Weaving Knowledge: An Analysis of the Convergences between 21st Century Competencies, Mathematics and Art

El objetivo del presente artículo es analizar la incidencia de las competencias del siglo XXI marcadas por los avances tecnológicos, en las dinámicas y exigencias de los contextos sociales actuales particularmente en los procesos de enseñanza aprendizaje. Dicho análisis se realiza desde la perspectiva educativa a partir de la (revisión teórica) interpretación de investigaciones y estudios relacionados con el desarrollo de Competencias para el Siglo XXI. Las dinámicas de esta nueva realidad de la sociedad exigen transformaciones en los perfiles de los ciudadanos y eso obliga a que los sistemas educativos y en términos generales, a que todo lo relacionado con educación, a generar cambios, resultado de reflexionar sobre las necesidades del contexto. Se abordan aspectos relevantes hoy en día relacionados con las competencias para el siglo XXI, como las competencias digitales y el pensamiento computacional en relación con el arte, las matemáticas y la neuroeducación; esto es importante en los procesos de enseñanza-aprendizaje: comprender que cada individuo tiene unas características especiales, insinuando que no todos los estudiantes aprenden igual. Preguntas orientadoras ¿Qué es competencia? ¿Qué significa competencia del siglo XXI? ¿Cómo están categorizadas las competencias del siglo XXI? ¿Cuáles son las competencias del siglo XXI? ¿De qué manera convergen las competencias del siglo XXI en el desarrollo de habilidades matemáticas, artísticas? ¿Cuál es el papel de las neurociencias en el ámbito educativo?The objective of this article is to analyze the impact of 21st century competencies marked by technological advances, on the dynamics and demands of current social contexts, particularly in teaching-learning processes. This analysis is carried out from an educational perspective based on the (theoretical review) interpretation of research and studies related to the development of Competencies for the 21st Century. Without a doubt, the dynamics of this new reality of society demand transformations in the profiles of citizens and that forces educational systems and, in general terms, everything that has to do with education, to generate some changes, as a result of reflect on the needs of the context. Aspects that are relevant today and that have to do with skills for the 21st century are then addressed, such as digital skills and computational thinking in relation to, for example, art, mathematics and neuroeducation; the latter as a very important factor in the teaching-learning processes: understanding that each individual has special characteristics implies that not all students in a classroom learn in the same way

Forest growth responses to drought at short- and long-term scales in Spain: squeezing the stress memory from tree rings

Drought-triggered declines in forest productivity and associated die-off events have increased considerably due to climate warming in the last decades. There is an increasing interest in quantifying the resilience capacity of forests against climate warming and drought to uncover how different stands and tree species will resist and recover after more frequent and intense droughts. Trees form annual growth rings that represent an accurate record of how forest growth responded to past droughts. Here we use dendrochronology to quantify the radial growth of different forests subjected to contrasting climatic conditions in Spain during the last half century. Particularly, we considered four climatically contrasting areas where dominant forests showed clear signs of drought-induced dieback. Studied forests included wet sites dominated by silver fir (Abies alba) in the Pyrenees and beech (Fagus sylvatica) stands in northern Spain, and drought-prone sites dominated by Scots pine (Pinus sylvestris) in eastern Spain and black pine (Pinus nigra) in the semi-arid south-eastern Spain. We quantified the growth reduction caused by different droughts and assessed the short-and long-term resilience capacity of declining vs. non-declining trees in each forest. In all cases, drought induced a marked growth reduction regardless tree vigor. However, the capacity to recover after drought (resilience) at short- and long-term scales varied greatly between declining and non-declining individuals. .

