When zero doesn't mean it and other geomathematical mischief

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

L´onconasa com a model per l´estudi de les bases moleculars de la citotoxicitat de les ribonucleases pancreàtiques. Aplicacions a la construcció de ribonucleases amb activitat antimicrobiana

La primera part d'aquest treball s´ha centrat en la caracterització i optimització del procés d'activació de l´onconasa recombinant per tal d'obtenir l´enzim igual a la forma nativa. Per això, les reaccions d'eliminació de la Met-1 i la ciclació de la Glu1, necessàries per generar el piroglutamic han estat seguides per MALDI-TOF MS. La segona part d´aquest treball s´ha centrat en l´estudi de la contribució del pont disulfur 30-75 de l´onconasa a les seves propietats biològiques. Els resultats suggereixen que el potencial redox del citosol cel·lular podria reduir el pont disulfur 30-75 de l´onconasa salvatge afectant la unió onconasa -inhibidor proteic de ribonucleases. La tercera part ha consistit en la construcció de variants de l´HP-RNasa i onconasa amb activitat bactericida. Per això, s´ha introduït el determinant bactericida (YRWR) descrit per la proteïna catiònica d'eosinòfils en els dos enzims. Els resultats obtinguts han evidenciat que les dues ribonucleases amb el determinant bactericida presenten activitat citotòxica contra bacteris gram-negatius preferentment.