GEBV calculation of evaluated traits was achieved by using 364 SNPs identified using the regularization methods. CONCLUSIONS: Together, these results provide an understanding of the genetic variations underlying the heritable phenotypic variability in yield in contemporary tomato breeding and the information necessary for improving such economically important and complex quantitative trait through breeding.

Efficacy of Hand Behind Back Mobilization With Movement for Acute Shoulder Pain and Movement Impairment: A Randomized Controlled Trial.

OBJECTIVE: The aim of this study was to investigate the effects of hand-behind-back (HBB) Mulligan mobilization with movement (MWM) techniques on acute shoulder pain, impairment, and disability. METHODS: This double-blind, randomized, controlled trial recruited 44 patients with acute shoulder pain and movement impairment presenting to an Indian general hospital. Participants were allocated to receive either MWM and exercise/hot pack (n = 22) or exercise/hot pack alone (n = 22). The average duration of symptoms was 4.1 and 4.7 weeks in the exercise and MWM groups, respectively. The primary outcome was HBB range of motion (ROM). Secondary variables were shoulder internal rotation ROM, pain intensity score, and shoulder disability identified by the shoulder pain and disability index. All variables were evaluated by a blinded assessor before and immediately after 9 treatment sessions over 3 weeks. RESULTS: Paired t tests revealed that both groups demonstrated statistically significant improvements (P < .001) with large effect sizes for all variables. However, for all variables, the MWM-with-exercise group showed significantly greater improvements (P < .05) than the exercise group. Hand-behind-back ROM showed a mean difference of 9.31° (95% confidence interval, 7.38-11.27), favoring greater improvement in the MWM-with-exercise group. CONCLUSIONS: In this study, the outcomes of patients with acute shoulder pain and disability receiving shoulder HBB MWM with exercise improved greater than those receiving exercise/hot packs alone.

Using machine learning and partial dependence to evaluate robustness of best linear unbiased prediction (BLUP) for phenotypic values.

Best linear unbiased prediction (BLUP) is widely used in plant research to address experimental variation. For phenotypic values, BLUP accuracy is largely dependent on properly controlled experimental repetition and how variable components are outlined in the model. Thus, determining BLUP robustness implies the need to evaluate contributions from each repetition. Here, we assessed the robustness of BLUP values for simulated or empirical phenotypic datasets, where the BLUP value and each experimental repetition served as dependent and independent (feature) variables, respectively. Our technique incorporated machine learning and partial dependence. First, we compared the feature importance estimated with the neural networks. Second, we compared estimated average marginal effects of individual repetitions, calculated with a partial dependence analysis. We showed that contributions of experimental repetitions are unequal in a phenotypic dataset, suggesting that the calculated BLUP value is likely to be influenced by some repetitions more than others (such as failing to detect simulated true positive associations). To resolve disproportionate sources, variable components in the BLUP model must be further outlined.

postQTL: a QTL mapping R workflow to improve the accuracy of true positive loci identification.

OBJECTIVE: The determination of the location of quantitative trait loci (QTL) (i.e., QTL mapping) is essential for identifying new genes. Various statistical methods are being incorporated into different QTL mapping functions. However, statistical errors and limitations may often occur in a QTL mapping, implying the risk of false positive errors and/or failing to detect a true positive QTL effect. We simulated the power to detect four simulated QTL in tomato using cim() and stepwiseqtl(), widely adopted QTL mapping functions, and QTL.gCIMapping(), a derivative of the composite interval mapping method. While there is general agreement that those three functions identified simulated QTL, missing or false positive QTL were observed, which were prevalent when more realistic data (such as smaller population size) were provided. RESULTS: To address this issue, we developed postQTL, a QTL mapping R workflow that incorporates (i) both cim() and stepwiseqtl(), (ii) widely used R packages developed for model selection, and (iii) automation to increase the accuracy, efficiency, and accessibility of QTL mapping. QTL mapping experiments on tomato F2 populations in which QTL effects were simulated or calculated showed advantages of postQTL in QTL detection.

Meropenem-induced Stevens-Johnson syndrome/toxic epidermal necrolysis in a patient with known type IV penicillin hypersensitivity.

Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) are considered variants of a disease continuum that results in a life-threatening exfoliative mucocutaneous disease. These are categorised as type IV cell-mediated delayed hypersensitivity reactions, and antibiotics are often implicated as a cause. Penicillins and other beta-lactam antibiotics are known to cause both immediate and delayed hypersensitivity reactions. While immediate IgE-mediated cross-reactivity between penicillins and carbapenems is well studied, less information on the risk of type IV delayed cell-mediated cross-reactivity between the two is available. We present a case of meropenem-induced SJS in a patient with documented history of SJS from amoxicillin. There are few cases of cross-reactivity with carbapenems reported in the literature, but based on the potential for life-threatening reaction, it is likely prudent to avoid the use of any beta-lactams in a patient with a history of SJS, TEN or any other severe cutaneous adverse reactions to another beta-lactam antibiotic.