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Record Nr. |
UNINA9910137204303321 |
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Autore |
Sharon M. Kolk |
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Titolo |
Deciphering serotonin's role in neurodevelopment / / topic editors Judith R. Homberg, Sharon M. Kolkand Dirk Schubert |
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Pubbl/distr/stampa |
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Frontiers Media SA, 2014 |
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France : , : Frontiers Media SA, , 2014 |
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ISBN |
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Descrizione fisica |
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1 online resource (131 pages) : illustrations |
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Collana |
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Frontiers Research Topics |
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Disciplina |
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Soggetti |
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Serotonin |
Serotonin - Physiological effect |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Bibliographic Level Mode of Issuance: Monograph |
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Nota di bibliografia |
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Includes bibliographical references. |
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Sommario/riassunto |
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This Research Topic is devoted to studies pinpointing the neurodevelopmental effects of serotonin in relation to prenatal SSRI exposure, serotonin transporter gene variation, and autism/neurodevelopmental disorders, using a wide-variety of cellular and molecular neurobiological techniques like, (epi)genetics, knockout, knockdown, neuroanatomy, physiology, MRI and behaviour in rodents and humans. We especially encouraged attempts to cross-link the neurodevelopmental processes across the fields of prenatal SSRI exposure, serotonin transporter gene variation, and autism/neurodevelopmental disorders, as well as new views on the positive or beneficial effects on serotonin-mediated neurodevelopmental changes. |
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2. |
Record Nr. |
UNINA9910437975503321 |
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Autore |
March Marisa Cristina |
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Titolo |
Advanced statistical methods for astrophysical probes of cosmology : doctoral thesis accepted by the Astrophysics Group of Imperial College London / / Marisa Cristina March |
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Pubbl/distr/stampa |
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New York, : Springer, 2013 |
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ISBN |
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1-299-19786-8 |
3-642-35060-7 |
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Edizione |
[1st ed. 2013.] |
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Descrizione fisica |
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1 online resource (191 p.) |
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Collana |
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Springer theses : recognizing outstanding Ph.D. research, , 2190-5053 |
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Disciplina |
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Soggetti |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Introduction -- Cosmology background -- Dark energy and apparent late time acceleration -- Supernovae Ia -- Statistical techniques -- Bayesian Doubt: Should we doubt the Cosmological Constant? -- Bayesian parameter inference for SNeIa data -- Robustness to Systematic Error for Future Dark Energy Probes -- Summary and Conclusions -- Index. |
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Sommario/riassunto |
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This thesis explores advanced Bayesian statistical methods for extracting key information for cosmological model selection, parameter inference and forecasting from astrophysical observations. Bayesian model selection provides a measure of how good models in a set are relative to each other - but what if the best model is missing and not included in the set? Bayesian Doubt is an approach which addresses this problem and seeks to deliver an absolute rather than a relative measure of how good a model is. Supernovae type Ia were the first astrophysical observations to indicate the late time acceleration of the Universe - this work presents a detailed Bayesian Hierarchical Model to infer the cosmological parameters (in particular dark energy) from observations of these supernovae type Ia. |
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