Direct determination of the solar neutrino fluxes from solar neutrino data

We determine the solar neutrino fluxes from a global analysis of the solar and terrestrial neutrino data in the framework of three-neutrino mixing. Using a Bayesian approach we reconstruct the posterior probability distribution function for the eight normalization parameters of the solar neutrino fluxes plus the relevant masses and mixing, with and without imposing the luminosity constraint. This is done by means of a Markov Chain Monte Carlo employing the Metropolis-Hastings algorithm. We also describe how these results can be applied to test the predictions of the Standard Solar Models. Our results show that, at present, both models with low and high metallicity can describe the data with good statistical agreement.Comment: 24 pages, 1 table, 7 figures. Acknowledgments correcte

Sero-epidemiology of Toxocara canis infection in children attending four selected health facilities in the central region of Ghana

Objective: The study determined the seroprevalence of Toxocara canis infection among children attending four selected health facilities in the Central Region of Ghana.Design: Cross-sectional studyMethod: Sera from 566 children aged 1-15 years attending four selected health facilities in the Central Region of Ghana between July and September 2012 was used in a Toxocara excretory-secretory antigenbased ELISA to detect serum IgG. A short questionnaire was designed to obtain data on respondents as to age, gender, educational level, locality of residence, habits of washing of fruits, vegetable and hands before eating, keeping of pet (dogs or cats) , and history of playing with soil and pets. Clinical information was also collected. Associations between sero-positivity and age group, gender, risk factors, educational level and other variables were determined by Chi square test.Results: The overall sero-prevalence was 53.5% (n=566). Age, educational level and hospital visited were significantly associated with sero-positivity (p< 0.05). Children with history of playing with soil (χ2=9.03, p=0.003), pet-keeping (χ2=14.77, p=0.001) and not washing hands with soap before eating (χ2=5.82, p=0.016) were significantly associated with sero-positivity.Conclusion: The sero-prevalence of T. canis infection in children in the study was high. The children should be educated to desist from risk factors such as playing with soil and pets and be encouraged to ensure proper personal hygiene.Keywords: Seroprevalence, Toxocariasis, risk factors, children, Ghan

Youth of West Cameroon are at high risk of developing IDD due to low dietary iodine and high dietary thiocyanate.

Objectives: Hypothyroidism in utero leading to mental retardation is highly prevalent and recurrent in developing countries where iodine deficiency and thiocyanate overload are combined. So, to explore and identify human population's risks for developing iodine deficiency disorders and their endemicity in Western Cameroon, with the aim to prevent this deficiency and to fight again it, urinary iodine and thiocyanate levels were determined. Methods: The district of Bamougoum in Western Cameroon was selected for closer study due to its geographic location predisposing for iodine deficiency disorders (IDD). A comprehensive sampling strategy included 24-h urine samples collected over three days from 120 school-aged children. Urinary iodine and thiocyanate levels were measured by colorimetric methods. Results: Twenty one percent of boys between the ages 3 and 19 were classified as iodine deficient. The prevalence of thiocyanate overload in the same population was found to be 20%. Conclusion: Presence of endemic iodine deficiency and excessive thiocyanate in the population indicates that the region is at risk of iodine deficiency disorder. A multifactorial approach that includes improvement of diet, increasing iodine and minimizing goitrogen substances intake, soil and crop improvement and an iodine supplementation program may help alleviate IDD in the affected area studied. African Health Sciences Vol. 8 (4) 2008: pp. 227-23

Boundary Terms, Spinors and Kerr/CFT

Similarly as in AdS/CFT, the requirement that the action for spinors be stationary for solutions to the Dirac equation with fixed boundary conditions determines the form of the boundary term that needs to be added to the standard Dirac action in Kerr/CFT. We determine this boundary term and make use of it to calculate the two-point function for spinor fields in Kerr/CFT. This two-point function agrees with the correlator of a two dimensional relativistic conformal field theory.Comment: 15 page

Intergenerational spillover effects of language training for refugees

Children of refugees are among the most economically disadvantaged youth in several European countries. They are more likely to drop out of school and to commit crime. We find that a reform in Denmark in 1999 that expanded language training for adult refugees and was shown to improve their earnings and job market outcomes permanently, also increased lower secondary school completion rates and decreased juvenile crime rates for their children. The crime effect is entirely due to boys who were below school age when their parents received language training. The older cohorts who were in elementary school when their parents received language training performed better in lower secondary school. Boys were more likely to finish lower secondary school and to sit the final exams, and girls achieved higher grade point averages in the exams

