Data quality in biofilm high-throughput routine analysis: intralaboratory protocol adaptation and experiment reproducibility

Biofilm research is growing more diverse and dependent on high-throughput technologies and the large-scale production of results aggravates data substantiation. In particular, it is often the case that experimental protocols are adapted to meet the needs of a particular laboratory and no statistical validation of the modified method is provided. This paper discusses the impact of intra-laboratory adaptation and non-rigorous documentation of experimental protocols on biofilm data interchange and validation. The case study is a non-standard, but widely used, workflow for Pseudomonas aeruginosa biofilm development, considering three analysis assays: the crystal violet (CV) assay for biomass quantification, the XTT assay for respiratory activity assessment, and the colony forming units (CFU) assay for determination of cell viability. The ruggedness of the protocol was assessed by introducing small changes in the biofilm growth conditions, which simulate minor protocol adaptations and non-rigorous protocol documentation. Results show that even minor variations in the biofilm growth conditions may affect the results considerably, and that the biofilm analysis assays lack repeatability. Intra-laboratory validation of non-standard protocols is found critical to ensure data quality and enable the comparison of results within and among laboratories.

Data supporting the role of enzymes and polysaccharides during cassava postharvest physiological deterioration

This data article is referred to the research article entitled The role of ascorbate peroxidase, guaiacol peroxidase, and polysaccharides in cassava (Manihot esculenta Crantz) roots under postharvest physiological deterioration by Uarrota et al. (2015). Food Chemistry 197, Part A, 737746. The stress duo to PPD of cassava roots leads to the formation of ROS which are extremely harmful and accelerates cassava spoiling. To prevent or alleviate injuries from ROS, plants have evolved antioxidant systems that include non-enzymatic and enzymatic defence systems such as ascorbate peroxidase, guaiacol peroxidase and polysaccharides. In this data article can be found a dataset called newdata, in RData format, with 60 observations and 06 variables. The first 02 variables (Samples and Cultivars) and the last 04, spectrophotometric data of ascorbate peroxidase, guaiacol peroxidase, tocopherol, total proteins and arcsined data of cassava PPD scoring. For further interpretation and analysis in R software, a report is also provided. Means of all variables and standard deviations are also provided in the Supplementary tables (data.long3.RData, data.long4.RData and meansEnzymes.RData), raw data of PPD scoring without transformation (PPDmeans.RData) and days of storage (days.RData) are also provided for data analysis reproducibility in R software.

Joint modelling of longitudinal and survival data on breast cancer

Tese de Doutoramento em Ci��ncias (Especialidade em Matem��tica)

Estimativa dos par��metros de compacta����o de solos usando t��cnicas de data mining

Disserta����o de mestrado integrado em Engenharia Civil

Tapping the wealth of microbial data in high-throughput metabolic model reconstruction

Genome-scale metabolic models are valuable tools in the metabolic engineering process, based on the ability of these models to integrate diverse sources of data to produce global predictions of organism behavior. At the most basic level, these models require only a genome sequence to construct, and once built, they may be used to predict essential genes, culture conditions, pathway utilization, and the modifications required to enhance a desired organism behavior. In this chapter, we address two key challenges associated with the reconstruction of metabolic models: (a) leveraging existing knowledge of microbiology, biochemistry, and available omics data to produce the best possible model; and (b) applying available tools and data to automate the reconstruction process. We consider these challenges as we progress through the model reconstruction process, beginning with genome assembly, and culminating in the integration of constraints to capture the impact of transcriptional regulation. We divide the reconstruction process into ten distinct steps: (1) genome assembly from sequenced reads; (2) automated structural and functional annotation; (3) phylogenetic tree-based curation of genome annotations; (4) assembly and standardization of biochemistry database; (5) genome-scale metabolic reconstruction; (6) generation of core metabolic model; (7) generation of biomass composition reaction; (8) completion of draft metabolic model; (9) curation of metabolic model; and (10) integration of regulatory constraints. Each of these ten steps is documented in detail.

