Extension Package Research Articles

From DNA to FBA: How to Build Your Own Genome-Scale Metabolic Model.

Microbiological studies are increasingly relying on in silico methods to perform exploration and rapid analysis of genomic data, and functional genomics studies are supplemented by the new perspectives that genome-scale metabolic models offer. A mathematical model consisting of a microbe’s entire metabolic map can be rapidly determined from whole-genome sequencing and annotating the genomic material encoded in its DNA. Flux-balance analysis (FBA), a linear programming technique that uses metabolic models to predict the phenotypic responses imposed by environmental elements and factors, is the leading method to simulate and manipulate cellular growth in silico. However, the process of creating an accurate model to use in FBA consists of a series of steps involving a multitude of connections between bioinformatics databases, enzyme resources, and metabolic pathways. We present the methodology and procedure to obtain a metabolic model using PyFBA, an extensible Python-based open-source software package aimed to provide a platform where functional annotations are used to build metabolic models (http://linsalrob.github.io/PyFBA). Backed by the Model SEED biochemistry database, PyFBA contains methods to reconstruct a microbe’s metabolic map, run FBA upon different media conditions, and gap-fill its metabolism. The extensibility of PyFBA facilitates novel techniques in creating accurate genome-scale metabolic models.

An efficient and numerically stable procedure for generating sextic force fields in normal mode coordinates.

In this paper, we outline a general, scalable, and black-box approach for calculating high-order strongly coupled force fields in rectilinear normal mode coordinates, based upon constructing low order expansions in curvilinear coordinates with naturally limited mode-mode coupling, and then transforming between coordinate sets analytically. The optimal balance between accuracy and efficiency is achieved by transforming from 3 mode representation quartic force fields in curvilinear normal mode coordinates to 4 mode representation sextic force fields in rectilinear normal modes. Using this reduced mode-representation strategy introduces an error of only 1 cm(-1) in fundamental frequencies, on average, across a sizable test set of molecules. We demonstrate that if it is feasible to generate an initial semi-quartic force field in curvilinear normal mode coordinates from ab initio data, then the subsequent coordinate transformation procedure will be relatively fast with modest memory demands. This procedure facilitates solving the nuclear vibrational problem, as all required integrals can be evaluated analytically. Our coordinate transformation code is implemented within the extensible PyPES library program package, at http://sourceforge.net/projects/pypes-lib-ext/.

HYPERDIRE—HYPERgeometric functions DIfferential REduction: Mathematica-based packages for the differential reduction of generalized hypergeometric functions: Lauricella function [formula omitted] of three variables

We present a further extension of the HYPERDIRE project, which is devoted to the creation of a set of Mathematica-based program packages for manipulations with Horn-type hypergeometric functions on the basis of differential equations. Specifically, we present the implementation of the differential reduction for the Lauricella function FC of three variables. Program summaryProgram title: HYPERDIRECatalogue identifier: AEPP_v4_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEPP_v4_0.htmlProgram obtainable from: CPC Program Library, Queen’s University, Belfast, N. IrelandLicensing provisions: GNU General Public License, version 3No. of lines in distributed program, including test data, etc.: 243461No. of bytes in distributed program, including test data, etc.: 61610782Distribution format: tar.gzProgramming language: Mathematica.Computer: All computers running Mathematica.Operating system: Operating systems running Mathematica.Classification: 4.4.Does the new version supersede the previous version?: No, it significantly extends the previous version.Nature of problem:Reduction of hypergeometric function FC of three variables to a set of basis functions.Solution method:Differential reduction.Reasons for new version:The extension package allows the user to handle the Lauricella function FC of three variables.Summary of revisions:The previous version goes unchanged.Running time:Depends on the complexity of the problem.

Power Supply Problems: Proposed Extension Package

This aimed to assess the electric power supply problems of San Francisco, Cebu as perceived by the electrical consumers. Factors considered were: electrical household consumers profile as to the type of electric cooperative membership combined monthly family income, kind/ type and number of electrical appliances, kind and number of electrical lamp, and average monthly electrical consumption; problems related to power supply as perceived by the respondents as to fuel supply, engine breakdown, maintenance operation, and low Voltage. Also considered was the significant mean difference in perception between the consumers and management/ maintenance personnel as to the problems met in the electrical power supply distribution and the extension package that could be formulated. The descriptive method of research was used in this study. The questionnaire was uses as the main instrument for gathering data and was supplemented by direct observation. Simple percentage computation, weighted mean, and Z-test were employed for the treatment of data.

