Ultra scale‐down approach to correct dispersive and retentive effects in small‐scale columns when predicting larger scale elution profiles

Ultra scale‐down approaches represent valuable methods for chromatography development work in the biopharmaceutical sector, but for them to be of value, scale‐down mimics must predict large‐scale process performance accurately. For example, one application of a scale‐down model involves using it to predict large‐scale elution profiles correctly with respect to the size of a product peak and its position in a chromatogram relative to contaminants. Predicting large‐scale profiles from data generated by small laboratory columns is complicated, however, by differences in dispersion and retention volumes between the two scales of operation. Correcting for these effects would improve the accuracy of the scale‐down models when predicting outputs such as eluate volumes at larger scale and thus enable the efficient design and operation of subsequent steps. This paper describes a novel ultra scale‐down approach which uses empirical correlations derived from conductivity changes during operation of laboratory and pilot columns to correct chromatographic profiles for the differences in dispersion and retention. The methodology was tested by using 1 mL column data to predict elution profiles of a chimeric monoclonal antibody obtained from Protein A chromatography columns at 3 mL laboratory‐ and 18.3 L pilot‐scale. The predictions were then verified experimentally. Results showed that the empirical corrections enabled accurate estimations of the characteristics of larger‐scale elution profiles. These data then provide the justification to adjust small‐scale conditions to achieve an eluate volume and product concentration which is consistent with that obtained at large‐scale and which can then be used for subsequent ultra scale‐down operations. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009     

A nonproportional hazards Weibull accelerated failure time regression model

We present a study of risk factors measured in mean before age 50 and subsequent incidence of heart disease over 32 years of follow-up. The data are from the Framingham Heart Study. The standard accelerated failure time model assumes the logarithm of time until an event has a constant dispersion parameter and a location parameter that is a linear function of covariates. Parameters are estimated by maximum likelihood. We reject a standard Weibull model for these data in favor of a model with the dispersion parameter depending on the location parameter. This model suggests that the cumulative hazard ratio for two individuals shrinks towards unity over the follow-up period. Thus, not only the standard Weibull, but also the semiparametric proportional hazards (Cox) model is inadequate for this data. The model improvement appears particularly valuable when estimating the difference in predicted outcome probabilities for two individuals.     

Clustering of diverse genomic data using information fusion

MOTIVATION: Genome sequencing projects and high-through-put technologies like DNA and Protein arrays have resulted in a very large amount of information-rich data. Microarray experimental data are a valuable, but limited source for inferring gene regulation mechanisms on a genomic scale. Additional information such as promoter sequences of genes/DNA binding motifs, gene ontologies, and location data, when combined with gene expression analysis can increase the statistical significance of the finding. This paper introduces a machine learning approach to information fusion for combining heterogeneous genomic data. The algorithm uses an unsupervised joint learning mechanism that identifies clusters of genes using the combined data. RESULTS: The correlation between gene expression time-series patterns obtained from different experimental conditions and the presence of several distinct and repeated motifs in their upstream sequences is examined here using publicly available yeast cell-cycle data. The results show that the combined learning approach taken here identifies correlated genes effectively. The algorithm provides an automated clustering method, but allows the user to specify apriori the influence of each data type on the final clustering using probabilities. AVAILABILITY: Software code is available by request from the first author. CONTACT: jkasturi@cse.psu.edu.     

Modeling spatiotemporal abundance of mobile wildlife in highly variable environments using boosted GAMLSS hurdle models

