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

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-

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-

Mechanically switchable wetting petal effect in self‐patterned nanocolumnar films on poly(Dimethylsiloxane)

Switchable mechanically induced changes in the wetting behavior of surfaces are of para-mount importance for advanced microfluidic, self‐cleaning and biomedical applications. In this work we show that the well‐known polydimethylsiloxane (PDMS) elastomer develops self‐patterning when it is coated with nanostructured TiO2 films prepared by physical vapor deposition at glancing angles and subsequently subjected to a mechanical deformation. Thus, unlike the disordered wrinkled surfaces typically created by deformation of the bare elastomer, well‐ordered and aligned micro‐scaled grooves form on TiO2/PDMS after the first post‐deposition bending or stretching event. These regularly patterned surfaces can be reversibly modified by mechanical deformation, thereby inducing a switchable and reversible wetting petal effect and the sliding of liquid droplets. When performed in a dynamic way, this mechanical actuation produces a unique capacity of liquid droplets (water and diiodomethane) transport and tweezing, this latter through their selective capture and release depending on their volume and chemical characteristics. Scanning electron and atomic force microscopy studies of the strained samples showed that a dual‐scale rough-ness, a parallel alignment of patterned grooves and their reversible widening upon deformation, are critical factors controlling this singular sliding behavior and the possibility to tailor their response by the appropriate manufacturing of surface structures.European Union 899352Ministerio de Ciencia e Innovación PID2019- 110430GB-C21, PID2019-109603RA-I0, MAT2013-40852-R, MAT2013- 42900-PMinisterio de Economía y Competitividad 201560E055Junta de Andalucía AT17-6079, P18-RT-348

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

Solid-State Hydrolysis (SSH) Improves the Nutritional Value of Plant Ingredients in the Diet of Mugil cephalus

The possibility of improving the nutritional quality of plant byproducts (brewers’ spent grain and rice bran) through an enzyme treatment was tested in a formulated feed for grey mullet (Mugil cephalus). The enzyme treatment was carried out by Solid-State Hydrolysis (SSH) using a commercial preparation including carbohydrases and phytase. A feed prepared without the treatment and a commercial feed for carp were used as controls. In a preliminary short-term trial carried out at laboratory facilities, fish receiving the enzyme-treated feed showed significant improvement in both FCR and SGR when compared to those obtained with the untreated diet, although both experimental diets presented worse values than those obtained with the commercial feed. Different metabolic indicators including higher values of muscle glycogen and plasmatic triglycerides supported the positive effect of the enzyme treatment on the nutritional condition of the fish over those fed on the diet containing non-treated ingredients. Results of growth and feed efficiency that were obtained in a second long-term trial developed for 148 days under real production conditions evidenced the equivalence among the experimental and commercial diets and confirmed that enzyme pretreatment of plant ingredients by SSH may be a useful procedure to improve the nutritive value of high fiber plant byproducts when included in practical diets for this species and others with similar nutritional features

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-