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Research Findings: The present study evaluated the impact of a working memory (WM) stimulation program on the development of WM and early literacy skills (ELS) in preschoolers from socioeconomically deprived rural and urban schools in Chile. The sample consisted of 268 children, 144 in the intervention group and 124 in the comparison group. The computer-based intervention comprised 16 sessions of 30 min each. Children in the intervention group demonstrated significantly more progress in WM than those in the comparison group when we evaluated them 3 months after exposure to the program and controlled for initial differences with an analysis of covariance. ELS were significantly stronger in children who were exposed to the stimulation program, which supports a link between WM and ELS. Practice or Policy: Results suggest that children's WM can be improved from an early age regardless of socioeconomic context or geographic location (rural or urban). This has direct implications for early education and may compensate for some of the difficulties that children experience when starting school.

It is not possible to think about the quality of education in a country without considering the training of teachers. Among the many skills that teachers in Chile need to develop, one of the weakest in this country is monitoring and assessing student learning. This represents a challenge for initial teacher training. The purpose of this study was to analyze the learning opportunities in assessment reported by 724 students training to be basic education teachers in the last year of their programs at 23 Chilean universities, who answered a survey about their preparation in terms of assessment of student learning in late 2014. The results show that little attention is given to the specificities of assessing the subjects they will teach, although there is a balance between theoretical and practical approaches. Given the sample studied, the availability of the instrument, and that the cohort analyzed was trained before the publication of the standards for initial teacher training (2012) and before the regulations for initial training were introduced under Law 20,903, this study constitutes a baseline to monitor possible changes as a consequence of these policies. [ABSTRACT FROM AUTHOR]

The Asteroid Terrestrial impact Last Alert System (ATLAS) system consists of two 0.5 m Schmidt telescopes with cameras covering 29 square degrees at plate scale of 1.86 arcsec per pixel. Working in tandem, the telescopes routinely survey the whole sky visible from Hawaii (above) every two nights, exposing four times per night, typically reaching magnitude per exposure when the moon is illuminated and magnitude per exposure in dark skies. Construction is underway of two further units to be sited in Chile and South Africa which will result in an all-sky daily cadence from 2021. Initially designed for detecting potentially hazardous near earth objects, the ATLAS data enable a range of astrophysical time domain science. To extract transients from the data stream requires a computing system to process the data, assimilate detections in time and space and associate them with known astrophysical sources. Here we describe the hardware and software infrastructure to produce a stream of clean, real, astrophysical transients in real time. This involves machine learning and boosted decision tree algorithms to identify extragalactic and Galactic transients. Typically we detect 10–15 supernova candidates per night which we immediately announce publicly. The ATLAS discoveries not only enable rapid follow-up of interesting sources but will provide complete statistical samples within the local volume of 100 Mpc. A simple comparison of the detected supernova rate within 100 Mpc, with no corrections for completeness, is already significantly higher (factor 1.5 to 2) than the current accepted rates. [ABSTRACT FROM AUTHOR]

As globalization continues, the spread of invasive species is accelerating, posing a severe threat to native biodiversity. To manage such species, reduce their negative impact on native biota and utilize management costs efficiently, a profound understanding of their geographical distribution pattern is mandatory. In this study, the species distribution model Maxent was used to predict the potential spatial distribution of U. europaeus. To account for sampling bias, three bias correction methods were applied, including a novel approach to increase the number of presence points by sampling occurrences based on satellite images. Furthermore, a decision structured process was used to evaluate and select optimal Maxent parameterization and account for limitations of single evaluation criteria. The currently suitable area of U. europaeus is primarily distributed in the coastal and central regions of Chilean natural region Zona Sur in south-central Chile. Annual mean temperature (bio1), annual precipitation (bio12), and precipitation seasonality (bio15) were the most important environmental variables that affected the distribution of U. europaeus. The sampling of additional presence points could effectively correct for sampling bias in species occurrence data. The use of a decision structured process for model evaluation proved to be useful in determining optimal model parameterization for decreased model complexity. This study highlights the importance of optimized Maxent calibrations to yield results as accurately as possible. The predicted suitable habitats can inform nature conservation planners and landscape managers to guide and prioritize conservation measures. • Various bias correction methods were compared for predicting U. europaeus in Chile using a species distribution model • The method including satellite images received highest precision when used for sampling bias correction • A structured decision process was used successfully for optimal Maxent parameterization • Annual mean temperature, annual precipitation and precipitation seasonality were the most important environmental variables [ABSTRACT FROM AUTHOR]

A sample of volcanic ashes emanated from the Osorno volcano, southern Chile, was characterized with X-ray fluorescence, X-ray diffraction and 57Fe Mössbauer spectroscopy, in an attempt to identify the iron-bearing minerals of that geologically recent magmatic deposit. X-ray patterns indicated that the sample is mainly constituted of anorthite, Fe-diopside-type and Ca-magnetite. The crystallographic structures of these dominant iron minerals are proposed on basis of their chemical composition and corresponding Mössbauer data to support models refined by fitting powder X-ray diffraction data with the Rietveld algorithm. [ABSTRACT FROM AUTHOR]

Introduction. There is substantial research literature emphasizing the positive impact of early literacy learning on school achievement, mainly in the case of children at-risk, but there aren't adequate conditions at school to develop them. Here we examine the effects of teaching reading and writing strategies on at-risk children's literacy learning (mean 5 years 3 months). Material and method: 339 at-risk Chilean children (132 in the intervention group and 207 in the comparison group) were assessed, at the beginning and the end of the school year. Teachers participated in a 1-year professional development intervention that included training and expert coaching. Results: Positive intervention effects were observed on children's knowledge of the alphabet, emergent writing and word identification. There was no effect on children's oral language out-comes. Discussion: The results were contrasted with other programs, showing the importance of training and coaching educators. [ABSTRACT FROM AUTHOR]

