Showing papers in "F1000Research in 2014"

Ultrafast approximation for phylogenetic bootstrap

Abstract: Nonparametric bootstrap has been a widely used tool in phylogenetic analysis to assess the clade support of phylogenetic trees. However, with the rapidly growing amount of data, this task remains a computational bottleneck. Recently, approximation methods such as the RAxML rapid bootstrap (RBS) and the Shimodaira-Hasegawa-like approximate likelihood ratio test have been introduced to speed up the bootstrap. Here, we suggest an ultrafast bootstrap approximation approach (UFBoot) to compute the support of phylogenetic groups in maximum likelihood (ML) based trees. To achieve this, we combine the resampling estimated log-likelihood method with a simple but effective collection scheme of candidate trees. We also propose a stopping rule that assesses the convergence of branch support values to automatically determine when to stop collecting candidate trees. UFBoot achieves a median speed up of 3.1 (range: 0.66-33.3) to 10.2 (range: 1.32-41.4) compared with RAxML RBS for real DNA and amino acid alignments, respectively. Moreover, our extensive simulations show that UFBoot is robust against moderate model violations and the support values obtained appear to be relatively unbiased compared with the conservative standard bootstrap. This provides a more direct interpretation of the bootstrap support. We offer an efficient and easy-to-use software (available at http://www.cibiv.at/software/iqtree) to perform the UFBoot analysis with ML tree inference.

GeneMANIA: Fast gene network construction and function prediction for Cytoscape

Abstract: The GeneMANIA Cytoscape app enables users to construct a composite gene-gene functional interaction network from a gene list. The resulting network includes the genes most related to the original list, and functional annotations from Gene Ontology. The edges are annotated with details about the publication or data source the interactions were derived from. The app leverages GeneMANIA’s database of 1800+ networks, containing over 500 million interactions spanning 8 organisms: A. thaliana, C. elegans, D. melanogaster, D. rerio, H. sapiens, M. musculus, R. norvegicus, and S. cerevisiae. Users may also import their own organisms, networks, and expression profiles. The app is compatible with Cytoscape versions 2 and 3.

ReactomeFIViz: a Cytoscape app for pathway and network-based data analysis.

Abstract: High-throughput experiments are routinely performed in modern biological studies. However, extracting meaningful results from massive experimental data sets is a challenging task for biologists. Projecting data onto pathway and network contexts is a powerful way to unravel patterns embedded in seemingly scattered large data sets and assist knowledge discovery related to cancer and other complex diseases. We have developed a Cytoscape app called “ReactomeFIViz”, which utilizes a highly reliable gene functional interaction network combined with human curated pathways derived from Reactome and other pathway databases. This app provides a suite of features to assist biologists in performing pathway- and network-based data analysis in a biologically intuitive and user-friendly way. Biologists can use this app to uncover network and pathway patterns related to their studies, search for gene signatures from gene expression data sets, reveal pathways significantly enriched by genes in a list, and integrate multiple genomic data types into a pathway context using probabilistic graphical models. We believe our app will give researchers substantial power to analyze intrinsically noisy high-throughput experimental data to find biologically relevant information.

Ethical research standards in a world of big data

Abstract: In 2009 Ginsberg et al. reported using Google search query volume to estimate influenza activity in advance of traditional methodologies. It was a groundbreaking example of digital disease detection, and it still remains illustrative of the power of gathering data from the internet for important research. In recent years, the methodologies have been extended to include new topics and data sources; Twitter in particular has been used for surveillance of influenza-like-illnesses, political sentiments, and even behavioral risk factors like sentiments about childhood vaccination programs. As the research landscape continuously changes, the protection of human subjects in online research needs to keep pace. Here we propose a number of guidelines for ensuring that the work done by digital researchers is supported by ethical-use principles. Our proposed guidelines include: 1) Study designs using Twitter-derived data should be transparent and readily available to the public. 2) The context in which a tweet is sent should be respected by researchers. 3) All data that could be used to identify tweet authors, including geolocations, should be secured. 4) No information collected from Twitter should be used to procure more data about tweet authors from other sources. 5) Study designs that require data collection from a few individuals rather than aggregate analysis require Institutional Review Board (IRB) approval. 6) Researchers should adhere to a user’s attempt to control his or her data by respecting privacy settings. As researchers, we believe that a discourse within the research community is needed to ensure protection of research subjects. These guidelines are offered to help start this discourse and to lay the foundations for the ethical use of Twitter data.

