Ermeloite is a new aluminium phosphate mineral from Galicia, Spain, in the northwest of the Iberian Peninsula. It is the third formally recognised mineral discovered in Galicia since morenosite and cervantite in the 19th Century. The name and symbol (Erm) were approved by the Commission on New Minerals, Nomenclature and Classification of the International Mineralogical Association (IMA2021–017a) in recognition of the geographical location where it was found. The mineral occurs as a light blue to white fine aggregate over quartz and microcline associated with wardite. Crystals of ~0.04 mm are transparent and have a waxy lustre. The simplified empirical formula determined using electron microprobe analysis is Al1.02P0.95F0.06O3.88⋅1.06 H2O, which is near to the ideal formula Al(PO4)⋅H2O. The mineral is an alteration product within a phosphate pegmatite. Ermeloite is the second phosphate isostructural with the sulfates of the kieserite group. Single-crystal X-ray diffraction showed that ermeloite crystallises in the monoclinic C2/c space group with cell parameters a = 6.5371(4) Å, b = 7.5670(5) Å, c = 7.1146(5) Å, β = 115.335(2)°, V = 318.08(4) Å3 and Z = 4 at room temperature. Comparative analysis of the crystallographic data, with isostructural phosphates, revealed an interesting behaviour for these compounds.

A common task in Earth Sciences is to infer climate information at local and regional scales from global climate models. Dynamical downscaling requires running expensive numerical models at high resolution, which can be prohibitive due to long model runtimes. On the other hand, statistical downscaling techniques present an alternative approach for learning links between the large- and local-scale climate in a more efficient way. A large number of deep neural network-based approaches for statistical downscaling have been proposed in recent years, mostly based on convolutional architectures developed for computer vision and super-resolution tasks. This paper presents deep learning for empirical downscaling (DL4DS), a python library that implements a wide variety of state-of-the-art and novel algorithms for downscaling gridded Earth Science data with deep neural networks. DL4DS has been designed with the goal of providing a general framework for training convolutional neural networks with configurable architectures and learning strategies to facilitate the conduction of comparative and ablation studies in a robust way. We showcase the capabilities of DL4DS on air quality Copernicus Atmosphere Monitoring Service (CAMS) data over the western Mediterranean area. The DL4DS library can be found in this repository: https://github.com/carlos-gg/dl4ds

Social factors impact sentence comprehension in a first language (L1), suggesting that semantic processing cannot be dissociated from social and moral emotions in relation to pro/antisocial individuals. Given that integrating multiple types of information and processing emotion-laden pragmatic information is costlier in a second language (L2), we investigated whether social factors would affect discourse comprehension similarly in L2. Processing the outcomes of scenarios involving pro/antisocial protagonists provoked similar neural patterns in L2 as in L1 (Rodríguez-Gómez, Martín-Loeches, Colmenares, Romero Ferreiro & Moreno, 2020), suggesting that L2 users simultaneously integrate semantic and discourse-pragmatic information during sentence comprehension.

The burden of depression is increasing worldwide, specifically in older adults. Unhealthy dietary patterns may partly explain this phenomenon. In the Spanish PREDIMED-Plus study, we explored (1) the cross-sectional association between the adherence to the Prime Diet Quality Score (PDQS), an a priori-defined high-quality food pattern, and the prevalence of depressive symptoms at baseline (cross-sectional analysis) and (2) the prospective association of baseline PDQS with changes in depressive symptomatology after 2 years of follow-up. After exclusions, we assessed 6612 participants in the cross-sectional analysis and 5523 participants in the prospective analysis. An energy-adjusted high-quality dietary score (PDQS) was assessed using a validated FFQ. The cross-sectional association between PDQS and the prevalence of depression or presence of depressive symptoms and the prospective changes in depressive symptoms were evaluated through multivariable regression models (logistic and linear models and mixed linear-effects models). PDQS was inversely associated with depressive status in the cross-sectional analysis. Participants in the highest quintile of PDQS (Q5) showed a significantly reduced odds of depression prevalence as compared to participants in the lowest quartile of PDQS (Q1) (OR (95 %) CI = 0·82 (0·68, 0·98))). The baseline prevalence of depression decreased across PDQS quintiles (Pfor trend = 0·015). A statistically significant association between PDQS and changes in depressive symptoms after 2-years follow-up was found (β (95 %) CI = −0·67 z-score (–1·17, −0·18). A higher PDQS was cross-sectionally related to a lower depressive status. Nevertheless, the null finding in our prospective analysis raises the possibility of reverse causality. Further prospective investigation is required to ascertain the association between PDQS and changes in depressive symptoms along time.

We record for the first time a nominal species of the genus Microscleroderma in the Western Atlantic, and the first record of the genus Amphibleptula in Mexico. Two new species of ‘lithistid’ Tetractinellida are described: Microscleroderma mexica sp. nov. from crevices of two reefs in Veracruz, Mexico, and Amphibleptula aaktun sp. nov. from the anchialine cave El Aerolito, Cozumel Island, Mexico. Microscleroderma mexica sp. nov. is characterized by its tubular and cup-shaped to wavy laminar habitus, hirsute surface and two size categories of oxea diactines. Amphibleptula aaktun sp. nov. is characterized by its ficiform or lobular habitus with an exhalant area at the flat top of the body, divided into numerous vertical septa-like structures; its desmas have thinner epirhabds, and thinner oxea than those of Amphibleptula madrepora. This is the second species of Amphibleptula that has been described; hence, we propose the redefinition of the genus. Both species described have a shallow distribution (≤21 m depth); they are differentiated from each other by the disjunction between their habitats as well as by their morphology.

