Generative topographic mapping applied to clustering and visualization of motor unit action potentials

The identification and visualization of clusters formed by motor unit action potentials (MUAPs) is an essential step in investigations seeking to explain the control of the neuromuscular system. This work introduces the generative topographic mapping (GTM), a novel machine learning tool, for clustering of MUAPs, and also it extends the GTM technique to provide a way of visualizing MUAPs. The performance of GTM was compared to that of three other clustering methods: the self-organizing map (SOM), a Gaussian mixture model (GMM), and the neural-gas network (NGN). The results, based on the study of experimental MUAPs, showed that the rate of success of both GTM and SOM outperformed that of GMM and NGN, and also that GTM may in practice be used as a principled alternative to the SOM in the study of MUAPs. A visualization tool, which we called GTM grid, was devised for visualization of MUAPs lying in a high-dimensional space. The visualization provided by the GTM grid was compared to that obtained from principal component analysis (PCA).     

Engelmann Spruce Site Index Models: A Comparison of Model Functions and Parameterizations

Engelmann spruce (Picea engelmannii Parry ex Engelm.) is a high-elevation species found in western Canada and western USA. As this species becomes increasingly targeted for harvesting, better height growth information is required for good management of this species. This project was initiated to fill this need. The objective of the project was threefold: develop a site index model for Engelmann spruce; compare the fits and modelling and application issues between three model formulations and four parameterizations; and more closely examine the grounded-Generalized Algebraic Difference Approach (g-GADA) model parameterization. The model fitting data consisted of 84 stem analyzed Engelmann spruce site trees sampled across the Engelmann Spruce – Subalpine Fir biogeoclimatic zone. The fitted models were based on the Chapman-Richards function, a modified Hossfeld IV function, and the Schumacher function. The model parameterizations that were tested are indicator variables, mixed-effects, GADA, and g-GADA. Model evaluation was based on the finite-sample corrected version of Akaike’s Information Criteria and the estimated variance. Model parameterization had more of an influence on the fit than did model formulation, with the indicator variable method providing the best fit, followed by the mixed-effects modelling (9% increase in the variance for the Chapman-Richards and Schumacher formulations over the indicator variable parameterization), g-GADA (optimal approach) (335% increase in the variance), and the GADA/g-GADA (with the GADA parameterization) (346% increase in the variance). Factors related to the application of the model must be considered when selecting the model for use as the best fitting methods have the most barriers in their application in terms of data and software requirements.     

Visual to Parametric Interaction (V2PI)

Typical data visualizations result from linear pipelines that start by characterizing data using a model or algorithm to reduce the dimension and summarize structure, and end by displaying the data in a reduced dimensional form. Sensemaking may take place at the end of the pipeline when users have an opportunity to observe, digest, and internalize any information displayed. However, some visualizations mask meaningful data structures when model or algorithm constraints (e.g., parameter specifications) contradict information in the data. Yet, due to the linearity of the pipeline, users do not have a natural means to adjust the displays. In this paper, we present a framework for creating dynamic data displays that rely on both mechanistic data summaries and expert judgement. The key is that we develop both the theory and methods of a new human-data interaction to which we refer as “ Visual to Parametric Interaction” (V2PI). With V2PI, the pipeline becomes bi-directional in that users are embedded in the pipeline; users learn from visualizations and the visualizations adjust to expert judgement. We demonstrate the utility of V2PI and a bi-directional pipeline with two examples.     

Estimating Population-Level Coancestry Coefficients by an Admixture F Model

In this article, we develop an admixture F model (AFM) for the estimation of population-level coancestry coefficients from neutral molecular markers. In contrast to the previously published F model, the AFM enables disentangling small population size and lack of migration as causes of genetic differentiation behind a given level of F_ST. We develop a Bayesian estimation scheme for fitting the AFM to multiallelic data acquired from a number of local populations. We demonstrate the performance of the AFM, using simulated data sets and real data on ninespine sticklebacks (Pungitius pungitius) and common shrews (Sorex araneus). The results show that the parameterization of the AFM conveys more information about the evolutionary history than a simple summary parameter such as F_ST. The methods are implemented in the R package RAFM.     

Reconciling the omnivory-stability debate

Despite attempts at reconciliation, the role of omnivory in food web stability remains unclear. Here we develop a novel community matrix approach that is analogous to the bifurcation method of modular food web theory to show that the stability of omnivorous food chains depends critically on interaction strength. We find that there are only six possible ways that omnivorous interaction strengths can influence the stability of linear food chains. The results from these six cases suggest that: (1) strong omnivory is always destabilizing, (2) stabilization by weak to intermediate omnivorous interaction strengths dominates the set of possible stability responses, and, (3) omnivory can be occasionally strictly destabilizing or intermittently destabilizing. We then revisit the classical results of Pimm and Lawton to show that although their parameterization tends to produce a low percentage of stable omnivorous webs, the same parameterization shows strong theoretical support for the weak interaction effect. Finally, we end by arguing that our current empirical knowledge of omnivory resonates with this general theory.     

