Self and cross-velocity correlation functions and diffusion coefficients in liquids: a molecular dynamics study of binary mixtures of soft spheres

Molecular dynamics simulation is applied to the study of the diffusion properties in binary liquid mixtures made up of soft-sphere particles with different sizes and masses. Self- and distinct velocity correlation functions and related diffusion coefficients have been calculated. Special attention has been paid to the dynamic cross correlations which have been computed through recently introduced relative mean molecular velocity correlation functions which are independent on the reference frame. The differences between the distinct velocity correlations and diffusion coefficients in different reference frames (mass-fixed, number-fixed, and solvent-fixed) are discussed.

Mineralization of the recalcitrant oxalic and oxamic acids by electrochemical advanced oxidation processes using a boron-doped diamond anode

Oxalic and oxamic acids are the ultimate and more persistent by-products of the degradation of N-aromatics by electrochemical advanced oxidation processes (EAOPs). In this paper, the kinetics and oxidative paths of these acids have been studied for several EAOPs using a boron-doped diamond (BDD) anode and a stainless steel or an air-diffusion cathode. Anodic oxidation (AO-BDD) in the presence of Fe2+ (AO-BDD-Fe2+) and under UVA irradiation (AO-BDD-Fe2+-UVA), along with electro-Fenton (EF-BDD), was tested. The oxidation of both acids and their iron complexes on BDD was clarified by cyclic voltammetry. AO-BDD allowed the overall mineralization of oxalic acid, but oxamic acid was removed much more slowly. Each acid underwent a similar decay in AO-BDD-Fe2+ and EFBDD, as expected if its iron complexes were not attacked by hydroxyl radicals in the bulk. The faster and total mineralization of both acids was achieved in AO-BDD-Fe2+-UVA due to the high photoactivity of their Fe(III) complexes that were continuously regenerated by oxidation of their Fe(II) complexes. Oxamic acid always released a larger proportion of NH4 + than NO3- ion, as well as volatile NOx species. Both acids were independently oxidized at the anode in AO-BDD, but in AO-BDD-Fe2+-UVA oxamic acid was more slowlydegraded as its content decreased, without significant effect on oxalic acid decay. The increase in current density enhanced the oxidation power of the latter method, with loss of efficiency. High Fe2+ contents inhibited the oxidation of Fe(II) complexes by the competitive oxidation of Fe2+ to Fe3+. Low current densities and Fe2+ contents are preferable to remove more efficiently these acids by the most potent AO-BDD-Fe2+-UVA method.

Performance of correlation functionals in ab initio chemisorption cluster-model calculations: Alkali metals on Si(111)

The performance of different correlation functionals has been tested for alkali metals, Li to Cs, interacting with cluster models simulating different active sites of the Si(111) surface. In all cases, the ab initio Hartree-Fock density has been obtained and used as a starting point. The electronic correlation energy is then introduced as an a posteriori correction to the Hartree-Fock energy using different correlation functionals. By making use of the ionic nature of the interaction and of different dissociation limits we have been able to prove that all functionals tested introduce the right correlation energy, although to a different extent. Hence, correlation functionals appear as an effective and easy way to introduce electronic correlation in the ab initio Hartree-Fock description of the chemisorption bond in complex systems where conventional configuration interaction techniques cannot be used. However, the calculated energies may differ by some tens of eV. Therefore, these methods can be employed to get a qualitative idea of how important correlation effects are, but they have some limitations if accurate binding energies are to be obtained.

Correlation between species composition and soil properties in the pastures of Plana de Vic (Catalonia, Spain)

The correlation between the species composition of pasture communities and soil properties in Plana de Vic has been studied using two multivariate methods, Correspondence Analysis (CA) for the vegetation data and Principal Component Analysis (PCA) for the soil data. To analyse the pastures, we took 144 vegetation relevés (comprising 201 species) that have been classified into 10 phytocoenological communities elsewhere. Most of these communities are almost entirely built up by perennials, ranging from xerophilous, clearly Mediterranean, to mesophilous, related to medium-European pastures, but a few occurring in shallow soils are dominated by therophytes. As for the soil properties, we analysed texture, pH, depth, bulk density, organic matter, C/N ratio and the carbonates content of 25 samples, correspondingto representative relevés of the communities studied.

