13 research outputs found

    A self-driven approach for multi-class discrimination in Alzheimer’s disease based on wearable EEG

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    Early detection is critical to control Alzheimer’s disease (AD) progression and postpone cognitive decline. Traditional medical procedures such as magnetic resonance imaging are costly, involve long waiting lists, and require complex analysis. Alternatively, for the past years, researchers have successfully evaluated AD detection approaches based on machine learning and electroencephalography (EEG). Nonetheless, these approaches frequently rely upon manual processing or involve non-portable EEG hardware. These aspects are suboptimal regarding automated diagnosis, since they require additional personnel and hinder porta- bility. In this work, we report the preliminary evaluation of a self-driven AD multi-class discrimination approach based on a commercial EEG acquisition system using sixteen channels. For this purpose, we recorded the EEG of three groups of participants: mild AD, mild cognitive impairment (MCI) non-AD, and controls, and we implemented a self-driven analysis pipeline to discriminate the three groups. First, we applied automated artifact rejection algorithms to the EEG recordings. Then, we extracted power, entropy, and complexity features from the preprocessed epochs. Finally, we evaluated a multi-class classification problem using a multi-layer perceptron through leave-one-subject-out cross-validation. The preliminary results that we obtained are comparable to the best in literature (0.88 F1-score), what suggests that AD can potentially be detected through a self-driven approach based on commercial EEG and machine learn- ing. We believe this work and further research could contribute to opening the door for the detection of AD in a single consultation session, therefore reducing the costs associated to AD screening and poten- tially advancing medical treatment.Spanish Government PGC2018-098813-B-C31European Commission Operative Program FEDER 2014-2020 BTIC-352-UGR20Economy, Universities and Science Office of the Andalusian Regional GovernmentUniversidad de Granada/CBU

    An Automated Approach for the Detection of Alzheimer’s Disease From Resting State Electroencephalography

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    Early detection is crucial to control the progression of Alzheimer’s disease and to postpone intellectual decline. Most current detection techniques are costly, inaccessible, or invasive. Furthermore, they require laborious analysis, what delays the start of medical treatment. To overcome this, researchers have recently investigated AD detection based on electroencephalography, a non-invasive neurophysiology technique, and machine learning algorithms. However, these approaches typically rely on manual procedures such as visual inspection, that requires additional personnel for the analysis, or on cumbersome EEG acquisition systems. In this paper, we performed a preliminary evaluation of a fully-automated approach for AD detection based on a commercial EEG acquisition system and an automated classification pipeline. For this purpose, we recorded the resting state brain activity of 26 participants from three groups: mild AD, mild cognitive impairment (MCI-non-AD), and healthy controls. First, we applied automated data-driven algorithms to reject EEG artifacts. Then, we obtained spectral, complexity, and entropy features from the preprocessed EEG segments. Finally, we assessed two binary classification problems: mild AD vs. controls, and MCI-non-AD vs. controls, through leave-one-subject-out cross-validation. The preliminary results that we obtained are comparable to the best reported in literature, what suggests that AD detection could be automatically detected through automated processing and commercial EEG systems. This is promising, since it may potentially contribute to reducing costs related to AD screening, and to shortening detection times, what may help to advance medical treatment.PID2021-128529OA-I00 Spanish Ministry of Science, Innovation and UniversitiesEuropean Regional Development FundsBTIC- 352-UGR20Operative Program FEDER 2014–2020Economy, Universities and Science Office of the Andalusian Regional Governmen

    Synthesis of ZnO:Li films using an Arduino-based Hydrothermal reactor: An application to solar cells

