[Implementation of a Geriatric liason team in coordination with Primary Care attending 60 nursing homes in the northwest community healthcare network of the Community of Madrid]. / Implantación de un programa de Geriatría de enlace coordinado con Atención Primaria en 60 centros sociosanitarios del área asistencial noroeste de la Comunidad de Madrid.

OBJECTIVE: To describe interventions included in the implementation of a multidisciplinary Geriatrics Program that gives support to nursing homes, in coordination with Primary Care and Public Health, in collaboration with other hospital departments. METHODS: An observational descriptive study was conducted in an area that includes 60 nursing homes with nearly 4600 residents from June 1 st, 2020 to October 1 st, 2021. The program consists of different interventions including Telemedicine and support of a Geriatric Consultation Liaison Team. An estimation of avoided costs through these interventions was carried out. RESULTS: The activity recorded was 11502 telephone calls, 2247 e-mails, 313 visits to these centres in where 4085 patients underwent comprehensive geriatric assessment. During this period of time 442 patients received intravenous therapy in their nursing homes, including 7541 different types of medication which 5850 of them were antibiotics. According to the Diagnosis-related-Group (DRG) of the patients that received intravenous treatment in their nursing homes, was estimated a cost reduction of 1,500,00€ and a total of 2800 days of hospital stay avoided. In the group of 198 patients that received video consultation was estimated reduction of costs of 37,026€. A hospital multidisciplinary care team focused on the nursing home patients was created. CONCLUSIONS: This program improves continuity of nursing homes patients care and to enhance communication and coordination among Primary Care, Hospitals and Public Health services and secondarily, reducing hospital costs.

Application of multi-trait Bayesian decision theory for parental genomic selection.

In all breeding programs, the decision about which individuals to select and intermate to form the next selection cycle is crucial. The improvement of genetic stocks requires considering multiple traits simultaneously, given that economic value and net genetic merits depend on many traits; therefore, with the advance of computational and statistical tools and genomic selection (GS), researchers are focusing on multi-trait selection. Selection of the best individuals is difficult, especially in traits that are antagonistically correlated, where improvement in one trait might imply a reduction in other(s). There are approaches that facilitate multi-trait selection, and recently a Bayesian decision theory (BDT) has been proposed. Parental selection using BDT has the potential to be effective in multi-trait selection given that it summarizes all relevant quantitative genetic concepts such as heritability, response to selection and the structure of dependence between traits (correlation). In this study, we applied BDT to provide a treatment for the complexity of multi-trait parental selection using three multivariate loss functions (LF), Kullback-Leibler (KL), Energy Score, and Multivariate Asymmetric Loss (MALF), to select the best-performing parents for the next breeding cycle in two extensive real wheat data sets. Results show that the high ranking lines in genomic estimated breeding value (GEBV) for certain traits did not always have low values for the posterior expected loss (PEL). For both data sets, the KL LF gave similar importance to all traits including grain yield. In contrast, the Energy Score and MALF gave a better performance in three of four traits that were different than grain yield. The BDT approach should help breeders to decide based not only on the GEBV per se of the parent to be selected, but also on the level of uncertainty according to the Bayesian paradigm.

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.

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.

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.

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.

Genomic prediction in biparental tropical maize populations in water-stressed and well-watered environments using low-density and GBS SNPs.

One of the most important applications of genomic selection in maize breeding is to predict and identify the best untested lines from biparental populations, when the training and validation sets are derived from the same cross. Nineteen tropical maize biparental populations evaluated in multienvironment trials were used in this study to assess prediction accuracy of different quantitative traits using low-density (~200 markers) and genotyping-by-sequencing (GBS) single-nucleotide polymorphisms (SNPs), respectively. An extension of the Genomic Best Linear Unbiased Predictor that incorporates genotype × environment (GE) interaction was used to predict genotypic values; cross-validation methods were applied to quantify prediction accuracy. Our results showed that: (1) low-density SNPs (~200 markers) were largely sufficient to get good prediction in biparental maize populations for simple traits with moderate-to-high heritability, but GBS outperformed low-density SNPs for complex traits and simple traits evaluated under stress conditions with low-to-moderate heritability; (2) heritability and genetic architecture of target traits affected prediction performance, prediction accuracy of complex traits (grain yield) were consistently lower than those of simple traits (anthesis date and plant height) and prediction accuracy under stress conditions was consistently lower and more variable than under well-watered conditions for all the target traits because of their poor heritability under stress conditions; and (3) the prediction accuracy of GE models was found to be superior to that of non-GE models for complex traits and marginal for simple traits.

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.

Genome-enabled methods for predicting litter size in pigs: a comparison.

Predictive ability of models for litter size in swine on the basis of different sources of genetic information was investigated. Data represented average litter size on 2598, 1604 and 1897 60K genotyped sows from two purebred and one crossbred line, respectively. The average correlation (r) between observed and predicted phenotypes in a 10-fold cross-validation was used to assess predictive ability. Models were: pedigree-based mixed-effects model (PED), Bayesian ridge regression (BRR), Bayesian LASSO (BL), genomic BLUP (GBLUP), reproducing kernel Hilbert spaces regression (RKHS), Bayesian regularized neural networks (BRNN) and radial basis function neural networks (RBFNN). BRR and BL used the marker matrix or its principal component scores matrix (UD) as covariates; RKHS employed a Gaussian kernel with additive codes for markers whereas neural networks employed the additive genomic relationship matrix (G) or UD as inputs. The non-parametric models (RKHS, BRNN, RNFNN) gave similar predictions to the parametric counterparts (average r ranged from 0.15 to 0.23); most of the genome-based models outperformed PED (r = 0.16). Predictive abilities of linear models and RKHS were similar over lines, but BRNN varied markedly, giving the best prediction (r = 0.31) when G was used in crossbreds, but the worst (r = 0.02) when the G matrix was used in one of the purebred lines. The r values for RBFNN ranged from 0.16 to 0.23. Predictive ability was better in crossbreds (0.26) than in purebreds (0.15 to 0.22). This may be related to family structure in the purebred lines.

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.