Meta-structure correlation in protein space unveils different selection rules for folded and intrinsically disordered proteins

The number of existing protein sequences spans a very small fraction of sequence space. Natural proteins have overcome a strong negative selective pressure to avoid the formation of insoluble aggregates. Stably folded globular proteins and intrinsically disordered proteins (IDP) use alternative solutions to the aggregation problem. While in globular proteins folding minimizes the access to aggregation prone regions IDPs on average display large exposed contact areas. Here, we introduce the concept of average meta-structure correlation map to analyze sequence space. Using this novel conceptual view we show that representative ensembles of folded and ID proteins show distinct characteristics and responds differently to sequence randomization. By studying the way evolutionary constraints act on IDPs to disable a negative function (aggregation) we might gain insight into the mechanisms by which function - enabling information is encoded in IDPs.

High pressure induced changes in beef muscle proteome: correlation with quality parameters

The relationship between pressure induced changes on individual proteins and selected quality parameters in bovine longissimus thoracis et lumborum (LTL) muscle was studied. Pressures ranging from 200 to 600 MPa at 20 °C were used. High pressure processing (HPP) at pressures above 200 MPa induced strong modifications of protein solubility, meat colour and water holding capacity (WHC). The protein profiles of non-treated and pressure treated meat were observed using two dimensional electrophoresis. Proteins showing significant differences in abundance among treatments were identified by mass spectrometry. Pressure levels above 200 MPa strongly modified bovine LTL proteome with main effects being insolubilisation of sarcoplasmic proteins and solubilisation of myofibrillar proteins. Sarcoplasmic proteins were more susceptible to HPP effects than myofibrillar. Individual protein changes were significantly correlated with protein solubility, L*, b* and WHC, providing further insights into the mechanistic processes underlying HPP influence on quality and providing the basis for the future development of protein markers to assess the quality of processed meats.

Partial correlation : Network analysis

The final year project came to us as an opportunity to get involved in a topic which has appeared to be attractive during the learning process of majoring in economics: statistics and its application to the analysis of economic data, i.e. econometrics.Moreover, the combination of econometrics and computer science is a very hot topic nowadays, given the Information Technologies boom in the last decades and the consequent exponential increase in the amount of data collected and stored day by day. Data analysts able to deal with Big Data and to find useful results from it are verydemanded in these days and, according to our understanding, the work they do, although sometimes controversial in terms of ethics, is a clear source of value added both for private corporations and the public sector. For these reasons, the essence of this project is the study of a statistical instrument valid for the analysis of large datasets which is directly related to computer science: Partial Correlation Networks.The structure of the project has been determined by our objectives through the development of it. At first, the characteristics of the studied instrument are explained, from the basic ideas up to the features of the model behind it, with the final goal of presenting SPACE model as a tool for estimating interconnections in between elements in large data sets. Afterwards, an illustrated simulation is performed in order to show the power and efficiency of the model presented. And at last, the model is put into practice by analyzing a relatively large data set of real world data, with the objective of assessing whether the proposed statistical instrument is valid and useful when applied to a real multivariate time series. In short, our main goals are to present the model and evaluate if Partial Correlation Network Analysis is an effective, useful instrument and allows finding valuable results from Big Data.As a result, the findings all along this project suggest the Partial Correlation Estimation by Joint Sparse Regression Models approach presented by Peng et al. (2009) to work well under the assumption of sparsity of data. Moreover, partial correlation networks are shown to be a very valid tool to represent cross-sectional interconnections in between elements in large data sets.The scope of this project is however limited, as there are some sections in which deeper analysis would have been appropriate. Considering intertemporal connections in between elements, the choice of the tuning parameter lambda, or a deeper analysis of the results in the real data application are examples of aspects in which this project could be completed.To sum up, the analyzed statistical tool has been proved to be a very useful instrument to find relationships that connect the elements present in a large data set. And after all, partial correlation networks allow the owner of this set to observe and analyze the existing linkages that could have been omitted otherwise.

Empirical study of correlated survival times for recurrent events with proportional hazards margins and the effect of correlation and censoring.

