1,233 research outputs found
Tendencias históricas de la comunidad de carnívoros del Monte de El Pardo (Madrid, España central)
Ab initio lattice dynamics and electron-phonon coupling of Bi(111)
We present a comprehensive ab initio study of structural, electronic, lattice
dynamical and electron-phonon coupling properties of the Bi(111) surface within
density functional perturbation theory. Relativistic corrections due to
spin-orbit coupling are consistently taken into account. As calculations are
carried out in a periodic slab geometry, special attention is given to the
convergence with respect to the slab thickness. Although the electronic
structure of Bi(111) thin films varies significantly with thickness, we found
that the lattice dynamics of Bi(111) is quite robust and appears converged
already for slabs as thin as 6 bilayers. Changes of interatomic couplings are
confined mostly to the first two bilayers, resulting in super-bulk modes with
frequencies higher than the optic bulk spectrum, and in an enhanced density of
states at lower frequencies for atoms in the first bilayer. Electronic states
of the surface band related to the outer part of the hole Fermi surfaces
exhibit a moderate electron-phonon coupling of about 0.45, which is larger than
the coupling constant of bulk Bi. States at the inner part of the hole surface
as well as those forming the electron pocket close to the zone center show much
increased couplings due to transitions into bulk projected states near
Gamma_bar. For these cases, the state dependent Eliashberg functions exhibit
pronounced peaks at low energy and strongly deviate in shape from a Debye-like
spectrum, indicating that an extraction of the coupling strength from measured
electronic self-energies based on this simple model is likely to fail.Comment: 30 pages, 11 figure
Comparación de dos métodos de captura para los micromamíferos (insectivora y rodentia) de campos de cultivo
Transnational, Social, and Neighborhood Ties and Smoking Among Latino Immigrants: Does Gender Matter?
Objectives. We examined whether transnational ties, social ties, and neighborhood ties were independently associated with current smoking status among Latino immigrants. We also tested interactions to determine whether these associations were moderated by gender.
Methods. We conducted a series of weighted logistic regression analyses (i.e., economic remittances, number of return visits, friend support, family support, and neighborhood cohesion) using the Latino immigrant subsample (n = 1629) of the National Latino and Asian American Study in 2002 and 2003.
Results. The number of past-year return visits to the country-of-origin was positively associated with current smoker status. Gender moderated the association between economic remittances, friend support, and smoking. Remittance behavior had a protective association with smoking, and this association was particularly pronounced for Latino immigrant women. Friendship support lowered the odds of smoking among men, but not women.
Conclusions. Our results underscore the growing importance of transnational networks for understanding Latino immigrant health and the gendered patterns of the associations between social ties, transnational ties, and health risk behaviors
Deep neural networks for the quantile estimation of regional renewable energy production
Wind and solar energy forecasting have become crucial for the inclusion of renewable energy in electrical power systems. Although most works have focused on point prediction, it is currently becoming important to also estimate the forecast uncertainty. With regard to forecasting methods, deep neural networks have shown good performance in many fields. However, the use of these networks for comparative studies of probabilistic forecasts of renewable energies, especially for regional forecasts, has not yet received much attention. The aim of this article is to study the performance of deep networks for estimating multiple conditional quantiles on regional renewable electricity production and compare them with widely used quantile regression methods such as the linear, support vector quantile regression, gradient boosting quantile regression, natural gradient boosting and quantile regression forest methods. A grid of numerical weather prediction variables covers the region of interest. These variables act as the predictors of the regional model. In addition to quantiles, prediction intervals are also constructed, and the models are evaluated using different metrics. These prediction intervals are further improved through an adapted conformalized quantile regression methodology. Overall, the results show that deep networks are the best performing method for both solar and wind energy regions, producing narrow prediction intervals with good coverage
Pareto optimal prediction intervals with hypernetworks
As the relevance of probabilistic forecasting grows, the need of estimating multiple high-quality prediction intervals (PI) also increases. In the current state of the art, most deep neural network gradient descent-based methods take into account interval width and coverage into a single loss function, focusing on a unique nominal coverage target, and adding additional parameters to control the coverage-width trade-off. The Pareto Optimal Prediction Interval Hypernetwork (POPI-HN) approach developed in this work has been derived to treat this coverage-width trade-off as a multi-objective problem, obtaining a complete set of Pareto Optimal solutions (Pareto front). POPI-HN are able to be trained through gradient descent with no need to add extra parameters to control the width-coverage trade-off of PIs. Once the Pareto set has been obtained, users can extract the PI with the required coverage. Comparative results with recently introduced Quality-Driven loss show similar behavior in coverage while improving interval width for the majority of the studied domains, making POPI-HN a competing alternative for estimating uncertainty in regression tasks where PIs with multiple coverages are needed.This publication is part of the I+D+i project PID2019-107455RB-C22, funded by MCIN /AEI/10.13039/501100011033. This work was also supported by the Comunidad de Madrid Excellence Program. Funding for APC: Universidad Carlos III de Madrid (Read & Publish Agreement CRUE-CSIC 2022