GOexpress: an R/Bioconductor package for the identification and visualisation of robust gene ontology signatures through supervised learning of gene expression data.

BackgroundIdentification of gene expression profiles that differentiate experimental groups is critical for discovery and analysis of key molecular pathways and also for selection of robust diagnostic or prognostic biomarkers. While integration of differential expression statistics has been used to refine gene set enrichment analyses, such approaches are typically limited to single gene lists resulting from simple two-group comparisons or time-series analyses. In contrast, functional class scoring and machine learning approaches provide powerful alternative methods to leverage molecular measurements for pathway analyses, and to compare continuous and multi-level categorical factors.ResultsWe introduce GOexpress, a software package for scoring and summarising the capacity of gene ontology features to simultaneously classify samples from multiple experimental groups. GOexpress integrates normalised gene expression data (e.g., from microarray and RNA-seq experiments) and phenotypic information of individual samples with gene ontology annotations to derive a ranking of genes and gene ontology terms using a supervised learning approach. The default random forest algorithm allows interactions between all experimental factors, and competitive scoring of expressed genes to evaluate their relative importance in classifying predefined groups of samples.ConclusionsGOexpress enables rapid identification and visualisation of ontology-related gene panels that robustly classify groups of samples and supports both categorical (e.g., infection status, treatment) and continuous (e.g., time-series, drug concentrations) experimental factors. The use of standard Bioconductor extension packages and publicly available gene ontology annotations facilitates straightforward integration of GOexpress within existing computational biology pipelines.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-0971-3) contains supplementary material, which is available to authorized users.

Are There Too Many R Packages?

The number of R extension packages available from the CRAN repository has tremendously grown over the past 10 years. We look at this phenomenon in more detail, and discuss some of its consequences. In particular, we argue that the statistical computing community needs a more common understanding of software quality, and better domain-specific semantic resources.

Dose-Response Analysis Using R.

Dose-response analysis can be carried out using multi-purpose commercial statistical software, but except for a few special cases the analysis easily becomes cumbersome as relevant, non-standard output requires manual programming. The extension package drc for the statistical environment R provides a flexible and versatile infrastructure for dose-response analyses in general. The present version of the package, reflecting extensions and modifications over the last decade, provides a user-friendly interface to specify the model assumptions about the dose-response relationship and comes with a number of extractors for summarizing fitted models and carrying out inference on derived parameters. The aim of the present paper is to provide an overview of state-of-the-art dose-response analysis, both in terms of general concepts that have evolved and matured over the years and by means of concrete examples.

Automated detection of Antarctic blue whale calls.

This paper addresses the problem of automated detection of Z-calls emitted by Antarctic blue whales (B. m. intermedia). The proposed solution is based on a subspace detector of sigmoidal-frequency signals with unknown time-varying amplitude. This detection strategy takes into account frequency variations of blue whale calls as well as the presence of other transient sounds that can interfere with Z-calls (such as airguns or other whale calls). The proposed method has been tested on more than 105 h of acoustic data containing about 2200 Z-calls (as found by an experienced human operator). This method is shown to have a correct-detection rate of up to more than 15% better than the extensible bioacoustic tool package, a spectrogram-based correlation detector commonly used to study blue whales. Because the proposed method relies on subspace detection, it does not suffer from some drawbacks of correlation-based detectors. In particular, it does not require the choice of an a priori fixed and subjective template. The analytic expression of the detection performance is also derived, which provides crucial information for higher level analyses such as animal density estimation from acoustic data. Finally, the detection threshold automatically adapts to the soundscape in order not to violate a user-specified false alarm rate.

DynaMet: a fully automated pipeline for dynamic LC-MS data.

Dynamic isotope labeling data provides crucial information about the operation of metabolic pathways and are commonly generated via liquid chromatography-mass spectrometry (LC-MS). Metabolome-wide analysis is challenging as it requires grouping of metabolite features over different samples. We developed DynaMet for fully automated investigations of isotope labeling experiments from LC-high-resolution MS raw data. DynaMet enables untargeted extraction of metabolite labeling profiles and provides integrated tools for expressive data visualization. To validate DynaMet we first used time course labeling data of the model strain Bacillus methanolicus from (13)C methanol resulting in complex spectra in multicarbon compounds. Analysis of two biological replicates revealed high robustness and reproducibility of the pipeline. In total, DynaMet extracted 386 features showing dynamic labeling within 10 min. Of these features, 357 could be fitted by implemented kinetic models. Feature identification against KEGG database resulted in 215 matches covering multiple pathways of core metabolism and major biosynthetic routes. Moreover, we performed time course labeling experiment with Escherichia coli on uniformly labeled (13)C glucose resulting in a comparable number of detected features with labeling profiles of high quality. The distinct labeling patterns of common central metabolites generated from both model bacteria can readily be explained by one versus multicarbon compound metabolism. DynaMet is freely available as an extension package for Python based eMZed2, an open source framework built for rapid development of LC-MS data analysis workflows.

Increasing the competitiveness of e-vehicles in Europe

This paper is concerned with incentives for the take-up and use of e-vehicles that are in place in different European countries. Especially, it analyses Norway and Austria, in order to establish and understand factors influencing the competitiveness of e-vehicles and potential market penetration. Norway currently enjoys the world’s largest take-up of electric cars per capita, achieved through an extensive package of incentives. Austria has used the concept of Model Regions with government support to stimulate market introduction. So far, this has been a less effective approach. The paper brings in and combine analyses of national travel survey data and web surveys to e-vehicle owners and non-e-vehicle owners. It considers socio-economic factors including convenience and time savings due to e-vehicle policies. Analysing national travel surveys, we find a considerable potential for e-vehicles based on people’s everyday travel. Social networks play a crucial role in spreading knowledge about this relatively new technology. The take-up of battery electric vehicles correlates relatively closely with the user value of e-vehicle incentives. The fiscal effects of e-vehicle incentives are non-trivial – especially in the longer run. The cost of lifting a new technology into the market by means of government incentives is significant. We point to the importance of a strategy for the gradual phasing out of e-vehicle policies in countries with large incentives when the cost of vehicles goes down and the technology improves. Successful market uptake and expansion of electric vehicles requires massive, expensive and combined policies. Central government backing, long term commitment and market-oriented incentives help reduce the perceived risk for market players like car importers and allow the e-vehicle market to thrive. For countries with low e-vehicle market shares the potential is promising. Battery electric vehicles are already a real option for the majority of peoples’ everyday trips and trip chains. However, their relative disadvantages must be compensated by means of incentives – at least in the initial market launch phase. Diffusion mechanisms play a sizeable role. The lack of knowledge in the population at large must be addressed.

ETRAN--R Extension Package for Eye Tracking Results Analysis.

Novel open source R extension package for general-purpose eye tracking results analysis proposed. Now supported features are data loading from SMI eye trackers, different methods of fixations detection, various imaging techniques for raw data, and detected fixations (time sequence, scanpath, heatmap, and dynamic visualization). The modular structure of the package and a detailed description of each function provide a convenient way to further extend the functionality. Effective use of package requires knowledge of R programming language and environment.

The NEWMEDS rodent touchscreen test battery for cognition relevant to schizophrenia.

The NEWMEDS initiative (Novel Methods leading to New Medications in Depression and Schizophrenia, http://www.newmeds-europe.com ) is a large industrial-academic collaborative project aimed at developing new methods for drug discovery for schizophrenia. As part of this project, Work package 2 (WP02) has developed and validated a comprehensive battery of novel touchscreen tasks for rats and mice for assessing cognitive domains relevant to schizophrenia. This article provides a review of the touchscreen battery of tasks for rats and mice for assessing cognitive domains relevant to schizophrenia and highlights validation data presented in several primary articles in this issue and elsewhere. The battery consists of the five-choice serial reaction time task and a novel rodent continuous performance task for measuring attention, a three-stimulus visual reversal and the serial visual reversal task for measuring cognitive flexibility, novel non-matching to sample-based tasks for measuring spatial working memory and paired-associates learning for measuring long-term memory. The rodent (i.e. both rats and mice) touchscreen operant chamber and battery has high translational value across species due to its emphasis on construct as well as face validity. In addition, it offers cognitive profiling of models of diseases with cognitive symptoms (not limited to schizophrenia) through a battery approach, whereby multiple cognitive constructs can be measured using the same apparatus, enabling comparisons of performance across tasks. This battery of tests constitutes an extensive tool package for both model characterisation and pre-clinical drug discovery.

Integrative multi-omics module network inference with Lemon-Tree.

Module network inference is an established statistical method to reconstruct co-expression modules and their upstream regulatory programs from integrated multi-omics datasets measuring the activity levels of various cellular components across different individuals, experimental conditions or time points of a dynamic process. We have developed Lemon-Tree, an open-source, platform-independent, modular, extensible software package implementing state-of-the-art ensemble methods for module network inference. We benchmarked Lemon-Tree using large-scale tumor datasets and showed that Lemon-Tree algorithms compare favorably with state-of-the-art module network inference software. We also analyzed a large dataset of somatic copy-number alterations and gene expression levels measured in glioblastoma samples from The Cancer Genome Atlas and found that Lemon-Tree correctly identifies known glioblastoma oncogenes and tumor suppressors as master regulators in the inferred module network. Novel candidate driver genes predicted by Lemon-Tree were validated using tumor pathway and survival analyses. Lemon-Tree is available from http://lemon-tree.googlecode.com under the GNU General Public License version 2.0.

Opportunities, and Challenges of Latrine Utilization among Rural Communities of Awabel District, Northwest Ethiopia, 2014

Ethiopia is working hard to increase access to improved sanitation to its rapidly growing population; however, its proper utilization is questioned. Update on its utilization is required. The objective of the study was to assess latrine utilization and associated factors among rural communities in Awabel District, Northwest Ethiopia, 2014. Community based cross-sectional study was conducted on 575 households. Participants were selected through multi-stage sampling technique. Pre-tested interviewer administered structured questioner and observational checklist was used to collect data. The collected data were coded and entered using Epi-Info 3.5.3 and analysis was done using SPSS version 20. Bivariable and multivariable logistic regressions were used to identify the associated factors for proper utilization of latrine facilities. Possible association and statistical significance were measured using odds ratio at 95% confidence interval. Proper utilization of latrine was practiced by about 297 (52%) of households. Utilization was significantly associated with presence of school children in the households [AOR: 1.824, 95%CI (1.189 - 2.799)], and latrine which did not need maintenance [AOR: 4.294, 95%CI (2.625 - 7.025)], households who were advised to construct latrine by health professionals [AOR: 4.194, 95%CI: (1.674 - 12.358)] and self initiation to construct latrine [AOR: 7.461, 95%CI: (1.850 - 30.084)]. The most outstanding findings of this study were the following: latrine utilization will be sustainable when the toilet is constructed following proper household based health education, when the toilet is well constructed, and when there is supportive supervision. This implies that health extension workers do have a pivotal role for increasing toilet construction as well as its proper utilization. The current level of proper latrine utilization seems fairly good for rural communities in the district. However, there is a lot to do to make it part of their culture. Its sustainability seems to be achievable via health extension package. Therefore, motivating health extension workers is recommended.

HYPERDIRE HYPERgeometric functions DIfferential REduction: Mathematica-based packages for the differential reduction of generalized hypergeometric functions: Horn-type hypergeometric functions of two variables

HYPERDIRE is a project devoted to the creation of a set of Mathematica-based programs for the differential reduction of hypergeometric functions. The current version allows for manipulations involving the full set of Horn-type hypergeometric functions of two variables, including 30 functions. Program summaryProgram title: HYPERDIRECatalogue identifier: AEPP_v2_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEPP_v2_0.htmlProgram obtainable from: CPC Program Library, Queen’s University, Belfast, N. IrelandLicensing provisions: GNU General Public LicenseNo. of lines in distributed program, including test data, etc.: 896No. of bytes in distributed program, including test data, etc.: 27172Distribution format: tar.gzProgramming language: Mathematica.Computer: All computers running Mathematica.Operating system: Operation systems running Mathematica.Classification: 4.4.Does the new version supersede the previous version?: No, it significantly extends the previous version.Nature of problem: Reduction of Horn-type hypergeometric functions of two variables to a set of basis functionsSolution method: Differential reductionReasons for new version: The extension package allows the user to handle all Horn-type hypergeometric functions of two variables.Summary of revisions: The previous version goes unchanged.Running time: Depends on the complexity of the problem

PLACE: an open-source python package for laboratory automation, control, and experimentation.