Ionic Currents of Human Trabecular Meshwork Cells from Control and Glaucoma Subjects

Increasing evidence suggests that trabecular meshwork (TM) cells participate in the regulation of intraocular pressure by controlling the rate of filtration of the aqueous humor. Ionic conductances that regulate cell volume and shape have been suggested to play an important role in TM cell volume regulation. Here, we compared ionic currents from TM cells derived from a normal subject (CTM) and from an individual affected by glaucoma (GTM). We found that while the ionic current types were similar, the current amplitudes and percentage of cells endowed with specific current at baseline were different in the two cell lines. Thus, we found that the majority of CTM cells were endowed with a swelling-activated Cl^? current at baseline, whereas in the majority of GTM cells this current was not active at baseline and became activated only after perfusion with a hypotonic solution. An inward rectifier K⁺ current was also more prevalent in CTM than in GTM cells. Our work suggests that disregulation of one or more of these ionic currents may be at the basis of TM cell participation in the development of glaucoma.     

De novo ChIP-seq analysis

Methods for the analysis of chromatin immunoprecipitation sequencing (ChIP-seq) data start by aligning the short reads to a reference genome. While often successful, they are not appropriate for cases where a reference genome is not available. Here we develop methods for de novo analysis of ChIP-seq data. Our methods combine de novo assembly with statistical tests enabling motif discovery without the use of a reference genome. We validate the performance of our method using human and mouse data. Analysis of fly data indicates that our method outperforms alignment based methods that utilize closely related species.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-015-0756-4) contains supplementary material, which is available to authorized users.     

Site and Strain-Specific Variation in Gut Microbiota Profiles and Metabolism in Experimental Mice

Background

The gastrointestinal tract microbiota (GTM) of mammals is a complex microbial consortium, the composition and activities of which influences mucosal development, immunity, nutrition and drug metabolism. It remains unclear whether the composition of the dominant GTM is conserved within animals of the same strain and whether stable GTMs are selected for by host-specific factors or dictated by environmental variables.

Methodology/Principal Findings

The GTM composition of six highly inbred, genetically distinct strains of mouse (C3H, C57, GFEC, CD1, CBA nu/nu and SCID) was profiled using eubacterial –specific PCR-DGGE and quantitative PCR of feces. Animals exhibited strain-specific fecal eubacterial profiles that were highly stable (c. >95% concordance over 26 months for C57). Analyses of mice that had been relocated before and after maturity indicated marked, reproducible changes in fecal consortia and that occurred only in young animals. Implantation of a female BDF1 mouse with genetically distinct (C57 and Agoutie) embryos produced highly similar GTM profiles (c. 95% concordance) between mother and offspring, regardless of offspring strain, which was also reflected in urinary metabolite profiles. Marked institution-specific GTM profiles were apparent in C3H mice raised in two different research institutions.

Conclusion/Significance

Strain-specific data were suggestive of genetic determination of the composition and activities of intestinal symbiotic consortia. However, relocation studies and uterine implantation demonstrated the dominance of environmental influences on the GTM. This was manifested in large variations between isogenic adult mice reared in different research institutions.     

Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies

We describe extensions to the method of Pritchard et al. for inferring population structure from multilocus genotype data. Most importantly, we develop methods that allow for linkage between loci. The new model accounts for the correlations between linked loci that arise in admixed populations ("admixture linkage disequilibium"). This modification has several advantages, allowing (1) detection of admixture events farther back into the past, (2) inference of the population of origin of chromosomal regions, and (3) more accurate estimates of statistical uncertainty when linked loci are used. It is also of potential use for admixture mapping. In addition, we describe a new prior model for the allele frequencies within each population, which allows identification of subtle population subdivisions that were not detectable using the existing method. We present results applying the new methods to study admixture in African-Americans, recombination in Helicobacter pylori, and drift in populations of Drosophila melanogaster. The methods are implemented in a program, structure, version 2.0, which is available at http://pritch.bsd.uchicago.edu.     

Testing by artificial intelligence: computational alternatives to the determination of mutagenicity.

In order to develop methods for evaluating the predictive performance of computer-driven structure-activity methods (SAR) as well as to determine the limits of predictivity, we investigated the behavior of two Salmonella mutagenicity data bases: (a) a subset from the Genetox Program and (b) one from the U.S. National Toxicology Program (NTP). For molecules common to the two data bases, the experimental concordance was 76% when "marginals" were included and 81% when they were excluded. Three SAR methods were evaluated: CASE, MULTICASE and CASE/Graph Indices (CASE/GI). The programs "learned" the Genetox data base and used it to predict NTP molecules that were not present in the Genetox compilation. The concordances were 72, 80 and 47% respectively. Obviously, the MULTICASE version is superior and approaches the 85% interlaboratory variability observed for the Salmonella mutagenicity assays when the latter was carried out under carefully controlled conditions.     

