La influencia de la edad y el género sobre los determinantes de la adopción del smartphone para la planificación de viajes

Vallespín, M., Molinillo, S. & Pérez-Aranda, J. (2016).La influencia de la edad y el género sobre los determinantes de la asopción del smartphone para la planificación de viajes. In Correia, M., Santos, J.A.C., Santos, M., Aguas, P. & Baptista (Eds.), Tourism & Management Studies International Conference TMS Algarve (p. 176), Portugal.Objetivo: Esta investigación analiza el comportamiento del turista ante la llegada de un nuevo canal de comercialización como es el canal móvil. Para ello, y con el objetivo de guiar a las empresas del sector en sus estrategias de segmentación, se analiza por separado, el comportamiento de los diferentes segmentos. Metodología: Mediante análisis ANOVA, sobre una muestra de 624 consumidores turísticos españoles, se estudia la influencia de la edad y el género para la planificación de viajes a través del canal móvil. En concreto, las variables dependientes consideradas son: la facilidad de uso, la utilidad percibida y el hábito que perciben hacia el canal móvil, su futura intención de uso, el apoyo que perciben de su círculo más cercano y los hábitos arraigados a otros canales como son el canal online y el canal offline. Principales resultados: Se encuentra que la variable edad genera diferentes comportamientos en casi todas las variables analizadas. De este modo, son los turistas menores de 45 años los que parecen presentar un mayor potencial ya que presentan un mayor hábito hacia el canal online y hacia el canal móvil en detrimento de los canales offline. Además, este segmento (menores de 45 años) también encuentran esta canal más fácil de usar. En cambio, el género parece no generar apenas diferencias, tan solo los hombres parecen presentar un mayor arraigo hacia las agencias de viajes. Podría concluirse, por tanto, que las diferencias de género encontradas antaño sobre el uso de las tecnologías parecen estar desapareciendo.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Bone marrow-derived mesenchymal stem cells transplantation produces a tissue recovery in hydrocephalic mice

In congenital hydrocephalus, cerebrospinal fluid accumulation is associated to ischemia/hypoxia, metabolic impairment, neuronal damage and astrocytic reaction, which cause significant mortality and life-long neurological complications. Currently, there are no effective therapies for congenital hydrocephalus. Bone marrow-derived mesenchymal stem cells (BM-MSC) are considered as a potential therapeutic tool for neurodegenerative diseases due to their ability for migrating and producing neuroprotector factors when they are transplanted. The aim of this research was to study the ability of BM-MSC to reach the degenerated regions and to detect their neuroprotector effects, using an animal model of congenital hydrocephalus, the hyh mouse. Fluorescent BM-MSC were analyzed by flow-cytometry and multilineage cell differentiation. BM-MSC were brain-ventricle injected into hyh mice. Wild-type and saline-injected hyh mice were used as controls. Inmunohistochemical, RT-PCR and High Resolution Magic Angle Spinning spectroscopy (HRMAS) analyses were carried out. After administration, integrated BM-MSC were identified inside the periventricular astrocyte reaction. They were detected producing glial-derived neuroprotector factor (GDNF), neural growth factor (NGF), and brain-derived neuroprotector factor (BDNF). Tissue recovery was detected with a reduction of apoptotic cells in the periventricular walls and of the levels of glutamate, glutamine, taurine, and creatine, all of them markers of tissue damage in hydrocephalus.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. ISCIII PI15/00619 y FEDE

A patient-specific FE-based methodology to simulate prosthesis insertion during an augmentation mammoplasty