The effect of siesta in parameters of cardiac structure and in interpretation of ambulatory arterial blood pressure monitoring

OBJECTIVE: To evaluate the influence of the siesta in ambulatory blood pressure (BP) monitoring and in cardiac structure parameters. METHODS: 1940 ambulatory arterial blood pressure monitoring tests were analyzed (Spacelabs 90207, 15/15 minutes from 7:00 to 22:00 hours and 20/20 minutes from 22:01 to 6.59hours) and 21% of the records indicated that the person had taken a siesta (263 woman, 52��14 years). The average duration of the siesta was 118��58 minutes. RESULTS: (average �� standard deviation) The average of systolic/diastolic pressures during wakefulness, including the napping period, was less than the average for the period not including the siesta (138��16/85��11 vs 139��16/86��11 mmHg, p<0.05); 2) pressure loads during wakefulness including the siesta, were less than those observed without the siesta); 3) the averages of nocturnal sleep blood pressures were similar to those of the siesta, 4) nocturnal sleep pressure drops were similar to those in the siesta including wakefulness with and without the siesta; 5) the averages of BP in men were higher (p<0.05) during wakefulness with and without the siesta, during the siesta and nocturnal sleep in relation to the average obtained in women; 6) patients with a reduction of 0- 5% during the siesta had thickening of the interventricular septum and a larger posterior wall than those with a reduction during the siesta >5%. CONCLUSION: The siesta influenced the heart structure parameters and from a statistical point of view the average of systolic and diastolic pressures and the respective pressure loads of the wakeful period.

An R package for the integrated analysis of metabolomics and spectral data

Recently, there has been a growing interest in the field of metabolomics, materialized by a remarkable growth in experimental techniques, available data and related biological applications. Indeed, techniques as Nuclear Magnetic Resonance, Gas or Liquid Chromatography, Mass Spectrometry, Infrared and UV-visible spectroscopies have provided extensive datasets that can help in tasks as biological and biomedical discovery, biotechnology and drug development. However, as it happens with other omics data, the analysis of metabolomics datasets provides multiple challenges, both in terms of methodologies and in the development of appropriate computational tools. Indeed, from the available software tools, none addresses the multiplicity of existing techniques and data analysis tasks. In this work, we make available a novel R package, named specmine, which provides a set of methods for metabolomics data analysis, including data loading in different formats, pre-processing, metabolite identification, univariate and multivariate data analysis, machine learning, and feature selection. Importantly, the implemented methods provide adequate support for the analysis of data from diverse experimental techniques, integrating a large set of functions from several R packages in a powerful, yet simple to use environment. The package, already available in CRAN, is accompanied by a web site where users can deposit datasets, scripts and analysis reports to be shared with the community, promoting the efficient sharing of metabolomics data analysis pipelines.

DESARROLLO DE APLICACIONES ESTAD��STICAS PARA LA AGRICULTURA DE PRECISI��N. STATISTICAL APPLICATIONS FOR PRECISION AGRICULTURE