Modeling organism distributions from survey data involves numerous statistical challenges, including accounting for zero‐inflation, overdispersion, and selection and incorporation of environmental covariates. In environments with high spatial and temporal variability, addressing these challenges often requires numerous assumptions regarding organism distributions and their relationships to biophysical features. These assumptions may limit the resolution or accuracy of predictions resulting from survey‐based distribution models. We propose an iterative modeling approach that incorporates a negative binomial hurdle, followed by modeling of the relationship of organism distribution and abundance to environmental covariates using generalized additive models (GAM) and generalized additive models for location, scale, and shape (GAMLSS). Our approach accounts for key features of survey data by separating binary (presence‐absence) from count (abundance) data, separately modeling the mean and dispersion of count data, and incorporating selection of appropriate covariates and response functions from a suite of potential covariates while avoiding overfitting. We apply our modeling approach to surveys of sea duck abundance and distribution in Nantucket Sound (Massachusetts, USA), which has been proposed as a location for offshore wind energy development. Our model results highlight the importance of spatiotemporal variation in this system, as well as identifying key habitat features including distance to shore, sediment grain size, and seafloor topographic variation. Our work provides a powerful, flexible, and highly repeatable modeling framework with minimal assumptions that can be broadly applied to the modeling of survey data with high spatiotemporal variability. Applying GAMLSS models to the count portion of survey data allows us to incorporate potential overdispersion, which can dramatically affect model results in highly dynamic systems. Our approach is particularly relevant to systems in which little a priori knowledge is available regarding relationships between organism distributions and biophysical features, since it incorporates simultaneous selection of covariates and their functional relationships with organism responses.     

An Adaptive Location‐Scale Test

Increasing locations are often accompanied by an increase in variability. In this case apparent heteroscedasticity can indicate that there are treatment effects and it is appropriate to consider an alternative involving differences in location as well as in scale. As a location‐scale test the sum of a location and a scale test statistic can be used. However, the power can be raised through weighting the sum. In order to select values for this weighting an adaptive design with an interim analysis is proposed: The data of the first stage are used to calculate the weights and with the second stage's data a weighted location‐scale test is carried out. The p‐values of the two stages are combined through Fisher's combination test. With a Lepage‐type location‐scale test it is illustrated that the resultant adaptive test can be more powerful than the ‘optimum’ test with no interim analysis. The principle to calculate weights, which cannot be reasonably chosen a priori, with the data of the first stage may be useful for other tests which utilize weighted statistics, too. Furthermore, the proposed test is illustrated with an example from experimental ecology.     

"Neighbourhood" size, dispersal and density estimates in the prickly forest skink (Gnypetoscincus queenslandiae) using individual genetic and demographic methods

Dispersal, or the amount of dispersion between an individual's birthplace and that of its offspring, is of great importance in population biology, behavioural ecology and conservation, however, obtaining direct estimates from field data on natural populations can be problematic. The prickly forest skink, Gnypetoscincus queenslandiae, is a rainforest endemic skink from the wet tropics of Australia. Because of its log-dwelling habits and lack of definite nesting sites, a demographic estimate of dispersal distance is difficult to obtain. Neighbourhood size, defined as 4piDsigma2 (where D is the population density and sigma2 the mean axial squared parent-offspring dispersal rate), dispersal and density were estimated directly and indirectly for this species using mark-recapture and microsatellite data, respectively, on lizards captured at a local geographical scale of 3 ha. Mark-recapture data gave a dispersal rate of 843 m2/generation (assuming a generation time of 6.5 years), a time-scaled density of 13 635 individuals * generation/km2 and, hence, a neighbourhood size of 144 individuals. A genetic method based on the multilocus (10 loci) microsatellite genotypes of individuals and their geographical location indicated that there is a significant isolation by distance pattern, and gave a neighbourhood size of 69 individuals, with a 95% confidence interval between 48 and 184. This translates into a dispersal rate of 404 m2/generation when using the mark-recapture density estimation, or an estimate of time-scaled population density of 6520 individuals * generation/km2 when using the mark-recapture dispersal rate estimate. The relationship between the two categories of neighbourhood size, dispersal and density estimates and reasons for any disparities are discussed.     

An enlarged European Union challenges priority settings in conservation

Biodiversity is declining worldwide under increasing human pressure. Since the location of and the threats are unevenly distributed and the resources available for conservation are limited, prioritization is essential to reduce the losses. Most conservation efforts until now proved to be ineffective in stopping the present worldwide decline of threatened species. We focus on the European Union (EU) after the repeated enlargements in the last decade, from 15 to 27 countries, by considering the present conservation priorities that have shifted towards a continental scale approach. The situation in the EU indicates that despite the differences in wealth across countries, there are no significant differences in the number and surface of protected areas between them, so re-evaluating conservation priorities at a continental scale and a reallocation of funds is required. A major limitation in priority settings for conservation is data availability. We recommend including in the decision process data provided by phylogeographic studies. This will prevent the decline of populations and species with evolutionary potential from centres of speciation and climate refugia. Recent EU members from central and eastern Europe still retain high biodiversity with a rather good conservation status. A large number of areas with high evolutionary potential identified by phylogeographic studies are located there and should be considered priorities within the context of global changes, as a proactive approach. We recommend a periodic re-evaluation of the status of species and habitats based on current research results, harmonization between the priority species listed in the conventions, directives and Red Lists at both EU and national levels.     