[EN] Breast augmentation surgery is a widespread practice for aesthetic purposes. Current techniques, however, are not able to reliably predict the desired final aspect of the breast after the intervention, whose success relies almost completely on the surgeon's skill. In this way, patient-specific methodologies capable of predicting the outcomes of such interventions are of particular interest. In this paper, a finite element biomechanical model of the breast of a female patient before an augmentation mammoplasty was generated using computer tomography images. Prosthesis insertion during surgery was simulated using the theory of finite elasticity. Hyperelastic constitutive models were considered for breast tissues and silicone implants. The deformed geometry obtained from finite element analysis was compared qualitatively and quantitatively with the real breast shape of the patient lying in supine position, with root-mean-squared errors less than 3. mm. The results indicate that the presented methodology is able to reasonably predict the aspect of the breast in an intermediate step of augmentation mammoplasty, and reveal the potential capabilities of finite element simulations for visualization and prediction purposes. However, further work is required before this methodology can be helpful in aesthetic surgery planning. © 2011 IPEM.The support of Instituto de Salud Carlos III (ISCIII) through the CIBER initiative, and the support of Platform for Biological Tissue Characterization of the Centro de Investigacion Biomedica en Red de Bioingenieria, Biomateriales y Nanomedicina (CIBER-BBN) are highly appreciated. The translation of this paper was funded by the Universitat Politecnica de Valencia, Spain.Lapuebla-Ferri, A.; Perez Del Palomar, A.; Herrero, J.; Jimenez Mocholi, AJ. (2011). A patient-specific FE-based methodology to simulate prosthesis insertion during an augmentation mammoplasty. Medical Engineering & Physics. 33(9):1094-1102. https://doi.org/10.1016/j.medengphy.2011.04.014S1094110233

Microglial response differences between amyloidogenic transgenic models and Alzheimer’s disease patients

Aims: The continuing failure to develop an effective treatment for Alzheimer’s disease (AD) reveals the complexity for AD pathology. Increasing evidence indicates that neuroinflammation involving particularly microglial cells contributes to disease pathogenesis. Here we analyze the differences in the microglial response between APP/PS1 model and human brains. Methods: RT-PCR, western blots, and immunostaining were performed in the hippocampus of human post mortem samples (from Braak II to Braak V-VI) and APP751SL/PS1M146L mice. In vitro studies to check the effect of S1 fractions on microglial cells were assayed. Results: In APP based models the high Abeta accumulation triggers a prominent microglial response. On the contrary, the microglial response detected in human samples is, at least, partial or really mild. This patent difference could simple reflect the lower and probably slower Abeta production observed in human hippocampal samples, in comparison with models or could reflect the consequence of a chronic long-standing microglial activation. However, beside this differential response, we also observed a prominent microglial degenerative process in Braak V-VI samples that, indeed, could compromise their normal role of surveying the brain environment and respond to the damage. This microglial degeneration, particularly relevant at the dentate gyrus of the hippocampal formation, might be mediated by the accumulation of toxic soluble phospho-tau species. Conclusions: These differences need to be considered when delineating animal models that better integrate the complexity of AD pathology and, therefore, guarantee clinical translation. Correcting dysregulated brain inflammatory responses might be a promising avenue to restore cognitive function. Supported by grants FIS PI15/00796 and FIS PI15/00957 co-financed by FEDER funds from European Union, and by Junta de Andalucia Proyecto de Excelencia CTS385 2035.Financiado por FIS PI15/00796 y FIS PI15/0095, cofinanciado por los fondos FEDER de la Unión Europea, y por Junta de Andalucia Proyecto de Excelencia CTS385 2035. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Deep convolutional neural network for damaged vegetation segmentation from RGB images based on virtual NIR-channel estimation

Performing accurate and automated semantic segmentation of vegetation is a first algorithmic step towards more complex models that can extract accurate biological information on crop health, weed presence and phenological state, among others. Traditionally, models based on normalized difference vegetation index (NDVI), near infrared channel (NIR) or RGB have been a good indicator of vegetation presence. However, these methods are not suitable for accurately segmenting vegetation showing damage, which precludes their use for downstream phenotyping algorithms. In this paper, we propose a comprehensive method for robust vegetation segmentation in RGB images that can cope with damaged vegetation. The method consists of a first regression convolutional neural network to estimate a virtual NIR channel from an RGB image. Second, we compute two newly proposed vegetation indices from this estimated virtual NIR: the infrared-dark channel subtraction (IDCS) and infrared-dark channel ratio (IDCR) indices. Finally, both the RGB image and the estimated indices are fed into a semantic segmentation deep convolutional neural network to train a model to segment vegetation regardless of damage or condition. The model was tested on 84 plots containing thirteen vegetation species showing different degrees of damage and acquired over 28 days. The results show that the best segmentation is obtained when the input image is augmented with the proposed virtual NIR channel (F1=0.94) and with the proposed IDCR and IDCS vegetation indices (F1=0.95) derived from the estimated NIR channel, while the use of only the image or RGB indices lead to inferior performance (RGB(F1=0.90) NIR(F1=0.82) or NDVI(F1=0.89) channel). The proposed method provides an end-to-end land cover map segmentation method directly from simple RGB images and has been successfully validated in real field conditions