Cytoscape: the network visualization tool for GenomeSpace workflows.

Abstract: Modern genomic analysis often requires workflows incorporating multiple best-of-breed tools. GenomeSpace is a web-based visual workbench that combines a selection of these tools with mechanisms that create data flows between them. One such tool is Cytoscape 3, a popular application that enables analysis and visualization of graph-oriented genomic networks. As Cytoscape runs on the desktop, and not in a web browser, integrating it into GenomeSpace required special care in creating a seamless user experience and enabling appropriate data flows. In this paper, we present the design and operation of the Cytoscape GenomeSpace app, which accomplishes this integration, thereby providing critical analysis and visualization functionality for GenomeSpace users. It has been downloaded over 850 times since the release of its first version in September, 2013.

Software Carpentry: lessons learned.

Abstract: Since its start in 1998, Software Carpentry has evolved from a week-long training course at the US national laboratories into a worldwide volunteer effort to improve researchers' computing skills. This paper explains what we have learned along the way, the challenges we now face, and our plans for the future.

Enrichment Map - a Cytoscape app to visualize and explore OMICs pathway enrichment results.

Abstract: High-throughput OMICs experiments generate signals for millions of entities (i.e. genes, proteins, metabolites or any measurable biological entity) in the cell. In an effort to summarize and explore these signals, expression results are examined in the context of known pathways and processes, through enrichment analysis to generate a set of pathways and processes that is significantly enriched. Due to the high redundancy in annotation resources this often results in hundreds of sets. To facilitate the analysis of these results, we have developed the Enrichment Map app to visualize enrichments as a network. We have updated Enrichment Map to support Cytoscape 3, and have added additional features including new data formats and command line access.

Subdivisions of the adult zebrafish pallium based on molecular marker analysis.

Abstract: Background: The telencephalon shows a remarkable structural diversity among vertebrates. In particular, the everted telencephalon of ray-finned fishes has a markedly different morphology compared to the evaginated telencephalon of all other vertebrates. This difference in development has hampered the comparison between different areas of the pallium of ray-finned fishes and the pallial nuclei of all other vertebrates. Various models of homology between pallial subdivisions in ray-finned fishes and the pallial nuclei in tetrapods have been proposed based on connectional, neurochemical, gene expression and functional data. However, no consensus has been reached so far. In recent years, the analysis of conserved developmental marker genes has assisted the identification of homologies for different parts of the telencephalon among several tetrapod species. Results: We have investigated the gene expression pattern of conserved marker genes in the adult zebrafish ( Danio rerio) pallium to identify pallial subdivisions and their homology to pallial nuclei in tetrapods. Combinatorial expression analysis of ascl1a, eomesa, emx1, emx2, emx3, and Prox1 identifies four main divisions in the adult zebrafish pallium. Within these subdivisions, we propose that Dm is homologous to the pallial amygdala in tetrapods and that the dorsal subdivision of Dl is homologous to part of the hippocampal formation in mouse. We have complemented this analysis be examining the gene expression of emx1, emx2 and emx3 in the zebrafish larval brain. Conclusions: Based on our gene expression data, we propose a new model of subdivisions in the adult zebrafish pallium and their putative homologies to pallial nuclei in tetrapods. Pallial nuclei control sensory, motor, and cognitive functions, like memory, learning and emotion. The identification of pallial subdivisions in the adult zebrafish and their homologies to pallial nuclei in tetrapods will contribute to the use of the zebrafish system as a model for neurobiological research and human neurodegenerative diseases.

shinyMethyl: interactive quality control of Illumina 450k DNA methylation arrays in R.

