Breast Cancer Automatic Diagnosis System using Faster Regional Convolutional Neural Networks

Breast cancer is one of the most frequent causes of mortality in women. For the early detection of breast cancer, the mammography is used as the most efficient technique to identify abnormalities such as tumors. Automatic detection of tumors in mammograms has become a big challenge and can play a crucial role to assist doctors in order to achieve an accurate diagnosis. State-of-the-art Deep Learning algorithms such as Faster Regional Convolutional Neural Networks are able to determine the presence of an object and also its position inside the image in a reduced computation time. In this work, we evaluate these algorithms to detect tumors in mammogram images and propose a detection system that contains: (1) a preprocessing step performed on mammograms taken from the Digital Database for Screening Mammography (DDSM) and (2) the Neural Network model, which performs feature extraction over the mammograms in order to locate tumors within each image and classify them as malignant or benign. The results obtained show that the proposed algorithm has an accuracy of 97.375%. These results show that the system could be very useful for aiding physicians when detecting tumors from mammogram images.Ministerio de Economía y Competitividad TEC2016-77785-

An Automated Fall Detection System Using Recurrent Neural Networks

Falls are the most common cause of fatal injuries in elderly people, causing even death if there is no immediate assistance. Fall detection systems can be used to alert and request help when this type of accident happens. Certain types of these systems include wearable devices that analyze bio-medical signals from the person carrying it in real time. In this way, Deep Learning algorithms could automate and improve the detection of unintentional falls by analyzing these signals. These algorithms have proven to achieve high effectiveness with competitive performances in many classification problems. This work aims to study 16 Recurrent Neural Networks architectures (using Long Short-Term Memory and Gated Recurrent Units) for falls detection based on accelerometer data, reducing computational requirements of previous research. The architectures have been tested on a labeled version of the publicly available SisFall dataset, achieving a mean F1-score above 0.73 and improving state-of-the-art solutions in terms of network complexity.Ministerio de Economía y Competitivida TEC2016-77785-

Socio-economic factors linked with mental health during the recession: a multilevel analysis

Background Periods of financial crisis are associated with higher psychological stress among the population and greater use of mental health services. The objective is to analyse contextual factors associated with mental health among the Spanish population during the recession. Methodology Cross-sectional, descriptive study of two periods: before the recession (2006) and after therecession (2011-2012). The study population comprised individuals aged 16+ years old, polled for the National Health Survey. There were 25,234 subjects (2006) and 20,754 subjects (2012). The dependent variable was psychic morbidity. Independent variables: 1) socio-demographic (age, socio-professional class, level of education, nationality, employment situation, marital status), 2) psycho-social (social support) and 3) financial (GDP per capita, risk of poverty, income per capita per household), public welfare services (health spending per capita), labour market (employment and unemployment rates, percentage of temporary workers). Multilevel logistic regression models with mixed effects were constructed to determine change in psychic morbidity according to the variables studied. Results The macroeconomic variables associated with worse mental health for both males and females were lower health spending per capita and percentage of temporary workers. Among women, the risk of poor mental health increased 6% for each 100€ decrease in healthcare spending per capita. Among men, the risk of poor mental health decreased 8% for each 5-percentage point increase in temporary workers. Conclusions Higher rates of precarious employment in a region have a negative effect on people’s mental health; likewise lower health spending per capita. Policies during periods of recession should focus on support and improved conditions for vulnerable groups such as temporary workers. Healthcare cutbacks should be avoided in order to prevent increased prevalence of poor mental health.This study was partially funded by the Regional Government of Andalusia Ministry of Health PI 0360-2012 and CIBER Epidemiología y Salud Pública.Ye

Superhydrophobic supported Ag-NPs@ZnO-nanorods with photoactivity in the visible range

