Relaxation Dynamics in See-Saw Shaped Dy(III) Single-Molecule Magnets

Utilizing a terphenyl bisanilide ligand, two Dy(III) compounds [K(DME)n][LArDy(X)2] (LAr = {C6H4[(2,6-iPrC6H3)NC6H4]2}2−), X = Cl (1) and X = I (2) were synthesized. The ligand imposes an unusual see-saw shaped molecular geometry leading to a coordinatively unsaturated metal complex with near-linear N–Dy–N (avg. 159.9° for 1 and avg. 160.4° for 2) angles. These compounds exhibit single-molecule magnet (SMM) behavior with significant uniaxial magnetic anisotropy as a result of the transverse coordination of the bisanilide ligand which yields high energy barriers to magnetic spin reversal of Ueff = 1334 K/927 cm−1 (1) and 1278 K/888 cm−1 (2) in zero field. Ab initio calculations reveal that the dominant crystal field of the bisanilide ligand controls the orientation of the main magnetic axis which runs nearly parallel to the N–Dy–N bonds, despite the identity of the halide ligand. Analysis of the relaxation dynamics reveals a ca. 14-fold decrease in the rate of quantum tunneling of the magnetisation when X = I (2). Most notably, the relaxation times were on average 5.6× longer at zero field when the heavier group 17 congener was employed. However, no direct evidence of a heavy atom effect on the Orbach relaxation was obtained as the height of the barrier is defined by the dominant bisanilide ligand.<br/

Fighting obesity: non-pharmacological interventions

The abnormal or excessive fat accumulation that impairs health is one of the criteria that fulfills obesity. According to epidemiological data, obesity has become a worldwide public health problem that in turn would trigger additional pathologies such as cardiorespiratory dysfunctions, cancer, gastrointestinal disturbances, depression, sleep disorders, just to mention a few.info:eu-repo/semantics/publishedVersio

Integrating data types to estimate spatial patterns of avian migration across the Western Hemisphere

For many avian species, spatial migration patterns remain largely undescribed, especially across hemispheric extents. Recent advancements in tracking technologies and high-resolution species distribution models (i.e., eBird Status and Trends products) provide new insights into migratory bird movements and offer a promising opportunity for integrating independent data sources to describe avian migration. Here, we present a three-stage modeling framework for estimating spatial patterns of avian migration. First, we integrate tracking and band re-encounter data to quantify migratory connectivity, defined as the relative proportions of individuals migrating between breeding and nonbreeding regions. Next, we use estimated connectivity proportions along with eBird occurrence probabilities to produce probabilistic least-cost path (LCP) indices. In a final step, we use generalized additive mixed models (GAMMs) both to evaluate the ability of LCP indices to accurately predict (i.e., as a covariate) observed locations derived from tracking and band re-encounter data sets versus pseudo-absence locations during migratory periods and to create a fully integrated (i.e., eBird occurrence, LCP, and tracking/band re-encounter data) spatial prediction index for mapping species-specific seasonal migrations. To illustrate this approach, we apply this framework to describe seasonal migrations of 12 bird species across the Western Hemisphere during pre- and postbreeding migratory periods (i.e., spring and fall, respectively). We found that including LCP indices with eBird occurrence in GAMMs generally improved the ability to accurately predict observed migratory locations compared to models with eBird occurrence alone. Using three performance metrics, the eBird + LCP model demonstrated equivalent or superior fit relative to the eBird-only model for 22 of 24 species–season GAMMs. In particular, the integrated index filled in spatial gaps for species with over-water movements and those that migrated over land where there were few eBird sightings and, thus, low predictive ability of eBird occurrence probabilities (e.g., Amazonian rainforest in South America). This methodology of combining individual-based seasonal movement data with temporally dynamic species distribution models provides a comprehensive approach to integrating multiple data types to describe broad-scale spatial patterns of animal movement. Further development and customization of this approach will continue to advance knowledge about the full annual cycle and conservation of migratory birds