Longitudinal association of dietary acid load with kidney function decline in an older adult population with metabolic syndrome

Background: Diets high in acid load may contribute to kidney function impairment. This study aimed to investigate the association between dietary acid load and 1-year changes in glomerular filtration rate (eGFR) and urine albumin/creatinine ratio (UACR). Methods: Older adults with overweight/obesity and metabolic syndrome (mean age 65 ± 5 years, 48% women) from the PREDIMED-Plus study who had available data on eGFR (n = 5,874) or UACR (n = 3,639) at baseline and after 1 year of follow-up were included in this prospective analysis. Dietary acid load was estimated as potential renal acid load (PRAL) and net endogenous acid production (NEAP) at baseline from a food frequency questionnaire. Linear and logistic regression models were fitted to evaluate the associations between baseline tertiles of dietary acid load and kidney function outcomes. One year-changes in eGFR and UACR were set as the primary outcomes. We secondarily assessed ≥ 10% eGFR decline or ≥10% UACR increase. Results: After multiple adjustments, individuals in the highest tertile of PRAL or NEAP showed higher one-year changes in eGFR (PRAL, β: -0.64 ml/min/1.73 m2; 95% CI: -1.21 to -0.08 and NEAP, β: -0.56 ml/min/1.73 m2; 95% CI: -1.13 to 0.01) compared to those in the lowest category. No associations with changes in UACR were found. Participants with higher levels of PRAL and NEAP had significantly higher odds of developing ≥10% eGFR decline (PRAL, OR: 1.28; 95% CI: 1.07-1.54 and NEAP, OR: 1.24; 95% CI: 1.03-1.50) and ≥10 % UACR increase (PRAL, OR: 1.23; 95% CI: 1.04-1.46) compared to individuals with lower dietary acid load. Conclusions: Higher PRAL and NEAP were associated with worse kidney function after 1 year of follow-up as measured by eGFR and UACR markers in an older Spanish population with overweight/obesity and metabolic syndrome. Keywords: albuminuria; chronic kidney disease (CKD); dietary acid load; glomerular filtration rate (GFR); kidney function; net endogenous acid production (NEAP); potential renal acid load (PRAL); renal nutrition

Longitudinal association of dietary acid load with kidney function decline in an older adult population with metabolic syndrome

Background: Diets high in acid load may contribute to kidney function impairment. This study aimed to investigate the association between dietary acid load and 1-year changes in glomerular filtration rate (eGFR) and urine albumin/creatinine ratio (UACR). Methods: Older adults with overweight/obesity and metabolic syndrome (mean age 65 ± 5 years, 48% women) from the PREDIMED-Plus study who had available data on eGFR (n = 5,874) or UACR (n = 3,639) at baseline and after 1 year of follow-up were included in this prospective analysis. Dietary acid load was estimated as potential renal acid load (PRAL) and net endogenous acid production (NEAP) at baseline from a food frequency questionnaire. Linear and logistic regression models were fitted to evaluate the associations between baseline tertiles of dietary acid load and kidney function outcomes. One year-changes in eGFR and UACR were set as the primary outcomes. We secondarily assessed ≥ 10% eGFR decline or ≥10% UACR increase. Results: After multiple adjustments, individuals in the highest tertile of PRAL or NEAP showed higher one-year changes in eGFR (PRAL, β: –0.64 ml/min/1.73 m2; 95% CI: –1.21 to –0.08 and NEAP, β: –0.56 ml/min/1.73 m2; 95% CI: –1.13 to 0.01) compared to those in the lowest category. No associations with changes in UACR were found. Participants with higher levels of PRAL and NEAP had significantly higher odds of developing ≥10% eGFR decline (PRAL, OR: 1.28; 95% CI: 1.07–1.54 and NEAP, OR: 1.24; 95% CI: 1.03–1.50) and ≥10 % UACR increase (PRAL, OR: 1.23; 95% CI: 1.04–1.46) compared to individuals with lower dietary acid load. Conclusions: Higher PRAL and NEAP were associated with worse kidney function after 1 year of follow-up as measured by eGFR and UACR markers in an older Spanish population with overweight/obesity and metabolic syndrome

Plasticity in dendroclimatic response across the distribution range of Aleppo pine (Pinus halepensis)

