Expression of nuclear-cytoplasmic interactions on quantitatively inherited traits from interspecific matings of oat (Avena sativa L. and A. sterilis L.)

From a biological perspective, cytoplasmic effects are probably due to interactions of nuclear and cytoplasmic factors. For example, an enzyme involved in photosynthesis, ribulose-1,5-biscarboxylase, is encoded by both cytoplasmic and nuclear DNA. In this study, the phenomena of cytoplasmic and nuclear-cytoplasmic interaction effects on quantitatively inherited traits of oats (Avena sativa L. and A. sterilis L.) were investigated;Genetic models were fit to generation means of isopopulations from four matings involving A. sterilis accessions and the Cornbelt oat (A. sativa) variety \u27CI 9170\u27. One mating had an isopopulation that exhibited nuclear-cytoplasmic heterosis. The models accurately described generation means in three matings and included additive nuclear and cytoplasmic interaction effects. It appeared that assumptions that simplified the algebra of the genetic models caused a lack of fit in one mating, and that alternative models should be developed. Based upon the best fitting model for each mating, predicted graing yield of advanced backcross generations was calculated. Actual grain yields of advanced backcross populations did not agree with the predicted values;The failure of the genetic models to describe generation means from all four matings prompted the development of an alternative model based upon molecular studies of cytoplasmic genetics. Unlike the nuclear genome, the cytoplasmic genome (a) consists of single, circular, duplex molecules of DNA, (b) is transmitted solely through the maternal parent in oats, and (c) exhibits little or no variability within cytoplasmic lines of descent. These biological features were incorporated into a quantitative genetic model which describes the genotypic value of an individual. The model was used to derive theoretical components of genetic variability in random mating population. Estimation of some variance components from the model is possible with a reciprocal mating design;Twelve F(,2)-derived lines of 76 cytoplasmic isopopulations were evaluated for seven traits. All traits exhibited significant nuclear-cytoplasmic interactions, but none exhibited consistent cytoplasmic effects. Nuclear-cytoplasmic heterosis was detected for each trait in 5 to 20% of the isopopulations with A. sterilis cytoplasm

Measuring the response of human head and neck squamous cell carcinoma to irradiation in a microfluidic model allowing customized therapy

Radiotherapy is the standard treatment for head and neck squamous cell carcinoma (HNSCC), however, radioresistance remains a major clinical problem despite significant improvements in treatment protocols. Therapeutic outcome could potentially be improved if a patient's tumour response to irradiation could be predicted ex vivo before clinical application. The present study employed a bespoke microfluidic device to maintain HNSCC tissue whilst subjecting it to external beam irradiation and measured the responses using a panel of cell death and proliferation markers. HNSCC biopsies from five newly-presenting patients [2 lymph node (LN); 3 primary tumour (PT)] were divided into parallel microfluidic devices and replicates of each tumour were subjected to single-dose irradiation (0, 5, 10, 15 and 20 Gy). Lactate dehydrogenase (LDH) release was measured and tissue sections were stained for cytokeratin (CK), cleaved-CK18 (cCK18), phosphorylated-H2AX (λH2AX) and Ki.67 by immunohistochemistry. In addition, fragmented DNA was detected using terminal deoxynucleotidyl transferase dUTP nick end labelling (TUNEL). Compared with non.irradiated controls, higher irradiation doses resulted in elevated CK18-labelling index in two lymph nodes [15 Gy; 34.8% on LN1 and 31.7% on LN2 (p=0.006)] and a single laryngeal primary tumour (20 Gy; 31.5%; p=0.014). Significantly higher levels of DNA fragmentation were also detected in both lymph node samples and one primary tumour but at varying doses of irradiation, i.e., LN1 (20 Gy; 27.6%; p=0.047), LN2 (15 Gy; 15.3%; p=0.038) and PT3 (10 Gy; 35.2%; p=0.01). The λH2AX expression was raised but not significantly in the majority of samples. The percentage of Ki.67 positive nuclei reduced dose-dependently following irradiation. In contrast no significant difference in LDH release was observed between irradiated groups and controls. There is clear interand intra-patient variability in response to irradiation when measuring a variety of parameters, which offers the potential for the approach to provide clinically valuable information

Parametric and Nonparametric Statistical Methods for Genomic Selection of Traits with Additive and Epistatic Genetic Architectures

