Paleomagnetism: principles and applications

This article summarizes the basic principles of Paleomagnetism, with examples of applications in geology that illustrate the capabilities of the technique.

Secondary Ion Mass Spectrometry (SIMS): principles and applications

This article outlines the basis of the technique and shows some examples of applications in order to exhibit the expectations of this technique invaried scientific fields.

Analyzing peptides and proteins by mass spectrometry: principles and applications in proteomics

The study of proteins has been a key element in biomedicine and biotechnology because of their important role in cell functions or enzymatic activity. Cells are the basic unit of living organisms, which are governed by a vast range of chemical reactions. These chemical reactions must be highly regulatedin order to achieve homeostasis. Proteins are polymeric molecules that havetaken on the evolutionary process the role, along with other factors, of controlthese chemical reactions. Learning how proteins interact and control their up anddown regulations can teach us how living cells regulate their functions, as well asthe cause of certain anomalies that occur in different diseases where proteins areinvolved. Mass spectrometry (MS) is an analytical widely used technique to studythe protein content inside the cells as a biomarker point, which describesdysfunctions in diseases and increases knowledge of how proteins are working.All the methodologies involved in these descriptions are integrated in the fieldcalled Proteomics.

An application of capital allocation principles to operational risk

The cost of operational risk refers to the capital needed to a fford the loss generated by ordinary activities of a firm. In this work we demonstrate how allocation principles can be used to the subdivision of the aggregate capital so that the firm can distribute this cost across its various constituents that generate operational risk. Several capital allocation principles are revised. Proportional allocation allows to calculate a relative risk premium to be charged to each unit. An example of fraud risk in the banking sector is presented and some correlation scenarios between business lines are compared. Keywords: solvency, quantile, value at risk, copulas

Predicting spinor condensate dynamics from simple principles

We study the spin dynamics of quasi-one-dimensional F=1 condensates both at zero and finite temperatures for arbitrary initial spin configurations. The rich dynamical evolution exhibited by these nonlinear systems is explained by surprisingly simple principles: minimization of energy at zero temperature and maximization of entropy at high temperature. Our analytical results for the homogeneous case are corroborated by numerical simulations for confined condensates in a wide variety of initial conditions. These predictions compare qualitatively well with recent experimental observations and can, therefore, serve as a guidance for ongoing experiments.

Identifying quantitative operation principles in metabolic pathways: a systematic method for searching feasible enzyme activity patterns leading to cellular adaptive responses

Background: Optimization methods allow designing changes in a system so that specific goals are attained. These techniques are fundamental for metabolic engineering. However, they are not directly applicable for investigating the evolution of metabolic adaptation to environmental changes. Although biological systems have evolved by natural selection and result in well-adapted systems, we can hardly expect that actual metabolic processes are at the theoretical optimum that could result from an optimization analysis. More likely, natural systems are to be found in a feasible region compatible with global physiological requirements. Results: We first present a new method for globally optimizing nonlinear models of metabolic pathways that are based on the Generalized Mass Action (GMA) representation. The optimization task is posed as a nonconvex nonlinear programming (NLP) problem that is solved by an outer- approximation algorithm. This method relies on solving iteratively reduced NLP slave subproblems and mixed-integer linear programming (MILP) master problems that provide valid upper and lower bounds, respectively, on the global solution to the original NLP. The capabilities of this method are illustrated through its application to the anaerobic fermentation pathway in Saccharomyces cerevisiae. We next introduce a method to identify the feasibility parametric regions that allow a system to meet a set of physiological constraints that can be represented in mathematical terms through algebraic equations. This technique is based on applying the outer-approximation based algorithm iteratively over a reduced search space in order to identify regions that contain feasible solutions to the problem and discard others in which no feasible solution exists. As an example, we characterize the feasible enzyme activity changes that are compatible with an appropriate adaptive response of yeast Saccharomyces cerevisiae to heat shock Conclusion: Our results show the utility of the suggested approach for investigating the evolution of adaptive responses to environmental changes. The proposed method can be used in other important applications such as the evaluation of parameter changes that are compatible with health and disease states.

