Cardiovascular fitness influences self-concept during the adolescence

Self-concept is one of the most researched constructs within the framework of educational psychology, psychology of the personality, and social psychology. Self-concept can be defined as the perception that an individual has of him or herself, with contents that are both descriptive, as well as evaluative 1. In the field of sport and exercise, physical selfconcept is also considered playing a considerable role in sports performance 2. The present study aims to investigate the relationship between sports practice, cardiovascular fitness and physical self-concept in adolescents.Ciencias de la Actividad Física y del Deport

Industry 4.0-based dynamic Social Organizational Life Cycle Assessment to target the social circular economy in manufacturing

Nowadays in manufacturing, the topic of sustainability plays a key role. However, over the years, economic crises and the climate change debate have focused the attention of scholars, industrialists and policy makers mainly on environmental sustainability, putting social sustainability on the back burner. This is also evident in the scientific literature which highlights several knowledge gaps. The digital transition of factories and Industry 4.0 technologies have not yet been fully exploited to correlate production and social metrics. As a result, there is a lack of adequate tools for monitoring social performance in the factory environment. In this context, the social dimension of the circular economy is still an under-researched topic. This study aims to fill these gaps by integrating Social Organizational Life Cycle Assessment (SO-LCA) and Industry 4.0 technologies in a blended methodological approach designed to dynamically monitor the social performance of a major manufacturing industry. Using primary data, a set of site-specific social indicators and indexes were created to assess the organization's social impact against key stakeholder categories and subcategories. Finally, within that set, those social metrics that the organization considers essential to moving toward the circular economy were identified. Therefore, this study, has contributed to fill the literature gaps by demonstrating that the digitization of production processes, not only enables the assessment of environmental impact, but can also play a key role in knowing the social performance of a manufacturing organization and to identify the hidden social dimension in the circular economy

El botulismo: una de las causas de mortalidad bovina en los Llanos Orientales de Colombia.

Ilustra al ganadero sobre el conocimiento del botulismo bovino, su identificación, métodos para prevenirlo y controlarlo, y recomendaciones prácticas para evitarlo y diferenciarlo de otras enfermedades que también ocasionan mortalidad bovinaGanadería bovin

Levodopa-Induced Dyskinesia in Parkinson Disease Specifically Associates with Dopaminergic Depletion in Sensorimotor-Related Functional Subregions of the Striatum

[Purpose] To determine whether the development of levodopa-induced dyskinesia (LID) in Parkinson disease (PD) specifically relates to dopaminergic depletion in sensorimotor-related subregions of the striatum.[Methods] Our primary study sample consisted of 185 locally recruited PD patients, of which 73 (40%) developed LID. Retrospective 123I-FP-CIT SPECT data were used to quantify the specific dopamine transporter (DAT) binding ratio within distinct functionally defined striatal subregions related to limbic, executive, and sensorimotor systems. Regional DAT levels were contrasted between patients who developed LID (PD + LID) and those who did not (PD-LID) using analysis of covariance models controlled for demographic and clinical features. For validation of the findings and assessment of the evolution of LID-associated DAT changes from an early disease stage, we also studied serial 123I-FP-CIT SPECT data from 343 de novo PD patients enrolled in the Parkinson Progression Marker’s Initiative using mixed linear model analysis.[Results] Compared with PD-LID, DAT level reductions in PD + LID patients were most pronounced in the sensorimotor striatal subregion (F = 5.99, P = 0.016) and also significant in the executive-related subregion (F = 5.30, P = 0.023). In the Parkinson Progression Marker’s Initiative cohort, DAT levels in PD + LID (n = 161, 47%) were only significantly reduced compared with PD-LID in the sensorimotor striatal subregion (t = −2.05, P = 0.041), and this difference was already present at baseline and remained largely constant over time.[Conclusion] Measuring DAT depletion in functionally defined sensorimotor-related striatal regions of interest may provide a more sensitive tool to detect LID-associated dopaminergic changes at an early disease stage and could improve individual prognosis of this common clinical complication in PD.Peer reviewe

The ALHAMBRA Survey: Bayesian Photometric Redshifts with 23 bands for 3 squared degrees

The ALHAMBRA (Advance Large Homogeneous Area Medium Band Redshift Astronomical) survey has observed 8 different regions of the sky, including sections of the COSMOS, DEEP2, ELAIS, GOODS-N, SDSS and Groth fields using a new photometric system with 20 contiguous ~ $300\AA$ filters covering the optical range, combining them with deep $JHKs$ imaging. The observations, carried out with the Calar Alto 3.5m telescope using the wide field (0.25 sq. deg FOV) optical camera LAICA and the NIR instrument Omega-2000, correspond to ~700hrs on-target science images. The photometric system was designed to maximize the effective depth of the survey in terms of accurate spectral-type and photo-zs estimation along with the capability of identification of relatively faint emission lines. Here we present multicolor photometry and photo-zs for ~438k galaxies, detected in synthetic F814W images, complete down to I~24.5 AB, taking into account realistic noise estimates, and correcting by PSF and aperture effects with the ColorPro software. The photometric ZP have been calibrated using stellar transformation equations and refined internally, using a new technique based on the highly robust photometric redshifts measured for emission line galaxies. We calculate photometric redshifts with the BPZ2 code, which includes new empirically calibrated templates and priors. Our photo-zs have a precision of $dz/(1+z_s)=1$ for I<22.5 and 1.4% for 22.5<I<24.5. Precisions of less than 0.5% are reached for the brighter spectroscopic sample, showing the potential of medium-band photometric surveys. The global $P(z)$ shows a mean redshift =0.56 for I=0.86 for I<24.5 AB. The data presented here covers an effective area of 2.79 sq. deg, split into 14 strips of 58.5'x15.5' and represents ~32 hrs of on-target.Comment: The catalog data and a full resolution version of this paper is available at https://cloud.iaa.csic.es/alhambra

Transfer learning for galaxy morphology from one survey to another

© 2018 The Author(s). Published by Oxford University Press on behalf of the Royal Astronomical Society.Deep Learning (DL) algorithms for morphological classification of galaxies have proven very successful, mimicking (or even improving) visual classifications. However, these algorithms rely on large training samples of labelled galaxies (typically thousands of them). A key question for using DL classifications in future Big Data surveys is how much of the knowledge acquired from an existing survey can be exported to a new dataset, i.e. if the features learned by the machines are meaningful for different data. We test the performance of DL models, trained with Sloan Digital Sky Survey (SDSS) data, on Dark Energy survey (DES) using images for a sample of $\sim$5000 galaxies with a similar redshift distribution to SDSS. Applying the models directly to DES data provides a reasonable global accuracy ($\sim$ 90%), but small completeness and purity values. A fast domain adaptation step, consisting in a further training with a small DES sample of galaxies ($\sim$500-300), is enough for obtaining an accuracy > 95% and a significant improvement in the completeness and purity values. This demonstrates that, once trained with a particular dataset, machines can quickly adapt to new instrument characteristics (e.g., PSF, seeing, depth), reducing by almost one order of magnitude the necessary training sample for morphological classification. Redshift evolution effects or significant depth differences are not taken into account in this study.Peer reviewedFinal Accepted Versio