Paleoetnología de la Península Ibérica. Reflexiones y perspectivas de futuro

Sin resume

Excision Dynamics of Vibrio Pathogenicity Island-2 from Vibrio Cholerae: Role of a Recombination Directionality Factor VefA

Vibrio Pathogenicity Island-2 (VPI-2) is a 57 kb region present in choleragenic V. cholerae isolates that is required for growth on sialic acid as a sole carbon source. V. cholerae non-O1/O139 pathogenic strains also contain VPI-2, which in addition to sialic acid catabolism genes also encodes a type 3 secretion system in these strains. VPI-2 integrates into chromosome 1 at a tRNA-serine site and encodes an integrase intV2 (VC1758) that belongs to the tyrosine recombinase family. ntV2 is required for VPI-2 excision from chromosome 1, which occurs at very low levels, and formation of a non-replicative circular intermediate

A New Celtiberian Hacksilber Hoard, c. 200 BCE

This study presents a Hacksilber hoard recently acquired by the ANS and argues for a significant role for Haccksilber in the monetization of Iberia in the third centur

Bone accumulation by leopards in the Late Pleistocene in the Moncayo Massif (Zaragoza, NE Spain)

Eating habits of Panthera pardus are well known. When there are caves in its territory, prey accumulates inside them. This helps to prevent its kill from being stolen by other predators like hyenas. Although the leopard is an accumulator of bones in caves, few studies have been conducted on existing lairs. There are, however, examples of fossil vertebrate sites whose main collecting agent is the leopard. During the Late Pleistocene, the leopard was a common carnivore in European faunal associations. Here we present a new locality of Quaternary mammals with a scarce human presence, the cave of Los Rincones (province of Zaragoza, Spain); we show the leopard to be the main accumulator of the bones in the cave, while there are no interactions between humans and leopards. For this purpose, a taphonomic analysis is performed on different bone-layers of the cave

Estimating soil organic carbon changes in managed temperate moist grasslands with RothC

Temperate grassland soils store significant amounts of carbon (C). Estimating how much livestock grazing and manuring can influence grassland soil organic carbon (SOC) is key to improve greenhouse gas grassland budgets. The Rothamsted Carbon (RothC) model, although originally developed and parameterized to model the turnover of organic C in arable topsoil, has been widely used, with varied success, to estimate SOC changes in grassland under different climates, soils, and management conditions. In this paper, we hypothesise that RothC-based SOC predictions in managed grasslands under temperate moist climatic conditions can be improved by incorporating small modifications to the model based on existing field data from diverse experimental locations in Europe. For this, we described and evaluated changes at the level of: (1) the soil water function of RothC, (2) entry pools accounting for the degradability of the exogenous organic matter (EOM) applied (e.g., ruminant excreta), (3) the month-on-month change in the quality of C inputs coming from plant residues (i.e above-, below-ground plant residue and rhizodeposits), and (4) the livestock trampling effect (i.e., poaching damage) as a common problem in areas with higher annual precipitation. In order to evaluate the potential utility of these changes, we performed a simple sensitivity analysis and tested the model predictions against averaged data from four grassland experiments in Europe. Our evaluation showed that the default model''s performance was 78% and whereas some of the modifications seemed to improve RothC SOC predictions (model performance of 95% and 86% for soil water function and plant residues, respectively), others did not lead to any/or almost any improvement (model performance of 80 and 46% for the change in the C input quality and livestock trampling, respectively). We concluded that, whereas adding more complexity to the RothC model by adding the livestock trampling would actually not improve the model, adding the modified soil water function and plant residue components, and at a lesser extent residues quality, could improve predictability of the RothC in managed grasslands under temperate moist climatic conditions. © 2021 Jebari et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Motivación y adherencia a la práctica de baloncesto en adolescentes