Hypoxia-Induced Changes in DNA Methylation Alter RASAL1 and TGFβ1 Expression in Human Trabecular Meshwork Cells

PurposeFibrosis and a hypoxic environment are associated with the trabecular meshwork (TM) region in the blinding disease glaucoma. Hypoxia has been shown to alter DNA methylation, an epigenetic mechanism involved in regulating gene expression such as the pro-fibrotic transforming growth factor (TGF) β1 and the anti-fibrotic Ras protein activator like 1 (RASAL1). The purpose of this study was to compare DNA methylation levels, and the expression of TGFβ1 and RASAL1 in primary human normal (NTM) with glaucomatous (GTM) cells and in NTM cells under hypoxic conditions.MethodsGlobal DNA methylation was assessed by ELISA in cultured age-matched NTM and GTM cells. qPCR was conducted for TGFβ1, collagen 1α1 (COL1A1), and RASAL1 expression. Western immunoblotting was used to determine protein expression. For hypoxia experiments, NTM cells were cultured in a 1%O₂, 5%CO₂ and 37°C environment. NTM and GTM cells were treated with TGFβ1 (10ng/ml) and the methylation inhibitor 5-azacytidine (5-aza) (0.5μM) respectively to determine their effects on DNA Methyltransferase 1 (DNMT1) and RASAL1 expression.ResultsWe found increased DNA methylation, increased TGFβ1 expression and decreased RASAL1 expression in GTM cells compared to NTM cells. Similar results were obtained in NTM cells under hypoxic conditions. TGFβ1 treatment increased DNMT1 and COL1A1, and decreased RASAL1 expression in NTM cells. 5-aza treatment decreased DNMT1, TGFβ1 and COL1A1 expression, and increased RASAL1 expression in GTM cells.ConclusionsTGFβ1 and RASAL1 expression, global DNA methylation, and expression of associated methylation enzymes were altered between NTM and GTM cells. We found that hypoxia in NTM cells induced similar results to the GTM cells. Furthermore, DNA methylation, TGFβ1 and RASAL1 appear to have an interacting relationship that may play a role in driving pro-fibrotic disease progression in the glaucomatous TM.     

Coregionalized single- and multiresolution spatially varying growth curve modeling with application to weed growth

Modeling of longitudinal data from agricultural experiments using growth curves helps understand conditions conducive or unconducive to crop growth. Recent advances in Geographical Information Systems (GIS) now allow geocoding of agricultural data that help understand spatial patterns. A particularly common problem is capturing spatial variation in growth patterns over the entire experimental domain. Statistical modeling in these settings can be challenging because agricultural designs are often spatially replicated, with arrays of subplots, and interest lies in capturing spatial variation at possibly different resolutions. In this article, we develop a framework for modeling spatially varying growth curves as Gaussian processes that capture associations at single and multiple resolutions. We provide Bayesian hierarchical models for this setting, where flexible parameterization enables spatial estimation and prediction of growth curves. We illustrate using data from weed growth experiments conducted in Waseca, Minnesota, that recorded growth of the weed Setaria spp. in a spatially replicated design.     

Gastrointestinal microbiota of wild and inbred individuals of two house mouse subspecies assessed using high‐throughput parallel pyrosequencing

The effects of gastrointestinal tract microbiota (GTM) on host physiology and health have been the subject of considerable interest in recent years. While a variety of captive bred species have been used in experiments, the extent to which GTM of captive and/or inbred individuals resembles natural composition and variation in wild populations is poorly understood. Using 454 pyrosequencing, we performed 16S rDNA GTM barcoding for 30 wild house mice (Mus musculus) and wild‐derived inbred strain mice belonging to two subspecies (M. m. musculus and M. m. domesticus). Sequenced individuals were selected according to a 2 × 2 experimental design: wild (14) vs. inbred origin (16) and M. m. musculus (15) vs. M. m. domesticus (15). We compared alpha diversity (i.e. number of operational taxonomic units – OTUs), beta diversity (i.e. interindividual variability) and microbiota composition across the four groups. We found no difference between M. m. musculus and M. m. domesticus subspecies, suggesting low effect of genetic differentiation between these two subspecies on GTM structure. Both inbred and wild populations showed the same level of microbial alpha and beta diversity; however, we found strong differentiation in microbiota composition between wild and inbred populations. Relative abundance of ~ 16% of OTUs differed significantly between wild and inbred individuals. As laboratory mice represent the most abundant model for studying the effects of gut microbiota on host metabolism, immunity and neurology, we suggest that the distinctness of laboratory‐kept mouse microbiota, which differs from wild mouse microbiota, needs to be considered in future biomedical research.     

