Astroinformatics of galaxies and quasars: a new general method for photometric redshifts estimation

With the availability of the huge amounts of data produced by current and future large multi-band photometric surveys, photometric redshifts have become a crucial tool for extragalactic astronomy and cosmology. In this paper we present a novel method, called Weak Gated Experts (WGE), which allows to derive photometric redshifts through a combination of data mining techniques. \noindent The WGE, like many other machine learning techniques, is based on the exploitation of a spectroscopic knowledge base composed by sources for which a spectroscopic value of the redshift is available. This method achieves a variance \sigma^2(\Delta z)=2.3x10^{-4} (\sigma^2(\Delta z) =0.08), where \Delta z = z_{phot} - z_{spec}) for the reconstruction of the photometric redshifts for the optical galaxies from the SDSS and for the optical quasars respectively, while the Root Mean Square (RMS) of the \Delta z variable distributions for the two experiments is respectively equal to 0.021 and 0.35. The WGE provides also a mechanism for the estimation of the accuracy of each photometric redshift. We also present and discuss the catalogs obtained for the optical SDSS galaxies, for the optical candidate quasars extracted from the DR7 SDSS photometric dataset {The sample of SDSS sources on which the accuracy of the reconstruction has been assessed is composed of bright sources, for a subset of which spectroscopic redshifts have been measured.}, and for optical SDSS candidate quasars observed by GALEX in the UV range. The WGE method exploits the new technological paradigm provided by the Virtual Observatory and the emerging field of Astroinformatics.Comment: 36 pages, 22 figures and 8 table

Photometric classification of emission line galaxies with machine-learning methods

In this paper, we discuss an application of machine-learning-based methods to the identification of candidate active galactic nucleus (AGN) from optical survey data and to the automatic classification of AGNs in broad classes. We applied four different machine-learning algorithms, namely the Multi Layer Perceptron, trained, respectively, with the Conjugate Gradient, the Scaled Conjugate Gradient, the Quasi Newton learning rules and the Support Vector Machines, to tackle the problem of the classification of emission line galaxies in different classes, mainly AGNs versus non-AGNs, obtained using optical photometry in place of the diagnostics based on line intensity ratios which are classically used in the literature. Using the same photometric features, we discuss also the behaviour of the classifiers on finer AGN classification tasks, namely Seyfert I versus Seyfert II, and Seyfert versus LINER. Furthermore, we describe the algorithms employed, the samples of spectroscopically classified galaxies used to train the algorithms, the procedure followed to select the photometric parameters and the performances of our methods in terms of multiple statistical indicators. The results of the experiments show that the application of self-adaptive data mining algorithms trained on spectroscopic data sets and applied to carefully chosen photometric parameters represents a viable alternative to the classical methods that employ time-consuming spectroscopic observations

Photometric classification of emission line galaxies with Machine Learning methods

In this paper we discuss an application of machine learning based methods to the identification of candidate AGN from optical survey data and to the automatic classification of AGNs in broad classes. We applied four different machine learning algorithms, namely the Multi Layer Perceptron (MLP), trained respectively with the Conjugate Gradient, Scaled Conjugate Gradient and Quasi Newton learning rules, and the Support Vector Machines (SVM), to tackle the problem of the classification of emission line galaxies in different classes, mainly AGNs vs non-AGNs, obtained using optical photometry in place of the diagnostics based on line intensity ratios which are classically used in the literature. Using the same photometric features we discuss also the behavior of the classifiers on finer AGN classification tasks, namely Seyfert I vs Seyfert II and Seyfert vs LINER. Furthermore we describe the algorithms employed, the samples of spectroscopically classified galaxies used to train the algorithms, the procedure followed to select the photometric parameters and the performances of our methods in terms of multiple statistical indicators. The results of the experiments show that the application of self adaptive data mining algorithms trained on spectroscopic data sets and applied to carefully chosen photometric parameters represents a viable alternative to the classical methods that employ time-consuming spectroscopic observations

DAME: A distributed data mining and exploration framework within the virtual observatory

Nowadays, many scientific areas share the same broad requirements of being able to deal with massive and distributed datasets while, when possible, being integrated with services and applications. In order to solve the growing gap between the incremental generation of data and our understanding of it, it is required to know how to access, retrieve, analyze, mine and integrate data from disparate sources. One of the fundamental aspects of any new generation of data mining software tool or package which really wants to become a service for the community is the possibility to use it within complex workflows which each user can fine tune in order to match the specific demands of his scientific goal. These workflows need often to access different resources (data, providers, computing facilities and packages) and require a strict interoperability on (at least) the client side. The project DAME (DAta Mining & Exploration) arises from these requirements by providing a distributed WEB-based data mining infrastructure specialized on Massive Data Sets exploration with Soft Computing methods. Originally designed to deal with astrophysical use cases, where first scientific application examples have demonstrated its effectiveness, the DAME Suite results as a multi-disciplinary platformindependent tool perfectly compliant with modern KDD (Knowledge Discovery in Databases) requirements and Information & Communication Technology trends

Reconstructing neural representations of tactile space

Psychophysical experiments have demonstrated large and highly systematic perceptual distortions of tactile space. Such a space can be referred to our experience of the spatial organisation of objects, at representational level, through touch, in analogy with the familiar concept of visual space. We investigated the neural basis of tactile space by analysing activity patterns induced by tactile stimulation of nine points on a 3 × 3 square grid on the hand dorsum using functional magnetic resonance imaging. We used a searchlight approach within pre-defined regions of interests to compute the pairwise Euclidean distances between the activity patterns elicited by tactile stimulation. Then, we used multidimensional scaling to reconstruct tactile space at the neural level and compare it with skin space at the perceptual level. Our reconstructions of the shape of skin space in contralateral primary somatosensory and motor cortices reveal that it is distorted in a way that matches the perceptual shape of skin space. This suggests that early sensorimotor areas critically contribute to the distorted internal representation of tactile space on the hand dorsum