Using unmanned aerial vehicle (UAV) surveys and image analysis in the study of large surface‐associated marine species: a case study on reef sharks Carcharhinus melanopterus shoaling behaviour

A novel image analysis‐based technique applied to unmanned aerial vehicle (UAV) survey data is described to detect and locate individual free‐ranging sharks within aggregations. The method allows rapid collection of data and quantification of fine‐scale swimming and collective patterns of sharks. We demonstrate the usefulness of this technique in a small‐scale case study exploring the shoaling tendencies of blacktip reef sharks Carcharhinus melanopterus in a large lagoon within Moorea, French Polynesia. Using our approach, we found that C. melanopterus displayed increased alignment with shoal companions when distributed over a sandflat where they are regularly fed for ecotourism purposes as compared with when they shoaled in a deeper adjacent channel. Our case study highlights the potential of a relatively low‐cost method that combines UAV survey data and image analysis to detect differences in shoaling patterns of free‐ranging sharks in shallow habitats. This approach offers an alternative to current techniques commonly used in controlled settings that require time‐consuming post‐processing effort.     

Beyond mean modelling: Bias due to misspecification of dispersion in Poisson‐inverse Gaussian regression

In clinical trials one traditionally models the effect of treatment on the mean response. The underlying assumption is that treatment affects the response distribution through a mean location shift on a suitable scale, with other aspects of the distribution (shape/dispersion/variance) remaining the same. This work is motivated by a trial in Parkinson's disease patients in which one of the endpoints is the number of falls during a 10‐week period. Inspection of the data reveals that the Poisson‐inverse Gaussian (PiG) distribution is appropriate, and that the experimental treatment reduces not only the mean, but also the variability, substantially. The conventional analysis assumes a treatment effect on the mean, either adjusted or unadjusted for covariates, and a constant dispersion parameter. On our data, this analysis yields a non‐significant treatment effect. However, if we model a treatment effect on both mean and dispersion parameters, both effects are highly significant. A simulation study shows that if a treatment effect exists on the dispersion and is ignored in the modelling, estimation of the treatment effect on the mean can be severely biased. We show further that if we use an orthogonal parametrization of the PiG distribution, estimates of the mean model are robust to misspecification of the dispersion model. We also discuss inferential aspects that are more difficult than anticipated in this setting. These findings have implications in the planning of statistical analyses for count data in clinical trials.     

A comparison of parametric and nonparametric methods for normalising cDNA microarray data

Normalisation is an essential first step in the analysis of most cDNA microarray data, to correct for effects arising from imperfections in the technology. Loess smoothing is commonly used to correct for trends in log-ratio data. However, parametric models, such as the additive plus multiplicative variance model, have been preferred for scale normalisation, though the variance structure of microarray data may be of a more complex nature than can be accommodated by a parametric model. We propose a new nonparametric approach that incorporates location and scale normalisation simultaneously using a Generalised Additive Model for Location, Scale and Shape (GAMLSS, Rigby and Stasinopoulos, 2005, Applied Statistics, 54, 507-554). We compare its performance in inferring differential expression with Huber et al.'s (2002, Bioinformatics, 18, 96-104) arsinh variance stabilising transformation (AVST) using real and simulated data. We show GAMLSS to be as powerful as AVST when the parametric model is correct, and more powerful when the model is wrong.     

