Brown bear (Ursus arctos) attacks resulting in human casualties in Scandinavia 1977-2016; management implications and recommendations.

Human persecution and habitat loss have endangered large carnivore populations worldwide, but some are recovering, exacerbating old conflicts. Carnivores can injure and kill people; the most dramatic form of wildlife-human conflict. In Scandinavia, the brown bear (Ursus arctos) population increased from ~500 bears in 1977 to ~3300 in 2008, with an increase in injuries, fatalities, and public fear of bear attacks. We reviewed media coverage and interviewed victims to explore how bear population trends, hunter education, and other factors may have influenced the number of injuries and fatalities in Scandinavia from 1977 to 2016. We found 42 incidents with 42 injuries and 2 fatalities; 42 were adult men, one was an adult woman conducting forestry work, and one was a boy skiing off-piste. Thirty-three adult men were hunting bears, moose, or small game, often with a hunting dog, and 26 had shot at the bear at 8±11 m before injury. Eleven nonhunters were conducting forestry work, inspecting a hunting area, picking berries, tending livestock, hiking, harassing a denned bear, and one person was killed outside his house at night. Eight of the 11 incidents of nonhunters involved female bears with cubs; three of these family groups were in dens and two were on carcasses. The annual number of hunters injured/killed was mostly influenced by the increase in the bear population size. The pattern was similar regarding injuries/fatalities to other outdoor users, but the relation with the bear population size was weaker than for hunters, and the null model was equally supported. Bear physiology at denning may make encounters with bears more risky in the fall, when bears show prehibernation behavior. Awareness and education efforts, especially among hunters, seem important to ensure human safety. Recreationists and forestry workers should avoid dense vegetation or make noise to warn bears of their presence.

Development of minimal enzyme cocktails for hydrolysis of sulfite-pulped lignocellulosic biomass.

Despite recent progress, saccharification of lignocellulosic biomass is still a major cost driver in biorefining. In this study, we present the development of minimal enzyme cocktails for hydrolysis of Norway spruce and sugarcane bagasse, which were pretreated using the so-called BALI™ process, which is based on sulfite pulping technology. Minimal enzyme cocktails were composed using several glycoside hydrolases purified from the industrially relevant filamentous fungus Trichoderma reesei and a purified commercial ß-glucosidase from Aspergillus niger. The contribution of in-house expressed lytic polysaccharide monooxygenases (LPMOs) was also tested, since oxidative cleavage of cellulose by such LPMOs is known to be beneficial for conversion efficiency. We show that the optimized cocktails permit efficient saccharification at reasonable enzyme loadings and that the effect of the LPMOs is substrate-dependent. Using a cocktail comprising only four enzymes, glucan conversion for Norway spruce reached >80% at enzyme loadings of 8mg/g glucan, whereas almost 100% conversion was achieved at 16mg/g.

Den Entry Behavior in Scandinavian Brown Bears: Implications for Preventing Human Injuries.

Encounters between Scandinavian brown bears (Ursus arctos) and humans that result in human injuries and fatalities typically coincide with den entry in October and November, and commonly occur near a den. Our aim was to determine when bears arrive at their dens, identify potential predictors of this event, document behavior and activity associated with this period, and attempt to explain the increased risk of bear-caused human injuries in this period. We analyzed global positioning system (GPS) location and activity data from brown bears in south-central Sweden, using generalized linear mixed models, statistical process control, and activity analyses. Bears arrived at their den sites between 6 October and 1 December. Timing varied by reproductive category, bear age, and year. Half of all bears significantly reduced their activity before arriving at the den area: on average 2,169 m away from the den and 1.8 days before arrival. The other half reduced their activity after arriving at the den area. The latter bears took longer time to reach hibernation activity levels, but we did not find a difference in the start date of hibernation between the 2 groups. Bears also appeared to be sensitive to disturbance in this period, with higher den abandonment rates than later in winter, particularly for males and for bears that had not visited their den sites previously. Den entry occurred from October to December, with high variability and poor predictability of its timing. Therefore, restricting hunting or other recreation activities to reduce risk of injury by bears and disturbing bears probably would be both impractical and ineffective. Our findings can be used to educate hunters about bear behavior at this time of year. Many people associate dens with an increased risk of a bear responding aggressively to disturbance to defend its den, but our results indicate that other behavioral, and possibly physiological, changes in this period also may be involved. © 2014 The Authors. The Journal of Wildlife Management published by The Wildlife Society.

Models and implementation for relationship problems with dropout.

Allelic dropout in relationship problems may commonly appear in areas such as disaster victim identification and the identification of missing persons. If dropout is not accounted for, the results may be incorrect interpretation of profiles, loss of valuable information and biased results. In this paper, we explore different models for dropout in kinship cases and present an efficient implementation for one of the models. The implementation allows for dropout to be handled simultaneously with phenomena like silent alleles and mutations that may also cause discordances in relationship data, in addition to subpopulation correction. The implemented dropout model is freely available in the new version of the Familias software. The concepts and methods are illustrated on real and simulated data.

Rotation gene set testing for longitudinal expression data.

Gene set analysis methods are popular tools for identifying differentially expressed gene sets in microarray data. Most existing methods use a permutation test to assess significance for each gene set. The permutation test's assumption of exchangeable samples is often not satisfied for time-series data and complex experimental designs, and in addition it requires a certain number of samples to compute p-values accurately. The method presented here uses a rotation test rather than a permutation test to assess significance. The rotation test can compute accurate p-values also for very small sample sizes. The method can handle complex designs and is particularly suited for longitudinal microarray data where the samples may have complex correlation structures. Dependencies between genes, modeled with the use of gene networks, are incorporated in the estimation of correlations between samples. In addition, the method can test for both gene sets that are differentially expressed and gene sets that show strong time trends. We show on simulated longitudinal data that the ability to identify important gene sets may be improved by taking the correlation structure between samples into account. Applied to real data, the method identifies both gene sets with constant expression and gene sets with strong time trends.

Exact computation of the distribution of likelihood ratios with forensic applications.

If complex DNA profiles, conditioned on multiple individuals are evaluated, it may be difficult to assess the strength of the evidence based on the likelihood ratio. A likelihood ratio does not give information about the relative weights that are provided by separate contributors. Alternatively, the observed likelihood ratio can be evaluated with respect to the distribution of the likelihood ratio under the defense hypothesis. We present an efficient algorithm to compute an exact distribution of likelihood ratios that can be applied to any LR-based model. The distribution may have several applications, but is used here to compute a p-value that corresponds to the observed likelihood ratio. The p-value is the probability that a profile under the defense hypothesis, substituted for a questioned contributor e.g. suspect, would attain a likelihood ratio which is at least the same magnitude as that observed. The p-value can be thought of as a scaled version of the likelihood ratio, giving a quantitative measure of the strength of the evidence relative to the specified hypotheses and the model used for the analysis. The algorithm is demonstrated on examples based on real data. R code for the algorithm is freely available in the R package euroMix.

Found in transcription: accurate Parkinson's disease classification in peripheral blood.

BACKGROUND: A blood-based test for the early detection of Parkinson's disease (PD) would be an important diagnostic tool and useful for patient selection when developing novel drugs or treatments for the disease. OBJECTIVE: Here, we aimed to identify potential biomarkers associated with PD. METHODS: We applied gene expression profiling to the study of peripheral blood from 75 healthy control subjects and 79 PD patients at different stages of the disease. Healthy control subjects were matched for age and gender with PD subjects, and the diagnosis of patients was based on clinical evaluation by specialists in movement disorders. RNA was extracted from the blood samples and the gene expressions were measured using the Illumina HumanHT-12 v4.0 Expression BeadChip. RESULTS: Our results support previous studies that gene expression in blood may be instrumental in the search for molecular biomarkers for PD. Single cross-validation results show that PD can be correctly classified from healthy controls with an agreement of 88% to clinical diagnosis. De novo PD patients are classified with a sensitivity of 87%, which is close to what was achieved for the patients having a confirmed PD diagnosis with disease duration <5 and >5 years (93% and 88%). A double cross-validation procedure showed that using a selected set of around 650 informative genes, similar results are achieved. Functional analysis of the selected genes showed genes significantly associated to mitochondrial dysfunction, protein ubiquitination, gene expression and cell death. CONCLUSIONS: PD affects gene expression in blood, suggesting the potential for the development of a blood-based gene expression test.