A partir de las ��ltimas d��cadas se ha impulsado el desarrollo y la utilizaci��n de los Sistemas de Informaci��n Geogr��ficos (SIG) y los Sistemas de Posicionamiento Satelital (GPS) orientados a mejorar la eficiencia productiva de distintos sistemas de cultivos extensivos en t��rminos agron��micos, econ��micos y ambientales. Estas nuevas tecnolog��as permiten medir variabilidad espacial de propiedades del sitio como conductividad el��ctrica aparente y otros atributos del terreno as�� como el efecto de las mismas sobre la distribuci��n espacial de los rendimientos. Luego, es posible aplicar el manejo sitio-espec��fico en los lotes para mejorar la eficiencia en el uso de los insumos agroqu��micos, la protecci��n del medio ambiente y la sustentabilidad de la vida rural. En la actualidad, existe una oferta amplia de recursos tecnol��gicos propios de la agricultura de precisi��n para capturar variaci��n espacial a trav��s de los sitios dentro del terreno. El ��ptimo uso del gran volumen de datos derivado de maquinarias de agricultura de precisi��n depende fuertemente de las capacidades para explorar la informaci��n relativa a las complejas interacciones que subyacen los resultados productivos. La covariaci��n espacial de las propiedades del sitio y el rendimiento de los cultivos ha sido estudiada a trav��s de modelos geoestad��sticos cl��sicos que se basan en la teor��a de variables regionalizadas. Nuevos desarrollos de modelos estad��sticos contempor��neos, entre los que se destacan los modelos lineales mixtos, constituyen herramientas prometedoras para el tratamiento de datos correlacionados espacialmente. M��s a��n, debido a la naturaleza multivariada de las m��ltiples variables registradas en cada sitio, las t��cnicas de an��lisis multivariado podr��an aportar valiosa informaci��n para la visualizaci��n y explotaci��n de datos georreferenciados. La comprensi��n de las bases agron��micas de las complejas interacciones que se producen a la escala de lotes en producci��n, es hoy posible con el uso de ��stas nuevas tecnolog��as. Los objetivos del presente proyecto son: (l) desarrollar estrategias metodol��gicas basadas en la complementaci��n de t��cnicas de an��lisis multivariados y geoestad��sticas, para la clasificaci��n de sitios intralotes y el estudio de interdependencias entre variables de sitio y rendimiento; (ll) proponer modelos mixtos alternativos, basados en funciones de correlaci��n espacial de los t��rminos de error que permitan explorar patrones de correlaci��n espacial de los rendimientos intralotes y las propiedades del suelo en los sitios delimitados. From the last decades the use and development of Geographical Information Systems (GIS) and Satellite Positioning Systems (GPS) is highly promoted in cropping systems. Such technologies allow measuring spatial variability of site properties including electrical conductivity and others soil features as well as their impact on the spatial variability of yields. Therefore, site-specific management could be applied to improve the efficiency in the use of agrochemicals, the environmental protection, and the sustainability of the rural life. Currently, there is a wide offer of technological resources to capture spatial variation across sites within field. However, the optimum use of data coming from the precision agriculture machineries strongly depends on the capabilities to explore the information about the complex interactions underlying the productive outputs. The covariation between spatial soil properties and yields from georeferenced data has been treated in a graphical manner or with standard geostatistical approaches. New statistical modeling capabilities from the Mixed Linear Model framework are promising to deal with correlated data such those produced by the precision agriculture. Moreover, rescuing the multivariate nature of the multiple data collected at each site, several multivariate statistical approaches could be crucial tools for data analysis with georeferenced data. Understanding the basis of complex interactions at the scale of production field is now within reach the use of these new techniques. Our main objectives are: (1) to develop new statistical strategies, based on the complementarities of geostatistics and multivariate methods, useful to classify sites within field grown with grain crops and analyze the interrelationships of several soil and yield variables, (2) to propose mixed linear models to predict yield according spatial soil variability and to build contour maps to promote a more sustainable agriculture.

Statistical Characteristics of Flash Flood in Georgia

Results are presented from the analysis of observations data on flash flood in Georgia over a period of 45 years, from 1961 to 2005, provided of the of Hydro-meteorology Service of Georgia.

Modeling usage of medical care services: the medical expenditure panel survey data, 1996-2000

We explore the determinants of usage of six different types of health care services, using the Medical Expenditure Panel Survey data, years 1996-2000. We apply a number of models for univariate count data, including semiparametric, semi-nonparametric and finite mixture models. We find that the complexity of the model that is required to fit the data well depends upon the way in which the data is pooled across sexes and over time, and upon the characteristics of the usage measure. Pooling across time and sexes is almost always favored, but when more heterogeneous data is pooled it is often the case that a more complex statistical model is required.

Real equilibrium exchange rates. A panel data approach for advanced and emerging economies.

Based on an behavioral equilibrium exchange rate model, this paper examines the determinants of the real effective exchange rate and evaluates the degree of misalignment of a group of currencies since 1980. Within a panel cointegration setting, we estimate the relationship between exchange rate and a set of economic fundamentals, such as traded-nontraded productivity differentials and the stock of foreign assets. Having ascertained the variables are integrated and cointegrated, the long-run equilibrium value of the fundamentals are estimated and used to derive equilibrium exchange rates and misalignments. Although there is statistical homogeneity, some structural differences were found to exist between advanced and emerging economies.

