Analyzing Digital Image by Deep Learning for Melanoma Diagnosis

Image classi cation is an important task in many medical applications, in order to achieve an adequate diagnostic of di erent le- sions. Melanoma is a frequent kind of skin cancer, which most of them can be detected by visual exploration. Heterogeneity and database size are the most important di culties to overcome in order to obtain a good classi cation performance. In this work, a deep learning based method for accurate classi cation of wound regions is proposed. Raw images are fed into a Convolutional Neural Network (CNN) producing a probability of being a melanoma or a non-melanoma. Alexnet and GoogLeNet were used due to their well-known e ectiveness. Moreover, data augmentation was used to increase the number of input images. Experiments show that the compared models can achieve high performance in terms of mean ac- curacy with very few data and without any preprocessing.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Quantitative Chevalley-Weil theorem for curves

The classical Chevalley-Weil theorem asserts that for an \'etale covering of projective varieties over a number field K, the discriminant of the field of definition of the fiber over a K-rational point is uniformly bounded. We obtain a fully explicit version of this theorem in dimension 1.Comment: version 4: minor inaccuracies in Lemma 3.4 and Proposition 5.2 correcte

Semiparametric Multivariate Accelerated Failure Time Model with Generalized Estimating Equations

The semiparametric accelerated failure time model is not as widely used as the Cox relative risk model mainly due to computational difficulties. Recent developments in least squares estimation and induced smoothing estimating equations provide promising tools to make the accelerate failure time models more attractive in practice. For semiparametric multivariate accelerated failure time models, we propose a generalized estimating equation approach to account for the multivariate dependence through working correlation structures. The marginal error distributions can be either identical as in sequential event settings or different as in parallel event settings. Some regression coefficients can be shared across margins as needed. The initial estimator is a rank-based estimator with Gehan's weight, but obtained from an induced smoothing approach with computation ease. The resulting estimator is consistent and asymptotically normal, with a variance estimated through a multiplier resampling method. In a simulation study, our estimator was up to three times as efficient as the initial estimator, especially with stronger multivariate dependence and heavier censoring percentage. Two real examples demonstrate the utility of the proposed method

New mutations at the imprinted Gnas cluster show gene dosage effects of Gsα in postnatal growth and implicate XLαs in bone and fat metabolism, but not in suckling

The imprinted Gnas cluster is involved in obesity, energy metabolism, feeding behavior, and viability. Relative contribution of paternally expressed proteins XLαs, XLN1, and ALEX or a double dose of maternally expressed Gsα to phenotype has not been established. In this study, we have generated two new mutants (Ex1A-T-CON and Ex1A-T) at the Gnas cluster. Paternal inheritance of Ex1A-T-CON leads to loss of imprinting of Gsα, resulting in preweaning growth retardation followed by catch-up growth. Paternal inheritance of Ex1A-T leads to loss of imprinting of Gsα and loss of expression of XLαs and XLN1. These mice have severe preweaning growth retardation and incomplete catch-up growth. They are fully viable probably because suckling is unimpaired, unlike mutants in which the expression of all the known paternally expressed Gnasxl proteins (XLαs, XLN1 and ALEX) is compromised. We suggest that loss of ALEX is most likely responsible for the suckling defects previously observed. In adults, paternal inheritance of Ex1A-T results in an increased metabolic rate and reductions in fat mass, leptin, and bone mineral density attributable to loss of XLαs. This is, to our knowledge, the first report describing a role for XLαs in bone metabolism. We propose that XLαs is involved in the regulation of bone and adipocyte metabolism

Polymorphisms of SP110 are associated with both pulmonary and extra-pulmonary tuberculosis among the Vietnamese

Background: Tuberculosis (TB) is an infectious disease that remains a major cause of morbidity and mortality worldwide, yet the reasons why only 10% of people infected with Mycobacterium tuberculosis go on to develop clinical disease are poorly understood. Genetically determined variation in the host immune response is one factor influencing the response to M. tuberculosis. SP110 is an interferon-responsive nuclear body protein with critical roles in cell cycling, apoptosis and immunity to infection. However association studies of the gene with clinical TB in different populations have produced conflicting results. Methods: To examine the importance of the SP110 gene in immunity to TB in the Vietnamese we conducted a case-control genetic association study of 24 SP110 variants, in 663 patients with microbiologically proven TB and 566 unaffected control subjects from three tertiary hospitals in northern Vietnam. Results: Five SNPs within SP110 were associated with all forms of TB, including four SNPs at the C terminus (rs10208770, rs10498244, rs16826860, rs11678451) under a dominant model and one SNP under a recessive model, rs7601176. Two of these SNPs were associated with pulmonary TB (rs10208770 and rs16826860) and one with extra-pulmonary TB (rs10498244). Conclusion: SP110 variants were associated with increased susceptibility to both pulmonary and extra-pulmonary TB in the Vietnamese. Genetic variants in SP110 may influence macrophage signaling responses and apoptosis during M. tuberculosis infection, however further research is required to establish the mechanism by which SP110 influences immunity to tuberculosis infection. © 2014 Fox et al

ABCD Neurocognitive Prediction Challenge 2019: Predicting individual residual fluid intelligence scores from cortical grey matter morphology

We predicted residual fluid intelligence scores from T1-weighted MRI data available as part of the ABCD NP Challenge 2019, using morphological similarity of grey-matter regions across the cortex. Individual structural covariance networks (SCN) were abstracted into graph-theory metrics averaged over nodes across the brain and in data-driven communities/modules. Metrics included degree, path length, clustering coefficient, centrality, rich club coefficient, and small-worldness. These features derived from the training set were used to build various regression models for predicting residual fluid intelligence scores, with performance evaluated both using cross-validation within the training set and using the held-out validation set. Our predictions on the test set were generated with a support vector regression model trained on the training set. We found minimal improvement over predicting a zero residual fluid intelligence score across the sample population, implying that structural covariance networks calculated from T1-weighted MR imaging data provide little information about residual fluid intelligence.Comment: 8 pages plus references, 3 figures, 2 tables. Submission to the ABCD Neurocognitive Prediction Challenge at MICCAI 201

How mothers feel: validation of a measure of maternal mood

© 2019 The Authors. Journal of Evaluation in Clinical Practice published by John Wiley & Sons Ltd Rationale: Low mood may affect developing relationships with a new baby, partner and family. Early identification of mood disturbance is crucial to improve outcomes for women perinatally. Instruments such as the Edinburgh Postnatal Depression Scale (EPDS) are used routinely, with evidence that some women do not feel comfortable with how they are asked about their mental health. Objective: To develop a mood checklist as a user-friendly, effective measure of well-being in post-partum women, for use by health professionals. Methods: Cognitive interviews with women who had recently given birth assessed response format and face validity of a prototype measure. A cross-sectional survey followed. A random split-half instrument development protocol was used. Exploratory factor analysis determined factor structure with the first sample,. The second sample confirmed factor structure and evaluationof key psychometric variables and known-groups discriminant validity (KGDV), requiring a supplementary between-subjects design with stratification based on case negative/case positive classification using EPDSscreening cut-off criteria. Results: Cognitive interview data confirmed the face validity of the measure. Exploratory factor analysis indicated an 18 item two-factor model with two (negatively) correlated factors. Factor 1 loaded with items reflecting positive mood and factor 2 negative items. Confirmatory factor analysis showed a good fit to the two-factor model across the full spectrum of fit indices. Statistically significant differences between groups were observed in relation to as EPDS caseness classification. Cronbach alpha coefficients for the positive and negative subscales revealed acceptable internal consistency of 0.79 and 0.72, respectively. Conclusion: The outcome checklist may be appropriate for use in clinical practice. It demonstrated effective psychometric properties and clear cross-validation with existing commonly used measures

BézierSketch: A Generative Model for Scalable Vector Sketches

The study of neural generative models of human sketches is a fascinating contemporary modeling problem due to the links between sketch image generation and the human drawing process. The landmark SketchRNN provided breakthrough by sequentially generating sketches as a sequence of waypoints. However this leads to low-resolution image generation, and failure to model long sketches. In this paper we present B\'ezierSketch, a novel generative model for fully vector sketches that are automatically scalable and high-resolution. To this end, we first introduce a novel inverse graphics approach to stroke embedding that trains an encoder to embed each stroke to its best fit B\'ezier curve. This enables us to treat sketches as short sequences of paramaterized strokes and thus train a recurrent sketch generator with greater capacity for longer sketches, while producing scalable high-resolution results. We report qualitative and quantitative results on the Quick, Draw! benchmark.Comment: Accepted as poster at ECCV 202