Inverse Batschelet distributions for circular data

We provide four-parameter families of distributions on the circle which are unimodal and display the widest ranges of both skewness and peakedness yet available. Our approach is to transform the scale of a generating distribution, such as the von Mises, using various nontrivial extensions of an approach first used in Batschelet's (1981, Circular Statistics in Biology) book. The key is to employ inverses of Batschelet-type transformations in certain ways; these exhibit considerable advantages over direct Batschelet transformations. The skewness transformation is especially appealing as it has no effect on the normalizing constant. As well as a variety of interesting theoretical properties, when likelihood inference is explored these distributions display orthogonality between elements of a pairing of parameters into (location, skewness) and (concentration, peakedness). Further, the location parameter can sometimes be made approximately orthogonal to all the other parameters. Profile likelihoods come to the fore in practice. Two illustrative applications, one concerning the locomotion of a Drosophila fly larva, the other analyzing a large set of sudden infant death syndrome data, are investigated.     

Disease Prevention versus Data Privacy: Using Landcover Maps to Inform Spatial Epidemic Models

The availability of epidemiological data in the early stages of an outbreak of an infectious disease is vital for modelers to make accurate predictions regarding the likely spread of disease and preferred intervention strategies. However, in some countries, the necessary demographic data are only available at an aggregate scale. We investigated the ability of models of livestock infectious diseases to predict epidemic spread and obtain optimal control policies in the event of imperfect, aggregated data. Taking a geographic information approach, we used land cover data to predict UK farm locations and investigated the influence of using these synthetic location data sets upon epidemiological predictions in the event of an outbreak of foot-and-mouth disease. When broadly classified land cover data were used to create synthetic farm locations, model predictions deviated significantly from those simulated on true data. However, when more resolved subclass land use data were used, moderate to highly accurate predictions of epidemic size, duration and optimal vaccination and ring culling strategies were obtained. This suggests that a geographic information approach may be useful where individual farm-level data are not available, to allow predictive analyses to be carried out regarding the likely spread of disease. This method can also be used for contingency planning in collaboration with policy makers to determine preferred control strategies in the event of a future outbreak of infectious disease in livestock.     

Implementing plant hydraulic architecture within the LPJ Dynamic Global Vegetation Model

Aim To implement plant hydraulic architecture within the Lund–Potsdam–Jena Dynamic Global Vegetation Model (LPJ–DGVM), and to test the model against a set of observational data. If the model can reproduce major patterns in vegetation and ecosystem processes, we consider this to be an important linkage between plant physiology and larger‐scale ecosystem dynamics. Location The location is global, geographically distributed. Methods A literature review was carried out to derive model formulations and parameter values for representing the hydraulic characteristics of major global plant functional types (PFTs) in a DGVM. After implementing the corresponding formulations within the LPJ–DGVM, present‐day model output was compared to observational data. Results The model reproduced observed broad‐scale patterns in potential natural vegetation, but it failed to distinguish accurately between different types of grassland and savanna vegetation, possibly related to inadequate model representations of water fluxes in the soil and wildfire effects. Compared to a version of the model using an empirical formulation for calculating plant water supply without considering plant hydraulic architecture, the new formulation improved simulated patterns of vegetation in particular for dry shrublands. Global‐scale simulation results for runoff and actual evapotranspiration (AET) corresponded well to available data. The model also successfully reproduced the magnitude and seasonal cycle of AET for most EUROFLUX forests, while modelled variation in NPP across a large number of sites spanning several biomes showed a strong correlation with estimates from field measurements. Main conclusions The model was generally confirmed by comparison to observational data. The novel model representation of water flow within plants makes it possible to resolve mechanistically the effects of hydraulic differences between plant functional groups on vegetation structure, water cycling, and competition. This may be an advantage when predicting ecosystem responses to nonextant climates, in particular in areas dominated by dry shrubland vegetation.     

A non-parametric approach to translating gene region heterogeneity associated with phenotype into location heterogeneity

MOTIVATION: The analysis of genetic data poses statistical problems in the form of high dimensionality with small sample sizes. The construction of a composite gene region (sequence pair) heterogeneity measure is one technique for reducing the dimensionality of the problem. This approach however is not without cost, since the contribution of locations to observed gene region differences between groups becomes entangled in this summary measure. This is problematic since it is of scientific interest to identify locations that together depict phenotype. RESULTS: A method is proposed for relating observed gene region heterogeneity back to the location level. In the spirit of a factor analysis-type setting, the approach focuses on identifying a latent variable structure among locations to explain within and between group genetic differences associated with phenotype. The method is flexible for identifying either the additive contribution from individual locations or the additive contribution from a group of locations, to observed gene region heterogeneity, depending upon the weighting scheme used in constructing a gene region heterogeneity measure. The approach is illustrated with clinical trial data, where the problem of altered HIV drug susceptibility is examined through characterizing location contributions to HIV protease gene region differences associated with a phenotypic treatment response. AVAILABILITY: The Splus (MathSoft, Inc. S-Plus 2000, Seattle, WA, 1999) developed menu-driven functions for obtaining results, GENE_ S (J.Kowalski, Harvard School of Public Health, Boston, MA 2001), is available from the author upon request.     

A Keyword Approach to Finding Common Ground in Community-Based Definitions of Human Well-Being

Ecosystem-based management involves the integration of ecosystem services and their human beneficiaries into decision making. This can occur at multiple scales; addressing global issues such as climate change down to local problems such as flood protection and maintaining water quality. At the local scale it can be challenging to achieve a consistent and sustainable outcome across multiple communities, particularly when they differ in resource availability and management priorities. A key requirement for consistent decision support at the community level is to identify common community objectives, as these can form the basis for readily transferable indices of ecosystem benefit and human well-being. We used a keyword-based approach to look for common terminology in community fundamental objectives as a basis for transferable indices of human well-being and then compared those commonalities to community demographics, location, and type. Analysis centered on strategic planning documents readily available from coastal communities in the conterminous United States. We examined strategic planning documents based on eight domains of human well-being, and found that Living Standards and Safety and Security were the most commonly addressed domains, and Health and Cultural Fulfillment were the least. In comparing communities, regional differences were observed in only one well-being domain, Safety and Security, while community type yielded significant differences in five of the eight domains examined. Community type differences followed an urban to rural trend with urban communities focusing on Education and Living Standards, and more rural communities focused on Social Cohesion and Leisure Time. Across all eight domains multivariate analysis suggested communities were distributed along two largely orthogonal gradients; one between Living Standards and Leisure Time and or Connection to Nature, and a second between Safety and Security and Social Priorities (Education/Health/Culture/Social Cohesion). Overall these findings demonstrate the use of automated keyword analysis for obtaining information from community strategic planning documents. Moreover, the results indicate measures and perceptions of well-being at the local scale differ by community type. This information could be used in management of ecosystem services and development of indices of community sustainability that are applicable to multiple communities with similar demographics, regional location, and type.     

Propagation of the hip joint centre location error to the estimate of femur vs pelvis orientation using a constrained or an unconstrained approach

To estimate hip joint angles during selected motor tasks using stereophotogrammetric data, it is necessary to determine the hip joint centre position. The question is whether the errors affecting that determination propagate less to the angles estimates when a three degrees of freedom (DOFs) constraint (spherical hinge) is used between femur and pelvis, rather than when the two bones are assumed to be unconstrained (six DOFs). An analytical relationship between the hip joint centre location error and the joint angle error was obtained limited to the planar case. In the 3-D case, a similar relationship was obtained using a simulation approach based on experimental data. The joint angle patterns resulted in a larger distortion using a constrained approach, especially when wider rotations occur. The range of motion of the hip flexion-extension, obtained simulating different location errors and without taking into account soft tissue artefacts, varied approximately 7 deg using a constrained approach and up to 1 deg when calculated with an unconstrained approach. Thus, the unconstrained approach should be preferred even though its estimated three linear DOFs most unlikely carry meaningful information.     

Assessing endemism at multiple spatial scales, with an example from the Australian vascular flora

Aim  To develop an approach for assessing the spatial scale of centres of endemism among species level data.
Location Australia.
Methods  Endemism is inherently scale dependent. Therefore, the Corrected Weighted Endemism (CWE) index used by Crisp et al. [ J. Biogeogr. (2001)28:183] is extended to account for species samples in local neighbourhoods as a Spatial CWE index. This then allows an analysis of how the degree of endemism of a location (cell) changes with spatial scale. The quality of the Spatial CWE index results are assessed using three spatial randomizations at the species level with and without preserving species richness and distributional patterns. We show that CWE is equivalent to beta diversity and predict that it should show high rates of change around centres of endemism.
Results  Similar patterns to those found by Crisp et al. using a data set of vascular flora from Australia are retrieved, but the extent to which they are scale dependent is more easily identified. For example, the Central Australian centre discounted by Crisp et al. is identified when a three-cell radius neighbourhood is used. However, the level of endemism in this centre is no greater than in the margins of many of the coastal centres of endemism. Most of the identified centres of endemism are better than random at all scales and are increasingly so as the spatial scale increases. As predicted, the highest rate of change in Spatial CWE (beta diversity) is most often between zero- and one-cell radius neighbours in most centres of endemism.
Main conclusions  The explicit incorporation of geographical space in analyses allows for a greater understanding of the scale-dependence of phenomena, in this case endemism and beta diversity.     

A Comparative Study of Scores for Correspondence Analysis with Ordered Categories

Ordered categorical data can be analysed using correspondence analysis with the ordered categories taken into consideration. Such an analysis was proposed by Beh (1997) and uses orthogonal polynomials which require the input of a scoring scheme to reflect the ordered structure of the categories. This method of correspondence analysis visualises the relationship between the categories, in terms of the location, dispersion and higher order components. The impact of the scoring method on the orthogonal polynomials, and hence upon the correspondence plot and other output of the analysis should therefore be considered. This paper aims at identifying this impact by considering four scoring schemes: integer valued (natural) scores, midrank scores, Nishisato scores and singular vectors from the classical correspondence analysis of the data. It is shown that while the latter two maximise the location component, generally there is little difference when comparing them with the output of the former two scoring schemes. A simple comparative study of profile co-ordinates using different scoring schemes is also discussed.     

Structural type diversity: measuring structuredness of communities by type diversity

Recently, the notion of diversity, which is directed towards (effective) numbers of types (states of a trait such as species and genotypes), is increasingly used as an umbrella term akin to “variation”, thus including classical metrics of dispersion among others. This is probably due to the growing interest in functional aspects of variation which involve variable differences between types. Though the traditional notion of diversity does not cover these aspects, it shows up in many interpretations. To overcome this ambiguity, the traditional notion of diversity is extended in this paper to include variable differences with emphasis on their general significance as structuring features. For this purpose, structure is conceived to be captured by the representation of types via variable differences and abundances. Structural diversity then results from application of traditional measures of diversity to the relative structural representations of types in addition to their relative abundances. Since diversity as effective number of types alone provides no information about their mutual distinctness and the range covered by them, connections to measures of dispersion are indispensable. This is considered via two approaches that rely on dispersion characteristics and one approach that allows for an assessment of structural diversity for controlled levels of type distinctness. Effects of structure on dispersion and diversity are analyzed. The use of the approaches for discovering rarely considered characteristics of phylogenetic structure is demonstrated.     

Identifying spatial patterns in the tropical rain forest structure using hemispherical photographs

This work emphasises the usefulness of hemispherical photography for identifying spatial patterns in the tropical rain forest structure. Structural variability was investigated at a local (intra-site) scale, in relation to the forest mosaic, and at a regional (inter-sites) scale, for its implication in forest typology. Four primary forest sites, from 0.6 to 1 ha, were investigated in Central Sumatra, Indonesia.In a first instance, a qualitative analysis of the forest 3-D structure was found to be very helpful in the interpretation of quantitative results related to forest dynamics. The quantitative analysis was undertaken through the assessment of three structural characteristics: (1) the canopy openness (CO), or visible sky as seen from all directions of the hemisphere, (2) the spherical variance (SV), which quantifies the spatial dispersion of gaps and, (3) the plant area index (L_p), defined as half the surface area of canopy elements per unit ground area. At the local scale, maps of the CO values gave an indication about disturbance location and extent, providing an interesting document for studies on forest dynamics. At the regional scale, between-sites comparisons of CO, SV and L_p values added new information on forest structural differences when compared to dendrometric measurements. These results emphasised the fact that rain forest may exhibit high structural variability, even within a same bioclimatic region and a narrow altitude range. Hemispherical photographs could be a quick means of further investigating this spatial variability and it's relation to physical environment, thus providing information that is crucial for the refining of forest typology in the area.