Background: In longitudinal studies where subjects experience recurrent incidents over a period of time, such as respiratory infections, fever or diarrhea, statistical methods are required to take into account the within-subject correlation. Methods: For repeated events data with censored failure, the independent increment (AG), marginal (WLW) and conditional (PWP) models are three multiple failure models that generalize Cox"s proportional hazard model. In this paper, we revise the efficiency, accuracy and robustness of all three models under simulated scenarios with varying degrees of within-subject correlation, censoring levels, maximum number of possible recurrences and sample size. We also study the methods performance on a real dataset from a cohort study with bronchial obstruction. Results: We find substantial differences between methods and there is not an optimal method. AG and PWP seem to be preferable to WLW for low correlation levels but the situation reverts for high correlations. Conclusions: All methods are stable in front of censoring, worsen with increasing recurrence levels and share a bias problem which, among other consequences, makes asymptotic normal confidence intervals not fully reliable, although they are well developed theoretically.

Electrochemical synthesis of mesoporous CoPt nanowires for methanol oxidation

A new electrochemical method to synthesize mesoporous nanowires of alloys has been developed. Electrochemical deposition in ionic liquid-in-water (IL/W) microemulsion has been successful to grow mesoporous CoPt nanowires in the interior of polycarbonate membranes. The viscosity of the medium was high, but it did not avoid the entrance of the microemulsion in the interior of the membrane"s channels. The structure of the IL/W microemulsions, with droplets of ionic liquid (4 nm average diameter) dispersed in CoPt aqueous solution, defined the structure of the nanowires, with pores of a few nanometers, because CoPt alloy deposited only from the aqueous component of the microemulsion. The electrodeposition in IL/W microemulsion allows obtaining mesoporous structures in which the small pores must correspond to the size of the droplets of the electrolytic aqueous component of the microemulsion. The IL main phase is like a template for the confined electrodeposition. The comparison of the electrocatalytic behaviours towards methanol oxidation of mesoporous and compact CoPt nanowires of the same composition, demonstrated the porosity of the material. For the same material mass, the CoPt mesoporous nanowires present a surface area 16 times greater than compact ones, and comparable to that observed for commercial carbon-supported platinum nanoparticles.

Self-Organising Maps and Correlation Analysis as a Tool to Explore Patterns in Excitation-Emission Matrix Data Sets and to Discriminate Dissolved Organic Matter Fluorescence Components

Dissolved organic matter (DOM) is a complex mixture of organic compounds, ubiquitous in marine and freshwater systems. Fluorescence spectroscopy, by means of Excitation-Emission Matrices (EEM), has become an indispensable tool to study DOM sources, transport and fate in aquatic ecosystems. However the statistical treatment of large and heterogeneous EEM data sets still represents an important challenge for biogeochemists. Recently, Self-Organising Maps (SOM) has been proposed as a tool to explore patterns in large EEM data sets. SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure. In this paper, we show how SOM, coupled with a correlation analysis of the component planes, can be used both to explore patterns among samples, as well as to identify individual fluorescence components. We analysed a large and heterogeneous EEM data set, including samples from a river catchment collected under a range of hydrological conditions, along a 60-km downstream gradient, and under the influence of different degrees of anthropogenic impact. According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics. On the other hand, river samples collected under flash flood conditions showed homogeneous EEM shapes. The correlation analysis of the component planes suggested the presence of four fluorescence components, consistent with DOM components previously described in the literature. A remarkable strength of this methodology was that outlier samples appeared naturally integrated in the analysis. We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.