Looking for an objective parameter to identify early vocal dysfunctions in healthy prceived singers

The finding of minimal laryngeal dysfunctions in professional voice users is essential to prevent the onset of organic vocal pathologies. The purpose of this study is to identify an objective parameter that supports the phoniatric evaluation in detecting minimal laryngeal dysfunctions in singers. 54 professional and non-professional singers have been evaluated with laryngostroboscopy, Multi-Dimensional Voice Program (MDVP), Dysphonia Severity Index (DSI), maximum phonation time (TMF), minimum intensity of sound emission (I-min), maximum frequency (F-max), voice handicap index (VHI), singing voice handicap index (SVHI), manual phonogram and audiometric examination. The SVHI of all the “healthy” singers was on average 23.7 ± 22.5, while that of the “dysfunctional” 20.9 ± 18. No statistically significant difference was found between the SVHI scores of the total of healthy singers compared to the scores of the dysfunctional ones on the VSL (p = 0.6). The between-group comparison of the means of individual parameter values of DSI, TMF, F-max, Jitter, Shimmer, NHR, and SPI was not statistically significant (respectively p = 0.315, 0.2, 0.18, 0.09, 0.2, 0.08, 0.3). The only parameter analyzed that was statistically significant was the I-min (p < 0.05). SVHI is a valid instrument for the evaluation after a therapy but in our experience, it is not useful in distinguishing healthy from dysfunctional patients. The minimum intensity of sound emission measured with the sound level meter (I-low2) resulted a reliable parameter to identify minimal laryngeal dysfunctions and a useful tool in supporting the phoniatric diagnostic-therapeutic process in singers

Electrospun Membranes Designed for Burst Release of New Gold-Complexes Inducing Apoptosis of Melanoma Cells

Two non-commercial metallic Au-based complexes were tested against one of the most aggressive malignant melanomas of the skin (MeWo cells), through cell viability and time-lapse live-cell imaging system assays. The tests with the complexes were carried out both in the form of free metallic complexes, directly in contact with the MeWo cell line culture, and embedded in fibers of Polycaprolactone (PCL) membranes produced by the electrospinning technique. Membranes functionalized with complexes were prepared to evaluate the efficiency of the membranes against the melanoma cells and therefore their feasibility in the application as an antitumoral patch for topical use. Both series of tests highlighted a very effective antitumoral activity, manifesting a very relevant cell viability inhibition after both 24 h and 48 h. In the case of the AuM1 complex at the concentration of 20 mM, melanoma cells completely died in this short period of time. A mortality of around 70% was detected from the tests performed using the membranes functionalized with AuM1 complex at a very low concentration (3 wt.%), even after 24 h of the contact period. The synthesized complexes also manifest high selectivity with respect to the MeWo cells. The peculiar structural and morphological organization of the nanofibers constituting the membranes allows for a very effective antitumoral activity in the first 3 h of treatment. Experimental points of the release profiles were perfectly fitted with theoretical curves, which easily allow interpretation of the kinetic phenomena occurring in the release of the synthesized complexes in the chosen medium

The use of neural networks to probe the structure of the nearby universe

In the framework of the European VO-Tech project, we are implementing new machine learning methods specifically tailored to match the needs of astronomical data mining. In this paper, we shortly present the methods and discuss an application to the Sloan Digital Sky Survey public data set. In particular, we discuss some preliminary results on the 3-D taxonomy of the nearby (z < 0.5) universe. Using neural networks trained on the available spectroscopic base of knowledge we derived distance estimates for ca. 30 million galaxies distributed over 8,000 sq. deg. We also use unsupervised clustering tools to investigate whether it is possible to characterize in broad morphological bins the nature of each object and produce a reliable list of candidate AGNs and QSOs

Economic evaluation of a bioinductive implant for the repair of rotator cuff tears compared with standard surgery in Italy

Introduction: Rotator cuff tear (RCT) is a painful, progressive condition resulting from damage to the rotator cuff tendons and is the leading cause of shoulder-related disability. Surgical repair of rotator cuff is an established standard of care (SOC); however, failure of the procedure can occur. In this context, the use of collagen-based bioinductive implant REGENETEN showed long-term improvements in clinical scores. The aim of the study was to assess the cost-effectiveness of REGENETEN combined with SOC (SOC + REGENETEN) compared to SOC alone from both National Healthcare Service (NHS) and societal perspectives in Italy. Methods: A decision analytic model was developed to estimate the number of tears healed and costs for the two considered treatment strategies over 1 year. Clinical data were retrieved from the literature, and the clinical pathways for the management of patients with RCTs were retrieved from four key opinion leaders in Italy. Results: Over a 1-year time horizon, healed lesions were 90.70% and 72.90% for surgical repair of RCTs with and without REGENETEN, respectively. Considering the NHS perspective, mean costs per patient were €7828 and €4650 for the two strategies, respectively, leading to an incremental cost-effectiveness ratio (ICER) of €17,857 per healed tear. From the societal perspective, the mean costs per patient were €12,659 for SOC and €11,784 for REGENETEN, thus showing savings of €4918 per healed tear when the bioinductive implant is used. The sensitivity analyses confirmed the robustness of the model results. Conclusion: In the context of paucity of cost-effectiveness studies, our findings provide additional evidence for clinicians and payers regarding the value of a new treatment option that supports a tailored approach for the management of patients with RCTs