Additive genetic variance and covariance between relatives in synthetic wheat crosses with variable parental ploidy levels

Cultivated bread wheat (Triticum aestivum L.) is an allohexaploid species resulting from the natural hybridization and chromosome doubling of allotetraploid durum wheat (T. turgidum) and a diploid goatgrass Aegilops tauschii Coss (Ae. tauschii). Synthetic hexaploid wheat (SHW) was developed through the interspecific hybridization of Ae. tauschii and T. turgidum, and then crossed to T. aestivum to produce synthetic hexaploid wheat derivatives (SHWDs). Owing to this founding variability, one may infer that the genetic variances of native wild populations vs improved wheat may vary due to their differential origin and evolutionary history. In this study, we partitioned the additive variance of SHW and SHWD with respect to their breed origin by fitting a hierarchical Bayesian model with heterogeneous covariance structure for breeding values to estimate variance components for each breed category, and segregation variance. Two data sets were used to test the proposed hierarchical Bayesian model, one from a multi-year multi-location field trial of SHWD and the other comprising the two species of SHW. For the SHWD, the Bayesian estimates of additive variances of grain yield from each breed category were similar for T. turgidum and Ae. tauschii, but smaller for T. aestivum. Segregation variances between Ae. tauschii-T. aestivum and T. turgidum-T. aestivum populations explained a sizable proportion of the phenotypic variance. Bayesian additive variance components and the Best Linear Unbiased Predictors (BLUPs) estimated by two well-known software programs were similar for multi-breed origin and for the sum of the breeding values by origin for both data sets. Our results support the suitability of models with heterogeneous additive genetic variances to predict breeding values in wheat crosses with variable ploidy levels.

Litterfall Hg deposition to an oak forest soil from southwestern Europe

Litterfall constitutes one of the main vectors for mercury (Hg) transfer to forested ecosystems, so we studied the deposition of Hg through senescent vegetation (oak leaves, twigs and miscellaneous) in a deciduous forest plot of Southwest Europe dominated by Quercus robur in 2015 and 2016. Total Hg concentrations increased in the following order: bole wood (1.4 μg kg^-1) < bark (8.3 μg kg^-1) < twigs (12.2 μg kg^-1) < miscellaneous (36.0 μg kg^-1) < oak leaves (39.3 μg kg^-1) < mineral soil (42.4 μg kg^-1) < Oi horizons (48.7 μg kg^-1) < Oe + Oa horizons (71.6 μg kg^-1). Mercury accumulation rates in oak leaves during the growing season were 0.15-0.18 μg kg^-1 day^-1. Mercury deposition fluxes were 26 and 21 μg m^-2 yr^-1 for 2015 and 2016, respectively, with oak leaves being the fraction that contributed the most. Mercury determination in litterfall sorted biomass fractions lead to a more accurate estimation of the total annual Hg deposition fluxes through litterfall. Higher Hg content was obtained for organic horizons (average of 60.2 μg kg^-1) than for mineral soil (mean of 42.4 μg kg^-1), but the soil Hg pool was higher in the latter. The results confirmed the necessity of taking into account the Hg pool in the deeper mineral soil layers as they accumulate substantial quantities of Hg associated to organic C and Al compounds, preventing its mobilization to other compartments of the terrestrial ecosystems.

Impact of Social Isolation Due to COVID-19 on Health in Older People: Mental and Physical Effects and Recommendations

OBJECTIVES

To review the impact of social isolation during COVID-19 pandemic on mental and physical health of older people and the recommendations for patients, caregivers and health professionals.

DESIGN

Narrative review.

SETTING

Non-institutionalized community-living people.

PARTICIPANTS

20.069 individuals from ten descriptive cross-sectional papers.

MEASUREMENTS

Articles since 2019 to 2020 published on Pubmed, Scielo and Google Scholar databases with the following MeSh terms ('COVID-19', 'coronavirus', 'aging', 'older people', 'elderly', 'social isolation' and 'quarantine') in English, Spanish or Portuguese were included. The studies not including people over 60 were excluded. Guidelines, recommendations, and update documents from different international organizations related to mental and physical activity were also analysed.

RESULTS

41 documents have been included in this narrative review, involving a total of 20.069 individuals (58% women), from Asia, Europe and America. 31 articles included recommendations and 10 addressed the impact of social distancing on mental or physical health. The main outcomes reported were anxiety, depression, poor sleep quality and physical inactivity during the isolation period. Cognitive strategies and increasing physical activity levels using apps, online videos, telehealth, are the main international recommendations.

CONCLUSION

Mental and physical health in older people are negatively affected during the social distancing for COVID-19. Therefore, a multicomponent program with exercise and psychological strategies are highly recommended for this population during the confinement. Future investigations are necessary in this field.

Copper and zinc in rhizospheric soil of wild plants growing in long-term acid vineyard soils. Insights on availability and metal remediation

Total and available Cu and Zn levels were assessed in plant biomass, as well as in two rhizosphere fractions (tightly adhering rhizosphere (TAR), and loosely adhering rhizosphere (LAR)), in wild plants species from vineyard soils. Both TAR and LAR fractions were enriched in total Cu and Zn (1.7 and 1.6 times, respectively), and in available Cu and Zn (2.2 and 19.5 times, respectively), with the former being significantly higher for TAR than for LAR fractions. Mean values for total Cu accumulation in root and aerial biomass of the studied wild plants were 84 and 66 mg kg^-1, respectively, being 57 and 79 mg kg^-1 for Zn. No correlations were found among metal contents in plant biomass and available Cu and Zn concentrations in the rhizosphere fractions. Translocation factor (TF) values for Zn (range 1.0-3.5) indicate preferential accumulation in the aerial biomass in all the studied wild plants. On the contrary, TF for Cu shows a greater variability, depending on plant species, and ranging from 0.2 to 5.9. Regarding bioaccumulation factor (BAF), ranges were 0.03-0.27 and 0.13-0.58, for Cu and Zn, respectively. Results suggest that D. sanguinalis, P. hieracioides, S. viridis, and T. barbata could be useful for Cu remediation in the studied soils, by means of phytostabilization processes.