In this article we present a new type of 1D nanostructures consisting of supported hollow ZnO nanorods (NRs) decorated with Ag nanoparticles (NPs). The 3D reconstruction by high-angle annular dark field scanning transmission electron microscopy (HAADF-STEM) electron tomography reveals that the Ag NPs are distributed along the hollow interior of the ZnO NRs. Supported and vertically aligned Ag-NPs@ZnO-NRs grow at low temperature (135 °C) by plasma enhanced chemical vapour deposition on heterostructured substrates fabricated by sputtered deposition of silver on flat surfaces of Si wafers, quartz slides or ITO. The growth mechanisms of these structures and their wetting behavior before and after visible light irradiation are critically discussed. The as prepared surfaces are superhydrophobic with water contact angles higher than 150°. These surfaces turn into superhydrophilic with water contact angles lower than 10° after prolonged irradiation under both visible and UV light. The evolution rate of the wetting angle and its dependence on the light characteristics are related to the nanostructure and the presence of silver embedded within the ZnO NRs. ÂEuropean Union NMP3-CT-2006- 032583Ministerio de Ciencia e Innovación MAT2010-21228, MAT2010-18447, CSD2008-00023Junta de Andalucía P09-TEP-5283, CTS-518

Centennial olive trees as a reservoir of genetic diversity

Background and AimsGenetic characterization and phylogenetic analysis of the oldest trees could be a powerful tool both for germplasm collection and for understanding the earliest origins of clonally propagated fruit crops. The olive tree (Olea europaea L.) is a suitable model to study the origin of cultivars due to its long lifespan, resulting in the existence of both centennial and millennial trees across the Mediterranean Basin.MethodsThe genetic identity and diversity as well as the phylogenetic relationships among the oldest wild and cultivated olives of southern Spain were evaluated by analysing simple sequence repeat markers. Samples from both the canopy and the roots of each tree were analysed to distinguish which trees were self-rooted and which were grafted. The ancient olives were also put into chronological order to infer the antiquity of traditional olive cultivars.Key ResultsOnly 9·6 % out of 104 a priori cultivated ancient genotypes matched current olive cultivars. The percentage of unidentified genotypes was higher among the oldest olives, which could be because they belong to ancient unknown cultivars or because of possible intra-cultivar variability. Comparing the observed patterns of genetic variation made it possible to distinguish which trees were grafted onto putative wild olives.ConclusionsThis study of ancient olives has been fruitful both for germplasm collection and for enlarging our knowledge about olive domestication. The findings suggest that grafting pre-existing wild olives with olive cultivars was linked to the beginnings of olive growing. Additionally, the low number of genotypes identified in current cultivars points out that the ancient olives from southern Spain constitute a priceless reservoir of genetic diversity

Glioma Diagnosis Aid through CNNs and Fuzzy-C Means for MRI

Glioma is a type of brain tumor that causes mortality in many cases. Early diagnosis is an important factor. Typically, it is detected through MRI and then either a treatment is applied, or it is removed through surgery. Deep-learning techniques are becoming popular in medical applications and image-based diagnosis. Convolutional Neural Networks are the preferred architecture for object detection and classification in images. In this paper, we present a study to evaluate the efficiency of using CNNs for diagnosis aids in glioma detection and the improvement of the method when using a clustering method (Fuzzy C-means) for preprocessing the input MRI dataset. Results offered an accuracy improvement from 0.77 to 0.81 when using Fuzzy C-Means.Ministerio de Economía y Competitividad TEC2016-77785-

Anisotropic in-plane conductivity and dichroic gold plasmon resonance in plasma assisted ITO thin films e-beam evaporated at oblique angles

ITO thin films have been prepared by electron beam evaporation at oblique angles (OA), directly and while assisting their growth with a downstream plasma. The films microstructure, characterized by scanning electron microscopy, atomic force microscopy and glancing incidence small angle X-ray scattering, consisted of tilted and separated nanostructures. In the plasma 2 assisted films, the tilting angle decreased and the nanocolumns became associated in the form of bundles along the direction perpendicular to the flux of evaporated material. The annealed films presented different in-depth and sheet resistivity as confirmed by scanning conductivity measurements taken for the individual nanocolumns. In addition, for the plasma assisted thin films, two different sheet resistance values were determined by measuring along the nanocolumn bundles or along the perpendicular direction. This in-plane anisotropy induces the electrochemical deposition of elongated gold nanostructures. The obtained Au-ITO composite thin films were characterized by anisotropic plasmon resonance absorption and a dichroic behavior when examined with linearly polarized light