Parametric and nonparametric methods have been developed for purposes of predicting phenotypes. These methods are based on retrospective analyses of empirical data consisting of genotypic and phenotypic scores. Recent reports have indicated that parametric methods are unable to predict phenotypes of traits with known epistatic genetic architectures. Herein, we review parametric methods including least squares regression, ridge regression, Bayesian ridge regression, least absolute shrinkage and selection operator (LASSO), Bayesian LASSO, best linear unbiased prediction (BLUP), Bayes A, Bayes B, Bayes C, and Bayes Cπ. We also review nonparametric methods including Nadaraya-Watson estimator, reproducing kernel Hilbert space, support vector machine regression, and neural networks. We assess the relative merits of these 14 methods in terms of accuracy and mean squared error (MSE) using simulated genetic architectures consisting of completely additive or two-way epistatic interactions in an F2 population derived from crosses of inbred lines. Each simulated genetic architecture explained either 30% or 70% of the phenotypic variability. The greatest impact on estimates of accuracy and MSE was due to genetic architecture. Parametric methods were unable to predict phenotypic values when the underlying genetic architecture was based entirely on epistasis. Parametric methods were slightly better than nonparametric methods for additive genetic architectures. Distinctions among parametric methods for additive genetic architectures were incremental. Heritability, i.e., proportion of phenotypic variability, had the second greatest impact on estimates of accuracy and MSE

Parametric and Nonparametric Statistical Methods for Genomic Selection of Traits with Additive and Epistatic Genetic Architectures

Parametric and nonparametric methods have been developed for purposes of predicting phenotypes. These methods are based on retrospective analyses of empirical data consisting of genotypic and phenotypic scores. Recent reports have indicated that parametric methods are unable to predict phenotypes of traits with known epistatic genetic architectures. Herein, we review parametric methods including least squares regression, ridge regression, Bayesian ridge regression, least absolute shrinkage and selection operator (LASSO), Bayesian LASSO, best linear unbiased prediction (BLUP), Bayes A, Bayes B, Bayes C, and Bayes Cπ. We also review nonparametric methods including Nadaraya-Watson estimator, reproducing kernel Hilbert space, support vector machine regression, and neural networks. We assess the relative merits of these 14 methods in terms of accuracy and mean squared error (MSE) using simulated genetic architectures consisting of completely additive or two-way epistatic interactions in an F2 population derived from crosses of inbred lines. Each simulated genetic architecture explained either 30% or 70% of the phenotypic variability. The greatest impact on estimates of accuracy and MSE was due to genetic architecture. Parametric methods were unable to predict phenotypic values when the underlying genetic architecture was based entirely on epistasis. Parametric methods were slightly better than nonparametric methods for additive genetic architectures. Distinctions among parametric methods for additive genetic architectures were incremental. Heritability, i.e., proportion of phenotypic variability, had the second greatest impact on estimates of accuracy and MSE

Application of Response Surface Methods To Determine Conditions for Optimal Genomic Prediction

An epistatic genetic architecture can have a significant impact on prediction accuracies of genomic prediction (GP) methods. Machine learning methods predict traits comprised of epistatic genetic architectures more accurately than statistical methods based on additive mixed linear models. The differences between these types of GP methods suggest a diagnostic for revealing genetic architectures underlying traits of interest. In addition to genetic architecture, the performance of GP methods may be influenced by the sample size of the training population, the number of QTL, and the proportion of phenotypic variability due to genotypic variability (heritability). Possible values for these factors and the number of combinations of the factor levels that influence the performance of GP methods can be large. Thus, efficient methods for identifying combinations of factor levels that produce most accurate GPs is needed. Herein, we employ response surface methods (RSMs) to find the experimental conditions that produce the most accurate GPs. We illustrate RSM with an example of simulated doubled haploid populations and identify the combination of factors that maximize the difference between prediction accuracies of best linear unbiased prediction (BLUP) and support vector machine (SVM) GP methods. The greatest impact on the response is due to the genetic architecture of the population, heritability of the trait, and the sample size. When epistasis is responsible for all of the genotypic variance and heritability is equal to one and the sample size of the training population is large, the advantage of using the SVM method vs. the BLUP method is greatest. However, except for values close to the maximum, most of the response surface shows little difference between the methods. We also determined that the conditions resulting in the greatest prediction accuracy for BLUP occurred when genetic architecture consists solely of additive effects, and heritability is equal to one

