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- Blanton, Michael R., et al.
(författare)
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Sloan Digital Sky Survey IV : Mapping the Milky Way, Nearby Galaxies, and the Distant Universe
- 2017
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Ingår i: Astronomical Journal. - : IOP Publishing Ltd. - 0004-6256 .- 1538-3881. ; 154:1
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Tidskriftsartikel (refereegranskat)abstract
- 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 z similar to 0.03). The extended Baryon Oscillation Spectroscopic Survey (eBOSS) is mapping the galaxy, quasar, and neutral gas distributions between z similar to 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.
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- Efferth, Thomas, et al.
(författare)
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Biopiracy versus One-World Medicine-From colonial relicts to global collaborative concepts
- 2019
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Ingår i: Phytomedicine. - : Elsevier. - 0944-7113 .- 1618-095X. ; 53, s. 319-331
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Forskningsöversikt (refereegranskat)abstract
- Background: Practices of biopiracy to use genetic resources and indigenous knowledge by Western companies without benefit-sharing of those, who generated the traditional knowledge, can be understood as form of neo-colonialism. Hypothesis: The One-World Medicine concept attempts to merge the best of traditional medicine from developing countries and conventional Western medicine for the sake of patients around the globe. Study design: Based on literature searches in several databases, a concept paper has been written. Legislative initiatives of the United Nations culminated in the Nagoya protocol aim to protect traditional knowledge and regulate benefit-sharing with indigenous communities. The European community adopted the Nagoya protocol, and the corresponding regulations will be implemented into national legislation among the member states. Despite pleasing progress, infrastructural problems of the health care systems in developing countries still remain. Current approaches to secure primary health care offer only fragmentary solutions at best. Conventional medicine from industrialized countries cannot be afforded by the impoverished population in the Third World. Confronted with exploding costs, even health systems in Western countries are endangered to burst. Complementary and alternative medicine (CAM) is popular among the general public in industrialized countries, although the efficacy is not sufficiently proven according to the standards of evidence-based medicine. CAM is often available without prescription as over-the-counter products with non-calculated risks concerning erroneous self-medication and safety/toxicity issues. The concept of integrative medicine attempts to combine holistic CAM approaches with evidence-based principles of conventional medicine. Conclusion: To realize the concept of One-World Medicine, a number of standards have to be set to assure safety, efficacy and applicability of traditional medicine, e.g. sustainable production and quality control of herbal products, performance of placebo-controlled, double-blind, randomized clinical trials, phytovigilance, as well as education of health professionals and patients.
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- Kino, S., et al.
(författare)
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A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects
- 2021
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Ingår i: SSM - Population Health. - : Elsevier BV. - 2352-8273. ; 15
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Tidskriftsartikel (refereegranskat)abstract
- Background: Machine learning (ML) has spread rapidly from computer science to several disciplines. Given the predictive capacity of ML, it offers new opportunities for health, behavioral, and social scientists. However, it remains unclear how and to what extent ML is being used in studies of social determinants of health (SDH). Methods: Using four search engines, we conducted a scoping review of studies that used ML to study SDH (published before May 1, 2020). Two independent reviewers analyzed the relevant studies. For each study, we identified the research questions, Results, data, and algorithms. We synthesized our findings in a narrative report. Results: Of the initial 8097 hits, we identified 82 relevant studies. The number of publications has risen during the past decade. More than half of the studies (n = 46) used US data. About 80% (n = 66) utilized surveys, and 70% (n = 57) employed ML for common prediction tasks. Although the number of studies in ML and SDH is growing rapidly, only a few studies used ML to improve causal inference, curate data, or identify social bias in predictions (i.e., algorithmic fairness). Conclusions: While ML equips researchers with new ways to measure health outcomes and their determinants from non-conventional sources such as text, audio, and image data, most studies still rely on traditional surveys. Although there are no guarantees that ML will lead to better social epidemiological research, the potential for innovation in SDH research is evident as a result of harnessing the predictive power of ML for causality, data curation, or algorithmic fairness. © 2021
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