Educar para la sostenibilidad. Un problema del que podemos hacernos cargo

La reciente toma de conciencia sobre la insostenibilidad del actual desarrollo nos ha llevado a la idea central de sostenibilidad. Nos encontramos ante un problema complejo y global, cuya comprensión también es compleja y está influida por una serie de factores que hemos de tener en cuenta para buscar soluciones desde la ciencia, la política, la educación, etc. Desde la perspectiva de la Didáctica de las Ciencias, si queremos contribuir a que la ciudadanía se haga cargo de la situación del planeta y pase a la acción para dar lugar a un futuro sostenible hay que considerar, además del conocimiento científico, las implicaciones éticas, políticas o económicas del problema. Para llevar a la práctica estas ideas se sugiere un enfoque interdisciplinar en torno a proyectos relacionados con problemas socio-científicos como el del cambio climático, que permitan reconocer los diferentes aspectos de los problemas, sus interrelaciones o las posibles soluciones y su puesta en acción

Bleomycin in the treatment of keloids and hypertrophic scars by multiple needle punctures

BACKGROUND: The treatment of keloids and hypertrophic scars has been difficult and a recent French study showed that bleomycin has been useful in the treatment of these lesions. OBJECTIVE: To determine the effectiveness and safety of bleomycin in the treatment of hypertrophic scars and keloids when this drug is administered through multiple superficial punctures. METHODS: We applied bleomycin to keloids and hypertrophic scars in 13 patients using a multiple-puncture method on the surface of the skin. All patients were given bleomycin at a concentration of 1.5 IU/ml. Clinical response after treatment was classified according to the following scale: complete flattening (100%), highly significant flattening (>90%), or significant flattening (75-90%). RESULTS: The clinical response was very positive in all cases: complete flattening in six cases, highly significant flattening in six cases, and significant flattening in one case. Two patients presented a recurrence as a small nodule 10 and 12 months after the last infiltration. CONCLUSIONS: These clinical findings show that administration of bleomycin in keloids and hypertrophic scars shows promise and needs further investigation

Molecular rearrangement of an Aza-Scorpiand macrocycle induced by pH: A computational study

Rearrangements and their control are a hot topic in supramolecular chemistry due to the possibilities that these phenomena open in the design of synthetic receptors and molecular machines. Macrocycle aza-scorpiands constitute an interesting system that can reorganize their spatial structure depending on pH variations or the presence of metal cations. In this study, the relative stabilities of these conformations were predicted computationally by semi-empirical and density functional theory approximations, and the reorganization from closed to open conformations was simulated by using the Monte Carlo multiple minimum method

Improved dead-time correction for PET scanners: Application to small-animal PET

Pile-up and dead-time are two main causes of nonlinearity in the response of a PET scanner as a function of activity in the field of view (FOV). For a given scanner and acquisition system, pile-up effects depend on the material and size of the object being imaged and on the distribution of activity inside and outside the FOV, because these factors change the singles-to-coincidences ratio (SCR). Thus, it is difficult to devise an accurate correction that would be valid for any acquisition. In this work, we demonstrate a linear relationship between SCR and effective dead-time, which measures the effects of both dead-time (losses) and pile-up (gains and losses). This relationship allows us to propose a simple method to accurately estimate dead-time and pile-up corrections using only two calibration acquisitions with, respectively, a high and low SCR. The method has been tested with simulations and experimental data for two different scanner geometries: a scanner with large area detectors and no pile-up rejection, and a scanner composed of two full rings of smaller detectors. Our results show that the SCR correction method is accurate within 7%, even for high activities in the FOV, and avoids the bias of the standard single-parameter method. © 2013 Institute of Physics and Engineering in Medicine.This work was partially funded by AMIT project (CEN-20101014) from the CDTICENIT program, CIBERsam (CB07/09/0031), projects TEC2010-21619-C04-01 and TEC2011-28972-C02-01 from Spanish Ministerio de Ciencia e Innovación, Spanish Government (ENTEPRASE Grant, PSE-300000-2009-5), PRECISION grant IPT-300000- 2010-3, CPAN (CSD-2007-00042@Ingenio2010), MEC (FPA2010-17142) and ARTEMIS program (S2009/DPI-1802) from Spanish Comunidad de Madrid and EU-ERDF program.Peer Reviewe

A Fuzzy k-Nearest Neighbors Classifier to Deal with Imperfect Data

© 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This document is the Accepted version of a Published Work that appeared in final form in Soft Computing. To access the final edited and published work see https://doi.org/10.1007/s00500-017-2567-xThe k-nearest neighbors method (kNN) is a nonparametric, instance-based method used for regression and classification. To classify a new instance, the kNN method computes its k nearest neighbors and generates a class value from them. Usually, this method requires that the information available in the datasets be precise and accurate, except for the existence of missing values. However, data imperfection is inevitable when dealing with real-world scenarios. In this paper, we present the kNNimp classifier, a k-nearest neighbors method to perform classification from datasets with imperfect value. The importance of each neighbor in the output decision is based on relative distance and its degree of imperfection. Furthermore, by using external parameters, the classifier enables us to define the maximum allowed imperfection, and to decide if the final output could be derived solely from the greatest weight class (the best class) or from the best class and a weighted combination of the closest classes to the best one. To test the proposed method, we performed several experiments with both synthetic and realworld datasets with imperfect data. The results, validated through statistical tests, show that the kNNimp classifier is robust when working with imperfect data and maintains a good performance when compared with other methods in the literature, applied to datasets with or without imperfection