A crossover trial was conducted to compare hand rub and hand scrub-brush methods for reducing bacterial loads when using propan-1-ol-60% according to European regulations. Both methods significantly reduced the bacterial load immediately after antisepsis, but only the hand rub method achieved significant bacterial load reduction 3 hours after the procedure.

Saccocoelioides is a genus of trematodes associated with fishes from the Americas. In the current research, morphologically distinct specimens of Saccocoelioides spp. were collected from six countries in Middle America. Specimens were sequenced using three molecular markers, the domains D1–D3 of the large subunit (LSU) from the nuclear rDNA, the cytochrome c oxidase subunit 1 (cox1) and nicotinamide adenine dinucleotide dehydrogenase subunit 1 (nad1) from mitochondrial DNA. A total of 74 new sequences were compared and aligned with other sequences available in GenBank. Maximum likelihood and Bayesian inference analyses were inferred from the LSU and cox1 datasets, revealing unequivocally that all the specimens correspond to S. lamothei. A haplotype network was built with 119 sequences of the nad1 gene. The network detected 57 distinct haplotypes divided into three haplogroups. To explore morphological differences among samples of S. lamothei, 17 morphological features were measured from 53 specimens from three fish families: Eleotridae, Mugilidae and Gobiidae. Principal component analysis yielded three main polygons that corresponded with each family analysed, suggesting host-induced phenotypic plasticity. The current evidence suggests that S. lamothei infects at least five fish families along the Pacific coasts of Mexico, Guatemala, El Salvador, Honduras, Nicaragua and Costa Rica.

The formation of massive stars remains one of the most intriguing questions in astrophysics today. The main limitations result from the difficulty to obtain direct observational constraints on the formation process itself. In this context, the Carina High-contrast Imaging Project of massive Stars (CHIPS) aims to observe all 80+ O stars in the Carina nebula using the new VLT 2nd-generation extreme-AO instrument SPHERE. This instrument offers unprecedented imaging contrast allowing us to detect the faintest companions around massive stars. These novel observational constraints will help to discriminate between the different formation scenarios by comparing their predictions for companion statistics and properties.

This study’s aim is to adapt the Health Care Communication Questionnaire in a Spanish sample, and then test the psychometric properties of the adapted instrument. To do so, the questionnaire was adapted for the Spanish context and then applied in a pilot study as well as a final study. The final sample consisted of 200 patients at Morales Meseguer Hospital in Murcia, Spain. The results show that this adaptation’s psychometric properties were similar to those of the original questionnaire. As for item analysis, all items obtained discriminant indices > .30. Confirmatory factor analysis revealed the same structure as that of the original questionnaire (χ2/df = 1.345; CFI = .983; IFI = .983; TLI = .977; RMSEA = .042), with indices reflecting adequate goodness of fit. Also, results from the analysis of each dimension’s internal consistency had coefficients between .71 and .86. We conclude that the Spanish version of the HCCQ has adequate psychometric properties, is useful, and will serve its purpose in the context in which it will be used.

Our objective in this study was to determine the density of the jaguar Panthera onca from camera-trap data, using an open population model, in a private protected natural area, the Northern Jaguar Reserve, and 10 adjoining cattle ranches in the state of Sonora, Mexico. The region is considered a long–term jaguar conservation unit. As well as being the most northerly recorded reproductive population of the jaguar, the arid habitat of this region is atypical for the species. During 16 months of sampling we identified 10 individual jaguars and the data met the three main assumptions of open population models. The estimated mean density was 1.05±SE 0.4 individuals per 100 km2, with a constant survival probability of 0.94 and capture probability of 0.23. This estimate of density is lower than reported in studies of the jaguar from more southerly locations in Mexico, Belize, Costa Rica, Bolivia and Brazil but cannot be attributed to a single factor even though in general there is an apparent relationship between jaguar density and precipitation. The main objectives of the management of the Northern Jaguar Reserve are to reduce the impact of cattle and restore jaguar habitat, with strategies focused on water retention, removal of invasive grass, reforestation and environmental education. Livestock have been gradually excluded since 2003 and, combined with the protection provided under the agreements with the surrounding ranches, the area is now a suitable place for long-term studies of the jaguar.

Solidification of a simple casting made of ductile iron is mathematically modeled in this work. The model is able to numerically simulate the cooling rate and solidification of the whole casting system composed by a cubic piece, a blind riser connected through a rectangular neck, and immersed in a green sand mold. The center of the neck acts as a valve that allows the flow of liquid metal between the casting and the riser based on the feeding technique known as Pressure Control Risering (PCR). The developed model couples the energy conservation equation and the solidification kinetics of ductile iron, through the statement of proper nucleation and growth laws. This model is satisfactorily validated by comparing the thermal histories predictions with experimental cooling curves obtained in the foundry laboratory for the same casting. According to a process analysis developed in this work, the pouring temperature is the variable that affects the most the solidification and the feeding behavior, since it increases significantly the solidification times in all regions of the casting system.