Improving stability and understandability of genotype-phenotype mapping in Saccharomyces using regularized variable selection in L-PLS regression.

BACKGROUND: Multivariate approaches have been successfully applied to genome wide association studies. Recently, a Partial Least Squares (PLS) based approach was introduced for mapping yeast genotype-phenotype relations, where background information such as gene function classification, gene dispensability, recent or ancient gene copy number variations and the presence of premature stop codons or frameshift mutations in reading frames, were used post hoc to explain selected genes. One of the latest advancement in PLS named L-Partial Least Squares (L-PLS), where 'L' presents the used data structure, enables the use of background information at the modeling level. Here, a modification of L-PLS with variable importance on projection (VIP) was implemented using a stepwise regularized procedure for gene and background information selection. Results were compared to PLS-based procedures, where no background information was used. RESULTS: Applying the proposed methodology to yeast Saccharomyces cerevisiae data, we found the relationship between genotype-phenotype to have improved understandability. Phenotypic variations were explained by the variations of relatively stable genes and stable background variations. The suggested procedure provides an automatic way for genotype-phenotype mapping. The selected phenotype influencing genes were evolving 29% faster than non-influential genes, and the current results are supported by a recently conducted study. Further power analysis on simulated data verified that the proposed methodology selects relevant variables. CONCLUSIONS: A modification of L-PLS with VIP in a stepwise regularized elimination procedure can improve the understandability and stability of selected genes and background information. The approach is recommended for genome wide association studies where background information is available.

Microarray-based prediction of Parkinson's disease using clinical data as additional response variables.

Recent discoveries and developments in the field of genomics have led to the commercialization of novel diagnostic devices for studying disease or estimating therapeutic outcomes in individual patients. With this emerging field, the emphasis is shifting to integration of clinical research into product development. Data acquisition is primary in the initial exploratory phase of product development, and during the process of sample collection and data generation in clinical microarray studies, great amounts of additional information, such as demographic, clinical, and study design variables associated with the data, are often accumulated and made available. Including additional information in classification has been addressed in many different ways. However, in previous studies, the additional information have consistently been treated as extra predictors, which can be a problem for future prediction if such information are not available or collectable for the new samples. We instead propose to adopt a method called canonical partial least squares, which for our purpose, only uses the additional information at the model building stage to stabilize the construction of a classifier for disease status from microarray data. The canonical partial least squares method is compared with regular partial least squares for the classification of Parkinson's disease from gene expression in peripheral blood samples and also through computer simulations. The present study showed that including clinical data in the model building produces simpler and more stable models for prediction of Parkinson's disease from gene expression data.

Exploration of multivariate analysis in microbial coding sequence modeling.

BACKGROUND: Gene finding is a complicated procedure that encapsulates algorithms for coding sequence modeling, identification of promoter regions, issues concerning overlapping genes and more. In the present study we focus on coding sequence modeling algorithms; that is, algorithms for identification and prediction of the actual coding sequences from genomic DNA. In this respect, we promote a novel multivariate method known as Canonical Powered Partial Least Squares (CPPLS) as an alternative to the commonly used Interpolated Markov model (IMM). Comparisons between the methods were performed on DNA, codon and protein sequences with highly conserved genes taken from several species with different genomic properties. RESULTS: The multivariate CPPLS approach classified coding sequence substantially better than the commonly used IMM on the same set of sequences. We also found that the use of CPPLS with codon representation gave significantly better classification results than both IMM with protein (p < 0.001) and with DNA (p < 0.001). Further, although the mean performance was similar, the variation of CPPLS performance on codon representation was significantly smaller than for IMM (p < 0.001). CONCLUSIONS: The performance of coding sequence modeling can be substantially improved by using an algorithm based on the multivariate CPPLS method applied to codon or DNA frequencies.

Light traps fail to estimate reliable malaria mosquito biting rates on Bioko Island, Equatorial Guinea.

BACKGROUND: The human biting rate (HBR), an important parameter for assessing malaria transmission and evaluating vector control interventions, is commonly estimated by human landing collections (HLC). Although intense efforts have been made to find alternative non-exposure mosquito collection methods, HLC remains the standard for providing reliable and consistent HBRs. The aim of this study was to assess the relationship between human landing and light trap collections (LTC), in an attempt to estimate operationally feasible conversion factors between the two. The study was conducted as part of the operational research component of the Bioko Island Malaria Control Project (BIMCP), Equatorial Guinea. METHODS: Malaria mosquitoes were collected indoors and outdoors by HLCs and LTCs in three villages on Bioko Island, Equatorial Guinea during five bimonthly collections in 2009. Indoor light traps were suspended adjacent to occupied long-lasting, insecticide-treated bed nets. Outdoor light traps were placed close to the outer wall under the roof of the collection house. Collected specimens were subjected to DNA extraction and diagnostic PCR to identify species within the Anopheles gambiae complex. Data were analysed by simple regression of log-transformed values and by Bayesian regression analysis. RESULTS: There was a poor correlation between the two collection methods. Results varied by location, venue, month, house, but also by the statistical method used. The more robust Bayesian analyses indicated non-linear relationships and relative sampling efficiencies being density dependent for the indoor collections, implying that straight-forward and simple conversion factors could not be calculated for any of the locations. Outdoor LTC:HLC relationships were weak, but could be estimated at 0.10 and 0.07 for each of two locations. CONCLUSIONS: Light trap collections in combination with bed nets are not recommended as a reliable method to assess human biting rates on Bioko Island. Different statistical analyses methods give variable and inconsistent results. Substantial variation in collection methods prevents the determination of reliable and operationally feasible conversion factors for both indoor and outdoor data. Until improved mosquito collection methods are developed that can provide reliable and unbiased HBR estimates, HLCs should continue to serve as the reference method for HBR estimation.

A Partial Least Squares based algorithm for parsimonious variable selection.

BACKGROUND: In genomics, a commonly encountered problem is to extract a subset of variables out of a large set of explanatory variables associated with one or several quantitative or qualitative response variables. An example is to identify associations between codon-usage and phylogeny based definitions of taxonomic groups at different taxonomic levels. Maximum understandability with the smallest number of selected variables, consistency of the selected variables, as well as variation of model performance on test data, are issues to be addressed for such problems. RESULTS: We present an algorithm balancing the parsimony and the predictive performance of a model. The algorithm is based on variable selection using reduced-rank Partial Least Squares with a regularized elimination. Allowing a marginal decrease in model performance results in a substantial decrease in the number of selected variables. This significantly improves the understandability of the model. Within the approach we have tested and compared three different criteria commonly used in the Partial Least Square modeling paradigm for variable selection; loading weights, regression coefficients and variable importance on projections. The algorithm is applied to a problem of identifying codon variations discriminating different bacterial taxa, which is of particular interest in classifying metagenomics samples. The results are compared with a classical forward selection algorithm, the much used Lasso algorithm as well as Soft-threshold Partial Least Squares variable selection. CONCLUSIONS: A regularized elimination algorithm based on Partial Least Squares produces results that increase understandability and consistency and reduces the classification error on test data compared to standard approaches.

Mining for genotype-phenotype relations in Saccharomyces using partial least squares.

BACKGROUND: Multivariate approaches are important due to their versatility and applications in many fields as it provides decisive advantages over univariate analysis in many ways. Genome wide association studies are rapidly emerging, but approaches in hand pay less attention to multivariate relation between genotype and phenotype. We introduce a methodology based on a BLAST approach for extracting information from genomic sequences and Soft- Thresholding Partial Least Squares (ST-PLS) for mapping genotype-phenotype relations. RESULTS: Applying this methodology to an extensive data set for the model yeast Saccharomyces cerevisiae, we found that the relationship between genotype-phenotype involves surprisingly few genes in the sense that an overwhelmingly large fraction of the phenotypic variation can be explained by variation in less than 1% of the full gene reference set containing 5791 genes. These phenotype influencing genes were evolving 20% faster than non-influential genes and were unevenly distributed over cellular functions, with strong enrichments in functions such as cellular respiration and transposition. These genes were also enriched with known paralogs, stop codon variations and copy number variations, suggesting that such molecular adjustments have had a disproportionate influence on Saccharomyces yeasts recent adaptation to environmental changes in its ecological niche. CONCLUSIONS: BLAST and PLS based multivariate approach derived results that adhere to the known yeast phylogeny and gene ontology and thus verify that the methodology extracts a set of fast evolving genes that capture the phylogeny of the yeast strains. The approach is worth pursuing, and future investigations should be made to improve the computations of genotype signals as well as variable selection procedure within the PLS framework.

A gene expression pattern in blood for the early detection of Alzheimer's disease.

A whole genome screen was performed using oligonucleotide microarray analysis on blood from a large clinical cohort of Alzheimer's disease (AD) patients and control subjects as clinical sample. Blood samples for total RNA extraction were collected in PAXgene tubes, and gene expression analysis performed on the AB1700 Whole Genome Survey Microarrays. When comparing the gene expression of 94 AD patients and 94 cognitive healthy controls, a Jackknife gene selection based method and Partial Least Square Regression (PLSR) was used to develop a disease classifier algorithm, which gives a test score indicating the presence (positive) or absence (negative) of AD. This algorithm, based on 1239 probes, was validated in an independent test set of 63 subjects comprising 31 AD patients, 25 age-matched cognitively healthy controls, and 7 young controls. This algorithm correctly predicted the class of 55/63 (accuracy 87%), including 26/31 AD samples (sensitivity 84%) and 29/32 controls (specificity 91%). The positive likelihood ratio was 8.9 and the area under the receiver operating characteristic curve (ROC AUC) was 0.94. Furthermore, the algorithm also discriminated AD from Parkinson's disease in 24/27 patients (accuracy 89%). We have identified and validated a gene expression signature in blood that classifies AD patients and cognitively healthy controls with high accuracy and show that alterations specific for AD can be detected distant from the primary site of the disease.

Smoothing gene expression data with network information improves consistency of regulated genes.

Gene set analysis methods have become a widely used tool for including prior biological knowledge in the statistical analysis of gene expression data. Advantages of these methods include increased sensitivity, easier interpretation and more conformity in the results. However, gene set methods do not employ all the available information about gene relations. Genes are arranged in complex networks where the network distances contain detailed information about inter-gene dependencies. We propose a method that uses gene networks to smooth gene expression data with the aim of reducing the number of false positives and identify important subnetworks. Gene dependencies are extracted from the network topology and are used to smooth genewise test statistics. To find the optimal degree of smoothing, we propose using a criterion that considers the correlation between the network and the data. The network smoothing is shown to improve the ability to identify important genes in simulated data. Applied to a real data set, the smoothing accentuates parts of the network with a high density of differentially expressed genes.

Gene expression profiling of peripheral blood cells for early detection of breast cancer.

INTRODUCTION: Early detection of breast cancer is key to successful treatment and patient survival. We have previously reported the potential use of gene expression profiling of peripheral blood cells for early detection of breast cancer. The aim of the present study was to refine these findings using a larger sample size and a commercially available microarray platform. METHODS: Blood samples were collected from 121 females referred for diagnostic mammography following an initial suspicious screening mammogram. Diagnostic work-up revealed that 67 of these women had breast cancer while 54 had no malignant disease. Additionally, nine samples from six healthy female controls were included. Gene expression analyses were conducted using high density oligonucleotide microarrays. Partial Least Squares Regression (PLSR) was used for model building while a leave-one-out (LOO) double cross validation approach was used to identify predictors and estimate their prediction efficiency. RESULTS: A set of 738 probes that discriminated breast cancer and non-breast cancer samples was identified. By cross validation we achieved an estimated prediction accuracy of 79.5% with a sensitivity of 80.6% and a specificity of 78.3%. The genes deregulated in blood of breast cancer patients are related to functional processes such as defense response, translation, and various metabolic processes, such as lipid- and steroid metabolism. CONCLUSIONS: We have identified a gene signature in whole blood that classifies breast cancer patients and healthy women with good accuracy supporting our previous findings.

Rotation testing in gene set enrichment analysis for small direct comparison experiments.