Use of physiological constraints to identify quantitative design principles for gene expression in yeast adaptation to heat shock

Background: Understanding the relationship between gene expression changes, enzyme activity shifts, and the corresponding physiological adaptive response of organisms to environmental cues is crucial in explaining how cells cope with stress. For example, adaptation of yeast to heat shock involves a characteristic profile of changes to the expression levels of genes coding for enzymes of the glycolytic pathway and some of its branches. The experimental determination of changes in gene expression profiles provides a descriptive picture of the adaptive response to stress. However, it does not explain why a particular profile is selected for any given response. Results: We used mathematical models and analysis of in silico gene expression profiles (GEPs) to understand how changes in gene expression correlate to an efficient response of yeast cells to heat shock. An exhaustive set of GEPs, matched with the corresponding set of enzyme activities, was simulated and analyzed. The effectiveness of each profile in the response to heat shock was evaluated according to relevant physiological and functional criteria. The small subset of GEPs that lead to effective physiological responses after heat shock was identified as the result of the tuning of several evolutionary criteria. The experimentally observed transcriptional changes in response to heat shock belong to this set and can be explained by quantitative design principles at the physiological level that ultimately constrain changes in gene expression. Conclusion: Our theoretical approach suggests a method for understanding the combined effect of changes in the expression of multiple genes on the activity of metabolic pathways, and consequently on the adaptation of cellular metabolism to heat shock. This method identifies quantitative design principles that facilitate understating the response of the cell to stress.

A priori parameterisation of the CERES soil-crop models and tests against several European data sets

Mechanistic soil-crop models have become indispensable tools to investigate the effect of management practices on the productivity or environmental impacts of arable crops. Ideally these models may claim to be universally applicable because they simulate the major processes governing the fate of inputs such as fertiliser nitrogen or pesticides. However, because they deal with complex systems and uncertain phenomena, site-specific calibration is usually a prerequisite to ensure their predictions are realistic. This statement implies that some experimental knowledge on the system to be simulated should be available prior to any modelling attempt, and raises a tremendous limitation to practical applications of models. Because the demand for more general simulation results is high, modellers have nevertheless taken the bold step of extrapolating a model tested within a limited sample of real conditions to a much larger domain. While methodological questions are often disregarded in this extrapolation process, they are specifically addressed in this paper, and in particular the issue of models a priori parameterisation. We thus implemented and tested a standard procedure to parameterize the soil components of a modified version of the CERES models. The procedure converts routinely-available soil properties into functional characteristics by means of pedo-transfer functions. The resulting predictions of soil water and nitrogen dynamics, as well as crop biomass, nitrogen content and leaf area index were compared to observations from trials conducted in five locations across Europe (southern Italy, northern Spain, northern France and northern Germany). In three cases, the model’s performance was judged acceptable when compared to experimental errors on the measurements, based on a test of the model’s root mean squared error (RMSE). Significant deviations between observations and model outputs were however noted in all sites, and could be ascribed to various model routines. In decreasing importance, these were: water balance, the turnover of soil organic matter, and crop N uptake. A better match to field observations could therefore be achieved by visually adjusting related parameters, such as field-capacity water content or the size of soil microbial biomass. As a result, model predictions fell within the measurement errors in all sites for most variables, and the model’s RMSE was within the range of published values for similar tests. We conclude that the proposed a priori method yields acceptable simulations with only a 50% probability, a figure which may be greatly increased through a posteriori calibration. Modellers should thus exercise caution when extrapolating their models to a large sample of pedo-climatic conditions for which they have only limited information.

First principles analysis of the stability and diffusion of oxygen vacancies in meetal oxides

Oxygen vacancies in metal oxides are known to determine their chemistry and physics. The properties of neutral oxygen vacancies in metal oxides of increasing complexity (MgO, CaO, alpha-Al2O3, and ZnO) have been studied using density functional theory. Vacancy formation energies, vacancy-vacancy interaction, and the barriers for vacancy migration are determined and rationalized in terms of the ionicity, the Madelung potential, and lattice relaxation. It is found that the Madelung potential controls the oxygen vacancy properties of highly ionic oxides whereas a more complex picture arises for covalent ZnO.

MgO/Ag(001) interface structure and STM images from first principles

The interface of MgO/Ag(001) has been studied with density functional theory applied to slabs. We have found that regular MgO films show a small adhesion to the silver substrate, the binding can be increased in off-stoichiometric regimes, either by the presence of O vacancies at the oxide film or by a small excess of O atoms at the interface between the ceramic to the metal. By means of theoretical methods, the scanning tunneling microscopy signatures of these films is also analyzed in some detail. For defect free deposits containing 1 or 2 ML and at low voltages, tunnelling takes place from the surface Ag substrate, and at large positive voltages Mg atoms are imaged. If defects, oxygen vacancies, are present on the surface of the oxide they introduce much easier channels for tunnelling resulting in big protrusions and controlling the shape of the image, the extra O stored at the interface can also be detected for very thin films.