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1. |
Record Nr. |
UNICAMPANIAVAN0249414 |
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Autore |
Wilson, Jeffrey R. |
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Titolo |
Marginal Models in Analysis of Correlated Binary Data with Time Dependent Covariates / Jeffrey R. Wilson, Elsa Vazquez-Arreola, (Din) Ding-Geng Chen |
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Pubbl/distr/stampa |
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Titolo uniforme |
Marginal Models in Analysis of Correlated Binary Data with Time Dependent Covariates |
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Descrizione fisica |
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xxiii, 166 p. : ill. ; 24 cm |
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Altri autori (Persone) |
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Chen, Ding-Geng |
Vazquez-Arreola, Elsa |
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Soggetti |
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62-XX - Statistics [MSC 2020] |
62H20 - Measures of association (correlation, canonical correlation, etc.) [MSC 2020] |
62P10 - Applications of statistics to biology and medical sciences; meta analysis [MSC 2020] |
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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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2. |
Record Nr. |
UNINA9910557298603321 |
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Autore |
Lattanzio Veronica Maria Teresa |
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Titolo |
Improved Analytical Technologies for the Detection of Natural Toxins and Their Metabolites in Food |
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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 (156 p.) |
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Soggetti |
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Research & 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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Food, by nature, is a biological substrate and is therefore capable of supporting the growth of microbials that are potential producers of toxic compounds. Among them mycotoxins, marine biotoxins, plant toxins, cyanogenic glycosides, and toxins occurring in poisonous mushrooms pose not only a risk to both human and animal health but also impact food security and nutrition by reducing people's access to healthy food. This book collects some of the recent key improvements of analytical methodologies for the detection of natural toxins and their metabolites in food, and highlights the challenges yet to be resolved. Special emphasis is given to emerging or less-investigated toxins, to provide the scientific community with new tools and/or data supporting a better understanding of related food safety issues. |
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