How Polarized Have We Become? A Multimodal Classification of Trump Followers and Clinton Followers

Polarization in American politics has been extensively documented and analyzed for decades, and the phenomenon became all the more apparent during the 2016 presidential election, where Trump and Clinton depicted two radically different pictures of America. Inspired by this gaping polarization and the extensive utilization of Twitter during the 2016 presidential campaign, in this paper we take the first step in measuring polarization in social media and we attempt to predict individuals' Twitter following behavior through analyzing ones' everyday tweets, profile images and posted pictures. As such, we treat polarization as a classification problem and study to what extent Trump followers and Clinton followers on Twitter can be distinguished, which in turn serves as a metric of polarization in general. We apply LSTM to processing tweet features and we extract visual features using the VGG neural network. Integrating these two sets of features boosts the overall performance. We are able to achieve an accuracy of 69%, suggesting that the high degree of polarization recorded in the literature has started to manifest itself in social media as well.Comment: 16 pages, SocInfo 2017, 9th International Conference on Social Informatic

Value-adding post harvest processing of cooking bananas (Musa spp. AAB and ABB genome groups)

Cooking bananas (including plantains) are among the major commodities used in Sub Saharan Africa to combat food insecurity. It is estimated that more than 30% of the banana production are lost afterharvest. The losses are mostly due to the rapid ripening of the fruits, poor handling, inadequate storage and transportation means, and poor knowledge of food processing options. Processing the fresh fruits into food products with a longer shelf life can provide a major outlet to use surpluses and to exploit a greater number of marketing options. In this paper, we provide ingredients and recipes for food products made by the International Institute of Tropical Agriculture (IITA) from its improved hybrids of cooking bananas to decrease post harvest losses, diversify the industrial potentials of bananas, and add value to farmers’ products. Some of these processing methods can be used by farmers and ruralentrepreneurs in their communities to ensure food security and raise their incomes, or upgraded by the private sector in a value chain approach to curb production losses in bananas.Keywords: Bananas, food security, post harvest, food processing, value additio

Bragg projection ptychography on niobium phase domains

Bragg projection ptychography (BPP) is a coherent x-ray diffraction imaging technique which combines the strengths of scanning microscopy with the phase contrast of x-ray ptychography. Here we apply it for high resolution imaging of the phase-shifted crystalline domains associated with epitaxial growth. The advantages of BPP are that the spatial extent of the sample is arbitrary, it is nondestructive, and it gives potentially diffraction limited spatial resolution. Here we demonstrate the application of BPP for revealing the domain structure caused by epitaxial misfit in a nanostructured metallic thin film. Experimental coherent diffraction data were collected from a niobium thin film, epitaxially grown on a sapphire substrate as the beam was scanned across the sample. The data were analyzed by BPP using a carefully selected combination of refinement procedures. The resulting image shows a close packed array of epitaxial domains, shifted with respect to each other due to misfit between the film and its substrate

A transfer-learning approach to feature extraction from cancer transcriptomes with deep autoencoders

Publicado en Lecture Notes in Computer Science.The diagnosis and prognosis of cancer are among the more challenging tasks that oncology medicine deals with. With the main aim of fitting the more appropriate treatments, current personalized medicine focuses on using data from heterogeneous sources to estimate the evolu- tion of a given disease for the particular case of a certain patient. In recent years, next-generation sequencing data have boosted cancer prediction by supplying gene-expression information that has allowed diverse machine learning algorithms to supply valuable solutions to the problem of cancer subtype classification, which has surely contributed to better estimation of patient’s response to diverse treatments. However, the efficacy of these models is seriously affected by the existing imbalance between the high dimensionality of the gene expression feature sets and the number of sam- ples available for a particular cancer type. To counteract what is known as the curse of dimensionality, feature selection and extraction methods have been traditionally applied to reduce the number of input variables present in gene expression datasets. Although these techniques work by scaling down the input feature space, the prediction performance of tradi- tional machine learning pipelines using these feature reduction strategies remains moderate. In this work, we propose the use of the Pan-Cancer dataset to pre-train deep autoencoder architectures on a subset com- posed of thousands of gene expression samples of very diverse tumor types. The resulting architectures are subsequently fine-tuned on a col- lection of specific breast cancer samples. This transfer-learning approach aims at combining supervised and unsupervised deep learning models with traditional machine learning classification algorithms to tackle the problem of breast tumor intrinsic-subtype classification.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech