stam – a Bioconductor compliant R package for structured analysis of microarray data

Background  

Genome wide microarray studies have the potential to unveil novel disease entities. Clinically homogeneous groups of patients can have diverse gene expression profiles. The definition of novel subclasses based on gene expression is a difficult problem not addressed systematically by currently available software tools.     

detrendeR – A Graphical User Interface to process and visualize tree-ring data using R

In this paper, we present the package detrendeR, a Graphical User Interface to facilitate the visualization and analysis of dendrochronological data, using the R computing environment. This package offers an easy way to perform most of the traditional tasks in dendrochronology: detrending, chronology building and graphical presentation of time series. The advantage of detrendeR, compared with the program ARSTAN, is the graphical interface that provides the user with an easy way to use R language, rich in graphics and handling routines, with no need to type commands. The detrendeR uses a simple and familiar dialog-box interface and it can read Tucson decadal-format files (*.rwl and *.crn) as well as plain text files. In addition, detrendeR has the ability to test temporal changes of the common signal using moving intervals. The detrendeR should make it easier to perform detrending and chronology building of tree-ring series, taking advantage of the R statistical programming environment.     

Topology and weights in a protein domain interaction network – a novel way to predict protein interactions

Background

While the analysis of unweighted biological webs as diverse as genetic, protein and metabolic networks allowed spectacular insights in the inner workings of a cell, biological networks are not only determined by their static grid of links. In fact, we expect that the heterogeneity in the utilization of connections has a major impact on the organization of cellular activities as well.

Results

We consider a web of interactions between protein domains of the Protein Family database (PFAM), which are weighted by a probability score. We apply metrics that combine the static layout and the weights of the underlying interactions. We observe that unweighted measures as well as their weighted counterparts largely share the same trends in the underlying domain interaction network. However, we only find weak signals that weights and the static grid of interactions are connected entities. Therefore assuming that a protein interaction is governed by a single domain interaction, we observe strong and significant correlations of the highest scoring domain interaction and the confidence of protein interactions in the underlying interactions of yeast and fly.Modeling an interaction between proteins if we find a high scoring protein domain interaction we obtain 1, 428 protein interactions among 361 proteins in the human malaria parasite Plasmodium falciparum. Assessing their quality by a logistic regression method we observe that increasing confidence of predicted interactions is accompanied by high scoring domain interactions and elevated levels of functional similarity and evolutionary conservation.

Conclusion

Our results indicate that probability scores are randomly distributed, allowing to treat static grid and weights of domain interactions as separate entities. In particular, these finding confirms earlier observations that a protein interaction is a matter of a single interaction event on domain level. As an immediate application, we show a simple way to predict potential protein interactions by utilizing expectation scores of single domain interactions.

     

Use of genomic DNA control features and predicted operon structure in microarray data analysis: ArrayLeaRNA – a Bayesian approach

Background  

Microarrays are widely used for the study of gene expression; however deciding on whether observed differences in expression are significant remains a challenge.     

Stability of vaccines – Bridging from stability data to continuous safety and efficacy throughout shelf life – An always reliable approach?

Stability studies are important tools to reliably ensure that efficacy and safety of medicinal products will remain unchanged from release of drug product until the end of shelf life. For complex medicinal products such as biological medicinal products, including vaccines, design and conduct of such studies requires particularly careful considerations in order to ensure that technical data resulting from stability studies are indeed indicative for unchanged clinical performance. Ideally, relevance of specifications controlled by stability studies as well as definition of shelf life should be justified by acceptable clinical data obtained with product at the end of the shelf life claimed.     

Longevity and life history of cave bears – a review and novel data from tooth cementum and relative emergence of permanent dentition

Abstract

Longevity and other life history variables are key to understanding evolutionary processes and the biology of extinct animals. For the past 20 years, the lifespan of cave bears received an increased interest. Studies focusing on incremental lines of tooth cementum resulted in detailed mortality patterns from different localities. In this review, we summarise literature on age estimation as well as mortality of different European cave bear localities and present novel data on longevity from 94 teeth originating from 20 European localities. Additionally, the relative tooth emergence pattern of the permanent dentition is investigated under the Schultz’s rule framework of possible life history implications. For this, the known sequences of extant bear species are compared with the one of cave bears. Our results suggest that the typical duration of the life of cave bears was 19 years but data from literature show that in rare cases ages of up to 30–32 years were achieved. Additionally, we present the oldest known age for the Middle Pleistocene cave bear Ursus deningeri, 29 years. The tooth eruption pattern of cave bears exhibits a heterochronic shift that implies, under the assumption of Schulz’ rule, a slightly faster life history than closely related species.     

Stephanoeca arndti spec. nov. – First cultivation success including molecular and autecological data from a freshwater acanthoecid choanoflagellate from Samoa

Until recently acanthoecid choanoflagellates have been described only from marine and brackish waters. Here I describe a distinct, strictly freshwater acanthoecid species from Samoa based on its morphology, ecology and molecular biological data (partial Small Subunit rDNA). The lorica of the species is characterised by two extensions at the posterior chamber which are used for attachment to the substratum. The posterior chamber is constructed of irregularly arranged costae. The anterior chamber consists of four transverse costal rings and 14–18 longitudinal costae. Despite its sturdy appearance, the lorica was extremely sensitive to water turbulence and movements of the water. The species showed a salinity tolerance of 0.5 practical salinity units with reduced growth rates and a temperature tolerance range of 20–34 °C. According to the morphology, phylogenetic analysis, and autecology of the species it was classified as a member of the genus Stephanoeca.     

Apolipoprotein E ε4 genotype as a risk factor for cognitive decline and dementia: data from the Canadian Study of Health and Aging

Background

Apolipoprotein E (ApoE) ε4 genotype is a well-established risk factor for Alzheimer''s disease (AD). However, its effect on predicting conversion from normal to “cognitive impairment, no dementia” (CIND) and from CIND to AD is less clear.

Methods

We used a nested case–control design from the population-based Canadian Study of Health and Aging (CSHA) to examine the effect of ApoE ε4 genotype on the conversion of subjects from normal to CIND and from CIND to AD. We also contrasted these findings with incident cases of AD and vascular dementia (VaD) in the CSHA cohort.

Results

The ApoE ε4 genotype was a significant risk factor for conversion from CIND to AD and from normal to AD and VaD. However, it was not a significant risk factor for conversion from normal to CIND. This effect is robust to adjustment for age, sex and education level. There is significant interaction between the ApoE ε4 genotype and age for AD and for conversion from CIND to AD. No interaction between ApoE ε4 genotype, sex, age, ethnicity and education level was found in other subgroup analyses. The positive predictive value of ApoE ε4 for predicting CIND conversion to AD was 0.48, and the negative predictive value was 0.65.

Interpretation

Possession of an ApoE ε4 allele increases the risk of AD developing from CIND. It is also associated with a decrease in the age at onset of AD. Its predictive values do not support its utility as a diagnostic test for predicting progression from CIND to AD, but it may be useful in research studies to enrich study samples that have a higher rate of progression to AD.Dementia has a profound impact on patients, families, caregivers and society in general. Data from the Canadian Study of Health and Aging (CSHA) show that 252 600 people had dementia in Canada in 1991; probable Alzheimer''s disease (AD) was diagnosed in 64% of those people.¹ It was also estimated that the net annual cost to society of care for dementia in Canada in 1991 was over $3.9 billion.² The prevalence of AD rises exponentially, doubling approximately every 5 years between the ages of 65 and 85. In recent years, rapid progress in molecular genetics has fostered the discovery of at least 4 genes associated with AD: the amyloid precursor protein (APP), the presenilin-1 gene (PS-1), the presenilin-2 gene (PS-2) and the apolipoprotein E gene (ApoE).^3,4,5Mutations in APP, PS-1 and PS-2 account for virtually all autosomal dominant inherited early-onset forms of AD. However, this form of AD represents less than 10% of all AD cases. By contrast, ApoE ε4 polymorphism does not cause AD, but it operates as a susceptibility gene or genetic risk factor. The gene exists in 3 different allele polymorphisms — ε2, ε3 and ε4 — in the general population. From previous epidemiological studies, it is estimated that people who carry 1 ε4 allele are 3 times more likely to have AD than those who do not carry any ε4 allele, and those who carry 2 ε4 alleles are 9 times more likely to develop AD than those who do not.^6,7 In addition, the ε4 allele appears to exert maximal effect in patients in whom AD is diagnosed between the ages of 55 and 75.^8,9 The ApoE ε4 allele also has been implicated as a risk factor for vascular dementia (VaD), but the findings have been inconsistent, with some studies showing positive association^{10,11,12,13,14} and others not.^{15,16,17,18,19} Recently, it has been recognized that patients who have “cognitive impairment but no dementia” (CIND) are an important group at risk for dementia. Few studies have examined ApoE ε4 as a predictor for progression from normal to CIND and from CIND to dementia.^17,19,20 To further define the relation between ApoE ε4 polymorphism and the risk of dementia in the Canadian population, we examined ApoE ε4 genotype as a predictor for conversion from normal to CIND and from CIND to AD or VaD using data obtained from the CSHA cohort. We also investigated the role of ApoE ε4 genotype as a risk factor for incident cases of AD and VaD, while controlling for the effects of age, sex and level of education.     

Correction to: Abiotic resource depletion potentials (ADPs) for elements revisited—updating ultimate reserve estimates and introducing time series for production data

The International Journal of Life Cycle Assessment - The original version of this article unfortunately contained a mistake which was missed during typesetting. The caption to Fig. 5 was...     

Assessment and improvement of the appropriateness of an LCI data set on a system level – application to textile manufacturing

Purpose

This paper aims at assessing the appropriateness at the system level of different Life Cycle Inventory (LCI) data sets (including default models) selected by the Life Cycle Assessment (LCA) practitioner. This means that the uncertainty measurements are applied on some specific main parameters of the LCI data set instead of measured input values. This approach aims at providing a pragmatic method to approximate and reduce the uncertainties resulting from a lack of information on a specific step.