We investigated the variability of the climate-growth relationship of Aleppo pine across its distribution range in the Mediterranean Basin. We constructed a network of tree-ring index chronologies from 63 sites across the region. Correlation function analysis identified the relationships of tree-ring index to climate factors for each site. We also estimated the dominant climatic gradients of the region using principal component analysis of monthly, seasonal, and annual mean temperature and total precipitation from 1,068 climatic gridpoints. Variation in ring width index was primarily related to precipitation and secondarily to temperature. However, we found that the dendroclimatic relationship depended on the position of the site along the climatic gradient. In the southern part of the distribution range, where temperature was generally higher and precipitation lower than the regional average, reduced growth was also associated with warm and dry conditions. In the northern part, where the average temperature was lower and the precipitation more abundant than the regional average, reduced growth was associated with cool conditions. Thus, our study highlights the substantial plasticity of Aleppo pine in response to different climatic conditions. These results do not resolve the source of response variability as being due to either genetic variation in provenance, to phenotypic plasticity, or a combination of factors. However, as current growth responses to inter-annual climate variability vary spatially across existing climate gradients, future climate-growth relationships will also likely be determined by differential adaptation and/or acclimation responses to spatial climatic variation. The contribution of local adaptation and/or phenotypic plasticity across populations to the persistence of species under global warming could be decisive for prediction of climate change impacts across populations. In this sense, a more complex forest dynamics modeling approach that includes the contribution of genetic variation and phenotypic plasticity can improve the reliability of the ecological inferences derived from the climate-growth relationships.This work was partially supported by Spanish Ministry of Education and Science co-funded by FEDER program (CGL2012-31668), the European Union and the National Ministry of Education and Religion of Greece (EPEAEK- Environment – Archimedes), the Slovenian Research Agency (program P4-0015), and the USDA Forest Service. The cooperation among international partners was supported by the COST Action FP1106, STREeSS

Comparison of PM10 concentrations and metal content in three different sites of the Venice Lagoon: An analysis of possible aerosol sources

The Venice Lagoon is exposed to atmospheric pollutants from industrial activities, thermoelectric power plants, petrochemical plants, incinerator, domestic heating, ship traffic, glass factories and vehicular emissions on the mainland. In 2005, construction began on the mobile dams (MOSE), one dam for each channel connecting the lagoon to the Adriatic Sea as a barrier against high tide. These construction works could represent an additional source of pollutants. PM10 samples were taken on random days between 2007 and 2010 at three different sites: Punta Sabbioni, Chioggia and Malamocco, located near the respective dam construction worksites. Chemical analyses of V, Cr, Fe, Co, Ni, Cu, Zn, As, Mo, Cd, Sb, Tl and Pb in PM10 samples were performed by Inductively coupled plasmaquadrupole mass spectrometry (ICP-QMS) and results were used to identify the main aerosol sources. The correlation of measured data with meteorology, and source apportionment, failed to highlight a contribution specifically associated to the emissions of the MOSE construction works. The comparison of the measurements at the three sites showed a substantial homogeneity of metal concentrations in the area. Source apportionment with principal component analysis (PCA) and positive matrix factorization (PMF) showed that a four principal factors model could describe the sources of metals in PM10. Three of them were assigned to specific sources in the area and one was characterised as a source of mixed origin (anthropogenic and crustal). A specific anthropogenic source of PM10 rich in Ni and Cr, active at the Chioggia site, was also identified

Clonal chromosomal mosaicism and loss of chromosome Y in elderly men increase vulnerability for SARS-CoV-2

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, COVID-19) had an estimated overall case fatality ratio of 1.38% (pre-vaccination), being 53% higher in males and increasing exponentially with age. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, we found 133 cases (1.42%) with detectable clonal mosaicism for chromosome alterations (mCA) and 226 males (5.08%) with acquired loss of chromosome Y (LOY). Individuals with clonal mosaic events (mCA and/or LOY) showed a 54% increase in the risk of COVID-19 lethality. LOY is associated with transcriptomic biomarkers of immune dysfunction, pro-coagulation activity and cardiovascular risk. Interferon-induced genes involved in the initial immune response to SARS-CoV-2 are also down-regulated in LOY. Thus, mCA and LOY underlie at least part of the sex-biased severity and mortality of COVID-19 in aging patients. Given its potential therapeutic and prognostic relevance, evaluation of clonal mosaicism should be implemented as biomarker of COVID-19 severity in elderly people. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, individuals with clonal mosaic events (clonal mosaicism for chromosome alterations and/or loss of chromosome Y) showed an increased risk of COVID-19 lethality

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches