5 resultados para When milk
em Universitat de Girona, Spain
Resumo:
Traditionally, compositional data has been identified with closed data, and the simplex has been considered as the natural sample space of this kind of data. In our opinion, the emphasis on the constrained nature of compositional data has contributed to mask its real nature. More crucial than the constraining property of compositional data is the scale-invariant property of this kind of data. Indeed, when we are considering only few parts of a full composition we are not working with constrained data but our data are still compositional. We believe that it is necessary to give a more precise definition of composition. This is the aim of this oral contribution
Resumo:
It is well known that regression analyses involving compositional data need special attention because the data are not of full rank. For a regression analysis where both the dependent and independent variable are components we propose a transformation of the components emphasizing their role as dependent and independent variables. A simple linear regression can be performed on the transformed components. The regression line can be depicted in a ternary diagram facilitating the interpretation of the analysis in terms of components. An exemple with time-budgets illustrates the method and the graphical features
Resumo:
There is almost not a case in exploration geology, where the studied data doesn’t includes below detection limits and/or zero values, and since most of the geological data responds to lognormal distributions, these “zero data” represent a mathematical challenge for the interpretation. We need to start by recognizing that there are zero values in geology. For example the amount of quartz in a foyaite (nepheline syenite) is zero, since quartz cannot co-exists with nepheline. Another common essential zero is a North azimuth, however we can always change that zero for the value of 360°. These are known as “Essential zeros”, but what can we do with “Rounded zeros” that are the result of below the detection limit of the equipment? Amalgamation, e.g. adding Na2O and K2O, as total alkalis is a solution, but sometimes we need to differentiate between a sodic and a potassic alteration. Pre-classification into groups requires a good knowledge of the distribution of the data and the geochemical characteristics of the groups which is not always available. Considering the zero values equal to the limit of detection of the used equipment will generate spurious distributions, especially in ternary diagrams. Same situation will occur if we replace the zero values by a small amount using non-parametric or parametric techniques (imputation). The method that we are proposing takes into consideration the well known relationships between some elements. For example, in copper porphyry deposits, there is always a good direct correlation between the copper values and the molybdenum ones, but while copper will always be above the limit of detection, many of the molybdenum values will be “rounded zeros”. So, we will take the lower quartile of the real molybdenum values and establish a regression equation with copper, and then we will estimate the “rounded” zero values of molybdenum by their corresponding copper values. The method could be applied to any type of data, provided we establish first their correlation dependency. One of the main advantages of this method is that we do not obtain a fixed value for the “rounded zeros”, but one that depends on the value of the other variable. Key words: compositional data analysis, treatment of zeros, essential zeros, rounded zeros, correlation dependency
Resumo:
Functional Data Analysis (FDA) deals with samples where a whole function is observed for each individual. A particular case of FDA is when the observed functions are density functions, that are also an example of infinite dimensional compositional data. In this work we compare several methods for dimensionality reduction for this particular type of data: functional principal components analysis (PCA) with or without a previous data transformation and multidimensional scaling (MDS) for diferent inter-densities distances, one of them taking into account the compositional nature of density functions. The difeerent methods are applied to both artificial and real data (households income distributions)
Resumo:
Una soca de Lactobacillus salivarius resistent a la rifampicina, CTC2197, es va assajar com a probiòtic en pollastres, estudiant la seva capacitat de prevenir la colonització de Salmonella enteritidis C-114 en pollastres. Quan la soca probiòtica es va administrar via oral juntament amb S.enteritidis C-114 directament al proventricle en pollets Leghorn de 1 dia, el patògen fou eliminat completament després de 21 dies. Els mateixos resultats es van obtenir quan la soca es va administrar a través del menjar i l'aigua a més de la inoculació directa al proventricle. La inclusió de L.salivarius CTC2197 en el menjar del primer dia va mostrar que una concentració de 105 UFC g-1 era suficient per assegurar la colonització dels tracte gastrointestinal dels pollets després de 1 setmana. No obstant, entre els 21 i 28 dies, L.salivarius CTC2197 no va ser detectable en el tracte gastrointestinal d'alguns pollets, mostrant que seria necessària més d'una dosis per assegurar la seva presència fins al final de l'etapa d'engreix. La liofilització i la congelació per glicerol o llet descremada com a agents crioprotector, van semblar mètodes adequats per preservar la soca probiòtica. La inclusió de L.salivarius CTC2197 en un pinso comercial va semblar ser un bon mètode per subministrar-lo en granja, tot i que la soca va mostrar sensibilitat a les temperatures utilitzades durant l'emmagatzematge del pinso i a les incubadores dels pollets. A més, la supervivència va millorar després de diverses reinoculacions en pinso.