Abstract: We present shinyMethyl, a Bioconductor package for interactive quality control of DNA methylation data from Illumina 450k arrays. The package summarizes 450k experiments into small exportable R objects from which an interactive interface is launched. Reactive plots allow fast and intuitive quality control assessment of the samples. In addition, exploration of the phenotypic associations is possible through coloring and principal component analysis. Altogether, the package makes it easy to perform quality assessment of large-scale methylation datasets, such as epigenome-wide association studies or the datasets available through The Cancer Genome Atlas portal. The shinyMethyl package is implemented in R and available via Bioconductor. Its development repository is at https://github.com/jfortin1/shinyMethyl.

edgeR: a versatile tool for the analysis of shRNA-seq and CRISPR-Cas9 genetic screens

Abstract: Pooled library sequencing screens that perturb gene function in a high-throughput manner are becoming increasingly popular in functional genomics research. Irrespective of the mechanism by which loss of function is achieved, via either RNA interference using short hairpin RNAs (shRNAs) or genetic mutation using single guide RNAs (sgRNAs) with the CRISPR-Cas9 system, there is a need to establish optimal analysis tools to handle such data. Our open-source processing pipeline in edgeR provides a complete analysis solution for screen data, that begins with the raw sequence reads and ends with a ranked list of candidate genes for downstream biological validation. We first summarize the raw data contained in a fastq file into a matrix of counts (samples in the columns, genes in the rows) with options for allowing mismatches and small shifts in sequence position. Diagnostic plots, normalization and differential representation analysis can then be performed using established methods to prioritize results in a statistically rigorous way, with the choice of either the classic exact testing methodology or generalized linear modeling that can handle complex experimental designs. A detailed users' guide that demonstrates how to analyze screen data in edgeR along with a point-and-click implementation of this workflow in Galaxy are also provided. The edgeR package is freely available from http://www.bioconductor.org.

Interpretation of mRNA splicing mutations in genetic disease: review of the literature and guidelines for information-theoretical analysis.

Abstract: The interpretation of genomic variants has become one of the paramount challenges in the post-genome sequencing era. In this review we summarize nearly 20 years of research on the applications of information theory (IT) to interpret coding and non-coding mutations that alter mRNA splicing in rare and common diseases. We compile and summarize the spectrum of published variants analyzed by IT, to provide a broad perspective of the distribution of deleterious natural and cryptic splice site variants detected, as well as those affecting splicing regulatory sequences. Results for natural splice site mutations can be interrogated dynamically with Splicing Mutation Calculator, a companion software program that computes changes in information content for any splice site substitution, linked to corresponding publications containing these mutations. The accuracy of IT-based analysis was assessed in the context of experimentally validated mutations. Because splice site information quantifies binding affinity, IT-based analyses can discern the differences between variants that account for the observed reduced (leaky) versus abolished mRNA splicing. We extend this principle by comparing predicted mutations in natural, cryptic, and regulatory splice sites with observed deleterious phenotypic and benign effects. Our analysis of 1727 variants revealed a number of general principles useful for ensuring portability of these analyses and accurate input and interpretation of mutations. We offer guidelines for optimal use of IT software for interpretation of mRNA splicing mutations.

Data publication consensus and controversies.

Abstract: The movement to bring datasets into the scholarly record as first class research products (validated, preserved, cited, and credited) has been inching forward for some time, but now the pace is quickening. As data publication venues proliferate, significant debate continues over formats, processes, and terminology. Here, we present an overview of data publication initiatives underway and the current conversation, highlighting points of consensus and issues still in contention. Data publication implementations differ in a variety of factors, including the kind of documentation, the location of the documentation relative to the data, and how the data is validated. Publishers may present data as supplemental material to a journal article, with a descriptive “data paper,” or independently. Complicating the situation, different initiatives and communities use the same terms to refer to distinct but overlapping concepts. For instance, the term published means that the data is publicly available and citable to virtually everyone, but it may or may not imply that the data has been peer-reviewed. In turn, what is meant by data peer review is far from defined; standards and processes encompass the full range employed in reviewing the literature, plus some novel variations. Basic data citation is a point of consensus, but the general agreement on the core elements of a dataset citation frays if the data is dynamic or part of a larger set. Even as data publication is being defined, some are looking past publication to other metaphors, notably “data as software,” for solutions to the more stubborn problems.

Biological network analysis with CentiScaPe: centralities and experimental dataset integration.

Abstract: The growing dimension and complexity of the available experimental data generating biological networks have increased the need for tools that help in categorizing nodes by their topological relevance. Here we present CentiScaPe, a Cytoscape app specifically designed to calculate centrality indexes used for the identification of the most important nodes in a network. CentiScaPe is a comprehensive suite of algorithms dedicated to network nodes centrality analysis, computing several centralities for undirected, directed and weighted networks. The results of the topological analysis can be integrated with data set from lab experiments, like expression or phosphorylation levels for each protein represented in the network. Our app opens new perspectives in the analysis of biological networks, since the integration of topological analysis with lab experimental data enhance the predictive power of the bioinformatics analysis.