Column leaching experiments were conducted to test the ability of Chilean volcanic soils in retaining the mineral constituents and metals in sewage sludge and sludge ash that were incorporated into the soils. Small or negligible amounts of the total content of Pb, Fe, Cr, Mn, Cd, and Zn (0 to <2%), and more significant amounts of mineral constituents such as Na (7–9%), Ca (7–13%), PO4 (4–10%), and SO4 (39–46%) in the sludge and sludge ash were readily soluble. When they were incorporated on the surface layer of the soils and leached with 12 pore volumes of water over a 3 month period of time, less than 0. 1% of the total amount of heavy metals and PO4 in the sludge and sludge ash were collected in the drainage water. Cation exchange selectivity, specific anion adsorption and solubility are the processes that cause the reduction of leaching. The volcanic soils were capable of retaining the mineral constituents, P, and metals in applied sewage sludge and sludge ash and gradually released them as nutrients for plant growth. [Copyright &y& Elsevier]

The empirical Rothmund-Kornfeld power function approach (in terms of equivalent fractions) was applied to potassium-calcium (K-Ca) exchange equilibrium carried out at 25°C and 0.05 M ionic strength on 4 soils derived from volcanic materials in southern Chile. Results were compared with those obtained by the Gaines-Thomas approach, where a K preference was observed in all samples, with equilibrium constant values ranging from 0.097 to 0.492. The equivalent fraction form of Rothmund-Kornfeld fit the exchange isotherm curves obtained by the Gaines-Thomas convention and provides a good description of experimental exchange data. The exchange equilibrium constants and adsorbed ion activity coefficients calculated with Rothmund-Kornfeld parameters are comparable with those obtained by the Gaines-Thomas approach. [ABSTRACT FROM AUTHOR]

The volcanic ash-derived soils have strong capacity to retain phosphorus (P). The P in 10 virgin Ultisols and Andisols in Southern Chile and their clay size fractions were fractionated into inorganic, fulvic, and humic fractions using an acid and base sequential extraction procedure. These soils had similar mineralogy, and were formed under different climatic conditions, and were under different native vegetation. They are representative of the major agricultural areas. The total content ranges from 733 mg P kg[SUP-1] to 3470 mg P kg[SUP-1]. As groups, Andisols had higher total P concentrations than the Ultisols. The organic P content was linearly correlated to organic carbon (C). In general, soils with higher organic C content had higher percentage of its P in the organic form. Clay size fraction accounted for 35 to 60% of the soil and had significantly higher concentrations of P in all fractions. In the clay fraction, the organic P content was also influence by their organic C. Soil depth did not affect the P distribution neither in the soils nor in the clay fractions. [ABSTRACT FROM AUTHOR]

Invasive plant species can pose major threats to biodiversity, ecosystem functioning and services. Satellite based remote sensing has evolved as an important technology to spatially map the occurrence of invasive species in space and time. With the new era of the Sentinel missions, Synthetic Aperture Radar (SAR) and multispectral data are now freely available and repeatedly acquired on a high spatial and temporal resolution for the entire globe. However, the high potential of such sensors for automatic mapping procedures cannot be fully harnessed without sufficient and appropriate reference data for model calibration. Reference data are commonly acquired in field surveys, which however, are often relatively expensive and affected by sampling and observer bias. Moreover, a direct transferability to the remote sensing perspective and scale is difficult. Accordingly, we firstly assess the potential of Unmanned Aerial Vehicles (UAV) for semi-automatic reference data acquisition on species cover of three woody invasive species Pinus radiata , Ulex europaeus and Acacia dealbata occurring in Chile. Secondly, we test the upscaling of the estimated species cover to the spatial scale of Sentinel-1 and Sentinel-2. The proposed workflow includes the visual sampling of respective canopies in UAV orthomosaics and the subsequent spatial extrapolations using MaxEnt with spectral (RGB, Hyperspectral), textural (2D) and canopy structural (3D) predictors derived from UAV-based photogrammetry. These UAV-based maps are then used to train random forest models with multitemporal Sentinel-1 and Sentinel-2 data to map the invasive species cover on large spatial scales. Our results show that the semi-automatic UAV-based mapping of the three invasive species results in accurate predictions. Depending on the predictor combination, the correlation was 0.70, 0.77 and 0.90 for Pinus radiatia , Ulex europaeus , Acacia dealbata , respectively. Among the three species, we observed clear differences in the model performance between the tested photogrammetric predictors and their combinations (spectral, 2D texture or 3D structure). For scaling up the UAV-based estimates to the satellite-scale, the Sentinel-2 data (multispectral) were more important than Sentinel-1 data (SAR). An independent validation revealed that the R2 of the upscaling accounted for 0.78 or higher for all species and RMSE lower than 12%. Our results hence demonstrate that UAV-based reference data acquisitions are a promising alternative to traditional field surveys if the target species are directly identifiable in the UAV data. • We combine UAV and multitemporal Sentinel-1 and Sentinel-2 data to map three invasive species. • Spectral, textural and structural UAV-based predictors are compared. • Up-scaling from UAV to Sentinel-scale is very accurate. • UAV-based reference data is a valuable alternative to traditional field sampling. • Sentinel-2 (spectral) outperforms Sentinel-1 (SAR) for mapping invasive species. [ABSTRACT FROM AUTHOR]