Estimation of protein coding density in a corpus of DNA sequence data

A number of experimental methods have been reported for estimating the number of genes in a genome, or the closely related coding density of a genome, defined as the fraction of base pairs in codons. Recently, DNA sequence data representative of the genome as a whole have become available for several organisms, making the problem of estimating coding density amenable to sequence analytic methods. Estimates of coding density for a single genome vary widely, so that methods with characterized error bounds have become increasingly desirable. We present a method to estimate the protein coding density in a corpus of DNA sequence data, in which a ‘coding statistic’ is calculated for a large number of windows of the sequence under study, and the distribution of the statistic is decomposed into two normal distributions, assumed to be the distributions of the coding statistic in the coding and noncoding fractions of the sequence windows. The accuracy of the method is evaluated using known data and application is made to the yeast chromosome III sequence and to C.elegans cosmid sequences. It can also be applied to fragmentary data, for example a collection of short sequences determined in the course of STS mapping.

Fusion of data sets in multivariate linear regression with errors-in-variables

We consider the application of normal theory methods to the estimation and testing of a general type of multivariate regressionmodels with errors--in--variables, in the case where various data setsare merged into a single analysis and the observable variables deviatepossibly from normality. The various samples to be merged can differ on the set of observable variables available. We show that there is a convenient way to parameterize the model so that, despite the possiblenon--normality of the data, normal--theory methods yield correct inferencesfor the parameters of interest and for the goodness--of--fit test. Thetheory described encompasses both the functional and structural modelcases, and can be implemented using standard software for structuralequations models, such as LISREL, EQS, LISCOMP, among others. An illustration with Monte Carlo data is presented.

Shrews in suburbia: an application of Goodman's extinction model

An equation is applied for calculating the expected persistence time of an unstructured population of the white-toothed shrew Crocidura russula from Preverenges, a suburban area in western Switzerland. Population abundance data from March and November between 1977 and 1988 were fit to the logistic density dependence model to estimate mean population growth rate as a function of population density. The variance in mean growth rate was approximated with two different models. The largest estimated persistence time was less than a few decades, the smallest less than 10 years. The results are sensitive to the magnitude of variance in population growth rate. Deviations from the logistic density dependence model in November are quite well explained by weather variables but those in March are uncorrelated with weather variables. Variability in population growth rates measured in winter months may be better explained by behavioural mechanisms. Environmental variability, dispersal of juveniles and refugia within the range of the population may contribute to its long-term survival.

Mitochondrial uncoupling as a regulator of life-history trajectories in birds: an experimental study in the zebra finch.

Mitochondria have a fundamental role in the transduction of energy from food into ATP. The coupling between food oxidation and ATP production is never perfect, but may nevertheless be of evolutionary significance. The 'uncoupling to survive' hypothesis suggests that 'mild' mitochondrial uncoupling evolved as a protective mechanism against the excessive production of damaging reactive oxygen species (ROS). Because resource allocation and ROS production are thought to shape animal life histories, alternative life-history trajectories might be driven by individual variation in the degree of mitochondrial uncoupling. We tested this hypothesis in a small bird species, the zebra finch (Taeniopygia guttata), by treating adults with the artificial mitochondrial uncoupler 2,4-dinitrophenol (DNP) over a 32-month period. In agreement with our expectations, the uncoupling treatment increased metabolic rate. However, we found no evidence that treated birds enjoyed lower oxidative stress levels or greater survival rates, in contrast to previous results in other taxa. In vitro experiments revealed lower sensitivity of ROS production to DNP in mitochondria isolated from skeletal muscles of zebra finch than mouse. In addition, we found significant reductions in the number of eggs laid and in the inflammatory immune response in treated birds. Altogether, our data suggest that the 'uncoupling to survive' hypothesis may not be applicable for zebra finches, presumably because of lower effects of mitochondrial uncoupling on mitochondrial ROS production in birds than in mammals. Nevertheless, mitochondrial uncoupling appeared to be a potential life-history regulator of traits such as fecundity and immunity at adulthood, even with food supplied ad libitum.

Application of K-feldspar-jadeite-quartz barometry to eclogite facies metagranites and metapelites in the Sesia Lanzo Zone (Western Alps, Italy)

The eclogite facies assemblage K-feldspar-jadeite-quartz in metagranites and metapelites from the Sesia-Lanzo Zone (Western Alps, Italy) records the equilibration pressure by dilution of the reaction jadeite + quartz = albite. The metapelites show partial transformation from a pre-Alpine assemblage of garnet (Alm(63)Prp(26)Grs(10))-K-feldspar-plagioclase-biotite +/- sillimanite to the Eo-Alpine high-pressure assemblage garnet (Alm(50)Prp(14)Grs(35))-jadeite (Jd(80-97)Di(0-4)Hd(0-8)Acm(0-7))=zoisite-phengite. Plagioclase is replaced by jadeite-zoisite-kyanite-K-feldspar-quartz and biotite is replaced by garnet-phengite or omphacite-kyanite-phengite. Equilibrium was attained only in local domains in the metapelites and therefore the K-feldspar-jadeite-quartz (KJQ) barometer was applied only to the plagioclase pseudomorphs and K-feldspar domains. The albite content of K-feldspar ranges from 4 to 11 mol% in less equilibrated assemblages from Val Savenca and from 4 to 7 mol% in the partially equilibrated samples from Monte Mucrone and the equilibrated samples from Montestrutto and Tavagnasco. Thermodynamic calculations on the stability of the assemblage K-feldspar-jadeite-quartz using available mixing data for K-feldspar and pyroxene indicate pressures of 15-21 kbar (+/- 1.6-1.9 kbar) at 550 +/- 50 degrees C. This barometer yields direct pressure estimates in high-pressure rocks where pressures are seldom otherwise fixed, although it is sensitive to analytical precision and the choice of thermodynamic mixing model for K-feldspar. Moreover, the KJQ barometer is independent of the ratio P-H2O/P-T. The inferred limiting a(H2O) for the assemblage jadeite-kyanite in the metapelites from Val Savenca is low and varies from 0.2 to 0.6.

The application of trend surface models to the analysis of time factors in Swiss cancer mortality.

To study different temporal components on cancer mortality (age, period and cohort) methods of graphic representation were applied to Swiss mortality data from 1950 to 1984. Maps using continuous slopes ("contour maps") and based on eight tones of grey according to the absolute distribution of rates were used to represent the surfaces defined by the matrix of various age-specific rates. Further, progressively more complex regression surface equations were defined, on the basis of two independent variables (age/cohort) and a dependent one (each age-specific mortality rate). General patterns of trends in cancer mortality were thus identified, permitting definition of important cohort (e.g., upwards for lung and other tobacco-related neoplasms, or downwards for stomach) or period (e.g., downwards for intestines or thyroid cancers) effects, besides the major underlying age component. For most cancer sites, even the lower order (1st to 3rd) models utilised provided excellent fitting, allowing immediate identification of the residuals (e.g., high or low mortality points) as well as estimates of first-order interactions between the three factors, although the parameters of the main effects remained still undetermined. Thus, the method should be essentially used as summary guide to illustrate and understand the general patterns of age, period and cohort effects in (cancer) mortality, although they cannot conceptually solve the inherent problem of identifiability of the three components.

Estimate of Groundwater Recharge Based on Water Balance in The Unsaturated Soil Zone

ABSTRACT Groundwater management depends on the knowledge on recharge rates and water fluxes within aquifers. The recharge is one of the water cycle components most difficult to estimate. As a result, despite the chosen method, the estimates are subject to uncertainties that can be identified by means of comparison with other approaches. In this study, groundwater recharge estimates based on the water balance in the unsaturated zone is assessed. Firstly, the approach is evaluated by comparing the results with those of another method. Then, the estimates are used as inputs in a transient groundwater flow model in order to assess how the water table would respond to the obtained recharges rates compared to measured levels. The results suggest a good performance of the adopted approach and, despite some inherent limitations, it has advantages over other methods since the data required are easier to obtain.

Data-driven clustering : new methods and applications

Abstract : This work is concerned with the development and application of novel unsupervised learning methods, having in mind two target applications: the analysis of forensic case data and the classification of remote sensing images. First, a method based on a symbolic optimization of the inter-sample distance measure is proposed to improve the flexibility of spectral clustering algorithms, and applied to the problem of forensic case data. This distance is optimized using a loss function related to the preservation of neighborhood structure between the input space and the space of principal components, and solutions are found using genetic programming. Results are compared to a variety of state-of--the-art clustering algorithms. Subsequently, a new large-scale clustering method based on a joint optimization of feature extraction and classification is proposed and applied to various databases, including two hyperspectral remote sensing images. The algorithm makes uses of a functional model (e.g., a neural network) for clustering which is trained by stochastic gradient descent. Results indicate that such a technique can easily scale to huge databases, can avoid the so-called out-of-sample problem, and can compete with or even outperform existing clustering algorithms on both artificial data and real remote sensing images. This is verified on small databases as well as very large problems. Résumé : Ce travail de recherche porte sur le développement et l'application de méthodes d'apprentissage dites non supervisées. Les applications visées par ces méthodes sont l'analyse de données forensiques et la classification d'images hyperspectrales en télédétection. Dans un premier temps, une méthodologie de classification non supervisée fondée sur l'optimisation symbolique d'une mesure de distance inter-échantillons est proposée. Cette mesure est obtenue en optimisant une fonction de coût reliée à la préservation de la structure de voisinage d'un point entre l'espace des variables initiales et l'espace des composantes principales. Cette méthode est appliquée à l'analyse de données forensiques et comparée à un éventail de méthodes déjà existantes. En second lieu, une méthode fondée sur une optimisation conjointe des tâches de sélection de variables et de classification est implémentée dans un réseau de neurones et appliquée à diverses bases de données, dont deux images hyperspectrales. Le réseau de neurones est entraîné à l'aide d'un algorithme de gradient stochastique, ce qui rend cette technique applicable à des images de très haute résolution. Les résultats de l'application de cette dernière montrent que l'utilisation d'une telle technique permet de classifier de très grandes bases de données sans difficulté et donne des résultats avantageusement comparables aux méthodes existantes.

Evaluation of Fly Ash in Water Reduced Paving Mixtures, June 1985

Fly ash was used to replace 15% of the cement in C3WR and C6WR concrete paving mixes containing ASTM C494 Type A water reducin9 admixtures. Two Class C ashes and one Class F ash from Iowa approved sources were examined in each mix. When Class C ashes were used they were substituted on the basis of 1 pound of ash added for each pound of cement deleted. When Class F was used it was substituted on the basis of 1.25 pounds of ash added for each pound of cement deleted. Compressive strengths of the water reduced mixes, with and without fly ash, were determined at 7, 28, and 56 days of age. In every case except one the mixes containing the fly ash exhibited higher strengths than the same concrete mix without the fly ash. An excellent correlation existed between the C3WR and C6WR mixes both with and without fly ash substitutions. The freeze-thaw durability of the concrete studied was not affected by presence or absence of fly ash. The data gathered suggests that the present Class C water reduced concrete paving mixes can be modified to allow the substitution of 15% of the cement with an approved fly ash.

Concrete Pavement Mixture Design and Analysis (MDA): Application of a Portable X-Ray Fluorescence Technique to Assess Concrete Mix Proportions , TPF-5(205), 2012

Any transportation infrastructure system is inherently concerned with durability and performance issues. The proportioning and uniformity control of concrete mixtures are critical factors that directly affect the longevity and performance of the portland cement concrete pavement systems. At present, the only means available to monitor mix proportions of any given batch are to track batch tickets created at the batch plant. However, this does not take into account potential errors in loading materials into storage silos, calibration errors, and addition of water after dispatch. Therefore, there is a need for a rapid, cost-effective, and reliable field test that estimates the proportions of as-delivered concrete mixtures. In addition, performance based specifications will be more easily implemented if there is a way to readily demonstrate whether any given batch is similar to the proportions already accepted based on laboratory performance testing. The goal of the present research project is to investigate the potential use of a portable x-ray fluorescence (XRF) technique to assess the proportions of concrete mixtures as they are delivered. Tests were conducted on the raw materials, paste and mortar samples using a portable XRF device. There is a reasonable correlation between the actual and calculated mix proportions of the paste samples, but data on mortar samples was less reliable.

Multiple Kernel Learning of Environmental Data. Case study: Analysis and Mapping of Wind Fields

The paper presents the Multiple Kernel Learning (MKL) approach as a modelling and data exploratory tool and applies it to the problem of wind speed mapping. Support Vector Regression (SVR) is used to predict spatial variations of the mean wind speed from terrain features (slopes, terrain curvature, directional derivatives) generated at different spatial scales. Multiple Kernel Learning is applied to learn kernels for individual features and thematic feature subsets, both in the context of feature selection and optimal parameters determination. An empirical study on real-life data confirms the usefulness of MKL as a tool that enhances the interpretability of data-driven models.

Inclusion of soil erosion impacts in life cycle assessment on a global scale: application to energy crops in Spain