Sampling Frequency Evaluation on Recurrent Neural Networks Architectures for IoT Real-time Fall Detection Devices

Falls are one of the most frequent causes of injuries in elderly people. Wearable Fall Detection Systems provided a ubiquitous tool for monitoring and alert when these events happen. Recurrent Neural Networks (RNN) are algorithms that demonstrates a great accuracy in some problems analyzing sequential inputs, such as temporal signal values. However, their computational complexity are an obstacle for the implementation in IoT devices. This work shows a performance analysis of a set of RNN architectures when trained with data obtained using different sampling frequencies. These architectures were trained to detect both fall and fall hazards by using accelerometers and were tested with 10-fold cross validation, using the F1-score metric. The results obtained show that sampling with a frequency of 25Hz does not affect the effectiveness, based on the F1-score, which implies a substantial increase in the performance in terms of computational cost. The architectures with two RNN layers and without a first dense layer had slightly better results than the smallest architectures. In future works, the best architectures obtained will be integrated in an IoT solution to determine the effectiveness empirically.Ministerio de Economía y Competitividad TEC2016-77785-

A Full Vacuum Approach for the Fabrication of Hybrid White-Light-Emitting Thin Films and Wide-Range In Situ Tunable Luminescent Microcavities

This study shows the fabrication by a dry approach at mild temperature (<150 °C) of a photoluminescence white light emitting hybrid layer. The white light emitter is obtained by evaporation of two photoluminescent small molecules, a blue (1,3,5-triphenyl-2-pyrazoline (TPP)) and an orange (Rubrene) dye within the porous of an SiO host film fabricated by glancing angle deposition. Fluorescence (Föster) resonant energy transfer between the two organic dyes allows the emission of the combined system upon excitation of the TPP molecule at wavelength of 365 nm. The distribution of the organic molecule within the host layer is analyzed as a function of the substrate temperature and vacuum conditions and the required conditions for the white emission determined by finely controlling the TPP:Rubrene ratio. The full vacuum processing of the hybrid layers provides a straightforward route for the incorporation of the white light emitters as optical defect within 1D Bragg microcavities. As a consequence, directional emission of the system is achieved which allows the development of wide-range in situ tunable photoluminescent devices.Junta de Andalucía TEP8067, FQM-6900, P12-FQM-2265Ministerio de Economía y Competitividad MAT2013-40852-R, MAT2013-42900-

Localización e identificación automática de pólipos mediante una red neuronal convolucional por regiones

Este trabajo expone la metodología llevada a cabo para la aplicación de un modelo Deep Learning con el fin de detectar pólipos de forma automática, así como su posición en videos de colonoscopia. Se plantearon diferentes métodos y diversas técnicas que pudieran aplicarse sobre el conjunto de datos proporcionado por el 2018 Sub-challenge Gastrointestinal Image ANAlysis. Seleccionamos el método Faster Regional Convolutional Neural Network para abarcar el problema planteado. Para la extracción de características empleamos el modelo ResNet50. Aplicamos técnicas de data augmentation para incrementar el conjunto de datos empleado en el entrenamiento del modelo. También aplicamos hard negative mining para reforzar el aprendizaje del background o fondo, reducir el porcentaje de falsos positivos y mejorar el rendimiento.This work exposes the methodology carried out for the application of a Deep Learning model in the context of automatic polyp detection and its location in colonoscopy videos. Different methods were proposed as well as the different techniques that can be applied on the given dataset provided by the 2018 Sub-challenge Gastrointestinal Image ANAlysis. We chose the Faster Regional Convolutional Neural Network method to solve this problem. We used ResNet50 in the first part of this algorithm to extract the main image features. We applied hard negative mining and data augmentation techniques to increase the dataset used in the training of the model. We also used hard negative mining to get a better learning of background, reducing false negatives and improving the performance