Parametric and Nonparametric Statistical Methods for Genomic Selection of Traits with Additive and Epistatic Genetic Architectures

Parametric and nonparametric methods have been developed for purposes of predicting phenotypes. These methods are based on retrospective analyses of empirical data consisting of genotypic and phenotypic scores. Recent reports have indicated that parametric methods are unable to predict phenotypes of traits with known epistatic genetic architectures. Herein, we review parametric methods including least squares regression, ridge regression, Bayesian ridge regression, least absolute shrinkage and selection operator (LASSO), Bayesian LASSO, best linear unbiased prediction (BLUP), Bayes A, Bayes B, Bayes C, and Bayes Cp. We also review nonparametric methods including Nadaraya-Watson estimator, reproducing kernel Hilbert space, support vector machine regression, and neural networks. We assess the relative merits of these 14 methods in terms of accuracy and mean squared error (MSE) using simulated genetic architectures consisting of completely additive or two-way epistatic interactions in an F2 population derived from crosses of inbred lines. Each simulated genetic architecture explained either 30% or 70% of the phenotypic variability. The greatest impact on estimates of accuracy and MSE was due to genetic architecture. Parametric methods were unable to predict phenotypic values when the underlying genetic architecture was based entirely on epistasis. Parametric methods were slightly better than nonparametric methods for additive genetic architectures. Distinctions among parametric methods for additive genetic architectures were incremental. Heritability, i.e., proportion of phenotypic variability, had the second greatest impact on estimates of accuracy and MSE

Effect of the source charge on charged-beam interferometry

We investigate quantal perturbations of the interferometric correlations of charged bosons by the Coulomb field of an instantaneous, charged source. The source charge increases the apparent source size by weakening the correlation at non-zero relative momenta. The effect is strongest for pairs with a small total momentum and is stronger for kaons than for pions of the same momenta. The experimental data currently available are well described by this effect without invoking Pratt's exploding source model. A simple expression is proposed to account for the effect.Comment: 9 pages TEX, 3 Postscript figures available at http://www.krl.caltech.edu/preprints/MAP.htm

Pbar Annihilation in Au+Au at AGS Energies

Antinucleon production in heavy ion collisions is potentially an excellent signal for unusual phenomena in hot and dense matter. However, at the low energies available at the AGS the annihilation process must be handled with care. In this Comment, we consider the case of Au + Au collisions at approximately 11 GeV/c, applying the ARC treatment of pbar production and annihilation to the analysis of experiment E878. It is apparent that classical screening introduced for Si + Au is crucial in the understanding of data obtained with the more massive projectile. Unfortunately, there seems no necessity for invoking unusual behaviour in the Au + Au system.Comment: 1 page in revtex, 1 postscript fil

Critical care admission following elective surgery was not associated with survival benefit: prospective analysis of data from 27 countries

PURPOSE: As global initiatives increase patient access to surgical treatments, there is a need to define optimal levels of perioperative care. Our aim was to describe the relationship between the provision and use of critical care resources and postoperative mortality. METHODS: Planned analysis of data collected during an international 7-day cohort study of adults undergoing elective in-patient surgery. We used risk-adjusted mixed-effects logistic regression models to evaluate the association between admission to critical care immediately after surgery and in-hospital mortality. We evaluated hospital-level associations between mortality and critical care admission immediately after surgery, critical care admission to treat life-threatening complications, and hospital provision of critical care beds. We evaluated the effect of national income using interaction tests. RESULTS: 44,814 patients from 474 hospitals in 27 countries were available for analysis. Death was more frequent amongst patients admitted directly to critical care after surgery (critical care: 103/4317 patients [2%], standard ward: 99/39,566 patients [0.3%]; adjusted OR 3.01 [2.10–5.21]; p < 0.001). This association may differ with national income (high income countries OR 2.50 vs. low and middle income countries OR 4.68; p = 0.07). At hospital level, there was no association between mortality and critical care admission directly after surgery (p = 0.26), critical care admission to treat complications (p = 0.33), or provision of critical care beds (p = 0.70). Findings of the hospital-level analyses were not affected by national income status. A sensitivity analysis including only high-risk patients yielded similar findings. CONCLUSIONS: We did not identify any survival benefit from critical care admission following surgery