Shaping the Future of Research: a perspective from junior scientists

Abstract: The landscape of scientific research and funding is in flux and affected by tight budgets, evolving models of both publishing and evaluation, and questions about training and workforce stability. As future leaders, junior scientists are uniquely poised to shape the culture and practice of science in response to these challenges. A group of postdocs in the Boston area who are invested in improving the scientific endeavor, planned a symposium held on October 2 nd and 3 rd , 2014, as a way to join the discussion about the future of US biomedical research. Here we present a report of the proceedings of participant-driven workshops and the organizers’ synthesis of the outcomes.

KEGGscape: a Cytoscape app for pathway data integration

Abstract: In this paper, we present KEGGscape a pathway data integration and visualization app for Cytoscape ( http://apps.cytoscape.org/apps/keggscape). KEGG is a comprehensive public biological database that contains large collection of human curated pathways. KEGGscape utilizes the database to reproduce the corresponding hand-drawn pathway diagrams with as much detail as possible in Cytoscape. Further, it allows users to import pathway data sets to visualize biologist-friendly diagrams using the Cytoscape core visualization function (Visual Style) and the ability to perform pathway analysis with a variety of Cytoscape apps. From the analyzed data, users can create complex and interactive visualizations which cannot be done in the KEGG PATHWAY web application. Experimental data with Affymetrix E. coli chips are used as an example to demonstrate how users can integrate pathways, annotations, and experimental data sets to create complex visualizations that clarify biological systems using KEGGscape and other Cytoscape apps.

SkateBase, an elasmobranch genome project and collection of molecular resources for chondrichthyan fishes.

Abstract: Chondrichthyan fishes are a diverse class of gnathostomes that provide a valuable perspective on fundamental characteristics shared by all jawed and limbed vertebrates. Studies of phylogeny, species diversity, population structure, conservation, and physiology are accelerated by genomic, transcriptomic and protein sequence data. These data are widely available for many sarcopterygii (coelacanth, lungfish and tetrapods) and actinoptergii (ray-finned fish including teleosts) taxa, but limited for chondrichthyan fishes. In this study, we summarize available data for chondrichthyes and describe resources for one of the largest projects to characterize one of these fish, Leucoraja erinacea, the little skate. SkateBase ( http://skatebase.org) serves as the skate genome project portal linking data, research tools, and teaching resources.

The second touch hypothesis: T cell activation, homing and polarization.

Abstract: The second touch hypothesis states that T cell activation, proliferation, induction of homing receptors and polarization are distinguishable and, at least in part, sequential. The second touch hypothesis maintains that full T cell polarization requires T cell interaction with antigen-presenting cells (DCs, macrophages, B cells and certain activated stromal cells) in the non-lymphoid tissue where the antigen resides. Upon initial antigen encounter in peripheral lymph nodes (PLN), T cells become activated, proliferate and express homing receptors that enable them to recirculate to the (inflamed) tissue that contains the antigen. Differentiation into the T helper lineages Th1, Th2, Th17 and induced regulatory T cells (iTreg) requires additional antigen presentation by tissue macrophages and other antigen presenting cells (APCs) in the inflamed tissue. Here, I present a conceptual framework for the importance of peripheral (non-lymphoid) antigen presentation to antigen-experienced T cells.

Which fMRI clustering gives good brain parcellations

Abstract: Analysis and interpretation of neuroimaging data often require one to divide the brain into a number of regions, or parcels, with homogeneous characteristics, be these regions defined in the brain volume or on on the cortical surface. While predefined brain atlases do not adapt to the signal in the individual subjects images, parcellation approaches use brain activity (e.g. found in some functional contrasts of interest) and clustering techniques to define regions with some degree of signal homogeneity. In this work, we address the question of which clustering technique is appropriate and how to optimize the corresponding model. We use two principled criteria: goodness of fit (accuracy), and reproducibility of the parcellation across bootstrap samples. We study these criteria on both simulated and two task-based functional Magnetic Resonance Imaging datasets for the Ward, spectral and K-means clustering algorithms. We show that in general Ward’s clustering performs better than alternative methods with regards to reproducibility and accuracy and that the two criteria diverge regarding the preferred models (reproducibility leading to more conservative solutions), thus deferring the practical decision to a higher level alternative, namely the choice of a trade-off between accuracy and stability.