Microstructure Order Flow: Statistical and Economic Evaluation of Nonlinear Forecasts

In this paper we propose a novel empirical extension of the standard market microstructure order flow model. The main idea is that heterogeneity of beliefs in the foreign exchange market can cause model instability and such instability has not been fully accounted for in the existing empirical literature. We investigate this issue using two di��erent data sets and focusing on out- of-sample forecasts. Forecasting power is measured using standard statistical tests and, additionally, using an alternative approach based on measuring the economic value of forecasts after building a portfolio of assets. We nd there is a substantial economic value on conditioning on the proposed models.

Estimating United States Phillips Curves With Expectations Consistent With The Statistical Process Of Inflation

���Modern��� Phillips curve theories predict inflation is an integrated, or near integrated, process. However, inflation appears bounded above and below in developed economies and so cannot be ���truly��� integrated and more likely stationary around a shifting mean. If agents believe inflation is integrated as in the ���modern��� theories then they are making systematic errors concerning the statistical process of inflation. An alternative theory of the Phillips curve is developed that is consistent with the ���true��� statistical process of inflation. It is demonstrated that United States inflation data is consistent with the alternative theory but not with the existing ���modern��� theories.

Comparing population distributions from bin-aggregated sample data: an application to historical height data from France

This paper develops a methodology to estimate the entire population distributions from bin-aggregated sample data. We do this through the estimation of the parameters of mixtures of distributions that allow for maximal parametric flexibility. The statistical approach we develop enables comparisons of the full distributions of height data from potential army conscripts across France's 88 departments for most of the nineteenth century. These comparisons are made by testing for differences-of-means stochastic dominance. Corrections for possible measurement errors are also devised by taking advantage of the richness of the data sets. Our methodology is of interest to researchers working on historical as well as contemporary bin-aggregated or histogram-type data, something that is still widely done since much of the information that is publicly available is in that form, often due to restrictions due to political sensitivity and/or confidentiality concerns.

Enzyme replacement therapy with agalsidase alfa in patients with Fabry's disease: an analysis of registry data.

BACKGROUND: We analysed 5-year treatment with agalsidase alfa enzyme replacement therapy in patients with Fabry's disease who were enrolled in the Fabry Outcome Survey observational database (FOS). METHODS: Baseline and 5-year data were available for up to 181 adults (126 men) in FOS. Serial data for cardiac mass and function, renal function, pain, and quality of life were assessed. Safety and sensitivity analyses were done in patients with baseline and at least one relevant follow-up measurement during the 5 years (n=555 and n=475, respectively). FINDINGS: In patients with baseline cardiac hypertrophy, treatment resulted in a sustained reduction in left ventricular mass (LVM) index after 5 years (from 71.4 [SD 22.5] g/m(2.7) to 64.1 [18.7] g/m(2.7), p=0.0111) and a significant increase in midwall fractional shortening (MFS) from 14.3% (2.3) to 16.0% (3.8) after 3 years (p=0.02). In patients without baseline hypertrophy, LVM index and MFS remained stable. Mean yearly fall in estimated glomerular filtration rate versus baseline after 5 years of enzyme replacement therapy was -3.17 mL/min per 1.73 m(2) for men and -0.89 mL/min per 1.73 m(2) for women. Average pain, measured by Brief Pain Inventory score, improved significantly, from 3.7 (2.3) at baseline to 2.5 (2.4) after 5 years (p=0.0023). Quality of life, measured by deviation scores from normal EuroQol values, improved significantly, from -0.24 (0.3) at baseline to -0.17 (0.3) after 5 years (p=0.0483). Findings were confirmed by sensitivity analysis. No unexpected safety concerns were identified. INTERPRETATION: By comparison with historical natural history data for patients with Fabry's disease who were not treated with enzyme replacement therapy, long-term treatment with agalsidase alfa leads to substantial and sustained clinical benefits. FUNDING: Shire Human Genetic Therapies AB.