In modern laboratories, software can drive the full experimental process from data acquisition to storage, processing, and analysis. The automation of laboratory data acquisition is an important consideration for every laboratory. When implementing a laboratory automation scheme, important parameters include its reliability, time to implement, adaptability, and compatibility with software used at other stages of experimentation. In this article, we present an open-source, flexible, and extensible Python package for Laboratory Automation, Control, and Experimentation (PLACE). The package uses modular organization and clear design principles; therefore, it can be easily customized or expanded to meet the needs of diverse laboratories. We discuss the organization of PLACE, data-handling considerations, and then present an example using PLACE for laser-ultrasound experiments. Finally, we demonstrate the seamless transition to post-processing and analysis with Python through the development of an analysis module for data produced by PLACE automation.

Determinants of Keeping Small Ruminants and Non-ruminant Livestock in Malawi: A Simulated Maximum Likelihood Multivariate Probit

ABSTRACTAn initial understanding of socio-economic factors that determine households’ decision to keep small ruminants and non-ruminant livestock is imperative when formulating technologies and policies that augment livestock production. Therefore, this article was started with the objective of bringing to light the socio-economic factors affecting the decision of smallholder farmers’ households to keep small ruminants and non-ruminant livestock. We used the Malawi Third Integrated Household Survey data, commissioned by National Statistical Office, in which 5 711 households were randomly sampled and interviewed from March 2010 to March 2011. The article fits a simulated maximum likelihood multivariate probit to the data. With respect to the policy variables considered in the analysis, unlike higher education levels and older household heads, younger household heads are more likely to keep small ruminants and non-ruminant livestock. Extension contact and credit access did not promote the keeping of small ruminants and non-ruminant livestock. There is a strong location bias towards keeping small ruminants in the central region of Malawi while there is also a bias against keeping pigs in southern Malawi. Reshaping extension packages toward well rounded crop-livestock messages is an important factor to consider in promoting small ruminant and non-ruminant production in Malawi.

English

          This paper was the first to use nationally representative data from the Agricultural Sample Surveys of Ethiopia to examine the factors affecting the adoption of the fertilizer-seed technology “package” promoted by Ethiopia’s government. We used a double hurdle model to analyze fertilizer adoption among four major cereal crops (barley, maize, teff, and wheat). This model allowed us to identify factors affecting farmers’ access to fertilizer and factors affecting fertilizer demand conditional to input access. Extension was proven to have the biggest impact on fertilizer adoption. We found that knowledge required to adopt new technology represented a high cost for farmers. In addition to extension, other factors that could reduce the cost to access knowledge include farmers’ knowledge and skills in cereal production, risk aversion behavior, household wealth and land fragmentation. Substantial yield gain in maize and teff could be achieved from locally tailored extension packages.   Key words: Technology adoption, double hurdle model, Ethiopia, fertilizer, cereal. JEL Codes: O33, O38, Q16, Q18.          

Simultaneous Small Sample Inference for Linear Combinations of Generalized Linear Model Parameters

A method is proposed to construct simultaneous confidence intervals for multiple linear combinations of generalized linear model parameters, that uses a multivariate normal- or t-distribution together with the signed likelihood root statistic. In an application to a case study simultaneous confidence bands for logistic regression are calculated. A simulation study based on the example evaluation suggests superior performance compared to the common Wald-type approaches. The proposed methods are readily implemented in the R extension package mcprofile.

Under five diarrhea among model household and non model households in Hawassa, South Ethiopia: a comparative cross-sectional community based survey

BackgroundEthiopia has been implementing a community-level health intervention package (referred to as “Health Extension Program”) to improve the health of children in particular.However, its effect on the major childhood illnesses in Ethiopia has not been studied. This study was conducted to determine whether a fully-implemented health extension program reduces diarrhea in children under the age of five.MethodA Community-based comparative cross-sectional study was carried out by comparing model households (i.e. households that fully implemented the health extension package) with non-model households (i.e. households that did not fully implement the health extension package). The study participants were mothers having children under the age of five. Data were collected through a household survey. A multiple logistic regression model was used to control known confounders.ResultAfter controlling potential confounding factors using a logistic regression model, under five year children residing in non-model households, were more likely to have diarrhea in the two weeks preceding the survey compared to those residing in model households [AOR: 2.65, 95%CI (1.11, 6.27)].ConclusionDiarrhea among under five children significantly reduced among families who fully implemented basic health packages. The finding suggests that being a model HH can have a positive impact on diarrhea morbidity among under five children.