Identifying environmental signals from population abundance data using multivariate time-series analysis

Individual organisms are affected by various natural and anthropogenic environmental factors throughout their life history. This is reflected in the way population abundance fluctuates. Consequently, observed population dynamics are often produced by the superimposition of multiple environmental signals. This complicates the analysis of population time-series. Here, a multivariate time-series method called maximum autocorrelation factor analysis (MAFA) was used to extract underlying signals from multiple population time series data. The extracted signals were compared with environmental variables that were suspected to affect the populations. Finally, a simple multiple regression analysis was applied to the same data set, and the results from the regression analysis were compared with those from MAFA. The extracted signals with MAFA were strongly associated with the environmental variables, suggesting that they represent environmental factors. On the other hand, with the multiple regression analysis, one of the important signals was not identifiable, revealing the shortcoming of the conventional approach. MAFA summarizes data based on their lag-one autocorrelation. This allows the identification of underlying signals with a small effect size on population abundance during the observation. It also uses multiple time series collected in parallel; this enables us to effectively analyze short time series. In this study, annual spawning adult counts of Chinook salmon at various locations within the Klamath Basin, California, were analyzed.     

Investigating the response of soil and vegetable crops to poultry and cow manure using ground and satellite data

Based on the massive production of cow and poultry manures, farmers in Saudi Arabia are moving towards the application of organic fertilizers in their farms. Therefore, the present work was conducted to study the response of soil and selected vegetable crops to poultry and cow manures, using ground data and Landsat-8 and Hyperion images. The studied vegetable crops are cabbage, cauliflower, broccoli, and lettuce. A total of 100 t ha^?1 organic manures were applied as a pre-planting treatment. A 12.5 ha field in Tawdeehiya Farms, 200 km southeast of Riyadh, was earmarked for this study. The field was divided into sectors cultivated with the above-mentioned vegetable crops. Soil characteristics, including the soil pH, the electric conductivity (EC), the nitrogen (N), the phosphorus (P) and the potassium (K), were examined before the application of manures and 25 days after the transplanting process. Observations on crops chlorophyll content, number of leaves, the diameter of merchantable products and yield were also investigated. Furthermore, the relationship between the crop performance and yield was investigated through the satellite images generated vegetation indices (VIs). This study revealed the better performance of poultry manure compared to cow manure in terms of development and production parameters of the experimental crops. Dynamics of the chlorophyll content across the crop growth period revealed that all the tested crops responded significantly (R² = 0.69; P = 0.001) to the poultry manure treatments. Among the tested crops, the chlorophyll content, curd or head sizes and crop yields were quite better in poultry manure applied plots. The investigation of crop yield was significant with poultry manure (R² = 0.64; P = 0.001) than cow manure (R² = 0.57; P = 0.001) using the OSAVI and mNDVI, respectively.     

Numerical modelling of label-structured cell population growth using CFSE distribution data

Background

Drug-drug interactions resulting from the inhibition of an enzymatic process can have serious implications for clinical drug therapy. Quantification of the drugs internal exposure increase upon administration with an inhibitor requires understanding to avoid the drug reaching toxic thresholds. In this study, we aim to predict the effect of the CYP3A4 inhibitors, itraconazole (ITZ) and its primary metabolite, hydroxyitraconazole (OH-ITZ) on the pharmacokinetics of the anesthetic, midazolam (MDZ) and its metabolites, 1' hydroxymidazolam (1OH-MDZ) and 1' hydroxymidazolam glucuronide (1OH-MDZ-Glu) using mechanistic whole body physiologically-based pharmacokinetic simulation models. The model is build on MDZ, 1OH-MDZ and 1OH-MDZ-Glu plasma concentration time data experimentally determined in 19 CYP3A5 genotyped adult male individuals, who received MDZ intravenously in a basal state. The model is then used to predict MDZ, 1OH-MDZ and 1OH-MDZ-Glu concentrations in an CYP3A-inhibited state following ITZ administration.

Results

For the basal state model, three linked WB-PBPK models (MDZ, 1OH-MDZ, 1OH-MDZ-Glu) for each individual were elimination optimized that resulted in MDZ and metabolite plasma concentration time curves that matched individual observed clinical data. In vivo K_m and V_max optimized values for MDZ hydroxylation were similar to literature based in vitro measures. With the addition of the ITZ/OH-ITZ model to each individual coupled MDZ + metabolite model, the plasma concentration time curves were predicted to greatly increase the exposure of MDZ as well as to both increase exposure and significantly alter the plasma concentration time curves of the MDZ metabolites in comparison to the basal state curves. As compared to the observed clinical data, the inhibited state curves were generally well described although the simulated concentrations tended to exceed the experimental data between approximately 6 to 12 hours following MDZ administration. This deviations appeared to be greater in the CYP3A5 *1/*1 and CYP3A5 *1/*3 group than in the CYP3A5 *3/*3 group and was potentially the result of assuming that ITZ/OH-ITZ inhibits both CYP3A4 and CYP3A5, whereas in vitro inhibition is due to CYP3A4.

Conclusion

This study represents the first attempt to dynamically simulate metabolic enzymatic drug-drug interactions via coupled WB-PBPK models. The workflow described herein, basal state optimization followed by inhibition prediction, is novel and will provide a basis for the development of other inhibitor models that can be used to guide, interpret, and potentially replace clinical drug-drug interaction trials.     

Prediction of soil property distribution in paddy soil landscapes using terrain data and satellite information as indicators

Sustainable land management and land use planning require reliable information about the spatial distribution of the physical and chemical soil properties affecting both landscape processes and services. Although many studies have been conducted to identify the spatial patterns of soil property distribution on various scales and in various landscapes, only little is known about the relationships underlying the spatial distribution of soil properties in intensively used, finely structured paddy soil landscapes in the southeastern part of China. In order to provide adequate soil information for the modelling of landscape processes, such as soil water movement, nutrient leaching, soil erosion and plant growth, this study investigates to what extent cheap and readily available ancillary information derived from digital elevation models and remote sensing data can be used to support soil mapping and to indicate soil characteristics on the landscape scale. This article focuses on the spatial prediction of the total carbon and nitrogen content and of physical soil properties such as topsoil silt, sand and clay content, topsoil depth and plough pan thickness. Correlation analyses indicate that, on the one side, the distribution of C, N and silt contents is quite closely related to the NDVI of vegetated surfaces and that, on the other side, it corresponds significantly to terrain attributes such as relative elevation, elevation above nearest drainage channel and topographical wetness index. Geostatistical analyses furthermore reflect a moderately structured spatial correlation of these soil variables. The combined use of the above mentioned terrain variables and the NDVI in a multiple linear regression accounted for 29% (silt) to 41% (total C) of the variance of these soil properties. In order to select the best prediction method to accurately map soil property distribution, we compared the performance of different regionalization techniques, such as multi-linear regression, simple kriging, inverse distance to a power, ordinary kriging and regression kriging. Except for the prediction of topsoil clay content, in all cases regression kriging model “C” performed best. Compared to simple kriging, the spatial prediction was improved by up to 14% (total C), 13% (total N) and 10% (silt). Since the autocorrelation lengths of these spatially well correlated soil variables range between three and five times the soil sampling density, we consider regression kriging model “C” to be a suitable method for reducing the soil sampling density. It should help to save time and costs when doing soil mapping on the landscape scale, even in intensively used paddy soil landscapes.     

Linking environmental and demographic data to predict future population viability of a perennial herb

Recent advances in stochastic demography provide tools to examine the importance of random and periodic variation in vital rates for population dynamics. In this study, we explore with simulations the effect of disturbance regime on population dynamics and viability. We collected 7 years of demographic data in three populations of the perennial herb Primula farinosa, and used these data to examine how variation in vital rates affected population viability parameters (stochastic growth rate, λ_S), and how vital rates were related to weather conditions. Elasticity analysis indicated that the stochastic growth rate was very sensitive to changes in regeneration, quantified as the production, survival, and germination of seeds. In one of the study years, all seedlings and mature plants in the demography plots died. This extinction coincided with the driest summer during the study period. Simulations suggested that a future increase in the frequency of high-mortality years due to climate change would result in reduced population growth rate, and an increased importance of survival in the seed bank for population viability. The results illustrate how the limited demographic data typically available for many natural systems can be used in simulation models to assess how environmental change will affect population viability.     

Mixture models for assessing differential expression in complex tissues using microarray data

MOTIVATION: The use of DNA microarrays has become quite popular in many scientific and medical disciplines, such as in cancer research. One common goal of these studies is to determine which genes are differentially expressed between cancer and healthy tissue, or more generally, between two experimental conditions. A major complication in the molecular profiling of tumors using gene expression data is that the data represent a combination of tumor and normal cells. Much of the methodology developed for assessing differential expression with microarray data has assumed that tissue samples are homogeneous. RESULTS: In this paper, we outline a general framework for determining differential expression in the presence of mixed cell populations. We consider study designs in which paired tissues and unpaired tissues are available. A hierarchical mixture model is used for modeling the data; a combination of methods of moments procedures and the expectation-maximization algorithm are used to estimate the model parameters. The finite-sample properties of the methods are assessed in simulation studies; they are applied to two microarray datasets from cancer studies. Commands in the R language can be downloaded from the URL http://www.sph.umich.edu/~ghoshd/COMPBIO/COMPMIX/.     

Species trees for spiny lizards (Genus Sceloporus): Identifying points of concordance and conflict between nuclear and mitochondrial data

Spiny lizards (genus Sceloporus) represent one of the most diverse and species rich clades of squamate reptiles in continental North America. Sceloporus contains 90+ species, which are partitioned into 21 species groups containing anywhere from one to 15 species. Despite substantial progress towards elucidating the phylogeographic patterns for many species of Sceloporus, efforts to resolve the phylogenetic relationships among the major species groups remain limited. In this study, the phylogenetic relationships of 53 species of Sceloporus, representing all 21 species groups, are estimated based on four nuclear genes (BDNF, PNN, R35, RAG-1; >3.3 kb) and contrasted with a new mitochondrial DNA genealogy based on six genes (12S, ND1, ND4, and the histidine, serine, and leucine tRNA genes; >2.5 kb). Species trees estimated from the nuclear loci using data concatenation or a coalescent-based inference method result in concordant topologies, but the coalescent approach provides lower resolution and support. When comparing nuclear versus mtDNA-based topologies for Sceloporus species groups, conflicting relationships outnumber concordant relationships. Incongruence is not restricted to weak or unresolved nodes as might be expected under a scenario of rapid diversification, but extends to conflicts involving strongly support clades. The points of concordance and conflict between the nuclear and mtDNA data are discussed, and arguments for preferring the species trees estimated from the multilocus nuclear data are presented.     

Fuzzy methods for assessing criteria and indicators of sustainable forest management

This paper describes the general concepts, meaning, and definitions of sustainability and proposes the use of soft methodologies, particularly fuzzy set theory, for its assessment. Criteria and indicators (C&I) are described as instruments to assess forest sustainability. Basic elements and concepts of fuzzy sets are described, including membership functions and their interpretations in the context of sustainable forest management. Moreover, fuzzy operators that can combine the operational concepts of sustainability, namely criteria and indicators are described. A simple illustrative example is described to demonstrate the application of these methodologies.     

A demonstration of pharmacokinetics and physiological modelling using a microcomputer for data capture and analysis

We describe a simple and inexpensive demonstration of mass transportand exchange using dye clearance from a hydro-dynamic model.A microcomputer was used for data acquisition and storage, non-linearleast squares curve fitting, compartmental analysis and parameterestimation. The system is useful for demonstrating the indicator— dilution technique for fluid volume measurement andcompartmental analysis in pharmacokinetics. Received on August 8, 1986; accepted on September 25, 1986     

Studying and modelling dynamic biological processes using time-series gene expression data

Biological processes are often dynamic, thus researchers must monitor their activity at multiple time points. The most abundant source of information regarding such dynamic activity is time-series gene expression data. These data are used to identify the complete set of activated genes in a biological process, to infer their rates of change, their order and their causal effects and to model dynamic systems in the cell. In this Review we discuss the basic patterns that have been observed in time-series experiments, how these patterns are combined to form expression programs, and the computational analysis, visualization and integration of these data to infer models of dynamic biological systems.     

Methods for delimiting species via population genetics and phylogenetics using genotype data

In species delimitation, a formidable goal in the discipline of systematic biology, we identify and describe species morphologically and ecologically based on phenotypic data. Efficient genotyping technologies produce genetic and genomic data with relative ease, which promotes species discovery and validation using genotype data. For the last two decades, we have seen the development of species delimitation methods based on genetic distances and phylogenetic trees using genotype data. However, speciation processes via evolutionary relationship among species were mostly divorced from species delimitation. Recent approaches to drawing species boundaries use multi-locus sequence data to account for evolutionary processes including speciation and gene flow. They allow us to learn of jointly speciation and species delimitation, leveraging computational and statistical techniques developed in population genetics and phylogenetics. Here, we review the recent progress in the development of species delimitation using genotype data and discuss the future outlook for the research of developing species delimitation methods.     

A method for distinguishing consanguinity and population substructure using multilocus genotype data

We use the patterns of homozygosity at multiple loci to distinguish between excess homozygosity caused by consanguineous mating and that due to undetected population subdivision (the Wahlund effect). Clarification of the underlying causes of excess homozygosity is of practical importance in explaining the occurrence of recessive genetic disorders and in forensic match probability calculations. We calculated a likelihood surface for two parameters: C, the proportion of the population practicing consanguinity, and theta, the genetic correlation due population subdivision. To illustrate the method, we applied it to multilocus genotypic data of two U.K. Asian populations, one practicing a high frequency of cousin marriage, and another in which caste endogamy was suspected. The method was able to successfully distinguish the different patterns of relatedness. The method also returned accurate estimates of C and theta using simulated data sets. We show how our method can be extended to allow for degrees of inbreeding closer than cousin unions, including selfing. With closer inbreeding, the relatedness of recent ancestors beyond the parents becomes an issue.     

A reaction norm model for genomic selection using high-dimensional genomic and environmental data

Key message

New methods that incorporate the main and interaction effects of high-dimensional markers and of high-dimensional environmental covariates gave increased prediction accuracy of grain yield in wheat across and within environments.

Abstract

In most agricultural crops the effects of genes on traits are modulated by environmental conditions, leading to genetic by environmental interaction (G × E). Modern genotyping technologies allow characterizing genomes in great detail and modern information systems can generate large volumes of environmental data. In principle, G × E can be accounted for using interactions between markers and environmental covariates (ECs). However, when genotypic and environmental information is high dimensional, modeling all possible interactions explicitly becomes infeasible. In this article we show how to model interactions between high-dimensional sets of markers and ECs using covariance functions. The model presented here consists of (random) reaction norm where the genetic and environmental gradients are described as linear functions of markers and of ECs, respectively. We assessed the proposed method using data from Arvalis, consisting of 139 wheat lines genotyped with 2,395 SNPs and evaluated for grain yield over 8 years and various locations within northern France. A total of 68 ECs, defined based on five phases of the phenology of the crop, were used in the analysis. Interaction terms accounted for a sizable proportion (16 %) of the within-environment yield variance, and the prediction accuracy of models including interaction terms was substantially higher (17–34 %) than that of models based on main effects only. Breeding for target environmental conditions has become a central priority of most breeding programs. Methods, like the one presented here, that can capitalize upon the wealth of genomic and environmental information available, will become increasingly important.     

Sexual conflict and the evolution of female preferences for indicators of male quality

Males and females have opposing interests when it comes to the honesty of signals used in mate choice. The existence of this sexual conflict has long been acknowledged, but its consequences have not been fully investigated. By applying adaptive dynamics methods and individual-based computer simulations to a standard model for good-genes sexual selection, we show that sexual conflict over condition-dependent signaling can prevent the handicap process from ever attaining an evolutionary equilibrium. We outline the parameter conditions and properties of the underlying genetics conducive to nonequilibrium behavior and discuss the potential of such behavior to explain the elaboration and frequent phylogenetic loss of sexually selected traits. We also evaluate its consequences for well-established insights of sexual selection theory previously shown to apply when female mating preference and male ornament expression do converge on stable equilibrium levels. Contrary to equilibrium expectation, a continual change of condition-dependent signaling enables the evolution of a costly preference for a pure epistatic indicator and the evolution of preferences for redundant signals or a large number of independent ornaments. We thus conclude that seemingly general results of sexual selection theory, insofar as these are based on equilibrium considerations, do not extend to cases where nonequilibrium behavior occurs.     

Methods to impute missing genotypes for population data

For large-scale genotyping studies, it is common for most subjects to have some missing genetic markers, even if the missing rate per marker is low. This compromises association analyses, with varying numbers of subjects contributing to analyses when performing single-marker or multi-marker analyses. In this paper, we consider eight methods to infer missing genotypes, including two haplotype reconstruction methods (local expectation maximization-EM, and fastPHASE), two k-nearest neighbor methods (original k-nearest neighbor, KNN, and a weighted k-nearest neighbor, wtKNN), three linear regression methods (backward variable selection, LM.back, least angle regression, LM.lars, and singular value decomposition, LM.svd), and a regression tree, Rtree. We evaluate the accuracy of them using single nucleotide polymorphism (SNP) data from the HapMap project, under a variety of conditions and parameters. We find that fastPHASE has the lowest error rates across different analysis panels and marker densities. LM.lars gives slightly less accurate estimate of missing genotypes than fastPHASE, but has better performance than the other methods.     

Spot shape modelling and data transformations for microarrays

MOTIVATION: To study lowly expressed genes in microarray experiments, it is useful to increase the photometric gain in the scanning. However, a large gain may cause some pixels for highly expressed genes to become saturated. Spatial statistical models that model spot shapes on the pixel level may be used to infer information about the saturated pixel intensities. Other possible applications for spot shape models include data quality control and accurate determination of spot centres and spot diameters. RESULTS: Spatial statistical models for spotted microarrays are studied including pixel level transformations and spot shape models. The models are applied to a dataset from 50mer oligonucleotide microarrays with 452 selected Arabidopsis genes. Logarithmic, Box-Cox and inverse hyperbolic sine transformations are compared in combination with four spot shape models: a cylindric plateau shape, an isotropic Gaussian distribution and a difference of two-scaled Gaussian distribution suggested in the literature, as well as a proposed new polynomial-hyperbolic spot shape model. A substantial improvement is obtained for the dataset studied by the polynomial-hyperbolic spot shape model in combination with the Box-Cox transformation. The spatial statistical models are used to correct spot measurements with saturation by extrapolating the censored data. AVAILABILITY: Source code for R is available at http://www.matfys.kvl.dk/~ekstrom/spotshapes/