Metabolomics in psoriatic disease: pilot study reveals metabolite differences in psoriasis and psoriatic arthritis.

Abstract: Importance: While “omics” studies have advanced our understanding of inflammatory skin diseases, metabolomics is mostly an unexplored field in dermatology. Objective: We sought to elucidate the pathogenesis of psoriatic diseases by determining the differences in metabolomic profiles among psoriasis patients with or without psoriatic arthritis and healthy controls. Design: We employed a global metabolomics approach to compare circulating metabolites from patients with psoriasis, psoriasis and psoriatic arthritis, and healthy controls. Setting: Study participants were recruited from the general community and from the Psoriasis Clinic at the University of California Davis in United States. Participants: We examined metabolomic profiles using blood serum samples from 30 patients age and gender matched into three groups: 10 patients with psoriasis, 10 patients with psoriasis and psoriatic arthritis and 10 control participants. Main outcome(s) and measures(s): Metabolite levels were measured calculating the mean peak intensities from gas chromatography time-of-flight mass spectrometry. Results: Multivariate analyses of metabolomics profiles revealed altered serum metabolites among the study population. Compared to control patients, psoriasis patients had a higher level of alpha ketoglutaric acid (Pso: 288 ± 88; Control: 209 ± 69; p=0.03), a lower level of asparagine (Pso: 5460 ± 980; Control: 7260 ± 2100; p=0.02), and a lower level of glutamine (Pso: 86000 ± 20000; Control: 111000 ± 27000; p=0.02). Compared to control patients, patients with psoriasis and psoriatic arthritis had increased levels of glucuronic acid (Pso + PsA: 638 ± 250; Control: 347 ± 61; p=0.001). Compared to patients with psoriasis alone, patients with both psoriasis and psoriatic arthritis had a decreased level of alpha ketoglutaric acid (Pso + PsA: 186 ± 80; Pso: 288 ± 88; p=0.02) and an increased level of lignoceric acid (Pso + PsA: 442 ± 280; Pso: 214 ± 64; p=0.02). Conclusions and relevance: The metabolite differences help elucidate the pathogenesis of psoriasis and psoriatic arthritis and they may provide insights for therapeutic development.

Commercial antibodies and their validation

Abstract: Despite an impressive growth in the business of research antibodies a general lack of trust in commercial antibodies remains in place. A variety of issues, each one potentially causing an antibody to fail, underpin the frustrations that scientists endure. Lots of money goes to waste in buying and trying one failing antibody after the other without realizing all the pitfalls that come with the product: Antibodies can get inactivated, both the biological material and the assay itself can potentially be flawed, a single antibody featuring in many different catalogues can be deemed as a set of different products, and a bad choice of antibody type, wrong dilutions, and lack of proper validation can all jeopardize the intended experiments. Antibodies endorsed by scientific research papers do not always meet the scientist’s requirements either due to flawed specifications, or due to batch-to-batch variations. Antibodies can be found with Quality Control data obtained from previous batches that no longer represent the batch on sale. In addition, one cannot assume that every antibody is fit for every application. The best chance of success is to try an antibody that already was confirmed to perform correctly in the required platform.

Astrocytes require insulin-like growth factor I to protect neurons against oxidative injury

Abstract: Oxidative stress is a proposed mechanism in brain aging, making the study of its regulatory processes an important aspect of current neurobiological research. In this regard, the role of the aging regulator insulin-like growth factor I (IGF-I) in brain responses to oxidative stress remains elusive as both beneficial and detrimental actions have been ascribed to this growth factor. Because astrocytes protect neurons against oxidative injury, we explored whether IGF-I participates in astrocyte neuroprotection and found that blockade of the IGF-I receptor in astrocytes abrogated their rescuing effect on neurons. We found that IGF-I directly protects astrocytes against oxidative stress (H 2O 2). Indeed, in astrocytes but not in neurons, IGF-I decreases the pro-oxidant protein thioredoxin-interacting protein 1 and normalizes the levels of reactive oxygen species. Furthermore, IGF-I cooperates with trophic signals produced by astrocytes in response to H 2O 2 such as stem cell factor (SCF) to protect neurons against oxidative insult. After stroke, a condition associated with brain aging where oxidative injury affects peri-infarcted regions, a simultaneous increase in SCF and IGF-I expression was found in the cortex, suggesting that a similar cooperative response takes place in vivo. Cell-specific modulation by IGF-I of brain responses to oxidative stress may contribute in clarifying the role of IGF-I in brain aging.