Concentration levels of new-generation fungicides in throughfall released by foliar wash-off from vineyards

The presence of agricultural pesticides in the environment and their effects on ecosystems are major concerns addressed in a significant number of articles. However, limited information is available on the pesticide concentrations released from crops. This study reports losses of new-generation fungicides by foliar wash-off from vineyards and their potential impact on the concentrations of their main active substances (AS) in surface waters. Two experimental plots devoted to vineyards were treated with various combinations of commercial new-generation fungicide formulations. Then, up to sixteen throughfall collectors were installed under the canopy. Concentrations of sixteen different AS in throughfall were determined along nine rainfall episodes. Concentrations in throughfall far exceeded the maximum permissible levels for drinking water established by the European Union regulations. Dynamics of fungicide release indicated a first-flush effect in the wash-off founding the highest concentrations of AS in the first rain episodes after application of the fungicides. This article shows that foliar spray application of commercial formulations of new-generation fungicides does not prevent the release of their AS to soil or the runoff. Concentration data obtained in this research can be valuable in supporting the assessment of environmental effects of new-generation fungicides and modeling their environmental fate.

Rainfall-induced removal of copper-based spray residues from vines

The continuous use of copper against fungal diseases and off-target effects causes major environmental and agronomic problems. However, the rain-induced removal of Cu-based residues is known only for a limited number of crops. We present the results of rain-induced removal of fungicides from two monitored vineyard plots which were sprayed with two widely used Cu-based formulations: copper-oxychloride (CO) and Bordeaux mixture (BM), respectively. Cu removal per growing season was 0.60±0.12kgha(-1) (30% of the applied fungicide) for CO and 0.80±0.10kgha(-1) for BM (70% of the applied fungicide). Fractioning the Cu in soluble (CuS) and particulate fractions (CuP) showed that most of the Cu was removed as CuP, but CuS concentrations found in throughfall collectors exceeded the regulatory threshold for toxicity in surface waters. The first few millimeters of rain caused most of the Cu removal. Our findings agreed with the data reported in the scientific literature, in which a significant fraction of the Cu-based formulation is loosely attached to the plant surfaces. In addition, we found that rainfall energy had a minor influence on the removal.

Modeling raindrop strike performance on copper wash-off from vine leaves

Copper lost in foliar wash-off from vine leaves treated with Cu-based fungicides was analyzed with a single-drop rainfall simulator. The temporal losses of the particulate Cu (CuP) and the solution Cu (CuS) from raindrop strikes on leaves were modeled using a Poisson point process. This model estimated maximum detachment rates of 0.82 ng CuP and 0.033 ng CuS per raindrop. The total amount of Cu (CuT) in the leaves before rainfall ranged between 0.4 and 4.4 g Cu kg(-1) dry weight. Wash-off reduced the amount of CuT present in the leaves by 0.6 g kg(-1). Particulate losses of CuT ranged from 75 to 90%, while soluble losses of CuT ranged from 10 to 25%. The kinetic energy of the raindrops influenced the loss of CuS but not the loss of CuP. The Poisson point approach can provide an interesting starting point to model non-point source pollution produced from agricultural chemicals washed-off by rain.

Model averaging for genome-enabled prediction with reproducing kernel Hilbert spaces: a case study with pig litter size and wheat yield

Predictive ability of yet-to-be observed litter size (pig) grain yield (wheat) records of several reproducing kernel Hilbert spaces (RKHS) regression models combining different number of Gaussian or t kernels was evaluated. Predictive performance was assessed as the average (over 50 replicates) predictive correlation in the testing set. Predictions from these models were combined using three different types of model averaging: (i) mean of predicted phenotypes obtained in each model, (ii) weighted average using mean squared error as weight or (iii) using the marginal likelihood as weight. (ii) and (iii) were obtained in a validation set with 5% of the data. Phenotypes consisted of 2598, 1604 and 1879 average litter size records from three commercial pig lines and wheat grain yield of 599 lines evaluated in four macro-environments. SNPs from the PorcineSNP60 BeadChip and 1447 DArT markers were used as predictors for the pig and wheat data analyses, respectively. Gaussian and univariate t kernels led to same predictive performance. Multikernel RKHS regression models overcame shortcomings of single kernel models (increasing the predictive correlation of RKHS models by 0.05 where 3 Gaussian or t kernels were fitted in the RKHS models simultaneously). None of the proposed averaging strategies improved the predictive correlations attained with single models using multiple kernel fitting.

Technical note: An R package for fitting Bayesian regularized neural networks with applications in animal breeding

In recent years, several statistical models have been developed for predicting genetic values for complex traits using information on dense molecular markers, pedigrees, or both. These models include, among others, the Bayesian regularized neural networks (BRNN) that have been widely used in prediction problems in other fields of application and, more recently, for genome-enabled prediction. The R package described here (brnn) implements BRNN models and extends these to include both additive and dominance effects. The implementation takes advantage of multicore architectures via a parallel computing approach using openMP (Open Multiprocessing) for the computations. This note briefly describes the classes of models that can be fitted using the brnn package, and it also illustrates its use through several real examples.