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1. |
Record Nr. |
UNINA9910138252003321 |
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Titolo |
Computational fluid dynamics technologies and applications / / edited by Igor V. Minin, Oleg V. Minin |
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Pubbl/distr/stampa |
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Rijeka, Croatia : , : InTech, , [2011] |
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©2011 |
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ISBN |
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Descrizione fisica |
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1 online resource (410 pages) : illustrations |
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Disciplina |
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Soggetti |
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Computational fluid dynamics |
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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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Nota di bibliografia |
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Includes bibliographical references. |
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2. |
Record Nr. |
UNINA9910557701803321 |
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Autore |
David Robert |
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Titolo |
Stem Cell Research on Cardiology |
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Pubbl/distr/stampa |
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Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
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Descrizione fisica |
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1 online resource (356 p.) |
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Soggetti |
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Biology, life sciences |
Research and information: general |
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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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Sommario/riassunto |
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Even today, cardiovascular diseases are the main cause of death |
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worldwide, and therapeutic approaches are very restricted. Due to the limited regenerative capabilities of terminally differentiated cardiomyocytes post injury, new strategies to treat cardiac patients are urgently needed. Post myocardial injury, resident fibroblasts begin to generate the extracellular matrix, resulting in fibrosis, and finally, cardiac failure. Recently, preclinical investigations and clinical trials raised hope in stem cell-based approaches, to be an effective therapy option for these diseases. So far, several types of stem cells have been identified to be promising candidates to be applied for treatment: cardiac progenitor cells, bone marrow derived stem cells, embryonic and induced pluripotent stem cells, as well as their descendants. Furthermore, the innovative techniques of direct cardiac reprogramming of cells offered promising options for cardiovascular research, in vitro and in vivo. Hereby, the investigation of underlying and associated mechanisms triggering the therapeutic effects of stem cell application is of particular importance to improve approaches for heart patients. This Special Issue of Cells provides the latest update in the rapidly developing field of regenerative medicine in cardiology. |
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3. |
Record Nr. |
UNINA9910253937203321 |
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Autore |
Blasco Agustín |
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Titolo |
Bayesian Data Analysis for Animal Scientists : The Basics / / by Agustín Blasco |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
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ISBN |
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Edizione |
[1st ed. 2017.] |
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Descrizione fisica |
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1 online resource (XVIII, 275 p. 62 illus., 57 illus. in color.) |
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Disciplina |
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Soggetti |
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Agriculture |
Veterinary medicine |
Biomathematics |
Animal genetics |
Biometry |
Veterinary Medicine/Veterinary Science |
Mathematical and Computational Biology |
Animal Genetics and Genomics |
Biostatistics |
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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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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Foreword -- Notation -- 1. Do we understand classical statistics? -- 2. The Bayesian choice -- 3. Posterior distributions -- 4. MCMC -- 5. The “baby” model -- 6. The linear model. I. The “fixed” effects model -- 7. The linear model. II. The “mixed” model -- 8. A scope of the possibilities of Bayesian inference + MCMC -- 9. Prior information -- 10. Model choice -- Appendix -- References. |
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Sommario/riassunto |
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In this book, we provide an easy introduction to Bayesian inference using MCMC techniques, making most topics intuitively reasonable and deriving to appendixes the more complicated matters. The biologist or the agricultural researcher does not normally have a background in Bayesian statistics, having difficulties in following the technical books introducing Bayesian techniques. The difficulties arise from the way of making inferences, which is completely different in the Bayesian school, and from the difficulties in understanding complicated matters such as the MCMC numerical methods. We compare both schools, classic and Bayesian, underlying the advantages of Bayesian solutions, and proposing inferences based in relevant differences, guaranteed values, probabilities of similitude or the use of ratios. We also give a scope of complex problems that can be solved using Bayesian statistics, and we end the book explaining the difficulties associated to model choice and the use of small samples. The book has a practical orientation and uses simple models to introduce the reader in this increasingly popular school of inference. |
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