WikiPathways App for Cytoscape: Making biological pathways amenable to network analysis and visualization

Abstract: In this paper we present the open-source WikiPathways app for Cytoscape ( http://apps.cytoscape.org/apps/wikipathways) that can be used to import biological pathways for data visualization and network analysis. WikiPathways is an open, collaborative biological pathway database that provides fully annotated pathway diagrams for manual download or through web services. The WikiPathways app allows users to load pathways in two different views: as an annotated pathway ideal for data visualization and as a simple network to perform computational analysis. An example pathway and dataset are used to demonstrate the functionality of the WikiPathways app and how they can be combined and used together with other apps. More than 3000 downloads in the first 12 months following its release in August 2013 highlight the importance and adoption of the app in the network biology field.

Current treatment of early breast cancer: adjuvant and neoadjuvant therapy

Abstract: Breast cancer is the most commonly diagnosed cancer in women. The latest world cancer statistics calculated by the International Agency for Research on Cancer (IARC) revealed that 1,677,000 women were diagnosed with breast cancer in 2012 and 577,000 died. The TNM classification of malignant tumor (TNM) is the most commonly used staging system for breast cancer. Breast cancer is a group of very heterogeneous diseases. The molecular subtype of breast cancer carries important predictive and prognostic values, and thus has been incorporated in the basic initial process of breast cancer assessment/diagnosis. Molecular subtypes of breast cancers are divided into human epidermal growth factor receptor 2 positive (HER2 +), hormone receptor positive (estrogen or progesterone +), both positive, and triple negative breast cancer. By virtue of early detection via mammogram, the majority of breast cancers in developed parts of world are diagnosed in the early stage of the disease. Early stage breast cancers can be completely resected by surgery. Over time however, the disease may come back even after complete resection, which has prompted the development of an adjuvant therapy. Surgery followed by adjuvant treatment has been the gold standard for breast cancer treatment for a long time. More recently, neoadjuvant treatment has been recognized as an important strategy in biomarker and target evaluation. It is clinically indicated for patients with large tumor size, high nodal involvement, an inflammatory component, or for those wish to preserve remnant breast tissue. Here we review the most up to date conventional and developing treatments for different subtypes of early stage breast cancer.

Validation of the peroneal nerve test to diagnose critical illness polyneuropathy and myopathy in the intensive care unit: the multicentre Italian CRIMYNE-2 diagnostic accuracy study.

Abstract: Objectives: To evaluate the accuracy of the peroneal nerve test (PENT) in the diagnosis of critical illness polyneuropathy (CIP) and myopathy (CIM) in the intensive care unit (ICU). We hypothesised that abnormal reduction of peroneal compound muscle action potential (CMAP) amplitude predicts CIP/CIM diagnosed using a complete nerve conduction study and electromyography (NCS-EMG) as a reference diagnostic standard. Design: prospective observational study. Setting: Nine Italian ICUs. Patients: One-hundred and twenty-one adult (≥18 years) neurologic (106) and non-neurologic (15) critically ill patients with an ICU stay of at least 3 days. Interventions: None. Measurements and main results: Patients underwent PENT and NCS-EMG testing on the same day conducted by two independent clinicians who were blind to the results of the other test. Cases were considered as true negative if both NCS-EMG and PENT measurements were normal. Cases were considered as true positive if the PENT result was abnormal and NCS-EMG showed symmetric abnormal findings, independently from the specific diagnosis by NCS-EMG (CIP, CIM, or combined CIP and CIM). All data were centrally reviewed and diagnoses were evaluated for consistency with predefined electrophysiological diagnostic criteria for CIP/CIM. During the study period, 342 patients were evaluated, 124 (36.3%) were enrolled and 121 individuals with no protocol violation were studied. Sensitivity and specificity of PENT were 100% (95% CI 96.1-100.0) and 85.2% (95% CI 66.3-95.8). Of 23 patients with normal results, all presented normal values on both tests with no false negative results. Of 97 patients with abnormal results, 93 had abnormal values on both tests (true positive), whereas four with abnormal findings with PENT had only single peroneal nerve neuropathy at complete NCS-EMG (false positive). Conclusions: PENT has 100% sensitivity and high specificity, and can be used as a screening test to diagnose CIP/CIM in the ICU.