Genotypic classification of patients with Wolfram syndrome: insights into the natural history of the disease and correlation with phenotype

Purpose: Wolfram syndrome is a degenerative, recessive rare disease with an onset in childhood. It is caused by mutations in WFS1 or CISD2 genes. More than 200 different variations in WFS1 have been described in patients with Wolfram syndrome, which complicates the establishment of clear genotype-phenotype correlation. The purpose of this study was to elucidate the role of WFS1 mutations and update the natural history of the disease. Methods: This study analyzed clinical and genetic data of 412 patients with Wolfram syndrome published in the last 15 years. Results: (i) 15% of published patients do not fulfill the current ­inclusion criterion; (ii) genotypic prevalence differences may exist among countries; (iii) diabetes mellitus and optic atrophy might not be the first two clinical features in some patients; (iv) mutations are nonuniformly distributed in WFS1; (v) age at onset of diabetes mellitus, hearing defects, and diabetes insipidus may depend on the patient"s genotypic class; and (vi) disease progression rate might depend on genotypic class. Conclusion: New genotype-phenotype correlations were established, disease progression rate for the general population and for the genotypic classes has been calculated, and new diagnostic criteria have been proposed. The conclusions raised could be important for patient management and counseling as well as for the development of treatments for Wolfram syndrome.

Self-Organising Maps and Correlation Analysis as a Tool to Explore Patterns in Excitation-Emission Matrix Data Sets and to Discriminate Dissolved Organic Matter Fluorescence Components

Dissolved organic matter (DOM) is a complex mixture of organic compounds, ubiquitous in marine and freshwater systems. Fluorescence spectroscopy, by means of Excitation-Emission Matrices (EEM), has become an indispensable tool to study DOM sources, transport and fate in aquatic ecosystems. However the statistical treatment of large and heterogeneous EEM data sets still represents an important challenge for biogeochemists. Recently, Self-Organising Maps (SOM) has been proposed as a tool to explore patterns in large EEM data sets. SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure. In this paper, we show how SOM, coupled with a correlation analysis of the component planes, can be used both to explore patterns among samples, as well as to identify individual fluorescence components. We analysed a large and heterogeneous EEM data set, including samples from a river catchment collected under a range of hydrological conditions, along a 60-km downstream gradient, and under the influence of different degrees of anthropogenic impact. According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics. On the other hand, river samples collected under flash flood conditions showed homogeneous EEM shapes. The correlation analysis of the component planes suggested the presence of four fluorescence components, consistent with DOM components previously described in the literature. A remarkable strength of this methodology was that outlier samples appeared naturally integrated in the analysis. We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.

Electrochemical synthesis of mesoporous CoPt nanowires for methanol oxidation

A new electrochemical method to synthesize mesoporous nanowires of alloys has been developed. Electrochemical deposition in ionic liquid-in-water (IL/W) microemulsion has been successful to grow mesoporous CoPt nanowires in the interior of polycarbonate membranes. The viscosity of the medium was high, but it did not avoid the entrance of the microemulsion in the interior of the membrane"s channels. The structure of the IL/W microemulsions, with droplets of ionic liquid (4 nm average diameter) dispersed in CoPt aqueous solution, defined the structure of the nanowires, with pores of a few nanometers, because CoPt alloy deposited only from the aqueous component of the microemulsion. The electrodeposition in IL/W microemulsion allows obtaining mesoporous structures in which the small pores must correspond to the size of the droplets of the electrolytic aqueous component of the microemulsion. The IL main phase is like a template for the confined electrodeposition. The comparison of the electrocatalytic behaviours towards methanol oxidation of mesoporous and compact CoPt nanowires of the same composition, demonstrated the porosity of the material. For the same material mass, the CoPt mesoporous nanowires present a surface area 16 times greater than compact ones, and comparable to that observed for commercial carbon-supported platinum nanoparticles.

Correlation between charge transport and electroluminescence properties of Si-rich oxide/nitride/oxide-based light emitting capacitors.

The electrical and electroluminescence (EL) properties at room and high temperatures of oxide/ nitride/oxide (ONO)-based light emitting capacitors are studied. The ONO multidielectric layer is enriched with silicon by means of ion implantation. The exceeding silicon distribution follows a Gaussian profile with a maximum of 19%, centered close to the lower oxide/nitride interface. The electrical measurements performed at room and high temperatures allowed to unambiguously identify variable range hopping (VRH) as the dominant electrical conduction mechanism at low voltages, whereas at moderate and high voltages, a hybrid conduction formed by means of variable range hopping and space charge-limited current enhanced by Poole-Frenkel effect predominates. The EL spectra at different temperatures are also recorded, and the correlation between charge transport mechanisms and EL properties is discussed.