Gene Set Enrichment Analysis (GSEA) is a method for analysing gene expression data with a focus on a priori defined gene sets. The permutation test generally used in GSEA for testing the significance of gene set enrichment involves permutation of a phenotype vector and is developed for data from an indirect comparison design, i.e. unpaired data. In some studies the samples representing two phenotypes are paired, e.g. samples taken from a patient before and after treatment, or if samples representing two phenotypes are hybridised to the same two-channel array (direct comparison design). In this paper we will focus on data from direct comparison experiments, but the methods can be applied to paired data in general. For these types of data, a standard permutation test for paired data that randomly re-signs samples can be used. However, if the sample size is very small, which is often the case for a direct comparison design, a permutation test will give very imprecise estimates of the p-values. Here we propose using a rotation test rather than a permutation test for estimation of significance in GSEA of direct comparison data with a limited number of samples. Our proposed rotation test makes GSEA applicable to direct comparison data with few samples, by depending on rotations of the data instead of permutations. The rotation test is a generalisation of the permutation test, and can in addition be used on indirect comparison data and for testing significance of other types of test statistics outside the GSEA framework.

Inversely density-dependent natal dispersal in brown bears Ursus arctos.

There is considerable controversy in the literature about the presence of density dependence in dispersal. In this study, we exploit a data series from a long-term study (>18 years) on radio-marked brown bears (Ursus arctos L.) in two study areas in Scandinavia to investigate how individual-based densities influence the probability of natal dispersal and natal dispersal distances. Cumulatively, 32% and 46% of the females and 81% and 92% of the males dispersed before reaching 5 years of age in the northern and southern study area, respectively. Density had a negative effect on both the probability of dispersal and dispersal distances for the dispersing animals, when controlling for study area, sex and age, making this the first study to show that natal dispersal probability and distances are inversely density dependent in a large carnivore. We suggest that female-female competition for space caused females in higher density areas to settle closer to their natal area. For males, however, merging of demes, resulting in decreased relatedness and increased heterozygosity in an expanding population, might be the reason for shorter dispersal distances in males living at higher densities. This has been hypothesised for small mammals. The high proportion of dispersing female brown bears in Scandinavian compared with North American studies might be due to lower densities in Scandinavia and recent population expansion, with unoccupied areas available at the edges of the population. The longer dispersal distances in female Scandinavian brown bears suggest less social constraints on movements than for North American females. The longer dispersal distances by Scandinavian males may be due to increased searching for potential mates in peripheral areas with lower densities of females. These results, in addition to results of other brown bear studies, suggest that brown bears might be more territorial than previously thought, and that density is regulated by social interactions.

A genetic and spatial Bayesian analysis of mastitis resistance.

A nationwide health card recording system for dairy cattle was introduced in Norway in 1975 (the Norwegian Cattle Health Services). The data base holds information on mastitis occurrences on an individual cow basis. A reduction in mastitis frequency across the population is desired, and for this purpose risk factors are investigated. In this paper a Bayesian proportional hazards model is used for modelling the time to first veterinary treatment of clinical mastitis, including both genetic and environmental covariates. Sire effects were modelled as shared random components, and veterinary district was included as an environmental effect with prior spatial smoothing. A non-informative smoothing prior was assumed for the baseline hazard, and Markov chain Monte Carlo methods (MCMC) were used for inference. We propose a new measure of quality for sires, in terms of their posterior probability of being among the, say 10% best sires. The probability is an easily interpretable measure that can be directly used to rank sires. Estimating these complex probabilities is straightforward in an MCMC setting. The results indicate considerable differences between sires with regards to their daughters disease resistance. A regional effect was also discovered with the lowest risk of disease in the south-eastern parts of Norway.

MHC polymorphism and disease resistance in Atlantic salmon (Salmo salar); facing pathogens with single expressed major histocompatibility class I and class II loci.

Few studies have yet addressed the functional aspects of MHC molecules in fish. To lay the foundation for this, we evaluated the association between disease resistance and MHC class I and class II polymorphism in Atlantic salmon. Standardized disease challenge trials were performed on a semi-wild Atlantic salmon population with subsequent MHC typing and statistical analysis. The pathogens employed were infectious salmon anaemia virus (ISAV) causing infectious salmon anaemia and the Aeromonas salmonicida bacteria causing furunculosis. The material consisted of 1,182 Atlantic salmon from 33 families challenged with A. salmonicida and 1,031 Atlantic salmon from 25 families challenged with ISAV. We found highly significant associations between resistance towards infectious diseases caused by both pathogens and MH class I and class II polymorphism in Atlantic salmon. The observed associations were detected due to independently segregating MH class I and class II single loci, and inclusion of a large number of fish allowing an extensive statistical analysis.