Across population genomic prediction scenarios in which Bayesian variable selection outperforms GBLUP

<p>Background: The use of information across populations is an attractive approach to increase the accuracy of genomic prediction for numerically small populations. However, accuracies of across population genomic prediction, in which reference and selection individuals are from different populations, are currently disappointing. It has been shown for within population genomic prediction that Bayesian variable selection models outperform GBLUP models when the number of QTL underlying the trait is low. Therefore, our objective was to identify across population genomic prediction scenarios in which Bayesian variable selection models outperform GBLUP in terms of prediction accuracy. In this study, high density genotype information of 1033 Holstein Friesian, 105 Groningen White Headed, and 147 Meuse-Rhine-Yssel cows were used. Phenotypes were simulated using two changing variables: (1) the number of QTL underlying the trait (3000, 300, 30, 3), and (2) the correlation between allele substitution effects of QTL across populations, i.e. the genetic correlation of the simulated trait between the populations (1.0, 0.8, 0.4). Results: The accuracy obtained by the Bayesian variable selection model was depending on the number of QTL underlying the trait, with a higher accuracy when the number of QTL was lower. This trend was more pronounced for across population genomic prediction than for within population genomic prediction. It was shown that Bayesian variable selection models have an advantage over GBLUP when the number of QTL underlying the simulated trait was small. This advantage disappeared when the number of QTL underlying the simulated trait was large. The point where the accuracy of Bayesian variable selection and GBLUP became similar was approximately the point where the number of QTL was equal to the number of independent chromosome segments (M _e) across the populations. Conclusion: Bayesian variable selection models outperform GBLUP when the number of QTL underlying the trait is smaller than M _e. Across populations, M _e is considerably larger than within populations. So, it is more likely to find a number of QTL underlying a trait smaller than M _e across populations than within population. Therefore Bayesian variable selection models can help to improve the accuracy of across population genomic prediction.</p

Viscoelastic gels of guar and xanthan gum mixtures provide long-term stabilization of iron micro- and nanoparticles

Iron micro- and nanoparticles used for groundwater remediation and medical applications are prone to fast aggregation and sedimentation. Diluted single biopolymer water solutions of guar gum (GG) or xanthan gum (XG) can stabilize these particles for few hours providing steric repulsion and by increasing the viscosity of the suspension. The goal of the study is to demonstrate that amending GG solutions with small amounts of XG (XG/GG weight ratio 1:19; 3 g/L of total biopolymer concentration) can significantly improve the capability of the biopolymer to stabilize highly concentrated iron micro- and nanoparticle suspensions. The synergistic effect between GG and XG generates a viscoelastic gel that can maintain 20 g/L iron particles suspended for over 24 h. This is attributed to (i) an increase in the static viscosity, (ii) a combined polymer structure the yield stress of which contrasts the downward stress exerted by the iron particles, and (iii) the adsorption of the polymers to the iron surface having an anchoring effect on the particles. The XG/GG viscoelastic gel is characterized by a marked shear thinning behavior. This property, coupled with the low biopolymer concentration, determines small viscosity values at high shear rates, facilitating the injection in porous media. Furthermore, the thermosensitivity of the soft elastic polymeric network promotes higher stability and longer storage times at low temperatures and rapid decrease of viscosity at higher temperatures. This feature can be exploited in order to improve the flowability and the delivery of the suspensions to the target as well as to effectively tune and control the release of the iron particle

Intravesical Treatments of Bladder Cancer: Review

For bladder cancer, intravesical chemo/immunotherapy is widely used as adjuvant therapies after surgical transurethal resection, while systemic therapy is typically reserved for higher stage, muscle-invading, or metastatic diseases. The goal of intravesical therapy is to eradicate existing or residual tumors through direct cytoablation or immunostimulation. The unique properties of the urinary bladder render it a fertile ground for evaluating additional novel experimental approaches to regional therapy, including iontophoresis/electrophoresis, local hyperthermia, co-administration of permeation enhancers, bioadhesive carriers, magnetic-targeted particles and gene therapy. Furthermore, due to its unique anatomical properties, the drug concentration-time profiles in various layers of bladder tissues during and after intravesical therapy can be described by mathematical models comprised of drug disposition and transport kinetic parameters. The drug delivery data, in turn, can be combined with the effective drug exposure to infer treatment efficacy and thereby assists the selection of optimal regimens. To our knowledge, intravesical therapy of bladder cancer represents the first example where computational pharmacological approach was used to design, and successfully predicted the outcome of, a randomized phase III trial (using mitomycin C). This review summarizes the pharmacological principles and the current status of intravesical therapy, and the application of computation to optimize the drug delivery to target sites and the treatment efficacy

Reactive oxygen species in phagocytic leukocytes

Phagocytic leukocytes consume oxygen and generate reactive oxygen species in response to appropriate stimuli. The phagocyte NADPH oxidase, a multiprotein complex, existing in the dissociated state in resting cells becomes assembled into the functional oxidase complex upon stimulation and then generates superoxide anions. Biochemical aspects of the NADPH oxidase are briefly discussed in this review; however, the major focus relates to the contributions of various modes of microscopy to our understanding of the NADPH oxidase and the cell biology of phagocytic leukocytes

Comparison of proton channel, phagocyte oxidase, and respiratory burst levels between human eosinophil and neutrophil granulocytes.