JSim, an open-source modeling system for data analysis

Abstract: JSim is a simulation system for developing models, designing experiments, and evaluating hypotheses on physiological and pharmacological systems through the testing of model solutions against data. It is designed for interactive, iterative manipulation of the model code, handling of multiple data sets and parameter sets, and for making comparisons among different models running simultaneously or separately. Interactive use is supported by a large collection of graphical user interfaces for model writing and compilation diagnostics, defining input functions, model runs, selection of algorithms solving ordinary and partial differential equations, run-time multidimensional graphics, parameter optimization (8 methods), sensitivity analysis, and Monte Carlo simulation for defining confidence ranges. JSim uses Mathematical Modeling Language (MML) a declarative syntax specifying algebraic and differential equations. Imperative constructs written in other languages (MATLAB, FORTRAN, C++, etc.) are accessed through procedure calls. MML syntax is simple, basically defining the parameters and variables, then writing the equations in a straightforward, easily read and understood mathematical form. This makes JSim good for teaching modeling as well as for model analysis for research. For high throughput applications, JSim can be run as a batch job. JSim can automatically translate models from the repositories for Systems Biology Markup Language (SBML) and CellML models. Stochastic modeling is supported. MML supports assigning physical units to constants and variables and automates checking dimensional balance as the first step in verification testing. Automatic unit scaling follows, e.g. seconds to minutes, if needed. The JSim Project File sets a standard for reproducible modeling analysis: it includes in one file everything for analyzing a set of experiments: the data, the models, the data fitting, and evaluation of parameter confidence ranges. JSim is open source; it and about 400 human readable open source physiological/biophysical models are available at http://www.physiome.org/jsim/.

Using cost-effectiveness thresholds to determine value for money in low-and middle-income country healthcare systems: Are current international norms fit for purpose?

Abstract: Healthcare systems in low- and middle-income countries face considerable population healthcare needs with markedly fewer resources than those in higher income countries. The way in which available resources are allocated across competing priorities has a profound effect on how much health is generated overall, who receives healthcare interventions and who goes without. Judgements about whether interventions and programmes should be regarded as cost-effective and prioritised over others should be based on an assessment of the health benefits that will be lost because the resources required will not be available to implement other effective interventions and programmes that would benefit other patients in the same or different disease areas. Unfortunately, frequently adopted international norms, in particular the cost-effectiveness thresholds recommended by the World Health Organization (WHO), are not founded on this type of assessment. Consequently current judgements about which interventions and programmes are cost-effective are often aspirational and do not reflect the reality of resource constraints. As a consequence their use is likely to reduce overall population health and exacerbate healthcare inequalities. They also fail to identify the real (and greater) value of devoting more resources to these efforts. By obscuring the true implications of current arrangements they do not contribute to greater understanding of and accountability for global and local decisions made on behalf of populations in low and middle as well as in high income countries. We illustrate these points using examples from HIV/AIDS.

dendsort: modular leaf ordering methods for dendrogram representations in R.

Abstract: Dendrograms are graphical representations of binary tree structures resulting from agglomerative hierarchical clustering. In Life Science, a cluster heat map is a widely accepted visualization technique that utilizes the leaf order of a dendrogram to reorder the rows and columns of the data table. The derived linear order is more meaningful than a random order, because it groups similar items together. However, two consecutive items can be quite dissimilar despite proximity in the order. In addition, there are 2 n-1 possible orderings given n input elements as the orientation of clusters at each merge can be flipped without affecting the hierarchical structure. We present two modular leaf ordering methods to encode both the monotonic order in which clusters are merged and the nested cluster relationships more faithfully in the resulting dendrogram structure. We compare dendrogram and cluster heat map visualizations created using our heuristics to the default heuristic in R and seriation-based leaf ordering methods. We find that our methods lead to a dendrogram structure with global patterns that are easier to interpret, more legible given a limited display space, and more insightful for some cases. The implementation of methods is available as an R package, named ”dendsort”, from the CRAN package repository. Further examples, documentations, and the source code are available at [https://bitbucket.org/biovizleuven/dendsort/].