Methods

The method proposed in this paper to assess the percentage errors on appropriateness includes three main steps. First, different systems including different versions of the same process with technological or geographical changes are assessed. Second, a hierarchical cluster analysis (HCA) or a principal component analysis (PCA) is performed to identify the main variables influencing the results. Third, a multivariate analysis of variances (MANOVA) assesses the significance of the main variables on the results. An appropriate default model can be developed by setting the variables introducing high variations in results.

Results and discussion

When comparing the same spinning process located in different countries, the HCA enabled us to identify the electricity mix as the main variable influencing the results. The “world average default models” has proven inappropriate to represent country specific locations. When comparing spinning technologies, the PCAs identified the electricity and the cotton input required as the main variables influencing the results and explained the variations in results due to technological changes. The HCA performed on different yarn manufacturing procedures identified the location and the yarn thickness as the two main variables influencing the results. The MANOVA assessed the percentage marginal variance (PMV) explained by the variable location between 85 and 92 % for four impact categories. The MANOVA performed on different fabric manufacturing systems assessed the PMV explained by the variables harvest, spinning, and weaving locations above 68 % for all impact categories. The HCA and MANOVA analyses helped design an appropriate “technological average default model.”

Conclusions

From the identification of the main influencing variables (HCA and PCA) to the quantitative appropriateness assessment (MANOVA) and the development of appropriate default models, the method has proven effective in assisting the LCA practitioner in the modeling of textile manufacturing systems, and for other worldwide multi-step manufacturing systems.     

A comprehensive framework for detecting copy number variants from single nucleotide polymorphism data: ‘rCNV’, a versatile r package for paralogue and CNV detection

Recent studies have highlighted the significant role of copy number variants (CNVs) in phenotypic diversity, environmental adaptation and species divergence across eukaryotes. The presence of CNVs also has the potential to introduce genotyping biases, which can pose challenges to accurate population and quantitative genetic analyses. However, detecting CNVs in genomes, particularly in non-model organisms, presents a formidable challenge. To address this issue, we have developed a statistical framework and an accompanying r software package that leverage allelic-read depth from single nucleotide polymorphism (SNP) data for accurate CNV detection. Our framework capitalises on two key principles. First, it exploits the distribution of allelic-read depth ratios in heterozygotes for individual SNPs by comparing it against an expected distribution based on binomial sampling. Second, it identifies SNPs exhibiting an apparent excess of heterozygotes under Hardy–Weinberg equilibrium. By employing multiple statistical tests, our method not only enhances sensitivity to sampling effects but also effectively addresses reference biases, resulting in optimised SNP classification. Our framework is compatible with various NGS technologies (e.g. RADseq, Exome-capture). This versatility enables CNV calling from genomes of diverse complexities. To streamline the analysis process, we have implemented our framework in the user-friendly r package ‘rCNV’, which automates the entire workflow seamlessly. We trained our models using simulated data and validated their performance on four datasets derived from different sequencing technologies, including RADseq (Chinook salmon—Oncorhynchus tshawytscha), Rapture (American lobster—Homarus americanus), Exome-capture (Norway spruce—Picea abies) and WGS (Malaria mosquito—Anopheles gambiae).     

Habitat preferences of red-listed fungi and bryophytes in woodland key habitats in southern Sweden – analyses of data from a national survey

The aim of this study was to identify habitat preferences of red-listedepiphytic and epixylic bryophyte, lichenized and non-lichenized fungi speciesinwoodland key habitats (WKHS) (areas less than 10 ha, where foreststructures indicate occurrence of red-listed species) in southern Sweden. Therelative importance of different groups of environmental factors was assessedwith partial canonical correspondence analysis techniques and across-validationapproach using data from 7196 selected WKHs. Different woody substrates (oldtrees, logs and snags) made up the most important variable group for occurrenceof red-listed species (30% unique explainable variation). Species associatedwith Fagus sylvatica and Picea abieshabitats, but also species associated with Quercus spp.andPopulus tremula habitats showed distinct habitatpreferences. The second most important variable group (16% unique explainablevariation) was geographical location. A west–east gradient was identified, andspecies concentrated to Baltic islands in the east were separated from otherspecies. This gradient, and an identified south–north gradient, probablyreflect differences in temperatures and rainfall between different regions.Among the remaining variable groups, historical land-use, ground conditions andforest stand composition were of similar importance (5–7% uniqueexplainable variation). Traditional management regimes resulting in semi-openforest habitats (leaf harvesting, forest grazing and selective cutting) wereassociated with the occurrence of many species, probably due to differences inmicroclimate between sites of different openness. Furthermore, a groundmoisturegradient extending from species associated with dry sites (mainly lichenizedfungi) to species associated with wet sites (mainly bryophytes), and a nutrientgradient from species associated with nutrient-poor sites to species occurringat nutrient-rich sites, were identified. Thus, conservation measures are neededin a broad spectrum of habitats with different substrates. Also sites withsimilar substrates, but situated in different regions (and climates), or withdifferent ground moisture and nutrient conditions are needed to cover the fullspectrum of habitat conditions suitable for different red-listed bryophytes andfungi.