Biocatalyzed synthesis of antidiabetic drugs: A review
The biocatalyzed production of building blocks for synthesizing drugs is a very attractive research field, because of the sustainability introduced in a synthetic schedule when chemical steps are substituted by biocatalyzed protocols. In this article, we will show how different antidiabetic drugs, for treating diabetes mellitus Type 1 and Type 2, can be more efficiently and effectively synthetized with the help of different types of biocatalysts. The huge overall drug market for these drugs, as well as the great number of people suffering from diabetes (the prevalence of all types of diabetes is growing), makes this topic attractive enough to focus on more efficient synthetic protocols for preparing antidiabetic drugs. Examples covering biocatalyzed synthesis of insulin analogues, sensitizers (PPAR agonists), secretagogues (GLP-1 analogues, GPR119 agonists) and enzyme inhibitors (α-glucosidase inhibitors, DPP4-inhibitors, SGLT-2 inhibitors and 11β-HSD1 inhibitors) will be presented
Counteracting effects of soil biota on emergence and growth of herbaceous plants
https://doi.org/10.5281/zenodo.7972878Background
Plants condition the biotic composition of their rhizosphere. In turn, this plant legacy on the soil biota may affect the performance of plants recruiting in their vicinity. Unravelling how plant-soil legacies drive plant recruitment is key to understand vegetation dynamics and plant community assembly. Studies on the topic usually focus on the effects of soil microbiota as a whole, while the relative role of different guilds of soil organisms in the plant recruitment processes is not usually dissected.
Aims
Here, we used soils of Mediterranean woody plant species to test whether arbuscular mycorrhizal fungi (AMF) and small-size microbiota (< 50 µm) (MB) affect the germination success and growth of eight herbaceous plants.
Results
We documented a significant increase in seedling emergence probability when small-sized MB was present and no effect of AMF. In contrast, the aboveground plant biomass decreased with the presence of MB and increased with that of AMF. Interestingly, those plants growing in the absence of MB and in soils from woody plants associated with higher AMF richness developed higher aboveground biomass.
Conclusion
This study brings new evidence on how soil microbial communities can determine the performance of their associated herb community, and also, how the effects of different microbial guilds may change across the plant ontogeny. Given these results, the differential effect of soil microbial functional guilds should be considered to better understand plant soil legacies and feedbacks, potentially driving plant recruitment and community assembly.Universidad
de Jaén/CBUAThe Spanish
Ministerio de Economía y Competitividad (MEC) throughout
COEXMED II project (CGL2015-69118-C2-1-P)University of Jaén
through Acción 9 programme.Project LifeWatch-SUMHAL-WP5
(LWE2103014) (5.1.7
Intra-population comparison of vegetative and floral trait
12 páginas, 5 tablas.Measuring heritable genetic variation is important for understanding patterns of trait
evolution in wild populations, and yet studies of quantitative genetic parameters
estimated directly in the field are limited by logistic constraints, such as the difficulties of inferring relatedness among individuals in the wild. Marker-based approaches have received attention because they can potentially be applied directly to wild populations.
For long-lived, self-compatible plant species where pedigrees are inadequate, the
regression-based method proposed by Ritland has the appeal of estimating heritabilities
from marker-based estimates of relatedness. The method has been difficult to implement
in some plant populations, however, because it requires significant variance in relatedness across the population. Here, we show that the method can be readily applied to compare the ability of different traits to respond to selection, within populations. For several taxa of the perennial herb genus Aquilegia, we estimated heritabilities of floral and vegetative traits and, combined with estimates of natural selection, compared the ability to respond to selection of both types of traits under current conditions. The intra-population comparisons showed that vegetative traits have a higher potential for evolution, because although they are as heritable as floral traits,
selection on them is stronger. These patterns of potential evolution are consistent with macroevolutionary trends in the European lineage of the genus.We thank Santiago Donat-Caerols and Rafael Jaime for essential help in the laboratory. We are also grateful to Carlos M. Herrera for initial advice and ideas on relatedness analyses.
Anonymous reviewers provided comments that substantially improved the original manuscript. This work was funded by a
Generalitat Valenciana research grant for young researchers (GVPRE ⁄ 2008 ⁄ 076) and project CGL2006-02848 of the Ministerio de Educacio´n y Ciencia and FEDER.Peer reviewe
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