Purpose: Despite the fundamental role of ecosystem goods and services in sustaining human activities, there is no harmonized and internationally agreed method for including them in life cycle assessment (LCA). The main goal of this study was to develop a globally applicable and spatially resolved method for assessing land-use impacts on the erosion regulation ecosystem service.Methods: Soil erosion depends much on location. Thus, unlike conventional LCA, the endpoint method was regionalized at the grid-cell level (5 arc-minutes, approximately 10×10 km2) to reflect the spatial conditions of the site. Spatially explicit characterization factors were not further aggregated at broader spatial scales. Results and discussion: Life cycle inventory data of topsoil and topsoil organic carbon (SOC) losses were interpreted at the endpoint level in terms of the ultimate damage to soil resources and ecosystem quality. Human health damages were excluded from the assessment. The method was tested on a case study of five three-year agricultural rotations, two of them with energy crops, grown in several locations in Spain. A large variation in soil and SOC losses was recorded in the inventory step, depending on climatic and edaphic conditions. The importance of using a spatially explicit model and characterization factors is shown in the case study.Conclusions and outlook: The regionalized assessment takes into account the differences in soil erosion-related environmental impacts caused by the great variability of soils. Taking this regionalized framework as the starting point, further research should focus on testing the applicability of the method trough the complete life cycle of a product and on determining an appropriate spatial scale at which to aggregate characterization factors, in order to deal with data gaps on location of processes, especially in the background system. Additional research should also focus on improving reliability of the method by quantifying and, insofar as it is possible, reducing uncertainty.

Application of text-mining for updating protein post-translational modification annotation in UniProtKB.

BACKGROUND: The annotation of protein post-translational modifications (PTMs) is an important task of UniProtKB curators and, with continuing improvements in experimental methodology, an ever greater number of articles are being published on this topic. To help curators cope with this growing body of information we have developed a system which extracts information from the scientific literature for the most frequently annotated PTMs in UniProtKB. RESULTS: The procedure uses a pattern-matching and rule-based approach to extract sentences with information on the type and site of modification. A ranked list of protein candidates for the modification is also provided. For PTM extraction, precision varies from 57% to 94%, and recall from 75% to 95%, according to the type of modification. The procedure was used to track new publications on PTMs and to recover potential supporting evidence for phosphorylation sites annotated based on the results of large scale proteomics experiments. CONCLUSIONS: The information retrieval and extraction method we have developed in this study forms the basis of a simple tool for the manual curation of protein post-translational modifications in UniProtKB/Swiss-Prot. Our work demonstrates that even simple text-mining tools can be effectively adapted for database curation tasks, providing that a thorough understanding of the working process and requirements are first obtained. This system can be accessed at http://eagl.unige.ch/PTM/.

Discordances in the application of different criteria for quantification of paediatric abdominal obesity: an analysis of two Swiss studies.

Several definitions of paediatric abdominal obesity have been proposed but it is unclear whether they lead to similar results. We assessed the prevalence of abdominal obesity using five different waist circumference-based definitions and their agreement with total body fat (TBF) and abdominal fat (AF). Data from 190 girls and 162 boys (Ballabeina), and from 134 girls and 113 boys (Kinder-Sportstudie, KISS) aged 5-11 years were used. TBF was assessed by bioimpedance (Ballabeina) or dual energy X-ray absorption (KISS). On the basis of the definition used, the prevalence of abdominal obesity varied between 3.1 and 49.4% in boys, and 4.7 and 55.5% in girls (Ballabeina), and between 1.8 and 36.3% in boys and 4.5 and 37.3% in girls (KISS). Among children considered as abdominally obese by at least one definition, 32.0 (Ballabeina) and 44.7% (KISS) were considered as such by at least two (out of five possible) definitions. Using excess TBF or AF as reference, the areas under the receiver operating curve varied between 0.577 and 0.762 (Ballabeina), and 0.583 and 0.818 (KISS). We conclude that current definitions of abdominal obesity in children lead to wide prevalence estimates and should not be used until a standard definition can be proposed.

Data-driven topo-climatic mapping with machine learning methods

Automatic environmental monitoring networks enforced by wireless communication technologies provide large and ever increasing volumes of data nowadays. The use of this information in natural hazard research is an important issue. Particularly useful for risk assessment and decision making are the spatial maps of hazard-related parameters produced from point observations and available auxiliary information. The purpose of this article is to present and explore the appropriate tools to process large amounts of available data and produce predictions at fine spatial scales. These are the algorithms of machine learning, which are aimed at non-parametric robust modelling of non-linear dependencies from empirical data. The computational efficiency of the data-driven methods allows producing the prediction maps in real time which makes them superior to physical models for the operational use in risk assessment and mitigation. Particularly, this situation encounters in spatial prediction of climatic variables (topo-climatic mapping). In complex topographies of the mountainous regions, the meteorological processes are highly influenced by the relief. The article shows how these relations, possibly regionalized and non-linear, can be modelled from data using the information from digital elevation models. The particular illustration of the developed methodology concerns the mapping of temperatures (including the situations of Föhn and temperature inversion) given the measurements taken from the Swiss meteorological monitoring network. The range of the methods used in the study includes data-driven feature selection, support vector algorithms and artificial neural networks.