Potential protective role of reactive astrocytes in the periventricular parenchyma in congenital hydrocephalus

Background Cerebrospinal fluid accumulation in hydrocephalus produces an elevation of intraventricular pressure with pathological consequences on the periventricular brain parenchyma including ischemia, oedema, oxidative stress, and accumulation of metabolic waste products. Here we studied in the hyh mouse, an animal model of congenital hydrocephalus, the role of reactive astrocytes in this clinical degenerative condition. Materials and Methods Wild type and hydrocephalic hyh mice at 30 days of postnatal age were used. Three metabolites related to the oxidative and neurotoxic conditions were analysed in ex vivo samples (glutathione, glutamine and taurine) using High Resolution Magic Angle Spinning (HR-MAS). Glutathione synthetase and peroxidase, glutamine synthetase, kidney-type glutaminase (KGA), and taurine/taurine transporter were immunolocated in brain sections. Results Levels of the metabolites were remarkably higher in hydrocephalic conditions. Glutathione peroxidase and synthetase were both detected in the periventricular reactive astrocytes and neurons. Taurine was mostly found free in the periventricular parenchyma and in the reactive astrocytes, and the taurine transporter was mainly present in the neurons located in such regions. Glutamine synthetase was found in reactive astrocytes. Glutaminase was also detected in the reactive astrocytes and in periventricular neurons. These results suggest a possible protective response of reactive astrocytes against oxidative stress and neurotoxic conditions. Conclusions Astrocyte reaction seems to trigger an anti-oxidative and anti-neurotoxic response in order to ameliorate pathological damage in periventricular areas of the hydrocephalic mice.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. PI15-00619 to AJJ

El conocimiento profesional del profesorado de ciencias sobre la educación ambiental: Primera fase.

Este artículo presenta los elementos correspondientes a la fase de fundamentación y diseño metodológico del proyecto de tesis doctoral que se desarrolla en el marco del programa oficial de postgrado “Investigación en la Enseñanza y el Aprendizaje de las Ciencias Experimentales, Sociales y Matemáticas”, desde el curso 2007-2008 y que se encuentra adscrito al Departamento de Didáctica de las Ciencias y Filosofía de la Universidad de Huelva. Bajo la línea de Desarrollo Profesional, en este trabajo se caracterizan los elementos del conocimiento profesional del profesor de ciencias naturales en el campo de la educación ambiental y los factores que inciden para promover u obstaculizar el desarrollo profesional de los maestros

DPDnet: A Robust People Detector using Deep Learning with an Overhead Depth Camera

In this paper we propose a method based on deep learning that detects multiple people from a single overhead depth image with high reliability. Our neural network, called DPDnet, is based on two fully-convolutional encoder-decoder neural blocks based on residual layers. The Main Block takes a depth image as input and generates a pixel-wise confidence map, where each detected person in the image is represented by a Gaussian-like distribution. The refinement block combines the depth image and the output from the main block, to refine the confidence map. Both blocks are simultaneously trained end-to-end using depth images and head position labels. The experimental work shows that DPDNet outperforms state-of-the-art methods, with accuracies greater than 99% in three different publicly available datasets, without retraining not fine-tuning. In addition, the computational complexity of our proposal is independent of the number of people in the scene and runs in real time using conventional GPUs