El objetivo del estudio fue examinar y profundizar en las motivaciones de los jugadores de baloncesto adolescentes para seguir practicando su deporte. La muestra estuvo compuesta por un total de 6 jugadores de baloncesto (3 chicas y 3 chicos), con edades comprendidas entre los 14 y los 16 años. Se empleó una entrevista semi-estructurada con preguntas abiertas a cada deportista. Se realizó un análisis de contenido de las transcripciones de las entrevistas, en el que se empleó un procedimiento deductivo. Los resultados revelaron que los siguientes factores tienen una gran importancia para que los jugadores de baloncesto entrevistados mantengan su práctica: una motivación autodeterminada para practicar, la satisfacción de sus necesidades psicológicas, el clima de apoyo a la autonomía que genera el entrenador, así como la relación con éste, el apoyo familiar y la posibilidad de poder compatibilizar con los estudios. En esta línea, se pone de maniesto la necesidad de emplear estrategias para ayudar a que los deportistas adopten formas de motivación autodeterminadas, generar un clima de apoyo a la autonomía en los entrenamientos y en la competición, que facilite la satisfacción de las tres necesidades psicológicas y que, además, el adolescente cuente con el apoyo familiar en las diversas facetas de su vida; todo ello es fundamental para garantizar la adherencia a la práctica entre los jóvenes jugadores de baloncesto.

Active mesh and neural network pipeline for cell aggregate segmentation

Segmenting cells within cellular aggregates in 3D is a growing challenge in cell biology due to improvements in capacity and accuracy of microscopy techniques. Here, we describe a pipeline to segment images of cell aggregates in 3D. The pipeline combines neural network segmentations with active meshes. We apply our segmentation method to cultured mouse mammary gland organoids imaged over 24 h with oblique plane microscopy, a high-throughput light-sheet fluorescence microscopy technique. We show that our method can also be applied to images of mouse embryonic stem cells imaged with a spinning disc microscope. We segment individual cells based on nuclei and cell membrane fluorescent markers, and track cells over time. We describe metrics to quantify the quality of the automated segmentation. Our segmentation pipeline involves a Fiji plugin that implements active mesh deformation and allows a user to create training data, automatically obtain segmentation meshes from original image data or neural network prediction, and manually curate segmentation data to identify and correct mistakes. Our active meshes-based approach facilitates segmentation postprocessing, correction, and integration with neural network prediction

Modelling the distribution of white matter hyperintensities due to ageing on MRI images using Bayesian inference

White matter hyperintensities (WMH), also known as white matter lesions, are localised white matter areas that appear hyperintense on MRI scans. WMH commonly occur in the ageing population, and are often associated with several factors such as cognitive disorders, cardiovascular risk factors, cerebrovascular and neurodegenerative diseases. Despite the fact that some links between lesion location and parametric factors such as age have already been established, the relationship between voxel-wise spatial distribution of lesions and these factors is not yet well understood. Hence, it would be of clinical importance to model the distribution of lesions at the population-level and quantitatively analyse the effect of various factors on the lesion distribution model. In this work we compare various methods, including our proposed method, to generate voxel-wise distributions of WMH within a population with respect to various factors. Our proposed Bayesian spline method models the spatio-temporal distribution of WMH with respect to a parametric factor of interest, in this case age, within a population. Our probabilistic model takes as input the lesion segmentation binary maps of subjects belonging to various age groups and provides a population-level parametric lesion probability map as output. We used a spline representation to ensure a degree of smoothness in space and the dimension associated with the parameter, and formulated our model using a Bayesian framework. We tested our algorithm output on simulated data and compared our results with those obtained using various existing methods with different levels of algorithmic and computational complexity. We then compared the better performing methods on a real dataset, consisting of 1000 subjects of the UK Biobank, divided in two groups based on hypertension diagnosis. Finally, we applied our method on a clinical dataset of patients with vascular disease. On simulated dataset, the results from our algorithm showed a mean square error (MSE) value of , which was lower than the MSE value reported in the literature, with the advantage of being robust and computationally efficient. In the UK Biobank data, we found that the lesion probabilities are higher for the hypertension group compared to the non-hypertension group and further verified this finding using a statistical t-test. Finally, when applying our method on patients with vascular disease, we observed that the overall probability of lesions is significantly higher in later age groups, which is in line with the current literature