Robust production of reactive oxygen species (ROS) by phagocyte NADPH oxidase (phox) during the respiratory burst (RB) is a characteristic feature of eosinophil and neutrophil granulocytes. In these cells the voltage-gated proton channel (Hv1) is now considered as an ancillary subunit of the phox needed for intense ROS production. Multiple sources reported that the expression of phox subunits and RB is more intensive in eosinophils than in neutrophils. In most of these studies the eosinophils were not isolated from healthy individuals, and a comparative analysis of Hv1 expression had never been carried out. We performed a systematic comparison of the levels of essential phox subunits, Hv1 expression and ROS producing capacity between eosinophils and neutrophils of healthy individuals. The expression of phox components was similar, whereas the amount of Hv1 was approximately 10-fold greater in eosinophils. Furthermore, Hv1 expression correlated with Nox2 expression only in eosinophils. Additionally, in confocal microscopy experiments co-accumulation of Hv1 and Nox2 at the cell periphery was observed in resting eosinophils but not in neutrophils. While phorbol-12-myristate-13-acetate-induced peak extracellular ROS release was approximately 1.7-fold greater in eosinophils, oxygen consumption studies indicated that the maximal intensity of the RB is only approximately 1.4-fold greater in eosinophils. Our data reinforce that eosinophils, unlike neutrophils, generate ROS predominantly extracellularly. In contrast to previous works we have found that the two granulocyte types display very similar phox subunit expression and RB capacity. The large difference in Hv1 expression suggests that its support to intense ROS production is more important at the cell surface

Frequently asked questions about chlorophyll fluorescence, the sequel

[EN] Using chlorophyll (Chl) a fluorescence many aspects of the photosynthetic apparatus can be studied, both in vitro and, noninvasively, in vivo. Complementary techniques can help to interpret changes in the Chl a fluorescence kinetics. Kalaji et al. (Photosynth Res 122: 121-158, 2014a) addressed several questions about instruments, methods and applications based on Chl a fluorescence. Here, additionalChl a fluorescence-related topics are discussed again in a question and answer format. Examples are the effect of connectivity on photochemical quenching, the correction of F-V/F-M values for PSI fluorescence, the energy partitioning concept, the interpretation of the complementary area, probing the donor side of PSII, the assignment of bands of 77 K fluorescence emission spectra to fluorescence emitters, the relationship between prompt and delayed fluorescence, potential problems when sampling tree canopies, the use of fluorescence parameters in QTL studies, the use of Chl a fluorescence in biosensor applications and the application of neural network approaches for the analysis of fluorescence measurements. The answers draw on knowledge fromdifferent Chl a fluorescence analysis domains, yielding in several cases new insights.Kalaji, H.; Schansker, G.; Brestic, M.; Bussotti, F.; Calatayud, A.; Ferroni, L.; Goltsev, V.... (2017). Frequently asked questions about chlorophyll fluorescence, the sequel. 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Multibreed genomic prediction using multitrait genomic residual maximum likelihood and multitask Bayesian variable selection

Genomic prediction is applicable to individuals of different breeds. Empirical results to date, however, show limited benefits in using information on multiple breeds in the context of genomic prediction. We investigated a multitask Bayesian model, presented previously by others, implemented in a Bayesian stochastic search variable selection (BSSVS) model. This model allowed for evidence of quantitative trait loci (QTL) to be accumulated across breeds or for both QTL that segregate across breeds and breed-specific QTL. In both cases, single nucleotide polymorphism effects were estimated with information from a single breed. Other models considered were a single-trait and multitrait genomic residual maximum likelihood (GREML) model, with breeds considered as different traits, and a single-trait BSSVS model. All single-trait models were applied to each of the 2 breeds separately and to the pooled data of both breeds. The data used included a training data set of 6,278 Holstein and 722 Jersey bulls, as well as 374 Jersey validation bulls. All animals had genotypes for 474,773 single nucleotide polymorphisms after editing and phenotypes for milk, fat, and protein yields. Using the same training data, BSSVS consistently outperformed GREML. The multitask BSSVS, however, did not outperform single-trait BSSVS, which used pooled Holstein and Jersey data for training. Thus, the rigorous assumption that the traits are the same in both breeds yielded a slightly better prediction than a model that had to estimate the correlation between the breeds from the data. Adding the Holstein data significantly increased the accuracy of the single-trait GREML and BSSVS in predicting the Jerseys for milk and protein, in line with estimated correlations between the breeds of 0.66 and 0.47 for milk and protein yields, whereas only the BSSVS model significantly improved the accuracy for fat yield with an estimated correlation between breeds of only 0.05. The relatively high genetic correlations for milk and protein yields, and the superiority of the pooling strategy, is likely the result of the observed admixture between both breeds in our data. The Bayesian model was able to detect several QTL in Holsteins, which likely enabled it to outperform GREML. The inability of the multitask Bayesian models to outperform a simple pooling strategy may be explained by the fact that the pooling strategy assumes equal effects in both breeds; furthermore, this assumption may be valid for moderate- to large-sized QTL, which are important for multibreed genomic prediction