Choosing a Cluster Sampling Design for Lot Quality Assurance Sampling Surveys

Lot quality assurance sampling (LQAS) surveys are commonly used for monitoring and evaluation in resource-limited settings. Recently several methods have been proposed to combine LQAS with cluster sampling for more timely and cost-effective data collection. For some of these methods, the standard binomial model can be used for constructing decision rules as the clustering can be ignored. For other designs, considered here, clustering is accommodated in the design phase. In this paper, we compare these latter cluster LQAS methodologies and provide recommendations for choosing a cluster LQAS design. We compare technical differences in the three methods and determine situations in which the choice of method results in a substantively different design. We consider two different aspects of the methods: the distributional assumptions and the clustering parameterization. Further, we provide software tools for implementing each method and clarify misconceptions about these designs in the literature. We illustrate the differences in these methods using vaccination and nutrition cluster LQAS surveys as example designs. The cluster methods are not sensitive to the distributional assumptions but can result in substantially different designs (sample sizes) depending on the clustering parameterization. However, none of the clustering parameterizations used in the existing methods appears to be consistent with the observed data, and, consequently, choice between the cluster LQAS methods is not straightforward. Further research should attempt to characterize clustering patterns in specific applications and provide suggestions for best-practice cluster LQAS designs on a setting-specific basis.     

GOR V server for protein secondary structure prediction

SUMMARY: We have created the GOR V web server for protein secondary structure prediction. The GOR V algorithm combines information theory, Bayesian statistics and evolutionary information. In its fifth version, the GOR method reached (with the full jack-knife procedure) an accuracy of prediction Q3 of 73.5%. Although GOR V has been among the most successful methods, its online unavailability has been a deterrent to its popularity. Here, we remedy this situation by creating the GOR V server.     

Altered spin state equilibrium in the T309V mutant of cytochrome P450 2D6: a spectroscopic and computational study

Cytochrome P450 2D6 (CYP2D6) is one of the most important cytochromes P450 in humans. Resonance Raman data from the T309V mutant of CYP2D6 show that the substitution of the conserved I-helix threonine situated in the enzyme’s active site perturbs the heme spin equilibrium in favor of the six-coordinated low-spin species. A mechanistic hypothesis is introduced to explain the experimental observations, and its compatibility with the available structural and spectroscopic data is tested using quantum-mechanical density functional theory calculations on active-site models for both the CYP2D6 wild type and the T309V mutant. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users. Alois Bonifacio, André R. Groenhof and Peter H. J. Keizers contributed equally to this work.     

Identification of naturally processed ligands in the C57BL/6 mouse using large-scale mass spectrometric peptide sequencing and bioinformatics prediction

Most major histocompatibility complex (MHC) class I–peptide-binding motifs are currently defined on the basis of quantitative in vitro MHC–peptide-binding assays. This information is used to develop bioinformatics-based tools to predict the binding of peptides to MHC class I molecules. To date few studies have analyzed the performance of these bioinformatics tools to predict the binding of peptides determined by sequencing of naturally processed peptides eluted directly from MHC class I molecules. In this study, we performed large-scale sequencing of endogenous peptides eluted from H2K^b and H2D^b molecules expressed in spleens of C57BL/6 mice. Using sequence data from 281 peptides, we identified novel preferred anchor residues located in H2K^b and H2D^b-associated peptides that refine our knowledge of these H2 class I peptide-binding motifs. The analysis comparing the performance of three bioinformatics methods to predict the binding of these peptides, including artificial neural network, stabilized matrix method, and average relative binding, revealed that 61% to 94% of peptides eluted from H2K^b and H2D^b molecules were correctly classified as binders by the three algorithms. These results suggest that bioinformatics tools are reliable and efficient methods for binding prediction of naturally processed MHC class I ligands. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Estimating net primary productivity from grassland biomass dynamics measurements

To address the need for a high quality data set based upon field observations suitable for parameterization, calibration, and validation of terrestrial biosphere models, we have developed a comprehensive global database on net primary productivity (NPP). We have compiled field measurements of biomass and associated environmental data for multiple study sites in major grassland types worldwide. Where sufficient data were available, we compared aboveground and total NPP estimated by six computational methods (algorithms) for 31 grassland sites. As has been found previously, NPP estimates were 2–5 times higher using methods which accounted for the dynamics of dead matter, compared with what is still the most commonly applied estimate of NPP (maximum peak live biomass). It is suggested that assumptions such as the use of peak biomass as an indicator of NPP in grasslands may apply only within certain subbiomes, e.g. temperate steppe grasslands. Additional data on belowground dynamics, or other reliable estimates of belowground productivity, are required if grasslands are to be fully appreciated for their role in the global carbon cycle.