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    Solar energy becomes more important in society, due to the low level of pollution it produces compared to fossil fuels. One way to take advantage of solar energy is using solar cells, which are made with zinc oxide films. To have its synthesis it is necessary to use a hydrothermal reactor,which has become increasingly expensive. Making it unavailable to most educational institutionsand the private sector. To solve the problem of its high cost, the aim of this work was tomanufacture a hydrothermal reactor based on Arduino, with an automation system with agitation,temperature-pressure control and low cost. This through a simple and practical methodology, which is focuses on users without deep knowledge in electronics and programming. SolidWorks®software was used for physical design and stress validation in the hydrothermal reactor. The cost of the reactor proposed in this work is 6 times lower than the cost of a commercial reactor. To validate the reliability of the reactor, three lithium-doped zinc oxide films with different molarities were synthesized. These films presented a transmittance response equivalent to that reported in theliterature. Finally, it is intended that the relevance of this work is to encourage users, through the proposed methodology, to manufacture not only a hydrothermal reactor, but also to manufacturecomplex laboratory equipment.La energía solar cobra mayor importancia en la sociedad, debido al bajo nivel de contaminación que produce en comparación con los combustibles fósiles. Una forma de aprovechar la energía solar es mediante el uso de celdas solares, que se fabrican con películas de óxido de zinc. Para su síntesis, es necesario utilizar un reactor hidrotermal, que se ha vuelto cada vez más costoso.Haciéndolo inaccesible para la mayoría de las instituciones educativas y el sector privado. Para solucionar el problema de su alto costo, el objetivo de este trabajo fue fabricar un reactor hidrotermal basado en Arduino, con un sistema de automatización con agitación, control de temperatura-presión y de bajo costo. Esto a través de una metodología sencilla y práctica, que está enfocada a usuarios sin conocimientos profundos en electrónica y programación. Se utilizó el software SolidWorks® para el diseño físico y la validación de esfuerzos en el reactor hidrotermal.El costo del reactor propuesto en este trabajo es 6 veces menor que el costo de un reactor comercial.Para validar la confiabilidad del reactor, se sintetizaron tres películas de óxido de zinc dopadas con litio con diferentes molaridades. Estas películas presentaron una respuesta de transmitancia equivalente a la reportada en la literatura. Finalmente, se pretende que la relevancia de este trabajo sea incentivar a usuarios, a través de la metodología propuesta, a fabricar no solo un reactor hidrotermal, sino también en fabricar complejos equipos de laboratorio

    The Fourteenth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the extended Baryon Oscillation Spectroscopic Survey and from the second phase of the Apache Point Observatory Galactic Evolution Experiment

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    The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) has been in operation since July 2014. This paper describes the second data release from this phase, and the fourteenth from SDSS overall (making this, Data Release Fourteen or DR14). This release makes public data taken by SDSS-IV in its first two years of operation (July 2014-2016). Like all previous SDSS releases, DR14 is cumulative, including the most recent reductions and calibrations of all data taken by SDSS since the first phase began operations in 2000. New in DR14 is the first public release of data from the extended Baryon Oscillation Spectroscopic Survey (eBOSS); the first data from the second phase of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE-2), including stellar parameter estimates from an innovative data driven machine learning algorithm known as "The Cannon"; and almost twice as many data cubes from the Mapping Nearby Galaxies at APO (MaNGA) survey as were in the previous release (N = 2812 in total). This paper describes the location and format of the publicly available data from SDSS-IV surveys. We provide references to the important technical papers describing how these data have been taken (both targeting and observation details) and processed for scientific use. The SDSS website (www.sdss.org) has been updated for this release, and provides links to data downloads, as well as tutorials and examples of data use. SDSS-IV is planning to continue to collect astronomical data until 2020, and will be followed by SDSS-V.Comment: SDSS-IV collaboration alphabetical author data release paper. DR14 happened on 31st July 2017. 19 pages, 5 figures. Accepted by ApJS on 28th Nov 2017 (this is the "post-print" and "post-proofs" version; minor corrections only from v1, and most of errors found in proofs corrected

    The 16th Data Release of the Sloan Digital Sky Surveys: First Release from the APOGEE-2 Southern Survey and Full Release of eBOSS Spectra

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    This paper documents the 16th data release (DR16) from the Sloan Digital Sky Surveys (SDSS), the fourth and penultimate from the fourth phase (SDSS-IV). This is the first release of data from the Southern Hemisphere survey of the Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2); new data from APOGEE-2 North are also included. DR16 is also notable as the final data release for the main cosmological program of the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), and all raw and reduced spectra from that project are released here. DR16 also includes all the data from the Time Domain Spectroscopic Survey and new data from the SPectroscopic IDentification of ERosita Survey programs, both of which were co-observed on eBOSS plates. DR16 has no new data from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey (or the MaNGA Stellar Library "MaStar"). We also preview future SDSS-V operations (due to start in 2020), and summarize plans for the final SDSS-IV data release (DR17)

    The Seventeenth Data Release of the Sloan Digital Sky Surveys: Complete Release of MaNGA, MaStar and APOGEE-2 Data

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    This paper documents the seventeenth data release (DR17) from the Sloan Digital Sky Surveys; the fifth and final release from the fourth phase (SDSS-IV). DR17 contains the complete release of the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, which reached its goal of surveying over 10,000 nearby galaxies. The complete release of the MaNGA Stellar Library (MaStar) accompanies this data, providing observations of almost 30,000 stars through the MaNGA instrument during bright time. DR17 also contains the complete release of the Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2) survey which publicly releases infra-red spectra of over 650,000 stars. The main sample from the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), as well as the sub-survey Time Domain Spectroscopic Survey (TDSS) data were fully released in DR16. New single-fiber optical spectroscopy released in DR17 is from the SPectroscipic IDentification of ERosita Survey (SPIDERS) sub-survey and the eBOSS-RM program. Along with the primary data sets, DR17 includes 25 new or updated Value Added Catalogs (VACs). This paper concludes the release of SDSS-IV survey data. SDSS continues into its fifth phase with observations already underway for the Milky Way Mapper (MWM), Local Volume Mapper (LVM) and Black Hole Mapper (BHM) surveys

    Sloan Digital Sky Survey IV: Mapping the Milky Way, Nearby Galaxies, and the Distant Universe

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    We describe the Sloan Digital Sky Survey IV (SDSS-IV), a project encompassing three major spectroscopic programs. The Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2) is observing hundreds of thousands of Milky Way stars at high resolution and high signal-to-noise ratios in the near-infrared. The Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey is obtaining spatially resolved spectroscopy for thousands of nearby galaxies (median z0.03z\sim 0.03). The extended Baryon Oscillation Spectroscopic Survey (eBOSS) is mapping the galaxy, quasar, and neutral gas distributions between z0.6z\sim 0.6 and 3.5 to constrain cosmology using baryon acoustic oscillations, redshift space distortions, and the shape of the power spectrum. Within eBOSS, we are conducting two major subprograms: the SPectroscopic IDentification of eROSITA Sources (SPIDERS), investigating X-ray AGNs and galaxies in X-ray clusters, and the Time Domain Spectroscopic Survey (TDSS), obtaining spectra of variable sources. All programs use the 2.5 m Sloan Foundation Telescope at the Apache Point Observatory; observations there began in Summer 2014. APOGEE-2 also operates a second near-infrared spectrograph at the 2.5 m du Pont Telescope at Las Campanas Observatory, with observations beginning in early 2017. Observations at both facilities are scheduled to continue through 2020. In keeping with previous SDSS policy, SDSS-IV provides regularly scheduled public data releases; the first one, Data Release 13, was made available in 2016 July

    The Fourteenth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the Extended Baryon Oscillation Spectroscopic Survey and from the Second Phase of the Apache Point Observatory Galactic Evolution Experiment

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    The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) has been in operation since 2014 July. This paper describes the second data release from this phase, and the 14th from SDSS overall (making this Data Release Fourteen or DR14). This release makes the data taken by SDSS-IV in its first two years of operation (2014–2016 July) public. Like all previous SDSS releases, DR14 is cumulative, including the most recent reductions and calibrations of all data taken by SDSS since the first phase began operations in 2000. New in DR14 is the first public release of data from the extended Baryon Oscillation Spectroscopic Survey; the first data from the second phase of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE-2), including stellar parameter estimates from an innovative data-driven machine-learning algorithm known as "The Cannon"; and almost twice as many data cubes from the Mapping Nearby Galaxies at APO (MaNGA) survey as were in the previous release (N = 2812 in total). This paper describes the location and format of the publicly available data from the SDSS-IV surveys. We provide references to the important technical papers describing how these data have been taken (both targeting and observation details) and processed for scientific use. The SDSS web site (www.sdss.org) has been updated for this release and provides links to data downloads, as well as tutorials and examples of data use. SDSS-IV is planning to continue to collect astronomical data until 2020 and will be followed by SDSS-V

    First scientific observations with MEGARA at GTC

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    On June 25th 2017, the new intermediate-resolution optical IFU and MOS of the 10.4-m GTC had its first light. As part of the tests carried out to verify the performance of the instrument in its two modes (IFU and MOS) and 18 spectral setups (identical number of VPHs with resolutions R=6000-20000 from 0.36 to 1 micron) a number of astronomical objects were observed. These observations show that MEGARA@GTC is called to fill a niche of high-throughput, intermediateresolution IFU and MOS observations of extremely-faint narrow-lined objects. Lyman-α absorbers, star-forming dwarfs or even weak absorptions in stellar spectra in our Galaxy or in the Local Group can now be explored to a new level. Thus, the versatility of MEGARA in terms of observing modes and spectral resolution and coverage will allow GTC to go beyond current observational limits in either depth or precision for all these objects. The results to be presented in this talk clearly demonstrate the potential of MEGARA in this regard
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